Wetland Invertebrate Fauna Monitoring: November 2003. Data ... · Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC) Wetland Invertebrate Fauna Monitoring: November 2003

Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC)

Wetland Invertebrate Fauna Monitoring: November 2003.

Data Analysis & Interpretation

Report to

Department of Conservation & Land

Management, Mid-West Regional Office

by

A.W. Storey, R.J. Shiel & J. Lynas

May 2004

School of Animal Biology

Buntine Marchagee Aquatic Invertebrate Survey November 2003

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CONTENTS

EXECUTIVE SUMMARY................................................................................................................................. IV

INTRODUCTION................................................................................................................................................. 1

BACKGROUND:.................................................................................................................................................... 1 OBJECTIVES: ....................................................................................................................................................... 1

METHODS ............................................................................................................................................................ 2

FIELD METHODS ................................................................................................................................................. 2 SAMPLE PROCESSING .......................................................................................................................................... 3 SITES SAMPLED................................................................................................................................................... 4 STATISTICAL ANALYSES ..................................................................................................................................... 6

Background ................................................................................................................................................... 6 Analyses Performed....................................................................................................................................... 6 Multivariate Analyses.................................................................................................................................... 8

RESULTS ............................................................................................................................................................ 10

PHYSICO-CHEMISTRY........................................................................................................................................ 10 AQUATIC INVERTEBRATE FAUNA...................................................................................................................... 13

Taxonomy .................................................................................................................................................... 13 Taxa Richness.............................................................................................................................................. 14 Conservation Significance of Invertebrate Fauna....................................................................................... 17 Multivariate Analysis of Microinvertebrate Fauna ..................................................................................... 18

Abundance Data ....................................................................................................................................................... 18 Presence/absence Data.............................................................................................................................................. 20

Multivariate Analysis of Macroinvertebrate Fauna .................................................................................... 22 Abundance Data ....................................................................................................................................................... 22 Presence/absence Data.............................................................................................................................................. 24

Multivariate Analysis of Total Invertebrate Fauna ..................................................................................... 26 Abundance Data ....................................................................................................................................................... 26 Presence/absence Data.............................................................................................................................................. 27

Comparison with SAP Samples ................................................................................................................... 29

DISCUSSION ...................................................................................................................................................... 31

PHYSICO-CHEMISTRY........................................................................................................................................ 31 SPECIES CONSERVATION SIGNIFICANCE............................................................................................................ 31 WETLAND CONSERVATION SIGNIFICANCE........................................................................................................ 32 REGIONAL CONSERVATION SIGNIFICANCE........................................................................................................ 33 EVALUATION OF ANALYSIS............................................................................................................................... 34

RECOMMENDATIONS.................................................................................................................................... 35

ACKNOWLEDGEMENTS................................................................................................................................ 35

REFERENCES.................................................................................................................................................... 35

APPENDICES ..................................................................................................................................................... 37

APPENDIX 1. PHYSICO-CHEMICAL DATA FROM EACH WETLAND. ...................................................................... 38 APPENDIX 2. LIST OF VOUCHER SPECIMENS ...................................................................................................... 40 APPENDIX 3. TAXA OCCURRENCES ACROSS WETLANDS .................................................................................... 41

Frontispiece: images of the Rotifer Hexarthra propinqua collected from site W002 in November 2003


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LIST OF FIGURES Figure 1: Location of the wetlands ......................................................................................................................... 5 Figure 2: Scatter plots of Ec and pH in the field and laboratory ........................................................................... 11 Figure 3: UPGMA classification dendrogram using physico-chemistry............................................................... 12 Figure 4: MDS ordination using physico-chemical variables ............................................................................... 12 Figure 5: Number of taxa of microinvertebrates, macroinvertebrates and total number of invertebrate taxa....... 14 Figure 6: Rank scores for wetlands according to species diversity....................................................................... 17 Figure 7: UPGMA classification dendrogram using microfauna.......................................................................... 18 Figure 8: MDS ordination using microfauna . ...................................................................................................... 19 Figure 9: UPGMA classification dendrogram using microfauna presence/absence data...................................... 20 Figure 10: MDS ordination using microfauna presence/absence data .................................................................. 21 Figure 11: UPGMA classification dendrogram using macrofauna. ...................................................................... 22 Figure 12: MDS ordination using macrofauna...................................................................................................... 23 Figure 13: UPGMA classification dendrogram using macrofauna presence/absence data. .................................. 24 Figure 14: MDS ordination using macrofauna presence/absence data.................................................................. 25 Figure 15: UPGMA classification dendrogram using total fauna abundance. ...................................................... 26 Figure 16: MDS ordination using total fauna abundance data. ............................................................................. 27 Figure 17: UPGMA classification dendrogram using total fauna presence/absence data. .................................... 27 Figure 18: MDS ordination using total fauna presence/absence data. .................................................................. 28 Figure 19: Species richness at the SAP and BMRC sites using total fauna presence/absence data, with sites

ordered according to species richness.......................................................................................................... 29 Figure 20: UPGMA classification dendrogram of the SAP and BMRC sites using total fauna presence/absence

data. ............................................................................................................................................................. 30 Figure 21: MDS ordination of the SAP and BMRC sites using total fauna presence/absence data...................... 31


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LIST OF TABLES

Table 1: Site details of the eight wetlands sampled in November 2003.................................................................. 4 Table 2: Pearson product moment correlation matrix amongst physico-chemical parameters. ............................ 11 Table 3: Matrix of association values, with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity

and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on standardised physico-chemical parameters.................................................................................................. 13

Table 4: Abundance of each taxon at each site. .................................................................................................... 15 Table 5: Rank scores for wetlands according to species diversity. ....................................................................... 17 Table 6: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity

and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on microfauna abundance data. ........................................................................................................................ 19

Table 7: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on microfauna presence/absence data............................................................................................................... 20

Table 8: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on macrofauna abundance data......................................................................................................................... 22

Table 9: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on macrofauna presence/absence data. ............................................................................................................. 24

Table 10: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on total fauna abundance data. ......................................................................................................................... 26

Table 11: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on total fauna presence/absence data. ............................................................................................................... 27


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EXECUTIVE SUMMARY The Buntine-Marchagee catchment in the Northern Wheatbelt Region has been nominated a Recovery Catchment under the State Salinity Strategy. Recovery catchments have been selected as a key measure for biodiversity conservation and are based on the identification of major, high priority public assets that are at risk from salinity and warrant significant, ongoing investment in their recovery and protection. To this end, water chemistry and aquatic invertebrate fauna of eight wetlands in the Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC) were sampled in November 2003 by the Dept of CALM. The goal for the BMRC project is “to maintain the native species in a range of representative wetlands within the Buntine-Marchagee catchment by 2020”, and the samples collected in 2003 will establish a baseline condition against which future changes may be assessed. The unprocessed samples of aquatic invertebrates collected in November 2003, along with the water quality data from each wetland were provided to the School of Animal Biology, The University of Western Australia for taxonomic identification of the fauna, and subsequent statistical analysis of the data. The specific aims were to identify the resident fauna to the lowest possible taxonomic level, determine conservation status of the fauna of each wetland, compare biodiversity and invertebrate assemblages amongst wetlands, relate differences in fauna to differences in water quality, and make limited comparisons with samples previously collected from adjacent wetlands sampled under the Salinity Action Plan (SAP). Analysis of physico-chemical data placed the wetlands into three groups, principally on salinity, separating the hypersaline sites (W004, W006,W018 and W019) from the fresh to brackish sites (W009, W010 and W011), with the one saline site (W002) being distinct. This saline site also separated on pH, being the only acidic wetland, with all other wetlands circum-neutral to alkaline. The fresh-brackish sites were deeper, with higher pH, and lower colour and turbidity than the hypersaline sites. A total of 64 species were identified from the eight wetlands. This consisted of 16 species of microinvertebrates (four species of Rotifera, four Copepoda, three Cladocera, and five species of Ostracoda) and 48 species of macroinvertebrates. The macroinvertebrate fauna comprised one species of Hydracarina (watermite) two water shrimps, 10 Coleoptera (beetles), three Hemiptera (waterbugs), twenty-one Diptera (midges and flies), three Trichoptera (Caddisflies), one Ephemeroptera (Mayfly), six Odonata (Dragonflies), and one Lepidoptera. Analysis of the aquatic invertebrate fauna of the wetlands demonstrated a clear and consistent separation of sites into the same groupings identified from physico-chemical parameters, indicating that water quality likely was a major influence on invertebrate composition. The eight wetlands had relatively low biodiversity in terms of species richness when compared against previous studies from southwestern Australia, and also against adjacent wetlands in the same catchment sampled under the SAP. Similarly, apart from a possible new species of non-biting midge (Chironomidae) (?Cladopelma sp.nov.) and the first record of a species of microfauna Rotifera (Hexarthra propinqua) from Australia (previous records principally being from Europe), the fauna of the wetlands consisted of ubiquitous and cosmopolitan species, commonly found across southern Australia. The few recorded species that were considered endemic to southwestern Australia tended to be in the fresh to brackish water sites, which also supported the greatest overall species richness. Overall, the samples collected in November 2003 constitute a small data set, which limits the amount of interpretation possible. However, additional samples from these and adjacent wetlands in future years, will provide greater insights into their conservation significance and will provide the basis for ongoing monitoring, against which future changes may be assessed.


INTRODUCTION

Background:

The Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC) is located in the Northern Wheatbelt Region, in the vicinity of the towns of Dalwallinu and Coorow, approximately 250 km NNE of Perth. The BMRC was selected as a recovery catchment under the State Salinity Strategy. The Strategy describes Recovery Catchments as a key measure for biodiversity conservation under the 1996 Salinity Action Plan (SAP). Recovery Catchments are based on the identification of major, high priority public assets that are at risk from salinity and warrant significant, ongoing investment in their recovery and protection. The goal for the BMRC project is “to maintain the native species in a range of representative wetlands within the Buntine-Marchagee catchment by 2020”. It is recognised that achieving this goal will require management of a range of degradation issues besides salinity. To assist in the characterisation and prioritisation of the many wetlands throughout the Buntine-Marchagee catchment, and to begin the collection of baseline data to assess the effectiveness of management actions, twenty wetlands were chosen for detailed sampling. To maximise the biodiversity recorded, the wetlands were selected to provide a geographical spread along the main braided drainage system, a range of different physical characteristics and types of remnant vegetation communities. The selection focused on those with relatively intact remnant fringing vegetation. The selected wetlands were sampled in November 2003 by the Department of Conservation and Land Management (CALM) for a range of parameters including aquatic invertebrates, water chemistry, fringing and aquatic vegetation and wetland-scale water dynamics. The November 2003 invertebrate sampling constitutes the first of a number of planned invertebrate sampling events in these wetlands. Due to low water levels only eight of the twenty wetlands could be sampled, however, this limited sampling provided:

• a late spring sample to act as a baseline against which comparison may be made in later years;

• establishment of sites for on-going monitoring; and • a test of field sampling protocols.

The School of Animal Biology at The University of Western Australia was contracted to undertake taxonomic identification of the preserved aquatic invertebrate fauna samples that had been collected from the eight wetlands, statistical analysis of the resultant data, preparation of a report detailing these findings, and the production of a reference collection of specimens.

Objectives:

The objectives of this report were to detail the results of the identification of macro and micro invertebrates from “benthic” and “planktonic” samples collected from eight wetlands in the Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC) during November 2003, and provide an analysis and consideration of these results

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(including a comparison amongst wetlands using fauna and other selected parameters, and comparison with wetlands previously surveyed in the area under the SAP). METHODS

Field Methods

All field sampling was conducted by staff of the Department of CALM. The location of water and invertebrate sampling was marked with a star picket and its position determined with a GPS to allow standardisation with future surveys. Methods used are as detailed in the contract brief, and are here reproduced. Two subsamples (“benthic” and “planktonic”) were collected at each of the eight wetlands sampled during November 2003. A number of other parameters were also collected at each wetland, including basic fringing vegetation community composition, water quality parameters (pH, electrical conductivity, turbidity, colour, chlorophyll A, total soluble N and total soluble P), depth of water, substrate type, benthic mat development and % macrophyte cover. The invertebrate sampling methodology was consistent with that used in the CALM spatial survey project that sampled 200 wetlands between Kalbarri and Esperance as part of the State Salinity Action Plan (SAP). To maximize the number of wetlands sampled, only one site was sampled (with two net types) at each of the detailed wetlands. This is consistent with the SAP spatial survey project but varies from the two sites per wetland sampled in the SAP monitoring project. Sampling involved the collection of both macro and micro invertebrates. The following protocol is largely taken from Wetlands monitoring in the wheatbelt of south-west Western Australia: site descriptions, waterbird, aquatic invertebrate and groundwater data by Cale et al (2004). The “benthic” subsample was collected through 50 metres of vigorous discontinuous sweeping over a distance of about 200 metres with a 250µm mesh pond net on a D-shaped frame (350mm wide and 250mm high). All identified wetland habitats ≤ 1 m deep between the shore and center of the wetland were sampled including water column, submerged vegetation, bottom sediment, along submerged logs and around tree trunks. Lake substrates were vigorously disturbed. Contents of the pond-net were emptied into a bucket several times during sampling to reduce resistance of the net in the water. After elutriation and removal of large debris, the sample was preserved in 70% ethanol (by adding 95% ethanol) in 2 litre plastic drums. Samples were then labeled with permanent marker and an internal pencil written tag detailing the wetland identification number, date of collection, collectors name, and the words “benthic sample” and “70% ethanol”. The “planktonic” subsample was collected by 50 metres of more gentle, discontinuous sweeping over a 200m distance with a 50µm mesh pond net of the same sized frame in the same habitat, other than benthos. A transfer net was used to transfer the sample to 175ml vials. The sample was preserved in 1-2% formaldehyde (ie 37% formaldehyde solution diluted 10x to give 4% formaldehyde and then mixed with wet sample) for two to four days before being washed and transferred into 70% ethanol

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(by rinsing with tap water and adding 95% ethanol to the wet sample). It was labeled in the same manner as the benthic sample except for the words “planktonic sample” replacing “benthic sample”. The nets were sterilised with “Decon 90” and/or thoroughly dried between lakes to eliminate the risk of transfer of material between lakes and to allow for any specimens accidentally transferred into subsequent samples to be identified (desiccated) and excluded from analysis. Bulk, labelled, preserved samples were provided to the School of Animal Biology for processing and identification. Water samples were collected from midway through the profile before disturbance. Depth, EC, pH and temperature of the water in the wetland were all measured in the field. The water quality parameters EC, pH, chlorophyll a, total dissolved N, total dissolved P, colour and turbidity were measured in the laboratory from eight samples of solution and filter paper received by the Western Australian Chemical Centre on the 14th November 2003.

Sample Processing

Sample processing methods were as detailed in the contract brief and are here summarised. The aim of sample processing was to produce a species list and associated abundance estimation for each of the sixteen subsamples (eight benthic macroinvertebrate and eight planktonic microinvertebrate subsamples), with methods and taxonomy to be consistent with those used in the SAP spatial survey project, requiring specialist knowledge for the identification and naming of specimens. In the laboratory each macroinvertebrate subsample was separated into three size fractions using 2 mm, 500 µm and 250 µm sieves. Representatives of each species (or morphospecies) were picked out using a dissecting microscope with 10 – 50x magnification and the species scored for abundance on a log scale (1 – 10 animals = 1, 11 – 100 = 2, 101 – 1000 = 3, etc.). Microinvertebrate samples were processed by picking the first 200 – 300 individuals from an agitated sample decanted into a 125 mm2 gridded plastic tray, with all individuals identified to species, with the tray then scanned for additional missed taxa also taken to species. Macro and microinvertebrates were identified to species level and names reconciled with those used in the State Salinity Strategy Biological survey reports. Rotifers were identified to morphospecies level and then ranked for abundance accordingly. The species lists produced by the SAP spatial survey from four wetlands within and to the north of the Buntine-Marchagee catchment (not sampled in November 2003) and a complete species list from all SAP spatial survey lakes was provided for reference. Specimens that appeared desiccated (and were considered to be cross contamination from other sites) were identified and noted, however, they were excluded from further analysis.

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After identification, all specimens (except those retained for the reference collection) were stored in appropriate vials according to their site, sample type and size class, labeled appropriately and preserved in 70% ethanol and returned to the Midwest CALM office. Identifications and abundance data were entered onto an excel spreadsheet, indicating how many specimens of each taxon were contributed from each sample to the reference collection. To meet the above objectives, the eight macroinvertebrate subsamples were processed at the School of Animal Biology, The University of Western Australia using in-house expertise. The eight microinvertebrate subsamples were sent to Dr Russ Shiel, School of Environmental Biology, Adelaide University, S.A. for identification. Data on faunal composition of the micro fauna was then returned to the School of Animal Biology for statistical analysis along with the macroinvertebrate data.

Sites Sampled

The eight sites sampled in the BMRC, along with site codes and GPS locations are listed in Table 1. Table 1: The eight wetlands sampled in November 2003, listing site code, name of land holder, Northing and Easting, size and date sampled.

SITE CODE

LANDHOLDER NORTHING (GDA 94)

EASTING (GDA 94)

SIZE (HA) DATE SAMPLED

W002 Doley 420307 6686722 12 30/10/03 W004 Dobson 436633 6658696 2 03/11/03 W006 Dobson 435593 6658317 0.8 03/11/03 W009 Hunt 419736 6677930 1.4 05/11/03 W010 Hunt 419925 6678046 1 05/11/03 W011 Hunt 415399 6678363 3.4 06/11/03 W018 Counsel 446453 6667290 58.6 08/11/03 W019 Counsel 446124 6667125 1.1 08/11/03

In addition to the eight sites sampled in November 2003, data from four sites (SPS157, SPS158, SPS201, SPS203) sampled in the same catchment in September 1999, as part of the Salinity Action Plan Wheatbelt survey were provided by Dr Stuart Halse to facilitate broad comparisons in assemblage composition with the eight sites sampled in November 2003.

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Figure 1: Buntine-Marchagee Biodiversity Recovery Catchment, showing location of the eight wetlands sampled in November 2003 and the additional four wetlands sampled in the Salinity Action Plan (SAP) sampling program in September 1999.

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Statistical Analyses

Background Required analyses were specified in the contract brief and are here summarised. The aim of this component was to characterise the wetlands based on invertebrate assemblages determined from samples collected in November 2003, to assess the variation and redundancy of the aquatic invertebrates between the wetlands and to consider these results in comparison with the physico-chemical parameters measured. Results and interpretation will then be used by CALM to prioritize resources to maximize biodiversity protection. Analysis were to be conducted using both presence/absence and abundance data sets, with comparisons between these measures. Methods of statistical analysis followed those used by Storey (1998) for assessing spatial and temporal variation and conservation significance of wetlands in the Byenup-Muir peat swamp system, southwestern Australia, and used similar categories for assessing conservation significance, and multivariate approaches for ordination and classification of data. Specifically, analyses were required to address:

1. What is the correlation between conservation significance/ biodiversity/wetland groupings/species to the other parameters (eg. basic water parameters and surrounding vegetation type) measured in November 2003?

2. What is the similarity / dissimilarity between the parameters measured at each of the wetlands, with a measure of this?

3. Which wetlands have the most biodiversity? How does this ranking vary when different taxon types are considered? (e.g. Micro versus macro invertebrates)

4. What is the conservation significance of each taxa, assemblage and wetland?

5. What species are common across a number of wetlands? What species are unique to individual (or group of) wetlands?

6. Which of the November 2003 samples seem to have similar suites of invertebrate communities, with a measure of this similarity? Which species (or group of species) define these wetland types?

7. What differences and similarities are there with the limited data available for those wetlands previously sampled in the SAP spatial project? As these wetlands were not resampled during the November 2003 sampling (due to low water levels) a direct comparison will not be possible.

Analyses Performed Analyses used to address the specific issues identified in the brief are described below.

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Wetland physico-chemistry similarity Multivariate pattern analysis using ordination and classification was used to group wetlands according to physico-chemical properties to indicate groups of similar/dissimilar sites. Prior to analysis, Pearson Product Moment correlation was performed to detect inter-relationships amongst physico-chemical parameters. Gradients of each parameter were placed through ordination space to indicate parameters responsible for site groupings, and between group difference in parameters were tested by ANOVA, if appropriate. The dissimilarity measures calculated across wetlands were used to determine levels of similarity/dissimilarity within and between groups of wetlands identified from the analyses. Results were presented in classification dendrograms and ordination scatterplots. Only those physico-chemical parameters that were as integer data or could be classified to an ordinal scale could be utilised in this analysis. Variables used were Area of wetland (ha.), Water depth (m), Water temperature (oC), Electrical conductivity (EC) in the field (ms/m), EC measured in the laboratory, pH measured in the field, pH measured in the laboratory, Chlorophyll-a (mg/l), Colour (TCU), Total nitrogen (mg/l), Total phosphorus (mg/l) and Turbidity (NTU). Data for Total phosporus, however, were all less than detection limit and therefore were not included as this parameter was invariant. Wetland biodiversity Wetland biodiversity was assessed by ranking wetlands according to taxa richness for micro, macro and total fauna composition, whereby, within each taxonomic level, the wetland with the most species was given the highest rank score (i.e. “8”), and the wetland with the least species was given the lowest rank score of “1”. For tied ranks, the score was shared (i.e. two wetlands with the same number of species were given the same score). Wetlands were then ordered in descending order based on the mean of their rank scores across taxonomic levels, with results tabulated and graphed. Conservation significance Taxa were allocated to one of four conservation categories:

C = common taxa recorded from other states/territories; S = endemic to southwestern Australia, but commonly occurring; L = endemic to southwestern Australia, but with a restricted occurrence; and I = indeterminate status due to insufficient taxonomic information.

Wetlands were then ranked according to composition of fauna scored by conservation categories, with high endemicity or rarity attracting a high ranking (1) and cosmopolitan taxa attracting a low ranking (>> 1). Results were tabulated and presented graphically. Taxa distributions Determination of which taxa were common or unique to each wetland and across wetlands was achieved by cross-tabulation of taxa by sites, with sites ranked by number of shared taxa.

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Wetland fauna similarity Multivariate pattern analysis was performed using ordination and classification techniques to group wetlands according to macro, micro and total fauna assemblages with both presence/absence and abundance data. This approach indicates groups of similar/dissimilar sites based on fauna. The dissimilarity measures calculated across wetlands were used to determine levels of similarity/dissimilarity within and between groups of wetlands identified from the analyses. Gradients in abundance of individual species will be placed through ordination space to indicate taxa responsible for site groupings. Results were presented as classification dendrograms and ordination scatterplots. The approach taken is detailed in the multivariate analysis section. Inter-relationships amongst faunal attributes and physico-chemistry Gradient analysis through ordination space was used to examine relationships between conservation significance, biodiversity and wetland groupings to the physico-chemical parameters collected for each wetland. Similarities between SAP wetlands and BMRC wetlands. Similarities in the relationships between the invertebrate assemblages of the eight BMRC wetlands and the four SAP wetlands were investigated using the same multivariate techniques (ordination and classification). Acknowledging the possible temporal confounding effect, a conservative approach was taken to this analysis whereby only presence/absence (i.e. not abundance) data were used in analyses. General differences/similarities in biodiversity will also be reported in tabular and graphical forms.

Multivariate Analyses The CSIRO Pattern Analysis package PATN (Belbin, 1995) was used to ordinate and classify sites on physico-chemical parameters and on invertebrate assemblages, and then to investigate relationships between invertebrate assemblages and physico-chemical parameters. This approach demonstrated: • groups of sites with similar/dissimilar chemical/physical characteristics, with a

measure of this dissimilarity, • between site differences in the fauna, • groups of sites which contained similar/dissimilar macroinvertebrate assemblages,

with a measure of this dissimilarity, • relationships between macroinvertebrate assemblages and chemical/physical

characteristics of the sites/site groupings, and, • taxa that may be used as indicators of different groups of sites identified in the

analysis (i.e. taxa that are indicative of specific water quality conditions such as increased salinity).

Prior to ordination and classification, physico-chemical variables were standardised to a scale of zero to 1 by the equation: xnew = (x-xmin)/xrange, as recommended by Harch (1996) for mixed type variables. This was necessary to set all physico-chemical variables on the same scale and so avoid variables on a higher scale having an over-

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bearing influence on the analysis. For physico-chemical data that were reported at below detection limit (i.e. nutrients), half the detection limit was used as the value for analysis. However, the non-standardised physico-chemical data were used for correlation analysis (PCC; see below). Invertebrate data were analysed as log10 abundance scale and as presence/absence data, with three data sets utilised; microinvertebrate fauna, macroinvertebrate fauna, and combined total invertebrate fauna. Normally in such analyses, only taxa that occurred at greater than 10% of wetlands in each data set were included to avoid ‘low-occurrence’ taxa having a disproportionate effect on the results (Gaugh, 1982). However, this procedure was overlooked because of the small data sets (8 sites only), and the high proportion of singletons (taxa occurring only once in each data set), which, if omitted, would reduce the data sets to very few taxa. Wetlands were classified using Unweighted Pairgroup Arithmetic Averaging (UPGMA), an agglomerative hierarchical fusion technique which produces a dendrogram in which sites with similar physico-chemistry/invertebrate assemblages group together. This analysis was performed to identify groupings of sites with similar physico-chemistry/invertebrate communities. Sites were then ordinated using Semi-Strong Hybrid Multidimensional Scaling (SSH MDS) to produce an n-dimensional scatter plot of sites. For each analysis, similarity between sites was determined using the Bray-Curtis association measure. UPGMA groupings were superimposed on the respective ordination plots to assess the distinctiveness of the site groupings. To test the significance of the separation of these groups of sites in ordination space, the Analysis of Similarity (ANOSIM) option in PATN (Belbin, 1995) was invoked. The Principal Axis Correlation (PCC) option in PATN was used to place gradients of:

• physico-chemical parameters through the ordination of sites on physico-chemical data;

• physico-chemical parameters through the ordination of sites on invertebrate data; and

• invertebrate taxa through the ordination of sites on invertebrate data. Monte Carlo randomisations (n=100) of the data were performed to test the significance of these gradients. Given the small sample size (eight samples), randomisations were not considered robust, given the limited number of the random permutations possible without repeating patterns, and therefore should be treated with caution. Similarly, analysis of variance-type analyses could not be performed given the small sample size in some groups (i.e. groups with < 2 wetlands). To further assess the role physico-chemistry and wetland morphology may play in structuring invertebrate communities, the UPGMA groupings derived from the classifications of sites on physico-chemical data were superimposed on the ordination of sites by invertebrate data. Clear separation of these groupings would indicate that water quality and morphology influenced invertebrate community structure. The significance of separation of these groupings was tested using ANOSIM.

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RESULTS

Physico-chemistry

The physico-chemical characteristics of each wetland are detailed in Appendix 1. Water temperatures ranged from 20.6 – 27.6 oC, likely reflecting differences in water depth (i.e. shallower wetlands have greater diel ranges in temperature) and time of day when sampled (wetlands sampled in the morning will likely be cooler than those sampled in the afternoon). Conductivities ranged from 330 mS/m to 18220 mS/m (2 – 100 ppt), reflecting a large range in salinities, from brackish to hypersaline respectively. The wetlands were all circum-neutral to alkaline (pH 7 – 9), except for one acidic wetland (W002). Chlorophyll-a levels ranged from below detection to 0.039 mg/l, indicating algal growth in some wetlands (i.e. W004 & W009). None of the eight wetlands were highly coloured, ranging from 9 – 30 TCUs. Similarly, turbidity was generally low in all wetlands (< 1.0 NTUs). Total soluble phosphorus levels were below detection in all wetlands, however, total soluble nitrogen levels were > 1.5 mg/l in all wetlands and reached a maxima of 5.6 and 7.2 mg/l in W004 and W006, respectively. Nutrient data were compared against trigger values in the ANZECC/ARMCANZ (2000) water quality guidelines for the protection of ecosystems, using trigger values specific to slightly disturbed wetlands of south west Western Australia. Nutrient levels exceeded the trigger values for total nitrogen (1.5 mg/l) in all eight wetlands, however, the guideline for total phosphorus (0.060 mg/l), was never exceeded. The wetlands may be at an increased level of risk of algal blooms (i.e. potential for nuisance algal blooms exists) with respect to total nitrogen, however, phosphorus may be limiting and may restrict excessive algal growth. The numeric physico-chemical parameters selected for inclusion in analysis were analysed in the first instance with Pearson product moment correlation to determine inter-dependencies in the data (Table 2). The only significant correlations were between field and laboratory-measured conductivity and field and laboratory-measured pH (Table 2, Figure 2). These relationships indicate that these parameters did not change greatly between field and stored samples and confirmed the accuracy of field measurements. Data for Total phosphorus were all below detection and due to the invariant nature of these data, they did not correlate with any parameter.

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0

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EC Fie ld (m s /m )

EC L

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/m)

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Figure 2: x-y scatter plots of electrical conductivity (left) and pH (right) measured in the field (x-axes) and in the laboratory (y-axes), showing strong correlations, especially for conductivity.

Table 2: Pearson product moment correlation matrix amongst physico-chemical parameters, indicating correlation coefficient (r) and significance of the relationship (ns = not significant). For instances, n = 8, except for depth, where n = 7 due to a missing value for wetland W006.

Area Depth Temp EC_field EC_lab pH_field pH_lab chl_a colour Total_N Total_P

Depth 0.121 ns

Temp -0.312 0.121 ns ns

EC_field 0.396 -0.302 0.268 ns ns ns

EC_lab 0.397 -0.325 0.271 0.999 ns ns ns <.0001

pH_field -0.274 0.507 0.341 -0.456 -0.468 ns ns ns ns ns

pH_lab -0.240 0.540 0.283 -0.421 -0.439 0.975 ns ns ns ns ns <.0001

chl_a -0.252 -0.205 0.282 -0.147 -0.126 0.286 0.074 ns ns ns ns ns ns ns

colour -0.335 -0.416 0.065 -0.186 -0.175 0.352 0.359 0.197 ns ns ns ns ns ns ns ns

Total_N -0.128 -0.456 0.547 0.497 0.518 -0.289 -0.354 0.298 0.276 ns ns ns ns ns ns ns ns ns

Total_P . . . . . . . . . . .

Turbidity -0.057 -0.366 0.263 0.606 0.612 0.011 0.005 0.229 0.534 0.669 . ns ns ns ns ns ns ns ns ns ns .

11


Classification of sites on standardised physico-chemical parameters detected three main groups of sites (Figure 3). Ordination also showed a clear separation of samples into three groups in ordination space. ANOSIM showed a significant separation of these groupings in ordination space (p = 0.020) (Figure 3). The main separation was of the fresh-brackish water sites (W009, W010 & W011; 2 – 10 ppt) from the hypersaline sites (W004, W006, W018 & W019; 60 – 100 ppt), with the saline site (W002; 30 ppt) as an outlier of these two groups. Depth and pH were higher at the fresh-brackish sites, and colour, turbidity and conductivity were higher at the hypersaline sites. Site W002 appeared to have a salinity value intermediate between the fresher and saline sites. In addition, it was the only acidic wetland (pH = 5.0), compared with all the other wetlands which were circum-neutral ( ~ pH 7.5) to alkaline (pH 8.2 – 9.7). W002 _____________________________________________________________ Group 1

W004 _ | W006 |________________ | Group 2 W018 _ | | W019 |_______________|______________________ | W009 __________ | | W010 _________|______________ | | Group 3 W011 _______________________|______________|_____________________|

| | | | | | 0.1850 0.3188 0.4526 0.5864 0.7202 0.8540

Figure 3: UPGMA classification dendrogram of the eight sites using standardised physico-chemical parameters, indicating the three main groupings.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e n s io n 1

Dim

en

sio

n 2

Group 1 Group 2 Group 3 Gradients

Ec ( lab & f ie ld)

Turbid ity

Colour

Depth

pH ( lab & f ie ld)W 011

W 010

W 009

W 002

W 018 W 019

W 006

W 004

Figure 4: MDS ordination of the eight wetlands using standardised physico-chemical variables, with sites labeled by site codes, coloured by a posteriori groupings from UPGMA classification (Figure 3), and indicating significant PCC-derived gradients of unstandardised physico-chemical parameters through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1322).

12


Table 3: Matrix of association values, with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on standardised physico-chemical parameters. Site names are labeled according to the UPGMA groupings indicated in Figures 3 & 4.

W002 (Grp1) W004

W004 (Grp2) 0.706 (Grp2) W006 W006 (Grp2) 0.709 0.187 (Grp2) W009 W009 (Grp3) 0.831 0.510 0.571 (Grp3) W010 W010 (Grp3) 0.860 0.484 0.488 0.287 (Grp3) W011 W011 (Grp3) 0.879 0.465 0.484 0.441 0.412 (Grp3) W018 W018 (Grp2) 0.609 0.420 0.321 0.503 0.554 0.570 (Grp2) W019 (Grp2) 0.637 0.335 0.232 0.477 0.517 0.525 0.185

Between-site pairwise dissimilarity values were used to calculate mean within group dissimilarity for the fresh-brackish and hypersaline sites, being 0.280 and 0.380 respectively. This compared with mean dissimilarity of the fresh-brackish to the hypersaline sites and vice versa of 0.551 and 0.581 respectively, indicating a greater within group than a between group similarity (i.e. a lower dissimilarity).

Aquatic Invertebrate Fauna

Taxonomy In total, 64 taxa were recorded from the eight wetlands in November 2003 (Table 4). The microinvertebrate fauna comprised four species of Rotifera, four Copepoda, three Cladocera, and five species of Ostracoda (Table 4). The macroinvertebrate fauna comprised one species of Hydracarina, two ‘macro-crustacea’, 10 Coleoptera, three Hemiptera, twenty-one Diptera, three Trichoptera, one Ephemeroptera, six Odonata, and one Lepidoptera. All taxa are presented in systematic order in Table 4 and a voucher collection was compiled and lodged with Dept CALM (ref Appendix 2). All specimens identified from the eight wetlands were taken to species using available keys, reference collections and available expertise. Species were also compared against the SAP reference collection to confirm identifications and to standardise taxonomy as best as possible. Even so there were several groups requiring clarification. Taxonomy of the anostracan crustacea Artemia parthenogenetica is imperfect, and this identification was based on cross-referencing to vouchers held by Dr Brenton Knott, UWA and also to the absence of males in the wetlands sampled; the species is parthenogenic, and therefore does not require males to reproduce. Within the non-biting midges, Chironomidae, the taxonomy is incomplete and various species and complexes are open to conjecture. For example, the complex of Tanytarsus fuscithorax, Tanytarsus ?semibarbitarsus and Tanytarsus barbitarsus are separated principally on size and shape of anal papillae. However, Dr Don Edward, UWA considers this feature to be unreliable and subject to variation according to ambient salinity. Interestingly, the distribution of these three species matched the salinity data for the eight wetlands, with T. fuscithorax (large papillae) only occurring in the fresh-brackish sites, T. ?semibarbitarsus (medium papillae) only in the saline site, and T. barbitarsus (reduced papillae) only in the hypersaline sites. A potentially new species of chironomid, ?Cladopelma sp. was taken from site W011. It was

13


similar, but distinctly different to Polypedilum nubifer and was also distinctly different to C. curtivalva. Finally, within the Chironomidae, Procladius villosimanus and P. paludicola were difficult to separate with confidence, since their morphology is variable, and early instar P. villosimanus are very difficult to differentiate from P. paludicola. The taxonomic listing also includes records of larval and pupal stages for groups such as Diptera and Coleoptera. Current taxonomy in Australia is not sufficiently well developed to allow identification of these groups to species level. In many instances it is likely that these stages are the same species as the larval/adult stages recorded from the same location. However, because this could not be definitively determined, they were treated as separate taxa.

Taxa Richness Of the 64 taxa, only four were common to at least five or more sites (Ceratopogoninae spp., Ostracod sp.C, Chironomid pupae and Tabanidae spp.). Eleven percent of taxa (7) occurred at only four sites, 14% (9 taxa) at three sites, 27% (17 taxa) at two sites and 41% (26 taxa) were singletons occurring from only one site (Appendix 3). Analysis of taxa richness across sites indicated comparable richness in microinvertebrate fauna across sites, with the exception of site W006, at which no microinvertebrates were recorded (Figure 5). Sites W011 and W018 had the most microinvertebrate taxa with 6 each, and after site W006, sites W010 and W019 had the least with 3 taxa each (Figure 5). Richness of macroinvertebrate fauna varied across sites, with the freshwater sites W010 and W011 supporting the most macroinvertebrates (30 and 24 taxa respectively), and the hypersaline wetlands of W004, W006, W018 and W019 supporting the least richness (with 7, 7, 9 and 8 taxa respectively). Overall, the four fresh-brackish water sites of W002, W009, W010 and W011 supported the greatest total taxa richness with 18, 17, 33 and 30 taxa respectively (Figure 5).

0

10

20

30

40

W 002 W 004 W 006 W 009 W 010 W 011 W 018 W 019

Sites

Num

ber

of T

axa

No. microinvertebrate taxa No. macroinvertebrate taxa Total no. taxa

Figure 5: Number of taxa of microinvertebrates, macroinvertebrates and total number of invertebrate taxa taken from each site in November 2003.

14


Table 4: Abundance (log10 scale where 1 = 1 – 10 individuals, 2 = 11 – 100, 3 = 101 – 1000 and 4 = > 1001) of each taxon at each site, with total number of taxa and numbers of southwest Western Australia common endemic and restricted endemic taxa. The level of endemism of each taxa is indicated, where C = common taxa recorded from other states/territories, S = endemic to southwestern Australia, but commonly occurring, L = endemic to southwestern Australia, but with a restricted occurrence, I = indeterminate status due to insufficient taxonomic information, ? = awaiting taxonomic identification).Suffixes after species names refer to larval (L) and pupal (P) forms. CLASS

Order

Family

subfamily

Species

W00

2

W00

4

W00

6

W00

9

W01

0

W01

1

W01

8

W01

9

Ende

mis

m

ROTIFERA

Bdelloidea indet. contr. sp. 0 0 0 0 0 2 0 0 ?

Monogononta Brachionidae Brachionus plicatilis 0 0 0 0 0 2 1 0 C

Keratella australis 1 0 0 3 0 0 0 0 C

Hexarthridae Hexarthra propinqua 1 0 0 0 0 0 0 0 L*

ARTHROPODA

ARACHNIDA

ACARIFORMES Hydracarina

Arrenuridae Arrenurus sp. 0 0 0 0 1 0 0 0 C

CRUSTACEA

COPEPODA Cyclopoida Meridiecyclops baylyi 1 3 0 0 0 1 3 0 C

Calanoida Boeckella triarticulata 0 0 0 1 2 2 0 0 C

Calamoecia salina 0 0 0 0 0 0 2 0 C

Harpacticoida Cletocamptus deitersi. 0 0 0 0 1 0 0 0 C

CLADOCERA Daphnidae Daphnia carinata 0 0 0 1 0 0 0 0 C

Daphniopsis truncata 2 0 0 0 0 0 0 0 C

Macrothricidae Macrothrix breviseta 0 0 0 0 0 1 0 0 C

OSTRACODA Ostracoda Australocypris insularis. 0 1 0 0 0 0 1 1 C

Mytilocypris cf mytiloides. 0 1 0 0 0 0 0 0 C

Reticypris sp. 3 2 0 1 1 1 1 1 ?

Diacypris sp. 0 1 0 0 0 0 0 0 ?

Sarscypridopsis aculeata 0 0 0 0 0 0 1 1 C

ANOSTRACA Artemiidae Artemia parthenogenetica 2 0 3 0 0 0 2 4 C

AMPHIPODA Ceinidae Austrochiltonia subtenius 1 0 0 4 4 4 0 0 C

INSECTA

COLEOPTERA Dytiscidae Necterosoma penicillatus 0 0 1 0 2 1 1 0 C

Necterosoma sp. (L) 1 0 0 0 1 2 0 0 I

Sternopriscus mulimaculatus 0 0 0 0 1 1 0 0 I

Sternopriscus sp. (L) 0 0 0 0 1 1 0 0 I

Allodessus bistrigatus 0 0 0 1 1 0 0 0 C

Hydrophilidae Berosus discolor 1 0 0 0 1 0 0 0 C

Berosus sp. (L) 2 1 0 0 1 1 0 0 I

Helochares sp. (L) 0 0 0 0 0 1 0 0 I

Laccobius sp. (L) 0 0 0 0 1 0 0 0 I

Hydraenidae Hydraena sp. 0 0 0 0 1 0 0 0 I

HEMIPTERA Corixidae Micronecta robusta 0 0 0 3 3 3 0 0 C

Agraptocorixa eurynome 0 0 0 1 3 1 0 0 C

Notonectidae Anisops thienemanni 0 0 0 0 3 3 0 0 C

DIPTERA Chironomidae Chironomid spp. (P) 1 3 2 0 1 1 2 2 I

Chironominae Tanytarsus fuscithorax 0 0 0 0 3 3 0 0 C

Tanytarsus ?semibarbitarsus 1 0 0 0 0 0 0 0 C

Tanytarsus barbitarsus 0 4 4 0 0 0 4 4 C

15


CLASS

Order

Family

subfamily

Species

W00

2

W00

4

W00

6

W00

9

W01

0

W01

1

W01

8

W01

9

Ende

mis

m

Cryptochironomus griseidorsum 0 0 0 2 2 0 0 0 C

Dicrotendipes ?conjunctus 0 0 0 1 3 0 0 0 S

Polypedilum nubifer 0 0 0 1 3 1 0 0 C

?Cladopelma sp. 0 0 0 0 0 3 0 0 L

Tanypodinae Procladius villosimanus 0 0 0 0 3 3 0 0 S

Procladius paludicola 3 2 0 2 0 3 0 0 C

Ablabesmyia notabilis 0 0 0 0 0 1 0 0 C

Orthocladiinae ?Limnophyes pullulus 1 0 0 0 0 0 0 0 C

Culicidae Culicinae spp. (L) 1 0 0 0 1 0 0 0 I

Culicinae spp. (P) 0 0 0 0 1 0 0 0 I

Ceratopogonidae Ceratopogoninae spp. 3 2 3 2 2 2 2 1 I

Ceratopogonid spp. (P) 1 0 1 0 0 0 1 0 I

Dasyheleinae spp. (L) 0 0 0 0 0 0 1 0 I

Tabanidae Tabanidae spp. 1 1 1 0 1 0 2 2 I

Stratiomyidae Stratiomyidae spp. (P) 0 0 0 0 0 0 0 1 I

Ephydridae Ephydridae spp. (P) 0 1 0 0 0 0 1 1 I

Scatopsidae Scatopsidae sp. (L) 1 0 0 0 0 0 0 1 I

TRICHOPTERA Leptoceridae Triplectides australis 0 0 0 2 2 0 0 0 C

Notalina spira 0 0 0 2 2 1 0 0 C

Oecetis sp. 0 0 0 1 1 0 0 0 I

EPHEMEROPTERA Caenidae Tasmanocoenis tillyardi 0 0 0 1 0 0 0 0 C

ODONATA

ANISOPTERA Libellulidae Orthetrum caledonicum 0 0 0 0 0 1 0 0 C

Diplacodes bipunctata 0 0 0 0 0 1 0 0 C

Aeshnidae Hemianax papuensis 0 0 0 0 0 1 0 0 C

ZYGOPTERA Lestidae Austrolestes annulosus 0 0 0 0 2 2 0 0 C

Coenagrionidae Xanthagrion erythroneurum 0 0 0 0 0 1 0 0 C

Ischnura aurora 0 0 0 0 1 0 0 0 C

LEPIDOPTERA Pyralidae Nymphulinae sp. 39 0 0 0 0 1 0 0 0 C

Number of microinvertebrate taxa 5 5 0 4 3 6 6 3

Number of macroinvertebrate taxa 14 7 7 13 30 24 9 8

Total Number of taxa 19 12 7 17 33 30 15 11

No. SW common endemics (S) 0 0 0 1 2 1 0 0

No. SW restricted endemics (L) 2 0 0 1 1 3 0 0

*This is a European/Central Asian species, but is the first record in Australia and therefore has conservation significance and is therefore classified as a ‘restricted local endemic’. Overall wetland biodiversity was assessed by ranking wetlands according to taxa richness for micro, macro and total fauna composition, and then ordering sites on the mean of the ranks across taxonomic levels (Table 5, Figure 6). The fresher water sites (W011, W010, W002 and W009) consistently had the highest rankings, with the exception of site W018 which on one occasion had the joint highest ranking based on microfauna, but lower scores for macro and total fauna at this site reduced its overall ranking. The hypersaline sites (W004, W006 & W019) consistently had the lowest ranking reflecting low species diversity (Table 5, Figure 6).

16


Table 5: Rank scores for wetlands according to species diversity, based on microinvertebrate, macroinvertebrate and total fauna diversity, with wetlands in ascending order according to the mean of individual rank scores across taxonomic levels. The wetland with the highest species diversity was given a rank score of “8” and the lowest diversity a score of “1”.

Site

Micro-invertebrate

Fauna

Macro-invertebrate

Fauna

Total Fauna

Mean Ranking across taxonomic

levels W011 7 7 7 7.0 W010 2 8 8 6.0 W002 5 6 6 5.7 W018 7 4 4 5.0 W009 4 5 5 4.7 W004 5 1 3 3.0 W019 2 3 2 2.3 W006 1 1 1 1.0

0

2

4

6

8

10

W 011 W 010 W 002 W 018 W 009 W 004 W 019 W 006

W etlands

Ran

ked

Taxa

Div

ersi

ty

M icroinvertebrates Macroinvertebrates Total Fauna Mean Ranking

Figure 6: Rank scores for wetlands according to species diversity, based on microinvertebrate, macroinvertebrate and total fauna diversity, with wetlands ordered according to the mean of individual rank scores across taxonomic levels. The wetland with the highest species diversity was given a rank score of “8” and the lowest diversity a score of “1”.

Conservation Significance of Invertebrate Fauna Conservation status was determined based on levels of endemicity and rarity in the fauna. Of the 64 taxa, 60% (38 taxa) were cosmopolitan in distribution, occurring across Australia and overseas, 29% (18 taxa) were indeterminate due to insufficient information, 9.5% (6 taxa) are awaiting final taxonomic resolution before levels of endemism can be assigned, 3% (2 taxa) were considered as commonly occurring south-west endemics (the chironomids Dicrotendipes ?conjunctus and Procladius

17


villosimanus), and 3% (2 taxa) were considered to be rarely occurring southwest endemics (the rotifer Hexarthra propinqua (included in this group since it is the first record for Australia, although it has a widespread distribution in Europe and central Asia), and the chironomid ?Cladopelma sp. nov, which appears to be a new species) (Table 4). The fresh-brackish water sites (W009, W010 & W011) had the greatest occurrence of south-west endemics (both commonly occurring and rarely occurring taxa), with site W002 also supporting rarely occurring taxa endemic to the south-west of Western Australia (Table 4). The hypersaline sites tended to support predominantly cosmopolitan species.

Multivariate Analysis of Microinvertebrate Fauna Abundance Data

UPGMA classification of microfauna on abundance data separated the seven sites (site W006 contained no microfauna) into three main groups, the hypersaline sites (W004, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.02) (Figures 7 & 8). Conductivity was the only physico-chemical parameter to have a significant gradient through the ordination, placed in the direction of the hypersaline wetlands. Five taxa had significant gradients in abundance through the ordination, with species reflecting differing preferences for each site grouping (Figure 8). However, care must be taken when interpreting the significance of gradients of physico-chemical parameters and species abundances through all ordinations because of the low number of sites in the data set relative to the requirement for adequate sites to allow at least 100 independent and unique Monte-Carlo randomisations to determine significance. Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. Because of the number and small site membership of the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 6). W002 _________________________________________________ Group 1 W004 _ | W018 |________________________ | Group 2 W019 ________________________|_______________________|____________

W009 ______________________________________ | Group 3 W010 _____________________ | | W011 ____________________|________________|______________________| Group 4

| | | | | | 0.3750 0.4742 0.5734 0.6726 0.7718 0.8710

Figure 7: UPGMA classification dendrogram of the seven sites (W006 contained no microfauna) using microfauna log(10) abundance data, indicating the four main groupings.

18


- 1

- 0 .5

0

0 .5

1

1 .5

- 1 .5 - 1 - 0 .5 0 0 .5 1 1 .5

D im e n s io n 1

Dim

en

sio

n 2

G r o u p 1 G r o u p 2 G r o u p 3 G r o u p 4 G r a d ie n t s

W 0 1 1W 0 1 0

W 0 0 9

W 0 0 2W 0 1 8

W 0 1 9

W 0 0 4

E c ( la b & f ie ld )

- 1

- 0 .5

0

0 .5

1

1 .5

- 1 .5 - 1 - 0 .5 0 0 .5 1 1 .5

Dim e n s io n 1

Dim

en

sio

n 2

G r o u p 1 G r o u p 2 G r o u p 3 G r o u p 4 G r a d ie n ts

W 0 1 1

W 0 1 0

W 0 0 9

W 0 0 2W 0 1 8

W 0 1 9

W 0 0 4

H . p r o p in q u aD . tr u n c a ta

O s tr a c o d a s p .C

A u s tr a lo c y p r is s p .

B . tr ia r t ic u la ta

Figure 8: MDS ordination of the seven wetlands (W006 contained no microfauna) using microfauna log(10) abundance data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 7), and indicating significant PCC-derived gradients of (top) unstandardised physico-chemical parameters and (bottom) species abundances through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1650).

Table 6: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on microfauna log(10) abundance data.

W002 W004

W004 0.600 W009 W009 0.714 0.846 W010 W010 0.833 0.818 0.600 W011 W011 0.765 0.750 0.733 0.539 W018 W018 0.765 0.375 0.867 0.846 0.667 W019 0.818 0.600 0.778 0.714 0.833 0.500

19


Presence/absence Data UPGMA classification of microfauna on presence/absence data again separated the seven sites into three main groups, the hypersaline sites (W004, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.0001) (Figures 9 & 10). Conductivity and pH were the only physico-chemical parameters to have significant gradients through the ordination, with conductivity placed in the direction of the hypersaline wetlands and pH in the opposite direction to the saline site W002. Five taxa had significant gradients in abundance through the ordination, with species reflecting differing preferences for each site grouping (Figure 10). Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. As for the abundance data set, due to the number and small site membership of the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 7). W002 _______________________________________________ Group 1

W009 ______________ | W010 _____________|______________________ | Group 2

W011 ___________________________________|__________|______________ Group 3

W004 _____________ | W018 _ | | Group 4 W019 |___________|_______________________________________________| | | | | | | 0.3330 0.4230 0.5130 0.6030 0.6930 0.7830

Figure 9: UPGMA classification dendrogram of the seven sites (W006 contained no microfauna) using microfauna presence/absence data, indicating the four main groupings.

Table 7: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on microfauna presence/absence data.

W002 W004

W004 0.556 W009 W009 0.556 0.750 W010 W010 0.750 0.714 0.429 W011 W011 0.636 0.600 0.600 0.556 W018 W018 0.636 0.400 0.800 0.778 0.500 W019 0.750 0.429 0.714 0.667 0.778 0.333

20


-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2

Group 1 Group 2 Group 3 Group 4 Gradients

W011

W010

W009

W002

W018

W019

W004

Ec (lab & f ield)

pH (lab)

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009

W002

W018

W019

W004B. triarticulata

A ustralocypris sp.Ostracoda sp.C

H. propinqua

D. truncata

Figure 10: MDS ordination of the seven wetlands (W006 contained no microfauna) using microfauna presence/absence data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 9), and indicating significant PCC-derived gradients of (top) unstandardised physico-chemical parameters and (bottom) species abundances through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1651).

21


Multivariate Analysis of Macroinvertebrate Fauna Abundance Data

UPGMA classification of macrofauna on abundance data similarly separated the eight sites into three main groups, the hypersaline sites (W004, W006, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.010) (Figures 11 & 12). Conductivity was the only physico-chemical parameter to have a significant gradient through the ordination, with conductivity placed in the direction of the hypersaline wetlands. Eight taxa had significant gradients in abundance through the ordination, with species reflecting differing preferences for each site grouping (Figure 12). Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. Because there were few sites in the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 8). W002 ___________________________________ Group 1 W004 ________________ | W006 _ | | W018 |______ | | Group 2 W019 ______|________|__________________|__________________________

W009 __________________________ | Group 3

W010 ________________ | | W011 _______________|_________|__________________________________| Group 4

| | | | | | 0.1610 0.3448 0.5286 0.7124 0.8962 1.0800

Figure 11: UPGMA classification dendrogram of the eight sites using macrofauna log(10) abundance data, indicating the four main groupings.

Table 8: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on macrofauna log(10) abundance data.

W002 W004

W004 0.576 W006 W006 0.529 0.379 W009 W009 0.762 0.784 0.895 W010 W010 0.750 0.851 0.853 0.474 W011 W011 0.705 0.786 0.860 0.569 0.390 W018 W018 0.600 0.333 0.161 0.897 0.855 0.862 W019 0.706 0.379 0.267 0.947 0.912 0.930 0.226

22


-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009

W002

W018

W019

W004

Ec ( lab & f ield)

W006

-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009

W002

W018

W019

W004

W006

A . subteniusM. robusta

T. barbitarsus

Helochares sp. (L), A . notabilis ,O. caledonicum, D. bipunctata, A . annulosus

Figure 12: MDS ordination of the eight wetlands using macrofauna log(10) abundance data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 11), and indicating significant PCC-derived gradients of (top) unstandardised physico-chemical parameters and (bottom) species abundances through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1065).

23


Presence/absence Data UPGMA classification of macrofauna on presence/absence data similarly separated the eight sites into three main groups, the hypersaline sites (W004, W006, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.010) (Figures 13 & 14). Conductivity was the only physico-chemical parameter to have a significant gradient through the ordination, with conductivity placed in the direction of the hypersaline wetlands. Eleven taxa had significant gradients in abundance through the ordination, with species reflecting differing preferences for each site grouping (Figure 14). Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. Because there were few sites in the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 9). W002 __________________________________ Group 1

W004 ___________________ | W006 _ | | W018 |___________ | | Group 2 W019 ___________|______|______________|___________________________

W009 ________________________________ | Group 3

W010 ____________________ | | W011 ___________________|___________|____________________________| Group 4 | | | | | | 0.1250 0.3020 0.4790 0.6560 0.8330 1.0100

Figure 13: UPGMA classification dendrogram of the eight sites using macrofauna presence/absence data, indicating the four main groupings.

Table 9: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on macrofauna presence/absence data.

W002 W004

W004 0.500 W006 W006 0.500 0.429 W009 W009 0.769 0.800 0.900 W010 W010 0.628 0.784 0.784 0.488 W011 W011 0.676 0.742 0.807 0.622 0.407 W018 W018 0.546 0.375 0.125 0.909 0.795 0.818 W019 0.600 0.286 0.286 0.900 0.838 0.871 0.250

24


-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009W002

W018

W019

W004

Ec (lab & f ield)

W006

-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009W002

W018

W019

W004

W006

A . subtenius

T. barbitarsus

T. tillyardi

A . bis trigatus,D. ?conjunctus, T. australis , Oecetis sp.

M. robusta,A . macrops, P. nubifer, N. spira

Figure 14: MDS ordination of the eight wetlands using macrofauna presence/absence data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 13), and indicating significant PCC-derived gradients of (top) unstandardised physico-chemical parameters and (bottom) species abundances through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1154).

25


Multivariate Analysis of Total Invertebrate Fauna Abundance Data

UPGMA classification of the total fauna on abundance data again separated the eight sites into the same three main groups, the hypersaline sites (W004, W006, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.010) (Figures 15 & 16). Conductivity and pH were the only physico-chemical parameters to have significant gradients through the ordination, with conductivity placed in the direction of the hypersaline wetlands and pH in the opposite direction to the acidic site W002. Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. Because there were few sites in the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 10).

Table 10: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on total fauna log(10) abundance data.

W002 W004

W004 0.583 W006 W006 0.619 0.500 W009 W009 0.750 0.800 0.909 W010 W010 0.762 0.846 0.861 0.488 W011 W011 0.718 0.778 0.879 0.600 0.407 W018 W018 0.654 0.348 0.350 0.889 0.854 0.816 W019 0.733 0.436 0.333 0.915 0.893 0.913 0.302

W002 ___________________________________ Group 1

W004 _____________ | W006 _____ | | W018 _ | | | Group 2 W019 |___|_______|_____________________|__________________________

W009 ______________________ | Group 3 W010 __________ | | W011 _________|___________|______________________________________| Group 4 | | | | | | 0.3020 0.4496 0.5972 0.7448 0.8924 1.0400

Figure 15: UPGMA classification dendrogram of the eight sites using total fauna log(10) abundance data, indicating the four main groupings.

26


-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W002

W018

W019

W004

Ec (lab & f ield)W006

pH (f ield)

W009

Figure 16: MDS ordination of the eight wetlands using total fauna log(10) abundance data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 15), and indicating significant PCC-derived gradients of unstandardised physico-chemical parameters through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1288).

Presence/absence Data

UPGMA classification of the total fauna on presence/absence data again separated the eight sites into the same groups, the hypersaline sites (W004, W006, W018 & W019), the saline site (W002), and the fresh-brackish water sites (W009, W010 & W011), which separated into two subgroups. In MDS ordination space, these site groupings were significantly separate from each other (ANOSIM, p = 0.010) (Figures 17 & 18). Conductivity was the only physico-chemical parameter to have a significant gradient through the ordination, with conductivity placed in the direction of the hypersaline wetlands. W002 _________________________________ Group 1

W004 _________ | W018 _ | | W019 |_______|________ | Group 2 W006 ________________|_______________|____________________________ W009 __________________________ | Group 3 W010 ______________ | | W011 _____________|___________|__________________________________| Group 4 | | | | | | 0.2800 0.4154 0.5508 0.6862 0.8216 0.9570

Figure 17: UPGMA classification dendrogram of the eight sites using total fauna presence/absence data, indicating the four main groupings.

27


-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2


W011

W010

W009

W002

W018

W019W004

Ec (lab & f ield)

W006

Figure 18: MDS ordination of the eight wetlands using total fauna presence/absence data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 17), and indicating significant PCC-derived gradients of unstandardised physico-chemical parameters through ordination space (optimum solution for the ordination was in 2 dimensions with a stress of 0.1519).

Between-site pairwise dissimilarity values were used to quantify levels of site dissimilarity. Because there were few sites in the groups it was not possible to calculate mean within to between group dissimilarities, however, reference to the matrix of similarities clearly shows those pairs of sites with a high similarity as opposed to those with a high dissimilarity (Table 11).

Table 11: Matrix of association values with each value indicating pairwise dissimilarity (i.e. 0 – 100% similarity and 1 = 0% similarity) amongst each wetland, calculated using the Bray-Curtis association measure on total fauna presence/absence data.

W002 W004

W004 0.517 W006 W006 0.600 0.556 W009 W009 0.714 0.786 0.917 W010 W010 0.647 0.773 0.800 0.480 W011 W011 0.667 0.707 0.838 0.617 0.429 W018 W018 0.576 0.385 0.364 0.875 0.792 0.733 W019 0.643 0.333 0.412 0.852 0.814 0.850 0.280

28


Comparison with SAP Samples Similarities in invertebrate assemblages between the eight BMRC wetlands and the four SAP wetlands were investigated using UPGMA classification and MDS ordination. Prior to analysis, all data were transformed to presence/absence to minimise effects due to differences in sampling approaches and seasons. Taxonomy was also re-standardised as best as possible to minimse apparent differences due to level of resolution. For example, some Chironomidae reported as species complexes under SAP (i.e. Tanytarsus fuscithorax, Tanytarsus ?semibarbitarsus and Tanytarsus barbitarsu), but taken to individual species level for BMRC, were amalgamated to the same level. However, other groups were left un-amalgamated, which could have influenced apparent differences. For example, the SAP data set contained three species of Diacypris ostracod, and the BMRC dataset contained one as yet undetermined species of Diacypris. In the first instance, broad comparison in species richness across sites was made by determining total species richness for each site, ranking sites across studies based on richness and then plotting positions graphically (Figure 19). Analysis showed that the greatest species richness was taken from a SAP site, and the lowest species richness was recorded from a BMRC site, however, the four SAP sites were regularly dispersed amongst the BMRC sites in terms of ranked species richness. Overall, the four SAP sites contained 85 taxa, whilst the eight BMRC sites contained 59 taxa (after re-standardisation), suggesting that the SAP sites are more diverse, with more taxa for half the sampling effort.

0

10

20

30

40

50

60

W00

6

W01

9

W00

4

SP

S15

7

W01

8

W00

2

W00

9

SP

S15

8

SP

S20

1

W01

1

W01

0

SP

S20

3

S ites

Num

ber

of T

axa

Figure 19: Species richness at the four SAP (pink shading) and the eight BMRC sites (blue hashes) using total fauna presence/absence data, with sites ordered according to species richness.

29


UPGMA classification of the 12 samples detected four groups, whereby the four hypersaline BMRC samples (W004, W006, W018 & W019) formed a group, BMRC site W002 grouped with SAP SPS157 & SPS158 samples, the three fresher water BMRC sites (W009, W010 & W011) formed a group and SAP SPS201 & SPS203 formed a group. The latter five sites formed a single group at a higher level in the classification (Figure 20). SPS157 _______ SPS158 ______|_______ Group 1 W002 _____________|____________

W004 _____ | W018 _ | | W019 |___|____ | Group 2 W006 ________|________________|___________________________________ SPS201 _______________ | SPS203 ______________|_______________ | Group 3

W009 _________ | | W010 _____ | | | Group 4 W011 ____|___|____________________|______________________________|

| | | | | | 0.3040 0.6972 1.0904 1.4836 1.8768 2.2700

Figure 20: UPGMA classification dendrogram of the four SAP and the eight BMRC sites using total fauna presence/absence data, indicating four main groupings (a Beta values of – 0.3 was applied to compress groupings in the classification).

MDS ordination of the 12 samples showed a clear separation of the SAP and the BMRC samples in ordination space when grouped under the a priori SAP v BMRC levels (ANOSIM, p<0.0001) (Figure 21). When grouped using the a posteriori UPGMA classification groupings, there was still a significant separation of groups in ordination space (ANOSIM, p < 0.0001), however, there was overlap in site membership across the two data sets. Without knowing the physico-chemical characteristics of the SAP sites it was not possible to empirically compare SAP and BMRC sites on water chemistry, however, the SAP groupings matched the underlying classification of the BMRC samples from analyses above, and it is hypothesised that the hypersaline sites formed one group (W004, W006, W018, W019), saline sites formed another group (W002, SPS157, SPS158) and fresher water sites formed the last groups (W009, W010 & W011; SPS201 & SPS203).

30


-1.5

-1

-0.5

0

0.5

1

1.5

-1 -0.5 0 0.5 1 1.5

Dim e ns ion 1

Dim

en

sio

n 2

Group 1 Group 2 Group 3 Group 4

W011W010

W009

W002

W018

W019

W004

W006

SPS203

SPS201

SPS158

SPS157

Figure 21: MDS ordination of the four SAP and the eight BMRC sites using total fauna presence/absence data, with sites labeled by site codes, and coloured by a posteriori groupings from UPGMA classification (Figure 20). Coloured balloons enclose all samples from the SAP (orange balloon) and from the BMRC (green balloon) (optimum solution for the ordination was in 2 dimensions with a stress of 0.1488).

DISCUSSION

Physico-chemistry

Analysis of the eight wetlands from the BMRC identified a consistent and similar grouping of wetlands on physico-chemical parameters and aquatic invertebrate fauna (microinvertebrates, macroinvertebrates and total fauna) that appears to relate to between-wetland differences in salinity and pH. Fresher wetlands (W009, W010 & W011) consistently grouped together and were separate from hypersaline wetlands (W004, W006, W018 & W019), which also consistently grouped together. The data set contained one wetland (W002) with a salinity level intermediate between the other sites, and this site consistently separated from all other sites on the basis of water chemistry and invertebrate fauna. This may reflect wholly a salinity difference, however, W002 was the only acidic wetland in the suite, and it is more likely that the lower pH rather than the intermediate salinity levels had a greater role in causing this wetland to separate from other wetlands.

Species Conservation Significance

The most noteworthy taxa within the microinvertebrate fauna was the Hexarthrid rotifer Hexarthra propinqua taken from site W002, being the first record of this species from Australia. The type locality for this species is Bohemia (Czechoslovakia), with records from Asia Minor, but likely occurs across Europe and

31


into Asia. Its occurrence at site W002 may be due to an introduction by migratory birds. The only other record of this species from the southern hemisphere is from Lake Ngahewa taken in 1959, a crater lake in the Rotorua thermal area (41 mg/l TDS, pH 7.5, bicarbonate dominated water) (Shiel & Green, 1996). Most other microinvertebrates recorded either have an indeterminate biogeographic distribution, or have a southern Australian or cosmopolitan distribution. Within the macroinvertebrate fauna there were also noteworthy records. The introduced anostracan Crustacea Artemia parthenogenetica was recorded in high abundance from the saline to hypersaline wetlands (viz. W002, W006, W0018 & W019). Conversely, the amphipod Austrochiltonia subtenius, was recorded from all the fresh-brackish water sites (W009, W010 & W011) as well as the saline site W002. A. parthenogenetica is of particular interest as it appears to be an accidental introduction to Western Australia, possibly connected to salt mining, and possibly as far back as the mid-1800s. The species has a natural distribution from western Europe into northeastern Europe and is principally parthenogenetic, although there are records of males being recorded, which suggests sexual reproduction at times. Previously. A. parthenogenetica has only been recorded from highly disturbed wetlands suffering from secondary salination, and it tends to displace the native Parartemia from these wetlands. The occurrence of A. parthenogenetica at relatively undisturbed, primary-salinised wetlands in the Buntine-Marchagee catchment is of concern as it may reflect a change in colonising ability of this introduced species. Within the Chironomidae, a potentially new species ?Cladopelma sp. was recorded from W011 in reasonably large numbers. This species could not be matched with any other known species of chironomid in Western Australia (Dr Don Edward, UWA). The Chironomidae were also represented by several species endemic to the south-west of Western Australia, but which occurred widely throughout this region (e.g. Dicrotendipes ?conjunctus, Procladius villosimanus). The only other taxon of note was the pyralid Lepidopteran Nymphulinae sp. 39. This taxon was also recorded from the Buntine-Marchagee catchment in the SAP survey. Previously it has only been recorded from New South Wales, however, the taxonomy of this group is still developing and many past studies would have been unlikely to identify members of this Order to below family level.

Wetland Conservation Significance

In general terms the wetlands were dominated by cosmopolitan species, having pan-continental or southern Australian distributions. Species endemic to the south-west of Western Australian were few and mainly recorded from the fresher water sites. The samples contained several species of interest, including a possible new species of chironomid and only the second record in the southern hemisphere of a species of harpacticoid copepod. In general, the number of taxa of special interest are no greater, and possibly lower than would be expected for a survey of this kind in any part of the south-west of the State.

32


Regional Conservation Significance

On the basis of taxa richness, the eight wetlands were not particularly species rich when compared with other studies. Comparisons in taxa richness between this and other studies must be treated with some caution because of differences in aspects such as sampling method, level of taxonomic resolution, inclusion/exclusion of micro-fauna/micro-crustacea, number of times sites have been sampled, and types of wetlands (fresh versus saline). Bearing these limitations in mind, comparisons are possible with studies of wetlands in Two Peoples Bay Nature Reserve (Storey et al., 1993), wetlands on the Swan Coastal Plain (Davis et al., 1993), South Coast wetlands (Edward et al., 1994), southern peatlands and shrublands in WA (Horwitz, 1994), wetlands in the Perth Airport (Halse & Storey, 1996), the wetlands in the Buntine-Marchagee catchment sampled under the SAP in September 1999 (Stuart Halse, Dept CALM, unpub. dat.), wetlands in the Marchagee district, immediately to the west of the study area (Halse, 1981), the 223 wetlands sampled across the Wheatbelt region under the SAP (Pinder et al. 2004), and Lakes Toolibin and Walbyring, east of Narrogin (Halse et al., 2000). In the present study, average numbers of invertebrate taxa per site was 18, with a maximum of 33 and a minimum of seven. Storey et al (1993) sampled three wetlands in the Two Peoples Bay Nature Reserve (Lakes Angove, Moates and Gardner Lakes) in winter 1990 and summer 1991, and recorded a total of 170 taxa, which included 47 taxa of Protozoa and Rotifera, 52 taxa of microcrustacea (Cladocera, Copepoda and Ostracoda), and 71 taxa of macroinvertebrates. The average number of macroinvertebrate taxa recorded in each season at the three lakes were 22.3 (winter) and 20.6 (summer), with respective maxima of 26 and 31 taxa from any one site. Davis et al. (1993) studied the physico-chemistry and invertebrate fauna of 41 wetlands on the Swan Coastal Plain in summer of 1989 and spring of 1989 and 1990. A total of 253 taxa were recorded, which included 73 taxa of micro-invertebrates (37 species of Cladocera, 26 species of Ostracoda, and 10 species of Copepoda). Totals of 140, 203 and 187 taxa were recorded in summer 1989, spring 1989 and spring 1990, respectively. The average (and maximum) number of macroinvertebrate taxa per wetland were 25.8 (50), 35.1 (65), and 34.6 (66) in summer 1989, spring 1989 and spring 1990 respectively, with an average total (and maximum) per site of 52.8 (95) taxa for the three sampling occasions combined. Edward et al. (1994) sampled 15 permanent wetlands in winter and 12 wetlands in summer 1991 along the south coast of WA between Cape Naturalist and Albany. A total of 209 taxa were recorded, including 33 taxa of microcrustacea. Mean number of taxa (including microfauna) recorded in winter and summer were 32.4 and 39.0, with respective maxima of 51 and 56 taxa. Halse & Storey (1996) sampled nine swamps around Perth Airport in September and November 1995 and recorded a total of approximately 125 taxa, including 42 taxa of micro-crustacea. A maximum of 56 taxa (including 12 taxa of microcrustacea) was recorded at a wetland on one occasion, with an average per wetland of 30 taxa (including microcrustacea).

33


Halse (1981) recorded 18 species of aquatic invertebrate from five wetlands in the Marchagee area, with some sites sampled on five occasions. Overall diversity was lower than the present study, likely indicating differences in sampling methodology and levels of taxonomic resolution (i.e. rotifers were not identified). As for the present study, Halse (1981) noted that as salinity increased the number of species in a lake decreased. This would apply to biodiversity within a lake over time, but also amongst lakes of differing salinities. Pinder et al. (2004) also discussed the implications of salinity on aquatic invertebrate composition, suggesting that species richness is reduced as salinity increases, with replacements by salt-tolerant species. Halse (op. cit.) also noted that the fauna of the saline lakes was dominated by crustacea and microcrustacea, with insects more prevalent in the fresher lakes. The same was apparent from the BMRC samples, with insects most prevalent in the fresher wetlands (Appendix 3). Halse et al. (2000) suggested however, that the invertebrate fauna of the wheatbelt region was comparatively salt-tolerant, and that substantial changes in community composition would only be seen when salinities exceeded 10 000 mg L-1

(approx 1900 mS/m). On this basis, sites W002, W004, W006, W018 and W019 already exceed this threshold and will be exhibiting changes due to salinity, and sites W009, W010 and W011 are rapidly approaching the threshold, and will likely start to loose the less-tolerant components of the invertebrate fauna should salinities increase further. Generally, allowing for the above-listed differences between the various studies, invertebrate taxa richness in this study appears to be less than most previous studies. This was further reinforced by the tendency for greater taxa richness at the four SAP wetlands sampled in the Buntine-Marchagee catchment. Without more detailed information on the physico-chemical properties and times of sampling of the SAP wetlands, it is not possible to determine the reason for the differences in assemblage composition and species richness across adjacent wetlands. However, it is possible the differences reflect seasonal differences, with the SAP wetlands sampled at a different stage in the seasonal cycle, similarly, between-year differences in rainfall would affect salinity, which again could influence species composition. Finally, differences in taxa richness could reflect effects of ‘operator experience’, with samples from the SAP wetlands collected by personnel more experienced in sampling wetlands.

Evaluation of Analysis

Overall, the eight samples from the BMRC survey collected in November 2003 constitute a small data set, which limits the amount of interpretation possible and also the statistical confidence that can be placed on outputs from analyses. Furthermore, a high number of singleton species (41%) were found, likely contributing to the high level of variability and separation between sites. Similarly, in a study encompassing 223 wetlands across the wheatbelt, Pinder et al. (2004) found high levels of singletons. Of the over 1000 species collected, the vast majority were found infrequently (Pinder et al. 2004). The analyses indicate wetlands of differing conservation value based on levels of endemicity and overall biodiversity. Additional samples from these and adjacent wetlands in future years, standardising as much as possible for sampling method and season will provide greater insights into their conservation significance. Such a data

34


set will also provide the basis for ongoing monitoring, against which future changes may be assessed. RECOMMENDATIONS • The same eight wetlands, and additional adjacent wetlands in the BMRC should

be re-sampled in 2004, in late spring (rainfall dependent), using the same standardised approaches to further document invertebrate composition with respect to water quality.

• The same suite of wetlands should be sampled on at least three occasions, each

occasion being a separate year to fully characterise between-year variation in species composition and water quality within each wetland. This information will provide a sound baseline data set against which future changes in wetland condition may be assessed. Standardised sampling procedures for collecting invertebrates and water quality parameters must be adhered to so that operator differences are minimised.

• On-going monitoring using the same protocols then should occur on an infrequent

basis, with sampling initiated by either changes in water quality or in response to catchment management activities that have been shown to have a perceptible influence on catchment condition and therefore likely to influence wetland condition.

ACKNOWLEDGEMENTS The authors would like to thank the following for assistance with taxonomy; Don Edward, Uni of W.A. (Chironomidae), Brenton Knott, Uni of W.A. (Anostracans), Stuart Halse & Jane McRae, Dept CALM (Ostracods & various). The map of the study catchment was provided by CALM. Data from the adjacent wetlands sampled under the SAP were provided by Stuart Halse. Constructive comments on a draft were provided by Stuart Halse, Wesley Manson, Jodie Watts, Kelly Gillen and Anthony Desmond. Wesley Manson is thanked for overall management of this project on behalf of CALM. REFERENCES ANZECC/ARMCANZ (2000) Australian and New Zealand guidelines for fresh and

marine water quality. www.ea.gov.au/water/nwqms/index.html Belbin, L. (1995) PATN Pattern Analysis Package. Division of Wildlife & Ecology,

CSIRO, Canberra, Australia. D.J. Cale, S.A. Halse and C.D. Walker (2004) Wetlands monitoring in the Wheatbelt

of south-west Western Australia: site descriptions, waterbird, aquatic invertebrate and groundwater data. Conservation Science Western Australia 5, 20-135.

35


Davis, J.A., Rosich, R.S., Bradley, J.S., Growns, J.E., Schmidt, L.G. & Cheal, F. (1993) Wetland classification on the basis of water quality and invertebrate community data [In] Wetlands of the Swan Coastal Plain, volume 6, Report of the Water Authority of Western Australia and Environmental Protection Authority 242pp.

Edward, D.H., Gazey, P & Davies, P.M. (1994) Invertebrate community structure related to physico-chemical parameters of permanent lakes of the south coast of Western Australia. Journal of the Royal Society of Western Australia 77: 51-63

Gauch, H.G. (1982) Multivariate analysis in community ecology. Cambridge University Press, New York, USA.

Halse, S.A. (1981) Faunal assemblages of some saline lakes near Marchagee, Western Australia. Aust. J. Mar. Freshwat. Res., 32:133-142

Halse, S.A. & Storey, A.W. (1996) Aquatic invertebrate surveys and water quality of Perth Airport swamps. Unpublished report to the Perth Airport Authority, 23pp.

Halse, S.A., Pearson, G.B., McRae, J.M. & Shiel, R.J. (2000). Monitoring aquatic invertebrates and waterbirds at Toolibin and Walbyring Lakes in Western Australian wheatbelt. J. Roy. Soc. W.A. 83:17-28.

Harch, B.D., Basford, K.E. DeLacy, I.H., Lawrence P.K. & Cruickshank A. (1996) Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data. Genetic Resources and Crop Evolution 43:363-376

Pinder, A.M., Halse, S.A., McRae, J.M. & Shiel, R.J. (2004). Aquatic invertebrate assemblages of wetlands and rivers in the Wheatbelt region of Western Australia. Records of the Western Australian Museum, Supplement (in press).

Shiel, R.J. & Green, J.D. (1996) Rotifera recorded from New Zealand, 1859 – 1995, with comments on zoogeography. N.Z. J. Zool. 23: 193-209.

Storey, A.W., Halse, S.A. & Shiel, R.J. (1993) Aquatic invertebrate fauna of the Two Peoples Bay area, southwestern Australia. Journal of the Royal Society of Western Australia 76:25-32

36


APPENDICES

37


37


Appendix 1. Physico-chemical data from each wetland.

Appendix 1. Physico-chemical parameters measured from each of the eight wetlands sampled in November 2003, listing site number, name, location, water quality parameters, vegetation structure and various summary comments

38

ID

ALI

AS

LAN

DH

OLD

ER

LAN

DU

SE

LOC

ATI

ON

M

GA

(G

DA

94

)

SIZ

E (H

A)

PO

SIT

ION

A

CR

OS

S

LAN

DS

CA

PE

DA

TE

SA

MP

LED

MA

X D

EP

TH/

DE

PTH

AT

STA

KE

(m)

FIEL

D T

EMP.

(o C

)

FIE

LD E

C

(mS

/m)

LAB

EC

(m

S/m

)

FIEL

D p

H

LAB

pH

CH

LOR

OP

HY

LL a

(mg/

l)

CO

LOU

R

(TC

U)

TOTA

L N

(m

g/l)

TOTA

L P

(m

g/l)

TUR

BID

ITY

(N

TU)

AQ

UA

TIC

V

EG

ETA

TIO

N

SU

BS

TRA

TE/

SO

IL T

YP

E

FRIN

GIN

G

VE

GE

TATI

ON

TY

PE

FAU

NA

CO

MM

EN

TS

W002 Doleys Big Lake

Doley remnantvegetation patch and wheat/sheep

420307 6686722 12.0 on edge of drainage line

30/10/03 0.25 21.9 6010 5890 5.2 5 <0.001 8 3.2 <0.01 0.2 rupia,some algae also

clay Halosarciasps

none observed

no visible gypsum crystals, rubbish dump with old car bodies up stream. And iron in a salty drainage line upslope.

W004 Dobson remnantvegetation patch and wheat/sheep


11/03/03 0.15 27.4 10740 10400 8.2 7.2 0.039 25 5.6 <0.01 0.8 rupia fine brownsand over black clay

Halosarcia sp.

3 blackwinged stilts

gastropods, overflow area void of vegetation on east side

W006 Dobson remnant vegetation patch and wheat/sheep


11/03/03 ???? 27.6 14950 13900 7.5 7.4 <0.001 30 7.2 <0.01 1 none observed

coarse sand onhard pan layer

Halosarcia sp.

none observed

some stands of dead trees

W009 Hunt on edge of small remnant, wheat cropping adjacent

419736 6677930 1.4 midslope 11/05/03 1.0 24.6 330 343 9.3 8.7 0.016 9 2.5 <0.01 0.4 algae sandyloam, some clay

emergent sedges

4 blackwinged stilts, coots, motorbike frogs, 25 grey teals, fox

heavily impacted by macropods on southern edge

W010 Hunt banksiawoodland remnant and wheet/sheep

419925 6678046 1.0 midslope 11/05/03 0.6 27.5 854 862 9.7 9.4 0.002 21 1.7 <0.01 0.2 algae sandyclay loam

sedge 2 blackwinged stilts, 2 blacktailed native hens and fox. Large numbers of tadpoles, noted 3 types

sheep water hole

W011 Hunt remnant,wheat/sheep cropping

415399 6678363 3.4 upslope 11/06/03 0.3 20.6 1611 1640 8.7 8.5 0.007 37 1.6 <0.01 0.7 algae/macro-phyte

sandplain sedges andrushes

2 x Tiliqua rugosa, approximately 40blackwinged stilts and 100 grey teals, motorbike frogs. Many bird calls and man

y

windmill nearby, recent fire also. Many emergent dead stumps. Some dead eucalypts to north

Buntine Marchagee Aquatic Invertebrate Survey November 2003 ID

ALI

AS

LAN

DH

OLD

ER

LAN

DU

SE

LOC

ATI

ON

M

GA

(G

DA

94

)

SIZ

E (H

A)

PO

SIT

ION

A

CR

OS

S

LAN

DS

CA

PE

DA

TE

SA

MP

LED

MA

X D

EP

TH/

DE

PTH

AT

STA

KE

(m)

FIEL

D T

EMP.

(o C

)

FIE

LD E

C

(mS

/m)

LAB

EC

(m

S/m

)

FIEL

D p

H

LAB

pH

CH

LOR

OP

HY

LL a

(mg/

l)

CO

LOU

R

(TC

U)

TOTA

L N

(m

g/l)

TOTA

L P

(m

g/l)

TUR

BID

ITY

(N

TU)

AQ

UA

TIC

V

EG

ETA

TIO

N

SU

BS

TRA

TE/

SO

IL T

YP

E

FRIN

GIN

G

VE

GE

TATI

ON

TY

PE

FAU

NA

CO

MM

EN

TS

tracks - echidna, varanus, lerista.

W018 councilsbig lake

Counsel wheat crop close by. Large remnant adjacent, smaller remnant to north.

446453 6667290 58.6 drainage line

11/08/03 0.6 23.5 16630 15300 7.5 7.3 0.001 11 2.9 <0.01 0.6 algae sand overwhite clay

Halosarcia sp.

water birds Steep and high bank close to waters edge.

W019 councilssmaller lake

Counsel Large remnant,wheat/sheep.

446124 6667125 1.1 drainage line

11/08/03 0.4 25.5 18220 16400 7.5 7.4 <0.001 9 2.6 <0.01 0.7 algae sand overblack clay

Halosarcia and othersucculents

none observed

rubbish dump nearby

39


Appendix 2. List of voucher specimens

Appendix 2. Voucher specimens selected from the eight samples collected in November 2003 from the Buntine-Marchagee wetlands, indicating voucher code, family name, species names and details of which sample fraction, site code and date it was taken from Voucher #. Family Species Site details Hy 1 Arrenuridae Arrenurus sp. W010 5/11/03 250 um Ano 1 Artemiidae Artemia parthenogenetica W018 9/11/03 2mm Amph 1 Ceinidae Austrochiltonia subtenius W010 5/11/03 2mm Coleo 1 Dytiscidae Necterosoma penicillatus W010 5/11/03 2mm Coleo 2 Dytiscidae Necterosoma sp. (L) W011 6/11/03 500um Coleo 3 Dytiscidae Sternopriscus mulimaculatus W010 5/11/03 500um Coleo 4 Dytiscidae Sternopriscus sp. (L) W011 6/11/03 500um Coleo 5 Dytiscidae Allodessus bistrigatus W010 5/11/03 2mm Coleo 6 Hydrophilidae Berosus discolor W010 5/11/03 2mm Coleo 7 Hydrophilidae Berosus sp. (L) W002 no date 2mm Coleo 8 Hydrophilidae Helochares sp. (L) W011 6/11/03 2mm Coleo 9 Hydrophilidae Laccobius sp. (L) W010 5/11/03 500um Coleo 10 Hydraenidae Hydraena sp. W010 5/11/03 2mm Hemi 1 Corixidae Micronecta robusta W010 5/11/03 2mm Hemi 2 Corixidae Agraptocorixa eurynome W010 5/11/03 2mm Hemi 3 Notonectidae Anisops thienemanni W010 5/11/03 2mm Dip 1 Chironomidae Chironomid spp. (P) W018 9/11/03 2mm Dip 2 Chironomidae Tanytarsus fuscithorax W010 5/11/03 2mm Dip 3 Chironomidae Tanytarsus ?semibarbitarsus W002 no date 500um Dip 4 Chironomidae Tanytarsus barbitarsus W004 3/11/03 2mm Dip 5 Chironomidae Cryptochironomus griseidorsum W009 5/11/03 500um Dip 6 Chironomidae Dicrotendipes ?conjunctus W010 5/11/03 2mm and W009 5/11/03

500um Dip 7 Chironomidae Polypedilum nubifer W010 5/11/03 2mm Dip 8 Chironomidae ?Cladopelma sp. W011 6/11/03 2mm Dip 9 Chironomidae Procladius villosimanus W010 5/11/03 2mm Dip 10 Chironomidae Procladius paludicola W002 no date 2mm Dip 11 Chironomidae Ablabesmyia notabilis W011 6/11/03 2mm Dip 12 Chironomidae ?Limnophyes pullulus W002 no date 500um Dip 13 Culicidae Culicinae spp. (L) W002 no date 500um Dip 14 Culicidae Culicinae spp. (P) W010 5/11/03 500um Dip 15 Ceratopogonidae Ceratopogoninae spp. W002 no date 500um Dip 16 Ceratopogonidae Ceratopogonid spp. (P) W002 no date 500um Dip 17 Ceratopogonidae Dasyheleinae spp. (L) W018 9/11/03 500um Dip 18 Tabanidae Tabanidae spp. W002 no date 500um Dip 19 Stratiomyidae Stratiomyidae spp. pupae W019 9/11/03 2mm Dip 20 Ephydridae Ephydridae spp. Pupae W019 9/11/03 500um Dip 21 Scatopsidae Scatopsid spp. W019 9/11/03 500um Tri 1 Leptoceridae Triplectides australis W010 5/11/03 2mm Tri 2 Leptoceridae Notalina spira W010 5/11/03 2mm Tri 3 Leptoceridae Oecitis sp. W010 5/11/03 500um Eph 1 Caenidae Tasmanocoenis tillyardi W009 5/11/03 200mm Odo 1 Libellulidae Orthetrum caledonicum W011 6/11/03 2mm Odo 2 Libellulidae Diplacodes bipunctata W011 6/11/03 2mm Odo 3 Aeshnidae Hemianax papuensis W011 6/11/03 2mm Odo 4 Lestidae Austrolestes annulosus W010 5/11/03 2mm Odo 5 Coenagrionidae Xanthagrion erythroneurum W011 6/11/03 2mm Odo 6 Coenagrionidae Ischnura aurora W010 5/11/03 500um Lepi 1 Pyralidae Nymphulinae sp. 39 W010 5/11/03 2mm

40


Appendix 3. Taxa occurrences across wetlands

Appendix 3. Occurrence of each taxon across the eight sites sampled in the Buntine-Marchagee catchment (log-scale abundance classes; 1 = 1 – 10 individuals, 2 = 11 – 100, 3 = 101 – 1000, 4 = >1000) Taxa are in descending order of occurrence, and sites are arranged by their classification groupings.

Fresh-brackish Saline Hypersaline Taxon W010 W011 W009 W002 W018 W019 W004 W006 occurrences

Ceratopogoninae spp. 2 2 2 3 2 1 2 3 8 Reticypris sp. 1 1 1 3 1 1 2 7 Chironomid spp. (P) 1 1 1 2 2 3 2 7 Tabanidae spp. 1 1 2 2 1 1 6 Meridiecyclops baylyi 1 1 3 3 4 Artemia parthenogenetica 2 2 4 3 4 Austrochiltonia subtenius 4 4 4 1 4 Necterosoma penicillatus 2 1 1 1 4 Berosus sp. (L) 1 1 2 1 4 Tanytarsus barbitarsus 4 4 4 4 4 Procladius paludicola 3 2 3 2 4 Boeckella triarticulata 2 2 1 3 Australocypris insularis. 1 1 1 3 Necterosoma sp. (L) 1 2 1 3 Micronecta robusta 3 3 3 3 Agraptocorixa eurynome 3 1 1 3 Polypedilum nubifer 3 1 1 3 Ceratopogonid spp. (P) 1 1 1 3 Ephydridae spp (P) 1 1 1 3 Notalina spira 2 1 2 3 Brachionus plicatilis 2 1 2 Keratella australis 3 1 2 Sarscypridopsis aculeata 1 1 2 Sternopriscus mulimaculatus 1 1 2 Sternopriscus sp. (L) 1 1 2 Allodessus bistrigatus 1 1 2 Berosus discolor 1 1 2 Anisops thienemanni 3 3 2 Tanytarsus fuscithorax 3 3 2 Cryptochironomus griseidorsum 2 2 2 Dicrotendipes ?conjunctus 3 1 2 Procladius villosimanus 3 3 2 Culicinae spp. (L) 1 1 2 Scatopsidae spp. 1 1 2 Triplectides australis 2 2 2 Oecitis sp. 1 1 2 Austrolestes annulosus 2 2 2 Diacypris sp. 1 1 indet. contr. sp. 2 1 Hexarthra propinqua 1 1 Calamoecia salina 2 1 Cletocamptus deitersi. 1 1 Daphnia carinata 1 1 Daphniopsis truncata 2 1 Macrothrix breviseta 1 1 Mytilocypris cf. mytiloides. 1 1 Arrenurus sp. 1 1 Helochares sp. (L) 1 1 Laccobius sp. (L) 1 1 Hydraena sp. 1 1 Tanytarsus ?semibarbitarsus 1 1 ?Cladopelma sp. 3 1 Ablabesmyia notabilis 1 1 ?Limnophyes pullulus 1 1 Culicinae spp. (P) 1 1 Dasyheleinae spp. (L) 1 1 Stratiomyidae spp. (P) 1 1 Tasmanocoenis tillyardi 1 1 Orthetrum caledonicum 1 1 Diplacodes bipunctata 1 1 Hemianax papuensis 1 1 Xanthagrion erythroneurum 1 1 ?Agriocnemis rubsecens 1 1 Nymphulinae sp. 39 1 1

Number of Taxa = 33 30 17 19 15 11 12 7

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Wetland Invertebrate Fauna Monitoring: November 2003. Data ... · Buntine-Marchagee Natural Diversity Recovery Catchment (BMRC) Wetland Invertebrate Fauna Monitoring: November 2003