Salt is a natural component of the Australian landscape to which a number of biota inhabiting rivers and wetlands are adapted. Under natural flow conditions periods of low flow have resulted in the concentration of salts in wetlands and riverine pools. The organisms of these systems survive these salinities by tolerance or avoidance. Freshwater ecosystems in Australia are now becoming increasingly threatened by salinity because of rising saline groundwater and modification of the water regime reducing the frequency of high-flow (flushing) events, resulting in an accumulation of salt. Available data suggest that aquatic biota will be adversely affected as salinity exceeds 1000 mg L–1 (1500 EC) but there is limited information on how increasing salinity will affect the various life stages of the biota. Salinisation can lead to changes in the physical environment that will affect ecosystem processes. However, we know little about how salinity interacts with the way nutrients and carbon are processed within an ecosystem. This paper updates the knowledge base on how salinity affects the physical and biotic components of aquatic ecosystems and explores the needs for information on how structure and function of aquatic ecosystems change with increasing salinity.

Terminal restriction fragment length polymorphism (T-RFLP) is increasingly being used to examine microbial community structure and accordingly, a range of approaches have been used to analyze data sets. A number of published reports have included data and results that were statistically flawed or lacked rigorous statistical testing. A range of simple, yet powerful techniques are available to examine community data, however their use is seldom, if ever, discussed in microbial literature. We describe an approach that overcomes some of the problems associated with analyzing community datasets and offer an approach that makes data interpretation simple and effective. The Bray-Curtis coefficient is suggested as an ideal coefficient to be used for the construction of similarity matrices. Its strengths include its ability to deal with data sets containing multiple blocks of zeros in a meaningful manner. Non-metric multi-dimensional scaling is described as a powerful, yet easily interpreted method to examine community patterns based on T-RFLP data. Importantly, we describe the use of significance testing of data sets to allow quantitative assessment of similarity, removing subjectivity in comparing complex data sets. Finally, we introduce a quantitative measure of sample dispersion and suggest its usefulness in describing site heterogeneity.

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Terminal restriction fragment length polymorphism (T-RFLP) analysis has the potential to be useful for comparisons of complex bacterial communities, especially to detect changes in community structure in response to different variables. To do this successfully, systematic variations have to be detected above methodassociated noise, by standardizing data sets and assigning confidence estimates to relationships detected. We investigated the use of different standardizing methods in T-RFLP analysis of PCR-amplified 16S rRNA genes to elucidate the similarities between the bacterial communities in 17 soil and sediment samples. We developed a robust method for standardizing data sets that appeared to allow detection of similarities between complex bacterial communities. We term this the variable percentage threshold method. We found that making conclusions about the similarities of complex bacterial communities from T-RFLP profiles generated by a single restriction enzyme (RE) may lead to erroneous conclusions. Instead, the use of multiple REs, each individually, to generate multiple data sets allowed us to determine a confidence estimate for groupings of apparently similar communities and at the same time minimized the effects of RE selection. In conjunction with the variable percentage threshold method, this allowed us to make confident conclusions about the similarities of the complex bacterial communities in the 17 different samples.

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