Satish Choy

The taxonomic feedback loop: symbiosis of morphology and molecules

Here, we relate the ongoing taxonomic story of a species complex of problematic, cryptic Australian freshwater shrimp (Atyidae; Caridina) to highlight the relative strength and utility of different taxonomic methods in assessing species boundaries. We used popular ‘DNA barcode’ gene fragments cytochrome c oxidase 1 and 16S ribosomal DNA. We then assessed the morphological attributes of these specimens and developed an identification key to complement the molecular results, and conclude that, despite occasionally strident arguments in favour of either molecular or morphological taxonomy, the two are inseparably linked and form parts of a greater whole.

The macroinvertebrate fauna of an Australian dryland river: spatial and temporal patterns and environmental relationships

Waterholes within the dryland Cooper Creek, Lake Eyre Basin, Australia, are connected only during floods and are typically isolated for long periods. Spatial changes in the macroinvertebrate assemblages of 15 of these waterholes belonging to four regions were explored and these changes were related to environmental aspects of the waterholes measured at four spatial scales: floodplain, waterhole, within waterhole and sample habitat. To explore temporal patterns, one region was sampled on four occasions differing in time since connection. Spatial patterns were characterised by ‘differentiation by distance’ whereby samples collected closer to each other in the landscape were more similar in assemblage composition than those collected further apart. Thus, there were significant differences between the assemblages of the four regions. Although there was a correlation between macroinvertebrate spatial patterns and a combination of local habitat, geomorphology and water chemistry attributes, it appears unlikely that these variables were responsible for the faunal differentiation by distance. Temporal variability was larger than spatial variability and temporal assemblage patterns were best explained by the ‘connectivity potential’ of waterholes, reflecting the position of individual waterholes within the broader channel network and long-term connectivity relationships, rather than the actual time since hydrological connection.

Terrestrial invertebrates of dry river beds are not simply subsets of riparian assemblages

Dry river beds are common worldwide and are rapidly increasing in extent due to the effects of water management and prolonged drought periods due to climate change. While attention has been given to the responses of aquatic invertebrates to drying rivers, few studies exist on the terrestrial invertebrates colonizing dry river beds. Dry river beds are physically harsh and they often differ substantially in substrate, topography, microclimate and inundation frequency from adjacent riparian zones. Given these differences, we predicted that dry river beds provide a unique habitat for terrestrial invertebrates, and that their assemblage composition differs from that in adjacent riparian zones. Dry river beds and riparian zones in Australia and Italy were sampled for terrestrial invertebrates with pitfall traps. Sites differed in substrate type, climate and flow regime. Dry river beds contained diverse invertebrate assemblages and their composition was consistently different from adjacent riparian zones, irrespective of substrate, climate or hydrology. Although some taxa were shared between dry river beds and riparian zones, 66 of 320 taxa occurred only in dry river beds. Differences were due to species turnover, rather than shifts in abundance, indicating that dry river bed assemblages are not simply subsets of riparian assemblages. Some spatial patterns in invertebrate assemblages were associated with environmental variables (irrespective of habitat type), but these associations were statistically weak. We suggest that dry river beds are unique habitats in their own right. We discuss potential human stressors and management issues regarding dry river beds and provide recommendations for future research.

Response of stream macroinvertebrates to changes in salinity and the development of a salinity index

Many streams and wetlands have been affected by increasing salinity, leading to significant changes in flora and fauna. The study investigates relationships between macroinvertebrate taxa and conductivity levels (µ Sc m −1 ) in Queensland stream systems. The analysed dataset contained occurrence patterns of frequently found macroinvertebrate taxa from edge (2580 samples) and riffle (1367 samples) habitats collected in spring and autumn over 8 years. Sensitivity analysis with predictive artificial neural network models and the taxon-specific mean conductivity values were used to assign a salinity sensitivity score (SSS) to each taxon (1—very tolerant, 5— tolerant, 10—sensitive). Salinity index (SI) based on the cumulative SSS was proposed as a measurement of change in macroinvertebrate communities caused by salinity increase. Changes in macroinvertebrate communities were observed at relatively low salinities, with SI rapidly decreasing to ∼800-1000 µ Sc m −1 and decreasing further at a slower rate. Natural variability and water quality factors were ruled out as potential primary causes of the observed changes by using partial canonical correspondence analysis and subsets of the data with only good water quality.

Predictive modelling of macroinvertebrate assemblages for stream habitat assessments in Queensland (Australia)

This paper describes the iterative approach towards predictive Artificial Neural Network (ANN) models for 37 macroinvertebrate taxa based on 896 stream data sets from the Queensland stream system. Data preprocessing and sensitivity analyses proved to be crucial in order to create data consistency and non-redundancy in the context of this approach. The model validation by means of 167 independent data sets revealed 73% as lowest rate and 82% as average rate of correct ANN predictions of stream site habitats. The increase of correct predictions was 30%, if ANNs and the statistical stream model AusRivAS were compared based on the same data sets. The validation of the ANN models justified their application to the prediction and assessment of stream habitats based on an independent database for test sites. Implications to stream management and research were drawn from prediction results.

River conservation in a changing world: invertebrate diversity and spatial prioritisation in south-eastern coastal Australia

Concentration of human populations with likely impacts of climate change present major challenges for river conservation in the south-eastern coastal region of Australia. Quantitative methods for spatial prioritisation of conservation actions can play a major role in meeting these challenges. We examined how these methods may be applied to help plan for potential impacts of climate change in the region, using macroinvertebrate assemblages as surrogates of river biodiversity. Environmental gradients explaining broad-scale patterns in the composition of macroinvertebrate assemblages are well represented in protected areas; however, their effectiveness for conserving river biodiversity with climate change depends on linking management inside and outside protected areas. Projected increases in temperature and sea level may be used to prioritise conservation to counter likely major impacts in high-altitude zones and the coastal fringes, whereas elsewhere, considerable uncertainty remains in the absence of better downscaled projections of rainfall. Applying such spatial prioritisations using biodiversity surrogates could help river-focussed conservation around the world.

Relationships between habitat properties and the occurrence of macroinvertebrates in Queensland streams (Australia) discovered by a sensitivity analysis with artificial neural networks

1t has been widely demonstrated that interactions between chemical and physical processes create environmental conditions at a range of scales that strongly influence the distribution and abundance of lotic taxa, and thus the composition of lotic assemblages (e.g. HYNES 1970). Many studies have identified substrate composition, complexity and heterogeneity as major determinants of in-stream biota (e.g. DoWNES et al. 1998). Other abiotic factors, such as flow velocity (e.g. BARMUTA 1990) and water chemistry (e.g. BuNN et al. 1986), have also been found to influence biotic composition. The idea that local species assemblages are a consequence of local environmental conditions is paramount to the many biomonitoring programmes increasingly incorporated into water resource management practices throughout the world (NüRRIS & NüRRIS 1995). Empirical models have been developed to predict the occurrence of macroinvertebrate taxa based on their association with environmental variables (e.g. WRIGHT 1995, REYNOLDSON et al. 1997, SIMPSON et al. 1997). Machine learning techniques such as artificial neural networks (ANN) have recently been applied to this problem and they have the potential to provide greater predictive capacity than statistical modelling techniques (WALLEY & FoNTAMA 1998, PuoMENZKY et al. 1998, PucKRJDGE et al. 1999, SCHLEITER et al. 1999). H oAN G et al. (200 l) developed a series o f ANN models that accurately predict the presence/absence of macroinvertebrate taxa in Queensland streams. These models used a referential approach (sensu REYNOLDSON et al. 1997) to predict the fauna at impacted sites if they were unimpacted. Sensitivity analyses performed to refine the selection of input variables generated graphs illustrating the relationships between environmental variables and the presence/absence of taxa. These results provide new insights into the relationships between environmental variation and the occurrence of Queensland stream fauna. This paper discusses examples of such relationships and their potential to enhance the understanding of relationships between human impacts and stream fauna.