Structure and Dynamics of a Benthic Invertebrate Community in an Intertidal Area of the Tagus Estuary, Western Portugal: A Six Year Data Series

A predictive model, incorporating macroinvertebrate and environmental data, similar to that developed for Australian rivers (AUSRIVAS) and British rivers (RIVPACS) was constructed using a dataset collected from 23 reference (least altered) wetlands on the Swan Coastal Plain, Western Australia, sampled in summer and spring, 1989 and spring, 1990. Four main groups of reference wetlands were identified by UPGMA classification (using the Bray–Curtis dissimilarity measure). Distinguishing environmental variables identified by Stepwise Multiple Discriminant Function Analysis were: calcium, colour (gilvin), latitude, longitude, sodium and organic carbon. Observed to expected ratios of taxa with a >50% chance of occurrence (OE50) derived from the model for a suite of 23 test wetlands sampled in spring, 1997, were significantly correlated with pH and the depth of the sampling sites. Greater discrimination between the test wetlands was provided by the OE50 ratios than either raw richness (number of families) or a biotic index (SWAMPS). Results obtained for a subset of 11 test wetlands sampled with both a rapid bioassessment protocol (incorporating field picking of 200 invertebrates collected in 2 min sweeps from selected habitats) and a semi-quantitative protocol (incorporating laboratory picking of all invertebrates collected in sweeps along 10 m transects at randomly allocated sites) were not significantly different, indicating that the former could be used to reduce the time and costs associated with macroinvertebrate-based wetland monitoring programs. In addition to providing an objective method of assessing wetland condition, predictive modelling provides a list of taxa expected to occur under reference conditions, which can be used as a target in wetland restoration programs. The probable impediment to widespread adoption of predictive modelling for wetland bioassessment is the need to produce models tailored to specific geographic regions and specific climatic conditions. This may incur significant costs in countries, such as Australia, which span a wide range of climatic zones.