Can a naturally depauperate Ephemeroptera, Plectoptera and Trichoptera (EPT) fauna track river degradation in south-western Australia?

Freshwater aquatic ecosystems are threatened globally. Biological monitoring is required to deliver rapid and replicable assessment of changes in habitat quality. The Ephemeroptera, Plectoptera, Trichoptera (EPT) index is a globally recognised rapid bioassessment that measures taxa richness of three insect orders whose larvae are considered sensitive to freshwater habitat degradation. South-western Australia contains threatened freshwater ecosystems but has depauperate EPT fauna and high endemism, potentially reducing the capacity of the EPT index to track degradation. This study investigated if EPT species richness, composition or individual species tracked physical or chemical river degradation in three catchments in south-western Australia. We sampled EPT fauna and measured water chemistry, erosion, sedimentation, riparian vegetation cover and instream habitat at 98 sites in the winters of 2007 and 2023. We found 35 EPT taxa across the study area with a median number of species per site of two. EPT species richness had weak positive associations with a composite water quality index and dissolved oxygen and weak negative associations with electrical conductivity and total nitrogen. No association was found between physical and fringing zone degradation measures and EPT species richness. EPT community structure generally did not distinguish between sites with high or low degradation levels. The presence of the mayfly Nyungara bunni tracked salinity, dissolved oxygen and nitrogen levels, but its usefulness as a bioindicator could be limited by its restricted range. This study suggests that the EPT index would need modification or combination with other indices to be a useful rapid bioassessment in south-western Australia.

Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains.

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.