Biological indicators are vital to the monitoring of ecosystems and environmental conditions across the globe as representatives of broader ecological trends. In Australia, ants are widely employed as biological indicators, owing to their ubiquity, importance to ecosystem service provision, representativeness of broader ecological patterns and well-characterised disturbance response. Ants are also considered much simpler to sample and sort than alternative indicators. However, despite these advantages, the use of ants as indicator taxa remains time-consuming, costly, and inaccessible to non-specialists due to the difficulties of identifying Australia’s hyper-diverse and hyper-abundant ant fauna to species level, which limits their implementation in monitoring programmes and the research avenues that can be explored.
The drawbacks of using ants can be addressed through the use of simplified analyses which circumvent the need to use species abundance data by utilising higher taxa, restricted species lists or presence/absence data. In this thesis I analyse data from a long-term study of ant community change after mine-site rehabilitation at German Creek and Callide mines in Australia. I test four simplified analyses – Genera Abundance, Functional Group Abundance, Large-Bodied Abundance and Species Presence/Absence – in order to assess their suitability as a surrogate for species abundance data in the monitoring and evaluation of rehabilitated mine sites, by evaluating their ability to replicate key aspects of the results from a full species abundance assessment.
I found the performance of the four simplified analyses to be variable between the two mines, with the exception of Species Presence/Absence, which was able to consistently replicate key aspects of the species abundance assessment. I discuss the possible analytical and ecological factors which likely contribute to variation in performance of the four analyses and recommend a context-based approach to simplified analysis use and research, and discuss how this will enhance the use of bioindicators for monitoring environmental systems.
