From order to species: Modelling Amphipoda distributions in the Hamersley Range

Groundwater amphipods in the Pilbara region of Western Australia are under pressure from the impacts of mining operations, particularly from groundwater extraction. To help inform conservation planning for this subterranean group, we model their distribution using a suite of environmental variables and occurrence data, and a Random Forest (RF) classification method. Given the taxonomic uncertainty and cryptic diversity in groundwater amphipods, we compared model results across nested taxonomic datasets to assess the potential benefits and limitations of higher-level taxonomic modelling. In this analysis, we compared all amphipods (Order: Amphipoda), the Paramelitidae family, which comprises 92.2% of filtered amphipod records used in the model, and the species B08 Paramelitidae SOLOMON 1, which accounts for 40.36% of filtered Paramelitidae records. We investigated which environmental factors most strongly influenced patterns in fauna distribution to better understand habitat requirements. Species probability maps offer a geospatial view of species distribution based on model predictions. A range of metrics were used to evaluate models in terms of predictive performance. Comparing the three models, the Amphipoda model achieved higher accuracy (80.4%) than the Paramelitidae model (68.3%), and the B08 Paramelitidae SOLOMON 1 model (69.8%), but lower sensitivity (77.8% vs 84.9% vs 100%), implying that the latter may be more appropriate when detecting species presence is the priority over species absence. Variable importance analysis revealed factors such as the gradient of the landscape, distance to the nearest dyke, and seasonal precipitation played important roles in shaping the distribution of each taxonomic group. The results demonstrate the ability of RF to reliably predict the presence of taxa alongside a set of informative metrics. Our results suggest that order-level modelling may retain some broad-scale ecological patterns observed at the species level under data-limited scenarios. Such information aids the development of effective conservation strategies. The analytical framework developed in this study is adaptable for use in other underground systems, facilitating further research on subterranean environments and adding a vital perspective to conservation decision-making.