Capitalising on the Floristic Survey as a Non-Destructive Line of Evidence for Mineral Potential Modelling: A Case Study of Bauxite in South-Western Australia

Lewis Trotter (), Grant Wardell-Johnson, Andrew Grigg, Sarah Luxton and Todd P. Robinson
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Lewis Trotter: School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia
Grant Wardell-Johnson: School of Molecular and Life Sciences, Curtin University, Perth, WA 6845, Australia
Andrew Grigg: Environment Department, Alcoa of Australia Ltd., Pinjarra, WA 6208, Australia
Sarah Luxton: Environment Division, Commonwealth Scientific and Research Organisation (CSIRO), Canberra, ACT 2601, Australia
Todd P. Robinson: School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia

Land, 2024, vol. 13, issue 12, 1-25

Abstract: While geobotanists have long used plant occurrence to locate subsurface resources, none have utilised floristic surveys as evidence in models of mineral potential. Here, we combine plant species distributions with terrain metrics to produce predictive models showing the probability of bauxite presence. We identified nineteen taxa with statistically significant associations with known bauxite deposits and identified eleven terrain metrics from previous studies. We grouped variables into three variable sets (floristic, topographic, and topo-flora) and produced mineral potential models for each using four algorithms or approaches: (a) a generalised linear model (GLM); (b) random forest (RF); (c) maxent (ME); and (d) a heterogenous stacking ensemble (GLM-RF-ME). Overall, the random forest model outperformed all algorithms including the ensemble based on the area under the curve (AUC) metric. The floristic set of variables outperformed the topographic set (AUC: 0.86 v 0.82). However, together they had the greatest predictive capacity (AUC: 0.89). Six taxa, including Banksia grandis , Leucopogon verticillatus , and Persoonia longifolia , were indicators of bauxite presence, while five other taxa, including Xanthorrhoea preissii and Hypocalymma angustifolium , were associated with bauxite absence. Important topographic variables were topographic wetness, landscape position, and valley depth, which characterised bauxite locations as being well drained, in the upper slope positions of subdued hills, and at some distance from valleys. The addition of floristic surveys provides a new line of evidence about the overlying botanical life that tolerates, accumulates, or avoids bauxite or associated minerals. As opposed to drilling, both datasets can be collected and interrogated at low cost and without impact to the surrounding environment. These data are valuable additions to future applications of mineral potential modelling.

Keywords: bauxite; mineral potential modelling; geobotany; machine learning; topography; exploration; Jarrah Forest; kriging (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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