Resolving ecosystem complexity in ecological risk assessment for mine site rehabilitation

Andrew J Harford; Renee E Bartolo; Chris L Humphrey; Jaylen D Nicholson; Darren L Richardson; David Rissik; Michelle Iles; Jeffrey M Dambacher

doi:10.1016/j.jenvman.2022.115488

Resolving ecosystem complexity in ecological risk assessment for mine site rehabilitation

J Environ Manage. 2022 Oct 1:319:115488. doi: 10.1016/j.jenvman.2022.115488. Epub 2022 Aug 3.

Authors

Andrew J Harford¹, Renee E Bartolo², Chris L Humphrey², Jaylen D Nicholson², Darren L Richardson³, David Rissik³, Michelle Iles⁴, Jeffrey M Dambacher⁵

Affiliations

¹ Department of Agriculture, Water and the Environment, Supervising Scientist Branch, Darwin, Northern Territory, 0801, Australia. Electronic address: andrew.harford@environment.gov.au.
² Department of Agriculture, Water and the Environment, Supervising Scientist Branch, Darwin, Northern Territory, 0801, Australia.
³ BMT Australia, PO Box 203, Spring Hill, QLD, 4004, Australia.
⁴ Energy Resources Australia, Darwin, Northern Territory, 0801, Australia.
⁵ CSIRO, Hobart, Tasmania,7000, Australia.

PMID: 35982549
DOI: 10.1016/j.jenvman.2022.115488

Abstract

Context: Ecological Risk Assessments (ERAs) are important tools for supporting evidence-based decision making. However, most ERA frameworks rarely consider complex ecological feedbacks, which limit their capacity to evaluate risks at community and ecosystem levels of organisation.

Method: We used qualitative mathematical modelling to add additional perspectives to previously conducted ERAs for the rehabilitation of the Ranger uranium mine (Northern Territory, Australia) and support an assessment of the cumulative risks from the mine site. Using expert elicitation workshops, separate qualitative models and scenarios were developed for aquatic and terrestrial systems. The models developed in the workshops were used to construct Bayes Nets that predicted whole-of-ecosystem outcomes after components were perturbed.

Results: The terrestrial model considered the effect of fire and weeds on established native vegetation that will be important for the successful rehabilitation of Ranger. It predicted that a combined intervention that suppresses both weeds and fire intensity gave similar response predictions as for weed control alone, except for lower levels of certainty to tall grasses and fire intensity in models with immature trees or tall grasses. However, this had ambiguous predictions for short grasses and forbs, and tall grasses in models representing mature vegetation. The aquatic model considered the effects of magnesium (Mg), a key solute in current and predicted mine runoff and groundwater egress, which is known to adversely affect many aquatic species. The aquatic models provided support that attached algae and phytoplankton assemblages are the key trophic base for food webs. It predicted that shifts in phytoplankton abundance arising from increase in Mg to receiving waters, may result in cascading effects through the food-chain.

Conclusion: The qualitative modelling approach was flexible and capable of modelling both gradual (i.e. decadal) processes in the mine-site restoration and the comparatively more rapid (seasonal) processes of the aquatic ecosystem. The modelling also provides a useful decision tool for identifying important ecosystem sub-systems for further research efforts.

Keywords: Bayes nets; Contaminants; Cumulative risk assessment; Environmental stressors; Mining; Sign directed graphs.

MeSH terms

Bayes Theorem
Ecosystem*
Food Chain
Risk Assessment
Uranium* / analysis

Substances

Uranium