Applying microbial indicators of hydrocarbon toxicity to contaminated sites undergoing bioremediation on subantarctic Macquarie Island

Journal Pre-proof Applying microbial indicators of hydrocarbon toxicity to contaminated sites undergoing bioremediation on subantarctic Macquarie Island Josie van Dorst, Daniel Wilkins, Catherine K. King, Tim Spedding, Greg Hince, Eden Zhang, Sally Crane, Belinda Ferrari PII:

S0269-7491(19)34313-1

DOI:

https://doi.org/10.1016/j.envpol.2019.113780

Reference:

ENPO 113780

To appear in:

Environmental Pollution

Received Date: 2 August 2019 Revised Date:

4 December 2019

Accepted Date: 8 December 2019

Please cite this article as: van Dorst, J., Wilkins, D., King, C.K., Spedding, T., Hince, G., Zhang, E., Crane, S., Ferrari, B., Applying microbial indicators of hydrocarbon toxicity to contaminated sites undergoing bioremediation on subantarctic Macquarie Island, Environmental Pollution (2020), doi: https://doi.org/10.1016/j.envpol.2019.113780. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Credit Author statement Josie van Dorst: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Visualisation, Writing – original draft, Daniel Wilkins: Conceptualization, Methodology, Data curation, Visualisation, Writing – review and editing, Eden Zhang: Visualization, Writing – review and editing, Sally Crane: Methodology, Writing – review and editing Catherine King: Conceptualization, Validation, Methodology, Writing – review and editing Tim Spedding, Project administration, Funding acquisition, Supervision, Writing – review and editing Greg Hince Project administration, Funding acquisition, Supervision, Data curation, Writing – review and editing Belinda Ferrari: Conceptualization, Project administration, Resources, Funding acquisition, Supervision, Writing – review and editing.

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Applying microbial indicators of hydrocarbon toxicity to

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contaminated sites undergoing bioremediation on subantarctic

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Macquarie Island.

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Josie van Dorsta, Daniel Wilkinsb, Catherine K. Kingb, , Tim Speddingb, Greg Hinceb, Eden

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Zhanga, Sally Cranea, , , Belinda Ferraria*.

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a

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b

School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Australia

Antarctic Conservation and Management, Australian Antarctic Division, Kingston,

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Tasmania, Australia

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*Corresponding author

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KEYWORDS

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Ecotoxicology, microbial ecology, polar soil, hydrocarbons, bioremediation

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ABSTRACT

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Microorganisms are useful biological indicators of toxicity and play a key role in the

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functioning of healthy soils. In this study, we investigated the residual toxicity of

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hydrocarbons in aged contaminated soils and determined the extent of microbial community

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recovery during in-situ bioremediation at subantarctic Macquarie Island. Previously identified

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microbial indicators of hydrocarbon toxicity were used to understand interactions between

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hydrocarbon concentrations, soil physicochemical parameters and the microbial community.

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Despite the complexity of the field sites, which included active fuel storage areas with high

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levels of soil heterogeneity, multiple spill events and variable fuel sources, we observed

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consistent microbial community traits associated with exposure to high concentrations of

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hydrocarbons. These included; reductions in alpha diversity, inhibition of nitrification

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potential and a reduction in the ratio of oligotrophic to copiotrophic species. These observed

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responses and the sensitivity of microbial communities in the field, were comparable to

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sensitivity estimates obtained in a previous lab-based mesocosm study with hydrocarbon

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spiked soils. This study provides a valuable and often missing link between the quite

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disparate conditions of controlled lab-based spiking experiments and the complexity

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presented by ‘real-world’ contaminated field sites.

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MAIN FINDINGS

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Hydrocarbon concentration drives microbial community structure and function and inhibits

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key ecosystem services. Critical Effect Concentrations based on microbial responses are

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comparable between field and lab studies.

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1. INTRODUCTION

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Microbial communities have been proposed as ideal indicators of polar soil health [1-4]. They

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are the most diverse and numerous organisms within cold region soils and provide essential

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ecosystem services, such as decomposition, mineralisation, biogeochemical cycling and

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pollutant degradation [5]. Microbes respond to gradients and changes in the physical and

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chemical environment and in turn influence that same environment, underpinning ecosystem

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function through biogeochemical cycling. It is this dynamic feedback system that make

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microorganisms ideal, albeit complex, bio-indicators of ecosystem health.

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The structure and function of microbial communities is consistently observed to be altered in

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response to contamination [6-10]. The link between contamination in the environment and

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changes to microbial structure and function has been highlighted using a range of methods

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including community fingerprinting with automated ribosomal intergenic spacer analysis

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(ARISA), terminal restriction fragment length polymorphism (TRFLP) or denaturing gradient

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gel electrophoresis (DGGE); community structural diversity with phospholipid fatty acid

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(PLFA) profiles; functional gene analysis with quantitative PCR (qPCR), and combinations

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of the above [6, 10-13]. However, the greatest momentum has occurred over the last decade

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with the rapid development and application of “omics” in microbial ecology and

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ecotoxicology [4, 7-9, 14, 15].

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For the successful integration of microbial indicators into environmental assessments or

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environmental quality guidelines, there is a need to characterise and quantify microbial

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community response to contaminants. Traditionally, sensitivity estimates are generated 3|Page

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through toxicity tests based on single species. Sensitivity estimates for a range of taxa are

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then combined in a cumulative frequency distribution known as a Species Sensitivity

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Distribution from which Protective Concentrations and Remediation Targets can be derived.

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However, in polar and sub-polar soils where biodiversity of traditional test species including

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soil invertebrates and plants is low and organisms are slow to respond to contaminants, the

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reliance on such single species tests for derivation of environmental quality guidelines is

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impractical [16, 17]. Toxicity tests based on microbial community response therefore provide

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a useful and highly relevant alternative to assess ecosystem wide risk.

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Macquarie Island is home to an Australian subantarctic research station that has been in

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continuous operation since 1948. Dependency on diesel fuel for operation, transportation,

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storage and daily power generation has led to historical and more recent fuel spills, resulting

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in contaminated soil and water [18]. Hydrocarbon contaminated sites associated with fuel

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storage and power generation facilities were first identified and characterised in 1994 [19],

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followed by further site characterisation and remediation trials [20, 21]. In 2009, in-situ

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bioremediation strategies commenced, including micro-bioventing and nutrient addition to

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stimulate the hydrocarbon degrading potential of indigenous microorganisms. In 2014, a

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Permeable Reactive Barrier (PRB) system was installed to minimize the release of any

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remaining mobile contaminants into the environment [22].

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To inform the remediation efforts at Macquarie Island, there has been an intensive effort to

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overcome the limitations of traditional ecotoxicology methods by targeting endemic

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invertebrate community responses, microbial community dynamics and microbial mediated

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nutrient processes [23-27] In van Dorst, (2014) [24], the impacts of petroleum hydrocarbons 4|Page

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on the bacterial community were assessed in a lab-based mesocosm study with a series of

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spiked soil mesocosms. The microbial responses were evaluated with both broad and targeted

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community indices and the abundances of functional genes encoding key enzymes within the

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nitrogen cycle. The resulting concentration-response curves from the bacterial community

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were used to determine Critical Effect Concentrations (CECs), including EC20s (Effective

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Concentration causing a 20% effect). The high sensitivity, low errors associated with CECs,

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and sustained inhibition of the bacterial amoA gene across variable soil types from Macquarie

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Island suggested that inhibition of potential nitrification activity was likely to be one of the

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best microbial indicators of diesel fuel toxicity for subantarctic soils. In addition to amoA, the

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Acidobacteria/β-Proteobacteria ratio, Shannon diversity and UniFrac similarities were

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identified as potentially valuable microbial indicators. An average EC20 value of 155 (C9-

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C40 mg kg-1 dry soil basis) based on the abundance of the bacterial amoA gene was

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consistent with a previous microbial ecotoxicology investigation at Macquarie Island, where

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an IC20 (Inhibitory Concentration causing 20% inhibition) of 190 mg kg-1 was reported for

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an acute potential nitrification enzyme assay. This EC20 value was also comparable to

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sensitivity estimates for key invertebrate species indigenous to Macquarie Island including an

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EC20 of 48 mg kg-1 reported for the springtail Parisotoma insularis [26] and a Protective

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Concentration between 50 and 200 mg kg-1 reported for the earthworm, Microscolex

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macquariensis, based on avoidance, survival and reproduction [1, 25]. Using data from this

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range of studies, the Australian Antarctic Division is working to derive site-specific Soil

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Quality Guidelines and Remediation Targets for hydrocarbons to evaluate whether the site

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continues to pose any ongoing unacceptable environmental risk.

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The overall aim of this study is to investigate the microbial communities associated with

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contaminated sites undergoing remediation at Macquarie Island and examine the use of these

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biota in environmental risk assessment. Specifically, we aimed to use a range of microbial-

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based methods to assess microbial community structure, function and taxonomy in relation to

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the patterns of hydrocarbon contamination within the sites and identify the likely

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environmental drivers of microbial community dynamics. Following this, we applied

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regression modelling to microbial community responses to generate CECs from field data and

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compare these results to previously derived estimates from a lab-based mesocosm spiking

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experiment. Ultimately, we aimed to determine the degree to which lab-based experiments

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are representative of the responses observed under environmentally realistic, complex field

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conditions. The results will be used to verify the applicability of previously identified

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microbial community indicators for use in the derivation of site-specific Soil Quality

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Guidelines.

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2. METHODS

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2.1 Site description and soil sampling

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Soil samples were collected from the three contaminated sites over a 4-year period from

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February 2009 to February 2013, during which active remediation was on-going (Figure 1,

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Table 1). The active in-situ bioremediation strategies included micro-bioventing and nutrient

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addition. Two primary methods of nutrient application were used 1) Soluble liquid nutrient

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addition through the sub-surface remediation aeration array and; 2) Surface/near-surface

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application of controlled release fertiliser (Table S2). Target concentrations were calculated

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based on delivering approximately 800 mg N/kg soil H2O, consistent with the results from

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[28]. Samples were collected throughout the soil profile using cores at the Main Powerhouse 6|Page

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East (MPHE) and using soil pits at the Fuel Farm (FF) and MPH South (MPHS). The depth

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of soil sampling was dictated by site characteristics such as bedrock depth, adjacent

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infrastructure, soil cohesion (particularly in the sandier soils), worker safety and the available

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equipment, with 2 m being the maximum sampling depth. Due to the difference between

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sites, soil profiles range from 0-70 cm total depth at MPH East and 0-140 cm at MPH South

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and FF. Each soil core/soil pit was sampled at 5-6 intervals from the surface down through

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the soil profile. For microbial analysis, two to three samples from each soil core/soil pit were

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analysed. Soils samples were separated into two groups: ‘shallow’ and ‘deep’, the limits of

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which were dependent on the different soil profiles and diffusive properties of the soil at each

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site (defined in Table 1). The location of the soil cores/pits were selected randomly within a

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grid at each site, i.e. one sample within each 3m x 3m grid square (avoiding any areas with

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previous soil disturbance to the soil profile from infrastructure instalment or earlier soil

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core/pits). Soil samples (50 g) were collected aseptically into 50 ml vials for microbial

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analysis and paired with 100 g samples collected alongside in amber glass jars for analysis of

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soil physicochemical properties and Total Recoverable Hydrocarbons (TRH) in the C9 – C40

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range. Soil properties and TRH were measured as per methods in [25]. A summary of the soil

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physical and chemical properties is provided as supplementary material (Table S1).

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2.1 Community fingerprinting

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Total gDNA was extracted from 133 soil samples in triplicate using the FastDNA SPIN kit

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for soils (MP Biomedicals, Seven Hills, NSW, Australia), according to the manufacturers

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protocol. Extracts were quantified with Quant-iTTM PicogreenTM ds DNA reagent (Thermo

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Fisher Scientific). Once diluted to a standard working concentration (10 ng/µl), the gDNA

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was utilised in ARISA assays to generate bacterial community profiles for the 399 DNA 7|Page

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samples. Ordination plots based on the community dissimilarity were then created in

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PRIMER [29]. Analysis of similarity (ANOSIM) and distance-based linear models (DistLM)

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were performed on the multivariate community data set to identify significant factors and

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environmental drivers [29]. A selection of samples from each site were chosen at random for

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further analysis with pyrosequencing and qPCR.

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2.3 Amplicon sequencing

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DNA from the replicate extracts were combined for 30 samples across the three sites and sent

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to a sequencing facility in Lubbock, Texas (Research and Testing Lab, Lubbock, TX, USA)

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for 16S rRNA gene amplicon sequencing using the PCR primers 27F and 519R on the 454

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FLX titanium platform as described by [30] (Roche Life Sciences, Branford, CT, USA). The

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tag pyrosequencing was performed on the Roche 454 titanium platform with the 16S rRNA

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gene universal primers; 28F and 519R and the conditions outlined in [29]. The resulting

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sequence data was processed using the MOTHUR software package as described in [29].

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Briefly, sequences and flowgrams were extracted from the sff files, de-multiplexed and error-

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checked via the Pyronoise algorithm [31] in MOTHUR [32]. After further quality control

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screening, bacterial seed sequences were aligned to the curated SILVA secondary structure

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alignment [33]. Aligned 16S sequences were then clustered into OTUs (Operational

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Taxonomic Unit) based on 97% sequence similarity. Taxonomic assignment of the identified

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bacterial OTUs was performed using the Greengenes database trimmed to the same region as

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our amplicons (V1-V3). An OTU abundance-by-sample matrix was generated from the

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bacterial dataset with MOTHUR.

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2.4 Gene quantification with microfluidic qPCR (MFQPCR)

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Relative estimates of bacterial and fungal load, key hydrocarbon degrading genes and

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processes within the nitrogen cycle were targeted with qPCR on the microfluidic qPCR

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platform (MFQPCR) according to the methods outlined in [34]. As with traditional qPCR,

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strict quality control thresholds were applied to the MFQPCR data including an amplification

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efficiency of 90-110%, standard curve with an R2 >95%, consistent target amplification

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between standards and samples and no non-specific amplification as determined through a

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melt curve. Target genes that did not satisfy the quality control thresholds were excluded

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from the final analysis. The final primer pairs and targeted genes used in this study are listed

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in supplementary material (Table S3).

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2.5 Correlation of environmental parameters with microbial communities.

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The measured environmental parameters were transformed and normalised as described in

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[29] and an environmental similarity matrix was created using the Euclidian coefficient in

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PRIMER-E v7. Correlations between the environmental, functional (MFQPCR) and

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taxonomic (amplicon sequencing) similarity matrices was tested using the Pearson

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coefficient. The null hypothesis of no correlation between environmental parameters and the

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bacterial communities was tested (α = 0.05). Individual environmental parameters were tested

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for correlation with the Distance-based linear model (DistLM) function in PRIMER and with

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correlation plots in the R package ggplot2 [35]. The relationship between individual genera

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and TRH was further explored in the R package Deseq [36], which uses negative binomial

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distribution with variance and mean linked by local regression, proposed as a more

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appropriate method for count data. All environmental data is published in Mendeley Data.

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2.6 Concentration-response regression analysis

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A series of microbial community and functional targets were considered to quantify

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responses of the microbial community to TRH exposure. The selection of indices was based

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on correlation with TRH, sensitivity, low errors associated with concentration-response

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models from van Dorst et al. (2014), and the ecological relevance of the individual targets.

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The four indices selected were the Acidobacteria:β-proteobacteria ratio, which according to

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[37] are considered indicative of the ratio of oligotrophic to copiotrophic species; the

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abundances of AOB genes, a sensitive target known to be inhibited by hydrocarbons and

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responsible for a key step in the global nitrogen cycle; and the widely applicable Shannon

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diversity and Pielou’s evenness indices. The Acidobacteria:β-proteobacteria ratios and AOB

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were calculated from MFQPCR data. The diversity indices were calculated from the

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amplicon sequencing data. The four selected targets were used to model the dose-responses

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of the three field sites and to compare to a previous lab-based mesocosm spiking experiment

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in van Dorst et al. (2014). CECs generated from the previous spiking experiment were based

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on four separate soil types across a carbon gradient. To enable comparisons with CECs

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generated from field data in the present study, we combined all the soil types and re-modelled

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the dose-response curves for each of the indices to generate CECs that represented microbial

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responses across all soil types that were present at the contaminated sites included in this

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study.

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3. RESULTS

3.1 Hydrocarbon concentrations in soils

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On a site wide basis, significant reductions in average TRH concentration was not observed

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over the sampling period (Figure 2A). The greatest reductions observed were from 2009 to

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2010 (MPH East) and 2010 to 2011 (FF and MPH South), consistent with theoretical

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expectations of progressive hydrocarbon degradation [38]. In 2013, approximately 15% of

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samples returned elevated values of TRH (Figure 2A) (Table S1). This is likely due to the

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significant site heterogeneity and associated sample variation, and/or, disturbances at the

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sites. The rate of weathering of fuel is dependent on environmental conditions and soil

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parameters, which can be highly variable, even over small spatial scales. This is the case on

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Macquarie Island. In 2013, we saw evidence of both unweathered Special Antarctic Blend

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diesel fuel and weathered or degraded fuel signatures. Unweathered fuel signatures have

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relatively high ratios of n-alkanes to isoprenoids and a very small unresolved component,

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while fuel signatures consistent with a weathered or degraded diesel fuel have comparatively

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high unresolved component or “UCM”, and low n-alkane to isoprenoid ratios [39] (Figure

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2B). The disparity observed between fuel signatures could suggest either a different fuel

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type/source, or variable degradation conditions across the heterogeneous site. Additionally, in

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2012, adaptations were made to the remediation program including the installation of

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additional aeration infrastructure. It is possible that the physical disturbance associated with

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this installation caused localised hydrocarbon mobilisation.

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3.2 Microbial community responses to hydrocarbon contamination

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3.2.1 Genera level taxa inhibited and stimulated with TRH

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At FF and MPH East, there was a greater number of taxa inhibited, as opposed to stimulated

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with increasing TRH (Figure 4A). In contrast, at MPH South, a greater number of taxa were

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stimulated with increasing TRH concentrations (Figure 4A). Taxa known to associate with

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hydrocarbons in polar soils were observed to increase significantly (P < 0.05) in relative

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abundance with TRH concentration above 1 000 mg kg-1 (Figure 4). These included

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Rhodococcus, Polaromonas, Pseudomonas, Geobacter and unclassified species from

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Rhodocyclaceae. Additional taxa elevated at high TRH ranges included Alicycliphilus,

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Thiobacillus, Bacilli, unclassified species within Methylophilales, Desulfobulbaceae and the

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Candidate Phyla WCBH1 and OPB41 (Figure 4B). Taxa observed to be significantly (P <

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0.05) inhibited with TRH included Nitrospira, Rhodoplanes and unclassified species within

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Nitrospiraceae, Rhodospirillaceae, Acidobacteria (Elin6513), Proteobacteria, β-

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Proteobacteria (MND1) and Xanthomonadaceae (Figure 4B). The taxa at the genus level

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significantly correlated to TRH, with the highest relative abundances across all sites produced

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the greatest contributions to the community dissimilarity observed between sites and include

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Geobacter, Alicycliphilus, Nitrospira, Rhodoplanes, unclassified genera from Rhodocyclacea,

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Rhodospirillaceae Proteobacteria, β-Proteobacteria (MND1) and Acidobacteria (Elin6513).

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The distribution of these taxa across TRH concentration ranges is displayed in Figure S6

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(Figures S6).

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3.2.2 Microbial diversity within sites

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At MPH East and FF, highly contaminated soils (>5 000 mg kg-1) exhibited lower species

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richness, species evenness, Shannon diversity and Simpson diversity estimates, than soils

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with low to medium TRH concentrations (Figure 5). At MPH South, diversity indices 12 | P a g e

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increased with increasing TRH concentration (Figure 4A, Figure 5). Soils from MPH South

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had lower average species richness, evenness, Shannon and Simpson estimates than soils

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from MPH East and FF, which is consistent with expectations of less ecological niches in the

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nutrient limited, carbon poor, coarse sandy soils typical of the MPH South site.

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3.2.3 Microbial community structure, function and taxonomy

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The microbial community was compared between sites based on structure (ARISA), function

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(MFQPCR) and taxonomy (amplicon sequencing) (Figure 3). Based on Bray-Curtis

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dissimilarity measures and a null hypothesis of no difference between a-priori groups, the

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microbial community structure, function and taxonomy were all found to be significantly

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dissimilar between sites (R = 0.421, p = 0.04 / R = 0.23, p= 0.001 / R = 0.084, p = 0.03) and

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between TRH concentration ranges (R = 0.087, p = 0.001 / R = 0.21, p = 0.01 / R = 0.566, p

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= 0.001) (Table 2). Conversely, the year of sampling and sample depth had no significant

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effect. Overall, microbial communities within high TRH soils were similar, regardless of site,

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depth, or year of sampling (Figure D-F, pink triangles and red squares), particularly for

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function and taxonomy (Figure 3 E-F). Despite the higher number of samples, the community

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fingerprinting utilised to examine the microbial community similarity was not as effective at

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capturing the variation within the microbial community as the MFQPCR and amplicon

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sequencing (Figure 3) (Table 2). The Principal Co-Ordinate Analysis plot (PCO) constructed

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from the dissimilarity matrix only captured 12.2% percentage of the variation in the first two

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axes, as opposed to 64.4% and 37.1% from MFQPCR and amplicon sequencing respectively

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(Figure 3). This is not unexpected given the low resolution of ARISA fingerprinting methods,

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with a saturation point at approximately 100 datapoints per sample [29]. 13 | P a g e

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3.3 Environmental drivers of microbial community

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TRH was the most significant environmental driver of functional gene abundances,

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community structure and taxonomy (P < 0.005), followed by pH (P < 0.05) (Table 3). For

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individual functional genes, TRH was positively correlated to 16S rRNA, nitrogen fixation

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genes (nifH, nifD) and B-proteobacteria species, targeted as a proxy for copiotrophic species

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(P < 0.05) (Figure S5B). Alkane degrading (AlkB, ALK1) and nitrate reductase genes (narG)

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were also stimulated with TRH, though these observations were not statistically significant.

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Bacterial and archaeal ammonium oxidation genes (AOB26, AOB1, AOAB, AOA2, amoA)

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were significantly inhibited with increasing TRH (P < 0.05), along with species within the

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Acidobacteria, a proxy for oligotrophic species (Figure S5B). The abundances of naphthalene

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degradation genes (nahAC1, nahAC2, nah AC6, nahAC7, nagAC) had no significant

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correlation to TRH or any of the other measured environmental factors. This is consistent

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with the limited naphthalene observed in the later sampled soils and previous reports of rapid

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evaporation and degradation rates of naphthalene [40, 41]. After TRH and pH, ammonium

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concentration and conductivity were the most significant factors correlated to the variation

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observed in the measured microbial community function (Table 2, Figure S5B).

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When examining individual sites, TRH remained a dominant (Table S5) driving factor for

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bacterial taxonomy and function (Table S4, Figure S5). The microbial community at FF was

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most influenced by TRH, followed by phosphate. At MPH East, chloride was the dominant

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environmental driver of microbial communities after TRH, consistent with the proximity of

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MPH East to the ocean shore and exposure to sea spray. In contrast, at MPH South, microbial

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function (p = 0.05) and taxonomy (p = 0.001) were more significantly correlated with pH, 14 | P a g e

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than with TRH. The average pH at MPH South was 5.6, which is lower than the average of

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6.2 at MPH East and 7.4 at FF.

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3.4 Microbial dose-response modelling

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3.4.1 Sensitivity estimates from field sites and comparison to lab-based spiking experiment

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CECs

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A negative correlation of TRH and the Acidobacteria: β-Proteobacteria ratio was observed at

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all three sites (Figure 5). This ratio was the most sensitive and consistent of the microbial

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indices examined, with EC20 values ranging from 240-390 mg kg-1 for the three sites (Table

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4). These field based EC20 values were more sensitive than the EC20 value of 1940 mg kg-1

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obtained in the remodelled data from the lab-based mesocosm spiking experiment (Table 4).

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The higher EC20 values derived from the spiked samples can be attributed to the high carbon

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soils. The high organic load present in the high carbon soils drives existing low oligotrophic

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to copiotrophic ratios. As a result, the Acidobacteria: β-Proteobacteria ratio in the high

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carbon soils was less sensitive to disruption from hydrocarbon inputs than the other soils in

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the spiking experiment, and the three field sites. As limited nutrients are generally considered

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characteristic of polar soils, the high carbon soils were removed, and the data was re-

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modelled to gain a more applicable sensitivity estimate. The resulting EC20 of 50 mg kg-1

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represents a more sensitive response, and is more comparable to estimates obtained for the

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three field sites (Table 4).

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The abundances of the bacterial (AOB) and archaeal (AOA) ammonia oxidation genes were

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negatively correlated with TRH, but variable within sites (Figure 5B, Figure S5B). For AOB,

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we observed highly variable copy numbers, even at TRH concentrations < 100 mg kg-1

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(Figure 5). This suggests that environmental factors other than TRH were influencing AOB

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abundance. Similar sensitivity for AOB abundance were estimated for soils from both the FF

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and MPH South sites, with EC20 values of 1040 and 1470 mg kg-1 respectively (Table 4). At

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MPH East, there was high variability in AOB abundances and a weak correlation to TRH,

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with an EC20 value of 8790 mg kg-1 (Table 4). The high variability observed is reflected in

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the confidence levels surrounding the estimates, with an upper confidence interval of

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107600 mg kg-1, well beyond any TRH concentrations reported at the site (Table 4). In the

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lab-based mesocosm spiking experiment, the abundances of AOB were previously identified

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as the most sensitive microbial indicator to hydrocarbon toxicity with an average across the

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four soils of 155 mg kg-1 [24]. Combining all samples from the spiking experiment and re-

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modelling dose-response curves in this study generated a substantially higher EC20 value of

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880 mg kg-1, which was comparable to the EC20 values from FF (1040 mg kg-1) and MPH

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South (1470 mg kg-1) (Table 4).

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Both diversity indices, Shannon diversity (H’) and Pielou’s evenness, were negatively

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correlated to TRH at FF and MPH East. In contrast, diversity increased with TRH at MPH

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South (Figure 5). The increase in diversity was sensitive to TRH, with a 20% effect observed

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at very low concentrations. This stimulation effect is unique when compared to the other

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microbial community responses but was previously observed in van Dorst et al. (2014) for

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soils with low carbon. At MPH East, both Shannon diversity and Pielou’s evenness were

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relatively insensitive to TRH, with high EC20 values generated (Table 4). Sensitivity 16 | P a g e

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estimates for FF were similar for both diversity and evenness at 3900 and 4390 mg kg-1

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(Table 4). Previous estimates from the lab-based mesocosm spiking experiment were 1300

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mg kg-1for Shannon and 5100 mg kg-1 for evenness. These estimates were most comparable

343

to FF, particularly for Pielou’s evenness. (Figure 5, Table 4).

344

4. DISCUSSION

345

By the end of the sampling period in 2013, hydrocarbon concentrations had declined in some

346

areas and depths, but many ‘hot spots’ with high TRH remained. However, in lieu of

347

consistent TRH reductions across all sites, the selection towards hydrocarbon degrading

348

genera, the increase in hydrocarbon degradation genes, and the correlation of TRH with

349

microbial community structure and function, has provided evidence of biodegradation along

350

with indicators of persistent toxicity.

351

The soils with TRH > 1 000 mg kg -1 shared significantly similar bacterial community

352

structure and functional traits, irrelevant of sample site, sample year or soil depth (Figure 3).

353

Overall, these soils exhibited a shift towards known hydrocarbon degraders (Figure 4),

354

inhibition in oligotrophic niches based on the Acidobacteria:β-Proteobacteria ratio and

355

reduced ammonium oxidation potential as determined by bacterial amoA (AOB) gene

356

abundance (Figure 5). The most significant environmental determinants for the microbial

357

community structure and function across all three contaminated sites was TRH concentration

358

and pH (Table 3) (Figure S5A, S5B). For soils with undetected or low TRH, the microbial

359

communities were highly variable within and between sites, reflective of the observed high

360

level of soil heterogeneity (Figure 3). 17 | P a g e

361

Questions remain on the ecological relevance of shifts in microbial taxonomic composition in

362

response to anthropogenic perturbation [42]. There are strong arguments for functional

363

redundancy, and evidence that environmental conditions strongly predict the functional

364

profiles of microbial communities, but only weakly predict the taxonomic composition within

365

each functional group [43]. This study shows that while soil pH and TRH were key

366

environmental drivers for both microbial taxonomy and function, soil pH had a greater

367

influence on microbial taxonomy and TRH had a greater influence on microbial function

368

(Table 3, Figure S5B). This is consistent with a large bi-polar soil microbial study that found

369

pH was a key driver of microbial composition, while soil parameters a greater predictor of

370

microbial richness [44]. In environments with low diversity such as subantarctic ecosystems,

371

functional redundancy is likely to be low, thereby rendering subantarctic ecosystems

372

particularly susceptible to disturbances such as petroleum spills and subsequent remediation

373

works [18, 45]. We suggest that taxonomic information is still important in these low

374

diversity ecosystems, but that the inclusion of functional assessments are critical in assessing

375

disturbances, particularly in the case of contamination or geochemical gradients that are

376

likely to be shaping niche distribution [43].

377

Some of the most promising microbial indicators identified in van Dorst et al. (2014) were

378

associated with high variability when applied in this study. This is consistent with

379

expectations for field applications of laboratory derived indices [46, 47]. The variability

380

observed could not be entirely explained by the measured environmental parameters. As a

381

result, the targeted community and functional microbial indicators from field sites were not

382

always able to be reliably incorporated into predicative dose-response models, particularly at

383

MPH East. Despite this, the oligotrophic niches and both diversity indices, continue to 18 | P a g e

384

provide valuable indicators of persistent toxicity from residual hydrocarbons in subantarctic

385

soils. While the amoA gene remains a sensitive target for hydrocarbon toxicity, it appears to

386

be sensitive to environmental gradients and potentially of limited applicability to monitor

387

complex sites. Overall, the ratio of oligotrophic to copiotrophic species was the most

388

sensitive and consistent microbial indicator across all sites. The concentrations generated in

389

the lab-based mesocosm study were comparable to concentrations generated from the field

390

(Figure 5), and adherence to the concentrations generated would result in protection of the

391

identified microbial targets, for most samples, across the sites.

392

The MPH South site was the only site to not exhibit a reduction in microbial community

393

diversity indices in response to TRH (Figure 4). The soils within the MPH South site are

394

coarse beach sands with very low average organic carbon which corresponded to a lower

395

bacterial loading than the MPH East and FF sites (Table S4). We suggest the stimulation

396

effect, resulting from the additional carbon inputs and nutrient addition, may be outweighing

397

any inhibitory effects from petroleum hydrocarbon toxicity by creating additional microbial

398

niches. MPH South also had the lowest recorded pH values (Table S1), driving distinct

399

community structure and function. A high correlation with pH is common in soil microbial

400

communities and has been linked to bacterial community composition in polar soils [44]. The

401

interaction of low carbon, low pH and high TRH dictated the microbial responses at MPH

402

South, which was distinct from MPH East and FF.

403

For the successful incorporation of ecotoxicology studies into Environmental Risk

404

Assessments, studies need to be both reliable and relevant [48]. Validation of controlled lab-

405

based ecotoxicology studies with complex field sites is a direct way to test the ecological 19 | P a g e

406

relevance of the predictions made. However, the high degree of variation introduced by

407

uncontrollable abiotic and biotic influences, means field site validations are often absent from

408

current ecotoxicology and site management methods [46, 47]. This study provides an

409

important evaluation of the applicability of microbial community toxicity estimates in a field

410

situation. Consistent reductions in functional potential and microbial community structural

411

changes across lab-based spiking experiments and complex field sites, along with comparable

412

protective EC20 concentrations, confirm the applicability of using microbial indicators to

413

measure hydrocarbon toxicity in these subantarctic soils. In 2015, a review also concluded

414

that assessments targeting microorganisms should be prioritised when examining

415

ecotoxicological assessment of antibiotics, as they complemented single-species tests to offer

416

more targeted protection of key ecosystem services [49]. We suggest that the use of microbial

417

community indicators offers a less reductive perspective on contamination and provides

418

direct links to ecosystem function. As a result, the observations are more likely to be

419

ecologically relevant and have broader applicability to other hydrocarbon contaminated polar

420

sites and potentially other contamination sources.

421 422

5. ACKNOWLEDGEMENTS

423

The authors would like to thank all the individuals from the Australian Antarctic Division

424

who were involved in the installation of the remediation infrastructure at Macquarie Island

425

and sample collection over the remediation period. We would also like to thank our funding

426

sources including the Australian Antarctic Science project #1163 and #4036 (Remediation of

427

Petroleum Contaminants in the Antarctic and Subantarctic).

20 | P a g e

428

429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472

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562 23 | P a g e

563

Table 1. Soil sampling at each contaminated site at subantarctic Macquarie Island Fuel

years

number sampled

shallow

range

sample

deep sample

Site sampled

concentration sampling

b

range (cm )

method C9-C40

b

range (cm )

-1

(mg kg )

564

a

a

10-40

40-70

a

25-60

60-140

a

25-70

70-140

MPH East

2009-2013

42

Split corer

MRL -27 900

MPH South

2010-2013

37

Sampling pit

MRL -11 500

Fuel Farm

2010-2013

54

Sampling pit

MRL -16 400

-1

b

Method Reporting Limit (MRL) of TRH – 64 mg kg (C9-C40). Centimetres below ground surface.

565

566

24 | P a g e

Table 2. Analysis of similarity (ANOSIM) between microbial communities and measured factors. Global r

Significance

Site

0.421

0.04*

Depth

0.053

0.33

Year

0.056

0.37

Fuel conc range

0.087

0.01*

Site

0.23

0.001**

Depth

0.052

0.18

Year

-0.049

0.81

Fuel conc range

0.211

0.01*

Site

0.084

0.03*

Depth

-0.021

0.59

Year

-0.015

0.55

Fuel conc range

0.566

0.001**

ARISA

MFQPCR

Amplicon sequencing

Significance *(P < 0.05), ** (P < 0.005)

1|Page

Table 3. Distance-based linear model (DistLM) for the most significant environmental parameters contributing to the observed microbial community variation. Pseudo-F

P value

Proportion of

Cumulative

Variation ARISA TRH

3.542

0.001**

0.010

0.010

Chloride

1.905

0.002**

0.006

0.017

pH

1.891

0.001**

0.005

0.022

phosphate

1.754

0.003**

0.005

0.028

TRH

6.411

0.001**

0.079

0.079

Ammonium

6.166

0.002**

0.072

0.151

pH

4.022

0.020*

0.045

0.196

conductivity

2.155

0.068

0.023

0.220

TRH

6.848

0.001**

0.209

0.209

pH

4.826

0.001**

0.128

0.337

Conductivity

1.496

0.144

0.039

0.375

Chloride

1.594

0.092

0.040

0.416

MFQPCR

Amplicon sequencing

Significance *(P < 0.05), **(P < 0.005)

2|Page

Table 4. Effective Concentration causing a 20% effect (EC20s) generated from best fitting dose response models for the three contaminated field sites in this study and from a controlled lab-based mesocosm spiking experiment in a previous study. d

Experiment Community Measure

e

EC20

Model

f

AIC

Site

Model parameters

type

spiking

g

-1

TRH (C9-C40 mg kg ) Conc.

SE

lower

upper

(b)

(d)

(e)

1940

2500

160

24000

W2.3

57.2

-4.53

9

4470

(no high C)

50

1.92

12

190

W2.3

-8.66

-2.73

1

2

FF

240

100

60

1040

LL.3

52.0

26.08

14

320

MPH East

390

60

100

1430

LL.3

52.0

20.80

28

590

MPH South

320

630

40

2760

W1.3

52.0

13.39

6

600

c

880

30

370

2100

W2.3

290

-10.00

85

1000

FF

1470

50100

40

52240

LL.3

84.5

8.43

23

5400

MPH East

8790

2500

720

107600

LL.3

81.5

13.68

74

22900

MPH South

1040

60

300

3590

W2.3

36.4

-3.14

360

3200

Combined

c

experiment [24] log Acido / Proteo

a

field

spiking experiment

Combined

[24] b

log AOB

field

3|Page

spiking experiment

Combined

c

1300

250

210

8300

LL.3

240

4.70

99

15800

FF

3900

30

1700

8900

W2.3

14.4

-3.96

11220

11200

MPH East

12400

20

7700

20000

W2.3

13.0

-16.37

6900

16600

MPH South

10

20

6

20

W2.1

23.6

-41.89

2750

10

5100

40

2000

13000

W2.3

200

-1.94

100

50100

FF

4390

30

1860

10400

W2.3

-28.4

-3.45

6

15100

MPH East

15900

20

11300

22500

W2.3

-32.7

-12.42

5

23400

MPH South

10

10

4

24

LL.3

-5.6

-54.09

4

10

[24] Shannon diversity

field

spiking experiment

Combined

c

[24] Pielou’s evenness

field

a

b

c

log Acido/Proteo = ratio of Acidobacteria:β-Proteobacteria. AOB = bacterial ammonium oxidisers. Dose-response models were originally applied to soils separately, based on carbon gradient ranges in the spiked mesocosm experiment. For comparison to the field sites in this study, all samples from [24] were combined and the EC20 d concentrations were generated from new dose-response models. EC20 parameters; Conc. = Concentration resulting in 20% change, SE = Standard Error, lower = 95% e f lower confidence estimates, upper = 95% upper confidence estimates Model types; LL = log logistic, W2.3 = Weibell 2.3, W2.3 = Weibell 2.3. AIC = Akaike information criterion, used to estimate the quality of each model, relative to other models tested. Note that this is for each data set individually, so it is not comparable between sites f or experiments. Model parameters; b = slope, d = upper asymptote, e = medium point in curve decay.

4|Page

Figure 1. Map of study area. A) Macquarie Island (black square) is situated in the Southern Ocean, southeast of Tasmania, Australia. B) Macquarie Island, the black rectangle highlights the location of the research station towards the north of the island. C) Layout of the station on Macquarie Island, highlighting the areas of contamination at the Fuel Farm (FF) (yellow), MPH South (red), MPH East (orange). Buildings are represented by black rectangles.

5|Page

6|Page

Figure 2A. Scatterplot showing the total recoverable hydrocarbons (C9-C40 mg kg-1) measured from soil samples at each site, over time. Average TRH concentrations were reduced over time at MPH South, despite some high measurements in 2013. At FF and MPH East, average concentrations were reduced from 2010 to 2012, but increased again in 2013 due to a few samples with very high hydrocarbon signals. Figure 2B. Chromatograms showing fuel signatures from two different FF soils sampled in 2013. The fuel signature in the sample 104250 is representative of an unweathered Special Antarctic Blend diesel fuel based on relatively high ratios of n-alkanes to isoprenoids and a very small unresolved component, while the fuel signature of 104275 is consistent with a weathered or degraded diesel fuel (comparatively high unresolved component or “UCM”, and low n-alkane to isoprenoid ratios). The y axis shows relative signal intensity. The cyclo-octane, 1-bromoeicosane, para-dichlorobenzene, tetracosane-d50 and paraterphenyl peaks shown are internal and surrogate standards.

7|Page

8|Page

Figure 3. Microbial community structure, function and taxonomy response to hydrocarbon contamination as shown by beta diversity. The microbial community structure was evaluated with ARISA community fingerprinting (A and D), the microbial community function was analysed with microfluidic qPCR (MFQPCR) (B and E) and the microbial taxonomy was established with 16S amplicon pyrosequencing (C and F). Samples are ordered by TRH concentration ranges (A-C); 1 (0-250 mg kg-1), 2 (250 – 500 mg kg-1), 3 (500-1000 mg kg-1), 4 (1000-5 000 mg kg-1), 5 (> 5000 mg kg-1) and by contaminated sites (D-F); Fuel Farm (FF), Main Power House East (MPHE), Main Power House South (MPHS). Samples with high TRH (TRH range 4 (5 000 – 10000 mg kg-1, and 5 > 10001 mg kg-1) have the highest similarity. This observation was most significant with the functional and taxonomic analysis, which have greater power of inference than community fingerprinting methods.

9|Page

Figure 4. OTUs inhibited and stimulated with TRH (A) and Genera level taxonomy across sites (B). In 4A, blue dots represent significant correlations with TRH and red dots are non-significant. Dots < 0 are inhibited with TRH, dots > 0 are stimulated with TRH. In 4B, relative abundances of genera recovered from amplicon pyrosequencing across the three sites are represented by the size of the bubble and individual genera are coloured according to Phyla. Samples are ordered by site, TRH concentration range and year of collection, following the sample denotation; site_TRH range_year_uniqueID.

10 | P a g e

Figure 5. Dose response plots of four microbial community targets from MFQPCR and amplicon sequencing. (A) Acidobacteria:B-proteobacteria ratios, (B) AOB gene abundances, (C) Shannon diversity and (D) Pielou’s -1

evenness. The measured microbial response is plotted against the log TRH concentration (C9-C40 mg kg ). The Effective concentrations from a previous lab-based mesocosm spiking experiment (concentrations that cause a 20% effect) have been overlayed for comparison. Solid lines represent the EC20 concentrations, and the dotted lines their 95% upper and lower confidence levels.

11 | P a g e

HIGHLIGHTS

-

HC concentration drives microbial community structural and functional traits.

-

HC contamination inhibits both bacterial and archaea ammonium oxidation genes.

-

HC contamination reduces the ratio of oligotrophic to copiotrophic bacterial species.

-

Critical Effect concentrations are comparable between field and lab studies.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: