Abstract

Volatilomics is the study of the total biogenic volatile organic compounds (BVOCs) produced by an organism. This field has been used to assess organism and ecosystem health, as well as determine BVOC biomarkers for forensic purposes, including the detection of human remains, ignitable liquid residues and illicit drugs. For volatilomics to be applied in wildlife-victim casework (e.g. the illegal wildlife trade) a large reference database must be collected across each targeted species range. Adequate sample sizes must be collected from different habitats spanning across the species range of the targeted species to evaluate volatilome variability associated with different environmental and dietary characteristics. This will allow for assessment of chemical diversity and the determination of BVOC biomarkers that are relevant to wildlife forensic cases (e.g. detection, species identification, geographic origin assessment). This study collected the first live animal volatilome database, using the highly trafficked and widely distributed Australian shingleback lizard (Tiliqua rugosa). Optimised thermal desorption and analysis methods were used to examine 127 wild shingleback volatilome samples collected from sites across New South Wales, South Australia and Western Australia and 28 volatilome samples from captive shinglebacks. The results demonstrated that volatilome profiles and chemical diversity differed across each sampling region, potentially related to habitat and diet changes. At least 7 volatilome samples were required to capture chemical diversity in a sampling region. Forty-four tentatively identified BVOCs were shared across all sampling regions and captive animals which may aid in detection purposes. Bioregion-specific BVOCs were also identified, which will also aid in geographic assignment of confiscated individuals. This work demonstrates the importance of sample sizes in capturing chemical diversity within Bioregions prior to downstream volatilome analysis for the establishment of wildlife forensic databases and biomarker selection.

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