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Further investigation of lead exposure as a potential threatening process for a scavenging marsupial species.

D J HutchinsonE M JonesJ M PayJ R ClarkeM T LohrJordan O Hampton
Published in: Australian veterinary journal (2023)
There is a growing recognition of the harmful effects of lead exposure on avian and mammalian scavengers. This can lead to both lethal and non-lethal effects which may negatively impact wildlife populations. Our objective was to assess medium-term lead exposure in wild Tasmanian devils (Sarcophilus harrisii). Frozen liver samples (n = 41), opportunistically collected in 2017-2022, were analysed using inductively coupled plasma mass spectrometry (ICP-MS) to determine liver lead concentrations. These results were then used to calculate the proportion of animals with elevated lead levels (>5 mg/kg dry weight) and examine the role of explanatory variables that may have influenced the results. The majority of samples analysed were from the south-east corner of Tasmania, within 50 km of Hobart. No Tasmanian devil samples were found to have elevated lead levels. The median liver lead concentration was 0.17 mg/kg (range 0.05-1.32 mg/kg). Female devils were found to have significantly higher liver lead concentrations than males (P = 0.013), which was likely related to lactation, but other variables (age, location, body mass) were not significant. These results suggest that wild Tasmanian devil populations currently show minimal medium-term evidence of exposure to lead pollution, although samples were concentrated in peri-urban areas. The results provide a baseline level which can be used to assess the impact of any future changes in lead use in Tasmania. Furthermore, these data can be used as a comparison for lead exposure studies in other mammalian scavengers, including other carnivorous marsupial species.
Keyphrases
  • mass spectrometry
  • risk assessment
  • heavy metals
  • multiple sclerosis
  • weight loss
  • deep learning
  • air pollution
  • machine learning
  • weight gain
  • drinking water
  • gestational age
  • artificial intelligence