MSc scholarship – UoW

The structure and functioning of soil and litter invertebrate communities in kauri forests

New Zealand kauri (Agathis australis) produce large volumes of acidic, tannin-rich litter that creates unique soil conditions with relatively low decomposition rates. Thus, kauri represent ‘keystone structures’ in New Zealand forests, where they likely have an inordinate influence on the structure and functioning of belowground food webs. However, this taonga species is under multiple threats from pathogens and climate change. To date, our understanding of what impact these threats are having on whole kauri forest ecosystems is limited.

As part of the Ngā Rākau Taketake research theme Risk Assessment & Ecosystem Impacts, the EcoDiv lab at the University of Waikato is currently involved in studying the spatial and energetic structure of soil invertebrate food webs in kauri forests as part of this broader project.

Within this research program, we have funding for an MSc project that will focus on increasing our understanding of the spatial and energetic structure of soil and litter invertebrate communities in kauri forests. The student will have the opportunity to carry out research on the elemental composition of the leaf litter, and investigate how this and other environmental variables influence the composition and functioning of invertebrate communities in forests in fected with kauri dieback.

This MSc research project will be supervised by Marijke Struijk and Andrew Barnes within the EcoDiv lab. Funding will fully cover student fees and a stipend (total scholarship value of $24,000).

Skills and experience:

  • A good level of fitness and general ability to work in the field is essential.
  • Strong attention to detail while working with soil samples and equipment potentially
  • contaminated with an unwanted organism (kauri dieback).
  • Some experience in terrestrial ecology would be preferred.
  • Experience working in a laboratory, and some knowledge of arthropod identification
  • would be beneficial.
  • Strong analytical skills and experience analysing data in R (or willingness to learn)
  • would be preferred.

If you are interested in this exciting opportunity, please apply with a short cover letter, CV, and academic transcript sent to and

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