Many species face extinction within a few decades unless actions are taken to reduce biodiversity loss. The most important drivers of species decline worldwide are changes in land use and direct exploitation of natural resources. Environmental DNA (eDNA) combined with species distribution models are valuable tools that are rapidly advancing biodiversity surveys to support conservation management. Particularly for organisms that cannot be identified with traditional inventory-based methods, those for which identification requires deep taxonomic knowledge, for rare species and species with short life cycle.
Given that DNA-metabarcoding is a highly sensitive technique, its application to model species distribution must be more carefully evaluated, particularly for organisms that are highly mobile. As users of these tools for conducting biodiversity surveys, one of our main concerns is to quantify the level of uncertainty associated with eDNA sampling. The potential for false negatives - where a species is present in the environment but not detected in surveys - can lead to biases in estimates of species richness and individual species occupancy. These false negatives can potentially occur during sampling of heteogeneous substrates, sample processing or molecular steps (DNA extraction, PCR amplification and sequencing and bioinformatics filtering). Accounting for these biases can improve species occurrence estimates based on eDNA surveys and allow more robust ecological conclusions for making management decisions and informing sampling designs.
We team-up researchers with competences in eDNA-metabarcoding and species distribution modelling to investigate how multi-species occupancy modelling can be used for the analysis of community biodiversity data resulting from eDNA metabarcoding. In this MSc project, the student will focus on fungi and lichens as target substrates. These organismal groups are good models to study co-occurrence patterns as they host a complex communitiy of microorganisms, including other fungi. The candidate will investigate the potential of these detection models based on ecological replicates and technical replicates, for improving methodologies and drawing sound ecological inference.
Facilities: The candidate will be part of a larger research project, ForestService, focussing on overall fungal diversity in the forest ecosystem. The student will work in close collaboration with researchers from two sections EvoGene (for molecular biology and bioinformatics tasks) and CEES (for statistics and modelling tasks). DNA samples are readily available, the research group supports the candidate with funding and facilities for conducting molecular work and sequencing, and provides basic training in molecular methods and introduction to bioinformatics and statistics software. Preliminary sequencing data is available for model optimisation. The Oslo Mycology Group offers a highly includive workplace and the candidate should have teamwork ability to actively contribute to a well-functioning and productive research environment.
Main tasks: Design the experimental set-up, perform PCR amplification, prepare DNA libraries for illumina sequencing, statistics and modelling, literature review and writing
Competences: We are looking for a candidate with a basic knowledge in microbial biodiversity, an interest in statistical ecology, who is keen to handle big sequencing datasets, with a capacity to communicate in a group, and abilities to work independently.
Supervision team: Torbj?rn Ergon, Markus Fjelde, H?vard Kauserud, Sundy Maurice. The candidate will interact with other researchers in the Oslo Mycology Group and at the international-level.
Contact:
Sundy Maurice: sundy.maurice@ibv.uio.no