About this course

The course is a collaboration between BIOVIT, MINA, and KBM, and provides students with an introduction to the principles and applications of environmental DNA (eDNA) in ecological research and environmental monitoring. Teaching is organized into thematic modules and a final group project, where students must collaborate on dataanalyses and report writing. The first module offers a foundational introduction to eDNA, from sampling to data analysis, while the subsequent modules focus on specific cases related to ecosystem monitoring, species presence and abundance, ecosystem health, and surveillance of pathogens. Through group work, students will delve deeper into the analysis of eDNA datasets, apply statistical and bioinformatic methods. The course emphasizes interdisciplinary collaboration, critical evaluation of eDNA methodology, and reflection on ethical and societal aspects of using eDNA in environmental science.

Learning outcome

Knowledge

  • Understand the principles and applications of environmental DNA (eDNA) in ecological research and monitoring.
  • Gain insight into DNA metabarcoding, ecological indicators, and the role of eDNA in assessing biodiversity and ecosystem health.
  • Learn the theoretical foundations of sampling design, molecular techniques, and bioinformatic workflows used in eDNA studies.
  • Recognize the limitations, biases, and challenges associated with eDNA data generation and interpretation.

Skills

  • Use computational tools to process raw sequence data and perform ecological analyses, including biodiversity metrics and community composition.
  • Apply statistical methods to interpret eDNA datasets in ecological contexts.
  • Design and execute eDNA-based studies, including hypothesis formulation, data analysis, and result presentation.

General Competence

  • Critically evaluate the reliability and relevance of eDNA approaches for ecological assessment and decision-making.
  • Communicate scientific findings effectively to both expert and non-expert audiences, including stakeholders and policymakers.
  • Collaborate in interdisciplinary teams to address environmental challenges using eDNA-based methods.
  • Reflect on ethical, practical, and societal implications of using eDNA in environmental science.

  • Learning activities

    Learning activities will include: lectures, self study, data analyses labs (in R or Python), group work.

  • Teaching support

    Canvas

  • Syllabus

    All reading material will be provided on the BIO328-Canvas pages.

  • Prerequisites

    Experience with programming in R and/or Python.

  • Recommended prerequisites

    STIN100 or STIN300 (or a similar course), BIO120

  • Assessment method

    This course uses portfolio assessment, consisting of data analysis reports and an oral examination. All components of the portfolio must be approved to pass.

    Result: Pass / Fail



    Portfolio Karakterregel: Passed / Not Passed

  • About use of AI

    K3 - Full use of KI

    • In this category, all use of AI is permitted, but must comply with the guidelines for the use of artificial intelligence (AI) at NMBU.
    • Important note: Ethical principles must always be followed, even when full use of AI is allowed.
    • Example: Students are free to use AI tools for all aspects of an assignment, including content generation, analysis, and formatting, provided that the use is clearly stated and ethical guidelines are followed.

    Descriptions of AI-category codes.

  • Examiner scheme

    The examiner will be involved in the approval of the exam.

  • Mandatory activity

    Reports from data analyses labs, group work report, oral presentation of group work

  • Notes

    If the number of registered students is 10 or less, the course may be cancelled.

  • Teaching hours

    Lectures: 20 hours

    Data analyses labs: 60 hours

    Group assignment and presentation: 100 hours

    Self study and preparation: 70 hours

  • Admission requirements

    Special requirements in Science.