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DTSTART;VALUE=DATE:20261116
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SUMMARY:Methods in forest microbial ecology and eDNA – from sampling to data analysis
DESCRIPTION:Enrolment guidelines  \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules.\nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe course is designed to provide an understanding of microbiome analysis\, from sample collection to advanced data interpretation\, fostering the development of skills necessary for addressing ecological questions related to microbial communities.\nThe course offers a comprehensive exploration of microbiome data analysis with a specific focus on its application in the field of forest ecology. \nThe course is structured around the following components:\n• Lectures on Metabarcoding in Ecological Applications: Principles\, methodologies\, and applications of metabarcoding techniques within the context of forest ecology. Lectures will cover the latest advancements in DNA sequencing technologies and bioinformatics tools essential for understanding microbial communities in forest ecosystems. \n• Hands-on eDNA Sampling: Practical sessions where participants gain firsthand experience in collecting environmental DNA (eDNA) samples from forest ecosystems\, including sampling techniques\, preservation methods\, and quality assurance considerations. \n• Molecular Lab Analysis: Guidance through molecular laboratory analysis of eDNA samples\, covering DNA extraction\, PCR amplification\, and sequencing techniques. \n• Principles of Bioinformatics: Foundational introduction to bioinformatics tools and workflows for processing and interpreting large-scale microbiome datasets\, including data quality control and taxonomic assignment. \n• Practical Exercises on Microbiome Data Analysis using R: Exercises on data preprocessing\, statistical analysis\, and visualization of microbiome datasets derived from forest environments. Emphasis is placed\non ecological interpretation and integration of microbial data into ecosystem-level questions. \n• Written Report: The course concludes with an individual or group research project where participants analyze microbiome data (either provided or from their own research) and prepare a written report\ndetailing findings and ecological interpretations. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Gain in-depth knowledge of the principles\, methodologies\, and applications of metabarcoding in ecological research.\n• Demonstrate a thorough understanding of eDNA sampling techniques\, including sample collection\, preservation\, and quality control.\n• Understand bioinformatics tools specific to microbiome datasets and their application to ecological interpretation. \nSkills:\n• Develop hands-on skills in implementing metabarcoding workflows\, from experimental design to data analysis.\n• Demonstrate competence in troubleshooting challenges in molecular and bioinformatic workflows.\n• Apply R programming skills to perform advanced analysis of microbiome datasets (diversity metrics\, differential abundance\, and community structure).\n• Conduct eDNA sampling with methodological accuracy and reproducibility. \nCompetences:\n• Design and execute research projects involving microbiome analysis in forest ecology.\n• Integrate metabarcoding techniques\, eDNA sampling\, and data analysis to address ecological questions.\n• Demonstrate autonomy and critical reflection when interpreting complex microbiome data for ecological applications. \nTarget Group\nPhD students working with ecology and related ecological disciplines involving microbiome data. The course is relevant for students applying molecular and bioinformatic tools to understand biodiversity and ecosystem functioning. \nRecommended Academic Qualifications\nExperience working with the R environment; knowledge of community ecology metrics and analysis; basic experience in molecular lab techniques. \nResearch Area\nEcology\, Microbiology\, Environmental Science. \nTeaching and Learning Methods\n1. Lectures introducing microbiome analysis workflows and ecological applications.\n2. Hands-on collection and processing of eDNA samples.\n3. Practical exercises on microbiome data analysis using R (including work with participants’ own data). \nType of Assessment\nCompletion of exercises and submission of a written report based on microbiome data analysis. \nLiterature\nSelected scientific papers and tutorial materials will be provided before the course. \nCourse coordinator\nSebastian Kepfer Rojas\, Associate Professor\, Department of Geoscience and Natural Resource Management\nEmail: skro@ign.ku.dk\, Phone: +45 353 34843 \nGuest Lecturers\nProf. Leho Tedersoo\, Mycology and Microbiology Center\, University of Tartu (EE) — expert in microbiome and molecular identification methods; lectures on next-generation sequencing and metabarcoding applications in forest ecosystems. \nDr. Sofia Fernandes Gomes\, Institute of Biology\, University of Leiden (NL) — expert in above- belowground interactions and microbiome sequence analysis using R; leads data analysis exercises and theory sessions. \nDr. Giovanni Emiliani\, Institute of Sustainable Plant Protection\, NRC (IT) — expert in molecular and functional characterization of fungal species and endophytic bacteria; lectures on functional ecology and plant–microbe interactions in forest ecosystems. \nDates\n16 – 20 November 2026 \nExpected frequency\nTo be held biennially. \nCourse location\nUniversity of Copenhagen\, Department of Geoscience and Natural Resource Management. \nRequirements for signing up\nOpen to PhD students; basic experience in R and ecology required. Registration with waiting list. \n• Participant fee: DKK 1.000\n• PhD student enrolled at SCIENCE: DKK 0\n• PhD student from Danish PhD school Open market: DKK 0\n• PhD student from Danish PhD school not Open market: DKK 3.000\n• PhD student from foreign university: DKK 3.000\n• Master’s student from Danish university: DKK 0\n• Master’s student from foreign university: DKK 3.000\n• Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000\n• Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \nCancellation policy\n•Cancellations made up to two weeks before the course starts are free of charge.\n•Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n•Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n•No-show will result in a fee of DKK 5.000\n•Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/methods-in-forest-microbial-ecology-and-edna-from-sampling-to-data-analysis/
LOCATION:Department of Geoscience and Natural Resource Management
CATEGORIES:PhD Course
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