Methods in Biodiversity Analysis
Introductions
- Different academic backgrounds (IBL, CML, …)
- Different interests, needs, and expectations
- Different skill levels and experiences
Please introduce each other, mentioning at least the following:
- Name
- Which faculty (IBL, CML, …)
- Which master’s programme
- Any concurrent activities (internships, courses, etc.)
- Data analytical skills (e.g. programming, statistics, bioinformatics)
- Your expectations and needs
Prerequisites and assumptions
- We are biologists: we are aware of the principles and practices of molecular biology,
ecology, biogeography, systematics, and evolution
- We’ve had previous training in statistics (e.g. what is a
PCA? What are residuals? What is R2? What is Bayesian statistics?)
- We know how to read scientific publications, and know how to present research, orally
and in writing
- We are not computer scientists, but we’re not afraid of computers
- We are going to learn together: questions, responses, discussions, interruptions, are
always welcome
Learning goals
- To develop a data-centric view of biodiversity research
- To adopt principles and practices of open science
- To learn computational skills in biodiversity analysis
- To improve our communication and teamwork abilities
Course outline
Lectures in the morning, practicals in the afternoon, presentation, report, exam.
- Lecture topics: DNA sequencing techniques; Barcoding; Metabarcoding; Phylogenetics
- Homework: Open Science, Open Data, Open Source
- Practical: Analysis of mycorrhizal molecular diversity
- Presentations: 10 minute standup about a topical paper
- Lecture topics: GIS and the geographical approach; Data input, management, and
analysis; Niche modeling
- Homework: Collecting occurrence data from GBIF
- Practicals: ArcGIS, MAXENT
- Report: Niche modeling results for GBIF species
- Lecture topics: Trait diversity; Tree topologies; Comparative character analysis;
Diversification
- Practicals: Data carpentry, RMarkdown, Phylogenies in R (tree shape,
diversification), Likelihood and Bayesian ancestor reconstruction, trait analysis
- Lecture topics: Diversity in space, time, and function
- Exam
Red thread / model organisms
Some exercises across the weeks will deal with the same model organisms, for which we
all pick our own staple food crop:
- Barcode data collection
- Paper about barcode diversity
- Occurrence data collection and ENM
- Functional trait data collection
Guest lectures
Note that the guest lectures are where absenteeism is recorded (10% of final grade).
Links to other courses
Methods in Biodiversity Analysis as well as the following courses are compulsory in the
master Biodiversity and Sustainability, 2018-2019:
In addition, Methods in Biodiversity Analysis is an elective in the following Biology Masters:
Teaching materials
- There is no book except for OSODOS
- Most lectures reference several publications. You should be broadly aware of their
contents insofar as a biologist can (so skip over the formulas).
- Slides (re-formatted as handouts) are at http://github.com/naturalis/mebioda
- Handouts and slides are subject to ongoing (wiki-like) improvement.
- During the course we will learn how to use this platform to share data, scripts, files
with each other.
Teamwork
- With the hands-on parts of the course we will try to learn in pairs, helping each other
- At least one of you bring a laptop, also to the lectures
- All assignments are individual, though
Grading
- Exam: 50%
- Paper presentation week 1: 20%
- Report week 2: 20%
- Participation: 10%
Locations
- All lectures, afternoon practicals, and presentations are in Sylvius 1.5.03
- The GIS practicals, in the afternoons of 2/12 and 4/12, are in Van Steenis F101
- The exam is in Sylvius 1.4.11/16
Times
- Lecture 1: 09:15 - 10:00
- Lecture 2: 10:15 - 11:00
- Lecture 3: 11:15 - 12:00
- Practicals: 13:15 - …