lecture1.md

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.

Week 1: Molecular Biodiversity

• 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

Week 2: Geospatial Patterns

• 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

Week 3: Comparative Analysis

• 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

Week 4: Wrap-up and Exam

• 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).

• DNA WaterScan - Kevin Beentjes
• Fungal molecular diversity - Jorinde Nuytinck
• GIS - Maarten van 't Zelfde
• Birds of the Philippines - Merlijn van Weerd
• Remote sensing - Joris Timmermans
• Georeferencing - Jeroen Creuwels
• ENM - Nuno De Mesquita César de Sá / Rosaleen March
• Character data - Jeremy Miller
• Diversification - Vincent Merckx
• Biodiversity dynamics - Hans ter Steege

Links to other courses

Methods in Biodiversity Analysis as well as the following courses are compulsory in the master Biodiversity and Sustainability, 2018-2019:

• Orientation on Biodiversity and Sustainability
• Advanced Statistics - We will refresh our R skills but apply them more specifically to cases involving biodiversity.
• Genomic Architecture - We will give more attention to the data science and bioinformatics side of genome (and other) sequencing. Bioinformatics in relation to HTS will be more Unix-based.
• Ecosystem Services
• Human Impacts on Biodiversity - We will learn more about the quantitative methods for assessing the impacts discussed in this course.

In addition, Methods in Biodiversity Analysis is an elective in the following Biology Masters:

• Biology and Business Studies
• Biology and Education
• Biology and Science Communication & Society
• Evolutionary Biology
• From Cells To Organisms
• General Biology Programme (no research specialisation)
• Molecular Genetics & Biotechnology

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 - ...