Physalia courses: Big Data Biogeography

Date & time: November 4^th - November 8^th, 9:30 -- 17:00

Location: Profund Innovation, Freie Universität Berlin - Altensteinstraße 40, 14195 Berlin, Germany

Teachers: Alexander Zizka, German Center for Integrative Biodiversity research & Daniele Silvestro, University of Gothenburg Sweden

Before the course

• Please bring your own computer

• Install the current version of R and RStudio on your computer

• Read the course literature listed below, we will have a literature discussion on the first day.

• Please prepare 3-5 minutes (if you want including slides) to present yourself and your research. Please make sure to answer the following questions: a. Where are you from? b. What is your research about? c. How are your R skills? d. What are your expectations for this course?

Contact

Course content: Alexander Zizka Organisational: Physalia courses

Physalia courses webpage

Objectives

After this course, students will be able to:

1. Obtain and prepare large scale species occurrence records from public databases in R (including data mining, data cleaning and exploration)

2. Apply novel methods for handling and processing ‘big data’ in biogeographic research, including area classification, bioregionalization and automated conservation assessments

3. Reconstruct species ancestral ranges based on species occurrences and phylogenetic trees, using different evolutionary models

4. Understand the potential and caveats of fossil based biogeography, and be familiar with novel methods to estimate ancestral ranges and evolutionary rates from ranges of extinct and extant taxa

Background

The public availability of large-scale species distribution data has increased drastically over the last ten years. In particular, due to the aggregation of records from museums and herbaria, and citizen science in public databases such as the Global Biodiversity Information Facility (GBIF). This is leading to a ‘big data’ revolution in biogeography, which holds an enormous but still poorly explored potential for understanding large scale patterns and drivers of biodiversity in space and time.

Project assignment

During the course you will analyse a specific dataset. We strongly encourage you to bring your own data (Taxon of interest, phylogenetic tree, fossil data), which will give you the opportunity to chose questions and exercises most suitable for your work. If necessary we will provide example data during the course as well.

Course literature

1. Meyer et al. (2015) Global priorities for an effective information basis of biodiversity distributions. Nature Communications, 8 pp.

2. Antonelli et al. (2018) Amazonia is the primary source of Neotropical biodiversity. PNAS 115(23): 6034–6039.

3. Kostikova A et al. (2016) Bridging Inter- and Intraspecific Trait Evolution with a Hierarchical Bayesian Approach. Systematic Biology 65(3):417-431.

4. One of the following suggestions (depending on your own interests): a. Edler et al. (2017) Infomap Bioregions: Interactive mapping of biogeographical regions from species distributions. Systematic Biology 66(2):197–204.

   b. Zizka et al. (2019) CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution 10:744-751.

   c. Silvestro et al.(2016) Fossil biogeography: a new model to infer dispersal, extinction and sampling from palaeontological data. Philosophical Transactions of the Royal Society B 371:20150225.

   d. Price et al. (2019) Big data little help in megafauna mysteries. Nature 558(7):23-25