ONLINE, 13-17 July 2026

This course will provide an in-depth guide to constructing reproducible, automated workflows for biodiversity data acquisition, cleaning, and analysis in R. These workflows will be applicable to both palaeontological and neontological datasets, allowing for their seamless integration during biodiversity analysis through time and across space. The key discussion points include:
- quantifying biodiversity across spatial and temporal scales;
- how sampling links true and observed diversity patterns;

- comparative analyses of biodiversity variation in space and time.

Each day, we will discuss theory on the processes which generate biodiversity and its spatial and temporal patterns in both modern and palaeontological contexts, including latitudinal gradients, hotspots, mass extinctions, and popular hypotheses of how relevant patterns were shaped. These discussions provide the context for the introduction of popular techniques and a series of practicals, through which you will learn how to interrogate biodiversity datasets to identify basic errors and sampling artefacts, and then make robust estimates of diversity and diversification rates. Throughout the course, there will be a strong focus on visualising diversity data to ensure that its limitations can be properly understood during analysis.

- Understanding of occurrence data for fossil and modern biodiversity: data structure, key databases and limitations.
- Working knowledge of geographic information systems (GIS), chronostratigraphy, and palaeogeographic models in R
- An overview of structuring of taxonomic diversity across space and through time, diversity/diversification rate metrics and the diversification process.
- Understanding of sampling variation, the practical problems these pose for biodiversity analysis, and tools which can help alleviate these issues.
- Appreciation for empirical patterns of diversity across geographic space and through geological time, and how to test hypotheses for these phenomena.

PRE-COURSE (optional)

- Self-guided introduction / refresher on base R language

DAILY SCHEDULE:
Lecture/discussion: 15:00 to 18:00 ( Berlin-time)
Practical: self-paced (~4 hours)


Day 1

Lecture/discussion:
Introduction to biodiversity research
Types of biodiversity data, and the fundamentals of biological occurrences
Key occurrence databases (GBIF, Paleobiology Database [PBDB], Triton, IUCN Red List)
Introduction to Geographic Information Systems (GIS)
Downloading and cleaning modern biodiversity occurrence data

Practical:
R as a GIS
Map making, including the terra, sf, rnaturalearth, geodata R packages
GBIF, PBDB and IUCN Red List APIs for data acquisition and cleaning, including the CoordinateCleaner, robis, rgbif R packages

Day 2

Lecture/discussion:
Diversity metrics
Spatial binning and spatial structure of biodiversity (alpha, beta, gamma)
Diversity estimators (e.g. rarefaction, SQS)
Impacts of sampling variation across space

Practical:
Sampling metrics and discovery curves, spatial binning using terra and icosa
Diversity metrics under incomplete sampling using vegan and iNEXT


Day 3

Lecture/discussion:
Temporal perspectives on biogeographic patterns
Introduction to chronostratigraphy and palaeogeography
Cleaning stratigraphy and taxonomy in fossil  occurrence datasets
Temporal binning and uneven sampling through geological time

Practical:
PBDB data cleanup (temporal) and binning
Paleogeographic mapping using the chronosphere, rpaleoclim and rgplates packages
Diversity and samping patterns through time
Time series and detrending

Day 4

Lecture/discussion:
Measuring diversification rates
Introduction to macroevolution
Diversification models and factors (e.g. Red Queen and Court Jester)
Extinction, mass extinction and recovery

Practical:
Palaeobiological diversification rates using divDyn
Latitudinal diversity gradients and bioregion detection


Day 5

Lecture/discussion:
Biodiversity beyond taxonomic richness
Bridging modern and palaeontological diversity analysis
Conservation palaeobiology

Practical:
Quantifying ecological diversity
Visualising ecological diversity in space
Simple linear model fitting for diversity data

"I enjoyed this course very much! I learned how to analyse data with spatial and temporal elements considered together, and where to start -- specifically the biodiversity and paleological databases, the R packages, understanding of simple features and shapes, and the usage of maps for visualizing spatiotemporal events."

"This is my very first Physalia course and I really enjoyed it. The most useful things I learned were learning how to use packages like palaeoverse and vegan, performing spatial binning and learning the issues of using this kind of data. "

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