In this course instructors will introduce different conceptual biological reasons why it might be interesting to examine biodiversity data in a geographic context, focusing on the relationships between species richness, trait similarity and phylogeny, as well as patterns of turnover in those relationships. In this overview, they will discuss what these can reveal about patterns of community assembly, in situ diversification vs immigration, as well as the potential connections between these patterns and environmental/climatic/elevational gradients.
In this workshop, instructors will cover how to obtain the various types of data (geographic, environmental/climatic and elevation; morphological and phylogenetic), how to work with them in R, the metrics of diversity, and how to map them for purposes of visualization and how to conduct the statistical analyses, taking geography into account.
This workshop is primarily intended for (but is not exclusive to) graduate students and postdocs with a degree in biological sciences.
The instructors will supply datasets but participants are encouraged to bring (or download) geographic information, phylogeny and morphological data.
Some familiarity with R is strongly recommended (and will be needed to follow the workshop).
Participants must have a personal computer (Windows, Mac, Linux). The use of a webcam and headphones is strongly recommended, and a good internet connection.
Click here to see the full Program
Introduction to ecoPhyloMapper
- We will demonstrate how to work with the multiple types of data so that all can be integrated in the analysis of geographic patterns of biodiversity. One major hurdle is to align all the types of data, which can be done using the tools in ecoPhyloMapper, an R package with tools that facilitate the manipulation of these datasets. We also provide tools for calculating a variety of metrics per grid cell, and for generating turnover patterns in taxonomic, phylogenetic and multivariate trait space. The novelty of the R package lies in the data structures and helper tools, as well as in the spatial grid metrics intended for multivariate morphological data.
Sources and types of data
- We will briefly discuss the types of biodiversity data one might come across: Primary biodiversity data such as occurrence records, expert range maps (IUCN), suitability surfaces from SDM’s, etc. We will discuss how these fit into the ecoPhyloMapper framework (for instance, use occurrences directly, or make polygons from them?).
- We will briefly discuss types of environmental data, online sources of those data, and how to obtain and import them into R. Among the types of data we will cover are rasterized climate data and polygon land cover types, from sources such as Worldclim, Chelsa, EarthEnv, and Bio-Oracle.
- We will discuss the main formats for phylogenetic trees that can be imported into R, where to obtain them, and how to work with multiple trees, including how to produce the maximum credibility tree from a Bayesian posterior distribution of trees.
- We will briefly introduce the formats for size and shape data, and how to produce the formats needed to integrate both univariate and multivariate morphological data with geographic and phylogenetic data.
Producing an ecoPhyloMapper object
- We will review the main considerations for constructing a ecoPhyloMapper object, including (1) spatial resolution, (2) grid type, (2) spatial projection and how they capture what you are trying to measure.
Working with phylogenies
- We will present an overview of what you can infer from phylogenies, such as what “patristic distance” means, and what the “diversification rate” statistic (DR) means and what you can learn from phylogenetic signal and the geographic structure of phylogenetic turnover.
Working with shape data: An introduction to geometric morphometrics
- We will briefly summarize the basics of shape analysis, including the collection of landmark and semilandmark data, extracting shape and size data from them, and measuring distances between shapes and sizes.
- We will first present an overview of metrics of biodiversity and how they are related to each other, and what these mean for interpreting their geographic distributions, and how to visualize these on a map using ecoPhyloMapper.
Statistical analysis (SARS models)
- We will present an introduction to statistical methods for analyzing relationships among the metrics of biodiversity (Spatial autoregressive models), including how to obtain a random sample from the grid cells of the map, determine the best model for the error term (to take spatial autocorrelation into account in the statistical analysis), and plot the relationships between variables.