Partitioning and Mixed Models for Biodiversity Analysis in R

The LifeWatch eCampus offers courses, short schools and lifelong learning products dedicated to universities, post-doc and early-career researchers.

The biodiversity of all individuals in a given meta-community may be split into the diversity within and between local communities. From a conservational point of view several questions arise. What is the importance of the biodiversity of a single local community with respect to the entire metacommunity? Which local communities contribute more to the biodiversity of the meta-community? Is it possible to maintain the biodiversity of the meta-community preserving only the most diverse local communities or should we care more about the conservation of ecosystem peculiarities?

This study-lab training briefly introduces the R software and then focuses on biodiversity partitioning, describing methodology and software for γ, α and β diversity profiling. It then discusses the theory behind mixed effects modeling and how this can be applied to investigate the variation of biodiversity measures. The training concludes with a practical unit that examines the use of the R implementation of mixed effects modeling routines with data from ecological surveys. 

Knowledge of α-, β- and γ-biodiversity; biodiversity and entropy; linear models; mixed effects models; basics of the R software is preferable, but not mandatory.