Species traits in multivariate analysis

The March Eco-Stats Lab (Friday 27th, 2pm, Bioscience level 6) will be on incorporating species traits into multivariate analysis (or equivalently, community level models), using some functions recently added to the mvabund package.

Often ecologists collect data (especially abundance or presence-absence data) simultaneously across many taxa, with the intention of studying what occurs where (and why). This tutorial focusses on the why - methods to help us move towards a functional explanation of community abundances. McGill et al (2006) and Shipley (2010) argue passionately for the need for this.

A common strategy in any field looking at "why" is to look for predictor variables that can explain the response. In the case of studying why some taxa are abundant at a site while others are not, the relevant predictors are species traits. These come in a matrix, different traits in different columns, different taxa in different rows.

We will explore methods for using such a matrix of traits in a multivariate analysis, using the mvabund package (version 3.10.1 or later).  Full details here:
             http://rpubs.com/dwarton/68823
We will also discuss the relationship of these methods with standard analysis of multivariate abundance data, SDMs, and Bill Shipley's CATS models.

*** Note you need mvabund 3.10.1 or later... ***

UPDATE (13/5/15): mvabund 3.10.4 is now available from CRAN.  It has a formula argument for more control of the traitglm model you fit, and composition and col.intercepts arguments to control whether or not you include row/column effects in the model (to focus on relative abundance), and a block resampling option on anova.traitglm (useful for example if you have repeated measures).

September Eco-Stats lab: model-based multivariate analysis in ecology (mvabund package and recent additions)

What does mvabund do?
Analyses multivariate data (especially abundance of presence-absence data) using simultaneous univariate models and design-based inference.

The main functions are manyglm, which fits a GLM to each response variable, and anova/summary, which use row-resampling for valid multivariate inference (i.e. taking into account correlation between variables)

Designed specially for multivariate abundance data in ecology, species-by-site stuff, which has two key properties that need to be dealt with:
(1) strong mean-variance relationship.
(2) correlation between response variables (e.g. due to species interaction)

Why is mvabund better than using PRIMER, PC-ORD, etc?
A few reasons, see this R script for details (and code to work through):

mvabund v2.2-5.3 online now!

mvabund is a a set of tools for displaying, modeling and analysing multivariate abundance data in community ecology. mvabund v2.2-5.3 has been tested on R-2.13.