13a: Distance-Based Redundancy Analysis (db-RDA)

Practice Task

Practice Task

Work through these questions after reading the Distance-Based Redundancy Analysis chapter. This is a low-stakes rehearsal of the workflow you will need for the integrative project, carried out here on the familiar Doubs and mite data rather than the seaweed data.

1. Using the Doubs fish data as the response and the Doubs environmental data as predictors, run a db-RDA with capscale() on a Bray-Curtis dissimilarity, constraining the fish composition by a sensible subset of the environmental variables. Report the permutation test of the whole model (anova(...)), the adjusted \(R^2\) (RsquareAdj()), and the proportion of total inertia that is constrained.

2. Check multicollinearity among your predictors with vif.cca(). Remove predictors with a VIF above 10, one at a time, refitting after each removal, and report the final predictor set. How did the model summary change?

3. Test the significance of the individual constrained axes (by = "axis") and of the individual terms (by = "terms"). Which environmental variables significantly explain fish composition?

4. Produce and interpret a triplot showing sites, species, and the environmental arrows. Relate the constrained axes to the upstream-downstream gradient that has run through the whole module.

5. Apply the same workflow to the oribatid mite data in vegan (data(mite); data(mite.env)). Briefly discuss what constrains the mite community.

6. In one paragraph, contrast what the db-RDA told you with what an unconstrained PCoA or nMDS of the same fish data would have told you. What does the constraint add, and what does it cost?

Assessment Criteria

This Task is not formally assessed.