Reading for week 7, 09 October 2013: Characterizing scale-dependent community assembly using the functional-diversity-area relationship


I have attached the reading for this week.



Smith et al. 2013


About seeddispersal

I am a post-doc at Case Western Reserve University working on the population dynamics of mutualism.

3 responses to “Reading for week 7, 09 October 2013: Characterizing scale-dependent community assembly using the functional-diversity-area relationship”

  1. seeddispersal says :

    Discussion topics:
    1. To me, the abiotic v. biotic question was much less interesting than their approach of creating null models to which they can compare their data. The authors were able to generate quantitative expectations which can appear to be a more valid way of interpreting data. Do you think that this is a sound way to evaluate data? Are there pitfalls? Tautologies? What can null models do for your own resaerch?
    (For a free download of Null Models in Ecology:
    (For Oikos editor, Jeremey Fox’s, view on null models in ecology with a little bit of historical background:

    2. For two of their analyses the authors used a single trait to examine functional trait diversity. Is this valid? What would be the best way to choose traits if one were interested in examining functional trait diversity?

    3. Like virtually everything we read in ecology and evolutionary biology, the authors assume that competition is the driver that shapes all aspects of their ecosystems. What would incorporating facilitative interactions do to their hypotheses and quantitative predictions, if anything? (E.g., figure 1.b. here:


  2. Josh says :

    4.) What does functional diversity actually measure? What advantages and disadvantages does functional diversity have over other metrics that ecologists (including evolutionary ecologists) use?

    5.) Can you realistically use functional diversity to infer past ecological and evolutionary forces that actually influenced the assembly of a community? What problems might arise from using a community measure from a single snapshot in time to infer past events?

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