[R-sig-eco] accounting for linear sampling structure using PERMANOVA or dbRDA

[Gavin Simpson]

Hi Tim,

It sounds like you'd be best served by modelling the transect spatial
position and using free permutations. Spatial eigenvectors could be
used for example to model the transect position effect if you need a
more complex effect than a simple linear or polynomial function.

HTH

G

On 13 January 2017 at 22:53, Tim O'Connor <tko2 at berkeley.edu> wrote:
> Hello everyone,
>
> I’m trying to assess the effect of a factor on community structure while controlling for confounded spatial effects.
>
> I sampled herbivorous insect communities along transects spanning a contact zone between two host plants, A and B, with 6 sites per transect (e.g., start-A-A-A-B-B-B-end) and 10 plants per site. Each plant was censused for insects separately, so I began with 60 total communities per transect. The transects are basically linear, but sites are irregularly spaced. I would like to quantify the effect of plant type on insect community while controlling for possible environmental or spatial effects due to transect position.
>
> At the moment I attempt this with a PERMANOVA (or equivalently, dbRDA), permuting plant type among sites and finding the marginal effect of plant in a model that includes position.
>
> ctrl <- how(complete = T,
>         within = Within(type = "none"),
>         plots = Plots(type = “free", strata = site))
> perms <- shuffleSet(nobs(communities), control = ctrl)
> adonis2(vegdist(communities) ~ position + plant, permutations = perms, by = “margin")
>
> Although a linear permutation scheme seems most justified and would account for the adjacency of sites, there are only 5 such permutations for a transect of 6 sites (aside from the observed arrangement). Allowing free permutation of sites improves total permutations (up to 719) but no longer includes spatial information.
>
> I have two questions. First, is this approach correct in principle? Does it seem overly or underly conservative? Second, are there other approaches I should consider, especially those that allow uneven observations among sites? The challenge with the method I describe is that my final data set includes different numbers of plants per site due to data cleaning. The constrained permutations require me to sacrifice at least 1/3 of my cleaned data to ensure the same number of observations per site.
>
> Thanks for any suggestions.
>
> Best,
> Tim
>
> -------------------
> Tim O'Connor
> PhD Student
> Whiteman Laboratory
> Integrative Biology
> University of California, Berkeley
> http://noahwhiteman.org/tim-oconnor.html
>
>
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>
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-- 
Gavin Simpson, PhD