[R-sig-ME] spatial auto-correlation or more complicated pseudo-replication?
thom@@merk||ng00 @end|ng |rom gm@||@com
Wed Apr 22 11:44:01 CEST 2020
I'm wondering how to best model data from an experimental design
involving a spatial component. This is a study on seabirds nesting on
artificial cliffs: each nest has been attributed an experimental
treatment (supplemented or not), while making sure that there was a
variable proportion of surrounding nests of the opposite treatment. Our
main goal was to investigate if laying date of a focal pair was
influenced by its treatment and/or by the proportion of surrounding
nests of the opposite treatment (hereafter, "Prop"), which we calculated
at 3 different spatial scale (local, panel and global, see
https://drive.google.com/open?id=1OrJQCkNfBO6KOBHSlkOoQyAdTrqtIdY8 for a
Hence, the treatment information of a focal pair is used in the
"Treatment" predictor variable, but also in the calculation of "Prop"
for the surrounding pairs (the number of pairs affected depending on the
spatial scale considered), thereby leading to some pseudo-replication.
Since this is dependent on the distance (i.e. "Prop" of pairs closer to
a focal one are more influenced than pairs further away), we thought
that accounting for spatial auto-correlation for be sufficient. We used
the spaMM package to do so, and our models look something like:
Laying ~ Treatment * Prop + Year + (1|PairID) + Matern(Y2011|x + y) +
Matern(Y2012|x + y)
with two Matérn correlation random effects (one for each year of the
study) being included (x and y being the spatial coordinates of the nests).
My question is: Is this random effect structure taking into account the
fact that "Prop" of a focal pair depends on the "Treatment" of the
surrounding pairs or not ? If not, how can we account for that?
Thanks in advance for your help!
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