Dear Thierry,
Thanks for reply. We used a sample of the population for our experiment,
but for this sample we have information (treatment and Prop variable at
each scale for all the nests.
How would you suggest to test/check is there is spatial autocorrelation?
I tried with the DHARMa package (which makes a Moran's I test adapted to
mixed models), but it doesn't show if autocorrelation changes with
distance, it just gives a p-value. I tried with a model with PairID as
random effect (p = 0.33), but if I include nest as a random effect (some
pairs changed in between the 2 years of the experiment, so there are less
Nest IDs than Pair IDs) the p-value becomes 0.054 ...
Kind regards,
Thomas
On 22/04/2020 13:57, Thierry Onkelinx wrote:
Dear Thomas,
Do you have information on all the nests or only on a sample of the nests?
In case you have data on every nest, then I would look at a simple model
with only treatment and an iid nest effect. Then see if there is spatial
autocorrelation. Variation at small ranges would indicate an effect of the
treatment of the neighbouring nests.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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Op wo 22 apr. 2020 om 11:44 schreef Thomas Merkling <
thomasmerkling00 at gmail.com>:
Hi all,
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
visual representation).
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 Mat?rn 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!
Thomas
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