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What you are observing here is an example of the marginal versus conditional interpretation of the fixed effects coefficients from generalized linear mixed-effects models (GLMMs). Namely, in GLMMs the fixed effects have an interpretation conditional on the random effects.
For your particular model, and because you have random intercepts only the fixed ...
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Yes, you can use site as a random effect. The observations are not independent so you need to account for the site effect somehow. Whilst the landscape variables you mention might explain differences in the mean insect abundance between your 60 sites, they are unlikely to account for all the difference (and likely can't as you point out that the counts ...
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This is still not entirely clear, and you have many research objectives for the design. Some might be difficult to investigate with this design. Some points:
I will assume analyzing only one response at a time, so three analyzes for each of the responses. Maybe some questions need a multivariate analysis, but anyhow you should start with the univariate ...
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I think in this case it is recommended to do a parametric bootstrap: the mixed effect model gives you an estimate of the variance of the effects of words and subjects, so you can generate new random deviates from their distribution (thus without actually resampling the estimated values). It is not difficult to write the code yourself, but if you used the ...
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