#StackBounty: #mixed-model #lme4-nlme Mixed models in ecology: definition of random effect

Bounty: 50

My understanding of a random effect is based on this paper, specifically this definition:

Random effects: factors whose levels are sampled from a larger
population, or whose interest lies in the variation among them rather
than the specific effects of each level.
(Bolker et al., 2009)

In ecology random effects seem to be mostly used to avoid (psuedo-)replication from repeated measures, for example sampling from the same location repeatedly, or to account for phylogeny i.e. that closely related species are more likely to be similar due to shared evolutionary history.

This seems to me to be only a restricted application of a random effect, based on the above definition. The Bolker definition says to me that treating a variable as a random effect will control for unmeasured differences between sampling units that may affect the variables I’m interested in. Is this correct?

Say I have a study where I’m interested in measuring variable X. My sampling design involves paired sampling at a number of different locations (not repeated), on different dates. Pairs would be random effects, to avoid repeated measures as discussed above. What about location and date? I’m not interested in the differences between locations or date, only variable X. In fact, I’d like to control for the differences between location and date to get a better understanding of the effect of variable X on my response. Would treating location and date as random effects accomplish this? I.e.:

Response ~ X + (1|location/pair) + (1|date)


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