There is also an excellent section on what constitutes a random or fixed effect 
in Tom Hobbs and Mevin Hooten's "Bayesian Models: a Statistical Primer for 
Ecologists" using fecundity of spotted owls (adapted from Clark's work on the 
subject), and again using hypothetical sampling of aboveground biomass, as 
examples. Both examples are accompanied by clear and concise explanations of 
the implications for the underlying distributions and assumptions of the model 
one might seek to fit, and for the ecology informing the models.

Garrett Street
Assistant Professor
Wildlife, Fisheries, and Aquaculture
Mississippi State University

On May 17, 2016, at 4:34 PM, Brian Church 
<[email protected]<mailto:[email protected]>> wrote:

There is a fairly detailed discussion of fixed vs. random effects on 
CrossValidated here: 
http://stats.stackexchange.com/questions/4700/what-is-the-difference-between-fixed-effect-random-effect-and-mixed-effect-mode

Based on the discussion there, it seems like temperature, rainfall, and density 
could all be considered to be random effects for the following reasons:
1. You are unlikely to sample the entire populations for those variables.
2. They are not being controlled
3. They are likely continuous and distributed in some way (e.g., normal) rather 
than discrete values
4. You are unlikely to be interested in responses at a specific temperature, 
rainfall, and density; rather, it seems more interesting to understand effects 
relating to the underlying distributions of those variables.

Those commenting in the CrossValidated forum cite a few sources, though they 
seem to be general/mathematical rather than ecology-specific. Hope that helps 
some.

-Brian Church


On Tue, May 17, 2016 at 11:12 AM, Gary Grossman 
<[email protected]<mailto:[email protected]>> wrote:
I'm having a bit of difficulty getting a clear understanding of what should be 
considered a fixed vs. a random effect in a linear mixed model analysis of 
field data. Even the statisticians seem to say "it depends on who's defining 
it" or "sometimes the same treatment/variable can be either". Some examples may 
help, let's say I collected samples annually in three sites and wanted to test 
for the effect of daily rainfall, daily temperature, and density, on 
recruitment of individuals in the following year. Using the lmer function in R 
which of these would be fixed effects and which would be random? A reference or 
two would help. I really couldn't find much in a google search on field 
studies, but I didn't go to anything like zoological abstracts. TIA, g2

--
Gary D. Grossman, PhD
Fellow, American Fisheries Soc.

Professor of Animal Ecology
Warnell School of Forestry & Natural Resources
University of Georgia
Athens, GA, USA 30602

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