Dear list,

 

     I am working on a project in which I use linear mixed effects models to
determine how 

both species traits and environmental features contribute to community
diversity patterns in fragmented landscapes. I would really appreciate some
input regarding a conceptual challenge surrounding the modeling analyses
that I have performed.

 

     The gist of the potential issue is that there is some question as to
whether the models allow me to detect the influences of both species traits
and environmental attributes on diversity patterns. 

 

     The models themselves are parameterized with environmental attributes
(e.g. site area, habitat heterogeneity, etc.) and species traits (e.g. wing
length, diet breadth, etc.) as fixed effects, and site and species identity
as random effects (because the environmental attributes are not independent
of site, and the species traits are not independent of species). The dataset
thus consists of species-by-site occurrence records, such that each record
holds information regarding the environmental attributes of a site and
traits of a species that was found there. As a response variable I am using
relative population density, measured as the number of individuals of a
species at a site/all individuals of that species in the dataset. Finally,
in order to forge the correct link between the response variable and
predictors, I have log transformed the response variable and specified a
Gaussian error family for the models. In the end, the model statement looks
like this:

 

Model<- glmer(ln(individuals at site/all individuals)~ Envt. predictors +
Trait predictors + (1|site) + (1|spp), data, REML=F)*

 

* The ML specification here is because I go on to compare subset models
during model selection and averaging (in MuMIn). Also, the model exhibits
very good fit to the data (good diagnostic plots, etc.).

 

 

    Now for the conceptual issue that has been called to question: Does this
model allow me to detect the influences of both environmental attributes and
species traits? The model output contains parameter estimates for both types
of predictors, and several of each type are also identified as important to
the system following model selection and averaging. However, it has been
questioned whether the response variable is appropriate for the detection of
both environmental and trait influences on population densities, in the same
model, or if such a response variable even exists. The thought is that it
may be difficult to tease apart what is going on with both types of
variables given that traits are species properties (and don't vary by site)
while environmental features are site properties (which don't vary by
species). As one additional piece of information, I also tried fitting the
model with 0's in the dataset, which was expected (by another researcher) to
result in the influences of species traits disappearing from the model
output. Although this model did not exhibit good fit characteristics (not
surprisingly), the effects of traits did not fall away as expected.

 

    I am now seeking additional input regarding this question, and thank-you
in advance for any feedback!

 

Natalie Robinson

Department of Ecology and Evolutionary Biology

University of Colorado

(303) 492-5175

n.robinson@colorado.edu

http://www.nataliesrobinson.com <http://www.nataliesrobinson.com/> 

 


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