quantifying directed dependence of environmental factors

On Thu, 7 Mar 2013, Philippi, Tom wrote:

I would look at packages bio.infer, paltran, fossil, and analogue, and
search to see if anyone has pushed them in the direction you want to go.

To this list I would add Steve Juggins' excellent rioja package. In
addition to several WA methods it also includes maximum likelihood
regression and calibration in the flavour of bio.infer.

   bio.infer is based on the EPA's EMAP-West (Environmental Monitoring and
Analysis Program for the western states) and uses benthic macroinvertebrates
and fish with selected water chemistry parameters. It uses the ITIS
(International Taxonomic Identification System) to provide consistency in
naming taxa to the lowest reasonable level.

As far as I can tell, bio.infer contains all you say but as higher-level
utility functions.

However, IIRC at the heart of bio.infer is what we call maximum
likelihood regression and calibration; fit a Gaussian logistic
regression to each species to characterise species-env relationships,
then "invert" this set of models to find the value of the environmental
variable that maximises the likelihood of observing a sample of new
counts over the set of species. Invariably, the inversion involves
numerical optimisation to search for the value of the env that made the
new counts most likely.

You just need to give mlsolve() the relevant data objects, which seem to
be somewhat easy to create by hand if you don't need to look-up
harmonised or correct taxon names. You really don't need all the nice
ITIS hand-holding, though I'm sure it is very handy for those working on
relevant species groups.

G

   Conceptually, one could assemble equivalent dataframes for diatom taxa and
environmental conditions, but I don't know if ITIS has plants/algae in the
system; problably does. However, the biota-environments relationships would
be based on current conditions and whether this would be valid for sediment
core data would need to be judged by a limnologist, not a stream ecologist
like me.

Rich

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