Sit down and describe your research system. Every time you mention a cause and an effect, write them both down with an arrow connecting them. Soon you will have a tangled mass of interactions, with arrows pointing every which way.
This is real. This is nature. As scientists, we may carve out a piece of this mess to ask questions. But even this is full of direct and indirect interactions and quantities that, while we can measure proxies, we can ever quite fully understand.
Structural Equation Modeling (SEM) is a multivariate technique that lets us build and evaluate complex causal models using data from our study systems. I began using SEM to analyze the dynamics of food webs. It is a rich technique that is particularly well suited for large-scale community data sets, such as those being developed by the Long-Term Ecological Research site network. It is by no means a one-stop solution for all modeling needs. Its intuitive connection to how we conceive of our study systems, however, makes it a powerful and useful technique for ecologists.
Currently, I am working with statisticians to develope tools for SEM in R. I first developed the sem.additions package to address some common problems in ecological datasets (non-normality, easy construction of multiple alternative models, etc.). I am now continuing to collaborate with developers of the sem and lavaan packages, as well as working with colleagues to make newer SEM techniques easily accessible to scientists of all stripes.
For those who are interested in using SEM, are teaching themselves how to code them up, and want some examples of published, working, ecologically oriented models, see my github collection of Ecological SEMs in lavaan. You may find it useful.
I have developed wokrshops ranging from 3-5 days to teach the basic techniques of SEM to Ecologists using the lavaan package in R. Please contact me if you are interested in hosting a workshop.
Fox, J., Byrnes, J., Boker, S., and Neale, M. 2012. Structural equation modeling in R with the sem and OpenMX packages. In Handbook of Structural Equation Modeling. Rock H. Hole, David Kaplan, George Marcoulides, and Steve West, eds. pg. 325-340. [amazon]