Many problems in ecology and evolutionary biology require understanding of the relationships among variables and examining their relative influences and responses. For example, over the last few decades ecologists have been trying to quantify the relative importance of top-down control by predation and herbivory vs. bottom-up control by nutrients and recruitment driving food web dynamics. Rather than arguing which of these forces are more important, we can examine the relative importance of each and how these forces interact to influence food web dynamics.

Structural Equation Modeling (SEM) or path analysis is a multivariate technique that can test for the nature and magnitude of direct and indirect effects of multiple interacting factors. SEM is an approach that interprets information about the observed correlations among the traits of organisms or groups of organisms in order to evaluate complex causal relationships. It is a rich technique that is particularly well suited for large-scale observational community or population data sets. Its intuitive connection to how we conceive of our study systems makes it a powerful and useful technique for ecologists and evolutionary biologists. The goal of this workshop is to familiarize ecologists the basic techniques of SEM using the 'lavaan' package in R.

Hello everyone! I'm looking forward to our upcoming journey into the wild world of Structural Equation Modeling together. Before the class begins, I want to make sure you all are prepared so that you can get the most out of it and to have you work through this preclass-excercise and tutorial.

The course is going to be a mixture of lecture and hands-on exercises. Aside from model building and identification exercises, everything else will be in R. I am going to assume that you have at least basic proficiency in R. You will need basic proficiency in order to work through the the class. What does basic proficiency mean? I expect that

0) You have R installed on your computer and be able to access help files.
1) You can install and load new libraries, as shown in the preclass tutorial.
2) You are able to load in data files as shown in the preclass tutorial. 3) You can plot a relationship between two variables from your data. 4) You can perform a basic linear regression, and get summary statistics for parameter fits and F-tests.

If you cannot do these things, I encourage you to visit one (or more) of the following additional tutorials (I'm listing several, as I've found that different tutorials work more or less well for different people - so take a look at them and see what works for you.)

http://www.r-tutor.com/r-introduction
http://www.nceas.ucsb.edu/files/scicomp/Dloads/RProgramming/BestFirstRTutorial.pdf
http://www.youtube.com/user/wildsc0p


If you can do more than what is above, great. But make sure you work through the preclass tutorial.

In the tutorial, you'll be asked to install the following libraries, as we'll be using them throughout the course:

lavaan
plyr
ggplot2
AICcmodavg
car
ggm
semPlot
semTools
lavaan.survey

For the exercises, we will work on some common datasets.

However, I'll also provide some time to begin working with your own data. If you do not have data to work with, don't worry. I have a few public data sets from kelp forests in Santa Barbara and fouling communities in Bodega Bay. There are also some good sets in the nceas data catalog. However, I highly encourage you to bring your own data as you'll get a much deeper understanding working with SEM if you know the data. You're also welcome to share data sets with each other - who knows, someone may figure out that problem you've been hitting your head on for the past few months!

If you have any further questions, please don't hesitate to contact me!

• R - http://www.r-project.org/
• R Studio, a fantastic cross-platform interface for R - http://www.rstudio.org/
• The lavaan package for analysis of Structural Equation Models - http://www.lavaan.org
• Plyr - http://had.co.nz/plyr
• Ggplot2 - http://had.co.nz/ggplot2