# The Map of Science

Why does it take so long for awesome cutting-edge statistical to make their way over to ecology? There are a myriad of techniques out there that have been around for 20, 30, 40, or more years that could help so many ecologists from banging their head into a wall over and over and over and…well, you get the point. But, it takes quite a while for them to percolate over to us. This is not for lack of user-friendly tools, often. Rather it has to do with the connectivity of disiciplines.

For example, I was having a lovely conversation with Jim Grace the other day about using Structural Equation Modeling for predictive purposes, and we ended up chatting a little about history. SEM as it is done currently – using maximum likelihood approaches to fit a model to a covariance or correlation matrix – really dates to the late 1960s and early 1970s. Before then, scientists in a number of disciplines used a wide variety of approaches to examine path models (a là Sewall Wright’s Path Analysis), or perform Factor Analysis, or approach other multivariate models that often included latent variables. These techniques were fairly heterogeneous, even though they attempted to do roughly similar things.

It took Karl Jöreskog‘s wonderful papers outlining his LISREL technique and software using maximum likelihood to really bring the whole enterprise together into modern SEM.

And yet, despite the fact that this seminal work was published in the 70s, there are Ecological papers well into 90s that use piecewise regression models to fit path analyses. Why?

The answer can be summed up by this beautiful diagram detailing the connectivity of science in 2004 from the ever-interesting eigenfactor.org (and hat-tip to Jim for pointing it out to me).

Orange circles represent fields, with larger, darker circles indicating larger field size as measured by Eigenfactor score™. Blue arrows represent citation flow between fields. An arrow from field A to field B indicates citation traffic from A to B, with larger, darker arrows indicating higher citation volume. Image from eigenfactor.org.

Basically, these methods were developed for economics, and saw their first heavy use there and and sociology, political science, education, and psychology. In terms of connectivity, Ecology & Evolution sites on the other side of a doughnut hole of communication (with the occasional exception of psychology). Historically, the fields where the newest techniques are being developed are rarely examined by ecologists, and it is to our loss. Fortunately, I think this is a historical trend. With the rise of search engines, message-boards, and copious mailing lists, I do wonder if a connectivity graph from 2004-2010 would be much tighter.

Connectivity can only be a boon for science. With environmental issues beginning to impinge on every endeavor, it has become more important than ever to survey the breadth of what is out there.

So, hey, sign-up for alerts for a journal that you think will have no relevance to you. Who knows what might drop into your inbox.