It is common to begin the analysis of information in biology with an uncontroversial but minimal notion: a causal or correlational conception. Smoke in the air and fire in the forest are correlated with one another. If you see a forest burning at night, you can predict the air will be smoky. If you see a plume of smoke rising from above the trees, you can predict that below there is a fire. The correlation does not have to be perfect to be informative. Putting the point generally, a signal carries information about a source, in this sense, if we can predict the state of the source from the signal. This sense of information is associated with Claude Shannon (1948) who showed how the concept of information could be used to quantify facts about contingency and correlation in a useful way, initially for use in communication technology. For Shannon, anything is a source of information if it has a number of alternative states that might be realized on a particular occasion. Any other variable contains some information about that source, or carries information about it, if its state is correlated with the state of the source. This is a matter of degree; a signal carries more information about a source if its state is a better predictor of the source, less information if it is a worse predictor. 