The study of ecology and evolution is becoming increasingly more mathematical, but most of the theoretical tools seem to be coming from physics. However, in many cases the problems have a very discrete nature (see for example SLBS00) and could benefit from a computer science perspective. Yet, I am aware of only a few serious results from TCS that try to touch on specific questions in ecology and evolution. The two directions that spring to mind are:
Livnat, A., Papadimitriou, C., Dusho, J., & Feldman, M.W.  "A mixability theory for the role of sex in evolution" PNAS 105(50): 19803-19808. [pdf]
Valiant, L.G.  "Evolvability" Journal of the ACM 56(1): 3.
The former applies idea from analysis of genetic algorithms to show a qualitative difference between the way sexual and asexual organisms behave in fitness landscapes, and has lead to follow ups that help justify observed modularity. The latter connects evolution and computational learning theory, to try to prove evolvability and impositibility results. It has influenced a small collection of papers, but mostly by other computer scientists.
Are there more results in these veins? Are their other deep/non-trivial applications of theoretical computer science to understanding ecology and evolution as it is studied by biologists?
I am not interested in general engineering related genetic or evolutionary algorithms results. Although this is a very interesting and exciting part of computer science, its connection to evolution as studied by biologists is often superficial. Sometimes (as in LPDF08) concrete connections are made, but most standard results are not of biological interest, and hence I am not interested in them in this post.
Bioinformatics is a nearby field, but it is also not what I am looking for. Although it can be used to reconstruct things like phylogenetic trees and thus help evolution/ecology, the theoretical CS aspects do not take centre stage. Here, the CS results seem to be mostly to perfect a tool that can be used largely as a black-box from within existing well established theories, and not to build or extend new biological theories.
I prefer results that use modern-ish and non-trivial aspects of computer science to influence biology at a theoretic (but still relevant to biologists) level. As such, I am not that interested in things like Chaitin's metabiology.