How to succeed in evolutionary biology, without really trying

Lab Times has an interesting article by Ralf Neumann that analyses Europe's publications in evolutionary biology for the period 1996-2006. On page 36 there is a table of the 30 most cited authors in Europe, and the top five most cited papers. To my astonishment, I'm there at number 10 (accompanied by a photo taken in New York). What is interesting is that although the top 30 are varied in their interests, and include some well known names in the field, the top five papers in terms of citations are all about phylogenetic methods:

1. Page, RDM
   TreeView: An application to display phylogenetic trees on personal computers.
   COMPUTER APPLICATIONS IN THE BIOSCIENCES, 12 (4): 357-358 AUG 1996 (doi:10.1093/bioinformatics/12.4.357)


2. Strimmer, K; von Haeseler, A
   Quartet puzzling: A quartet maximum-likelihood method for reconstructing tree topologies.
   MOLECULAR BIOLOGY AND EVOLUTION, 13 (7): 964-969 SEP 1996


3. Ronquist, F; Huelsenbeck, JP
   MrBayes 3: Bayesian phylogenetic inference under mixed models.
   BIOINFORMATICS, 19 (12): 1572-1574 AUG 12 2003 (doi:10.1093/bioinformatics/btg180)


4. Yang, ZH
   PAML: a program package for phylogenetic analysis by maximum likelihood.
   COMPUTER APPLICATIONS IN THE BIOSCIENCES, 13 (5): 555-556 OCT 1997 (doi:10.1093/bioinformatics/13.5.555)


5. Guindon, S; Gascuel, O
   A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.
   SYSTEMATIC BIOLOGY, 52 (5): 696-704 OCT 2003 (doi:10.1080/10635150390235520)

Note also that most of these papers are short application notes. Of course, the number of pages in the publication bears no relation to the effort involved in writing the actual software. The other thing that's interesting is that of the 30 most cited authors, I have published the second smallest number of papers (40). A quick plot of the number of citations against number of papers suggests published suggests that while there is a correlation between effort (papers) and impact (citations), it's not perfect (ρ = 0.44, R²= 0.19). You can have a reasonable impact without generating lots of papers.

So, what can we learn from this? Well, it would be tempting to offer advice along the lines of "if you want to succeed in this field, write a piece of software that a lot of people find useful, and make sure you have a publication that they can cite." Oh, and getting in early helps. Of course, this advice should be taken with a pinch of salt. Beware the 100th idiot.