Even among RNA viruses, which generally exhibit high evolutionary plasticity due to low fidelity of their RNA polymerases, HIV-1 is second only to HCV for its ability to generate within-host genetic diversity [1]. HIV’s rapid generation time leads to this high genetic diversity. The unfortunate consequences of HIV’s rapid evolution are resistance to antiretroviral drugs [1], partial escape from immune responses [2–4], the ability to switch tropism for target cells [5], and potential threats to new therapeutic strategies [6, 7]. The forces driving and influencing HIV evolution include Darwinian selection, limited population size, linkage, recombination, epistasis, spatial aspects, and dynamic factors (particularly due to the immune response). These factors, and the parameters that define them, can be difficult to discern. One of the most elusive parameters critically important for the rate of evolution in every medically relevant scenario is the ‘‘effective population number’’(Neff)(Figure 1). By definition, the census population size of HIV is the total number of infectious proviruses integrated into the cellular DNA of an individual at a given time. However, the genetically relevant Neff may differ substantially from the census population size. In this volume of PLOS Genetics, Pennings and colleagues [8] use new insights into ‘‘hard’’and ‘‘soft’’selective sweeps to estimate the effective population size of HIV. The search for Neff (and other HIV evolutionary parameters) has gone on for almost two decades, following every turn and hitting each pothole on the eventful road of HIV modeling [9]. The rapidity of resistance to monotherapy (in 1–2 weeks) was explained by the deterministic selection of alleles that preexist therapy in minute quantities [1]. The large numbers of virus-producing cells (, 108) in the lymphoid tissue of experimentally infected macaques seemed to confirm this simple Darwinian selection model [10]. However, the Darwinian view has faced challenges. Tajima’s ‘‘neutrality test’’applied to HIV sequences in untreated patients assumed that selection was neutral and predicted much smaller ‘‘effective’’populations, of Neff, 103 [11]. Since Tajima’s approach was designed to detect isolated selective sweeps at one or a few mutant sites—while HIV exhibits hundreds of diverse sites in vivo—two groups re-tested the result. A linkage disequilibrium (LD) test [12] and analysis of the variation in the time to drug resistance [13] arrived at the same value, Neff=(5–10) 6105, for an average patient (with the mutation rate, 1025 per base). Such populations are sufficiently large for deterministic selection to dominate, yet not large enough to neglect stochastic effects altogether. The LD test [12] is affected by recombination, and HIV’s recombination rate had not been well measured at that time. The recent measurement of 561026 crossovers per base per HIV replication cycle in an average untreated individual [14–16] updates Neff to (1–2) 6105, not far from the original value. A recent study of the pattern of diversity accumulation in early and late HIV infection confirms the range of Neff [17]. However, all these estimates of Neff are lower bounds.

Pennings et al.[8] continue this quest for an effective population size of HIV using a new method based on a theoretical calculation of the probability of multiple introductions of a beneficial allele at a site before it is fixed in a population [18]. The prediction does not depend on whether mutations are new or result from standing variation prior to therapy. The authors use sequence data obtained from 30 patients who failed suboptimal antiretroviral regi-