Two general conclusions can be drawn from both studies analysed here. First, there is a clear indication of adaptation to a novel environment. All the adult traits analysed presented clear patterns of improvement throughout the first 14–15 generations assayed (with the exception of male starvation resistance in the second study, which took more generations to indicate clear convergence). This general improvement agrees with the expectations of positive covariances among life-history traits when populations start evolving in a novel environment (see Service & Rose, 1985; Matos et al., 2000a). But it is not in accordance with what might be expected considering Partridge and collaborators' study of laboratory adaptation in D. melanogaster, which indicated that adaptation to the novel, laboratory environment involved an improvement of early adult performance at the cost of late performance and starvation resistance (Sgrò & Partridge, 2000; Hoffmann et al., 2001). Although we did not measure either late fecundity or mortality rates, we did measure starvation resistance, which is expected to be strongly correlated with longevity, at least in D. melanogaster (e.g. see Rose et al., 1992) and plausibly in our species as well. This trait showed an increase throughout generations in both studies, and although female starvation resistance did give indications of a biphasic evolutionary trajectory in both studies (corresponding to higher values of the more recent populations from a certain phase onwards, which suggests the occurrence of a later drop if a convergent state is reached in relation to established populations), this is not the same as observing a drop of values since foundation. It seems to us that the most likely explanation for the disagreement between our results and those of Hoffmann et al. (2001) is a different methodological approach, i.e. inferences from a comparative approach in their case and a direct analysis of evolutionary trajectories in our case (see also below).
Secondly, in both our studies the evolutionary trajectories indicate a process of convergence towards the values presented by long-established populations. The fact that the best fit models for most traits in the second study were loglinear ones is in accordance with the expectation of an approach to an evolutionary equilibrium (plausibly a stable convergent state). Thus, although fecundity is still differentiated between experimental and established populations, it is likely that a state of convergence will be achieved.
Although convergent evolution is an important corollary of Darwinian evolution, adequate testing of this expectation is surprisingly lacking in the literature, particularly the empirical study of evolutionary trajectories such as those presented here (see introduction). A clear exception is the study by Teotónio & Rose (2000) of reverse evolution in D. melanogaster. In that study, 25 replicated populations (from five different selective regimes, starting from a common ancestral population) were followed for 50 generations, after being placed again in the ancestral environment. Their conclusion was that reverse evolution (which is a particular type of convergent evolution) occurred, but was not universal, being dependent both on previous evolutionary history and the traits involved. It would have been interesting to have more generations of reverse evolution data from this study in order to check whether the difference between our study and the reverse evolution study was because of the peculiarities of the selective regimes imposed, the traits analysed, or general differences of reverse evolution vs. convergent evolution to an overall novel environment.
There are different convergent patterns between the first and second study
Although there was a clear convergence process in both our studies, there were also differences in the initial differentiation and the rate of convergence, resulting in a higher number of generations (observed or expected) to attain full convergence in the second study.
These differences may have been the result of different genetic backgrounds at foundation (e.g. genetic changes in the natural population and genetic sampling effects, although foundation involved a relatively high number of individuals) and/or environmental factors, that, although apparently slight, might have affected the adaptive process to the laboratory environment between studies (see also Cohan, 1984b).
One cannot dismiss the possibility that changes in the control values may have contributed to the differences observed. There was in fact a generally better performance of NB values, in relation to B values, which can be seen in the averages across generations for most traits. These were, respectively: for age of first reproduction, 3.0 and 4.9; for fecundity of the first week, 119.6 and 28.0; for fecundity between days 8 and 12, 157.4 and 66.3; for male starvation resistance, 39.1 and 29.4; the only exception was female starvation resistance, in which NB had an average of 41.1 and B of 41.3. The differences in the first four traits were significant (two-tailed t-tests, 12 d.f., P < 0.01). It is unlikely that a different level of inbreeding depression in the B population caused the observed differences, given the population sizes during maintenance (in general above 1000 individuals during the first study, similar to or bigger than the NB replicate population sizes). Differences could also be because of the fact that the B population was still adapting to the standard laboratory environment between the first and second study. The stability of B phenotypic values during the 14 generations of the first study (see Matos et al., 2000a) makes this unlikely. Furthermore, an assay performed at generation 47 of W and 71 of B (in which there were no significant differences between populations; see Matos et al., 2000a) further suggests the hypothesis that neither inbreeding depression nor evolutionary disequilibrium were involved in the changes of the B population. Finally, a comparison of NW and W-values at generation 4 also found a significantly higher performance for the first (two-tailed t-tests, 106 d.f., P < 0.01 for all traits except female starvation resistance). This suggests environmental changes in laboratory conditions between the first and second studies, affecting both NB and NW performance.
All told, the most likely explanation for the differences between NB and B stocks are environmental factors contributing to a better performance in the second study, and acting simultaneously on the NB and NW populations (e.g. change of incubator, yeast used, occurring at least 2 years before the start of the second study). The interaction between these environmental factors and the genetic background of the founder population, together with possible effects of the slight changes in the culture regime (generation time, slightly shortened more than 2 years before the second study), may have changed the evolutionary scenario, causing a change in the tempo of convergence, both in single traits and in the relationship between the evolutionary rates and initial differentiation.
Our results confirm that microevolutionary processes are highly sensitive, leading to considerable differences in the evolution of life-history traits, whether caused by effects of foundation, changes in environment, or interactions between the two (e.g. see Fontdevila, 1989; Schlichting & Pigliucci, 1998). Although comparative studies may allow synchronous testing (e.g. Sgrò & Partridge, 2000; Hoffmann et al., 2001), the fact that different populations are involved obscures evolutionary processes, given the many potential sources of variation in evolutionary patterns (see Leroi et al., 1994).
Evolutionary rate is dependent on initial differentiation
In our study evolutionary rates were linearly related to initial differentiation, both in each separate study and when both were plotted together. However, there were differences in the relationship between initial differentiation and evolutionary rate between studies. It is an open question whether these differences were because of the different genetic backgrounds of the founder populations, or because of the effects of genotype vs. environment interactions. Whatever the source of the differences, they show that the linear relation found between these parameters is no mathematical certainty and deserves attention for its potential evolutionary significance. Is such a general linear relationship related to positive genetic covariances among traits, which may arise from the effects of a novel environment (Service & Rose, 1985; Matos et al., 2000a)? Only more studies of patterns of convergence among synchronously evolving populations can clarify this point.
The power of studying convergent evolution in action
Unlike divergent selection experiments, evolutionary convergence studies can test for a particular outcome of the evolutionary process. This gives an increased power to define clear predictions in evolutionary biology studies. In particular, they are a fine way of testing the evolutionary limitations possibly caused by genetic constraints, so often mentioned in the literature (see general reviews in Maynard Smith et al., 1985; Loeschcke, 1987; Roff, 1992, 1997; Stearns, 1992; Falconer & Mackay, 1996; Stearns & Hoekstra, 2000; Teotónio & Rose, 2001).
Thus, empirical studies involving reverse evolution and convergent evolution in a common environment are powerful tools for evolutionary biology. Few studies have used this approach. The most common approach for investigating adaptive evolution has been a comparative approach to infer evolutionary patterns and processes. Our data suggest that even in the same laboratory, slight temporal changes in the environment cause significant changes in the features of convergent evolution, suggesting that even more severe problems will likely arise in the comparison of natural populations (cf. Leroi et al., 1994; Matos & Avelar, 2001). The results also illustrate the difficulties of evolutionary studies in which the controls are dynamic populations themselves, possibly leading to misleading results (see Rose et al., 1996). In particular, tests for repeatability between experiments are important because replication within experiments is not enough to cover the effects of both the foundation and microenvironmental changes, in studies aimed at general evolutionary questions.