PATTERNS OF DISPARITY IN EAST AFRICAN CICHLIDS
Before assessing possible reasons for differences in disparity among the tribes, it is necessary to establish that differences in disparity are real and not simply sampling artifacts. In particular, because Cyprichromini has both the smallest sample size and the lowest disparity of the tribes that were simulated for this study, the low disparity could be due to poor sampling.
However, several lines of evidence suggest that the low variance of Cyprichromini is not due to differences in sample size. First, there are only an estimated 10 species in the tribe Cyprichromini. Furthermore, all six confirmed, described species (Brandstatter et al. 2005; Day et al. 2008) are included in this study. There are an estimated three (Day et al. 2008) or four (Genner et al. 2007) possible species that have yet to be described and are currently classified as geographic variants of Cyprichromis leptosoma. Thus, the six species for which data were available represent at least 60% of the total species within the tribe Cyprichromini, and the remaining possible species are unlikely to add significantly to the total disparity due to their similarity to C. leptosoma.
In contrast, less than 60% of the total estimated species diversity is represented by the data for the other three tribes (Table 1). Moreover, variance is independent of the sample size or the number of species in a clade (Foote 1993, 1997; Ricklefs 2006a). This is further supported by the bootstrap analyses, which indicate that sampling only six species with replacement yields the same variance as sampling the full sample size of Ectodini, Lamprologini, and Tropheini (Appendix S5). Moreover, both Limnochromini and Perrisodini have smaller sample sizes than Cyprichromini (both are represented by only five species), but have variance roughly comparable to that of Tropheini, which has a similar age. Finally, there is not a significant correlation between morphological variance and the number of samples for Ectodini, Lamprologini, Tropheini, Cyprichromini, Limnochromini, and Perrisodini (r2= 0.35, P = 0.21). Overall, this suggests that the differences in variance among the tribes are due to factors such as stochasticity, clade age, or differences in evolutionary rates rather than sample size.
There is no indication that the greater disparity of Ectodini is due to differences in either morphological change or turnover rates when compared with Lamprologini and Tropheini. In fact, there is strong support from the speciation-dependent model that Ectodini may have experienced a lower turnover rate than Lamprologini and Tropheini. For a speciation-dependent model, disparity increases with turnover rates. Thus, the high disparity of Ectodini relative to the other tribes is due to its greater age, similar morphological change rate, and equivalent or lower turnover rate. Likewise, there is little support for differences in evolutionary rates between Tropheini and Lamprologini, and thus, the disparity differences between these tribes are also likely due largely to age differences.
However, rates of morphological change are substantially greater in Ectodini, Lamprologini, and Tropheini when compared with Cyprichromini. Under the Min–Max model, the Akaike weights for lower rates in Cyprichromini than the other three tribes were all greater than 0.80 (Table 3). The other model variations provide slightly lower support for rate differences, but the interpretations are consistent with the findings from the Min–Max model. These results are also strongly supported under pure-birth conditions (Appendix S7), suggesting that these results are highly robust to assumptions about the value of ɛ. Furthermore, there is considerable support for higher turnover rates in both Lamprologini and Tropheini in comparison to Cyprichromini with average Akaike weights over 0.80 in both cases. Support for higher turnover rates in Ectodini than Cyprichromini is lower, with an average Akaike weight of only 0.60, which is too low to be conclusive. Under a pure-birth model, higher rates in the more disparate tribe are only supported for Tropheini relative to Cyprichromini (Appendix S7). Thus, interpretations of turnover rate differences may be slightly more susceptible to assumptions regarding the value of ɛ than those for morphological change rates.
The linear regression analysis provides a second approach to examining rate differences among the tribes. According to Foote (1996) and Ricklefs (2006a), for a constant number of species, disparity increases linearly with time if rates of speciation, extinction, and morphological change are constant. Pie and Weitz (2005) found that for the more realistic branching random walks, the relationship between variance and time is nonlinear, but is approximately linear after a short initial period (t = 1/λ). Thus, given maximum likelihood estimates for origination rates for the tribes in this study (ɛ= 0.9), disparity should increase linearly after about 0.5–2 million year (My) if the evolutionary rates are constant. All of the tribes examined in this study, including Perrisodini and Limnochromini, are substantially older than this threshold. Thus, if the rates are the same among the tribes, a linear relationship is expected between clade age and disparity.
While age and morphological variance are strongly positively correlated in the five tribes other than Cyprichromini, the latter falls well below the expected variance. Cyprichromini falls outside the 95% confidence intervals, based on the calculated regression error (Fig. 3A), indicating that Cyprichromini is a statistical outlier. Further, if Cyprichromini is included in the regression, there is not a significant correlation between age and variance even though one is expected under equal rates of evolution. This suggests that the differences between the tribes Ectodini, Tropheini, Lamprologini, Perrisodini, and Limnochromini are all consistent with evolution under the same turnover and morphological change rates, but that Cyprichromini likely had lower evolutionary rates to have the currently low standing disparity. This evidence, combined with the general agreement among all model variations, indicates that the Cyprichromini evolved with lower morphological change and turnover rates.
This approach may also provide a means for comparing rates among large numbers of clades without simulations. However, to use this approach without simulations, further testing is required to determine how much variation from a calculated correlation would be consistent with constant rates. Furthermore, linear regressions do not provide information on whether differences in turnover versus morphological change rates are most likely responsible for disparity patterns. Thus, the regression approach may be most useful as a way to determine whether simulation studies are likely to reveal information on rate differences or, as in this study, as a supplement to simulation-based study, rather than as the only method for assessing rate differences.
The strong linear correlation between disparity and age indicates that age differences are largely responsible for standing disparity patterns among Ectodini, Lamprologini, and Tropheini. Further evidence that these patterns are due at least partially to age differences and cannot be explained simply by the fact that stochastic variation comes from the simulations in which all tribes are treated as if they are of the same age. There would be moderate support for turnover rate differences if the tribes were of the same age, but there is no support for rate differences given the actual ages of the tribes. These results indicate that older tribes may have higher disparity because the three tribes evolved under similar evolutionary rates, but have had different amounts of time to diversify (Appendix S6).
The lower evolutionary rates experienced by Cyprichromini are most interesting within an ecological context. On the basis of previous work on the adaptive character of cichlid morphology, tribes with a low diversity of trophic niches and preferred water depths might be expected to have lower evolutionary rates in an adaptive radiation as these are the ecological traits that correlate most strongly with morphology. Cyprichromini, which has lower rates than in the other tribes described in this study, has become progressively more adapted to a pelagic lifestyle, and perhaps more importantly, to the planktotrophic niche (Brandstatter et al. 2005). In contrast, members of the Ectodini, Lamprologini, Tropheini, Limnochromini, and Perrisodini all consume multiple food types (Konings 1998; Koblmuller et al. 2004, 2007, 2010; Duftner et al. 2005; Day et al. 2007).
Given the ecology of Cyprichromini, there are two possible explanations for standing disparity patterns. The first is that the species of Cyprichromini reached an adaptive optimum as open-water planktotrophs that subsequently resulted in a low rate of morphological change. That the Cyprichromini species have become progressively more specialized (Brandstatter et al. 2005) may provide support for this hypothesis in that it indicates that the tribe has been evolving toward a particular niche, and perhaps thus also toward a particular morphological optimum. This might also indicate that the morphological evolution of Cyprichromini could be better explained by an Ornstein–Uhlenbeck model than a Brownian Motion model (e.g., Hunt et al. 2008). Sidlauskas (2008) found that the approach used in this study may indicate low morphological change rates in clades that repeatedly evolve the same morphology as opposed to those which diffuse through a larger portion of morphospace. A species-level phylogeny would be required to test this hypothesis (Sidlauskas 2008). The second possibility is that the Cyprichromini have only succeeded in specializing for one trophic niche because their low morphological change and turnover rates do not enable them to diversify as rapidly into the range of trophic niches occupied by the other tribes. However, if this second option is the case, then the underlying reason for low rates of morphological change in this group remains to be determined.
Regardless of the ordering of cause and effect, these results suggest congruence between trophic diversity and rate of morphological evolution. Similarly, in comparing the lack of disparity in younger clades (e.g., the species flocks of Lake Malawi and Lake Victoria) with the more morphologically disparate assemblage of Lake Tanganyika, the differences in taxonomic diversity, clade age, and stochastic variation should be examined. The results presented here emphasize the importance of establishing differences in evolutionary rates before making biological interpretations to avoid these confounding factors.
MODEL ASSESSMENT AND SENSITIVITY ANALYSIS
This study develops an approach for comparing rates among clades that are not sister taxa. However, the approach requires the use of ages calculated using fossil or Gondwanaland calibrations of molecular clocks are necessary for comparing clades of different ages. These two calibrations yield very different ages (Genner et al. 2007) and the results are controversial (Stauffer et al. 2006; Koblmuller et al. 2008). However, this study does not seek to calculate diversification or morphological rates, but rather, only to determine whether these rates are greater in some clades relative to others. As should be the case, the ratios between the ages of the tribes are identical (Table 1), regardless of whether the fossil calibrations or the Gondwanaland calibrations are used. Therefore, the assertion that each time step represents 100,000 years may be inaccurate, but the relative proportions of time for diversification of each tribe should represent the actual ages very well. Accordingly, inaccuracies in the absolute age estimates of the tribes should have a minimal effect on the results of this study.
Although it is the ratio of the ages rather than the absolute ages of the tribes that are important for this model, it is also informative to consider how errors in the estimates of these ages are likely to influence the interpretations presented in this article. Note that some phylogenies place Cyprichromini as the sister group to Ectodini (e.g., Takahashi 2003), although many studies differ in their interpretations of the relationships of the Tanganyikan cichlids (Koblmuller et al. 2008). The age used for Cyprichromini in this study is less than half that of Ectodini, and therefore, the expected variance in Cyprichromini would be significantly lower than in Ectodini due to the differences in clade age alone. Furthermore, the Day et al. (2008) ages for the six tribes examined in this study also indicate that the tribe Cyprichromini is older relative to the other tribes than estimated by Genner et al. (2007, See Table 1). If Cyprichromini is in fact relatively older than Genner et al. estimated, then the expected variance of Cyprichromini would be higher if the evolutionary rates were equal, and thus the difference between expected and observed disparity would be even larger for this clade. In this scenario, support for rate differences between Cyprichromini and the other tribes would be higher than estimated by this study. Therefore, this study represents conservative findings with respect to the likelihood of lower evolutionary rates in Cyprichromini.
Either a pure-birth or a birth–death model may be appropriate in representing the radiation of cichlids. However, these two models yield similar results. In particular, morphological change rate comparisons are very similar between the two types of model, although differences in turnover rates among clades are somewhat more dependent on the extinction rate (Appendix S7). This provides further support that the results of this model are robust to its underlying assumptions, and that the interpreted similarities and differences in evolutionary rates among cichlid tribes are not artifacts of model design.
One of the previously unresolved questions regarding this model (Sidlauskas 2007) was that differences in turnover rates were not determined to be a major factor in generating clades of differing levels of disparity, despite simulation studies that suggest that turnover rates should play a major role in determining standing disparity (O’Meara et al. 2006; Ricklefs 2006a). In contrast, differences in turnover rate are strongly supported by this study. Sidlauskas (2007) suggests that his model does not indicate differences in turnover rate because the model is conditioned upon achieving the modern species diversity (equivalent to the Min–Max model of this study). However, the Min–Max model of this study yielded results that are consistent with results from the other three model conditions (the No Condition, Extant, and Min models). All four models generally produce numbers of successes under the same parameter combinations that correlate well with each other (Appendix S8). It may be that including the nonuniform priors for turnover rates results in more support for differences in turnover rates. It is also possible that turnover rates have played a more substantial role in shaping the disparity of Tanganyikan cichlids than that of South American characiform fish. Either way, the results presented here indicate that this model can distinguish differences in turnover rates, regardless of the model conditioning variation that is employed.
Additional research could further our understanding of the effects of turnover rates on standing disparity in cichlids. For example, assumptions of time homogeneous rates or simple random extinction could contribute to the disparity patterns generated by the model. Temporally constant rates are less likely in large clades, and many studies suggest that evolutionary rates are heterogeneous with respect to time (Purvis 2008). Furthermore, simulations indicate that random extinction of 95% of species may reduce total branch length in a phylogeny by as little as 19%, whereas selective extinction may have a much more substantial influence on phylogenetic diversity (Nee and May 1997) and thus also on disparity. Thus, simulations that incorporate these elements could further assess the robustness of the results presented here.
Although future research may provide insight into the nature of evolutionary processes in East African cichlids, the interpretations presented in this study are generally robust to the type of conditioning and to multiple assumptions of the model. This includes time-dependent and speciation-dependent models, five different approaches to conditioning model results, pure-birth versus birth–death models, and two different approaches to comparing model results with empirical disparity. This finding has encouraging implications for future studies. That the results are generally consistent across such a wide range of assumptions implies that the choice of model conditioning does not substantially alter the interpretations of results and the models may be used interchangeably to some extent. However, when possible, using multiple versions of the model and testing assumptions to demonstrate the robustness of the model is advised.