Phylogenetic convergence and multiple shell shape optima for gliding scallops (Bivalvia: Pectinidae)

Abstract

An important question in evolutionary biology is how often, and to what extent, do similar ecologies elicit distantly related taxa to evolve towards the same phenotype? In some scenarios, the repeated evolution of particular phenotypes may be expected, for instance when species are exposed to common selective forces that result from strong functional demands. In bivalved scallops (Pectinidae), some species exhibit a distinct swimming behaviour (gliding), which requires specific biomechanical attributes to generate lift and reduce drag during locomotive events. Further, a phylogenetic analysis revealed that gliding behaviour has independently evolved at least four times, which raises the question as to whether these independent lineages have also converged on a similar phenotype. Here, we test the hypothesis that gliding scallops display shell shape convergence using a combination of geometric morphometrics and phylogenetic comparative methods that evaluate patterns of multivariate trait evolution. Our findings reveal that the gliding species display less morphological disparity and significant evolutionary convergence in morphospace, relative to expectations under a neutral model of Brownian motion for evolutionary phenotypic change. Intriguingly, the phylomorphospace patterns indicate that gliding lineages follow similar evolutionary trajectories to not one, but two regions of morphological space, and subsequent analyses identified significant differences in their biomechanical parameters, suggesting that these two groups of scallops accomplish gliding in different ways. Thus, whereas there is a clear gliding morphotype that has evolved convergently across the phylogeny, functionally distinct morphological subforms are apparent, suggesting that there may be two optima for the gliding phenotype in the Pectinidae.

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Seeing distinct groups where there are none: spurious patterns from between-group PCA

“…The iso F ratio is also given in analogy to the 6 usual F ratio and the proportion of the total variation accounted for by differences among means, 2 iso R , 7 is also given. Note that these are not the usual F and R 2 coefficients from an anova or a multiple 8 regression analysis -they are expected values assuming the isotropic model, unlike a standard 9 MANOVA where one estimates between-group variance relative to within-group using all original 10 variables, here computations are only within the g-1 dimensions of the bgPCA transformed data and 11 cannot be used for statistical testing. This means that the within-group component shown in the table 12 only refers to the residual variance left unexplained by groups in the g-1 dimensional bgPCA space 13 (i.e., the within-group variation one sees in the scatterplots such as in Fig.…”

“…Plot showing 2 iso R values for the results of sampling experiments 7for simulated data based on the models shown inFig 5A.The slope b was varied from 0 to 1 to8 increase the level of correlation among the variables. Experiments were performed using 10009 replicates for g = 3 groups of size i n = 20. The solid line shows the expected relationship, uncorrelated data that closely matches the results from this sampling 11…”

“…Studies using bgPCA are emphasized in bold 8 while the columns with pNn and N/g ratios are emphasized in light grey. 9…”

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