Evolutionary Biology最新文献

Landmark-based geometric morphometrics using the Procrustes approach has become the dominant family of methods in morphometrics. However, the superimposition (and sliding, if semilandmarks are present), that transforms raw coordinates into shape coordinates is biologically arbitrary. Procrustes has desirable statistical properties, but is not based on a biological model. The same is true for sliding methods. These techniques allow powerful statistical analyses of a full set of shape coordinates, but make the use of subsets of landmarks/semilandmarks problematic, inaccurate and misleading, if not totally wrong. Crucially, the biological arbitrariness of the superimposition prevents any meaningful quantification, analysis and interpretation of variation one landmark/semilandmark at a time. We exemplify how misleading this type of analyses can be by using a real dataset, as well as simulated data with isotropic variation. Both show inconsistencies in ‘per-landmark/semilandmark’ variances. The simulation in fact helps to make even more obvious that the pattern of variance is strongly influenced by the biologically arbitrary choice of the mathematical treatment. Unfortunately, despite this limitation of all superimposition methods being known since the early days of Procrustean morphometrics, there has been a recent series of papers in leading journals presenting results of ‘per-landmark’ analyses. Thus, we further clarify why these analyses are wrong and represent misleading examples that should not be followed: Procrustes shape data cannot be analyzed, visualized or interpreted one landmark at a time. For users who are in doubt, in the Conclusions, we provide a short list of recommendations on how to easily avoid this type of mistakes.

All skeletal specimens of the North American dinosaur Tyrannosaurus and a number of trace fossils have been attributed to the single species: T. rex. Although an unusual degree of variation in skeletal robustness among specimens and variability in anterior dentary tooth form have been noted, the possibility of sibling species within the genus Tyrannosaurus has never been tested in depth in both anatomical and stratigraphic terms. New analysis, based on a dataset of over three dozen specimens, finds that Tyrannosaurus specimens exhibit such a remarkable degree of proportional variations, distributed at different stratigraphic levels, that the pattern favors multiple species at least partly separated by time; ontogenetic and sexual causes being less consistent with the data. Variation in dentary incisiform counts correlate with skeletal robusticity and also appear to change over time. Based on the current evidence, three morphotypes are demonstrated, and two additional species of Tyrannosaurus are diagnosed and named. One robust species with two small incisors in each dentary appears to have been present initially, followed by two contemporaneous species (one robust and another gracile) both of which had one small incisor in each dentary, suggesting both anagenesis and cladogenesis occurred. The geological/geographic forces underlying the evolution of multiple Tyrannosaurus species are examined. A discussion of the issues involving the recognition and designation of multiple morphotypes/species within dinosaur genera is included.

Quantitative genetics is a powerful tool for predicting phenotypic evolution on a microevolutionary scale. This predictive power primarily comes from the Lande equation (Δz̅ = Gβ), a multivariate expansion of the breeder’s equation, where phenotypic change (Δz̅) is predicted from the genetic covariances (G) and selection (β). Typically restricted to generational change, evolutionary biologists have proposed that quantitative genetics could bridge micro- and macroevolutionary patterns if predictions were expanded to longer timescales. While mathematically possible, making quantitative genetic predictions across generations or species is contentiously debated, principally in assuming long-term stability of the G-matrix. Here we tested stability at a macroevolutionary timescale by conducting full- and half-sib breeding programs in two species of sigmodontine rodents from South America, the leaf-eared mice Phyllotis vaccarum and P. darwini and estimated the G-matrices for eight pelvic traits. To expand our phylogenetic breadth, we incorporated two additional G-matrices measured for the same traits from Kohn & Atchley’s 1988 study of the murine rodents Mus musculus and Rattus norvegicus. Using a phylogenetic comparative framework and four separate metrics of matrix divergence or similarity, we found no significant association between evolutionary divergence among species G-matrices and time, supporting the assumption of stability for at least some structures. However, the phylogenetic sample size is necessarily small. We suggest that small fluctuations in covariance structure can occur rapidly, but underlying developmental regulation prevents significant divergence at macroevolutionary scales, analogous to an Ornstein–Uhlenbeck pattern. Expanded taxonomic sampling will be needed to test this suggestion.

Identifying the role of quantitative variables on speciation rates is among the main purposes of trait-dependent diversification methods. ES-sim, a recent simulation-based approach that relies on Pearson’s correlations, allows testing trait-dependent diversification for single regression models. Here, we modified this approach to include generalized linear models and two independent variables. To examine the effects of multiple traits on speciation we modified ES-sim and integrated generalized linear models instead of Pearson’s correlations. We named the new approach as ES-sim-GLM. We further evaluated how this modified method performs in both single and multiple regression modelling. For this, we analyzed the relationship of speciation rates with geographic range size and snout-to-vent length in 216 species from the family Liolaemidae, a South American radiation of Andean lizards. Based on simulations, ES-sim-GLM for single regression models shows high power, low false discovery rates and is robust to incomplete taxon sampling. ES-sim-GLM for multiple regression models shows lower power but also low false-discovery rates. Both remained computationally efficient. Using Liolaemidae data, we found that larger species but with smaller species geographic range sizes were associated with higher speciation rates. To the best of our knowledge, no study as addressed these relationships in this clade. Our results provide new insights on macroevolutionary methods that should be relevant to all organisms and facilitate future studies that aim to understand diversification patterns across the Tree of Life.