Evolutionary Mode Routinely Varies Among Morphological Traits Within Fossil Species Lineages
Total Page：16
File Type：pdf, Size：1020Kb
Evolutionary mode routinely varies among morphological traits within fossil species lineages Melanie J. Hopkinsa,b,1 and Scott Lidgarda aDepartment of Geology, Field Museum of Natural History, Chicago, IL 60605; and bMuseum für Naturkunde, Leibniz Institute for Research on Evolution and Biodiversity at the Humboldt University Berlin, 10115 Berlin, Germany Edited by Neil H. Shubin, The University of Chicago, Chicago, IL, and approved October 19, 2012 (received for review June 11, 2012) Recent studies have revitalized interest in methods for detecting seen in the overall species morphology captured in discriminant evolutionary modes in both fossil sequences and phylogenies. analysis of the same set of traits. It is notable that these traits Most of these studies examine single size or shape traits, often were relatively unimportant in distinguishing species, particularly implicitly assuming that single phenotypic traits are adequate ancestors and descendents (21). Despite thousands of papers on representations of species-level change. We test the validity of this punctuated equilibrium and stasis, the question that Cheetham and assumption by tallying the frequency with which traits vary in others posed has remained unanswered: are (or similarly, when are) evolutionary mode within fossil species lineages. After fitting single traits adequate representations of species-level change? models of directional change, unbiased random walk, and stasis to We investigate this query further by posing a more specific a dataset of 635 traits across 153 species lineages, we find that empirical question: How often do single traits show conflicting within the majority of lineages, evolutionary mode varies across patterns in the same sequence? We define a sequence as a tem- traits and the likelihood of conflicting within-lineage patterns poral series of fossil samples belonging to a species lineage and increases with the number of traits analyzed. In addition, single apply Hunt’s (16) Akaike Information Criterion (AIC) based traits may show variation in evolutionary mode even in situations method for determining whether each sequence is best charac- where the overall morphological evolution of the lineage is terized by directional change (modeled as a generalized random dominated by one type of mode. These quantified, stratigraphi- walk with a non-zero mean step size), unbiased random walk cally based findings validate the idea that morphological patterns (modeled as a generalized random walk with a zero mean step of mosaic evolution are pervasive across groups of organisms size), or stasis (modeled following ref. 23). In addition, using ’ throughout Earth s history. parameters suitably scaled from an actual lineage (24), we sim- ulate a sequence in which a clear overall directional trend is integration | modularity | punctuated equilibrium | rate of evolution | evident, compile an exhaustive set of length:length shape traits, trends and fit the three modes of evolution to each trait. Finally, we compare results based on single traits with results based on uch of the research on stasis and punctuated equilibrium has multivariate traits describing the same species lineages. Mfocused on processes that could generate or influence pat- terns of morphological evolution, including stabilizing selection (1, Results and Discussion 2), metapopulation dynamics (3), environmental stability, habitat Within the full dataset of fossil sequences (n = 635, including tracking, and stress (3–6). Recently, however, renewed discussions traits derived from multivariate analyses), the relative frequency have highlighted the patterns themselves. These investigations of sequences best characterized by directional change, unbiased largely fall into one of two categories, each focusing on a different random walk, and stasis agree with Hunt (17): just over half show aspect of the theory of punctuated equilibrium (7, 8). The first an unbiased random walk, slightly fewer show stasis and very few considers whether morphological evolution is concentrated at spe- show directional change (Table 1). This result also holds for the ciation events or occurs gradually along branches of a phylogenetic subset of single (univariate) traits. However, among the subset tree. Here, methods applied to trees of extant taxa test whether the containing only strongly supported results (AICc weight is 2.7 variance in phenotypes increases as a function of the number of times higher than the next best supported model; Materials and speciation events (inferring a punctuational mode) or of total Methods), slightly more sequences show stasis versus an unbiased branch length, i.e., time (inferring a gradualist mode) (9–13). The random walk (Table 1). In general, static trends are more likely second category distinguishes morphological evolutionary patterns to be strongly supported than random walks (of weakly sup- within sequences of populations in the fossil record, particularly to ported sequences, 65% show an unbiased random walk, 22% determine the relative frequency of stasis compared with other show stasis, and 13% show directional change; G = 72.488, P < modes of change. Recent methods either expand on earlier work in 0.0001). This is unsurprising given that sequences either show treating unbiased random walks as null hypotheses [e.g., Hurst very high or very low support for stasis (Fig. 1) and that the mean measure (14), see ref. 15 for earlier work] or treat an unbiased AICc weight for sequences showing an unbiased random walk is random walk as a model of evolutionary change to be judged 0.706, as opposed to 0.868 for sequences showing stasis (Dataset alongside other models using model selection criteria (16–18). S1). In contrast to Hunt (17), we find less support that shape What studies in both categories have in common is that the traits are more likely than size traits to experience stasis (full quantitative assessment of morphological change is mostly based on dataset: G = 6.446, P = 0.040; single traits only: G = 3.549, P = single traits, either size or shape. In our dataset of 635 sequences 0.170; and strongly supported sequences: G = 4.234, P = 0.120). compiled from literature on the fossil record, only 17% were de- rived using multivariate analysis of several traits (Dataset S1). The remaining sequences are comprised primarily of trait lengths and Author contributions: M.J.H. and S.L. designed research; M.J.H. performed research; M.J.H. trait length:length ratios (Dataset S1). In many cases, more than and S.L. analyzed data; and M.J.H. and S.L. wrote the paper. one trait was measured from a sequence, but each was treated The authors declare no conflict of interest. separately. Potential problems with this approach have been noted This article is a PNAS Direct Submission. before (19–22). In 1987, Cheetham pointed out that among 46 1To whom correspondence should be addressed. E-mail: [email protected]. Metrar- single traits measured across sequences of nine species of This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. abdotos (bryozoan), a few traits departed from the static pattern 1073/pnas.1209901109/-/DCSupplemental. 20520–20525 | PNAS | December 11, 2012 | vol. 109 | no. 50 www.pnas.org/cgi/doi/10.1073/pnas.1209901109 Downloaded by guest on September 28, 2021 Table 1. Relative frequency of directional change (GRW), A unbiased random walk (URW), and stasis 300 Dataset GRW URW Stasis N Full dataset 36 (5.7) 333 (52.4) 266 (41.9) 635 Single traits 34 (6.4) 283 (53.5) 212 (40.1) 529 Frequency SS traits 8 (1.9) 192 (45.9) 218 (52.2) 418 Simulation 12 (13.3) 35 (38.9) 43 (47.8) 90 0 100 200 Simulation, SS 4 (7.5) 13 (24.5) 36 (67.9) 53 0.0 0.2 0.4 0.6 0.8 1.0 AICc weights, GRW Total number followed by percentage of total in parentheses. SS, strongly supported. Focusing next on evolutionary modes of just single traits within a species lineage (n = 529), two strong patterns emerge. First, the majority of species lineages show conflicting results among traits; in other words, within most lineages, different Frequency evolutionary modes characterize different traits. Second, even 0 50 100 though there are fewer studies where many traits were measured, 0.0 0.2 0.4 0.6 0.8 1.0 it is evident that a conflicting pattern among traits is more likely AICc weights, URW as the total number of traits analyzed increases (Fig. 2A, G test = 20.838, P = 0.035). These findings hold for subsets limited to size traits (Fig. 2C, G test = 17.513, P = 0.041), shape and meristic traits (Fig. 2D, G test = 21.141, P = 0.004), but not strongly supported traits (Fig. 2B, G test = 4.114, P = 0.767) where the signal is overwhelmed by the tendency for strongly supported 100 200 traits to show stasis. Within lineages that show conflict, traits may Frequency still be dominated by a particular mode (Fig. 3). For example, 0 among 10 length measurements taken from a sequence of sam- 0.0 0.2 0.4 0.6 0.8 1.0 Mandarina chichijimana ples of (land snail; ref. 25), 9 show an AICc weights, stasis unbiased random walk, and only 1 shows stasis. However, none of our lineages are comprised of traits showing only directional B Stasis change (Fig. 3). These patterns emerge despite known correla- tions among particular traits in some sequences that should bias us against finding variation in evolutionary modes. Even in lineages where the trend in overall morphology is 0.8 0.2 dominated by a certain mode, some traits will show other modes of change. Almost half of the variation in the simulation based on an actual trend in trilobite cranidial shape (24) is summarized 0.6 0.4 by the first principal component (PC), and directional change of PC 1 scores is strongly supported (Fig. 4B). However, most of the possible length:length ratios (90 in total) are better characterized by an unbiased random walk or stasis, and this characterization 0.4 0.6 remains true for the subset of strongly supported results (Fig.