Evolutionary mode routinely varies among morphological traits within fossil species lineages Melanie J. Hopkinsa,b,1 and Scott Lidgarda a Department 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
integration trends
| modularity | punctuated equilibrium | rate of evolution |
M
uch of the research on stasis and punctuated equilibrium has focused on processes that could generate or influence patterns of morphological evolution, including stabilizing selection (1, 2), metapopulation dynamics (3), environmental stability, habitat tracking, and stress (3–6). Recently, however, renewed discussions have highlighted the patterns themselves. These investigations largely fall into one of two categories, each focusing on a different aspect of the theory of punctuated equilibrium (7, 8). The first considers whether morphological evolution is concentrated at speciation events or occurs gradually along branches of a phylogenetic tree. Here, methods applied to trees of extant taxa test whether the variance in phenotypes increases as a function of the number of speciation events (inferring a punctuational mode) or of total branch length, i.e., time (inferring a gradualist mode) (9–13). The second category distinguishes morphological evolutionary patterns within sequences of populations in the fossil record, particularly to determine the relative frequency of stasis compared with other modes of change. Recent methods either expand on earlier work in treating unbiased random walks as null hypotheses [e.g., Hurst measure (14), see ref. 15 for earlier work] or treat an unbiased random walk as a model of evolutionary change to be judged alongside other models using model selection criteria (16–18). What studies in both categories have in common is that the quantitative assessment of morphological change is mostly based on single traits, either size or shape. In our dataset of 635 sequences compiled from literature on the fossil record, only 17% were derived using multivariate analysis of several traits (Dataset S1). The remaining sequences are comprised primarily of trait lengths and trait length:length ratios (Dataset S1). In many cases, more than one trait was measured from a sequence, but each was treated separately. Potential problems with this approach have been noted before (19–22). In 1987, Cheetham pointed out that among 46 single traits measured across sequences of nine species of Metrarabdotos (bryozoan), a few traits departed from the static pattern www.pnas.org/cgi/doi/10.1073/pnas.1209901109
seen in the overall species morphology captured in discriminant analysis of the same set of traits. It is notable that these traits were relatively unimportant in distinguishing species, particularly ancestors and descendents (21). Despite thousands of papers on punctuated equilibrium and stasis, the question that Cheetham and others posed has remained unanswered: are (or similarly, when are) single traits adequate representations of species-level change? We investigate this query further by posing a more specific empirical question: How often do single traits show conflicting patterns in the same sequence? We define a sequence as a temporal series of fossil samples belonging to a species lineage and apply Hunt’s (16) Akaike Information Criterion (AIC) based method for determining whether each sequence is best characterized by directional change (modeled as a generalized random walk with a non-zero mean step size), unbiased random walk (modeled as a generalized random walk with a zero mean step size), or stasis (modeled following ref. 23). In addition, using parameters suitably scaled from an actual lineage (24), we simulate a sequence in which a clear overall directional trend is evident, compile an exhaustive set of length:length shape traits, and fit the three modes of evolution to each trait. Finally, we compare results based on single traits with results based on multivariate traits describing the same species lineages. Results and Discussion Within the full dataset of fossil sequences (n = 635, including traits derived from multivariate analyses), the relative frequency of sequences best characterized by directional change, unbiased random walk, and stasis agree with Hunt (17): just over half show an unbiased random walk, slightly fewer show stasis and very few show directional change (Table 1). This result also holds for the subset of single (univariate) traits. However, among the subset containing only strongly supported results (AICc weight is 2.7 times higher than the next best supported model; Materials and Methods), slightly more sequences show stasis versus an unbiased random walk (Table 1). In general, static trends are more likely to be strongly supported than random walks (of weakly supported sequences, 65% show an unbiased random walk, 22% show stasis, and 13% show directional change; G = 72.488, P < 0.0001). This is unsurprising given that sequences either show very high or very low support for stasis (Fig. 1) and that the mean AICc weight for sequences showing an unbiased random walk is 0.706, as opposed to 0.868 for sequences showing stasis (Dataset S1). In contrast to Hunt (17), we find less support that shape traits are more likely than size traits to experience stasis (full dataset: G = 6.446, P = 0.040; single traits only: G = 3.549, P = 0.170; and strongly supported sequences: G = 4.234, P = 0.120).
Author contributions: M.J.H. and S.L. designed research; M.J.H. performed research; M.J.H. and S.L. analyzed data; and M.J.H. and S.L. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1
To whom correspondence should be addressed. E-mail:
[email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1209901109/-/DCSupplemental.
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Recent studies have revitalized interest in methods for detecting evolutionary modes in both fossil sequences and phylogenies. Most of these studies examine single size or shape traits, often implicitly assuming that single phenotypic traits are adequate representations of species-level change. We test the validity of this assumption by tallying the frequency with which traits vary in evolutionary mode within fossil species lineages. After fitting models of directional change, unbiased random walk, and stasis to a dataset of 635 traits across 153 species lineages, we find that within the majority of lineages, evolutionary mode varies across traits and the likelihood of conflicting within-lineage patterns increases with the number of traits analyzed. In addition, single traits may show variation in evolutionary mode even in situations where the overall morphological evolution of the lineage is dominated by one type of mode. These quantified, stratigraphically based findings validate the idea that morphological patterns of mosaic evolution are pervasive across groups of organisms throughout Earth’s history.
EVOLUTION
Edited by Neil H. Shubin, The University of Chicago, Chicago, IL, and approved October 19, 2012 (received for review June 11, 2012)
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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 evolutionary modes characterize different traits. Second, even though there are fewer studies where many traits were measured, it is evident that a conflicting pattern among traits is more likely 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 traits to show stasis. Within lineages that show conflict, traits may still be dominated by a particular mode (Fig. 3). For example, among 10 length measurements taken from a sequence of samples of Mandarina chichijimana (land snail; ref. 25), 9 show an unbiased random walk, and only 1 shows stasis. However, none of our lineages are comprised of traits showing only directional change (Fig. 3). These patterns emerge despite known correlations 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 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 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 remains true for the subset of strongly supported results (Fig. 4C and Table 1). Thus, even in this case where directional change seems obvious, there are ample traits that do not change congruently with the directional mode. Further, randomly chosen samples of length:length ratios are more likely to show variation in mode of evolution among traits, especially as sample size increases; at n > 9, no samples are entirely consistent in mode (Fig. 4D). The pattern of conflicting within-lineage patterns supports similar findings in empirical fossil sequences (Fig. 2). Another insightful example is based on data from a study of lacustrine bivalves (26). The relative beak height and the relative anterior length of the valve are best characterized by directional change. However, PC analysis of this and nine additional traits reveals that none of the first four principal components are best characterized by directional change. Rather, beak height and valve length load heavily on the two principal components that show only moderate support for directional change. This suggests that while some traits in this lineage do show some directional change, they do not account for a large proportion of variation within the traits chosen to represent the lineage. Because our ability to recognize directional change in a trait depends on that change being greater than change in other aspects of size or shape, it may be inevitable that some traits remain comparatively static even as others show a directional shift. An increase in horn size, for example, is no less notable because no other parts of
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Fig. 1. Distribution of AICc weights. (A) Frequency of AICc weights for directional change (GRW), unbiased random walk (URW), and stasis. (B) Ternary diagram showing AICc weights for each model for each analyzed trait. Strongly supported traits shown in gray.
the organism change significantly. However, there are also clear examples where the mode of evolution varies across traits within species lineages that appear static overall. For example, morphological change in the Globorotalia truncatulinoides (foraminiferan) from Deep Sea Drilling Project site 591 is strongly characterized by stasis if the three evolutionary modes are fit to the discriminant function scores extracted from an eigenanalysis of 34 size and shape traits (27). If the 34 traits are analyzed separately, however, over a third of them are best characterized by an unbiased random walk (Dataset S1). Although our results show that single measured traits may not fully represent species-level change, determining the general Hopkins and Lidgard
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Fig. 2. Stacked histograms showing distribution of species lineages where all measured traits show same evolutionary mode (black) and where evolutionary mode varies across measured traits (gray). (A) All single traits, (B) size traits only, (C) shape traits only, and (D) strongly supported traits only.
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tendency of the overall variation within the species lineage is hardly simple. In addition to the multivariate statistical methods— ordination, geometric morphometrics—used in the examples above, one might initially compare results with the distribution of modes among separate individual traits to extract complementary perspectives of species change. However, scientists will always be extrapolating from a small sample of available morphological (or genetic, or other) traits to characterize evolutionary
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Fig. 3. Boxplots showing proportion of traits within species lineages that show directional change (GRW), unbiased random walk (URW), and stasis. Tallied for sequences where at least four traits were measured.
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modes of species lineages. For paleobiological studies, this is an inevitable consequence of the nature of the fossil record. Typically, only skeletal elements are preserved and both preservation and sampling vary in uniformity and resolution. Our simulation data are a patent example: with rare exception, only trilobite exoskeletons are preserved in the fossil record and the high frequency of disarticulation among skeletal elements limited the study to just one part of the skeleton. Moreover, species themselves are all determined by subsets of possible traits and may be individuated using a variety of different protocols (28). Among paleontologists, species determinations are based on morphological variation. However, concepts differ in how variation is used for delimiting species, depending on the goal of the study. For example, studies based in cladistic parsimony look for changes in otherwise invariant traits, studies using numerical phenetics may claim measurable traits are all equivalent, and biostratigraphic studies may seek out (directionally) variable traits for finer temporal resolution. Here, we have accepted the species delimitations in the original studies, which in total may comprise different species concepts. Nor were the traits analyzed necessarily selected randomly in the original studies. Some may have been chosen because they permitted consistent measurement or had prior taxonomic importance, but also because they seemed to show change or lack of change. Because observed evolutionary patterns may include contributions from immigration and emigration on ecological timescales, and climatic, habitat, or facies shifts on much longer geological timescales, it is also possible that different modes characterize the same trait, contingent upon the particular interval of time or particular locality being considered. Changes in PNAS Early Edition | 3 of 6
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Fig. 4. Results from simulated trilobite cranidial shape data. (A) Landmarks representing overall shape of cranidium (required only from half because of bilateral symmetry) and all possible length measurements between landmarks. (B) Time series of selected PC axes scores. (Upper Left) Image shows example of typical morphology from oldest sample; (Upper Right) image shows typical morphology from stratigraphically youngest sample (scale bars = 1 mm). Change in morphology is dominated by an expansion and rotation of palpebral lobes relative to rest of the cranidium. (C) Time series of individual length:length measurements, divided up into four panels showing those that were best characterized by directional change (Upper Left) (black), unbiased random walk (Upper Right) (gray), stasis (Lower Left) (white), and strongly supported trends of all types (Lower Right). Bars on right show range of means for each mode of evolution at the end of the sequence. (D) Stacked histograms showing distribution of samples of randomly selected length:length measurements where all traits in the sample show the same evolutionary mode (black) and where evolutionary mode varies across traits (gray). Number of samples taken at each sample size = 1,000.
geographically patchy or clinal morphological variation likely contribute to all fine-scale temporal trends in the fossil record, although their general significance is disputed; in the few studies examining trends in replicate sections, both congruent and incongruent patterns are reported (29–31). Simulation or modeling approaches merit further investigation, but necessarily involve numerous interacting parameters that resist precise estimation, and are expected to differ among taxa, localities, and time intervals. None of these contributions negate our overall findings, which are supported by several lines of evidence. First, our findings are reinforced by an independent study of character rate distributions using phylogenetic compatibility, which rejected single4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1209901109
rate models of character change among nearly all cases in 115 fossil invertebrate clades (32). Second, our simulation results involved random resampling, which by virtue of subsetting, accommodates both the possibilities of temporal and geographic variation. Finally, the size and scope of our study lends assurance to at least the broad patterns it reveals. The dataset includes representatives from across eukaryotes and throughout the fossil record, compares many more quantified sequences than any other we are aware of, and does so using a consistent analytical protocol. Our results are so striking that they compel us to think differently about the whole question of single traits as proxies for species-level change: the apparent ubiquity of mosaic patterns of morphological evolution. Hopkins and Lidgard
Materials and Methods We began with the results from a previous study investigating the relative frequency of directional change, unbiased random walk, and stasis in the fossil record (17). This dataset consisted of AICc results for 251 sequences from 32 references. We excluded nine sequences because they did not meet our selection criteria. We added an additional 393 sequences from 61 references (Datasets S1 and S2). Model selection (16) requires trait means, variances, sample sizes, and relative stratigraphic position or age. New data
Hopkins and Lidgard
ACKNOWLEDGMENTS. We thank G. Hunt for making his 2007 AICc results available and for his help and advice using the paleoTS package. We also thank D. Lazarus, K. Kim, M. Kucera, B. Lauridsen, W. Theriot, E. Erba, and D. Anderson for providing additional data; and A. Haber for advice on sampling from multivariate normal distributions. This project was supported by a John Caldwell Meeker Postdoctoral Fellowship (Department of Geology, Field Museum of Natural History, Chicago) and by the VolkswagenStiftung.
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were largely collected from figures using WinDig (56); additional data were obtained directly from authors (Acknowledgments). Sequences were included if there were at least six temporal samples with five or more specimens; of these, sequences were not included if the analysis was at the genus level or higher (e.g., refs. 57, 58) or if one or more of the above four parameters went unreported (e.g., refs. 59, 60), including situations where only the order of samples was reported (e.g., ref. 61). We refer to sequences as species lineages because they may comprise series of samples belonging to two or more chronospecies as well as single species. The dataset is comprised of both micro- and macrofossils from marine shelf, deep sea, open ocean (pelagic), terrestrial, and lacustrine environments, including brachiopods, bryozoans, echinoderms, mammals, fish, mollusks, conodonts, ostracods, trilobites, planktonic and benthic foraminiferans, diatoms, and radiolarians. Sequences range from 0.01 to 35.6 million years long with a median duration of 2.8 million years. Finally, all of the traits are quantitative and measurable throughout each sequence analyzed and thus do not document the appearance of new traits within sequences. The generalized random walk, unbiased random walk, and stasis models were fit by maximum likelihood to each sequence using the paleoTS package for R, in accord with the protocols and model assumptions previously described by Hunt (16, 18). Model support was assessed using the bias-corrected Akaike Information Criterion; the model with the lowest AICc is the best supported. Akaike weights, which are a transformation of the AICc values that sum to one, are then interpreted as probabilities that each of the models considered respectively is the best supported model (62). To compare our results with those of Hunt (17), we used G tests to assess whether counts of best supported modes were independent of categorical variables such as trait type. However, instead of using Wilcoxen rank-sum tests to test for differences in median AIC weights for particular models across groups, we chose to evaluate separately the subset of sequences for which the bestsupported evolutionary mode was also strongly supported. Results were retained if the best supported mode had an AICc weight 2.7 times greater than the second best-supported evolutionary mode. This criterion corresponds to the “rule of 2,” where a model is considered well supported relative to other models under evaluation if the difference in likelihood values is greater than 2 (62). However, this is an arbitrary cutoff and should not be interpreted as a level of statistical significance (62). We chose not to use the stricter criterion of an AICc weight of 0.89 (the likelihood criterion of rejecting a hypothesis when an outcome is eight times [or more] less probable [63]) because this reduced the dataset by over 70%. It is notable, however, that applying this criterion reduces the dataset to 179 sequences, 91% of which are best characterized by stasis (see also Fig. 1B). We also applied the same analysis to subsets of data that included only the single traits. Finally, within the subset of sequences where single traits were measured, we tallied the relative frequencies of each evolutionary mode within species lineages from unique localities. Using geometric morphometrics, Hopkins and Webster (24) documented a directional shift in the shape of the cranidium from a series of five successive samples of the trilobite Zacanthopsis levis-palmeri. To simulate a larger number of samples that mathematically represented this trend, we randomly sampled 25 specimens from a multivariate normal distribution defined by 1 of 10 evenly spaced landmark configurations and the covariance matrix of one of the original samples (UCR 10097, Klondike Gap, Nevada, n = 32) using the mtvnorm package for R (64). This yielded 10 samples of 25 specimens where the mean of each was represented by 1 of 10 successive mean landmark configurations representing the overall directional trend. We extracted all of the between-landmark lengths and found the ratio between each length and the length of the cranidium (defined by two landmarks), for a total of 90 length:length ratios (derived from 14 landmarks) (Fig. 4A). We performed PC analysis of the warp scores derived from a thin-plate spline decomposition of the landmark coordinates (65, 66). We then fit the three evolutionary modes to each of the 90 length:length ratios as well as the sample mean scores from the PC analysis. Finally, we randomly sampled the 90 length:length ratios (1,000 iterations without replacement, starting at n = 2) and fit the three evolutionary models to each.
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Mosaic evolution is one among a historical welter of ideas relating to the organism as a combination of ancient and more derived morphological parts, or to these parts evolving at different rates within and among lineages (33, 34). The central idea, if not the term itself (35), extends back to the nineteenth century, to Louis Dollo’s principle of “crossing of specializations” (36, 37) and to conflicting rates of trait change in precise stratigraphic studies of fossil populations (38, 39). In 1944, G. G. Simpson “inextricably bound the tempo of evolution with the mode, or pattern, of evolution within the matrix of organism-environment interactions” (40, 41); indeed the measurement of rates is not separable from assumptions about the mode of evolution (42, 43). At the same time, Simpson primarily related varying rates and patterns of change in single characters or character complexes—not the aggregate of all characters evident within lineages—to environmental circumstances and adaptation (41). Thus, Simpson may also have inadvertently codified an analytical frame for morphological rates that persisted for decades, despite widespread acceptance of mosaic patterns by paleontologists, including himself. Given that mosaic evolution has long been acknowledged by evolutionary biologists, our results may not appear surprising. The question “What alternative exists?” is for us less relevant than “How does the explicit recognition of mosaic evolution help us to develop a more nuanced understanding?” (35, 44, 45). A critical problem in this vein is deducing independence of traits while inferring the overall mode of evolution. Patterns of trait independence versus integration affect all types of multivariate approaches. Thus, the use of multiple traits in studies of evolutionary mode in fossil lineages can be an opportunity to incorporate complementary insights from studies of morphological integration and modularity in living organisms (46, 47). For example, quantitative assessments of modularity may identify traits that can more appropriately be treated as independent in phylogenetic and morphological rate studies. Our analysis only considers mosaic evolution as pattern (48). However, because different forms of integration (e.g., functional integration of parts [49, 50], and developmental modularity [51]) can influence the variation, direction, rate, and magnitude of lineages’ morphological changes at different temporal scales (e.g., ref. 52), they are potential keys to process explanations of evolutionary modes. Variation in evolutionary mode among traits within species may occur because different traits vary in their response to the same selection pressure, are subjected to different selection pressures at different levels of organization, or are more constrained or more evolvable due to degrees of trait integration and functional and developmental modularity. A promising way forward will take the pervasiveness of mosaic evolution into account, not just whether single traits adequately represent species’ evolutionary tempos and modes. A more nuanced understanding invokes convergence of evidential inferences (53): paleontological approaches that compare canonical concepts of evolutionary mode (43) and their further development to accommodate more holistic, multivariate patterns of trait covariance (“overall morphology”); measuring congruence of these results with individual trait patterns within lineages; and developing ways of studying mosaic evolution and morphological integration that are shared by paleontology and neontology (45, 54, 55).
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