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Morphological volatility precedes ecological innovation in early echinoderms

Abstract

Origins of higher taxonomic groups entail dramatic and nearly simultaneous changes in morphology and ecological function, limiting our ability to disentangle the drivers of evolutionary diversification. Here we phylogenetically compare the anatomy and life habits of Cambrian–Ordovician echinoderms to test which facet better facilitates future success. Rates of morphological evolution are faster and involve more volatile trait changes, allowing morphological disparity to accrue faster and earlier in the Cambrian. However, persistent life-habit evolution throughout the early Palaeozoic, combined with iterative functional convergence within adaptive strategies, results in major expansion of ecospace and functional diversity. The interactions between tempo, divergence and convergence demonstrate not only that anatomical novelty precedes ecological success, but also that ecological innovation is constrained, even during a phylum’s origin.

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Fig. 1: Distinct trends in echinoderm morphology and ecology during the early Palaeozoic.
Fig. 2: Phylomorphospace and phyloecospace through the Cambrian and Ordovician periods.
Fig. 3: Phylogenetic distributions of divergence and convergence.

Data availability

Raw datasets are appended as Supplementary Data 15. Additional ancillary datasets, R script, time-scaled phylogenies and intermediate R workflow objects have been reposited in the open-source Harvard Dataverse (https://dataverse.harvard.edu/dataverse/CamOrdEchinoderms), under CC0 public domain license.

Code availability

All code (written in the programming language R) is available publicly at https://dataverse.harvard.edu/dataverse/CamOrdEchinoderms, under CC0 public domain licence.

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Acknowledgements

R. L. Parsley, W. I. Ausich and S. R. Cole discussed echinoderm life habits; J. Thompson, S. Zamora, I. Rahman, S. Sheffield, W. I. Ausich, C. Mah and T. Kammer assisted with compiling anatomical characters; G. J. Motz, P. D. Polly, C. C. Johnson, M. Florence, B. Hunda and R. Burkhalter provided access to museum collections; S. Lidgard, K. D. Angielczyk, P. Mayer, O. Rieppel and T. Lumbsch provided access to Field Museum Library; S. Larrondo located rare publications; G. T. Lloyd, M. J. Hopkins, D. W. Bapst, L. J. Revell and P. J. Wagner provided programming and analytical advice. ‘Assembling the Echinoderm Tree of Life’ supplementary grants NSF 1036416 and 1036356 provided funding to B.D., and NSF 1036260, 1213530, 1314236 and 1519658 provided funding to C.D.S. The Benjamin Meaker Visiting Professorship and the University of West Georgia Faculty Research Grants supported B.D. The Indiana University Summer Repository Research Fellowship, R. P. Rylaarsdam, B. Ng, M. de la Cámara, and the Benedictine University Faculty Development Committee supported P.M.N-G. This is Paleobiology Database publication no. 415.

Author information

Authors and Affiliations

Authors

Contributions

P.M.N.-G. and B.D. designed the study; P.M.N.-G. conducted analyses; P.M.N.-G., B.D. and C.D.S. wrote the paper; P.M.N.-G., B.D., C.D.S., A.S., N.S.S., J.P., K.E.H., R.L., I.R., C.C. and R.P. collected data.

Corresponding author

Correspondence to Philip M. Novack-Gottshall.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Juan Cantalapiedra and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Stacked richness trends, color coded according to operational taxonomic class.

a, Tree-based estimate of lineage richness measured using mean of 366 tip and 365 node ranges inferred across time-scaled phylogenies. b, Tree-based estimate of lineage richness measured using mean of only 366 tip-taxon ranges inferred from time-scaled phylogenies. c, Richness measured using only raw stratigraphic ranges from Paleobiology Database (PBDB). Ancestral nodes were assigned to taxonomic classes when both descendent tips were classified in the same class, and left as “UNCERTAIN” otherwise. For comparative purposes, these figures provide alternative representations of lineage richness through time. The legend is ordered to correspond to same order as in the stacked curves, with most-diverse (crinoids, primarily Ordovician) at lower right, and least diverse (Ctenoimbricata, early Cambrian) at upper left.

Extended Data Fig. 2 Sample-standardized mean trend in nine disparity metrics for the three life-habit propagation treatments (mode, constant, and raw) of the ecological data set.

Sample standardization used a sampling quota of 50 lineages and 3,000 replicates per stratigraphic age (sampling 50 time-scaled phylogenies 60 times each). Trends are percent-range-transformed to focus on shape of trends rather than absolute values. The standard deviation across the multi-tree resampling distribution for the ‘mode’ treatment is illustrated as a confidence polygon; the variability for the other treatments are of a similar magnitude. Metrics include number of unique life habits / morphotypes (trait-combinations) (H), mean pairwise Wills GED distance (D), maximum pairwise distance (M), sum of ranges (R), total variance (V), functional richness, or minimal convex-hull hypervolume (FRic), functional evenness (FEve), functional divergence (FDiv), and functional dispersion (FDis). See text for description of each metric. See Supplementary Table 5 for statistical correlation tests.

Extended Data Fig. 3 Sample-standardized mean trend in nine disparity metrics for the morphological and ecological (‘mode’ treatment) data sets.

Sample standardization used a sampling quota of 50 lineages and 3,000 replicates per stratigraphic age (sampling 50 time-scaled phylogenies 60 times each). Trends are percent-range-transformed to focus on shape of trends rather than absolute values. The standard deviation across the multi-tree resampling distribution for each trend is illustrated as a confidence polygon. See Extended Data Fig. 2 for abbreviations of each metric. Trends are not illustrated for the ‘constant’ life-habit treatment. See Supplementary Table 6 for statistical correlation tests (including for the ‘constant’ treatment).

Extended Data Fig. 4 Ecospace for first six PCoA axes (using the ‘mode’ data set), superimposing the k-means clusters and listing the taxonomic classes that are most frequent in each cluster.

PCoA output only illustrated for fiftieth time-scaled tree as one example; remaining time-scaled phylogenies produce similar ordination structures. Life-habit variables that are strongly correlated (positively or negatively) with each PCoA axis are illustrated. Cri = Crinoidea, Eocr = Eocrinoidea, Edrio and Edr = Edrioasteroidea, Rhomb = Rhombifera, Diplo = Diploporita, Paracr = Paracrinoidea, Hel = Helicoplacoidea, Stylo = Stylophora, Homo = Homostelea (order Cincta), Solut = Soluta, Ctenoc = Ctenocystoidea, Cyclo = Cyclocystoidea, Aster = Asteroidea, Ech = Echinoidea, Oph = Ophiuroidea, Holo = Holothuroidea, Somas = Somasteroidea, and Stenur = Stenuroidea. Additional classes with few genera not indicated. In general, these clusters correspond to four major life habits: Group 1 includes sedentary, epifaunal, high-density filter feeders on hard or biotic substrates; group 2 includes motile filter-feeders in soft-substrates, group 3 includes free-living and mobile epifaunal filter feeders on soft substrates; and group 4 includes deposit-feeders, carnivores, and scavengers. Although colors are chosen to match the classes illustrated in Fig. 2, note the points here correspond to the k-means life-habit clusters instead of taxonomic classes.

Extended Data Fig. 5 Rate distributions for morphological and ecological data sets, according to hierarchical character-level.

a, Per-character rate barplots (box-and-whisker plots) for morphological characters, separated according to hierarchical level. Center line is median, box limits are upper and lower quartiles; whiskers are 1.5x interquartile range; points are outliers. b, Same as panel a, but for ecological characters, which only has a single secondary character (filter density). c, Histogram of per-character rates for morphological characters, separated according to primary versus dependent (secondary through quinary) characters. d, Same as panel c, but for ecological characters. Note that panels c and d have different ordinate scaling to better illustrate the range of character rates, and panel c is reported as the proportion of characters instead of raw numbers. Per-character rates are the mean rate for each character across 50 time-scaled phylogenies.

Extended Data Fig. 6 Subsampled rate distribution for morphological and ecological data sets, after simultaneously standardizing for number of characters and lineages per stratigraphic age.

The subsampling algorithm drew, at random and without replacement, 30 characters and an average of 59 lineages per stratigraphic bin, evenly sampling all 50 time-scaled trees. After resampling, λmorphology = 0.679 and λecology = 0.390. 59.1% of replicates support (Akaike weight ≥ 0.90) that the morphological and ecological data sets evolve at different rates, with the median support equal to 0.986.

Extended Data Fig. 7 Rate distributions for morphological and ecological data sets, according to numbers of character states.

a, Per-character rate barplots (box-and-whisker plots) for morphological characters, separated according to number of states for characters. Center line is median, box limits are upper and lower quartiles; whiskers are 1.5x interquartile range; points are outliers. b, Same as panel a, but for ecological characters. c, Histogram of per-character rates for morphological characters, separated according to characters with 1–2 states versus those with 3 or more states. d, Same as panel c, but for ecological characters. Note that panels c and d have different ordinate scaling to better illustrate the range of character rates. Per-character rates are the mean rate for each character across 50 time-scaled phylogenies.

Extended Data Fig. 8 Trends in missing data through time for the morphological and ecological data sets, per stratigraphic age.

a, Proportion of lineages (tips and nodes) with at least one missing character state through time. b, Proportion of states, across characters and lineages, through time. c, Proportion of polymorphic (for example, 0&1) or uncertain (for example, 0/1) states through time. The trend is the median values in each interval across 50 time-scaled trees and the error polygon is the standard deviation across these time-scaled trees.

Extended Data Fig. 9 Barplots of the taxonomic rank at which all highly convergent (C1 ≥ 0.90) genus pairs are classified.

When a convergent pair shares a taxonomic class, Crinoidea for example, it means each genus is classified in the same class but in a different subordinate rank of that class, such as in different subclasses or orders. a, Taxonomic similarity for the morphological data set. b, Taxonomic similarity for the ecological data set. C1 for each genus pair was calculated as the modal (most frequent) C1 statistic for that pairing across all 50 time-scaled phylogenies.

Extended Data Fig. 10 Proportional distributions of branching distances among highly convergent genus pairs.

Distances were calculated as the sum tipward distance each genus traveled in Wills GED distance space since their most-recent common ancestor (Kolmogorov-Smirnov two-sample test: D = 0.578, P < 2.2e-16). Branching distance for each genus pair was calculated as the most frequent distance for that pairing across all 50 time-scaled phylogenies.

Supplementary information

Supplementary Information

Caption for Supplementary Tables 1–4, Tables 5 and 6, captions for Supplementary Data 1–6 and Extended Data Figs. 1–10, Notes, Discussion, Appendices 1 and 2, and References (including those in main text and continuing through Supplementary Information).

Reporting Summary.

Peer Review File.

Supplementary Tables 1–4

Supplementary tables describing dataset characters and per-character rates of evolution. a, Anatomical characters in morphological dataset. b, Life-habit characters in ecological dataset. c, Per-character evolutionary rates for morphological characters. d, Per-character evolutionary rates for ecological characters.

Supplementary Data 1

‘cal3trees’ is an R object of class ape::phylo that contains 50 ‘cal3’ time-scaled phylogenies used in all analyses, after removing zero-length branches and adjusting the root time accordingly.

Supplementary Data 2

‘EchinoTree_Morph.nex’ is a NEXUS file containing the anatomical character-state matrix for 413 anatomical characters for 366 Cambrian–Ordovician echinoderm tip genera used in morphological analyses. See ‘Compiling and formatting morphological and ecological datasets’ in Methods for additional details of coding schema. The file excludes the supertree topology, which is imported instead from the ‘cal3trees’ object (Supplementary Data 1).

Supplementary Data 3

‘EchinoTree_Mode.nex’ is a NEXUS file containing the anatomical character-state matrix for 40 life-habit characters for 366 Cambrian–Ordovician echinoderm tip genera used in morphological analyses, using the ‘mode’ life-habit propagation algorithm. See ‘Compiling and formatting morphological and ecological datasets’ in Methods for additional details of coding schema. The file excludes the supertree topology, which is imported instead from the ‘cal3trees’ object (Supplementary Data 1).

Supplementary Data 4

‘EchinoTree_Constant.nex’ is a NEXUS file containing the anatomical character-state matrix for 40 life-habit characters for 366 Cambrian–Ordovician echinoderm tip genera used in morphological analyses, using the ‘constant’ life-habit propagation algorithm. See ‘Compiling and formatting morphological and ecological datasets’ in Methods for additional details of coding schema. The file excludes the supertree topology, which is imported instead from the ‘cal3trees’ object (Supplementary Data 1).

Supplementary Data 5

‘EchinoTree_Raw.nex’ is a NEXUS file containing the anatomical character-state matrix for 40 life-habit characters for 366 Cambrian–Ordovician echinoderm tip genera used in morphological analyses, using the ‘raw’ life-habit propagation algorithm. See ‘Compiling and formatting morphological and ecological datasets’ in Methods for additional details of coding schema. The file excludes the supertree topology, which is imported instead from the ‘cal3trees’ object (Supplementary Data 1).

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Novack-Gottshall, P.M., Sultan, A., Smith, N.S. et al. Morphological volatility precedes ecological innovation in early echinoderms. Nat Ecol Evol 6, 263–272 (2022). https://doi.org/10.1038/s41559-021-01656-0

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