Abstract
Rates of lineage diversification vary considerably across the tree of life, often as a result of evolutionary innovations1,2,3,4,5. Although the ability to produce new traits can vary between clades and may drive ecological transitions6,7,8,9, the impact of differences in the pace at which innovations evolve at macroevolutionary scales has been overlooked. Complex teeth are one innovation that contributed to the evolutionary success of major vertebrate lineages10,11,12. Here we show that evolutionary lability of tooth complexity, but not complexity itself, spurs rapid diversification across ray-finned fishes. Speciation rates are five times higher when transitions between simple and complex teeth occur rapidly. We find that African cichlids are unique among all fishes; they are dominated by lineages that transition between simple and complex teeth at unparalleled rates. This innovation interacted with the ecological versatility of complex teeth to spur rapid adaptive radiations in lakes Malawi, Victoria and Barombi Mbo. The marked effect on diversification stems from the tight association of tooth complexity with microhabitat and diet. Our results show that phylogenetic variation in how innovations evolve can have a stronger effect on patterns of diversification than the innovation itself. Investigating the impact of innovations from this new perspective will probably implicate more traits in causing heterogeneous diversification rates across the tree of life.
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Data availability
All the data generated and analysed in the current study, including tooth complexity classifications for 30,915 species of ray-finned fishes, are available via figshare at https://doi.org/10.6084/m9.figshare.25661859 (ref. 51).
Code availability
All RevBayes scripts used for phylogenetic analyses are available at https://github.com/npeoples/fish_tooth_complexity.
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Acknowledgements
We thank the members of the Wainwright Lab and L. Kaufman for support and discussions. Funding for this work was provided by the University of California, Davis, College of Biological Sciences and the University of California, Davis, Center for Population Biology.
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The study was conceptualized and designed by N.P. and P.C.W. N.P. collected the data on tooth complexity and diet and performed all analyses. Data interpretation was carried out by N.P. with support from P.C.W., M.M. and M.D.B. N.P. wrote the manuscript with contributions from all authors.
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Extended data figures and tables
Extended Data Fig. 1 The phylogenetic distribution of complex teeth and high lability across ray-finned fishes.
a, Maximum a posteriori stochastic character map of tooth complexity and its evolutionary lability for 11,508 species of ray-finned fishes under a HR2-ARD model. Lineages with simple teeth are light grey (low labiltiy) and dark blue (high lability); lineages with complex teeth are red (low lability) and gold (high lability). DR statistic values, a measure of species’ tip speciation rate, are plotted at the tips; longer bars indicate higher speciation rates. b, The proportion of lineages with complex teeth and high lability across families of ray-finned fishes that have at least one complex lineage (n = 31 families). The color of the points corresponds to family mean speciation rate, estimated using the DR statistic. The shape of the points represent habitat (freshwater, marine, both) and the size of the points are scaled by the total number of species. African and Neotropical cichlids are indicated separately to highlight the difference between these groups.
Extended Data Fig. 2 The evolution of tooth complexity and effect of lability are robust to assumptions on the prior transition rate in ray-finned fishes.
a, Maximum a posteriori ancestral state reconstruction of tooth complexity and its evolutionary lability across ray-finned fishes (n = 11,508 species) under the HR2-ARD model, under five different priors (50, 150, 200, 250, 400) for the total number of transitions. The insets for 250 and 400 highlight that rapid consecutive state changes on long branches inflate the transition rates in the “high lability” state under these priors. b, The total number of lineages with high lability and c, the proportion of lineages with high lability that are African cichlids under five different prior number of transitions. d-f, Simulated data shows no effect of lability on speciation rates. Distribution of FiSSE tip speciation rates for d, low lability and e, high lability over 100 ancestral state reconstructions simulated under the observed HR2-ARD model. Median rates are indicated with blue lines. f, Distribution of FiSSE two-tailed p-values for 100 simulated character histories; the red line marks the significance level of 0.05. g-j, Transition rates are robust to assumptions on the prior number of transitions. Posterior distribution of estimated rates for g, low lability gain, h, low lability loss, i, high lability gain and j, high lability loss under the HR2-ARD model for five priors (50, 150, 200, 250, 400) on the number of transitions. k-p, Convergence of the HR2-ARD model. k, Posterior probability distribution of two replicate MCMC runs of the HR2-ARD model. l, Histogram of Kolmogorov-Smirnov (KS) scores for model parameters. The grey dotted line marks the threshold for the KS test of 0.0921 (α = 0.01, ESS > 625), indicating that the model parameters were drawn from the same distribution for both replicate runs. Posterior distribution of transition rates across replicate MCMC runs for m, low lability gain, n, low lability loss, o, high lability gain, and p, high lability loss.
Extended Data Fig. 3 Convergence and prior sensitivity analyses for the HR4-ARD model in African cichlids.
Posterior distribution of estimated transition rates under the HR4-ER model for three different priors (50, 100, 150) on the total number of transitions; a, very low lability, b, low lability, c, high lability, and d, very high lability. e, Posterior probability distribution of four replicate MCMC runs of the HR4-ER model; two chains ran for 50,000 generations and two chains ran for 500,000 generations. f, Histogram of Kolmogorov-Smirnov (KS) scores for model parameters. The grey dotted line marks the threshold for the KS test of 0.0921 (α = 0.01, ESS > 625), indicating that the model parameters were drawn from the same distribution for all replicate runs. Running the chain longer resulted in the same posterior probability distribution and transition rate estimates despite ESS scores <625 for some parameters for 50,000 generations. Posterior distribution of transition rates across replicate MCMC runs for g, very low lability h, low lability i, high lability and j, very high lability.
Extended Data Fig. 4 Evolutionary lability of tooth complexity increases net diversification rates across four background rate regimes in African cichlids.
Maximum a posteriori (MAP) ancestral state reconstruction of evolutionary lability for African cichlids (n = 1,069 species) under the MuHiSSE-4 model. Lineages in a, hidden state A, b, hidden state B, c, hidden state C, and d, hidden state D are highlighted separately. Selected clades are labeled for each hidden state. The posterior distribution of net diversification rates are reported for each level of lability, within each hidden state.
Extended Data Fig. 5 Convergence of the MuHiSSE-2 and MuHiSSE-4 models, fit across African cichlids.
a, Posterior probability distribution of two replicate MCMC runs of the MuHiSSE-2 model. b, Histogram of Kolmogorov-Smirnov (KS) scores for MuHiSSE-2 model parameters. The grey dotted line marks the threshold for the KS test of 0.0921 (α = 0.01, ESS > 625), indicating that the model parameters were drawn from the same distribution for both replicate runs. Two parameters fall just outside this threshold. c, Posterior probability distribution of two replicate MCMC runs of the MuHiSSE-4 model. d, Histogram of Kolmogorov-Smirnov (KS) scores for MuHiSSE-4 model parameters. The grey dotted line marks the threshold for the KS test of 0.0921 (α = 0.01, ESS > 625), indicating that the model parameters were drawn from the same distribution for both replicate runs.
Extended Data Fig. 6 Speciation rates for African cichlids under varying levels of lability and six relative extinction scenarios, estimated under a MuHiSSE-4 model.
Posterior distributions of speciation rates for four levels of lability across four hidden states (A-D). Rates were estimated under six different relative extinction scenarios by setting lower bounds on the extinction rate (µ).
Extended Data Fig. 7 Net diversification rates for African cichlids under varying levels of lability and six relative extinction scenarios, estimated under a MuHiSSE-4 model.
Posterior distributions of net diversification rates, measured as speciation (λ) minus extinction (µ), for four levels of lability across four hidden states (A-D). Rates were estimated under six different relative extinction scenarios by setting lower bounds on the extinction rate (µ).
Extended Data Fig. 8 Evolutionary lability of tooth complexity increases net diversification rates across two background rate regimes in African cichlids.
Maximum a posteriori (MAP) ancestral state reconstruction of evolutionary lability for African cichlids (n = 1,069 species) under the MuHiSSE-2 model. Lineages in a, hidden state A and b, hidden state B are highlighted separately. Selected clades are labeled for each hidden state. The posterior distribution of net diversification rates are reported for each level of lability, within each hidden state.
Extended Data Fig. 9 Speciation and net diversification rates for African cichlids under varying levels of lability and six relative extinction scenarios, estimated under a MuHiSSE-2 model.
a, Posterior distributions of net diversification rates (speciation – extinction) for two levels of lability across two hidden states (A, B). b, Posterior distributions of corresponding speciation rates (λ). Rates were estimated under six different relative extinction scenarios by setting lower bounds on the extinction rate (µ).
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Peoples, N., Burns, M.D., Mihalitsis, M. et al. Evolutionary lability of a key innovation spurs rapid diversification. Nature 639, 962–967 (2025). https://doi.org/10.1038/s41586-025-08612-z
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DOI: https://doi.org/10.1038/s41586-025-08612-z
