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Life rather than climate influences diversity at scales greater than 40 million years


The diversity of life on Earth is controlled by hierarchical processes that interact over wide ranges of timescales1. Here, we consider the megaclimate regime2 at scales ≥1 million years (Myr). We focus on determining the domains of ‘wandering’ stochastic Earth system processes (‘Court Jester’3) and stabilizing biotic interactions that induce diversity dependence of fluctuations in macroevolutionary rates (‘Red Queen’4). Using state-of-the-art multiscale Haar and cross-Haar fluctuation analyses, we analysed the global genus-level Phanerozoic marine animal Paleobiology Database record of extinction rates (E), origination rates (O) and diversity (D) as well as sea water palaeotemperatures (T). Over the entire observed range from several million years to several hundred million years, we found that the fluctuations of T, E and O showed time-scaling behaviour. The megaclimate was characterized by positive scaling exponents—it is therefore apparently unstable. E and O are also scaling but with negative exponents—stable behaviour that is biotically mediated. For D, there were two regimes with a crossover at critical timescale \(\Delta {t}_{{\rm{trans}}}\) ≈ 40 Myr. For shorter timescales, D exhibited nearly the same positive scaling as the megaclimate palaeotemperatures, whereas for longer timescales it tracks the scaling of macroevolutionary rates. At scales of at least \(\Delta {t}_{{\rm{trans}}}\) there is onset of diversity dependence of E and O, probably enabled by mixing and synchronization (globalization) of the biota by geodispersal (‘Geo-Red Queen’).

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Fig. 1: Scaling of macroevolutionary metrics and sea water palaeotemperatures.
Fig. 2: Scale-dependant correlations of macroevolutionary and palaeoclimate variables.
Fig. 3: Scale-dependant correlations of macroevolutionary and palaeoclimate variables.
Fig. 4: Space–time scaling of dominance in macroevolutionary modes.

Data availability

The palaeobiological genus occurrence data are freely available for download in the Paleobiology Database,quaternary&envtype=marine. Other data—palaeotemperatures, tectonic rates and sea levels are available as supplementary data in the original cited articles.

Code availability

Macroevolutionary metrics were calculated in R v.3.6.0 using functions available in divDyn package. The scripts needed for the Phanerozoic-scale analysis of marine diversity with divDyn can be found at Haar fluctuation, scaling and cross-Haar analyses were done using Mathematica custom-made code and can be accessed at


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We thank A. Kocsis for help with divDyn package functions. We also thank many contributors to the Paleobiology Database and the authors of descriptive taxonomic papers and geochemical analyses that generated the primary data used in this study. This research was supported by project S-MIP-21-9 ‘The role of spatial structuring in major transitions in macroevolution’. S.L. acknowledges the National Science and Engineering Council for some support. This paper is Paleobiology Database official publication 426.

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A.S. and S.L. developed the design of the study, analysed the data and wrote the text.

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Correspondence to Andrej Spiridonov or Shaun Lovejoy.

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Nature thanks Jurgen Kurths, Gene Hunt, Appy Sluijs 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 figures and tables

Extended Data Fig. 1 Macroevolutionary and paleoclimate time series.

A. Average global surface water temperatures (T) Song and others (2019) data28, B marine animal genus diversity (D), C second-for-third genus extinction rates per bin (E), D second-for-third origination rates per bin (O). Paleobiological patterns are per bin averages with \(\pm \sigma \) confidence intervals based on 100 bootstrap replications.

Extended Data Fig. 2 Paleoclimate time series and time scaling.

A. Song et al., 2019 (blue) and Veizer et al., 1999 (red) global sea water paleotemperature stacks32,33 expressed in \(\delta {}^{18}O\) ‰ units as a function of age (time flows from right to left). The Song et al., 2019 data was standardized by setting mean to zero and standard deviations were made equal. B Cretaceous sea surface water temperatures based on TEX86 and \(\delta {}^{18}O_{pl}\) data73 for low latitudes (black) [n = 2856 temperature estimates] and high latitudes (red)[n = 638 temperature estimates] in °C. C. Haar fluctuation scaling curves for high latitude (pink), and low latitude (brown) Cretaceous sea surface temperatures (in °C); timescales shown in \({\log }_{10}\) Myr; dashed line shows scaling pattern with H = +0.25.

Extended Data Fig. 3 Scaling of macroevolutionary time series with confidence bands.

Haar fluctuation scaling curves with standard errors based on the analysis of 100 subsamplings: A origination (red) and extinction (brown) rates (x10)[as in the Fig. 1]; B genus diversity (x0.01))[as in the Fig. 1]. Timescales shown in \({\log }_{10}\) Myr.

Extended Data Fig. 4 Time-dependant correlations.

scale-dependant correlations of fluctuations in A extinction rates with temperatures, time in Myr. Same for B origination rates with temperatures; C global diversity levels with extinction rates; D global diversity levels with origination rates. Mean (black) and one standard deviation confidence limits dashed red. Mean (black) and one standard deviation confidence limits dashed red (16–84%).

Extended Data Fig. 5 Time scaling of the global sea level and sea floor spreading.

Haar fluctuation scaling of the sea level75 (in m from the present level) during the Phanerozoic and Neoproterozoic (brown), and of the sea floor production rates76 through the Meso-Cenozoic (m2/year /3 x 104) (pink). Timescales shown in \({\log }_{10}\) Myr. Both geophyscal variables scale positively at least to the timescale of 100 Myr. \(\varDelta {t}_{trans}\) signifies critical transition time from the positive to the negative diversity scaling, and the start of synchronization of macroevolutionary rates. Sea level and tectonic activity scales positively well beyound this critical threshold.

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Discussion of uncertainties in the estimation of scaling relations and correlation of scaling time series and Supplementary Figs. 1–5 and references.

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Spiridonov, A., Lovejoy, S. Life rather than climate influences diversity at scales greater than 40 million years. Nature 607, 307–312 (2022).

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