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Diversity decoupled from ecosystem function and resilience during mass extinction recovery

Nature volume 574, pages 242–245 (2019)Cite this article

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Abstract

The Chicxulub bolide impact 66 million years ago drove the near-instantaneous collapse of ocean ecosystems. The devastating loss of diversity at the base of ocean food webs probably triggered cascading extinctions across all trophic levels1,2,3 and caused severe disruption of the biogeochemical functions of the ocean, and especially disrupted the cycling of carbon between the surface and deep sea4,5. The absence of sufficiently detailed biotic data that span the post-extinction interval has limited our understanding of how ecosystem resilience and biochemical function was restored; estimates6,7,8 of ecosystem ‘recovery’ vary from less than 100 years to 10 million years. Here, using a 13-million-year-long nannoplankton time series, we show that post-extinction communities exhibited 1.8 million years of exceptional volatility before a more stable equilibrium-state community emerged that displayed hallmarks of resilience. The transition to this new equilibrium-state community with a broader spectrum of cell sizes coincides with indicators of carbon-cycle restoration and a fully functioning biological pump9. These findings suggest a fundamental link between ecosystem recovery and biogeochemical cycling over timescales that are longer than those suggested by proxies of export production7,8, but far shorter than the return of taxonomic richness6. The fact that species richness remained low as both community stability and biological pump efficiency re-emerged suggests that ecological functions rather than the number of species are more important to community resilience and biochemical functions.

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Fig. 1: Nannoplankton abundance, variability and diversity records from the latest Cretaceous to early Eocene.
Fig. 2: ΣCV and magnitude of climate perturbation (δ13C excursion).
Fig. 3: Danian nannoplankton community variance, acme abundances, diversity, cell volume and key milestones.

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Data availability

The datasets generated or analysed during this study are included as Source Data for Figs. 1–3.

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Acknowledgements

This research used samples provided by the International Ocean Discovery Program. We thank the Royal Society for funding S.J.G. through a URF. R.M.S. was funded jointly through a Vice Chancellor’s studentship from the University of Southampton and a Natural Environment Research Council (NERC) studentship (award reference 1272561) and H.K. was supported in part by a UCL Dean’s prize. We thank the European Union for post-doctoral research funding for S.A.A. (grant ERC-2013-CoG-617303). A.R. was supported in part by an award from the Heising-Simons Foundation as well as by grant ERC-2013-CoG-617303. We thank T. Ezard for contributions to the interpretation of the datasets and P. Wilson for his independent editing of the manuscript.

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Author notes

  1. Sarah A. Alvarez

    Present address: University of Gibraltar, Europa Point Campus, Gibraltar, Gibraltar

Authors and Affiliations

  1. School of Geographical Sciences, University of Bristol, Bristol, UK

    Sarah A. Alvarez

  2. Department of Earth Sciences, University College London, London, UK

    Sarah A. Alvarez, Paul R. Bown & Hojung Kim

  3. Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK

    Samantha J. Gibbs

  4. Institute for Geoscience, Goethe-Universität Frankfurt, Frankfurt am Main, Germany

    Rosie M. Sheward

  5. Department of Earth Sciences, University of California at Riverside, Riverside, CA, USA

    Andy Ridgwell

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  1. Sarah A. Alvarez
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Contributions

S.J.G., P.R.B. and A.R. conceived and designed the study. S.A.A. developed the methodology and performed most of the data collection. S.A.A. and S.J.G. performed the data analyses. P.R.B., R.M.S., H.K. and A.R. contributed to data collection, analysis and interpretation. S.J.G. and P.R.B. wrote the manuscript and A.R., S.A.A. and R.M.S. participated in manuscript writing and editing.

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Correspondence to Sarah A. Alvarez or Samantha J. Gibbs.

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Peer review information Nature thanks Appy Sluijs and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Location of ODP Site 1209 (black star) with respect to model-simulated late Cretaceous major ocean current and circulation patterns.

a, Barotropic stream function (Sv) simulated in a late Cretaceous configuration of the cGENIE Earth system model47. b, Surface ocean current field (black arrows) for the same circulation state as a overlaid on annual average sea surface temperature (SST) (colours). Scale for current vectors on the right, along with a truncated temperature scale to highlight the distribution of comparable temperature regimes. Red arrows illustrate inferred flow paths relevant to the position of ODP Site 1209 (marked by a star).

Extended Data Fig. 2 Comparison of community structure metrics.

a–d, Left, δ13C bulk and stable isotopes as in Fig. 1a. Dark green, benthic32; light green, bulk. Downcore plots of ΣCV (a), Bray–Curtis dissimilarity (b), Simpson’s index (d; grey dashed lines; black line indicates the 150-kyr moving average) and the variance (in 150-kyr windows) in the Simpson’s index (c). Vertical grey lines in a and b show the level of background inferred from rank order plots of these data. All four metrics (ΣCV, Bray–Curtis dissimilarity, Simpson’s index and variance in the Simpson’s index) show a distinction of volatility between early Danian regime 1 (n = 137 data points) and regime 2 (the rest of the record, n = 861 data points). For example, the Wilcoxon rank-sum (W) value for the Simpson’s record was W = 46,646; P < 0.001 on first differences with 95% confidence limits of −0.013, −0.006. A W value of zero would support a null hypothesis. The test was two-sided. The Simpson’s index shows a diversity minimum in the earliest Danian and then a rapid increase and steady long-term trend towards more diverse, more even communities, but with high variability in the early Danian. This fluctuation in the Simpson’s index, as recorded by the variance of the record (c) shows similar patterns to Bray–Curtis dissimilarity and ΣCV with high variance in the early Danian before dropping down. The variance in the Simpson’s index also shows high background fluctuations and a sustained increase in amplitude of fluctuations around the isotope shift in the Palaeocene Carbon Isotope Maximum, reflecting oligotroph diversification, which the Simpson’s index shows strongly due to its higher sensitivity to rare taxa. In effect, metric sensitivity to the richness in taxa and rare taxa increased from a to c (from abundance variance to diversity variance). Note, the Simpson’s index can only be calculated on full assemblage data and therefore the record extends only from 66 to 55.5 Myr ago.

Extended Data Fig. 3 Relative abundance of key nannoplankton groups and abundance of reworked specimens per 100 nannofossils.

Relative abundance of coccoliths from all groups included in the ΣCV metric are shown, coloured according to clade (as in Fig. 1b) and ordered by stratigraphic appearance. Cretaceous survivor taxa were counted as individual species but have been grouped together here, comprising mostly Zeugrhabdotus with lower abundances of Cyclagelosphaera, Markalius and Neocrepidolithus.

Extended Data Fig. 4 Calcareous nannoplankton across the K/Pg boundary.

Stratigraphic distribution of important species grouped as incoming (brown), survivor (green) or disappearing (blue) taxa. A subset of Cretaceous taxa is shown, with the latest Maastrichtian diversity for families shown alongside the number of survivors. Gradualistic evolutionary transitions indicated by close spacing and arrows indicate genus-level transitions. The nannoplankton data are primarily from our work but are largely consistent with published sources10,15,16,37,74. Diversity and cell-volume records are also shown in Fig. 3. Cp., Cruciplacolithus; Dan-C2, L C29n, hyperthermals; LDE, Late Danian Event; Neobisc., Neobiscutum; NP = nannofossil biozone; Prae., Praeprinsius.

Extended Data Fig. 5 Effects of window duration and depth or age sampling on ΣCV and Bray–Curtis dissimilarity.

a–c, Influence of different window duration (75–1,000 kyr) (a) and sampling in either the depth or age domain (c) on ΣCV, and effect of window duration on Bray–Curtis dissimilarity (b). c, Side-by-side results of ΣCV calculated using evenly spaced samples in either the depth domain or the age domain using a depth window duration of 60 cm, which is broadly equivalent to the 150-kyr time window. When ΣCV is plotted in the depth domain the main patterns are retained, indicating that no significant artefacts arise from the applied age model. The boundaries between the Myr sections, at which the taxa included in the ΣCV change (black dots), are marked on a. There are no obvious artefacts across the Myr windows with changes in the most abundant taxa.

Extended Data Fig. 6 Phylogenetic models for the dominant Palaeocene nannoplankton.

a–c, Models range from a standard genus-level stratophenetic tree (a) through two successively conservative scenarios (b, c) grouping closely related taxa—that is, recently diverged taxa based on morphological and stratigraphic range data. Nannoplankton taxonomy is primarily based on the morphology and crystallographic ultrastructure of exoskeletal coccoliths but the addition of genetic data for modern taxa confirmed that this approach is robust75,76,77. Evolutionary models are stratophenetic, because we have high-quality stratigraphic information but lack the range of meaningful homologous morphological characters to allow a cladistic analysis. a, Genus-level phylogeny based on an extensive species-level stratophenetic tree. b, c, Different ancestry options used to test for artefacts and sensitivity in variance/dissimilarity that may result from equal weighting of closely related versus more-distantly related taxa. b, Ancestry model option 1 is highly conservative and merges major sub-family groups (shown by shaded boxes) around five nodes shown by black circles. c, Ancestry model option 2 merges the most-closely related genera (shaded boxes) around eight nodes.

Extended Data Fig. 7 Influences of ancestry on ΣCV and Bray–Curtis dissimilarity.

a, b, Analyses of the influences of two additional models of shared ancestry (Extended Data Fig. 6a) on the ΣCV (a) and Bray–Curtis dissimilarity (b) datasets. In red, the original analysis in which each genus is weighted equally. In grey, analysis of the conservative ancestry model that merges genera into major sub-family groups (ancestry model option 1; Extended Data Fig. 6b). In black, analysis of the moderately conservative ancestry model (option 2; Extended Data Fig. 6c), which merges the most-closely related genera. The Bray–Curtis dissimilarity analysis shows very little sensitivity to variation in the taxonomic hierarchies. The ΣCV displays some sensitivity, particularly at the Late Danian Event (around 62 Myr ago); however, the main patterns are retained between the original and option 2. Some variance is lost in the less-realistic analysis of option 1, in which grouping of key genera that are found in the same families dampens the variance, in particular, in the early Danian. However, the values of early Danian variance still remain anomalously high compared to the rest of the record.

Extended Data Table 1 Summary of main biometric lith and cell parameters measured and reconstructed
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Extended Data Table 2 Carbon isotope excursion events
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Alvarez, S.A., Gibbs, S.J., Bown, P.R. et al. Diversity decoupled from ecosystem function and resilience during mass extinction recovery. Nature 574, 242–245 (2019). https://doi.org/10.1038/s41586-019-1590-8

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