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Deep-sea diversity patterns are shaped by energy availability

Abstract

The deep ocean is the largest and least-explored ecosystem on Earth, and a uniquely energy-poor environment. The distribution, drivers and origins of deep-sea biodiversity remain unknown at global scales1,2,3. Here we analyse a database of more than 165,000 distribution records of Ophiuroidea (brittle stars), a dominant component of sea-floor fauna, and find patterns of biodiversity unlike known terrestrial or coastal marine realms. Both patterns and environmental predictors of deep-sea (2,000–6,500 m) species richness fundamentally differ from those found in coastal (0–20 m), continental shelf (20–200 m), and upper-slope (200–2,000 m) waters. Continental shelf to upper-slope richness consistently peaks in tropical Indo-west Pacific and Caribbean (0–30°) latitudes, and is well explained by variations in water temperature. In contrast, deep-sea species show maximum richness at higher latitudes (30–50°), concentrated in areas of high carbon export flux and regions close to continental margins. We reconcile this structuring of oceanic biodiversity using a species–energy framework, with kinetic energy predicting shallow-water richness, while chemical energy (export productivity) and proximity to slope habitats drive deep-sea diversity. Our findings provide a global baseline for conservation efforts across the sea floor, and demonstrate that deep-sea ecosystems show a biodiversity pattern consistent with ecological theory, despite being different from other planetary-scale habitats.

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Figure 1: Global patterns of ophiuroid species richness.
Figure 2: Estimated mean ophiuroid species richness plot as a function of depth and latitude.

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Acknowledgements

We thank all collectors and taxonomists involved for providing biodiversity data. This work is an output of the project ‘National maps of biodiversity and connectivity’ of the Marine Biodiversity Research Hub and Environmental Decisions Hub, funded through the Commonwealth National Environmental Research Program (NERP) and administered through the Australian Government’s Department of Environment. This work is also a product of the International Network for Scientific Investigations of Deep-Sea Ecosystems (INDEEP) working group on biogeography. We also thank the Centre of Excellence for Environmental Decisions (CEED) for travel funding that enabled collaboration between the University of Melbourne, Museum Victoria and Dalhousie University.

Author information

Authors and Affiliations

Authors

Contributions

S.N.C.W., T.O., D.T. and B.W. conceived the study. T.O. collected, refined and managed the biological dataset. S.N.C.W., T.O., D.T., B.A.W., G.G.A. and J.J.L.M. performed analyses. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Skipton N. C. Woolley.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Distribution of global sampling effort across deep-sea bathomes.

ac, Spatial plots of sampling effort for ophiuroid occurrence data at the same equal-area grid cells used in MSODM at 500-km equal area grid cells, maximum effort is capped at 100 surveys to help visualize low and high regions of repeated effort. Shelf effort from 20–200 m (a), slope effort from 200–2,000 m (b) and deep-water collection effort from 2,000–6,500 m (c). df, Ophiuroid distribution data are presented for shelf (d; red; 20–200 m), slope (e; orange; 200–2,000 m) and deep-water (f; yellow; 2,000–6,500 m); key represents depth profile. Land maps used in these figures are from the public domain Natural Earth and freely available for personal, educational, and commercial purposes. See http://www.naturalearthdata.com/about/terms-of-use/ for more details.

Extended Data Figure 2 Model estimated global deep-sea species richness across different depth strata.

ac, Maps of species counts () as calculated using MSODM are presented as shelf (a), slope (b) and deep-water species (c). is an estimate of species present per cell based on our occurrence matrix (Z). Z is a latent variable used to calculated presences and absences of species within each cell. The estimated count of species () per 500km equal area grid cell are reported in each figure key. Land maps used in these figures are from the public domain Natural Earth and freely available for personal, educational, and commercial purposes. See http://www.naturalearthdata.com/about/terms-of-use/ for more details.

Extended Data Figure 3 Linear partial residual plots as derived from SLMs.

ai, Partial residual plots for significant variables included in the models for global deep-sea richness at shelf (ac, 20–200 m), upper-slope (df, 200–2,000 m) and deep-water (LSA; gi, 2,000–6,500 m). Hatched lines are partial fits (red lines). Values on the x axis are centred and normalized (mean = 0, variance = 1), as derived from SLMs.

Extended Data Figure 4 Environmental relationships covariate estimated with the multi-species occupancy–detection model.

The shaded areas represent the regions delimited by the tenth–ninetieth percentiles of the estimates obtained from the responses of all species. From top to bottom, rows display the estimates of occupancy (ψ), for shelf (green), slope (blue) and abyss (red) species. All covariates were centred and normalized (mean = 0, variance = 1).

Extended Data Figure 5 Bayesian posterior estimates.

ac, Deep-water MSODM parameter estimates, for shelf (a), slope (b), and deep-water species (c). Posterior distributions of parameter estimates are across all species. All covariates were centred and normalized (mean = 0, variance = 1).

Extended Data Figure 6 Mean variance of MSODM predictions of species occupancy probabilities.

Variance for shelf diversity (a, 20–200 m), slope diversity (b, 200–2,000 m) and deep-water diversity (c, 2,000–6,500 m). Land maps used in these figures are from the public domain Natural Earth and freely available for personal, educational, and commercial purposes. See http://www.naturalearthdata.com/about/terms-of-use/ for more details.

Extended Data Table 1 Encapsulation of species richness hypotheses by environmental and physical predictors
Extended Data Table 2 Correlations between environmental predictors used in GLMs, SLMs and MSODMs by bathome
Extended Data Table 3 Top SLMs based on AIC under all model selection for each bathome (ΔAIC of <2)
Extended Data Table 4 Deviance reduction between null multi-species occupancy detection models and fully fitted models

Supplementary information

Supplementary Information

This file contains ophiuroid biological data sources used to construct species richness patterns. (PDF 55 kb)

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Woolley, S., Tittensor, D., Dunstan, P. et al. Deep-sea diversity patterns are shaped by energy availability. Nature 533, 393–396 (2016). https://doi.org/10.1038/nature17937

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