Deep-sea diversity patterns are shaped by energy availability

Journal name:
Nature
Volume:
533,
Pages:
393–396
Date published:
DOI:
doi:10.1038/nature17937
Received
Accepted
Published online

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.

At a glance

Figures

  1. Global patterns of ophiuroid species richness.
    Figure 1: Global patterns of ophiuroid species richness.

    ac, Multi-species occupancy detection models (MSODM) of summed occupancy probabilities at 500 km equal area resolution for shelf diversity (20−200 m; a), slope diversity (200−2,000 m; b) and deep-water diversity (2,000−6,500 m; c). 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.

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

    ac, Species richness predicted from MSODMs at depth intervals from surface to lower-abyss depths for binned equal-area latitudinal regions across the global extent of longitude. Mean species richness estimated from MSODMs for shelf diversity (20−200 m; a), slope diversity (200−2,000 m; b) and deep-water diversity (2,000−6,500 m; c). The vertical dashed line represents the equator. The grey contour lines represent the top 20% of species richness for each bathome. Depths are binned at 50 m intervals for shelf regions (0–50, 50–100, 100–150 and 150–200 m), at 200-m intervals for slope (200–2,000 m), and 500-m intervals for deep-water (2,000–6,500 m).

  3. Distribution of global sampling effort across deep-sea bathomes.
    Extended Data Fig. 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.

  4. Model estimated global deep-sea species richness across different depth strata.
    Extended Data Fig. 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.

  5. Linear partial residual plots as derived from SLMs.
    Extended Data Fig. 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.

  6. Environmental relationships covariate estimated with the multi-species occupancy–detection model.
    Extended Data Fig. 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).

  7. Bayesian posterior estimates.
    Extended Data Fig. 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).

  8. Mean variance of MSODM predictions of species occupancy probabilities.
    Extended Data Fig. 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.

Tables

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

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Author information

Affiliations

  1. Museum Victoria, GPO Box 666, Melbourne, Victoria 3001, Australia

    • Skipton N. C. Woolley &
    • Timothy D. O’Hara
  2. Quantitative and Applied Ecology Group, School of Biological Sciences, BioSciences Building 2, The University of Melbourne, Victoria 3010, Australia

    • Skipton N. C. Woolley,
    • Gurutzeta Guillera-Arroita,
    • José J. Lahoz-Monfort &
    • Brendan A. Wintle
  3. Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax B3H 4J1, Canada

    • Derek P. Tittensor &
    • Boris Worm
  4. United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 0DL, UK

    • Derek P. Tittensor
  5. CSIRO, Wealth from Oceans Flagship, Hobart, Tasmania 7000, Australia

    • Piers K. Dunstan

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Distribution of global sampling effort across deep-sea bathomes. (715 KB)

    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.

  2. Extended Data Figure 2: Model estimated global deep-sea species richness across different depth strata. (742 KB)

    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.

  3. Extended Data Figure 3: Linear partial residual plots as derived from SLMs. (390 KB)

    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.

  4. Extended Data Figure 4: Environmental relationships covariate estimated with the multi-species occupancy–detection model. (542 KB)

    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).

  5. Extended Data Figure 5: Bayesian posterior estimates. (136 KB)

    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).

  6. Extended Data Figure 6: Mean variance of MSODM predictions of species occupancy probabilities. (583 KB)

    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 Tables

  1. Extended Data Table 1: Encapsulation of species richness hypotheses by environmental and physical predictors (394 KB)
  2. Extended Data Table 2: Correlations between environmental predictors used in GLMs, SLMs and MSODMs by bathome (233 KB)
  3. Extended Data Table 3: Top SLMs based on AIC under all model selection for each bathome (ΔAIC of <2) (161 KB)
  4. Extended Data Table 4: Deviance reduction between null multi-species occupancy detection models and fully fitted models (92 KB)

Supplementary information

PDF files

  1. Supplementary Information (55 KB)

    This file contains ophiuroid biological data sources used to construct species richness patterns.

Additional data