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Spatiotemporal faunal connectivity across global sea floors

Nature volume 645, pages 423–428 (2025)Cite this article

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Abstract

Our knowledge of biogeographic patterns and processes in the deep sea has been limited by the lack of integrated datasets that cover its vast extent1. Here we analyse a new global dataset of genomic DNA sequences, spanning an entire taxonomic class of benthic invertebrates (Ophiuroidea), to obtain a broad understanding of phylogenetic divergence and biotic movement across all oceans, from coastal margins down to the abyssal plains. We show that regional faunas on the continental shelf are phylogenetically divergent, particularly at temperate and tropical latitudes. By contrast, assemblages in the deep sea are much more connected. Many temperate deep-sea lineages have achieved distribution ranges across the planet, including over the Quaternary period. A close relationship exists between deep-sea faunas of the northern Atlantic and, on the opposite side of the globe, southern Australia. Bathymetric interchange is not only reliant on vertical migration through isothermal polar waters but also occurs across the thermal depth gradients of tropical regions. The connected nature of deep-sea life should be an important consideration in marine conservation assessments.

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Fig. 1: Map of sample sites and phylogeny of the Ophiuroidea.
Fig. 2: Multidimensional scaling ordination showing evolutionary relationships between marine region-depth biomes.
Fig. 3: Relationships of biomes.
Fig. 4: The number of lineage transitions between marine biomes.

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Data availability

All data necessary to repeat the analyses described here are available at Dryad (https://doi.org/10.5061/dryad.xsj3tx9rh)9.

Code availability

All codes necessary to repeat the analyses described here are available at Dryad (https://doi.org/10.5061/dryad.xsj3tx9rh)9.

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Acknowledgements

CSIRO Marine National Facility provided sea time and personnel on the RV Investigator for the voyages IN2017_V03, IN2021_V04 and IN2022_V08. K. Naughton and C. Keely (Museums Victoria) assisted with DNA extractions. We acknowledge the numerous museum collection managers, researchers and voyage funders that enabled the collection of ophiuroid specimens included in this study9 and philanthropic support to Museums Victoria Research Institute.

Author information

Authors and Affiliations

  1. Museums Victoria, Melbourne, Victoria, Australia

    Timothy D. O’Hara, Andrew F. Hugall, Margaret L. Haines, Alexandra A.-T. Weber & Adnan Moussalli

  2. Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    Alexandra A.-T. Weber

  3. Deutsche Zentrum für Marine Biodiversitätsforschung (DZMB), Senckenberg am Meer, Wilhelmshaven, Germany

    Angelina Eichsteller & Pedro Martinez Arbizu

  4. Instituto de Biología de Organismos Marinos, IBIOMAR CONICET, Puerto Madryn, Argentina

    Martin I. Brogger

  5. Muséum national d’Histoire naturelle (MNHN), Paris, France

    Marc Eléaume

  6. National Museum of Nature and Science, Tsukuba-shi, Japan

    Toshihiko Fujita

  7. University Museum of Bergen, Bergen, Norway

    Jon A. Kongsrud

  8. National Institute of Water & Atmospheric Research Ltd (NIWA), Wellington, New Zealand

    Sadie Mills

  9. WILDTRUST, Pietermaritzburg, South Africa

    Jennifer M. Olbers

  10. Florida Museum of Natural History, University of Florida, Gainesville, FL, USA

    Gustav Paulay

  11. Centro de Investigación Mariña (CIM), Facultade de Ciencias do Mar, Universidade de Vigo, Vigo, Spain

    Fran Ramil

  12. Institut Systématique Evolution Biodiversité (ISYEB), CNRS, Sorbonne Université, EPHE, Université des Antilles, Muséum national d’Histoire naturelle, Paris, France

    Sarah Samadi

  13. British Antarctic Survey, Cambridge, UK

    Chester J. Sands

  14. Departmento de Biología Marina & Centro ESMOI, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile

    Javier Sellanes

  15. Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Francisco A. Solis-Marin

Authors
  1. Timothy D. O’Hara
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  2. Andrew F. Hugall
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Contributions

T.D.O’H., A.F.H. and A.M. designed the research T.D.O’H., A.F.H., M.L.H., A.A.-T.W., A.E., M.I.B., M.E., T.F., J.A.K., P.M.A., S.M., J.M.O., G.P., F.R., S.S., C.J.S., J.S. and F.A.S.-M. assembled the data. T.D.O’H., A.F.H. and M.L.H. performed the sequence bioinformatics and macro-evolutionary analyses. All authors contributed to interpretation and discussion of results. T.D.O’H. drafted the paper with substantial input from other authors.

Corresponding author

Correspondence to Timothy D. O’Hara.

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Extended data figures and tables

Extended Data Fig. 1 Biome diversity known from taxonomy and as sampled by our phylogenetic tree.

Undescribed species determined by T O’Hara (unpublished). Phylogenetic Diversity (PD) of each biome is expressed as a percentage of the total tree branch length (53,849 Myr). Each column is coloured separately with reds indicating high values, yellows medium and greens low.

Extended Data Fig. 2 Heat maps of geometric mean of unique nearest neighbour (GUNN) ages between biomes (lower left, the distance values used to create the MDS ordination in Fig. 2) and decisive cladogenetic biome transitions from CorHMM ER model (upper right, used to create the chord diagram in Fig. 3).

Mean between biome GUNN and sum of count of biome transitions within and between depth layers is shelf-shelf (83 Myr, 186 transitions), bathyal-bathyal (46, 403), abyss-abyss (40, 23), shelf-bathyal (83, 201), shelf-abyss (137, 0), bathyal-abyss (84, 33). If polar biomes are excluded, the mean GUNN of shelf-shelf relationships is 69 Myr. If comparisons are limited to only the 7 abyssal regions, shelf-shelf is 79 Myr and bathyal-bathyal 33 Myr. AN=Antarctic, AR=Arctic, AU = S Australia, EA = E Atlantic, EP = E Pacific, IN=Indian, IP=Indo-Pacific, NA = N Atlantic, NEP = NE Pacific, NWP = NW Pacific, NZ=New Zealand, SA = S Africa, SK=Kerguelen, SM = S America, WA = W Atlantic; S=Shelf, B=Bathyal, A=Abyssal.

Extended Data Fig. 3 Schematic of methodology to calculate GUNN (geometric mean of unique nearest neighbour ages) distance measures and resulting ordination (e.g., Fig. 2).

This hypothetical example uses an ultrametric chronogram of 9 samples (1–9) spread across 3 biomes (A, B, C). Step 1 is to identify all the Most Recent Common Ancestor nodes that span nearest-neighbour samples from each pair of biomes (bidirectionally). Step 2 is to reduce this list to the unique nodes. We interpret these nodes as representing unique biome-biome transitions (i.e., connectivity). Step 3 is to average the ages of each set of unique nodes, in this example using the geometric mean (geomean), to produce GUNN measures for each biome pair. Step 4 is to ordinate a triangular matrix of these GUNN measures.

Extended Data Fig. 4 Chord diagrams with number of lineage transitions between marine biomes stratified into temporal bands.

Transitions are counted as decisive cladogenetic events on the phylogeny that result in daughter lineages living in different biomes. Ancestral reconstruction of marginal likelihoods derived from an equal rates unordered Markov-k model of biome evolution. (a-b) Models with samples grouped into 37 bathy-regional biomes (Fig. 1). (c-d) Models with samples grouped into 9 depth (shelf, bathyal, abyss) and latitude (tropical, temperate, polar) categories.

Extended Data Fig. 5 Latitude-depth transects of annual sea-water temperature and dissolved oxygen.

(a) East Pacific, (b) West Atlantic, (c) East Atlantic, (d) West Pacific transects. Transects shown in lower right inset map. Values derived from the World Ocean Atlas (WOA) 2018. Temperature averaged across longitudes into latitude-depth bins based on WOA categories.

Extended Data Fig. 6 Location of our 1415 exons across the 20 assembled chromosomes of the Amphiura filiformis genome.

Exons of a target-capture sample of A. filiformis mapped by amino-acid matching against the genome (NCBI: Afil_fr2py GCA_039555335.1 Amphiura filiformis FM-2023a 46).

Extended Data Fig. 7 The influence of temperature and depth.

(a) Ultrametric phylogeny of the Ophiuroidea with depth and sea temperature where each sample was found (n = 2699) displayed as coloured rings (27 equal-sized categories, root=265 my). (b-e) Influence of site substitution rate variation aggregated at 3 organisational scales. (b) Phylogenetic Generalized Least Squares (PGLS) analysis showing a weak relationship between sample root-to-tip site substitution path length (RTTPL) from RAxML phylogram and temperature (df=2039, p = 1.4e-4, adjusted R2 = 0.018). (c) Regression showing a non-significant relationship between mean RTTPL per biome and mean age of the tip branch on our ultrametric chronogram per biome (df=35, p = 0.6), with point labels indicating A=abyssal, B=bathyal and S=shelf biomes. (d) A 2-factor multiple regression of biome means (df=34, linear regression t-tests, adjusted R2 = 0.62) showing a positive relationship of tip age and temperature, but a negative one with RTTPL. (e) Mean sample values aggregated into depth layers showing that the range of mean tip ages is far greater than for RTTPL or the smoothing rate applied to the tip branches.

Extended Data Fig. 8 Sampling and lineages through time plots.

(a) Mean (line), 75% (dark fill) and 95% (light fill) quantiles of 110, 65, and 30 Ma lineages sampled per number of tips on the tree, tip accumulation randomised 100 times. Additional sampling has led to few additional 110 Ma (family-level) lineages. (b) Lineage through time (LTT) plots for the species-level trees of Class Ophiuroidea and the 6 extant taxonomic Orders, along with Ophiuroidea birth-death simulations (n = 100) and model-fit net diversification. X-axis truncated to 220 Ma.

Extended Data Fig. 9 MDS reliability tests.

The MDS pattern in Fig. 2 (using all nodes in our phylogeny and the geomean statistic) is largely robust to the statistic phylogeny or procedure used, including: (a) mean, (b) median or (c) harmonic mean; temporal selection of input nodes: (d) restricted to <=65 Ma or (e) > 3 and <=65 Ma; (f) the mean of 100 jackknifed (90% without replacement) datasets (see methods for details); (g) addition of dummy samples to reflect the known distribution of species across biomes, and (h) the use of a non-ultrametric (RAxML v8.1.20) phylogeny with branch lengths based on site substitution rates (rather than ages). The position of the Arctic bathyal biome is labile reflecting its varying relationship to Arctic shelf and Eastern Atlantic bathyal biomes. The temperate shelf biomes are notably more dispersed when older nodes are excluded (d, e) or down-weighted (c).

Extended Data Fig. 10 Comparison of tested CorHMM models and node marginal likelihoods of final ER model.

(a) ER = Equal rates, ARD = all rates, SYM = symmetrical rates, ARD-2 and SYM-2 reduce the number of rates to be estimated by excluding transitions that tended to zero in the SYM model, ARD-3 (the final model) further reduces the number of rates estimated by excluding transitions that tended to zero in the ARD-2 model. The reported model in each case was the model with the highest Log Likelihood from 10 starts. (b) Decisive (ML > 0.67) marginal states (biomes) from the ER model, mapped onto edges on the phylogeny (root = 265 Ma). The majority (99%) of decisive edges date from less than 65 Ma.

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O’Hara, T.D., Hugall, A.F., Haines, M.L. et al. Spatiotemporal faunal connectivity across global sea floors. Nature 645, 423–428 (2025). https://doi.org/10.1038/s41586-025-09307-1

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