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Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues

Nature Astronomy volume 7, pages 406–422 (2023)Cite this article

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Abstract

In the search for biosignatures on Mars, there is an abundance of data from orbiters and rovers to characterize global and regional habitability, but much less information is available at the scales and resolutions of microbial habitats and biosignatures. Understanding whether the distribution of terrestrial biosignatures is characterized by recognizable and predictable patterns could yield signposts to optimize search efforts for life on other terrestrial planets. We advance an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature patterns at nested spatial scales in a polyextreme terrestrial environment. Drone flight imagery connected simulated HiRISE data to ground surveys, spectroscopy and biosignature mapping to reveal predictable distributions linked to environmental factors. Artificial intelligence–machine learning models successfully identified geologic features with high probabilities for containing biosignatures at spatial scales relevant to rover-based astrobiology exploration. Targeted approaches augmented by deep learning delivered 56.9–87.5% probabilities of biosignature detection versus <10% for random searches and reduced the physical search space by 85–97%. Libraries of biosignature distributions, detection probabilities, predictive models and search roadmaps for many terrestrial environments will standardize analogue science research, enabling agnostic comparisons at all scales.

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Fig. 1: Orbit-to-ground scales of investigation.
Fig. 2: Biosignatures exhibit significant spatial heterogeneity and occur in non-random clustered distributions at hierarchical scales.
Fig. 3: Macro- and microhabitat composition and biosignature detection probabilities.
Fig. 4: Nested spatial scale biosignature probability and habitat maps.
Fig. 5: Habitat and biosignature probability maps from CNN models and spatial GAMs.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. Sequence data from this study have been deposited to the DDBJ database under BioProject PRJDB14848 with accession numbers DRR425262 to DRR425263.

Code availability

Code and data to reproduce the results of the CNN models can be found at https://github.com/SETI/endo.

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Acknowledgements

This study was supported by the NASA Astrobiology Institute (NAI) via grant NNA15BB01A. We acknowledge XRD data and its respective analysis generated from the MAINI’s scientific equipment and Centro de Biotecnología at Universidad Católica del Norte. G.C.-D. and C.D. thank BHP Minerals Americas Project 32002137 (2016–2020). We thank K. Phillips (kennydphillips.com) for graphic design. V.P. thanks the Ministry of Science and Innovation (Spain) (grant RTI2018-094368-B-I00), State Agency of Research (MCIN/AEI/10.13039/501100011033) and ERDF ‘A way of making Europe’ for funding and M. García-Villadangos for technical support.

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Author notes

  1. A full list of authors and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. SETI Institute Carl Sagan Center, Mountain View, CA, USA

    Kimberley Warren-Rhodes, Nathalie A. Cabrol, Janice L. Bishop, Pablo Sobron & Philippe Sarazzin

  2. NASA Ames Research Center, Space Science Division and Astrobiology Division, Moffett Field, CA, USA

    Kimberley Warren-Rhodes, Nathalie A. Cabrol, Janice L. Bishop & Philippe Sarazzin

  3. Johns Hopkins, Applied Physics Laboratory, Laurel, MD, USA

    Michael Phillips, Jeffrey Moersch & Claire Mondro

  4. Universidad Católica del Norte, Antofagasta, Chile

    Cinthya Tebes-Cayo, Diego Ayma, Cecilia Demergasso, Guillermo Chong-Diaz, Camila Javiera & Connie Rodriguez

  5. Department of Computer Science, University of Oxford, Oxford, UK

    Freddie Kalaitzis

  6. Auckland University of Technology, Auckland, New Zealand

    Kevin Lee

  7. Department of Geosciences, University of Montana, Missoula, MT, USA

    Nancy Hinman & Michael H. Hofmann

  8. MarAlliance, San Francisco, CA, USA

    Kevin L. Rhodes

  9. University of Washington, Seattle, WA, USA

    Linda Ng Boyle

  10. Tropical Futures Institute, James Cook University, Singapore, Singapore

    Neil Hutchinson

  11. Old Dominion University, Norfolk, VA, USA

    Nora Nofke

  12. Centro de Astrobiologia (CAB), INTA-CSIC, Madrid, Spain

    Victor Parro

  13. Impossible Sensing, St. Louis, MO, USA

    Pablo Sobron

  14. Carnegie Mellon University Robotics Institute, Pittsburgh, PA, USA

    David Wettergreen

  15. Honeybee Robotics, Altadena, CA, USA

    Kris Zacny

Authors
  1. Kimberley Warren-Rhodes
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  2. Nathalie A. Cabrol
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  3. Michael Phillips
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  4. Cinthya Tebes-Cayo
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  5. Freddie Kalaitzis
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  6. Diego Ayma
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  7. Cecilia Demergasso
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  8. Guillermo Chong-Diaz
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  9. Kevin Lee
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  10. Nancy Hinman
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  11. Kevin L. Rhodes
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  12. Linda Ng Boyle
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  13. Janice L. Bishop
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  14. Michael H. Hofmann
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  15. Neil Hutchinson
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  16. Camila Javiera
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  17. Jeffrey Moersch
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  18. Claire Mondro
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  19. Nora Nofke
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  20. Victor Parro
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  21. Connie Rodriguez
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  22. Pablo Sobron
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  23. Philippe Sarazzin
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  24. David Wettergreen
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  25. Kris Zacny
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Consortia

the SETI Institute NAI Team

Contributions

K.W.-R., M.P., N. Hutchinson, N.A.C. and K.L.R. designed and implemented the experiment. K.W.-R., M.P., C.T.-C., K.L.R., N.A.C., N. Hinman., V.P., P. Sobron, P. Sarazzin, C.J., C.R. and C.D. conducted field operations and sampling. J.M., M.P., C.M., P. Sobron, P. Sarazzin and D.W. collected in situ visible, multi- and hyperspectral data from instruments and instrumented drones. M.P., F.K., D.A., L.N.B., K.L. and K.W.-R. developed deep learning and statistical models. K.W.-R., M.P., K.L.R., V.P., N. Hinman and J.L.B. processed samples and conducted geochemical and mineralogical analyses. K.W.-R., M.P., K.L.R., C.T.-C., N.H., C.D., G.C.-D., M.H.H. and V.P. performed geological and biological analyses. K.W.-R., K.L.R., D.W., G.C.-D., D.A. and C.T.-C. installed, collected and analysed microclimate data. All authors wrote the manuscript. N.A.C. conceived the NAI project study. K.W.-R., K.L.R. and N.H. designed the ecological study. All authors reviewed and edited a manuscript draft.

Corresponding author

Correspondence to Kimberley Warren-Rhodes.

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The authors declare no competing interests.

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Nature Astronomy thanks Jesus Martinez-Frias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Geomorphic units and habitats at Dome Field study location from orbital (left) to cm (right) views.

a, Large-scale polygonal network pattern (pink boxes). a1, Orbital view reveals large-sized polygons (mean diameters in study location = 10 m). a2, Large-scale polygon exteriors are composed of ridges and polygon interiors are composed of aeolian cover; a3, Ground view of ridge (mean height in study location = 33.5 ± 8.6 cm). Note the flat ridge tops covered in eroded float due to wind erosion and scouring by sand. The sides of ridges are typically composed of crystals and the bottoms by alabaster. a4, Arrangement of crystals along one ridge. View of Type II crystals with visible biosignature bands, as seen in a5 (~0.55 cm below ‘eggshell seal’23 surface duricrust); a5, In the close-up view of crystals, microbal laminae become visible. Pink-orange layer: desiccation and radiation-resistance bacteria such as Salinibacter spp., Halorubrum sp., Pyrococcus sp., Chloroflexota, Thermi (carotenoids, pink arrow); Green colonized layer: Cyanobacteria (chlorophyll pigments, green arrow); b, domes (red boxes). b1, Distribution of domes (red arrows) in orbital view; b2, Dome heights range from ~10 cm to ~3 m, diameters range from ~1 to 7.5 m. b3, Ground view of domes, with eroded crystals in left foreground and intact tightly embedded crystals of the dome structure; alabaster lines the bottom of the dome; b4, Type I jagged sharp crystals tightly embedded in dome; b5, Type II crystal topped with alabaster in a dome. Note brown thin (~0.5 cm) surface duricrust and white powdery alabster efflorescence material with green and pink biosignature layers. c, Patterned ground (green boxes); Orbital view of patterned ground geomorphic/macro-habitat unit; c1. Bare salar surface is visible as speckled tan features. c2-c3, Fractal nature of micro-structure network inhabited by biological soil crust (BSC) communities is apparent from aerial and ground views. Micro-structure network with BSC is the patterned darker material (1–3 cm height) covering the lighter-toned bare salar surface micro-habitat. c4, Micro-structure with pinnacles covering the bare open salar surface, which is visible in the bottom right of photo; c5, A flipped micro-structure reveals BSC photosynthetic communities with orange/pink and green biosignatures.

Extended Data Fig. 2 Microbial Landscape Ecology Methods: Nested Spatial Scale and Habitat Sampling Designs.

a) ES-1 nested scale sampling design. b) ES-2 habitat study surveys and sampling design. c) ES-3 fine-scale microhabitat and biosignature mapping sampling design.

Extended Data Fig. 3 Rules for the probability a sample contains endolithic biosignatures in microhabitats of a dome macrohabitat.

Visual representation of the main results of Supplementary Table 1. For example, in the first decision choice, if the microhabitat is alabaster, then the probability of endolithic biosignatures is 100% (%Col is: 27 colonized samples/27 total alabaster samples ×100). In the figure, if the microhabitat is not alabaster, this leads to the next decision choice of whether the microhabitat is a Type II (T2) crystal or not, and so forth.

Extended Data Fig. 4 Thin section photomicrographs (a,c) and binary images of pore networks (b,d) of Alabaster (a,b) and a porous selenite crystal (c,d), respectively.

a, b, Alabaster contains a well connected intercrystalline pore network (f = ~9.4%) without preferred orientation. c, d, the selenite crystal has a higher total porosity (f = ~11.9%) and larger pores, but pores are parallel to each other (elongated slot pores) and are less well connected in a horizontal direction (growth direction of crystal is up in photomicrograph) Each image is 900 ×900 mm (1750 px 1750 px).

Extended Data Table 1 Relevant ecological survey (black, this study) spatial (red, this study) and comparable rover sampling scales
Full size table
Extended Data Table 2 Detailed statistical ecology analyses and results
Full size table
Extended Data Table 3 Microclimate Data at Salar de Pajonales, liquid water availability in alabaster (519 sensor) and Type I crystals (517 sensor, east; 520 sensor, north) in dry and hydrated states; C: colonized; UC: uncolonized; T: temperature (oC); %RH: Percent Relative Humidity
Full size table
Extended Data Table 4 Comparison of evaluation metrics of neural network semantic segmentation results on orthophoto mosaics at 6.9 cm/pixel and 23.9 cm/pixel ground sampling distances (GSD)
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Discussion and Tables 1–4.

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Warren-Rhodes, K., Cabrol, N.A., Phillips, M. et al. Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues. Nat Astron 7, 406–422 (2023). https://doi.org/10.1038/s41550-022-01882-x

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