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

Warren-Rhodes, Kimberley, Cabrol, Nathalie A., Phillips, Michael, Tebes-Cayo, Cinthya, Kalaitzis, Freddie, Ayma, Diego, Demergasso, Cecilia, Chong-Diaz, Guillermo, Lee, Kevin, Hinman, Nancy, Rhodes, Kevin L., Boyle, Linda Ng, Bishop, Janice L., Hofmann, Michael H., Hutchinson, Neil, Javiera, Camila, Moersch, Jeffrey, Mondro, Claire, Nofke, Nora, Parro, Victor, Rodriguez, Connie, Sobron, Pablo, Sarazzain, Philippe, Wettergreen, David, Zacny, Kris, and the SETI Institute NAI Team, (2023) Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues. Nature Astronomy, 7. pp. 406-422.

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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.

Item ID: 69697
Item Type: Article (Research - C1)
ISSN: 2397-3366
Keywords: Microbial landscape ecology, Mars, deep learning, habitability, endolith, biological soil crust, biosignature, astrobiology, convolutional neural network, artificial intelligence, machine learning, model
Copyright Information: Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Funders: NASA Astrobiology Institute, BHP Minerals Americas Project, Spain Ministry of Science and Innovation grants
Date Deposited: 10 Mar 2023 01:42
FoR Codes: 31 BIOLOGICAL SCIENCES > 3103 Ecology > 310399 Ecology not elsewhere classified @ 30%
51 PHYSICAL SCIENCES > 5101 Astronomical sciences > 510101 Astrobiology @ 40%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 30%
SEO Codes: 96 ENVIRONMENT > 9699 Other Environment > 969999 Environment not elsewhere classified @ 50%
97 EXPANDING KNOWLEDGE > 970105 Expanding Knowledge in the Environmental Sciences @ 25%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 25%
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