Ecology

Day, J. W. et al. Pattern and process of land loss in the Mississippi Delta: a spatial and temporal analysis of wetland habitat change. Estuaries 23, 425–438 (2000).Article 

Google Scholar 
Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–686 (2009).Article 
CAS 

Google Scholar 
Higgins, S., Overeem, I., Tanaka, A. & Syvitski, J. P. Land subsidence at aquaculture facilities in the Yellow River delta, China. Geophys. Res. Lett. 40, 3898–3902 (2013).Article 

Google Scholar 
Giosan, L., Syvitski, J., Constantinescu, S. & Day, J. Climate change: protect the world’s deltas. Nature 516, 31–33 (2014).Couvillion, B. R., Beck, H., Schoolmaster, D. & Fischer, M. Land Area Change in Coastal Louisiana (1932 to 2016) (USGS, 2017); https://doi.org/10.3133/sim3381Corthell, E. L. The delta of the Mississippi River. Natl Geogr. Mag. 12, 351–354 (1897).
Google Scholar 
Blum, M. D. & Roberts, H. H. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nat. Geosci. 2, 488–491 (2009).Article 
CAS 

Google Scholar 
Gagliano, S. M., Meyer-Arendt, K. J. & Wicker, K. M. Land Loss in the Mississippi River Deltaic Plain. Gulf Coast Assoc. Geol. Soc. Trans. 31, 295–300 (1981).Day, J. W., Clark, H. C., Chang, C., Hunter, R. & Norman, C. R. Life cycle of oil and gas fields in the Mississippi River Delta: a review. Water 12, 1492 (2020).Article 
CAS 

Google Scholar 
Morton, R., Bernier, J., Barras, J. & Ferina, N. Rapid Subsidence and Historical Wetland Loss in the Mississippi Delta Plain: Likely Causes and Future Implications (USGS, 2005).Kolker, A. S., Allison, M. A. & Hameed, S. An evaluation of subsidence rates and sea‐level variability in the northern Gulf of Mexico. Geophys. Res. Lett. 38, L21404 (2011).Roy, S., Robeson, S. M., Ortiz, A. C. & Edmonds, D. A. Spatial and temporal patterns of land loss in the Lower Mississippi River Delta from 1983 to 2016. Remote Sens. Environ. 250, 112046 (2020).Sanks, K. M., Shaw, J. B. & Naithani, K. Field-based estimate of the sediment deficit in coastal Louisiana. J. Geophys. Res. Earth Surf. 125, e2019JF005389 (2020).Article 

Google Scholar 
Jankowski, K. L., Törnqvist, T. E. & Fernandes, A. M. Vulnerability of Louisiana’s coastal wetlands to present-day rates of relative sea-level rise. Nat. Commun. 8, 14792 (2017).Article 
CAS 

Google Scholar 
Turner, R. E. & McClenachan, G. Reversing wetland death from 35,000 cuts: opportunities to restore Louisiana’s dredged canals. PLoS ONE 13, e0207717 (2018).Article 

Google Scholar 
Falcini, F. et al. Linking the historic 2011 Mississippi River flood to coastal wetland sedimentation. Nat. Geosci. 5, 803–807 (2012).Chamberlain, E. L., Törnqvist, T. E., Shen, Z., Mauz, B. & Wallinga, J. Anatomy of Mississippi Delta growth and its implications for coastal restoration. Sci. Adv. 4, eaar4740 (2018).Article 

Google Scholar 
Roberts, H. H. Dynamic changes of the Holocene Mississippi River delta plain: the delta cycle. J. Coast. Res. 13, 605–627 (1997).
Google Scholar 
Siverd, C. G. et al. Coastal Louisiana landscape and storm surge evolution: 1850–2110. Clim. Change 157, 445–468 (2019).Article 

Google Scholar 
Tweel, A. W. & Turner, R. E. Watershed land use and river engineering drive wetland formation and loss in the Mississippi River birdfoot delta. Limnol. Oceanogr. 57, 18–28 (2012).Article 

Google Scholar 
Shen, Z. et al. Episodic overbank deposition as a dominant mechanism of floodplain and delta-plain aggradation. Geology 43, 875–878 (2015).Article 

Google Scholar 
Frederikse, T. et al. The causes of sea-level rise since 1900. Nature 584, 393–397 (2020).Article 
CAS 

Google Scholar 
Meade, R. H. & Moody, J. A. Causes for the decline of suspended‐sediment discharge in the Mississippi River system, 1940–2007. Hydrol. Process. 24, 35–49 (2010).
Google Scholar 
Xu, K., Bentley, S. J., Day, J. W. & Freeman, A. M. A review of sediment diversion in the Mississippi River Deltaic Plain. Estuar. Coast. Shelf Sci. 225, 106241 (2019).Article 

Google Scholar 
Vogel, H. D. Report on control of floods of the Lower Mississippi River, Annex no. 5, Basic data Mississippi River. House Doc. 798, 61–137 (1930).Craig, N. J., Turner, R. E. & Day, J. W. Land loss in coastal Louisiana (U.S.A.). Environ. Manage. 3, 133–144 (1979).Article 

Google Scholar 
Ko, J.-Y. & Day, J. W. A review of ecological impacts of oil and gas development on coastal ecosystems in the Mississippi Delta. Ocean Coast. Manage. 47, 597–623 (2004).Article 

Google Scholar 
Penland, S., Beall, A. D., Britsch, L. D. & Jeffress, W. S. Geologic classification of coastal land loss between 1932 and 1990 in the Mississippi River Delta Plain, Southeastern Louisiana. Gulf Coast Assoc. Geol. Soc. Trans. 52, 799–807 (2002).
Google Scholar 
Turner, R. Coastal wetland subsidence arising from local hydrologic manipulations. Estuaries 27, 265–272 (2004).Article 

Google Scholar 
Nienhuis, J. H., Törnqvist, T. E. & Erkens, G. Altered surface hydrology as a potential mechanism for subsidence in coastal Louisiana. Proc. IAHS 382, 333–337 (2020).Morton, R. A., Bernier, J. C. & Barras, J. A. Evidence of regional subsidence and associated interior wetland loss induced by hydrocarbon production, Gulf Coast region, USA. Environ. Geol. 50, 261–274 (2006).Karegar, M. A., Dixon, T. H. & Malservisi, R. A three-dimensional surface velocity field for the Mississippi Delta: implications for coastal restoration and flood potential. Geology 43, 519–522 (2015).Article 

Google Scholar 
Gambolati, G. & Teatini, P. Geomechanics of subsurface water withdrawal and injection. Water Resour. Res. 51, 3922–3955 (2015).Article 

Google Scholar 
Chang, C., Mallman, E. & Zoback, M. Time-dependent subsidence associated with drainage-induced compaction in Gulf of Mexico shales bounding a severely depleted gas reservoir. AAPG Bull. 98, 1145–1159 (2014).Article 

Google Scholar 
Guzy, A. & Malinowska, A. A. State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water 12, 2051 (2020).Article 

Google Scholar 
Ortiz, A. C., Roy, S. & Edmonds, D. A. Land loss by pond expansion on the Mississippi River Delta Plain. Geophys. Res. Lett. 44, 3635–3642 (2017).Article 

Google Scholar 
Mariotti, G. Revisiting salt marsh resilience to sea level rise: are ponds responsible for permanent land loss? J. Geophys. Res. Earth Surf. 121, 1391–1407 (2016).Article 

Google Scholar 
Louisiana’s Comprehensive Master Plan for a Sustainable Coast. Coastal Protection and Restoration Authority https://coastal.la.gov/wp-content/uploads/2023/01/230105_CPRA_MP-Draft_Final-for-web_spreads-main.pdf (2023).Siverd, C. G. et al. Hydrodynamic storm surge model simplification via application of land to water isopleths in coastal Louisiana. Coast. Eng. 137, 28–42 (2018).Article 

Google Scholar 
Twilley, R. R. et al. Co-evolution of wetland landscapes, flooding, and human settlement in the Mississippi River Delta Plain. Sustain. Sci. https://doi.org/10.1007/s11625-016-0374-4 (2016).Edmonds, D. A. et al. in Treatise on Geomorphology 2nd edn (ed. Shroder, J. F.) 110–140 (Academic Press, 2022).Xu, K., Harris, C. K., Hetland, R. D. & Kaihatu, J. M. Dispersal of Mississippi and Atchafalaya sediment on the Texas–Louisiana shelf: model estimates for the year 1993. Cont. Shelf Res. 31, 1558–1575 (2011).Article 

Google Scholar 
Baptist, M. et al. On inducing equations for vegetation resistance. J. Hydraul. Res. 45, 435–450 (2007).Article 

Google Scholar 
Hopkinson, C. S., Morris, J. T., Fagherazzi, S., Wollheim, W. M. & Raymond, P. A. Lateral marsh edge erosion as a source of sediments for vertical marsh accretion. J. Geophys. Res. Biogeosci. 123, 2444–2465 (2018).Article 
CAS 

Google Scholar 
Danielson, J. et al. Topobathymetric Model of the Northern Gulf of Mexico, 1885 to 2021 (USGS, 2022); https://doi.org/10.5066/P99JULDNBomer, E. J. et al. Deltaic morphodynamics and stratigraphic evolution of Middle Barataria Bay and Middle Breton Sound regions, Louisiana, USA: implications for river-sediment diversions. Estuar. Coast. Shelf Sci. 224, 20–33 (2019).Article 
CAS 

Google Scholar 
Wolinsky, M., Edmonds, D. A., Martin, J. M. & Paola, C. Delta allometry: growth laws for river deltas. Geophys. Res. Lett. 37, L21403 (2010).Article 

Google Scholar 
Mariotti, G., Elsey-Quirk, T., Bruno, G. & Valentine, K. Mud-associated organic matter and its direct and indirect role in marsh organic matter accumulation and vertical accretion. Limnol. Oceanogr. 65, 2627–2641 (2020).Article 
CAS 

Google Scholar  More

Des Marais, D. J. The biogeochemistry of hypersaline microbial mats. Adv. Microb. Ecol. 14, 251–274 (1995).Article 

Google Scholar 
Belnap, J., Welter, J., Grimm, N., Barger, N. & Ludwig, J. Linkages between microbial and hydrologic processes in arid and semiarid watersheds. Ecology 86, 298–307 (2005).Article 

Google Scholar 
Houghton, J. et al. Spatial variability in photosynthetic and heterotrophic activity drives locale δ13Corg fluctuations and carbonate precipitation in hypersaline microbial mats. Geobiology 12, 557–574 (2014).Article 

Google Scholar 
Allwood, A., Walter, M., Burch, I. & Kamber, B. 3.43 billion-year-old stromatolite reef from the Pilbara Craton of Western Australia: ecosystem-scale insights to early life on Earth. Precambrian Res. 158, 198–227 (2007).Article 
ADS 

Google Scholar 
Al-Najjar, M. et al. Spatial patterns and links between microbial community composition and function in cyanobacterial mats. Front. Microbiol. 5, 406 (2014).Article 

Google Scholar 
Warren-Rhodes, K., Dungan, J., Piatek, J. & McKay, C. Ecology and spatial pattern of cyanobacterial island patches in the Atacama Desert. J. Geophys. Res. Biogeosciences 112, G04S15 (2007).Article 

Google Scholar 
Allwood, A., Walter, M., Kamber, B., Marshall, C. & Burch, I. Stromatolite reef from the early Archaean era of Australia. Nature 441, 714–718 (2006).Article 
ADS 

Google Scholar 
Meslier, V. et al. Fundamental drivers for endolithic microbial community assemblies in the hyperarid Atacama Desert. Environ. Microbiol. 20, 1765–1781 (2018).Article 

Google Scholar 
Finstad, K. et al. Microbial community structure and the persistence of cyanobacterial populations in salt crusts of the hyperarid Atacama Desert from genome-resolved metagenomics. Front. Microbiol. 8, 1435 (2017).Article 

Google Scholar 
Wilhelm, M. et al. Constraints on the metabolic activity of microorganisms in Atacama surface soils inferred from refractory biomarkers: Implications for Martian habitability and biomarker detection. Astrobiology 18, 955–966 (2018).Dillon, J. et al. Spatial and temporal variability in a stratified microbial mat community. FEMS Microbiol. Ecol. 68, 46–58 (2009).Article 

Google Scholar 
Rillig, M. & Antonovics, J. Microbial biospherics: the experimental study of ecosystem function and evolution. Proc. Natl Acad. Sci. USA 116, 11093–11098 (2019).Article 
ADS 

Google Scholar 
Sephton, M. & Carter, J. The chances of detecting life on Mars. Planet. Space Sci. 112, 15–22 (2015).Article 
ADS 

Google Scholar 
Naveh, Z., & Lieberman, A. S. Landscape Ecology: Theory and Application (Springer, 2013).Mony, C., Vandenkoornhuyse, P., Bohannan, B. J. M., Peay, K. & Leibold, M. A. A landscape of opportunities for microbial ecology research. Front. Microbiol. 11, 2964 (2020).Article 

Google Scholar 
Summons, R. et al. Preservation of Martian organic and environmental records: final report of the Mars Biosignature Working Group. Astrobiology 11, 157–181 (2011).Article 
ADS 

Google Scholar 
Farmer, J. & Des Marais, D. J. Exploring for a record of ancient Martian life. J. Geophys. Res. 104, 26,977–26,995 (1999).Article 
ADS 

Google Scholar 
Stoker et al. We should search for extant life on Mars in this decade. Bull. AAS 53 (2021); https://doi.org/10.3847/25c2cfeb.36ef5e33Jakowsky, B. et al. Mars, the nearest habitable world—a comprehensive program for future Mars exploration. Bull. AAS 53 (2021); https://doi.org/10.3847/25c2cfeb.e5222017Hinman, N. et al. Surface morphologies in a Mars analog Ca sulfate salar, High Andes, Northern Chile. Front. Astron. Space Sci. 8, 797591 (2022).Article 

Google Scholar 
Cabrol, N. et al. Record solar UV irradiance in the tropical Andes. Front. Environ. Sci. 2, 19 (2014).Article 

Google Scholar 
Phillips, M.S. et al. Planetary mapping using Deep Learning: a method to evaluate feature identification confidence applied to habitats in Mars-analogue terrain. Astrobiology 23 (2023).Wierzchos, J. et al. Adaptation strategies of endolithic chlorophototrophs to survive the hyperarid and extreme solar radiation environment of the Atacama Desert. Front. Microbiol. 6, 934 (2015).Article 

Google Scholar 
Lynch, K. et al. Near-infrared spectroscopy of lacustrine sediments in the Great Salt Lake Desert: an analog study for Martian paleolake basins. J. Geophys. Res. Planets 120, 599–623 (2015).Article 
ADS 

Google Scholar 
El-Maarry, M., Pommerol, A. & Thomas, N. Analysis of polygonal cracking patterns in chloride-bearing terrains of Mars: indicators of ancient playa settings. J. Geophys. Res. 113, 2263–2278 (2013).Article 

Google Scholar 
Onstott, T. et al. Paleo-rock-hosted life on Earth and the search on Mars: a review and strategy for exploration. Astrobiology 19, 1230–1262 (2019).Article 
ADS 

Google Scholar 
Davila, A. & Schulze-Makuch, D. The last possible outposts for life on Mars. Astrobiology 16, 159–168 (2016).Article 
ADS 

Google Scholar 
Osterloo, M. M. et al. Geologic context of proposed chloride-bearing materials on Mars. J. Geophys. Res. 115, E10012 (2010).Article 
ADS 

Google Scholar 
Flauhaut, J., Martinot, M., Bishop, J.L., Davies, G.R. & Potts, N.J. Remote sensing and in situ mineralogic survey of the Chilean salars: an analog to Mars evaporate deposits? Icarus 282, 152–173 (2017).Bosak, T., Moore, K., Gong, J. & Grotzinger, J. Searching for biosignatures in sedimentary rocks from early Earth and Mars. Nat. Rev. Earth Environ. 2, 490–506 (2021).Article 
ADS 

Google Scholar 
Balci, N. et al. Biotic and abiotic imprints on Mg-rich stromatolites: lessons from Lake Salda, SW Turkey. Geomicrobiol. J. 37, 401–425 (2020).Article 

Google Scholar 
Williams, A., Buck, B., Soukup, D. & Merkler, D. Geomorphic controls on biological soil crust distribution: a conceptual model from the Mojave Desert (USA). Geomorphology 195, 99–109 (2013).Article 
ADS 

Google Scholar 
Warren, J. Evaporites: a Geological Compendium 2nd edn (Springer, 2016).Wierzchos, J. et al. Microbial colonization of Ca sulfate crusts in the hyperarid core of the Atacama Desert: implications for the search for life on Mars. Geobiology 9, 44–60 (2010).Article 

Google Scholar 
Robinson, C. K. et al. Microbial diversity and the presence of algae in halite endolithic communities are correlated to atmospheric moisture in the hyper-arid zone of the Atacama Desert. Environ. Microbiol. 17, 299–315 (2013).Article 

Google Scholar 
Jørgesen, B. & Des Marais, D. Optical properties of benthic photosynthetic communities: fiber-optic studies of cyanobacterial mats. Limnol. Oceanogr. 33, 99–113 (1988).Article 
ADS 

Google Scholar 
Szynkiewicz, A., Moore, C., Glamoclija, M., Bustos, D. & Pratt, L. Origin of coarsely crystalline gypsum domes in a saline playa environment at the White Sands National Monument, New Mexico. J. Geophys. Res. 115, F02021 (2010).Article 
ADS 

Google Scholar 
Walker, J., Spear, J. & Pace, N. Geobiology of a microbial endolithic community in the Yellowstone geothermal environment. Nature 434, 1011–1013 (2005).Article 
ADS 

Google Scholar 
Rasuk, M. et al. Microbial characterization of microbial ecosystems associated to evaporites domes of gypsum in Salar de Llamara in Atacama Desert. Microb. Ecol. 68, 483–494 (2014).Article 

Google Scholar 
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. Semantic image segmentation with deep convolutional nets and fully connected CRFs. Preprint at arXiv https://arxiv.org/abs/1412.7062 (2014).Chan, M. et al. Exploring, mapping and data management integration of habitable environments in astrobiology. Frontiers in Microbiology 10, 147 (2019).Farmer, J. in From Habitability to Life on Mars 1–12 (Elsevier, 2018).Hays, L. et al. Biosignature preservation and detection in Mars analog environments. Astrobiology 17, 363–400 (2017).Article 
ADS 

Google Scholar 
Fairen, A. et al. Astrobiology through the ages of Mars: the study of terrestrial analogues to understand the habitability of Mars. Astrobiology 10, 821 (2010).Article 
ADS 

Google Scholar 
Green, J. et al. Call for a framework for reporting evidence for life beyond Earth. Nature 598, 575–579 (2021).Article 
ADS 

Google Scholar 
He, K., Xiangyu, Z., Shaoqing, R. & Jian, S. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In 2015 IEEE International Conference on Computer Vision (ICCV) 1026–1034 (IEEE, 2015).Adams, J. B. & Filice, A. L. Spectral reflectance 0.4 to 2.0 microns of silicate rock powders. J. Geophys. Res. 72, 5705–5715 (1967).National Academies of Sciences, Engineering & Medicine. Origins, Worlds and Life: a Decadal Strategy for Planetary Science and Astrobiology 2023–2032 (National Academies Press, 2022).Rodríguez Albornoz, C. Geology and Controls on Microbiota of the Salar de Pajonales (7.209.000–7.226.500 N.–510.000–530.000 E), Antofagasta, Northern Chile. Master’s thesis, Univ. Católica del Norte Antofagasta (2018).Naranjo, J., Villa, V. & Venegas, C. Geology of the Salar de Pajonales Area and Cerro Moño. Antofagasta and Atacama Regions (Geological Maps of Chile Basic Geology Series No. 153 (1: 100.000), National Geological Service, Geology and Mining Subsection, 2013).Schween, J., Hoffmeister, D. & Löhnert, U. Filling the observational gap in the Atacama Desert with a new network of climate stations. Glob. Planet. Chang. 184, 103034 (2020).Gutiérrez, F. & Cooper, A. Surface morphology of gypsum karst. Treatise Geomorphol. 6, 425–437 (2013).Article 

Google Scholar 
Bishop, J. L. et al. Spectral properties of Ca-sulfates: gypsum, bassanite and anhydrite. Am. Mineral. 99, 2105–2115 (2014).Article 
ADS 

Google Scholar 
Green, A., Berman, M., Switzer, P. & Craig, M. D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26, 65–74 (1988).Article 
ADS 

Google Scholar 
Davis, W., Pater, I. & McKay, C. P. Rain infiltration and crust formation in the extreme arid zone of the Atacama Desert, Chile. Planet. Space Sci. 58, 616–622 (2010).Article 
ADS 

Google Scholar 
McKay, C. P. et al. Temperature and moisture conditions for life in the extreme arid region of the Atacama Desert: four years of observation including the El Niño of 1997–1998. Astrobiology 3, 393–406 (2003).Article 
ADS 

Google Scholar 
Warren-Rhodes, K., Rhodes, K., Liu, S., Zhou, P. & McKay, C. Nanoclimate environment of cyanobacterial communities in China’s hot and cold hyperarid deserts. J. Geophys. Res. 112, G01016 (2007).Article 
ADS 

Google Scholar 
Warren-Rhodes, K. et al. Physical ecology of hypolithic communities in the central Namib Desert: the role of fog, rain, rock habitat and light. J. Geophys. Res. 118, 1451–1460 (2013).Article 

Google Scholar 
Lange, O., Kilian, E. & Ziegler, H. Water vapor uptake and photosynthesis of lichens: performance differences in species with green and blue–green algae as phycobionts. Oecologia 71, 104–110 (1986).Article 
ADS 

Google Scholar 
Lange, O. L., Meyer, A. & Büdel, B. Net photosynthesis activation of a desiccated cyanobacterium without liquid water in high air humidity alone. Experiments with a Microcoleus sociatus isolated from a desert soil crust. Funct. Ecol. 8, 52–57 (1994).Article 

Google Scholar 
Palmer, R. & Friedmann, E. I. Water relations and photosynthesis in the cryptoendolithic microbial habitat of hot and cold deserts. Microb. Ecol. 18, 111–118 (1990).Article 

Google Scholar 
Potts, M. & Friedmann, E. Effects of water stress on cryptoendolithic cyanobacteria from hot desert rocks. Arch. Microbiol. 130, 267–271 (1981).Article 

Google Scholar 
Tracy, C. et al. Microclimate and limits to photosynthesis in a diverse community of hypolithic cyanobacteria in northern Australia. Environ. Microbiol. 12, 592–607 (2010).Article 

Google Scholar 
Azúa-Bustos, A. et al. Hypolithic cyanobacteria supported mainly by fog in the coastal range of the Atacama Desert. Microb. Ecol. 51, 568–581 (2011).Article 

Google Scholar 
Rull, F. et al. ExoMars Raman Laser Spectrometer for ExoMars. Proc. SPIE 8152, 81520J (2011).Kontoyannis, C. G., Orkoula, M. & Koutsoukos, P. Quantitative analysis of sulphated calcium carbonates using Raman spectrometry and X-ray powder diffraction. Analyst 122, 33–38 (1997).Lopez-Reyes, G. et al. Analysis of the scientific capabilities of the ExoMars Raman Laser Spectrometer Instrument. Eur. J. Mineral. 25, 721–733 (2013).Hunt, G. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42, 501–513 (1977).Article 
ADS 

Google Scholar 
Bishop, J. L. in Remote Compositional Analysis: Techniques for Understanding Spectroscopy, Mineralogy, and Geochemistry of Planetary Surfaces (eds Bishop, J. L. et al.) 68–101 (Cambridge Univ. Press, 2019).Morris, R. V. et al. Evidence for pigmentary hematite on Mars based on optical, magnetic and Mössbauer studies of superparamagnetic (nanocrystalline) hematite. J. Geophys. Res. 94, 2760–2778 (1989).Article 
ADS 

Google Scholar 
Bishop, J. L., Pieters, C. M. & Burns, R. G. Reflectance and Mössbauer spectroscopy of ferrihydrite–montmorillonite assemblages as Mars soil analog materials. Geochim. Cosmochim. Acta 57, 4583–4595 (1993).Article 
ADS 

Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
Underwood, A. J., Chapman, M. G. & Connell, S. D. Observations in ecology: you can’t make progress on processes without understanding the patterns. J. Exp. Mar. Biol. Ecol. 250, 97–115 (2000).Article 

Google Scholar 
Turner, M. G. Landscape ecology: the effect of pattern on process. Annu. Rev. Ecol. Syst. 20, 171–197 (1989).Article 

Google Scholar 
Turner, M. G., Gardner, R. H. & O’Neill, R. V. Landscape Ecology in Theory and Practice (Springer, 2001).Wiens, J. A., Chr, N., Van Horne, B. & Ims, R. A. Ecological mechanisms and landscape ecology. Oikos 66, 369–380 (1993).Article 

Google Scholar 
Urban, D., O’Neill, R. & Shugart, H. Landscape ecology. BioScience 37, 119–127 (1987).Article 

Google Scholar 
Underwood, A. J. et al. Experiments in Ecology: their Logical Design and Interpretation using Analysis of Variance (Cambridge Univ. Press, 1997).Quinn, G. P., & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002).Neyman, J. & Pearson, E. S. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. A 231, 289–337 (1933).Article 
ADS 
MATH 

Google Scholar 
Zar, J. H. Biostatistical Analysis 5th edn (Prentice-Hall/Pearson, 2010).Ripley, B. D. Journal of the Royal Statistical Society Series B (Methodological) 39, 172-212 (1977).Royle, J. A. & Nichols, J. D. Estimating abundance from repeated presence–absence data or point counts. Ecology 84, 777–790 (2003).Article 

Google Scholar 
Krebs, C. Ecological Methodology 2nd edn (Addison-Wesley, 1999).Warren-Rhodes, K., Dungan, J., Piatek, J. & McKay, C. Ecology and spatial pattern of cyanobacterial community island patches in the Atacama Desert. J. Geophys. Res. 112, G04S15 (2007).Article 

Google Scholar 
Belnap, J., Phillips, S., Witwicki, D. & Miller, M. Visually assessing the level of development and soil surface stability of cyanobacterially dominated biological soil crusts. J. Arid Environ. 72, 1257–1264 (2008).Article 
ADS 

Google Scholar 
Warren-Rhodes, K. et al. Hypolithic cyanobacteria, dry limit of photosynthesis, and microbial ecology in the hyperarid Atacama Desert. Microb. Ecol. 52, 389–398 (2006).Article 

Google Scholar 
Yingst, R. et al. Is a linear or a walkabout protocol more efficient when using a rover to choose biologically relevant samples in a small region of interest? Astrobiology 20, 327–347 (2020).Article 
ADS 

Google Scholar 
Shen, J., Wyness, A., Claire, M. & Zerkle, A. Spatial variability of microbial communities and salt distributions across a latitudinal gradient in the Atacama Desert. Microb. Ecol. 82, 442–458 (2021).Article 

Google Scholar 
Barrett, J. et al. Variation in biogeochemistry and soil biodiversity across spatial scales in a polar desert ecosystem. Ecology 85, 3105–3118 (2004).Article 

Google Scholar 
Pointing, S. B. et al. Highly specialized microbial diversity in hyper-arid polar desert. Proc. Natl Acad. Sci. USA 106, 19964–19969 (2009).Article 
ADS 

Google Scholar 
Chiodini, R. et al. Microbial population differentials between mucosal and submucosal intestinal tissues in advanced Crohn’s disease of the ileum. PloS ONE 10, e0134382 (2015).Article 

Google Scholar 
Rivas, L. A. et al. A 200-antibody microarray biochip for environmental monitoring: searching for universal microbial biomarkers through immunoprofiling. Anal. Chem. 80, 7970–7979 (2008).Article 

Google Scholar 
Sanchez-Garcia, L. et al. Microbial biomarker transition in high-altitude sinter mounds from El Tatio (Chile) through different stages of hydrothermal activity. Front. Microbiol. 9, 3350 (2019).Article 

Google Scholar 
Parro, V. et al. SOLID3, a multiplex antibody microarray-based optical sensor instrument for in situ life detection in planetary exploration. Astrobiology 11, 15–28 (2011).Article 
ADS 

Google Scholar 
Parro, V. et al. A microbial oasis in the hypersaline Atacama subsurface discovered by a life detector chip: implications for the search for life on Mars. Astrobiology 11, 969–996 (2011).Article 
ADS 

Google Scholar 
Blanco, Y., Moreno-Paz, M., Aguirre, J. & Parro, V. in Hydrocarbon and Lipid Microbiology Protocols (eds McGenity, T. J. et al.) Ch. 159 (Springer, 2017).Moreno-Paz, M. et al. Detecting nonvolatile life and nonlife-derived organics in a carbonaceous chrondrite analogue with a new multiplex immunoassay and its relevance for planetary exploration. Astrobiology 18, 1041–1056 (2018).Article 
ADS 

Google Scholar 
Ekwealor, J. & Fisher, K. Life under quartz: hypolithic mosses in the Mojave Desert. PLoS ONE 15, e0235928 (2020).Article 

Google Scholar 
Williams, A., Buck, B. & Beyene, M. Biological soil crusts in the Mojave Desert, USA: micromorphology and pedogenesis. Soil Sci. Soc. Am. 76, 1685–1695 (2012).Article 

Google Scholar 
Archer, S. et al. Endolithic microbial diversity in sandstone and granite from the McMurdo Dry Valleys, Antarctica. Polar Biol. 40, 997–1006 (2017).Article 

Google Scholar 
Noffke, N., Gerdes, G., Klenke, T. & Krumbein, W. Microbially induced sedimentary structures—a new category within the classification of primary sedimentary structures. J. Sediment. Res. 71, 649–656 (2001).Article 
ADS 

Google Scholar 
Fierer, N. & Jackson, R. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).Article 
ADS 

Google Scholar 
Caruso, T. et al. Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale. ISME J. 5, 1406–1413 (2011).Article 

Google Scholar 
Valverde et al. Prokaryotic community structure and metabolisms in shallow subsurface of Atacama Desert playas and alluvial fans after heavy rains: repairing and preparing for next dry period. Front. Microbiol. 10, 1641 (2019).Article 

Google Scholar 
Sun, H. Endolithic microbial life in extreme cold climate: snow is required, but perhaps less is more. Biology 2, 693–701 (2013).Maier, S. et al. Photoautotrophic organisms control microbial abundance, diversity and physiology in different types of biological soil crusts. ISME J. 12, 1032–1046 (2018).Article 

Google Scholar 
Roldan, M., Ascaso, C. & Weirzchos, J. Fluorescent fingerprint of endolithic phototrophic cyanobacteria living within halite rocks in the Atacama Desert. Appl. Environ. Microbiol. 80, 2998–3006 (2014).Article 
ADS 

Google Scholar 
Cockell, C. et al. 0.25 Ga salt deposits preserve geological signatures of habitable conditions and ancient lipids. Astrobiology 20, 864–877 (2019).Article 
ADS 

Google Scholar 
Ripley, B. D. Spatial Statistics (Wiley, 1981).Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (eds) Handbook of Spatial Statistics (CRC Press, 2010).Dixon, P. M. in Encyclopedia of Environmetrics, 1796-1803 (Wiley, 2006).Baddeley, A., Rubak, E. & Turner, R. Spatial point patterns: methodology and applications with R. J. Stat. Softw. 75, 2 (2016).Wood, S. Generalized Additive Models: an Introduction with R Ch 3–5 (Chapman and Hall/CRC, 2006).Simon, R. & Wood, N. GAMS in practice: mgcv. In Generalized Additive Models: an Introduction with R 2nd ed (eds Blitzstein, J., Faraway, J., Tanner, M. & Zidek, J.) Ch 7 (Chapman and Hall/CRC, 2017).Fang, X. & Chan, K.-S. Generalized Additive Models with Spatio-temporal Data (Univ. Iowa); https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr396.pdfLeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).Article 

Google Scholar 
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).Article 

Google Scholar 
Shelhamer, E., Long J. & Darrell, T. Fully convolutional networks for semantic segmentation. Preprint at arXiv https://arxiv.org/abs/1605.06211 (2016).Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Preprint at arXiv https://arxiv.org/abs/1506.02142 (2016).Bishop, J. L. & Murad, E. in Volcano–Ice Interactions on Earth and Mars (eds Smellie, J. L. & Chapman, M. G.) 357–370 (Special Publication No. 202, Geological Society, 2002).Buzgar, N., Buzatu, A. & Sanislav, I. V. The Raman study of certain sulfates. An. Stiintificie Univ. Al. I. Cuza IASI Geol. 55, 5–23 (2009).Jehlicka, J., Edwards, H. & Oren, A. Raman spectroscopy of microbial pigments. Appl. Environ. Microbiol. 80, 3286–3295 (2013). More

Forbes, B. C. & Kumpula, T. The ecological role and geography of reindeer (Rangifer tarandus) in Northern Eurasia. Geogr. Compass 3, 1356–1380 (2009).Article 

Google Scholar 
Post, E. & Pedersen, C. Opposing plant community responses to warming with and without herbivores. Proc. Natl Acad. Sci. USA 105, 12353–12358 (2008).Article 
CAS 

Google Scholar 
Berkes, F., Colding, J. & Folke, C. Navigating Social-Ecological Systems: Building Resilience for Complexity and Change (Cambridge Univ. Press, 2003).Tremblay, R., Landry-Cuerrier, M. & Humphries, M. M. Culture and the social-ecology of local food use by Indigenous communities in northern North America. Ecol. Soc. 25, 8 (2020).Kenny, T.-A., Fillion, M., Simpkin, S., Wesche, S. & Chan, L. Caribou (Rangifer tarandus) and Inuit nutrition security in Canada. Ecohealth 15, 590–607 (2018).Article 

Google Scholar 
Benson, K. Gwich’in Knowledge of Porcupine Caribou: State of Current Knowledge and Gaps Assessment (Department of Cultural Heritage, Gwich’in Tribal Council, 2019); https://thelastgreatherd.com/wp-content/uploads/2020/06/GTC-current-knowledge-and-gaps-assessment.pdfParlee, B. & Caine, K. When the Caribou Do Not Come: Indigenous Knowledge and Adaptive Management in the Western Arctic (UBC Press, 2018).Herds: Status of Herds (CircumArctic Rangifer Monitoring and Assessment Network, accessed 3 November 2021); https://carma.caff.is/herdsFesta-Bianchet, M., Ray, J. C., Boutin, S., Côté, S. D. & Gunn, A. Conservation of caribou (Rangifer tarandus) in Canada: an uncertain future. Can. J. Zool. 89, 419–434 (2011).Article 

Google Scholar 
Gunn, A. Voles, lemmings and caribou: population cycles revisited? Rangifer 23, 105–111 (2003).Article 

Google Scholar 
Ferguson, M. A. D., Williamson, R. G. & Messier, F. Inuit knowledge of long-term changes in a population of Arctic tundra caribou. Arctic 51, 201–219 (1998).Article 

Google Scholar 
Beaulieu, D. Dene traditional knowledge about caribou cycles in the Northwest Territories. Rangifer 32, 59–67 (2012).Article 

Google Scholar 
Mallory, C. D. & Boyce, M. S. Observed and predicted effects of climate change on Arctic caribou and reindeer. Environ. Rev. 26, 13–25 (2018).Article 

Google Scholar 
Uboni, A. et al. Long-term trends and role of climate in the population dynamics of Eurasian reindeer. PLoS ONE 11, e0158359 (2016).Article 

Google Scholar 
Chapin, F. S. III et al. Directional changes in ecological communities and social-ecological systems: a framework for prediction based on Alaskan examples. Am. Nat. 168, S36–S49 (2006).Article 

Google Scholar 
Tengö, M. et al. Weaving knowledge systems in IPBES, CBD and beyond – lessons learned for sustainability. Curr. Opin. Environ. Sustain. 26, 17–25 (2017).Article 

Google Scholar 
Berkes, F. Sacred Ecology 4th edn (Routledge, 2018).Stuart Chapin, F. III et al. Earth stewardship: science for action to sustain the human-earth system. Ecosphere 2, 89 (2011).Parlee, B. L., Sandlos, J. & Natcher, D. C. Undermining subsistence: barren-ground caribou in a ‘tragedy of open access’. Sci. Adv. 4, e1701611 (2018).Article 

Google Scholar 
Johnson, J. T. et al. Weaving Indigenous and sustainability sciences to diversify our methods. Sustain. Sci. 11, 1–11 (2016).Article 
CAS 

Google Scholar 
Reid, A. J. et al. ‘Two-eyed seeing’: an Indigenous framework to transform fisheries research and management. Fish Fish. 22, 243–261 (2021).Article 

Google Scholar 
Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. AMBIO 43, 579–591 (2014).Article 

Google Scholar 
Aminpour, P. et al. The diversity bonus in pooling local knowledge about complex problems. Proc. Natl Acad. Sci. USA 118, e2016887118 (2021).Article 
CAS 

Google Scholar 
Henri, D. A. et al. Weaving Indigenous knowledge systems and Western sciences in terrestrial research, monitoring and management in Canada: a protocol for a systematic map. Ecol. Solut. Evid. 2, e12057 (2021).Article 

Google Scholar 
Ljubicic, G. J., Mearns, R., Okpakok, S. & Robertson, S. Nunami iliharniq (learning from the land): reflecting on relational accountability in land-based learning and cross-cultural research in Uqšuqtuuq (Gjoa Haven, Nunavut). Arct. Sci. 8, 252–291 (2022).Article 

Google Scholar 
Stern, E. R. & Humphries, M. M. Interweaving local, expert, and Indigenous knowledge into quantitative wildlife analyses: a systematic review. Biol. Conserv. 266, 109444 (2022).Article 

Google Scholar 
Bourgeon, L., Burke, A. & Higham, T. Earliest human presence in North America dated to the last glacial maximum: new radiocarbon dates from Bluefish Caves, Canada. PLoS ONE 12, e0169486 (2017).Article 

Google Scholar 
Kuhnlein, H. V., McDonald, M., Spigelski, D., Vittrekwa, E. & Erasmus, B. in Indigenous Peoples’ Food Systems: the Many Dimensions of Culture, Diversity and Environment for Nutrition and Health (eds Kuhnlein, H. V. et al.) Ch. 3 (FAO, Centre for Indigenous Peoples’ Nutrition and Environment, 2009).Porcupine Caribou Technical Committee. Porcupine Caribou Annual Summary Report 2018–2019 (Porcupine Caribou Management Board, Whitehorse, Yukon, 2019); https://pcmb.ca/wp-content/uploads/2020/06/PCH_annual_summ_report_Nov29_2019_FINAL.pdfIPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Zhang, X. et al. in Canada’s Changing Climate Report (eds Bush, E. & Lemmen, D. S.) Ch. 4 (Government of Canada, 2019).Griffith, B. et al. in Arctic Refuge Coastal Plain Terrestrial Wildlife Research Summaries Biological Science Report USGS/BRD BSR-2002-0001 (eds Douglas, D. C. et al.) 8–37 (US Geological Survey, 2002).Russell, D. & Gunn, A. Vulnerability Analysis of the Porcupine Caribou Herd to Potential Development of the 1002 lands in the Arctic National Wildlife Refuge, Alaska (Environment Yukon, Canadian Wildlife Service and GNWT Department of Environment and Natural Resources, 2019); https://pcmb.ca/wp-content/uploads/2021/10/Russell-and-Gunn-PCH-vulnerability-analysis-2019.pdfKruse, J. A. et al. Modeling sustainability of Arctic communities: an interdisciplinary collaboration of researchers and local knowledge holders. Ecosystems 7, 815–828 (2004).Berman, M., Nicolson, C., Fofinas, G., Tetlichi, J. & Martin, S. Adaptation and sustainability in a small Arctic community: results of an agent-based simulation model. Arctic 57, 401–414 (2004).Article 

Google Scholar 
Kofinas, G., Aklavik, Arctic Village, Old Crow & Fort McPherson. in The Earth is Faster Now: Indigenous Observations of Arctic Environmental Change (eds Krupnik, I. & Jolly, D.) 55–91 (Arctic Research Consortium of the United States, 2002).Eamer, J. in Bridging Scales and Knowledge Systems: Concepts and Applications in Ecosystem Assessment (eds Reid, W. V. et al.) 185–206 (Island Press, 2006).Shipley, B. Cause and Correlation in Biology: a User’s Guide to Path Analysis, Structural Equations and Causal Inference with R 2nd edn (Cambridge Univ. Press, 2016).Parlee, B. & Furgal, C. Well-being and environmental change in the Arctic: a synthesis of selected research from Canada’s International Polar Year program. Clim. Change 115, 13–34 (2012).Article 

Google Scholar 
Kofinas, G. P. The Costs of Power Sharing: Community Involvment in Canadian Porcuine Caribou Co-management. PhD thesis, Univ. of British Columbia (1998).Ford, J. D. et al. Including indigenous knowledge and experience in IPCC assessment reports. Nat. Clim. Change 6, 349–353 (2016).Article 

Google Scholar 
Brinkman, T. J. et al. Arctic communities perceive climate impacts on access as a critical challenge to availability of subsistence resources. Clim. Change 139, 413–427 (2016).Article 

Google Scholar 
McNeil, P., Russell, D. E., Griffith, B., Gunn, A. & Kofinas, G. Where the wild things are: seasonal variation in caribou distribution in relation to climate change. Rangifer 25, 51–63 (2005).Berman, M. & Kofinas, G. Hunting for models: grounded and rational choice approaches to analyzing climate effects on subsistence hunting in an Arctic community. Ecol. Econ. 49, 31–46 (2004).Article 

Google Scholar 
Hansen, B. B. et al. Climate events synchronize the dynamics of a resident vertebrate community in the High Arctic. Science 339, 313–315 (2013).Article 
CAS 

Google Scholar 
Collings, P., Marten, M. G., Pearce, T. & Young, A. G. Country food sharing networks, household structure, and implications for understanding food insecurity in Arctic Canada. Ecol. Food Nutr. 55, 30–49 (2016).Article 

Google Scholar 
BurnSilver, S., Magdanz, J., Stotts, R., Berman, M. & Kofinas, G. Are mixed economies persistent or transitional? Evidence using social networks from Arctic Alaska. Am. Anthropol. 118, 121–129 (2016).Article 

Google Scholar 
Baggio, J. A. et al. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion. Proc. Natl Acad. Sci. USA 113, 13708–13713 (2016).Article 
CAS 

Google Scholar 
Gagnon, C. A. et al. Merging Indigenous and scientific knowledge links climate with the growth of a large migratory caribou population. J. Appl. Ecol. 57, 1644–1655 (2020).Article 

Google Scholar 
Houde, N. The six faces of traditional ecological knowledge: challenges and opportunities for Canadian co-management arrangements. Ecol. Soc. 12, 34 (2007).Article 

Google Scholar 
Fancy, S. G., Pank, L. F., Whitten, K. R. & Regelin, W. L. Seasonal movements of caribou in Arctic Alaska as determined by satellite. Can. J. Zool. 67, 644–650 (1989).Article 

Google Scholar 
Porcupine Caribou Technical Committee. Porcupine Caribou Annual Summary Report 2014 (Porcupine Caribou Management Board, Whitehorse, Yukon, 2014); https://pcmb.ca/wp-content/uploads/2021/07/PCH_annual_summ_report_2014_2015_NOV19_FINAL.pdfEastland, W. G. Influence of Weather on Movements and Migrations of Caribou. PhD thesis, Univ. of Alaska (1991).Tyler, N. J. C. Climate, snow, ice, crashes, and declines in populations of reindeer and caribou (Rangifer tarandus L.). Ecol. Monogr. 80, 197–219 (2010).Article 

Google Scholar 
Hansen, B. B., Aanes, R. & Saether, B. E. Feeding-crater selection by high-arctic reindeer facing ice-blocked pastures. Can. J. Zool. 88, 170–177 (2010).Article 

Google Scholar 
Solberg, E. J. et al. Effects of density-dependence and climate on the dynamics of a Svalbard reindeer population. Ecography 24, 441–451 (2001).Article 

Google Scholar 
Hansen, B. B., Aanes, R., Herfindal, I., Kohler, J. & Sæther, B.-E. Climate, icing, and wild arctic reindeer: past relationships and future prospects. Ecology 92, 1917–1923 (2011).Article 

Google Scholar 
Langlois, A. et al. Detection of rain-on-snow (ROS) events and ice layer formation using passive microwave radiometry: a context for Peary caribou habitat in the Canadian Arctic. Remote Sens. Environ. 189, 84–95 (2017).Article 

Google Scholar 
Russell, D. E., Gunn, A. & White, R. G. CircumArctic collaboration to monitor caribou and wild reindeer. Arctic 68, 6–10 (2015).Article 

Google Scholar 
Russell, D. E. et al. CARMA’s MERRA-based caribou range climate database. Rangifer 33, 145–152 (2013).Article 

Google Scholar 
ArcGIS version 10 (Environmental Systems Resource Institute, 2010).Cai, J., Russell, D. & Whitfield, P. Methodology and Algorithms for Constructing CARMA Bio-climate Tables (Simon Fraser Univ., 2011).Stenseth, N. C. & Mysterud, A. Weather packages: finding the right scale and composition of climate in ecology. J. Anim. Ecol. 74, 1195–1198 (2005).Article 

Google Scholar 
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).Bivand, R. S., Pebesma, E. J. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn (Springer, 2013).Bivand, R. S., Keitt, T. & Rowlingson, B. Rgdal: Bindings for the Geospatial Data Abstraction Library. R package version 0.8-16 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgdal/index.htmlBivand, R. S. & Rundel, C. Rgeos: Interface to Geometry Engine – Open Source (GEOS). R package version 0.3-4 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgeos/index.htmlLefcheck, J. S. piecewise SEM: piecewise structural equation modelling in R for ecology, evolution and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

Google Scholar 
Thomas, D. W. et al. Common paths link food abundance and ectoparasite loads to physiological performance and recruitment in nestling blue tits. Funct. Ecol. 21, 947–955 (2007).Article 

Google Scholar 
Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach (Springer, 2002).Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 593, 103–113 (2010).Article 

Google Scholar  More

Goal and scope of the studyThe goal of this study was to assess the planetary footprints of GHG mitigation strategies for the global production of plastics. To calculate planetary footprints, we apply LCA in combination with the planetary boundaries framework as proposed by ref. 22. As GHG mitigation strategies, we consider recycling, bio-based production and production via CCU, and compare their planetary footprints to the planetary footprints of fossil-based plastics. We use a bottom-up model covering >90% of global plastic production for 2030 (and 2050, Supplementary Information, section 3). The bottom-up model builds on the plastic production system from ref. 10 and includes plastic production, the supply chain and the disposal of plastics at the end of life.Functional unitIn LCA, the functional unit quantifies the functions of the investigated product system. In this study, the function of the product system is the production and disposal of >90% of global plastics. To cover >90% of global plastics, we define the functional unit as the yearly global production and disposal of 14 large-volume plastics (summarized in Supplementary Table 5). We estimated the yearly production volumes for 2030 and 2050 based on the production volumes in 2015 and the annual growth rates shown in Supplementary Table 5.Our assessment includes plastic disposal. However, the production and disposal of plastics do not necessarily occur in the same year. For instance, while polyolefins used for plastic packaging have an average lifetime of 6 months, the average lifetime of polyurethane used in construction is 35 years11. Including the lifetime of plastics, and hence, the temporal difference between production and disposal, would lead to an increasing plastic stock. An increasing stock, in turn, represents a carbon sink during the production year that appears to enable the production of net-negative GHG emission plastics based on biomass or CCU. However, the plastic stock is not a permanent carbon sink, which would be required for producing net-negative GHG emission plastics55. To avoid misleading conclusions about net-negative bio- and CCU-based plastics, we assign the planetary footprints from disposal to the year of plastic production. Thereby, we conservatively assess the planetary footprints of plastics.In addition, we address the challenge highlighted in ref. 56 that the increasing demand for plastics renders determining the absolute sustainability of plastics difficult. We meet this challenge by assuming a steady-state production system with a recurring functional unit in the same amount every year. We thereby analyse discrete scenarios with constant consumption levels for plastics. Therefore, our conclusions depend on the accuracy of the demand forecasts and apply only to the production volumes considered.System boundariesWe use cradle-to-grave system boundaries, including plastic production and supply chain, potential recycling and final disposal at the end of life. Assessing the use phase of plastics is not possible because of a lack of data. The versatile properties of plastics result in a wide range of applications that cannot be represented in a single study. Furthermore, it would be necessary to consider not only the emissions of the use phase (probably relatively small) but also the system-wide environmental consequences of using plastics in each application compared to other materials. Thus, a consequential assessment of the plastic use phase is desirable but beyond the scope of this study.The plastics supply chain includes several intermediate chemicals such as monomers, solvents or other reactants. The bottom-up model covers the production of all intermediate chemicals in the foreground system. As a background system, we use aggregated datasets from the LCA database ecoinvent. A list of all intermediate chemicals and all aggregated datasets can be found in Supplementary Information, section 1. In addition, the foreground system of the bottom-up model does not include environmental impacts from infrastructure and transportation because of a lack of data. However, we consider the environmental impacts of infrastructure and transportation from other industrial sectors by aggregated datasets, for example, from electricity generation and biomass cultivation.The bottom-up model includes the best available fossil-based technologies and the following technologies for plastic disposal and virgin production based on biomass and CCU.Plastic waste disposalThe bottom-up model includes three options for plastic waste disposal: landfilling, incineration with energy recovery and recycling. Plastic waste can occur in several forms: as sorted fraction of municipal solid waste, as mixed plastics and residues from sorting, and as residues from mechanical recycling. For all fractions, we include waste incineration with energy recovery and landfilling.Landfilled plastic waste is assumed to degrade by approximately 1% of the contained carbon, which is in line with the ecoinvent database45. Mechanical recycling is only modelled for sorted fractions of packaging waste owing to impurities of mixed and non-packaging wastes. In contrast, chemical recycling can be applied to all plastic fractions. In this study, we model chemical recycling as pyrolysis to refinery feedstock, that is, naphtha. The pyrolysis has yields of 29 to 69% depending on the type of plastic (details in Supplementary Information, section 1). Furthermore, we include options for chemical recycling of plastic waste to monomers, which are still early-stage technologies. To derive the minimal necessary recycling rate in Fig. 5, we apply an optimistic scenario with a 95% yield of chemical recycling processes following common modelling in life-cycle inventories of chemicals (Supplementary Information, section 3)57. All calculations are constrained to maximum recycling rates of 94% as the remaining 6% are assumed to be the minimal landfilling rate until the middle of the century11. The assumption is based on historical trends in end-of-life treatment of plastics.Bio-based productionBio-based GHG mitigation is frequently discussed in the literature and is often associated with competition with the food industry58. To avoid competition with the food industry, the bottom-up model is restricted to lignocellulosic biomass as feedstock, that is, energy crops, forest residues and by-products from other industrial biomass processes (for example, bagasse). In this study, unless mentioned otherwise, we model biomass as energy crops because of their potential for large-scale application (Supplementary Information, section 3). However, we conduct a sensitivity analysis for other lignocellulosic biomass sources to assess the sustainability of bio-based plastics in more detail.For each biomass type, we account for the carbon uptake during the biomass growth phase by giving a credit corresponding to the biomass carbon content. We do not consider land use change emissions as current literature lacks an assessment of land use change effects on other Earth-system processes besides climate change.For biomass processing, we include the following high-maturity processes: gasification to syngas and fermentation to ethanol, and the subsequent conversion to methanol and ethylene (Supplementary Table 1). Methanol and ethylene can be further converted to propylene and aromatics, which all together represent the building blocks for all plastics in this study.CCU-based productionCCU-based plastic production particularly requires CO2 and hydrogen. For CO2 supply, we consider CO2 capture from highly concentrated point sources within the plastics supply chain. Highly concentrated point sources include the conventional fossil-based processes, ammonia production, steam methane reforming, ethylene oxide production, the bio-based processes for ethanol and syngas, and plastic waste incineration. Capturing from processes within the plastics supply chain is limited by the amount of CO2 emitted by these processes and avoids the corresponding emissions. For these processes, we considered the energy demand for compressing the CO2 with 0.4 MJ of electricity59. For waste incineration, we consider a decrease in energy output when capturing CO2. All further CO2 sources are conservatively approximated by direct air capture. For 1 kg CO2 captured via direct air capture, we include an uptake of 1 kg of CO2 equivalent while considering the energy demand of 1.29 MJ electricity and 4.19 MJ heat60.Hydrogen for CCU is produced by water electrolysis, with an overall efficiency of 67%61. Previous studies have already shown that renewable electricity is required for CCU to be environmentally beneficial13. Thus, we conduct a sensitivity analysis for multiple electricity technologies to assess their influence on the sustainability of CCU-based plastics (Supplementary Information).For CCU-based production, we include high-maturity technologies, such as CO2-based methanol and methane, as well as subsequent production of olefins and aromatics (Supplementary Table 1). We do not consider CCS as an additional scenario, as fossil resources and storage capacities are ultimately limited. Therefore, CCS may serve as an interim solution for GHG mitigation but stands in contrast to long-term sustainability as the goal of this study.Pathway definitionWe assess nine pathways for the plastics industry towards sustainability. Pathway 1 is fossil-based plastic production (current recycling rate of 23%) that serves as a reference. We also include two pathways that combine all circular technologies: Pathway 2, which minimizes the climate change impact (climate-optimal), and pathway 3, which minimizes the maximal transgression of the share of SOS of the plastics industry (balanced) (Fig. 2). To assess the impact of switching from fossil to renewable feedstocks, we introduce pathway 4, which is bio-based, and pathway 5, which is CCU-based (Fig. 3). Pathways 4 and 5 include the current recycling rate of 23%. In addition, we introduce three pathways with the maximum recycling rates of 94%: pathway 6, in which the remaining virgin production is based on fossil resources; pathway 7, in which it is based on biomass; and pathway 8, in which it is based on CO2 (Fig. 3). Pathway 9 combines biomass, CCU and recycling, and additionally includes chemical recycling of polymers to monomers to calculate the minimal recycling rate to achieve sustainable plastics (Fig. 5).The planetary boundaries frameworkWe follow the recommendations for absolute environmental sustainability assessment in ref. 29 and choose the planetary boundaries framework for the assessment. The planetary boundaries framework suits the goal of the study best because of its precautionary principles for the definition of environmental thresholds, the SOS. We assess eight of the nine Earth-system processes suggested in ref. 21, namely, climate change, ocean acidification, changes in biosphere integrity, the biogeochemical flow of nitrogen and phosphorus (referred to as N cycle and P cycle), aerosol loading, freshwater use, stratospheric ozone depletion, and land-system change. We do not assess the Earth-system process of novel entities since neither control variables nor the boundary itself is yet adequately defined22. We consider the global boundaries for the Earth-system processes in line with the scope of this study. These global boundaries and the corresponding calculation of planetary footprints are subject to assumptions and thus incorporate uncertainty (Supplementary Information, section 2).For the two subprocesses for climate change (namely, atmospheric CO2 concentration and energy imbalance at the top-of-atmosphere), we only consider the energy imbalance at the top-of-atmosphere quantified by radiative forcing. We focus on radiative forcing, as the control variable is more inclusive and fundamental, and the global limits are stricter than for atmospheric CO2 concentration21. Thereby, we conservatively assess climate change.Biosphere integrity is divided into functional and genetic diversity of species. Preserving functional diversity ensures a stable ecosystem by maintaining all ecosystem services. We assess the functional diversity of species using the method proposed in ref. 18. The method covers the mean species abundance loss caused by the two main stressors, direct land use and GHG emissions, as a proxy for the biodiversity intactness index. Genetic diversity provides the long-term ability of the biosphere to persist under and adapt to gradual changes of the environment21. Genetic diversity is often approximated by the global extinction rate. However, using the global extinction rate does not fully cover variation of genetic composition, resulting in high uncertainties when quantifying genetic diversity18. Thus, we focus on functional diversity.Downscaling of the safe operating spaceAs the plastics industry accounts for only a fraction of all human activities, we assign a share of the SOS to plastics. The plastics industry should operate within its assigned share to be considered environmentally sustainable. To assign a share of SOS to the plastics industry, we apply utilitarian downscaling principles. Utilitarian downscaling principles are tailored to maximize welfare in society29. We approximate welfare by consumption expenditure on plastics as an economic indicator for consumer preferences and human needs62. An extensive discussion on the other downscaling principles and their implications can be found in Supplementary Information.Although the final consumption expenditures on plastics are negligible, the industry consumes plastics to produce other goods and services. Accordingly, plastics are produced mostly in the upstream supply chain to support the final consumption of other goods and services. Thus, consuming other goods and services induces plastic production. To account for this inducement of plastic production, we used the total global plastic production xplastics to represent the global intermediate and final consumption expenditure on plastics. For this purpose, we use the gross output vector x of the product-by-product input–output table of EXIOBASE for the year 2020 (ref. 63). To calculate the share of SOS of the plastics industry, we divide the total global plastic production xplastics by the gross world product. The gross world product equals the total global final consumption expenditure. Analogously, we also consider the end-of-life treatment of plastics to be consistent with the system boundaries of the environmental assessment.We estimate the share of SOS for the plastics industry for 2030 and 2050 based on data for the year 2020. Accordingly, we assume that the market share of the plastics industry and, therefore, its share of SOS do not change in the coming years despite the increasing production volume of plastics. Thereby, we implicitly assume that all industries grow equally economically. Alternatively, economic forecasting models could estimate future market shares of plastics. However, applying economic forecasting models is complex, and the results would still be highly uncertain, especially if industry pursues low-carbon technology pathways. Therefore, estimating future market shares is beyond the scope of this study.Technology choice modelTo calculate the planetary footprint of plastics, we use a bottom-up model of the plastics industry. The model builds on the technology choice model (TCM) that allows for linear optimization of production systems27. The TCM represents the production system based on the following elements: technologies, intermediate flows, elementary flows and final demands. Ref. 27 describes each element in detail.The TCM is based on the established computational structure of LCA64. This structure arranges the data that represent the physical production system in the technology matrix A and the elementary flow matrix B. In the technology matrix A, columns represent technologies, and rows represent intermediate flows. Therefore, the coefficient aij of the A matrix corresponds to an intermediate flow i that is either produced (aij  > 0) or consumed (aij  More

Higham, T. E. et al. Linking ecomechanical models and functional traits to understand phenotypic diversity. Trends Ecol. Evol. 36, 860–873 (2021).Article 
PubMed 

Google Scholar 
Kearney, M. R., Jusup, M., McGeoch, M. A., Kooijman, S. A. & Chown, S. L. Where do functional traits come from? The role of theory and models. Funct. Ecol. 35, 1385–1396 (2021).Article 
CAS 

Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).Article 

Google Scholar 
Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. R. Soc. B 275, 1469–1478 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Rubalcaba, J. G. & Jimeno, B. Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species. Funct. Ecol. 36, 64–72 (2022).Article 
CAS 

Google Scholar 
Anderson, R. O., White, C. R., Chapple, D. G. & Kearney, M. R. A hierarchical approach to understanding physiological associations with climate. Glob. Ecol. Biogeogr. 31, 332–346 (2022).Article 

Google Scholar 
Angilletta, M. J. Jr, Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).Article 

Google Scholar 
Olalla‐Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad‐scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33, 781–793 (2006).Article 

Google Scholar 
Amado, T., Moreno Pinto, M. G. & Olalla‐Tárraga, M. Á. Anuran 3D models reveal the relationship between surface area‐to‐volume ratio and climate. J. Biogeogr. 46, 1429–1437 (2019).
Google Scholar 
Castro, K. M. S. A. et al. Water constraints drive allometric patterns in the body shape of tree frogs. Sci. Rep. 11, 1218 (2021).Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. Testing the thermal melanism hypothesis: a macrophysiological approach. Funct. Ecol. 22, 232–238 (2008).Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 
PubMed 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muñoz, M. M. The Bogert effect, a factor in evolution. Evolution 76, 49–66 (2021).Article 
PubMed 

Google Scholar 
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).Article 
CAS 
PubMed 

Google Scholar 
Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).Article 
PubMed 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Messier, J., McGill, B. J., Enquist, B. J. & Lechowicz, M. J. Trait variation and integration across scales: is the leaf economic spectrum present at local scales? Ecography 40, 685–697 (2017).Article 

Google Scholar 
Ricklefs, R. E. & Schluter, D. (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives (Univ. Chicago Press, 1993).Angilletta, M. J. Jr, Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: fitting pieces of a life-history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).Article 
PubMed 

Google Scholar 
Pincheira-Donoso, D. The balance between predictions and evidence and the search for universal macroecological patterns: taking Bergmann’s rule back to its endothermic origin. Theory Biosci. 129, 247–253 (2010).Article 
PubMed 

Google Scholar 
Slavenko, A. et al. Global patterns of body size evolution in squamate reptiles are not driven by climate. Glob. Ecol. Biogeogr. 28, 471–483 (2019).Article 

Google Scholar 
Stevenson, R. D. Body size and limits to the daily range of body temperature in terrestrial ectotherms. Am. Nat. 125, 102–117 (1985).Article 

Google Scholar 
Rubalcaba, J. G., Gouveia, S. F. & Olalla‐Tárraga, M. A. A mechanistic model to scale up biophysical processes into geographical size gradients in ectotherms. Glob. Ecol. Biogeogr. 28, 793–803 (2019).Article 

Google Scholar 
Rubalcaba, J. G. & Olalla‐Tárraga, M. Á. The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude. J. Anim. Ecol. 89, 1277–1285 (2020).Article 
PubMed 

Google Scholar 
Pincheira-Donoso, D., Hodgson, D. J. & Tregenza, T. The evolution of body size under environmental gradients in ectotherms: why should Bergmann’s rule apply to lizards? BMC Evol. Biol. 8, 68 (2008).Jablonski, D. Biotic interactions and macroevolution: extensions and mismatches across scales and levels. Evolution 62, 715–739 (2008).Article 
PubMed 

Google Scholar 
Kearney, M. R., Porter, W. P. & Huey, R. B. Modelling the joint effects of body size and microclimate on heat budgets and foraging opportunities of ectotherms. Methods Ecol. Evol. 12, 458–467 (2021).Article 

Google Scholar 
Campbell-Staton, S. C., Bare, A., Losos, J. B., Edwards, S. V. & Cheviron, Z. A. Physiological and regulatory underpinnings of geographic variation in reptilian cold tolerance across a latitudinal cline. Mol. Ecol. 27, 2243–2255 (2018).Article 
CAS 
PubMed 

Google Scholar 
Boretto, J. M., Fernández, J. B., Cabezas-Cartes, F., Medina, M. S. & Ibargüengoytía, N. R. in Lizards of Patagonia (eds Morando, M. & Avila, L. J.) 335–371 (Springer, 2020).Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 
PubMed 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 
PubMed 

Google Scholar 
Hoffmann, A. A., Chown, S. L. & Clusella‐Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).Article 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

Google Scholar 
Huey, R. B. & Slatkin, M. Cost and benefits of lizard thermoregulation. Q. Rev. Biol. 51, 363–384 (1976).Article 
CAS 
PubMed 

Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Porter, W. P., Mitchell, J. W., Beckman, W. A. & DeWitt, C. B. Behavioral implications of mechanistic ecology. Oecologia 13, 1–54 (1973).Article 
CAS 
PubMed 

Google Scholar 
Hertz, P. E., Huey, R. B. & Stevenson, R. D. Evaluating temperature regulation by field-active ectotherms: the fallacy of the inappropriate question. Am. Nat. 142, 796–818 (1993).Article 
CAS 
PubMed 

Google Scholar 
Fey, S. B. et al. Opportunities for behavioral rescue under rapid environmental change. Glob. Change Biol. 25, 3110–3120 (2019).Article 

Google Scholar 
Martin, T. L. & Huey, R. B. Why ‘suboptimal’ is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).Article 
PubMed 

Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics 2nd edn (Springer-Verlag, 1998).Mao, J. & Yan, B. Global Monthly Mean Leaf Area Index Climatology, 1981–2015 (ORNL DAAC, 2019).Meiri, S. et al. Are lizards feeling the heat? A tale of ecology and evolution under two temperatures. Glob. Ecol. Biogeogr. 22, 834–845 (2013).Article 

Google Scholar 
Marino, S., Hogue, I. B., Ray, C. J. & Kirschner, D. E. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Renardy, M., Hult, C., Evans, S., Linderman, J. J. & Kirschner, D. E. Global sensitivity analysis of biological multiscale models. Curr. Opin. Biomed. Eng. 11, 109–116 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Carnell, R. lhs: Latin hypercube samples. R package version 1.1.1 (2020).Meiri, S. Traits of lizards of the world: variation around a successful evolutionary design. Glob. Ecol. Biogeogr. 27, 1168–1172 (2018).Article 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 
PubMed 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).Article 
PubMed 

Google Scholar 
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).Article 

Google Scholar 
Ives, A. R. R2s for correlated data: phylogenetic models, LMMs, and GLLMs. Syst. Biol. 68, 234–251 (2019).Article 
PubMed 

Google Scholar 
Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2020).Article 

Google Scholar 
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within‐species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar 
Koenker, R. et al. Package ‘quantreg’ (R-CRAN, 2018); https://cran.r-project.org/web/packages/quantreg/quantreg.pdfGriffith, D. A. & Peres-Neto, P. R. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613 (2006).Article 
PubMed 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
Rubalcaba, J. G. et al. Data: ‘Climate drives global functional trait variation in lizards’. figshare https://doi.org/10.6084/m9.figshare.19949315 (2022). More

Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth.A team of researchers have successfully trained an AI to map biosignatures — any feature which provides evidence of past or present life — in a three-square-kilometre area of Chile’s Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet. The results were reported on 6 March in Nature Astronomy1.Kimberley Warren-Rhodes, a senior research scientist at the SETI Institute in Mountain View, California, and lead author on the paper, has been chasing biosignatures since the early 2000s, when she realized how few tools existed to study the biology of other planets. She wanted to combine her background in statistical ecology with emerging technologies such as AI to help mission scientists, “who are under a lot of pressure to find biosignatures” but tightly constrained in how they do so. Rovers that are controlled remotely from Earth, for example, can travel only limited distances and collect relatively few specimens, placing a premium on sampling locations that are the most likely to yield life. Mission scientists base these predictions in part on Mars analogues on Earth, where scientists scour extreme habitats to determine how and where living organisms thrive.Searching for lifeBeginning in 2016, Warren-Rhodes’ group travelled to the high, parched plateau of the Atacama Desert — a proposed Mars analogue at an elevation of around 3,500 metres in the Chilean Andes — to search for rock-dwelling, photosynthetic organisms called endoliths. To fully characterize the environment, the researchers collected everything from drone footage to geochemical analyses to DNA sequences. Together, this data set mimics the types of information researchers are collecting on Mars with orbital satellites, drones and rovers.Warren-Rhodes’ team fed its data into an AI-based convolutional neural network (CNN) and a machine-learning algorithm that in turn predicted where life was most likely to be found in the Atacama.

Aerial view (left) and ground view from a rover of a biosignature probability map of the same area.Credit: M. Phillips, K. A. Warren-Rhodes & F. Kalaitzis

By targeting their sample collection on the basis of AI feedback, the researchers were able to reduce their search area by up to 97% and increase their likelihood of finding life by up to 88%. “At the end, you could plop us down, and instead of wandering around for a long time, it would take us a minute to find life,” Warren-Rhodes says. Specifically, the team found that endoliths in the Atacama were most often found in a mineral called alabaster — which is porous and retains water — and tended to aggregate in transitional areas between various microhabitats, such as where sand and alabaster crystals abut one another.“I’m very impressed and very happy to see this suite of work,” says Kennda Lynch, an astrobiologist at the Lunar and Planetary Institute in Houston, Texas, who studies biosignatures. “It’s really cool that they can show some success with an AI to help predict where to go and look.”Graham Lau, an astrobiologist at the Blue Marble Space Institute of Science who is based in Boulder, Colorado, worked on another Mars analogue in the Canadian Arctic as a graduate student, to study how biology influences the formation of rare minerals that can serve as biosignatures on other planets. “Ever since I first read Frank Herbert’s Dune as a young child, I was struck by this idea of applying ecology to planets,” he says. But up until the last decade or so, the tools and data weren’t available to address such questions with scientific rigour. “The place where we have almost unlimited data possibilities is through these orbital observations and drone imaging,” he says, “and I do see this paper as being one of many pieces along the pathway to doing these larger analyses.”Deceptively simpleThe new method will need to be verified across multiple ecosystems, Lau and Lynch say, including those with more complex geology and greater biodiversity. The Atacama, Lau notes, is relatively simple in terms of the habitats and the types of life that are likely to be found there. And on Mars, the high level of ultraviolet radiation striking the planet’s surface means that scientists might need to detect clues that hint at life below ground.

NASA’s Perseverance rover collected its first rock sample from an area in Mars’ Jezero Crater.Credit: NASA/JPL-Caltech/ASU/MSSS

Ultimately, Warren-Rhodes says she would like to see a comprehensive database of different Mars analogues that could feed valuable information to mission scientists planning their next sampling run. Her team’s advance, she adds, might appear “deceptively simple” to anyone who grew up watching Star Trek explorers scanning alien worlds with a tricorder. But, it represents an important advance in extraterrestrial research, in which biology has often lagged behind chemistry and geology. Imagine, for instance, virtual-reality headsets that feed mission scientists real-time data as they scan a surface, using a rover’s ‘eyes’ to direct their activities. “To have our team make one of these first steps towards reliably detecting biosignatures using AI is exciting,” she says. “It’s really a momentous time.” More

SettingThis study was conducted in approximately 40-year-old naturally regenerated P. densiflora stands in Wola National Experimental Forest in Gyeongnam province in South Korea (35°12′ N, 128°10′ E; Table 1). The productivity of this forest is low, with a dominant tree height of 10 m at 20 years of age. Over the last 10 years, the mean annual precipitation was 1490 mm, of which one third fell during summer (July–August), and the mean temperature was 13.1 °C. The vegetation growing season generally lasts for approximately 200 days, extending from early April to October. The soil texture is a silt loam originating from sandstone and shale (clay 13.0 ± 0.8%, silt 44.1 ± 1.3%, sand 42.9 ± 1.6%; n = 18). The given texture results in volumetric water contents at 13.4 ± 0.7% (m3 m−3) at permanent wilting point (1500 kPa) and 40.7 ± 1.2% at field capacity (10 kPa)55. The understory is covered with Lespedeza spp., Quercus variabilis, Q. serrata, Smilax china, and Lindera glauca.In 2010, we selected two adjacent P. densiflora stands approximately 100 m apart from each other (180 m and 195 m above sea level, on slopes of 15° and 33°, both stands face south). Following a completely randomized design, we established nine plots (10 × 10 m2 with a 5 m untreated buffer) within each stand, of which three were randomly assigned to annual NPK fertilization, three to PK fertilization, and the rest to a control treatment without fertilization. The fertilizer, composed of urea, fused superphosphate and potassium chloride (N3P4K1) or P4K1 was added manually by deposition on the forest floor for 3 years in April 2011, April 2012, and March 2013. Over these 3 years, the NPK plots received 33.9 g N, 45 g P, and 11.1 g K m−2, while the PK plots received 45 g P and 11.1 g K m−2.Tree and stand measurementsThe standing biomass of trees was estimated using a combination of site-specific allometric equations based on destructive harvesting56 and repeated measurements of the dimensions of all trees in each plot (5–18 trees plot−1). The stem diameter at 1.2 m (D) was measured for all trees in each plot for which D was ≥ 6 cm. Selecting a representative tree in size for each plot within the 4 × 4 m2 center of the plot, we measured the tree height (H) and crown base for the representative trees. Measurements were performed in April and September 2011, September 2012–2014, and October 2021. We observed no effect of fertilization on the relationship between D and H or between D and crown base, so we assumed no effect on the allometric functions for foliage or branch biomass. A dendrometer band (Series 5 Manual Band, Forestry Suppliers Inc., Jackson, MS, USA) was installed on 18 representative trees (one per plot) to monitor radial growth monthly.Three 0.25 m2 circular litter traps were installed 60 cm above the forest floor in each plot in April 2011. Litter was collected at 3-month intervals between June 2011 and March 2015. The litter from each trap was transported to the laboratory and then oven-dried at 65 °C for 48 h. All dried samples were separated into needles, bark, cones, branches, and miscellaneous components, and weighed separately.In September 2014, we estimated the biomass of understory vegetation, separately for woody plants and herbaceous plants. All woody plants  More

Description of the phenological, vegetative and yield traits of the accessions per habitatThe process of data management included the computation of mean squares for the assessed phenological, vegetative and yield traits of the accessions with the sampling habitats considered as the treatment factor. The error mean squares served in the multiple comparison of means reported in Table 1.Table 1 Means of the measured phonological, vegetative and flowering and yield traits of Cucurbita moschata genotypes sampled from seven habitats.Full size tableRegarding the phenological traits, the accessions from the habitat of Zh have the longest period from seeding to first male (102.39 d) and first female (108.14 d) flower appearances, and the longest period from seeding to physiological maturity (153.95 d). For those traits, the accessions from Tiassale and Soubre are not significantly different from those of Zh. And, accessions from Tiassale and Zh have the longest periods from seeding to 50% flowering. On the other hand, accessions from Korho, Ferke, Bondu and Burki develop their first male and female flowers and attain 50% flowering in a very short period. They also reach physiological maturity faster. Accessions from Korho, however, have the longest period from seeding to 50% emergence (6.07 d) and accessions from Bondu have the longest period from first female flower appearance to physiological maturity (53.04 d).
For the vegetative traits, accessions from Tiassale and Soubre have the largest girth size (4.43 cm and 4.63 cm, respectively). Accessions from Tiassale have the longest (24.98 cm) and widest (19.94 cm) leaves, the longest male (16.2 cm) and female (4.03 cm) peduncles and the longest petioles (34.94 cm). The measures for those organs on accessions from Soubre rank second to those of Tiassale. On the other hand, accessions from Korho, Ferke, Bondu and Burki are characterized by smaller girth size, smaller leaves, smaller petioles and smaller peduncles of male and female flowers. But the accessions from Bondu are the tallest (586.91 cm) followed by the accessions from Ferke (489.20 cm). And the accessions from Zh are the shortest (417.38 cm).For the flowering and yield traits, accessions from Tiassale and Soubre show the largest numbers of male (27.33 units and 22.58 units, respectively) and female (5.22 units and 6.05 units, respectively) flowers per plant, largest numbers of fruits per plant (2.78 units and 2.53 units, respectively) and largest measures of all fruit-related traits. Their seeds are very large, but in small numbers. In contrast, accessions from Korho, Ferke, Bondu and Burki have the smallest numbers of male and female flowers per plant, the smallest numbers of fruits per plant and the smallest measures of fruit-related traits. They have large numbers of seeds, but their seeds are smaller, except the seeds of the accessions from Burki. Refer to Table 1 for more detailed information.
Variability of the phenological, vegetative and yield traitsTable 2 shows the spread of the phenological and morphological traits of the assessed accessions of C. moschata. All the evaluated traits showed very wide ranges of distribution of the observations. Some conspicuously wide ranges of traits include number of days to 50% flowering (DTF) that goes from 52 to 152 d, plant height with a minimum of 48 cm and a maximum of 1510 cm, diameter of the fruit that is between 5.8 cm and 35 cm, weight of the fruit that varies between 150 g and 10,930 g and number of seeds per fruit that spreads in the interval from 32 units per fruit to 729 units per fruit. Excluding the number of days to 50% emergence (DTE), all the other assessed traits have remarkably wide ranges of phenotypic expressions (Table 2). All the traits but DTE, DTF, days from first female flower appearance to fruit maturity, fruit length and length of the dry seed, had outliers. The number of outliers ranged from 1 to 67. Except the outliers observed with the width of the dry seed, all the outliers were above 1.5*IQR + Q3 where IQR is the inter-quartile range and Q3 is the third quartile. The presence of outliers is indicative of the richness and large variability of the population of accessions. The outliers are exceptional performances that fall outside the normal distribution of the observations. They are a stock of unusual traits that can be used in a crop improvement program when beneficial. For example, the observed outliers for diameter of the fruit, weight of the fruit or thickness of the pulp can be used in a breeding program for the improvement of fruit yield. Similarly, outliers for beneficial traits related to the seed can be used to improve C. moschata crop for seed yield. Besides, the computed mean squares (data not reported) showed highly significant variations between accessions for the assessed traits. They all yielded p-values less than 0.01, providing additional support to the evidence of large variability among the accessions of C. moschata of Cote d’Ivoire. The computed standard deviation, and median absolute deviation for each trait are additional evidence. We should note that in most cases, the mean squares associated to year (data not reported) were not significant, indicating the relative stability of the assessed traits.Table 2 Minimum (Min), first quartile (Q1), median, third quartile (Q3), maximum (Max), standard deviation (SD), median absolute deviation (MAD) and outliers obtained from the phenological, vegetative and flowering and yield traits of 663 accessions of C. moschata.Full size tableThe components of variance, the quantitative genetic differentiation, the overall mean, and the coefficients of variation are reported in Table 3. The lme4 package37 used in the determination of the components of variance, does not provide p-values in the analysis of mixed or random models. The reported quantities in Table 3 are not accompanied with tests of significance. It is worth mentioning that the respective units of measure of the assessed traits are squared for the variances and the evaluated estimates will be reported without the units of measure. The phenotypic variance ((sigma_{p}^{2})) is partitioned into variance between morphotypes or genotypic variance ((sigma_{g}^{2})), and within morphotypes or residual variance ((sigma_{e}^{2})). For the class of phenological traits, considerable genotypic variances were observed with days to 50% flowering (266.21) and days to first male flower appearance (254.40), compared with their respective residual variances (148.13 and 199.50). Regarding the class of vegetative traits, only the peduncle length of male flowers had a genotypic variance (9.22) greater than its residual variance (8.86). In the class of flowering and yield traits, 8 of the 15 traits assessed showed large genotypic variances in comparison with their respective residual variances. They are number of female flowers per plant ((sigma_{g}^{2}) = 3.02 versus (sigma_{e}^{2}) = 2.36), length of the fruit ((sigma_{g}^{2}) = 53.96 versus (sigma_{e}^{2}) = 48.97), diameter of the fruit ((sigma_{g}^{2}) = 37.17 versus (sigma_{e}^{2}) = 16.76), volume of the fruit ((sigma_{g}^{2}) = 10,713,468 versus (sigma_{e}^{2}) = 3,904,590), weight of the fruit ((sigma_{g}^{2}) = 5,413,819 versus (sigma_{e}^{2}) = 1,420,187), diameter of the cavity enclosing the seed ((sigma_{g}^{2}) = 19.12 versus (sigma_{e}^{2}) = 7.75), thickness of the fruit pulp ((sigma_{g}^{2}) = 1.11 versus (sigma_{e}^{2}) = 0.94) and weight of the fruit pulp ((sigma_{g}^{2}) = 5,979,212 versus (sigma_{e}^{2}) = 1,088,750). For a trait to have a lager genotypic variance than the residual variance is synonymous to a relative ease of improvement of the crop for that trait through a breeding program.Table 3 Components of variances ((sigma_{p}^{2}), (sigma_{g}^{2}), (sigma_{e}^{2}), (sigma_{a}^{2})), quantitative genetic differentiation ((Q_{ST})), overall mean ((mu)), and coefficients of variation (%) ((CV_{p}),(CV_{g}),(CV_{e})), of the measured phenological, vegetative and yield traits of the accessions of C. moschata of Cote d’Ivoire.Full size tableThe coefficient of variation (CV) is another statistic that measures variation. It is actually the dispersion of a trait per unit measure of its mean, which can be used to compare variations of traits with different measurement units or different scales. As a rule-of-thumb, a coefficient of variation greater than 20% is indicative of large variation for the trait. The phenotypic coefficient of variation is considerably high for 25 of the 28 assessed traits. Only the number of days from seeding to physiological maturity, the first and second longest axes of the dry seed show coefficients of variation less than 20%. Traits with very large phenotypic coefficients of variation include the peduncle length of female flowers ((CV_{p}) = 93.98%), weight of the pulp ((CV_{p}) = 92.96%), volume of the fruit ((CV_{p}) = 89.17%), weight of the fruit ((CV_{p}) = 78.30%) and number of female flowers per plant ((CV_{p}) = 65.81%). With respect to the residual coefficients of variation, only the number of days from seeding to 50% emergence and number of days from first female flower appearance to physiological maturity have residual coefficients of variation greater than 20%, among the phenological traits. All the vegetative traits have residual coefficients of variation greater than 20%, and show a near-perfect linear relation (r = 0.98; p  More