More stories

  • in

    Land loss due to human-altered sediment budget in the Mississippi River Delta

    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

  • in

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

    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

  • in

    Climate, caribou and human needs linked by analysis of Indigenous and scientific knowledge

    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

  • in

    Towards circular plastics within planetary boundaries

    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

  • in

    Direct competition and potential displacement involving managed Trogoderma stored product pests

    Finkelman, S., Navarro, S., Rindner, M. & Dias, R. Effect of low pressure on the survival of Trogoderma granarium Everts, Lasioderma serricorne (F.) and Oryzaephilus surinamensis (L.) at 30°C. J. Stored. Prod. Res. 42, 23–30 (2006).Article 

    Google Scholar 
    Hosseininaveh, V. A., Bandani, A. P., Azmayeshfard, P. S., Hosseinkhani, S. & Kazzazi, M. Digestive proteolytic and amylolytic activities in Trogoderma granarium Everts (Dermestidae: Coleoptera). J. Stored. Prod. Res. 43, 515–522 (2007).Article 
    CAS 

    Google Scholar 
    Burges, H. D. Development of the khapra beetle, Trogoderma granarium, in the lower part of its temperature range. J. Stored. Prod. Res. 44, 32–35 (2008).Article 

    Google Scholar 
    Hagstrum D. W & Subramanyam, B. Stored-Product Insect Resource (AACC International, 2009).Beal, R. S. Synopsis of the economic species of Trogoderma occurring in the United States with description of a new species (Coleoptera: Dermestidae). Ann. Entomol. Soc. Am. 49, 559–566 (1956).Article 

    Google Scholar 
    Kerr, J. A. Khapra beetle returns. Pest Control 49(12), 24–25 (1984).
    Google Scholar 
    Sinha, R. N. & Utida, S. Climatic areas potentially vulnerable to stored product insects in Japan. Appl. Entomol. Zool. 2, 124–132 (1967).Article 

    Google Scholar 
    Banks, H. J. Distribution and establishment of Trogoderma granarium Everts (Coleoptera: Dermestidae): Climatic and other influences. J. Stored. Prod. Res. 13, 183–202 (1977).Article 

    Google Scholar 
    Kavallieratos, N. G., Athanassiou, C. G., Guedes, R. N. C., Drempela, J. D. & Boukouvala, M. C. Invader competition with local competitors: Displacement or coexistence among the invasive khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae), and two other major stored-grain beetles?. Front. Plant. Sci. 8, 1837 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lampiri, E., Baliota, G. V., Morrison, W. M., Domingue, M. J. & Athanassiou, C. Comparative population growth of the khapra beetle (Coleoptera: Dermestidae) and the warehouse beetle (Coleoptera: Dermestidae) on wheat and rice. J. Econ. Entomol. 115, 344–352 (2021).Article 

    Google Scholar 
    Athanassiou, C. G., Phillips, T. W. & Wakil, W. Biology and control of the khapra beetle, Trogoderma granarium, a major quarantine threat to global food security. Ann. Rev. Entomol. 64, 131–148 (2019).Article 
    CAS 

    Google Scholar 
    Stibick, J. New pest response guidelines: khapra beetle. APHIS– PPQ–Emergency and Domestic Programs. (U.S Department of Agriculture, 2009).Myers, S. W. & Hagstrum, D. W. Quarantine, In Stored stored product protection, (ed. Hagstrum D.W. Phillips T.W. & Cuperus G.) 297–304 (Kansas State University, 2012).Day, C. & White, B. Khapra beetle, Trogoderma granarium interceptions and eradications in Australia and around the world. In SARE working papers 1609. (Crawley: School of Agricul. Res. Econ. 2016).Burges, H. D. Diapause, pest status and control of the Khapra beetle. Trogoderma Granar. Everts Ann. Appl. Biol. 50, 614–617 (1962).Article 

    Google Scholar 
    Nair, K. & Desai, A. The termination of diapause in Trogoderma granarium Everts (Coleoptera, Dermestidae). J. Stored. Prod. Res. 8, 275–290 (1973).Article 

    Google Scholar 
    Burges, H. D. Studies on the Dermestid beetle Trogoderma granarium Everts—IV. Feeding, growth, and respiration with particular reference to diapause larvae. J. Insect. Physiol. 5, 317–334 (1960).Article 
    CAS 

    Google Scholar 
    Wilches, D., Laird, R. A., Floate, K. & Fields, P. G. A review of diapause and tolerance to extreme temperatures in dermestids (Coleoptera). J. Stored Prod. Res. 68, 50–62 (2016).Article 

    Google Scholar 
    Vick, K. W., Drummond, P. C. & Coffelt, J. A. Trogoderma inclusum and T. glabrum: Effects of time of day on production of female pheromone, male responsiveness and mating. Ann. Entomol. Soc. Am. 66, 1001–1004 (1973).Article 

    Google Scholar 
    Partida, G. J. & Strong, R. G. Distribution and relative abundance of Trogoderma spp. in relation to climate zones of California. J. Econ. Entomol. 63, 1553–1560 (1970).Article 

    Google Scholar 
    Hagstrum, D. W. Seasonal variation of stored wheat environment and insect populations. J. Econ. Entomol. 16, 77–83 (1987).
    Google Scholar 
    Mullen, M. A. & Arbogast, R. T. Insect succession in a stored-corn ecosystem in southeast Georgia. J. Econ. Entomol. 81, 899–912 (1988).
    Google Scholar 
    Partida, G. J. & Strong, R. G. Comparative studies on the biologies of six species of Trogoderma: T. inclusum. Ann. Entomol. Soc. Am. 68, 91–103 (1975).Article 

    Google Scholar 
    Beal, R. S. Biology and taxonomy of the nearctic species of Trogoderma. Univ. Calif. Misc. Publ. Entomol. 10, 35–102 (1954).
    Google Scholar 
    Castañé, C., Agustí, N., del Estal, P. & Riudavets, J. Survey of Trogoderma spp in Spanish mills and warehouses. J. Stored. Prod. Res. 88, 1061 (2020).Article 

    Google Scholar 
    Levinson, H. Z. & Mori, K. The pheromone activity of chiral isomers of trogodermal for male khapra beetles. Naturwissenschaften 67, 148–149 (1980).Article 
    CAS 

    Google Scholar 
    Silverstein, R. M. et al. Perception by Trogoderma species of chirality and methyl branching at a site far removed from a functional group in a pheromone component. J. Chem. Ecol. 6, 911–917 (1980).Article 
    CAS 

    Google Scholar 
    Vick, K. W. Effects of interspecific matings of Trogoderma glabrum and T. inclusum on oviposition and re-mating. Ann. Entomol. Soc. Am. 66, 237–239 (1973).Article 
    MathSciNet 

    Google Scholar 
    Drijfhout, S. et al. Catalogue of abrupt shifts in intergovernmental panel on climate change climate models. Proc. Natl. Acad. Sci. USA 112, E5777–E5786 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, T. W., Pfannenstiel, L. & Hagstrum, D. Survey of Trogoderma species (Coleoptera: Dermestidae) associated with international trade of dried distiller’s grains and solubles in the USA. Julius-Kühn-Archiv 1, 233–238 (2018).
    Google Scholar 
    Hadaway, A. The biology of the beetles, Trogoderma granarium Everts and Trogoderma versicolor (Creutz). Bull. Entomol. Res. 46, 781–796 (1956).Article 
    CAS 

    Google Scholar 
    Gorham, J. R. Insect and Mite Pests in Food: An Illustrated Key. Vols. 1 and 2, (U.S Department of Agriculture, 1991).Furui, S., Miyanoshita, A., Imamura, T., Minegishi, Y. & Kokutani, R. Qualitative real-time PCR identification of the khapra beetle, Trogoderma granarium (Coleoptera: Dermestidae). Appl. Entomol. Zool. 54, 101–107 (2019).Article 
    CAS 

    Google Scholar 
    Olson, R. L., Farris, R. E., Barr, N. B. & Cognato, A. I. Molecular identification of Trogoderma granarium (Coleoptera: Dermestidae) using the 16s gene. J Pest Sci 87, 701–710 (2014).Article 

    Google Scholar 
    Wu, Y. et al. Development of an array of molecular tools for the identification of khapra beetle (Trogoderma granarium), a destructive beetle of stored food products. Sci. Rep. 13, 3327 (2023).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lampiri, E., Athanassiou, C. & Arthur, F. H. Population growth and development of the khapra beetle (Coleoptera: Dermestidae), on different sorghum fractions. J. Econ. Entomol. 114, 424–429 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Athanassiou, C. G., Kavallieratos, N. G. & Boukouvala, M. C. Population growth of the khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae) on different commodities. J. Stored. Prod. Res. 69, 72–77 (2016).Article 

    Google Scholar 
    Karnavar, G. K. Mating behaviour and fecundity in Trogoderma granarium (Coleoptera: Dermestidae). J. Stored. Prod. Res. 8, 65–69 (1972).Article 

    Google Scholar 
    Pray, L. A. & Goodnight, C. J. Genetic variation in inbreeding depression in the red flour beetle Tribolium castaneum. Evolution 49, 176–188 (1995).Article 
    PubMed 

    Google Scholar 
    Barzin, S., Naseri, B., Fathi, S. A. A., Razmjou, J. & Aeinehchi, P. Feeding efficiency and digestive physiology of Trogoderma granarium Everts (Coleoptera: Dermestidae) on different rice cultivars. J. Stored. Prod. Res. 84, 101511 (2019).Article 

    Google Scholar 
    Naseri, B., Aeinehchi, P. & Ashjerdi, A. R. Nutritional responses and digestive enzymatic profile of Trogoderma granarium Everts (Coleoptera: Dermestidae) on 10 commercial rice cultivars. J. Stored. Prod. Res. 87, 101591 (2020).Article 

    Google Scholar 
    Sarwar, M. & Sattar, M. Varietals assessment of different wheat varieties for their resistance response to Khapra beetle Trogoderma granarium. Pak. J. Seed. Technol. 1(10), 1–7 (2007).
    Google Scholar 
    Wilches, D., Laird, R., Floate, K. & Fields, P. Effects of acclimation and diapause on the cold tolerance of Trogoderma granarium. Entomol. Exp. Appl. 165, 169–178 (2017).Article 
    CAS 

    Google Scholar 
    Paini, D. R. & Yemshanov, D. Modelling the arrival of invasive organisms via the international marine shipping network: a Khapra beetle study. PLoS ONE 7(9), e44589 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morrison, W. R., Grosdidier, R. F., Arthur, F. H., Myers, S. W. & Domingue, M. J. Attraction, arrestment, and preference by immature Trogoderma variabile and Trogoderma granarium to food and pheromonal stimuli. J. Pest Sci. 93, 135–147 (2020).Article 

    Google Scholar 
    Arthur, F. H. & Morrison, W. M. Methodology for assessing progeny production and grain damage on commodities treated with insecticides. Agronomy 10(6), 804 (2020).Article 
    CAS 

    Google Scholar  More

  • in

    Strong temporal variation of consumer δ13C value in an oligotrophic reservoir is related to water level fluctuation

    Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

    Google Scholar 
    Fry, B. Stable Isotope Ecology (Springer, 2007).
    Google Scholar 
    Boon, P. I. & Bunn, S. E. Variations in the stable isotope composition of aquatic plants and their implications for food web analysis. Aquat. Bot. 48, 99–108 (1994).Article 

    Google Scholar 
    Kling, G. W., Fry, B. & O’Brien, W. J. Stable isotopes and planktonic trophic structure in arctic lakes. Ecology 73, 561–566 (1992).Article 

    Google Scholar 
    Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

    Google Scholar 
    Coulter, A. A., Swanson, H. K. & Goforth, R. R. Seasonal variation in resource overlap of invasive and native fishes revealed by stable isotopes. Biol. Invasions 21, 315–321 (2019).Article 

    Google Scholar 
    Jung, A. S., Van Der Veer, H. W., Van Der Meer, M. T. & Philippart, C. J. Seasonal variation in the diet of estuarine bivalves. PLoS One 14, e0217003 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Devlin, S. P., Vander Zanden, M. J. & Vadeboncoeur, Y. Depth-specific variation in carbon isotopes demonstrates resource partitioning among the littoral zoobenthos. Freshw. Biol. 58, 2389–2400 (2013).CAS 

    Google Scholar 
    Possamai, B., Vieira, J. P., Grimm, A. M. & Garcia, A. M. Temporal variability (1997–2015) of trophic fish guilds and its relationships with El Niño events in a subtropical estuary. Estuar. Coast. Shelf Sci. 202, 145–154 (2018).Article 
    ADS 

    Google Scholar 
    Syvaranta, J., Hamalainen, H. & Jones, R. I. Within-lake variability in carbon and nitrogen stable isotope signatures. Freshw. Biol. 51, 1090–1102 (2006).Article 
    CAS 

    Google Scholar 
    Janbu, A. D., Paasche, Ø. & Talbot, M. R. Paleoclimate changes inferred from stable isotopes and magnetic properties of organic-rich lake sediments in Arctic Norway. J. Paleolimnol. 46, 29 (2011).Article 
    ADS 

    Google Scholar 
    Leng, M. et al. Late quaternary palaeoenvironmental reconstruction from Lakes Ohrid and Prespa (Macedonia/Albania border) using stable isotopes. Biogeosciences 7, 3109–3122 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Jiang, Q., Shen, J., Liu, X., Zhang, E. & Xiao, X. A high-resolution climatic change since holocene inferred from multi-proxy of lake sediment in westerly area of China. Chin. Sci. Bull. 52, 1970–1979 (2007).Article 

    Google Scholar 
    Finlay, J. C. & Kendall, C. Stable isotope tracing of temporal and spatial variability in organic matter sources to freshwater ecosystems. Stable Isot. Ecol. Environ. Sci. 2, 283–333 (2007).Article 

    Google Scholar 
    Harvey, C. J. & Kitchell, J. F. A stable isotope evaluation of the structure and spatial heterogeneity of a Lake Superior food web. Can. J. Fish. Aquat. Sci. 57, 1395–1403 (2000).Article 
    CAS 

    Google Scholar 
    Xu, D. et al. Spatial heterogeneity of food web structure in a large shallow eutrophic lake (Lake Taihu, China): Implications for eutrophication process and management. J. Freshw. Ecol. 34, 229–245 (2019).Article 
    CAS 

    Google Scholar 
    Ruokonen, T., Kiljunen, M., Karjalainen, J. & Hämäläinen, H. Invasive crayfish increase habitat connectivity: A case study in a large boreal lake. Knowl. Manag. Aquat. Ecosyst. https://doi.org/10.1051/kmae/2013034 (2012).Article 

    Google Scholar 
    Veselý, L. et al. The crayfish distribution, feeding plasticity, seasonal isotopic variation and trophic role across ontogeny and habitat in a canyon-shaped reservoir. Aquat. Ecol. 54, 1169–1183 (2020).Article 

    Google Scholar 
    Kalff, J. Limnology: Inland Water Ecosystems Vol. 592 (Prentice Hall, 2002).
    Google Scholar 
    Polačik, M., Harrod, C., Blažek, R. & Reichard, M. Trophic niche partitioning in communities of African annual fish: Evidence from stable isotopes. Hydrobiologia 721, 99–106 (2014).Article 

    Google Scholar 
    Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. Ontogenetic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. Mar. Ecol. Prog. Ser. 460, 169–181 (2012).Article 
    ADS 

    Google Scholar 
    Matthews, B. & Mazumder, A. Consequences of large temporal variability of zooplankton δ15N for modeling fish trophic position and variation. Limnol. Oceanogr. 50, 1404–1414 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Taipale, S., Kankaala, P., Tiirola, M. & Jones, R. I. Whole-lake dissolved inorganic 13C additions reveal seasonal shifts in zooplankton diet. Ecology 89, 463–474 (2008).Article 
    PubMed 

    Google Scholar 
    Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
    ADS 
    CAS 

    Google Scholar 
    Stenroth, P. et al. Stable isotopes as an indicator of diet in omnivorous crayfish (Pacifastacus leniusculus): The influence of tissue, sample treatment, and season. Can. J. Fish. Aquat. Sci. 63, 821–831 (2006).Article 
    CAS 

    Google Scholar 
    R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).Article 
    PubMed 

    Google Scholar 
    Stock, B. C. & Semmens, B. X. Unifying error structures in commonly used biotracer mixing models. Ecology 97, 2562–2569 (2016).Article 
    PubMed 

    Google Scholar 
    Irz, P., Laurent, A., Messad, S., Pronier, O. & Argillier, C. Influence of site characteristics on fish community patterns in French reservoirs. Ecol. Freshw. Fish 11, 123–136 (2002).Article 

    Google Scholar 
    Sutela, T., Aroviita, J. & Keto, A. Assessing ecological status of regulated lakes with littoral macrophyte, macroinvertebrate and fish assemblages. Ecol. Indic. 24, 185–192 (2013).Article 

    Google Scholar 
    Hunt, P. & Jones, J. The effect of water level fluctuations on a littoral fauna. J. Fish Biol. 4, 385–394 (1972).Article 

    Google Scholar 
    Kaster, J. & Jacobi, G. Benthic macroinvertebrates of a fluctuating reservoir. Freshw. Biol. 8, 283–290 (1978).Article 

    Google Scholar 
    Kraft, K. The effect of unnatural water level fluctuations on benthic invertebrates in Voyageurs National Park. Research⁄Resources Management Report MWR-12. US Department of the Interior, National Park Service. International Falls, Minnesota (1988).Glon, M., Larson, E. R. & Pangle, K. Comparison of 13C and 15N discrimination factors and turnover rates between congeneric crayfish Orconectes rusticus and O. virilis (Decapoda, Cambaridae). Hydrobiologia 768, 51–61 (2016).Article 
    CAS 

    Google Scholar 
    Hesslein, R. H., Hallard, K. & Ramlal, P. Replacement of sulfur, carbon, and nitrogen in tissue of growing broad whitefish (Coregonus nasus) in response to a change in diet traced by δ34S, δ13C, and δ15N. Can. J. Fish. Aquat. Sci. 50, 2071–2076 (1993).Article 
    CAS 

    Google Scholar  More

  • in

    Denser forests across the USA experience more damage from insects and pathogens

    Teale, S. A. & Castello, J. D. The past as key to the future: a new perspective on forest health. In Forest Health: An Integrated Perspective (eds Castello, J. D. & Teale, S. A.) 3–16 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511974977.002.Chapter 

    Google Scholar 
    Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).Article 
    PubMed 

    Google Scholar 
    Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    North, M. P. et al. Operational resilience in western US frequent-fire forests. For. Ecol. Manag. 507, 120004 (2022).Article 

    Google Scholar 
    Raffa, K. F. et al. A literal use of “forest health” safeguards against misuse and misapplication. J. For. 107, 276–277 (2009).
    Google Scholar 
    Kolb, T. E., Wagner, M. R. & Covington, W. W. Concepts of forest health: Utilitarian and ecosystem perspectives. J. For. 92, 10–15 (1994).
    Google Scholar 
    Cale, J. A. et al. A quantitative index of forest structural sustainability. Forests 5, 1618–1634 (2014).Article 

    Google Scholar 
    Lintz, H. E. et al. Quantifying density-independent mortality of temperate tree species. Ecol. Indic. 66, 1–9 (2016).Article 

    Google Scholar 
    Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bettinger, P., Boston, K., Siry, J. P. & Grebner, D. L. Chapter 2—Valuing and Characterizing Forest Conditions. In Forest Management and Planning (eds Bettinger, P. et al.) 21–63 (Academic Press, 2017). https://doi.org/10.1016/B978-0-12-809476-1.00002-3.Chapter 

    Google Scholar 
    Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fettig, C. J. et al. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. For. Ecol. Manag. 238, 24–53 (2007).Article 

    Google Scholar 
    Morin, R. S. & Liebhold, A. M. Invasions by two non-native insects alter regional forest species composition and successional trajectories. For. Ecol. Manag. 341, 67–74 (2015).Article 

    Google Scholar 
    Nowak, J. T., Meeker, J. R., Coyle, D. R., Steiner, C. A. & Brownie, C. Southern pine beetle infestations in relation to forest stand conditions, previous thinning, and prescribed burning: Evaluation of the southern pine beetle prevention program. J. For. 113, 454–462 (2015).
    Google Scholar 
    Asaro, C. & Chamberlin, L. A. Outbreak history (1953–2014) of spring defoliators impacting oak-dominated forests in Virginia, with emphasis on gypsy moth (Lymantria dispar L.) and fall cankerworm (Alsophila pometaria Harris). Am. Entomol. 61, 174–185 (2015).Article 

    Google Scholar 
    Negrón, J. F. Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. For. Ecol. Manag. 107, 71–85 (1998).Article 

    Google Scholar 
    Negrón, J. F. & Popp, J. B. Probability of ponderosa pine infestation by mountain pine beetle in the Colorado Front Range. For. Ecol. Manag. 191, 17–27 (2004).Article 

    Google Scholar 
    Schmid, J. M. & Frye, R. H. Spruce Beetle in the Rockies. Gen. Tech. Rep. RM-49 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1977).
    Google Scholar 
    Krivak-Tetley, F. E. et al. Aggressive tree killer or natural thinning agent? Assessing the impacts of a globally important forest insect. For. Ecol. Manag. 483, 118728 (2021).Article 

    Google Scholar 
    Bradford, J. B. et al. Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance. J. Appl. Ecol. 59, 549–559 (2022).Article 

    Google Scholar 
    Young, D. J. N. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).Article 
    PubMed 

    Google Scholar 
    Furniss, T. J., Das, A. J., van Mantgem, P. J., Stephenson, N. L. & Lutz, J. A. Crowding, climate, and the case for social distancing among trees. Ecol. Appl. 32, e2507 (2022).Article 
    PubMed 

    Google Scholar 
    Woodall, C. W. & Weiskittel, A. R. Relative density of United States forests has shifted to higher levels over last two decades with important implications for future dynamics. Sci. Rep. 11, 1–12 (2021).Article 

    Google Scholar 
    Gandhi, K. J. K., Campbell, F. & Abrams, J. Current status of forest health policy in the United States. Insects 10, 1–14 (2019).Article 

    Google Scholar 
    Ciesla, W. M. The role of human activities on forest insect outbreaks worldwide. Int. For. Rev. 17, 269–281 (2015).
    Google Scholar 
    Jactel, H. & Brockerhoff, E. G. Tree diversity reduces herbivory by forest insects. Ecol. Lett. 10, 835–848 (2007).Article 
    PubMed 

    Google Scholar 
    Marini, L., Ayres, M. P. & Jactel, H. Impact of stand and landscape management on forest pest damage. Annu. Rev. Entomol. 67, 181–199 (2022).Article 
    PubMed 

    Google Scholar 
    Guyot, V., Castagneyrol, B., Vialatte, A., Deconchat, M. & Jactel, H. Tree diversity reduces pest damage in mature forests across Europe. Biol. Lett. 12, 20151037 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kneeshaw, D. D. et al. The vision of managing for pest-resistant landscapes: Realistic or utopic? Curr. For. Rep. 7, 97–113 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Chisholm, P. J., Stevens-Rumann, C. S. & Davis, T. S. Interactions between climate and stand conditions predict pine mortality during a bark beetle outbreak. Forests 12, 360 (2021).Article 

    Google Scholar 
    Ferrell, G. T., Otrosina, W. J. & Demars, C. J. Predicting susceptibility of white fir during a drought-associated outbreak of the fir engraver, Scolytus ventralis in California. Can. J. For. Res. 24, 302–305 (1994).Article 

    Google Scholar 
    Asaro, C., Nowak, J. T. & Elledge, A. Why have southern pine beetle outbreaks declined in the southeastern U.S. with the expansion of intensive pine silviculture? A brief review of hypotheses. For. Ecol. Manag. 391, 338–348 (2017).Article 

    Google Scholar 
    Nowak, J. T., Klepzig, K. D., Coyle, D. R., Carothers, W. A. & Gandhi, K. J. K. Southern pine beetles in central hardwood forests: Frequency, spatial extent, and changes to forest structure. In Managing Forest Ecosystems Volume 32: Natural Disturbances and Historic Range of Variation (eds Greenberg, C. H. & Collins, B. S.) 73–88 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-21527-3_4.Chapter 

    Google Scholar 
    Crocker, S. J., Liknes, G. C., McKee, F. R., Albers, J. S. & Aukema, B. H. Stand-level factors associated with resurging mortality from eastern larch beetle (Dendroctonus simplex LeConte). For. Ecol. Manag. 375, 27–34 (2016).Article 

    Google Scholar 
    Mattson, W. J. & Addy, N. D. Phytophagous insects as regulators of forest primary production. Science 190, 515–522 (1975).Article 
    ADS 

    Google Scholar 
    Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).Article 
    PubMed 

    Google Scholar 
    Grégoire, J. C., Raffa, K. F. & Lindgren, B. S. Economics and politics of bark beetles. In Bark Beetles: Biology and Ecology of Native and Invasive Species (eds Vega, F. E. & Hofstetter, R. W.) 585–613 (Academic Press, 2015). https://doi.org/10.1016/B978-0-12-417156-5.00015-0.Chapter 

    Google Scholar 
    Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manag. 380, 321–334 (2016).Article 

    Google Scholar 
    Fettig, C. J. et al. Changing climates, changing forests: A western North American perspective. J. For. 111, 214–228 (2013).
    Google Scholar 
    Liebhold, A. M. et al. A highly aggregated geographical distribution of forest pest invasions in the USA. Divers. Distrib. 19, 1208–1216 (2013).Article 

    Google Scholar 
    Siegert, N. W., Mccullough, D. G., Liebhold, A. M. & Telewski, F. W. Dendrochronological reconstruction of the epicentre and early spread of emerald ash borer in North America. Divers. Distrib. 20, 847–858 (2014).Article 

    Google Scholar 
    Smith, A., Herms, D. A., Long, R. P. & Gandhi, K. J. K. Community composition and structure had no effect on forest susceptibility to invasion by the emerald ash borer (Coleoptera: Buprestidae). Can. Entomol. 147, 318–328 (2015).Article 

    Google Scholar 
    Aukema, J. E. et al. Historical accumulation of nonindigenous forest pests in the continental United States. Bioscience 60, 886–897 (2010).Article 

    Google Scholar 
    Hicke, J. A. et al. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob. Chang. Biol. 18, 7–34 (2012).Article 
    ADS 

    Google Scholar 
    Feeny, P. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 (1970).Article 

    Google Scholar 
    Schowalter, T. D., Hargrove, W. W. & Crossley, D. A. Herbivory in forested ecosystems. Annu. Rev. Entomol. 31, 177–196 (1986).Article 

    Google Scholar 
    Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).Article 

    Google Scholar 
    Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).Article 

    Google Scholar 
    Guyot, V. et al. Tree diversity limits the impact of an invasive forest pest. PLoS One 10, 1–16 (2015).Article 

    Google Scholar 
    Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: The fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

    Google Scholar 
    Acker, S. A., Boetsch, J. R., Fallon, B. & Denn, M. Stable background tree mortality in mature and old-growth forests in western Washington (NW USA). For. Ecol. Manag. 532, 120817 (2023).Article 

    Google Scholar 
    Shive, K. L. et al. Ancient trees and modern wildfires: Declining resilience to wildfire in the highly fire-adapted giant sequoia. For. Ecol. Manag. 511, 120110 (2022).Article 

    Google Scholar 
    Searle, E. B., Chen, H. Y. H. & Paquette, A. Higher tree diversity is linked to higher tree mortality. Proc. Natl. Acad. Sci. U.S.A. 119, 1–7 (2022).Article 

    Google Scholar 
    Hart, S. J., Veblen, T. T., Eisenhart, K. S., Jarvis, D. & Kulakowski, D. Drought induces spruce beetle (Dendroctonus rufipennis) outbreaks across northwestern Colorado. Ecology 95, 930–939 (2014).Article 
    PubMed 

    Google Scholar 
    Hart, S. J., Veblen, T. T. & Kulakowski, D. Do tree and stand-level attributes determine susceptibility of spruce-fir forests to spruce beetle outbreaks in the early 21st century? For. Ecol. Manag. 318, 44–53 (2014).Article 

    Google Scholar 
    Temperli, C. et al. Are density reduction treatments effective at managing for resistance or resilience to spruce beetle disturbance in the southern Rocky Mountains? For. Ecol. Manag. 334, 53–63 (2014).Article 

    Google Scholar 
    Six, D. L., Biber, E. & Long, E. Management for mountain pine beetle outbreak suppression: Does relevant science support current policy? Forests 5, 103–133 (2014).Article 

    Google Scholar 
    Black, S. H., Kulakowski, D., Noon, B. R. & Dellasala, D. A. Do bark beetle outbreaks increase wildfire risks in the central U.S. rocky mountains? Implications from recent research. Nat. Areas J. 33, 59–65 (2013).Article 

    Google Scholar 
    Oswalt, S. N., Smith, W. B., Miles, P. D. & Pugh, S. A. Forest Resources of the United States, 2017: A Technical Document Supporting the Forest Service 2020 RPA Assessment. Gen. Tech. Rep. WO-97 (US Department of Agriculture, Forest Service, 2019). https://doi.org/10.2737/WO-GTR-97.Book 

    Google Scholar 
    Cleland, D. et al. Terrestrial condition assessment for national forests of the USDA Forest Service in the continental US. Sustainability 9, 1–19 (2017).Article 

    Google Scholar 
    USDA Forest Service Forest Health Protection. Insect and Disease Detection Survey (IDS) data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/detection-surveys.shtml (2021). Accessed on 9 October 2021.Spruce, J. P. et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 115, 427–437 (2011).Article 
    ADS 

    Google Scholar 
    Gomez, D. F., Ritger, H. M. W., Pearce, C., Eickwort, J. & Hulcr, J. Ability of remote sensing systems to detect bark beetle spots in the southeastern US. Forests 11, 1–10 (2020).Article 

    Google Scholar 
    Hanavan, R. P. et al. Supplementing the forest health national aerial survey program with remote sensing during the COVID-19 pandemic: Lessons learned from a collaborative approach. J. For. 120, 125–132 (2021).
    Google Scholar 
    Johnson, E. W. & Wittwer, D. Aerial detection surveys in the United States. Aust. For. 71, 212–215 (2008).Article 

    Google Scholar 
    Bright, B. C. et al. Using satellite imagery to evaluate bark beetle-caused tree mortality reported in aerial surveys in a mixed conifer forest in Northern Idaho, USA. Forests 11, 1–19 (2020).Article 

    Google Scholar 
    Coleman, T. W. et al. Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. For. Ecol. Manag. 430, 321–336 (2018).Article 

    Google Scholar 
    Hicke, J. A., Xu, B., Meddens, A. J. H. & Egan, J. M. Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys. For. Ecol. Manag. 475, 118402 (2020).Article 

    Google Scholar 
    Kosiba, A. M. et al. Spatiotemporal patterns of forest damage and disturbance in the northeastern United States: 2000–2016. For. Ecol. Manag. 430, 94–104 (2018).Article 

    Google Scholar 
    Meigs, G. W., Kennedy, R. E., Gray, A. N. & Gregory, M. J. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region USA. For. Ecol. Manag. 339, 71–86 (2015).Article 

    Google Scholar 
    Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80 (US Department of Agriculture, Forest Service, Southern Research Station, 2005). https://doi.org/10.2737/SRS-GTR-80.Book 

    Google Scholar 
    Randolph, K. D. C. et al. Past and present individual-tree damage assessments of the US national forest inventory. Environ. Monit. Assess. 193, 116 (2021).Article 
    PubMed 

    Google Scholar 
    Kromroy, K. W., Juzwik, J., Castillo, P. & Hansen, M. H. Using forest service forest inventory and analysis data to estimate regional oak decline and oak mortality. North. J. Appl. For. 25, 17–24 (2008).Article 

    Google Scholar 
    Coulston, J. W., Edgar, C. B., Westfall, J. A. & Taylor, M. E. Estimation of forest disturbance from retrospective observations in a broad-scale inventory. Forests 11, 1298 (2020).Article 

    Google Scholar 
    Wilson, B. T., Lister, A. J. & Riemann, R. I. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. For. Ecol. Manag. 271, 182–198 (2012).Article 

    Google Scholar 
    Blackard, J. A. et al. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens. Environ. 112, 1658–1677 (2008).Article 
    ADS 

    Google Scholar 
    Brosofske, K. D., Froese, R. E., Falkowski, M. J. & Banskota, A. A review of methods for mapping and prediction of inventory attributes for operational forest management. For. Sci. 60, 733–756 (2014).Article 

    Google Scholar 
    Lister, A. J. et al. Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory. Forests 11, 1–41 (2020).Article 

    Google Scholar 
    USDA Forest Service Forest Health Protection. Individual Tree Species Parameter (ITSP) maps – GIS data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/indiv-tree-parameter-maps.shtml (2021). Accessed on 9 October 2021.Ellenwood, J. R., Krist, F. J. & Romero, S. A. National Individual Tree Species Atlas. FHTET-15-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2015).
    Google Scholar 
    Krist, F. J. et al. National Insect and Disease Forest Risk Assessment. FHTET-14-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2014).
    Google Scholar 
    Rulequest Inc. Cubist, release 2.07. https://www.rulequest.com/cubist-info.html (2011). Accessed on 15 July 2022.R Core Team. R: A language and environment for statistical computing. https://www.r-project.org (2021). Accessed on 4 March 2022.Esri Inc. ArcGIS Pro 2.8.0. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2021). Accessed on 4 March 2022. More

  • in

    Ecological divergence of syntopic marine bacterial species is shaped by gene content and expression

    Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.Article 
    CAS 
    PubMed 

    Google Scholar 
    Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappe MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 2019;8:e46497.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 2008;320:1081–5.Article 
    CAS 
    PubMed 

    Google Scholar 
    Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.Article 
    CAS 
    PubMed 

    Google Scholar 
    Rivas LR. A reinterpretation of the concepts “sympatric” and “allopatric” with proposal for the additional terms “syntopic” and “allotopic”. Syst Zool. 1964;13:42–3.Article 

    Google Scholar 
    Friedman J, Alm EJ, Shapiro BJ. Sympatric speciation: when is it possible in bacteria? PLoS One. 2013;8:e53539.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kiene RP, Nowinski B, Esson K, Preston C, Marin R III, Birch J, et al. Unprecedented DMSP concentrations in a massive dinoflagellate bloom in Monterey Bay. Ca Geophys Res Lett. 2019;46:12279–88.Article 

    Google Scholar 
    Scholin CA, Birch J, Jensen S, Marin R, Massion E, Pargett D, et al. The quest to develop ecogenomic sensors a 25-year history of the environmental sample processor (ESP) as a case study. Oceanography. 2017;30:100–13.Article 

    Google Scholar 
    Nowinski B, Smith CB, Thomas CM, Esson K, Marin R, Preston CM, et al. Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom. Sci Data. 2019;6:1–7.Article 
    CAS 

    Google Scholar 
    Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2012;14:41–51.Article 
    CAS 
    PubMed 

    Google Scholar 
    Connon SA, Giovannoni SJ. High-throughput methods for culturing microorganisms in very-low-nutrient media yield diverse new marine isolates. Appl Environ Microbiol. 2002;68:3878–85.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Feng X, Chu X, Qian Y, Henson MW, Lanclos VC, Qin F, et al. Mechanisms driving genome reduction of a novel Roseobacter lineage. ISME J. 2021;15:3576–86.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moran MA, Belas R, Schell M, González J, Sun F, Sun S, et al. Ecological genomics of marine roseobacters. Appl Environ Microbiol. 2007;73:4559–69.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.Article 
    CAS 
    PubMed 

    Google Scholar 
    Suzuki MT, Preston CM, Béjà O, De La Torre J, Steward G, DeLong EF. Phylogenetic screening of ribosomal RNA gene-containing clones in bacterial artificial chromosome (BAC) libraries from different depths in Monterey Bay. Micro Ecol. 2004;48:473–88.Article 
    CAS 

    Google Scholar 
    Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Giebel H-A, Kalhoefer D, Lemke A, Thole S, Gahl-Janssen R, Simon M, et al. Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate. ISME J. 2011;5:8–19.Article 
    PubMed 

    Google Scholar 
    Ottesen EA, Marin R, Preston CM, Young CR, Ryan JP, Scholin CA, et al. Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J. 2011;5:1881–95.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang Y, Sun Y, Jiao N, Stepanauskas R, Luo H. Ecological genomics of the uncultivated marine Roseobacter lineage CHAB-I-5. Appl Environ Microbiol. 2016;82:2100–11.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Nat Acad Sci. 2015;112:5443–8.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Nat Acad Sci. 2013;110:E488–E97.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nowinski B, Motard‐Côté J, Landa M, Preston CM, Scholin CA, Birch JM, et al. Microdiversity and temporal dynamics of marine bacterial dimethylsulfoniopropionate genes. Environ Microbiol. 2019;21:1687–701.Article 
    CAS 
    PubMed 

    Google Scholar 
    Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v. 8: overview and updates. Nucleic Acids Res. 2021;49:D723–D33.Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v. 5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D77.Article 
    CAS 
    PubMed 

    Google Scholar 
    Satinsky BM, Gifford SM, Crump BC, Moran MA. Use of internal standards for quantitative metatranscriptome and metagenome analysis. In: DeLong EF, editor. Methods in Enzymology 531: Elsevier; 2013. p. 237–50.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Internal genomic DNA standard for quantitative metagenome analysis V3. protocols io 2017; https://doi.org/10.17504/protocols.io.jxdcpi6p.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Preparation of custom synthesized RNAtranscript standard V3. protocols io. 2017; https://doi.org/10.17504/protocols.io.jxccpiwp.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Prepr. 2016. Report No.: 2167–9843Lee K, Choo Y-J, Giovannoni SJ, Cho J-C. Maritimibacter alkaliphilus gen. nov., sp. nov., a genome-sequenced marine bacterium of the Roseobacter clade in the order Rhodobacterales. Int J Syst Evol Microbiol. 2007;57:1653–8.Article 
    PubMed 

    Google Scholar 
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 2015;3:e1319.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2020;36:2251–2.Article 
    CAS 
    PubMed 

    Google Scholar 
    Bushnell B. BBMap: a fast, accurate, splice-aware aligner. No. LBNL-7065E. Lawrence Berkeley National Laboratory, Berkeley, CA (United States); 2014.Markowitz VM, Chen I-MA, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res. 2010;38:D382–D90.Article 
    CAS 
    PubMed 

    Google Scholar 
    Sun Y, Luo H. Homologous recombination in core genomes facilitates marine bacterial adaptation. Appl Environ Microbiol. 2018;84:e02545–17.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pfaffel O. ClustImpute: An R package for K-means clustering with build-in missing data imputation. https://www.researchgate.net/publication/341881683.Moran MA, Satinsky B, Gifford SM, Luo H, Rivers A, Chan L-K, et al. Sizing up metatranscriptomics. ISME J 2013;7:237–43.Article 
    CAS 
    PubMed 

    Google Scholar 
    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 
    PubMed 

    Google Scholar 
    Gifford SM, Zhao L, Stemple B, DeLong K, Medeiros PM, Seim H, et al. Microbial niche diversification in the Galápagos Archipelago and its response to El Niño. Front Microbiol. 2020;11:575194.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rich VI, Pham VD, Eppley J, Shi Y, DeLong EF. Time‐series analyses of Monterey Bay coastal microbial picoplankton using a ‘genome proxy’microarray. Environ Microbiol. 2011;13:116–34.Article 
    CAS 
    PubMed 

    Google Scholar 
    Riedel T, Tomasch J, Buchholz I, Jacobs J, Kollenberg M, Gerdts G, et al. Constitutive expression of the proteorhodopsin gene by a flavobacterium strain representative of the proteorhodopsin-producing microbial community in the North Sea. Appl Environ Microbiol. 2010;76:3187–97.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, Armbrust EV. Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 2012;335:587–90.Article 
    CAS 
    PubMed 

    Google Scholar 
    Yooseph S, Nealson KH, Rusch DB, McCrow JP, Dupont CL, Kim M, et al. Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature 2010;468:60–6.Article 
    CAS 
    PubMed 

    Google Scholar 
    Wagner-Döbler I, Biebl H. Environmental biology of the marine Roseobacter lineage. Annu Rev Microbiol. 2006;60:255–80.Article 
    PubMed 

    Google Scholar 
    West NJ, Obernosterer I, Zemb O, Lebaron P. Major differences of bacterial diversity and activity inside and outside of a natural iron‐fertilized phytoplankton bloom in the Southern Ocean. Environ Microbiol. 2008;10:738–56.Article 
    CAS 
    PubMed 

    Google Scholar 
    Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J 2017;11:1483–99.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Comm. 2018;9:1–8.Article 

    Google Scholar 
    Caro‐Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.Article 
    PubMed 

    Google Scholar 
    Tindall BJ, Rosselló-Móra R, Busse H-J, Ludwig W, Kämpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol. 2010;60:249–66.Article 
    CAS 
    PubMed 

    Google Scholar 
    Cohan FM. What are bacterial species? Ann Rev Microbiol. 2002;56:457–87.Article 
    CAS 

    Google Scholar 
    Mende DR, Sunagawa S, Zeller G, Bork P. Accurate and universal delineation of prokaryotic species. Nat Meth. 2013;10:881–4.Article 
    CAS 

    Google Scholar 
    Olm MR, Crits-Christoph A, Diamond S, Lavy A, Matheus Carnevali PB, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 2020;5:e00731–19.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Konstantinidis KT, Tiedje JM. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol. 2007;10:504–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Delmont TO, Eren EM. Linking pangenomes and metagenomes: The Prochlorococcus metapangenome. PeerJ 2018;2018:e4320–e.Article 

    Google Scholar 
    Neidhardt F, Umbarger H Chemical composition of Escherichia coli. In: FC N, Curtiss R III, JL I, ECC L, KB L, B M, et al., editors. Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology. Washington DC: ASM Press; 1996. p. 13-6.Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329:533–8.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rodríguez-Gijón A, Nuy JK, Mehrshad M, Buck M, Schulz F, Woyke T, et al. A genomic perspective across Earth’s microbiomes reveals that genome size in Archaea and Bacteria is linked to ecosystem type and trophic strategy. Front Microbiol. 2022;12:761869.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ryu K-S, Kim C, Kim I, Yoo S, Choi B-S, Park C. NMR application probes a novel and ubiquitous family of enzymes that alter monosaccharide configuration. J Biol Chem. 2004;279:25544–8.Article 
    CAS 
    PubMed 

    Google Scholar 
    Giachino A, Waldron KJ. Copper tolerance in bacteria requires the activation of multiple accessory pathways. Mol Microbiol. 2020;114:377–90.Article 
    CAS 
    PubMed 

    Google Scholar 
    Wang X, Zhang Y, Ren M, Xia T, Chu X, Liu C, et al. Cryptic speciation of a pelagic Roseobacter population varying at a few thousand nucleotide sites. ISME J. 2020;14:3106–19.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Uchimiya M, Schroer W, Olofsson M, Edison AS, Moran MA. Diel investments in metabolite production and consumption in a model microbial system. ISME J. 2022;16:1306–17.Article 
    CAS 
    PubMed 

    Google Scholar 
    Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, Hussain F, et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science 2012;337:1228–31.Article 
    CAS 
    PubMed 

    Google Scholar 
    Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 2012;3:e00036–12.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Environmental data from CTD during the Fall 2016 ESP deployment in Monterey Bay, CA. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756376.1.Environmental data from Niskin bottle sampling during the Fall 2016 ESP deployment in Monterey Bay. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756413.1. More