Ecology

Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

Google Scholar 
Esselman, P. C. & Allan, J. D. Application of species distribution models and conservation planning software to the design of a reserve network for the riverine fishes of northeastern Mesoamerica. Freshw. Biol. 56, 71–88 (2011).Article 

Google Scholar 
Valle, M. et al. Comparing the performance of species distribution models of Zostera marina: Implications for conservation. J. Sea Res. 83, 56–64 (2013).ADS 
Article 

Google Scholar 
Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4:421, (2017).Roberts, J. J. et al. Habitat-based cetacean density models for the US Atlantic and Gulf of Mexico. Sci. Rep. 6, 22615 (2016).Lambert, C. et al. How does ocean seasonality drive habitat preferences of highly mobile top predators? Part I: The north-western Mediterranean Sea. Deep Sea. Res. Part II Top. Stud. Oceanogr. 141, 115–132 (2017).ADS 
Article 

Google Scholar 
Lan, K.-W., Shimada, T., Lee, M.-A., Su, N.-J. & Chang, Y. Using remote-sensing environmental and fishery data to map potential Yellowfin Tuna habitats in the tropical pacific Ocean. Remote Sens. 9, 444 (2017).ADS 
Article 

Google Scholar 
Austin, R. A. et al. Predicting habitat suitability for basking sharks (Cetorhinus maximus) in UK waters using ensemble ecological niche modelling. J. Sea Res. 153, 101767 (2019).Article 

Google Scholar 
Hobday, A. J. et al. Impacts of climate change on marine top predators: Advances and future challenges. Deep Sea Res. Part II Top. Stud. Oceanogr. 113, 1–8 (2015).ADS 
Article 

Google Scholar 
Avila, I. C., Kaschner, K. & Dormann, C. F. Current global risks to marine mammals: Taking stock of the threats. Biol. Conserv. 221, 44–58 (2018).Article 

Google Scholar 
Panti, C. et al. Marine litter: One of the major threats for marine mammals. Outcomes from the European Cetacean Society workshop. Environ. Pollut. 247, 72–79 (2019).CAS 
PubMed 
Article 

Google Scholar 
Grémillet, D. et al. Spatial match-mismatch in the Benguela upwelling zone: should we expect chlorophyll and sea-surface temperature to predict marine predator distributions?. J. Appl. Ecol. 45, 610–621 (2008).Article 
CAS 

Google Scholar 
Österblom, H., Olsson, O., Blenckner, T. & Furness, R. W. Junk-food in marine ecosystems. Oikos 117, 967–977 (2008).Article 

Google Scholar 
Hazen, E. L., Nowacek, D. P., Laurent, L. S., Halpin, P. N. & Moretti, D. J. The relationship among oceanography, prey fields, and beaked whale foraging habitat in the tongue of the ocean. PLoS ONE 6, e19269 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Louzao, M. et al. Marine megafauna niche coexistence and hotspot areas in a temperate ecosystem. Cont. Shelf Res. 186, 77–87 (2019).ADS 
Article 

Google Scholar 
Rogan, E. et al. Distribution, abundance and habitat use of deep diving cetaceans in the North-East Atlantic. Deep Sea Res. Part II Top. Stud. Oceanogr. 141, 8–19 (2017).ADS 
Article 

Google Scholar 
Bangley, C. W., Curtis, T. H., Secor, D. H., Latour, R. J. & Ogburn, M. B. Identifying important juvenile dusky shark habitat in the northwest atlantic ocean using acoustic telemetry and spatial modeling. Mar. Coast. Fish. 12, 348–363 (2020).Article 

Google Scholar 
Yen, P. P. W., Sydeman, W. J. & Hyrenbach, K. D. Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: implications for trophic transfer and conservation. J. Mar. Syst. 50, 79–99 (2004).Article 

Google Scholar 
Redfern, J. V et al. Techniques for cetacean–habitat modeling. Mar. Ecol. Prog. Ser. 310, 271-295 (2006).Robison, B. H. Deep pelagic biology. J. Exp. Mar. Bio. Ecol. 300, 253–272 (2004).Article 

Google Scholar 
Reijnders, P. J. H., Aguilar, A. & Borrell, A. Pollution and marine mammals. In Encyclopedia of Marine Mammal (eds Perrin, W. F. et al.) 890–898 (Academic Press, New York, 2009).Chapter 

Google Scholar 
Spitz, J. et al. Prey preferences among the community of deep-diving odontocetes from the Bay of Biscay, Northeast Atlantic. Deep Sea Res. Part I Oceanogr. Res. Pap. 58, 273–282 (2011).ADS 
Article 

Google Scholar 
Cañadas, A. et al. The challenge of habitat modelling for threatened low density species using heterogeneous data: The case of Cuvier’s beaked whales in the Mediterranean. Ecol. Indic. 85, 128–136 (2018).Article 

Google Scholar 
Pirotta, E., Brotons, J. M., Cerdà, M., Bakkers, S. & Rendell, L. E. Multi-scale analysis reveals changing distribution patterns and the influence of social structure on the habitat use of an endangered marine predator, the sperm whale Physeter macrocephalus in the Western Mediterranean Sea. Deep Sea Res. Part I Oceanogr. Res. Pap. 155, 103169 (2020).Article 

Google Scholar 
Watwood, S. L., Miller, P. J. O., Johnson, M., Madsen, P. T. & Tyack, P. L. Deep-diving foraging behaviour of sperm whales (Physeter macrocephalus). J. Anim. Ecol. 75, 814–825 (2006).PubMed 
Article 

Google Scholar 
Warren, V. E. et al. Spatio-temporal variation in click production rates of beaked whales: Implications for passive acoustic density estimation. J. Acoust. Soc. Am. 141, 1962–1974 (2017).ADS 
PubMed 
Article 

Google Scholar 
Shearer, J. M. et al. Diving behaviour of Cuvier’s beaked whales (Ziphius cavirostris) off Cape Hatteras, North Carolina. R. Soc. Open Sci. 6: 181728 (2019).Brodie, S. et al. Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. 5:219 (2018).Becker, E. et al. Moving towards dynamic ocean management: How well do modeled ocean products predict species distributions?. Remote Sens. 8, 149 (2016).ADS 
Article 

Google Scholar 
Wood, S. N. On confidence intervals for generalized additive models based on penalized regression splines. Aust. N. Z. J. Stat. 48, 445–464 (2006).MathSciNet 
MATH 
Article 

Google Scholar 
Virgili, A. et al. Combining multiple visual surveys to model the habitat of deep-diving cetaceans at the basin scale. Glob. Ecol. Biogeogr. 28, 300–314 (2019).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference (Springer, New York, 2004).MATH 
Book 

Google Scholar 
Voss, N. A., Vecchione, M., Toll, R. B. & Sweeney, M. J. Systematics and Biogeography of Cephalopods (Smithsonian Institution Press, New York, 1998).
Google Scholar 
Kostylev, V. E., Erlandsson, J., Ming, M. Y. & Williams, G. A. The relative importance of habitat complexity and surface area in assessing biodiversity: Fractal application on rocky shores. Ecol. Complex. 2, 272–286 (2005).Article 

Google Scholar 
Pingree, R. D. & Cann, B. L. Three anticyclonic slope water oceanic eDDIES (SWODDIES) in the Southern Bay of Biscay in 1990. Deep Sea Res. Part A Oceanogr. Res. Pap. 39, 1147–1175 (1991).ADS 
Article 

Google Scholar 
Koutsikopoulos, C. & Cann, B. L. Physical processes and hydrological structures related to the Bay of Biscay anchovy. Sci. Mar. 60, 9–19 (1996).
Google Scholar 
Bost, C. A. et al. The importance of oceanographic fronts to marine birds and mammals of the southern oceans. J. Mar. Syst. 78, 363–376 (2009).Article 

Google Scholar 
Woodson, C. B. & Litvin, S. Y. Ocean fronts drive marine fishery production and biogeochemical cycling. Proc. Natl. Acad. Sci. 112, 1710–1715 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kiszka, J., Macleod, K., Van Canneyt, O., Walker, D. & Ridoux, V. Distribution, encounter rates, and habitat characteristics of toothed cetaceans in the Bay of Biscay and adjacent waters from platform-of-opportunity Data. ICES J. Mar. Sci. 64, 1033–1043 (2007).Article 

Google Scholar 
Waring, G. T., Hamazaki, T., Sheehan, D., Wood, G. & Baker, S. Characterization of beaked whale (Ziphiidae) and sperm whale (Physeter macrocephalus) summer habitat in shelf-edge and deeper waters off the Northeast U.S.. Mar. Mammal Sci. 17, 703–717 (2001).Article 

Google Scholar 
Moulins, A., Rosso, M., Nani, B. & Würtz, M. Aspects of the distribution of Cuvier’s beaked whale (Ziphius cavirostris) in relation to topographic features in the Pelagos Sanctuary (north-western Mediterranean Sea). J. Mar. Biol. Assoc. U. K. 87, 177–186 (2007).Article 

Google Scholar 
Mussi, B., Miragliuolo, A., Zucchini, A. & Pace, D. S. Occurrence and spatio-temporal distribution of sperm whale (Physeter macrocephalus) in the submarine canyon of Cuma (Tyrrhenian Sea, Italy). Aquat. Conserv. Mar. Freshw. Ecosyst. 24, 59–70 (2014).Article 

Google Scholar 
Moors-Murphy, H. B. Submarine canyons as important habitat for cetaceans, with special reference to the Gully: A review. Deep Res. Part II Top. Stud. Oceanogr. 104, 6–19 (2014).ADS 
Article 

Google Scholar 
Millot, C. & Taupier-Letage, I. Circulation in the Mediterranean Sea. In: Saliot, A. (eds) The Mediterranean Sea. Handbook of Environmental Chemistry, vol 5K. Springer, Berlin, Heidelberg. (2005). https://doi.org/10.1007/b107143Robbins, J. R., Bell, E., Potts, J., Babey, L. & Marley, S. A. Likely year-round presence of beaked whales in the Bay of Biscay. Hydrobiologia https://doi.org/10.1007/s10750-022-04822-y (2022).Article 

Google Scholar 
McSweeney, D. J., Baird, R. W. & Mahaffy, S. D. Site fidelity, associations, and movements of Cuvier’s (Ziphius cavirostris) and Blainville’s (Mesoplodon densirostris) beaked whales off the island of Hawai’i. Mar. Mammal Sci. 23, 666–687 (2007).Article 

Google Scholar 
Wimmer, T. & Whitehead, H. Movements and distribution of northern bottlenose whales, Hyperoodon ampullatus, on the Scotian Slope and in adjacent waters. Can. J. Zool. 82, 1782–1794 (2004).Article 

Google Scholar 
Mannocci, L., Monestiez, P., Spitz, J. & Ridoux, V. Extrapolating cetacean densities beyond surveyed regions: Habitat-based predictions in the circumtropical belt. J. Biogeogr. 42, 1267–1280 (2015).Article 

Google Scholar 
Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
Lambert, C., Mannocci, L., Lehodey, P. & Ridoux, V. Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions. PLoS ONE 9, e105958 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Virgili, A. et al. Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?. PLoS ONE 16, e0255667 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scales, K. L. et al. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol. Appl. 27, 2313–2329 (2017).PubMed 
Article 

Google Scholar 
Amano, M. & Yoshioka, M. Sperm whale diving behavior monitored using a suction-cup-attached TDR tag. Mar. Ecol. Prog. Ser. 258, 291–295 (2003).ADS 
Article 

Google Scholar 
Irvine, L., Palacios, D. M., Urbán, J. & Mate, B. Sperm whale dive behavior characteristics derived from intermediate-duration archival tag data. Ecol. Evol. 7, 7822–7837 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Shearer, J. M. et al. Diving behaviour of Cuvier’s beaked whales (Ziphius cavirostris) off Cape Hatteras, North Carolina. R. Soc. Open Sci. 6, 181728 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Towers, J. R. et al. Movements and dive behaviour of a toothfish-depredating killer and sperm whale. ICES J. Mar. Sci. 76, 298–311 (2019).Article 

Google Scholar 
ESRI. ArcGIS Desktop: Release 10.3. Redlands, CA: Environmental Systems Research Institute (2016).Roberts, J. J., Best, B. D., Dunn, D. C., Treml, E. A. & Halpin, P. N. Marine geospatial ecology tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environ. Model. Softw. 25, 1197–1207 (2010).Article 

Google Scholar 
Buckland, S. T., Rexstad, E. A., Marques, T. A. & Oedekoven, C. S. Distance Sampling: Methods and Applications (Springer International Publishing, New York, 2015).MATH 
Book 

Google Scholar 
Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 
Article 

Google Scholar 
Baird, R. W. et al. Diving behaviour of Cuvier’s (Ziphius cavirostris) and Blainville’s (Mesoplodon densirostris) beaked whales in Hawai‘i. Can. J. Zool. 84, 1120–1128 (2006).Article 

Google Scholar 
Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).ADS 
Article 

Google Scholar 
Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 3. 4–5. https://CRAN.R-project.org/package=raster (2020).Lau-Medrano, W. grec: Gradient-based recognition of spatial patterns in environmental data. R package version 1.4.1. (2020).Foster, S. D. & Bravington, M. V. A Poisson-Gamma model for analysis of ecological non-negative continuous data. Environ. Ecol. Stat. 20, 533–552 (2013).MathSciNet 
Article 

Google Scholar 
Wood, S. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc (B), 73(1), 3-36 (2011).Hedley, S. L. & Buckland, S. T. Spatial models for line transect sampling. J. Agric. Biol. Environ. Stat. 9, 181–199 (2004).Article 

Google Scholar 
Mannocci, L. et al. Predicting cetacean and seabird habitats across a productivity gradient in the South Pacific gyre. Prog. Oceanogr. 120, 383–398 (2014).ADS 
Article 

Google Scholar 
Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a correlation matrix (Version 0.84). https://github.com/taiyun/corrplot (2017).Spiess, A. qpcR: Modelling and analysis of real‐time PCR data. R package version 1.4‐1. https://CRAN.R-project.org/package=qpcR (2018).Fabozzi, F. J., Focardi, S. M., Rachev, S. T. & Arshanapalli, B. G. The basics of financial econometrics: Tools, concepts, and asset management applications. John Wiley & Sons (2014).Becker, E. A. et al. Habitat-based density models for three cetacean species off southern california illustrate pronounced seasonal differences. Front. Mar. Sci. 4:121 (2017).Neill, S. P. & Hashemi, M. R. Fundamentals of ocean renewable energy: Generating electricity from the sea. Academic Press (2018).Becker, E. A. et al. Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees. Ecol. Evol. 10, 5759–5784 (2020).PubMed 
PubMed Central 
Article 

Google Scholar  More

Zhou, J. Z. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110. https://doi.org/10.1038/nclimate1331 (2012).Li, F. L., Liu, M., Li, Z. P., Jiang, C. Y., Han, F. X. & Che, Y. P.Changes in soil microbial biomass and functional diversity with a nitrogen gradient in soil columns. Appl. Soil Ecol. 64, 1–6. https://doi.org/10.1016/j.apsoil.2012.10.006 (2013).Gryta, A., Frąc, M. & Oszust, K. The application of the Biolog EcoPlate approach in ecotoxicological evaluation of dairy sewage sludge. Appl. Biochem. Biotechnol. 174, 1434–1443. https://doi.org/10.1007/s12010-014-1131-8 (2014).Djukic, I., Zehetner, F., Mentler, A. & Gerzabek, M. H. Microbial community composition and activity in different Alpine vegetation zones. Soil Boil Biochem. 42, 155–161. https://doi.org/10.1016/j.soilbio.2009.10.006 (2010)Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature. 436 (7054), 1157–1160. https://doi.org/10.1038/nature03891 (2015).Zhang, X., Zhao, X. & Zhang, M. Functional diversity changes of microbial communities along a soil aquifer for reclaimed water recharge. FEMS Microbiol. Ecol. 80, 9–18. https://doi.org/10.1111/j.1574-6941.2011.01263.x (2012).Hügler, M. & Sievert, S. M. Beyond the Calvin cycle: Autotrophic carbon fixation in the ocean. Annu. Rev. Mar. Sci. 3, 261–289. https://doi.org/10.1146/annurev-marine-120709-142712 (2010)Falkowski, P. et al. The global carbon cycle: A test of our knowledge of earth as a system. Science 290, 291–296. https://doi.org/10.1126/science.290.5490.291 (2000).Tabita, F. R. Molecular and cellular regulation of autotrophic carbon dioxide fixation in microorganisms. Microbiol. Rev. 52, 155–189. https://doi.org/10.1128/mr.52.2.155-189.1988 (1988).Yuan, H., Ge, T., Chen, C., O’Donnell, A. G. & Wu, J. Significant role for microbial autotrophy in the sequestration of soil carbon. Appl. Environ. Microbiol. 78, 2328–2336. https://doi.org/10.1128/AEM.06881-11 (2012).Xu, H. H. & Tabita, F. R. Ribulose-1,5-bisphosphate carboxylase/oxygenase gene expression and diversity of Lake Erie planktonic microorganisms. Appl. Environ. Microbiol. 62, 1913–1921. https://doi.org/10.1128/aem.62.6.1913-1921.1996 (1996).Bräuer, S. L. et al. Dark carbon fixation in the Columbia River’s Estuarine Turbidity Maxima: Molecular characterization of red-type cbbL genes and measurement of DIC uptake rates in response to added electron donors. Estuaries Coast. 36(5), 1073–1083. https://doi.org/10.1007/s12237-013-9603-6 (2013).Hanson, T. E. & Tabita, F. R. A ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO)-like protein from chlorobium tepidum that is involved with sulfur metabolism and the response to oxidative stress. Proc. Natl. Acad. Sci. USA 98, 4397–4402. https://doi.org/10.1073/pnas.081610398 (2001).Selesi, D., Pattis, I., Schmid, M., Kandeler, Ellen. & Hartmann, A. Quantification of bacterial RubisCO genes in soils by cbbL targeted real-time PCR. J. Microbiol. Meth. 69, 497–503. https://doi.org/10.1016/j.mimet.2007.03.002 (2007).Shanmugam, S. G.et al. Bacterial diversity patterns differ in soils developing in sub-tropical and cool-temperate ecosystems. Microb. Ecol. 73, 556–569. https://doi.org/10.1007/s00248-016-0884-8 (2017).Guo, G., Kong, W., Liu, J., Zhao, J. & Du H. Diversity and distribution of autotrophic microbial community along environmental gradients in grassland soils on the Tibetan Plateau. Appl. Microbiol. Biotechnol. 99, 8765–8776. https://doi.org/10.1093/femsec/fiw160 (2015).Bryant, J. A., Lamanna, C., Morlon, H., Kerkhoff, A. J., Enquist, B. J. & Green, J. L. Microbes on mountainsides: Contrasting elevational patterns of bacterial and plant diversity. Proc Natl Acad Sci U S A. 105, 11505–11511. https://doi.org/10.1073/pnas.0801920105 (2008)Shen, C., Ni, Y., Liang, W. & Chu, H. Distinct soil bacterial communities along a small-scale elevational gradient in alpine tundra. Front. Microbiol. 6, 582. https://doi.org/10.3389/fmicb.2015.00582 (2015).Lugo, M. A., Ferrero, M., Menoyo, E., Estévez, M.C., Sieriz, F. & Anton, A. Arbuscular mycorrhizal fungi and rhizospheric bacteria diversity along an altitudinal gradient in South American Puna grassland. Microb. Ecol. 55, 705–713. https://doi.org/10.1007/s00248-007-9313-3 (2008).Singh, D., Takahashi, K., & Adams, J. M. Elevational patterns in archaeal diversity on Mt. Fuji. Plos One. 7, e44494. https://doi.org/10.1371/journal.pone.0044494 (2012)Miyamoto, Y., Nakano, T., Hattori, M. & Nara, K. The mid-domain effect in ectomycorrhizal fungi: Range overlap along an elevation gradient on Mount Fuji Japan. ISME J. 8(8), 1739–1746. https://doi.org/10.1038/ismej.2014.34 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Singh, D., Lee-Cruz, L., Kim, W. S. & Kerfahi D. Strong elevational trends in soil bacterial community composition on Mt. Halla, South Korea. Soil. Boil. Biochem. 68, 140–149. https://doi.org/10.1016/j.soilbio.2013.09.027 (2014).Qiu, J. China: The third pole. Nature 454, 393–396. https://doi.org/10.1038/454393a (2008)Singh, D., Takahashi, K., Kim, M., Chun, J. & Adams, J. M. A hump-backed trend in bacterial diversity with elevation on mount Fuji, Japan. Microb. Ecol. 63, 429–437. https://doi.org/10.1007/s00248-011-9900-1 (2012).Shen, C. et al. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai Mountain. Soil Boil. Biochem. 57, 204–211. https://doi.org/10.1016/j.soilbio.2012.07.013 (2013).Zhang, B., Chen, S. Y., Zhang, J. F. & Tian, C. Depth-related responses of soil microbial communities toexperimental warming in an alpine meadow on the Qinghai-Tibet Plateau. Eur. J. Soil Sci. 66, 496–504. https://doi.org/10.1111/ejss.12240 (2015).Liu, J.et al. High throughput sequencing analysis of biogeographical distribution of bacterial communities in the black soils of northeast China. Soil Boil. Biochem. 70, 113–122. https://doi.org/10.1016/j.soilbio.2013.12.014 (2014)Wu, X. D., Xu, H. Y., Liu, G. M., Ma, X., Mu, C. & Zhao L. Bacterial communities in the upper soil layers in the permafrost regions on the Qinghai-Tibetan Plateau. Appl. Soil Ecol. 120, 81–88. https://doi.org/10.1016/j.apsoil.2017.08.001 (2017).Horner-Devine, M. C., Lage, M., Hughes, J. B. & Bohannan, B. J. M.A taxa-area relationship for bacteria. Nature 432, 750–753. https://doi.org/10.1038/nature03073 (2004).Fuks, D. et al. Relationships between heterotrophic bacteria and cyanobacteria in the northern Adriatic in relation to the mucilage phenomenon. Sci. Total Environ. 353, 178–188. https://doi.org/10.1016/j.scitotenv.2005.09.015 (2005).Dziallas, C. & Grossart, H. P. Microbial interactions with the cyanobacterium Microcystis aeruginosa and their dependence on temperature. Mar Biol. 159, 2389–2398. https://doi.org/10.1007/s00227-012-1927-4 (2012).Shen, H., Niu, Y., Xie, P., Tao, M. & Yang, X. Morphological and physiological changes in Microcystis aeruginosa as a result of interactions with heterotrophic bacteria. Freshw. Biol. 56, 1065–1080. https://doi.org/10.1111/j.1365-2427.2010.02551.x (2011).Xun, L., Sun, M. L., Zhang, H. H., Xu, N. & Sun, G. Y. Use of mulberry-soybean intercropping in salt-alkali soil impacts the diversity of the soil bacterial community. Microb. Biotechnol. 9, 293–304. https://doi.org/10.1111/1751-7915.12342 (2016).Mohamed, H., Miloud, B., Zohra, F., García-Arenzana, J. M. & Rodríguez-Couto, S. Isolation and characterization of actinobacteria from Algerian Sahara soils with antimicrobial activities. Int. J. Mol. Cell Med. 6, 109–120. https://doi.org/10.22088/acadpub.BUMS.6.2.5 (2017).Wang, J. T. et al. Altitudinal distribution patterns of soil bacterial and archaeal communities along Mt. Shegyla on the Tibetan Plateau. Microb. Ecol. 69, 135–145. https://doi.org/10.1007/s00248-014-0465-7 (2015).Zhang, Y. G. et al. Soil bacterial diversity patterns and drivers along an elevational gradient on Shennongjia Mountain, China. Microb. Biotechnol. 8, 739–746. https://doi.org/10.1111/1751-7915.12288 (2015).Li, G., Xu, G., Shen, C., Yong, T., Zhang, Y., Ma, K.Contrasting elevational diversity patterns for soil bacteria between two ecosystems divided by the treeline. Sci. China Life Sci. 59, 1177–1186. https://doi.org/10.1007/s11427-016-0072-6 (2016).Liu, L., Hart M. M., Zhang, J., Cai, X. & Gai, J. Altitudinal distribution patterns of AM fungal assemblages in a Tibeta.n alpine grassland. FEMS Microbiol. Ecol. 91, fiv078. https://doi.org/10.1093/femsec/fiv078 (2015).Xiao, K. Q. et al. Quantitative analyses of ribulose-1, 5-bisphosphate carboxylase/oxygenase (RubisCO) large-subunit genes (cbb L) in typical paddy soils. FEMS Microbiol. Ecol. 87, 89–101. https://doi.org/10.1111/1574-6941.12193 (2014).Sardans, J., Peñuelas, J. & Estiarte, M. Changes in soil enzymes related to C and N cycle and in soil C and N content under prolonged warming and drought in a Mediterranean shrubland. Appl. Soil Ecol. 39, 223–235. https://doi.org/10.1016/j.apsoil.2007.12.011 (2008).Article 

Google Scholar 
Sidari, M., Ronzello, G., Vecchio, G. & Muscolo, A. Influence of slope aspects on soil chemical and biochemical properties in a Pinus Iaricio forest ecosystem of Aspromonte (Southern Italy). Eur. J. Soil Biol. 44, 364–372. https://doi.org/10.1016/j.ejsobi.2008.05.001(2008) (2008).CAS 
Article 

Google Scholar 
La, D., Zhang, Y. J., Pang, Y. Z., Cui, L. L., Liu J. & Suo, N. C.Numerical analysis on plant community and species richness patterns along an altitudinal gradient in the Mila Hill, Tibet. J. Tibet Univ. 12–20 (in Chinese) (2015). More

Assembly and annotation of the chloroplast genomesAssembly resulted in a whole cp genome sequence of C. hirtinoda with a length of 139, 561 bp (Fig. 1), consisting of 83, 166 bp large single-copy region, 20, 811 bp small single-copy regions, and two 21,792 bp IR regions, comprising the typical quadripartite structure of terrestrial plants. The cp genome of C. hirtinoda was annotated with 130 genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes (Table 1). Most of the 15 genes in the C. hirtinoda cp genome contain introns. Of these, 13 genes contain one intron (atpF, ndhA, ndhB, petB, petD, rpl2, rpl16, rps16, trnA-UGC, trnI-GAU, trnK-UUU, trnL-UAA, trnV-UAC) and only the gene cyf3 includes two introns, and the gene clpP intron was deleted (Supplementary Table S1). The rps12 gene contained two copies, and the three exons were spliced into a trans-splicing gene18.Figure 1Chloroplast genome map of C. hirtinoda. Different colors represent different functional genes groups. Genes outside the circle indicate counterclockwise transcription, and genes inside the clockwise transcription. The thick black line on the outer circle represents the two IR regions. The GC content is the dark gray area within the ring.Full size imageTable 1 Summary of the chloroplast genome of C. hirtinoda.Full size tableThe accD, ycf1, and ycf2 genes were missing in the cp genome of C. hirtinoda, and the introns in the genes clpP and rpoC1 were lost. This phenomenon is consistent with previous systematic evolutionary studies on the genome structure of plants in the Poaceae family19. The phenomenon of missing genes is reported in other plants20,21,22,23.The total GC content in the C. hirtinoda cp genome was 38.90%, and the content for each of the four bases, A, T, G, and C, was 30.63%, 30.46%, 19.57%, and 19.33%, respectively (Table 2). The LSC region (36.98%) and SSC region (33.21%) exhibited much lower values than the IR region (44.23%), indicating a non-uniform distribution of the base contents in the cp genome, probably because of four rRNAs in the IR region, which in turn makes the GC content higher in the IR region. These values were similar to cp genome results previously reported for some Poaceae plants24,25.Table 2 Base composition in the C. hirtinoda choloroplast genome.Full size tableRepeat sequences and codon analysisSSR consists of 10-bp-long base repeats and is widely used for exploring phylogenetic evolution and genetic diversity analysis26,27,28,29.In total, 48 SSRs were detected in C. hirtinoda, including 27 mononucleotide versions, accounting for 56.25% of the total SSRs, primarily consisting of A or T. Additionally, four dinucleotide repeats consisting of AT/TA and TC/CT repeats, and 3 tri, 13 tetra, and 1penta-repeats (Fig. 2A). From the SSRs distribution perspective, the majority (79%) of SSRs (38) were observed in the LSC area, whereas 6 SSRs in the IR region (13%) and 4 SSRs in the SSC region (8%) were discovered (Fig. 2B). Previous research suggests that the distribution of SSRs numbers in each region and the differences among locations in GC content are related to the expansion or contraction of the IR boundary30.Figure 2Analysis of simple sequence repeats in C. hirtinoda cp genome. (A) The percentage distribution of 45 SSRs in LSC, SSC, and IR regions. (B).Full size imageThe REPuter program revealed that the cp genome of C. hirtinoda was identified with 61 repeats, consisting of 15 palindromic, 19 forward and no reverse and complement repeats (Fig. 3). We noticed that repeat analyses of three Chimonobambusa genus species exhibited 61–65 repeats, with only one reverse in C. hejiangensis. Most of the repeat lengths were between 30 and 100 bp, and the repeat sequences were located in either IR or LSC region31 (Supplementary Table S2).Figure 3Information of chloroplast genome repeats of Chimonobambusa genus species.Full size imageWe identified 20,180 codons in the coding region of C. hirtinoda (Fig. 4, Supplementary Table S3). The codon AUU of Ile was the most used, and the TER of UAG was the least used codon (817 and 19), excluding the termination codons. Leu was the most encoded amino acid (2,170), and TER was the lowest (85). The Relative Synonymous Codon Usage (RSCU) value greater than 1.0 means a codon is used more frequently32. The RSCU values for 31 codons exceeded 1 in the C. hirtinoda cp genome, and of these, the third most frequent codon was A/U with 29 (93.55%), and the frequency of start codons AUG and UGG used demonstrated no bias (RSCU = 1).Figure 4Amino acid frequencies in C. hirtinoda cp genome protein coding sequences. The column diagrams indicate the number of amino acid codes, and the broken line indicates the proportion of amino acid codes.Full size imageComparative analysis of genome structureThe nucleotide variability (Pi) values of the three cp genomes discovered in the Chimonobambusa genus species ranged from 0 to 0.021 with an average value of 0.000544, as demonstrated from DnaSP 5.10 software analysis. Five peaks were observed in the two single-copy regions, and the highest peak was present in the trnT-trnE-trnY region of the LSC region (Fig. 5). The Pi value for LSC and SSC is significantly higher than that of the IR region. In the IR region, highly different sequences were not observed, a highly conserved region. The sequences of these highly variable regions are reported in other plants during examinations for species identification, phylogenetic analysis, and population genetics research33,34,35.Figure 5Sliding window analysis of Chimonobambusa genus complete chloroplast genome sequences. X-axis: position of the midpoint of a window, Y-axis: nucleotide diversity of each window.Full size imageThe structural information for the complete cp genomes among three Chimonobambusa genus species revealed that the sequences in most regions were conserved (Fig. 6). The LSC and SSC regions exhibit a remarkable degree of variation, higher than the IR region, and the non-coding region demonstrates higher variability than the coding region. In the non-coding areas, 7–9 k, 28–30 k, 36 k and other gene loci differed significantly. Genes rpoC2, rps19, ndhJ and other regions differ in the protein-coding region. However, the agreement between the tRNA and rRNA regions is 100%. A similar phenomenon has also been reported by others36.Figure 6Visualization of genome alignment of three species chloroplast genome sequences using Chimonobambusa hejiangensis as reference. The vertical scale shows the percent of identity, ranging from 50 to 100%. The horizontal axis shows the coordinates within the cp genome. Those are some colors represents protein coding, intron, mRNA and conserved non-coding sequence, respectively.Full size imageIR contraction and expansion in the chloroplast genomeDue to the unique circular structure of the cp genome, there are four junctions between the LSC/IRB/SSC/IRA regions. During species evolution, the stability of the two IR regions sequences was ensured by the IR region of the chloroplast genome expanding and contracting to some degree, and this adjustment is the primary reason for chloroplast genome length variation37,38.The variations at IR/SC boundary regions in the three Chimonobambusa genus chloroplast genomes were highly similar in the organization, gene content, and gene order. The size of IR ranges from 21,797 bp (C. tumidissinoda) to 21,835 bp (C. hejiangensis). The ndhH gene spans the SSC/IRa boundary, and this gene extended 181–224 bp into the IRa region for all three Chimonobambusa genus. The gene rps19 was extended from the IRb to the LSC region with a 31–35 bp gap. The rpl12 gene was located in the LSC region of all genomes, varied from 35–36 bp apart from the LSC/IRb (Fig. 7).Figure 7Comparison of LSC, SSC and IR boundaries of chloroplast genomes among the three Chimonobambusa species. The LSC, SSC and IRs regions are represented with different colors. JLB, JSB, JSA and JLA represent the connecting sites between the corresponding regions of the genome, respectively. Genes are showed by boxes.Full size imageThree chloroplast genomes of the Chimonobambusa genus were compared using the Mauve alignment. The results showed that all sequences show perfect synteny conservation with no inversion or rearrangements (Fig. 8).Figure 8The chloroplast genomes of three Chimonobambusa species rearranged by the software MAUVE. Locally collinear blocks (LCBs) are represented by the same color blocks connected by lines. The vertical line indicates the degree of conservatism among position. The small red bar represents rRNA.Full size imagePhylogenetic analysisWe performed a phylogenetic analysis using the complete chloroplast genomes and matK gene reflecting the phylogenetic position of C. hirtinoda. The maximum likelihood (ML) analysis based on the complete chloroplast genomes indicated seven nodes with entirely branch support (100% bootstrap value). However, the three Chimonobambusa genera exhibited a moderate relationship due to fewer samples used, supporting that C. hirtinoda is closely related to C. tumidissinoda with a 62% bootstrap value more than C. hejiangensis. A phylogenetic tree based on the matK gene revealed that Chimonobambusa species clustered in one branch was consistent with the phylogenetic tree constructed by the complete cp genome tree (Fig. 9). The results show that the whole chloroplast genome identified related species better than the former, consistent with the previous study39.Figure 9Maximum likelihood phylogenetic tree based on the complete chloroplast genomes (A) and matK gene (B).Full size image More

Koski, S. E. Broader horizons for animal personality research. Front. Ecol. Evol. 2, 70 (2014).Article 

Google Scholar 
Careere, C. & Eens, M. Unravelling animal personalities: How and why individuals consistently differ. Behaviour 142, 1149–1157 (2005).Article 

Google Scholar 
Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. Lond. B 271, 847–852 (2004).Article 

Google Scholar 
Bell, A. M. & Sih, A. Exposure to predation generates personality in three-spined sticklebacks (Gasterosteus aculeatus). Ecol. Lett. 10, 828–834 (2007).PubMed 
Article 

Google Scholar 
Cavigelli, S. A. Animal personality and health. Behaviour 142, 1223–1244 (2005).Article 

Google Scholar 
Barber, I. & Dingemanse, N. J. Parasitism and the evolutionary ecology of animal personality. Proc. R. Soc. Lond. B 365, 4077–4088 (2010).
Google Scholar 
Koprivnikar, J., Gibson, C. H. & Redfern, J. C. Infectious personalities: Behavioural syndromes and disease risk in larval amphibians. Proc. R. Soc. Lond. B 279, 1544–1550 (2012).
Google Scholar 
Turner, J. & Hughes, W. O. H. The effect of parasitism on personality in a social insect. Behav. Proc. 157, 532–539 (2018).Article 

Google Scholar 
Frost, A. J., Winrow-Giffen, A., Ashley, P. J. & Sneddon, L. U. Plasticity in animal personality traits: Does prior experience alter the degree of boldness?. Proc. Biol. Sci. 274, 333–339 (2007).PubMed 

Google Scholar 
Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Philos. Trans. R. Soc. B 365, 3947–3958 (2010).Article 

Google Scholar 
Müller, T. & Müller, C. Phenotype of a leaf beetle larva depends on host plant quality and previous test experience. Behav. Proc. 142, 40–45 (2017).Article 

Google Scholar 
Hart, B. L. Biological basis of behaviour in sick animals. Neurosci. Biobehav. Rev. 12, 123–137 (1988).CAS 
PubMed 
Article 

Google Scholar 
Hart, B. L. Behavioral adaptations to pathogens and parasites: Five strategies. Neurosci. Biobehav. Rev. 14, 273–294 (1990).CAS 
PubMed 
Article 

Google Scholar 
Johnson, R. W. The concept of sickness behavior: A brief chronological account of four key discoveries. Vet. Immunol. Immunopathol. 87, 443–450 (2002).CAS 
PubMed 
Article 

Google Scholar 
Klein, S. L. Parasite manipulation of the proximate mechanisms that mediate social behavior in vertebrates. Physiol. Behav. 79, 441–449 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Boyer, N., Reale, D., Marmet, J., Pisanu, B. & Chapuis, L. Personality, space use and tick load in an introduced population of Siberian chipmunks Tanias sibiricus. J. Anim. Ecol. 79, 538–547 (2010).PubMed 
Article 

Google Scholar 
Ezenwa, V. O. Host social behavior and parasitic infection: A multifactorial approach. Behav. Ecol. 15, 446–454 (2004).Article 

Google Scholar 
Finkemeier, M. A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures and relationship to welfare. Front. Vet. Sci. 5, 131 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Huntingford, F. & Adams, C. Behavioural syndromes in farmed fish: Implications for production and welfare. Behaviour 142, 1207–1221 (2005).Article 

Google Scholar 
Berggren, Å., Jansson, A. & Low, M. Approaching ecological sustainability in the emerging insects-as-food industry. Trends Ecol. Evol. 34, 132–138 (2019).PubMed 
Article 

Google Scholar 
Dochtermann, N. A. & Nelson, A. B. Multiple facets of exploratory behavior in house crickets (Acheta domesticus): Split personalities or simply different behaviors?. Ethology 120, 1110–1117 (2014).Article 

Google Scholar 
van Huis, A. & Tomberlin, J. K. Future prospects. In Insects as Food Feed: From Production to Consumption (eds van Huis, A. & Tomberlin, J. K.) 430–445 (Wageningen Academic Publishers, 2017).Szelei, J. et al. Susceptibility of North-American and European crickets to Acheta domesticus densovirus (AdDNV) and associated epizootics. J. Invert. Pathol 106, 394–399 (2011).CAS 
Article 

Google Scholar 
Eilenberg, J., Vlak, J. M., Nielsen-LeRoux, C., Cappellozza, S. & Jensen, A. B. Diseases in insects produced for food and feed. J. Insects Food Feed 1, 87–102 (2015).Article 

Google Scholar 
Raubenheimer, D. & Tucker, D. Associative learning by locusts: Pairing of visual cues with consumption of protein and carbohydrate. Anim. Behav. 54, 1449–1459 (1997).CAS 
PubMed 
Article 

Google Scholar 
Mallory, H. S., Howard, A. F. & Weiss, M. R. Timing of environmental enrichment affects memory in the house cricket, Acheta domesticus. PLoS One 11, e0152245 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: An integrative overview. Q. Rev. Biol. 79, 241–277 (2004).PubMed 
Article 

Google Scholar 
Siva-Jothy, J. A. & Vale, P. F. Viral infection causes sex-specific changes in fruit fly social aggregation behaviour. Biol. Lett. 15, 20190344 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Houte, S., Ros, V. I. D. & Van Oers, M. M. Walking with insects: Molecular mechanisms behind parasitic manipulation of host behaviour. Mol. Ecol. 22, 3458–3475 (2013).PubMed 
Article 

Google Scholar 
Vale, P. F., Siva-Jothy, J. A., Morrill, A. & Forbes, M. R. The influence of parasites. In Insect Behavior: From Mechanisms to Ecological and Evolutionary Consequences (eds Córdoba-Aguilar, A., González-Tokman, D. & González-Santoyo, I) (Oxford University Press, 2018).de Roode, J. C. & Lefèvre, T. Behavioral Immunity in Insects. Insects 3, 789–820 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Kutzer, M. A. M. & Armitage, S. A. O. Maximising fitness in the face of parasites: A review of host tolerance. Zoology 119, 281–289 (2016).PubMed 
Article 

Google Scholar 
Vossen, L. E., Roman, E. & Jansson, A. Fasting increases shelter use in house crickets (Acheta domestica). J. Insects Food Feed 8, 5–8 (2021).Article 

Google Scholar 
Schutgens, M., Cook, B., Gilbert, F. & Behnke, J. M. Behavioural changes in the flour beetle Tribolium confusum infected with the spirurid nematode Protospirura muricola. J. Helminthol. 89, 68–79 (2015).CAS 
PubMed 
Article 

Google Scholar 
Kazlauskas, N., Klappenbach, M., Depino, A. M. & Locatelli, F. F. Sickness behavior in honey bees. Front. Physiol. 7, 261 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Stahlschmidt, Z. R. & Adamo, S. A. Context dependency and generality of fever in insects. Naturwissenschaften 100, 691–696 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, S. Y. S., Tattersall, G. J. & Koprivnikar, J. Trematode parasite infection affects temperature selection in aquatic host snails. Physiol. Biochem. Zool. 92, 71–79 (2019).PubMed 
Article 

Google Scholar 
Berggren, Å., Jansson, A. & Low, M. Using current systems to inform rearing facility design in the insects-as-food industry. J. Insects Food Feed. 4, 167–170 (2018).Article 

Google Scholar 
Marshall, J. A. & Haes, E. C. M. Grasshoppers and Allied Insects of Great Britain and Ireland (Harley Books, Essex) (1988).GBIF Secretariat. Acheta domesticus (Linnaeus, 1758). GBIF Backbone Taxonomy. Checklist dataset. https://doi.org/10.15468/39omei accessed via GBIF.org on 12 Jan 2022 (2021).Holst, K. T. The Saltatoria (Bush-crickets, Crickets and Grasshoppers) of Northern Europe (E J Brill, 1986).Ingrisch, S. & Köhler, G. Die heuschrecken mitteleuropas. (Westarp Wissenschaften, 1998).Booth, D. T. & Kiddell, K. Temperature and the energetics of development in the house cricket (Acheta domesticus). J. Insect Physiol. 53, 950–953 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ghouri, A. S. K. & McFarlane, J. E. Observations on the development of crickets. Can. Entomol. 90, 158–165 (1958).Article 

Google Scholar 
Semberg, E. et al. Diagnostic protocols for the detection of Acheta domesticus densovirus (AdDV) in cricket frass. J. Virol. Methods 264, 61–64 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bergoin, M. & Tijssen, P. Parvoviruses of arthropods. In Encyclopedia of Virology. 76–85 (2008).Cotmore, S. F. et al. The family Parvoviridae. Arch. Virol. 159, 1239–1247 (2014).CAS 
PubMed 
Article 

Google Scholar 
Styer, E. L. & Hamm, J. J. Report of a densovirus in a commercial cricket operation in the southeastern United States. J. Invert. Pathol. 58, 283–285 (1991).Article 

Google Scholar 
Weissman, D. B., Gray, D. A., Pham, H. T. & Tijssen, P. Billions and billions sold: Pet-feeder crickets (Orthoptera: Gryllidae), commercial crickets farms, an epizootic densovirus, and government regulations make for a potential disaster. Zootaxa 3504, 67–88 (2012).Article 

Google Scholar 
Maciel-Vergara, G. & Ros, V. I. D. Viruses of insects reared for food and feed. J. Invert. Pathol. 147, 60–75 (2017).Article 

Google Scholar 
Liu, K. et al. The Acheta domesticus densovirus, isolated from the European house cricket, has evolved an expression strategy unique among parvoviruses. J. Virol. 85, 10069–10078 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, Y. et al. Densovirus crosses the insect midgut by transcytosis and disturbs the epithelial barrier function. J. Virol. 87, 12380–12391 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Miranda, J. R. et al. Virus diversity and loads in crickets reared for feed: Implications for husbandry. Front. Vet. Sci. 8, 642085 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
de Miranda, J. R., Granberg, F., Onorati, P., Jansson, A. & Berggren, Å. Virus prospecting in crickets: discovery and strain divergence of a novel Iflavirus in wild and cultivated Acheta domesticus. Viruses 13, 364 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Niemelä, P., Vainikka, A., Hedrick, A. & Kortet, R. Integrating behaviour with life history: Boldness of the field cricket, Gryllus integer, during ontogeny. Funct. Ecol. 26, 450–456 (2012).Article 

Google Scholar 
Hedrick, A. V. Crickets with extravagant mating songs compensate for predation risk with extra caution. Proc. R. Soc. Lond. B 267, 671–675 (2000).CAS 
Article 

Google Scholar 
Hedrick, A. V. & Kortet, R. Hiding behaviour in two cricket populations that differ in predation pressure. Anim. Behav. 72, 1111–1118 (2006).Article 

Google Scholar 
Kortet, R. & Hedrick, A. V. A behavioural syndrome in the field cricket Gryllus integer: Intrasexual aggression is correlated with activity in a novel environment. Biol. J. Linnean Soc. 91, 475–482 (2007).Article 

Google Scholar 
Fisher, D. N., David, M., Rodríguez-Muñoz, R. & Tregenza, T. Lifespan and age, but not residual reproductive value or condition, are related to behaviour in wild field crickets. Ethology 124, 338–346 (2018).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing. Vienna, Austria (2003).Le Galliard, J. F., Paquet, M., Cisel, M. & Montes-Poloni, L. Personality and the pace-of-life syndrome: Variation and selection on exploration, metabolism and locomotor performances. Funct. Ecol. 27, 136–144 (2013).Article 

Google Scholar 
Roche, D. G., Careau, V. & Binning, S. A. Demystifying animal ‘personality’ (or not): Why individual variation matters to experimental biologists. J. Exp. Biol. 219, 3832–3843 (2016).PubMed 

Google Scholar 
Low, M. et al. The importance of accounting for larval detectability in mosquito habitat-association studies. Malar. J. 15, 1–9 (2016).Article 

Google Scholar  More

Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993, https://doi.org/10.1126/science.1201609 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proceedings of the National Academy of Sciences 116, 4382–4387, https://doi.org/10.1073/pnas.1810512116 (2019).ADS 
CAS 
Article 

Google Scholar 
Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Science Advances 2, e1501639, https://doi.org/10.1126/sciadv.1501639 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214, https://doi.org/10.1038/nature16512 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550, https://doi.org/10.1038/s41586-020-2686-x (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lewis, S. L. et al. Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006, https://doi.org/10.1038/nature07771 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87, https://doi.org/10.1038/s41586-020-2035-0 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Nabuurs, G.-J. et al. First signs of carbon sink saturation in European forest biomass. Nature Climate Change 3, 792–796, https://doi.org/10.1038/nclimate1853 (2013).ADS 
CAS 
Article 

Google Scholar 
Köhl, M. et al. Changes in forest production, biomass and carbon: results from the 2015 UN FAO Global Forest Resource Assessment. Forest Ecology and Management 352, 21–34, https://doi.org/10.1016/j.foreco.2015.05.036 (2015).Article 

Google Scholar 
Asner, G. P. et al. High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences 107, 16738–16742, https://doi.org/10.1073/pnas.1004875107 (2010).ADS 
Article 

Google Scholar 
Asner, G. P. Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environmental Research Letters 4, https://doi.org/10.1088/1748-9326/4/3/034009 (2009).Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Science Advances 7, eabe9829, https://doi.org/10.1126/sciadv.abe9829 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Aalde, U. et al. in IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Eggleston, S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K.) Ch. 4 (IPPC, 2006).Brown, S. Measuring carbon in forests: current status and future challenges. Environmental Pollution 116, 363–372, https://doi.org/10.1016/s0269-7491(01)00212-3 (2002).CAS 
Article 
PubMed 

Google Scholar 
Woodall, C. W., Heath, L. S., Domke, G. M. & Nichols, M. C. Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. (U.S. Department of Agriculture, Forest Service, Northern Research Station, 2011).Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108 (2011).ADS 
Article 

Google Scholar 
Lamlom, S. H. & Savidge, R. A. A reassessment of carbon content in wood: variation within and between 41 North American species. Biomass Bioenergy 25, 381–388 (2003).CAS 
Article 

Google Scholar 
Van Der Werf, G. R. et al. CO2 emissions from forest loss. Nature Geoscience 2, 737–738, https://doi.org/10.1038/ngeo671 (2009).ADS 
CAS 
Article 

Google Scholar 
Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nature Geoscience 11, 915–920, https://doi.org/10.1038/s41561-018-0246-x (2018).ADS 
CAS 
Article 

Google Scholar 
Tavşanoğlu, Ç. & Pausas, J. G. A functional trait database for Mediterranean Basin plants. Scientific Data 5, 180135, https://doi.org/10.1038/sdata.2018.135 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecology Letters 12, 351–366, https://doi.org/10.1111/j.1461-0248.2009.01285.x (2009).Article 
PubMed 

Google Scholar 
Martin, A. R., Domke, G. M., Doraisami, M. & Thomas, S. C. Carbon fractions in the world’s dead wood. Nature Communications 12, https://doi.org/10.1038/s41467-021-21149-9 (2021).Martin, A. R. & Thomas, S. C. A Reassessment of carbon content in tropical trees. PLoS ONE 6, e23533, https://doi.org/10.1371/journal.pone.0023533 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, A. R., Gezahegn, S. & Thomas, S. C. Variation in carbon and nitrogen concentration among major woody tissue types in temperate trees. Canadian Journal of Forest Research 45, 744–757, https://doi.org/10.1139/cjfr-2015-0024 (2015).CAS 
Article 

Google Scholar 
Thomas, S. C. & Martin, A. R. Carbon content of tree tissues: a synthesis. Forests 3, 332–352, https://doi.org/10.3390/f3020332 (2012).Article 

Google Scholar 
Doraisami, M. et al. GLOWCAD: A global database of woody tissue carbon concentrations fractions. Dryad https://doi.org/10.5061/dryad.18931zcxk (2022)Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302–4315, https://doi.org/10.1002/joc.5086 (2017).ADS 
Article 

Google Scholar 
Guerrero‐Ramírez, N. R. et al. Global root traits (GRooT) database. Global Ecology and Biogeography 30, 25–37, https://doi.org/10.1111/geb.13179 (2021).Article 

Google Scholar 
Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Global Change Biology 26, 119–188, https://doi.org/10.1111/gcb.14904 (2020).ADS 
Article 
PubMed 

Google Scholar 
Iversen, C. M. et al. A global Fine-Root Ecology Database to address below-ground challenges in plant ecology. New Phytologist 215, 15–26, https://doi.org/10.1111/nph.14486 (2017).Article 
PubMed 

Google Scholar 
Isaac, M. E. et al. Intraspecific trait variation and coordination: Root and Leaf Economics Spectra in coffee across environmental gradients. Frontiers in Plant Science 8, https://doi.org/10.3389/fpls.2017.01196 (2017).Liu, C. et al. Variation in the functional traits of fine roots is linked to phylogenetics in the common tree species of Chinese subtropical forests. Plant and Soil 436, 347–364, https://doi.org/10.1007/s11104-019-03934-0 (2019).CAS 
Article 

Google Scholar 
Wang, R. et al. Different phylogenetic and environmental controls of first‐order root morphological and nutrient traits: Evidence of multidimensional root traits. Functional Ecology 32, 29–39, https://doi.org/10.1111/1365-2435.12983 (2018).Article 

Google Scholar 
Minden, V. & Kleyer, M. Internal and external regulation of plant organ stoichiometry. Plant Biology 16, 897–907, https://doi.org/10.1111/plb.12155 (2014).CAS 
Article 
PubMed 

Google Scholar 
Alameda, D. & Villar, R. Linking root traits to plant physiology and growth in Fraxinus angustifolia Vahl. seedlings under soil compaction conditions. Environmental and Experimental Botany 79, 49–57, https://doi.org/10.1016/j.envexpbot.2012.01.004 (2012).Article 

Google Scholar 
Aubin, I. et al. Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change. Environmental Reviews 24, 164–186, https://doi.org/10.1139/er-2015-0072 (2016).Article 

Google Scholar 
Fernández-García, N. et al. Intrinsic water use efficiency controls the adaptation to high salinity in a semi-arid adapted plant, henna (Lawsonia inermis L.). Journal of Plant Physiology 171, 64–75, https://doi.org/10.1016/j.jplph.2013.11.004 (2014).CAS 
Article 
PubMed 

Google Scholar 
Grechi, I. et al. Effect of light and nitrogen supply on internal C:N balance and control of root-to-shoot biomass allocation in grapevine. Environmental and Experimental Botany 59, 139–149, https://doi.org/10.1016/j.envexpbot.2005.11.002 (2007).CAS 
Article 

Google Scholar 
Ineson, P., Cotrufo, M. F., Bol, R., Harkness, D. D. & Blum, H. Quantification of soil carbon inputs under elevated CO2: C3 plants in a C4 soil. Plant and Soil 187, 345–350, https://doi.org/10.1007/bf00017099 (1995).Article 

Google Scholar 
Pregitzer, K. S. et al. Atmospheric CO2, soil nitrogen and turnover of fine roots. New Phytologist 129, 579–585, https://doi.org/10.1111/j.1469-8137.1995.tb03025.x (1995).Article 

Google Scholar 
Rohatgi, A. WebPlotDigitizer: Version 4.5. https://automeris.io/WebPlotDigitizer (2021).Harmon, M. E., Fasth, B., Woodall, C. W. & Sexton, J. Carbon concentration of standing and downed woody detritus: effects of tree taxa, decay class, position, and tissue type. Forest Ecology and Management 291, 259–267, https://doi.org/10.1016/j.foreco.2012.11.046 (2013).Article 

Google Scholar 
Boyle, B. H. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinformatics 14, 1-15, https://tnrs.biendata.org/ (2021).Krankina, O. N., Harmon, M. E. & Griazkin, A. V. Nutrient stores and dynamics of woody detritus in a boreal forest: modeling potential implications at the stand level. Canadian Journal of Forest Research 29, 20–32, https://doi.org/10.1139/x98-162 (1999).Article 

Google Scholar 
Whittaker, R. H. Classification of natural communities. Botanical Review 28, 1–239, https://doi.org/10.1007/BF02860872 (1962).Article 

Google Scholar 
Maiti, R. & Rodriguez, H. G. Wood carbon and nitrogen of 37 woody shrubs and trees in Tamaulipan thorn scrub, northeastern Mexico. Pakistan Journal of Botany 51, 979–984 (2019).CAS 
Article 

Google Scholar 
Durkaya, A. B. D., E. Makineci, I. Orhan Aboveground biomass and carbon storage relationship of Turkish Pines. Fresenius Environmental Bulletin 24, 3573–3583 (2015).CAS 

Google Scholar 
Tesfaye, M. A., Bravo-Oviedo, A., Bravo, F., Pando, V. & De Aza, C. H. Variation in carbon concentration and wood density for five most commonly grown native tree species in central highlands of Ethiopia: The case of Chilimo dry Afromontane forest. Journal of Sustainable Forestry 38, 769–790, https://doi.org/10.1080/10549811.2019.1607754 (2019).Article 

Google Scholar 
Abdallah, M. A. B., Mata-González, R., Noller, J. S. & Ochoa, C. G. Ecosystem carbon in relation to woody plant encroachment and control: Juniper systems in Oregon, USA. Agriculture, Ecosystems & Environment 290, 106762, https://doi.org/10.1016/j.agee.2019.106762 (2020).CAS 
Article 

Google Scholar 
Arias, D., Calvo-Alvarado, J., Richter, D. D. B. & Dohrenbusch, A. Productivity, aboveground biomass, nutrient uptake and carbon content in fast-growing tree plantations of native and introduced species in the Southern Region of Costa Rica. Biomass and Bioenergy 35, 1779–1788, https://doi.org/10.1016/j.biombioe.2011.01.009 (2011).CAS 
Article 

Google Scholar 
Assefa, D., Godbold, D. L., Belay, B., Abiyu, A. & Rewald, B. Fine Root Morphology, Biochemistry and Litter Quality Indices of Fast- and Slow-growing Woody Species in Ethiopian Highland Forest. Ecosystems 21, 482–494, https://doi.org/10.1007/s10021-017-0163-7 (2018).CAS 
Article 

Google Scholar 
Atkin, O. K., Schortemeyer, M., Mcfarlane, N. & Evans, J. R. The response of fast- and slow-growing Acacia species to elevated atmospheric CO2: an analysis of the underlying components of relative growth rate. Oecologia 120, 544–554, https://doi.org/10.1007/s004420050889 (1999).ADS 
Article 
PubMed 

Google Scholar 
Bardulis, A., Jansons, A., Bardule, A., Zeps, M. & LAzdins, A. Assessment of carbon content in root biomass in Scots Pine and silver birch young stands of Latvia. Baltic Forestry 23, 482–489 (2017).
Google Scholar 
Becker, G. S., Braun, D., Gliniars, R. & Dalitz, H. Relations between wood variables and how they relate to tree size variables of tropical African tree species. Trees 26, 1101–1112, https://doi.org/10.1007/s00468-012-0687-6 (2012).Article 

Google Scholar 
Bembenek, M. et al. Carbon content in Juvenile and mature wood of Scots Pine (Pinus sylyestris L.). Baltic Forestry 21, 279–284 (2015).
Google Scholar 
Bert, D. & Danjon, F. Carbon concentration variations in the roots, stem and crown of mature Pinus pinaster (Ait.). Forest Ecology and Management 222, 279–295, https://doi.org/10.1016/j.foreco.2005.10.030 (2006).Article 

Google Scholar 
Borden, K. A., Anglaaere, L. C. N., Adu-Bredu, S. & Isaac, M. E. Root biomass variation of cocoa and implications for carbon stocks in agroforestry systems. Agroforestry Systems 93, 369–381, https://doi.org/10.1007/s10457-017-0122-5 (2019).Article 

Google Scholar 
Borden, K. A., Isaac, M. E., Thevathasan, N. V., Gordon, A. M. & Thomas, S. C. Estimating coarse root biomass with ground penetrating radar in a tree-based intercropping system. Agroforestry Systems 88, 657–669, https://doi.org/10.1007/s10457-014-9722-5 (2014).Article 

Google Scholar 
Bueno-López, S. W., García-Lucas, E. & Caraballo-Rojas, L. R. Allometric equations for total aboveground dry biomass and carbon content of Pinus occidentalis trees. Madera y Bosques 25, https://doi.org/10.21829/myb.2019.2531868 (2019).Bulmer, R. H., Schwendenmann, L. & Lundquist, C. J. Allometric models for estimating aboveground biomass, carbon and nitrogen stocks in temperate Avicennia marina forests. Wetlands 36, 841–848, https://doi.org/10.1007/s13157-016-0793-0 (2016).Article 

Google Scholar 
Bütler, R., Patty, L., Le Bayon, R.-C., Guenat, C. & Schlaepfer, R. Log decay of Picea abies in the Swiss Jura Mountains of central Europe. Forest Ecology and Management 242, 791–799, https://doi.org/10.1016/j.foreco.2007.02.017 (2007).Article 

Google Scholar 
Cao, Y. & Chen, Y. Ecosystem C:N:P stoichiometry and carbon storage in plantations and a secondary forest on the Loess Plateau, China. Ecological Engineering 105, 125–132, https://doi.org/10.1016/j.ecoleng.2017.04.024 (2017).Article 

Google Scholar 
Castaño-Santamaría, J. & Bravo, F. Variation in carbon concentration and basic density along stems of sessile oak (Quercus petraea (Matt.) Liebl.) and Pyrenean oak (Quercus pyrenaica Willd.) in the Cantabrian Range (NW Spain). Annals of Forest Science 69, 663–672, https://doi.org/10.1007/s13595-012-0183-6 (2012).Article 

Google Scholar 
Chao, K.-J. et al. Carbon concentration declines with decay class in tropical forest woody debris. Forest Ecology and Management 391, 75–85, https://doi.org/10.1016/j.foreco.2017.01.020 (2017).Article 

Google Scholar 
Chen, Y. et al. Nutrient limitation of woody debris decomposition in a tropical forest: contrasting effects of N and P addition. Functional Ecology 30, 295–304, https://doi.org/10.1111/1365-2435.12471 (2016).Article 

Google Scholar 
Correia, A. C. et al. Biomass allometry and carbon factors for a Mediterranean pine (Pinus pinea L.) in Portugal. Forest Systems 19, 418, https://doi.org/10.5424/fs/2010193-9082 (2010).Article 

Google Scholar 
Cousins, S. J. M., Battles, J. J., Sanders, J. E. & York, R. A. Decay patterns and carbon density of standing dead trees in California mixed conifer forests. Forest Ecology and Management 353, 136–147, https://doi.org/10.1016/j.foreco.2015.05.030 (2015).Article 

Google Scholar 
Craven, D. et al. Seasonal variability of photosynthetic characteristics influences growth of eight tropical tree species at two sites with contrasting precipitation in Panama. Forest Ecology and Management 261, 1643–1653, https://doi.org/10.1016/j.foreco.2010.09.017 (2011).Article 

Google Scholar 
Cruz, P., Bascuñan, A., Velozo, J. & Rodriguez, M. Funciones alométricas de contenido de carbono para quillay, peumo, espino y litre. Bosque (Valdivia) 36, 375–381, https://doi.org/10.4067/s0717-92002015000300005 (2015).Article 

Google Scholar 
Currie, W. S. & Nadelhoffer, K. J. The imprint of land-use history: patterns of carbon and nitrogen in downed woody debris at the Harvard Forest. Ecosystems 5, 446–460, https://doi.org/10.1007/s10021-002-1153-x (2002).CAS 
Article 

Google Scholar 
Dong, L., Zhang, X. & Li Variation in carbon concentration and allometric equations for estimating tree carbon contents of 10 broadleaf species in natural forests in northeast China. Forests 10, 928, https://doi.org/10.3390/f10100928 (2019).Article 

Google Scholar 
Dossa, G. G. O., Paudel, E., Cao, K., Schaefer, D. & Harrison, R. D. Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species. Scientific Reports 6, 34153, https://doi.org/10.1038/srep34153 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Durkaya, B., Durkaya, A., Makineci, E. & Ülküdür, M. Estimation of above-ground biomass and sequestered carbon of Taurus Cedar (Cedrus libani L.) in Antalya, Turkey. iForest – Biogeosciences and Forestry 6, 278–284, https://doi.org/10.3832/ifor0899-006 (2013).Article 

Google Scholar 
Elias, M. & Potvin, C. Assessing inter- and intra-specific variation in trunk carbon concentration for 32 neotropical tree species. Canadian Journal of Forest Research 33, 1039–1045, https://doi.org/10.1139/x03-018 (2003).Article 

Google Scholar 
Fang, S., Xue, J. & Tang, L. Biomass production and carbon sequestration potential in poplar plantations with different management patterns. Journal of Environmental Management 85, 672–679, https://doi.org/10.1016/j.jenvman.2006.09.014 (2007).CAS 
Article 
PubMed 

Google Scholar 
Fonseca, W., Alice, F. E. & Rey-Benayas, J. M. Carbon accumulation in aboveground and belowground biomass and soil of different age native forest plantations in the humid tropical lowlands of Costa Rica. New Forests 43, 197–211, https://doi.org/10.1007/s11056-011-9273-9 (2012).Article 

Google Scholar 
Frangi, J. L., Richter, L. L., Barrera, M. D. & Aloggia, M. Decomposition of Nothofagus fallen woody debris in forests of Tierra del Fuego, Argentina. Canadian Journal of Forest Research 27, 1095–1102, https://doi.org/10.1139/x97-060 (1997).Article 

Google Scholar 
Freschet, G. T., Cornelissen, J. H. C., Van Logtestijn, R. S. P. & Aerts, R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. Journal of Ecology 98, 362–373, https://doi.org/10.1111/j.1365-2745.2009.01615.x (2010).Article 

Google Scholar 
Fukatsu, E., Fukuda, Y., Takahashi, M. & Nakada, R. Clonal variation of carbon content in wood of Larix kaempferi (Japanese larch). Journal of Wood Science 54, 247–251, https://doi.org/10.1007/s10086-007-0939-z (2008).CAS 
Article 

Google Scholar 
Ganamé, M., Bayen, P., Dimobe, K., Ouédraogo, I. & Thiombiano, A. Aboveground biomass allocation, additive biomass and carbon sequestration models for Pterocarpus erinaceus Poir. in Burkina Faso. Heliyon 6, e03805, https://doi.org/10.1016/j.heliyon.2020.e03805 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Ganjegunte, G. K., Condron, L. M., Clinton, P. W., Davis, M. R. & Mahieu, N. Decomposition and nutrient release from radiata pine (Pinus radiata) coarse woody debris. Forest Ecology and Management 187, 197–211, https://doi.org/10.1016/s0378-1127(03)00332-3 (2004).Article 

Google Scholar 
Gao, B., Taylor, A. R., Chen, H. Y. H. & Wang, J. Variation in total and volatile carbon concentration among the major tree species of the boreal forest. Forest Ecology and Management 375, 191–199, https://doi.org/10.1016/j.foreco.2016.05.041 (2016).Article 

Google Scholar 
Gillerot, L. et al. Inter- and intraspecific variation in mangrove carbon fraction and wood specific gravity in Gazi Bay, Kenya. Ecosphere 9, e02306, https://doi.org/10.1002/ecs2.2306 (2018).Article 

Google Scholar 
Gómez-Brandón, M. et al. Physico-chemical and microbiological evidence of exposure effects on Picea abies – coarse woody debris at different stages of decay. Forest Ecology and Management 391, 376–389, https://doi.org/10.1016/j.foreco.2017.02.033 (2017).Article 

Google Scholar 
Guner, S. T. & Comez, A. Biomass equations and changes in carbon stock in afforested Black Pine (Pinus nigra Arnold. subsp. pallasiana (Lamb.) Holmboe) stands in Turkey. Fresenius Environmental Bulletin 26, 2368–2379 (2017).CAS 

Google Scholar 
Guo, J., Chen, G., Xie, J., Yang, Z. & Yang, Y. Patterns of mass, carbon and nitrogen in coarse woody debris in five natural forests in southern China. Annals of Forest Science 71, 585–594, https://doi.org/10.1007/s13595-014-0366-4 (2014).Article 

Google Scholar 
Hanpattanakit, P., Chidthaisong, A., Sanwangsri, M. & Lichaikul, N. Improving allometric equations to estimate biomass and carbon in secondary dry dipterocarp forest. Singapore SG 18, 208–211 (2016).
Google Scholar 
Herrero De Aza, C., Turrión, M. B., Pando, V. & Bravo, F. Carbon in heartwood, sapwood and bark along the stem profile in three Mediterranean Pinus species. Annals of Forest Science 68, 1067–1076, https://doi.org/10.1007/s13595-011-0122-y (2011).Article 

Google Scholar 
Huet, S., Forgeard, F. O. & Nys, C. Above- and belowground distribution of dry matter and carbon biomass of Atlantic beech (Fagus sylvatica L.) in a time sequence. Annals of Forest Science 61, 683–694, https://doi.org/10.1051/forest:2004063 (2004).CAS 
Article 

Google Scholar 
Jacobs, D. F., Selig, M. F. & Severeid, L. R. Aboveground carbon biomass of plantation-grown American chestnut (Castanea dentata) in absence of blight. Forest Ecology and Management 258, 288–294, https://doi.org/10.1016/j.foreco.2009.04.014 (2009).Article 

Google Scholar 
Janssens, I. A. et al. Above- and belowground phytomass and carbon storage in a Belgian Scots pine stand. Annals of forest science 56, 81–90, https://doi.org/10.1051/forest:19990201 (1999).Article 

Google Scholar 
Jiménez Pérez, J., Treviño Garza, E. J. & Yerena Yamallel, J. I. Concentración de carbono en especies del bosque de pino-encino en la Sierra Madre Oriental. Revista mexicana de ciencias forestales 4, 50–61 (2013).Article 

Google Scholar 
Jomura, M. et al. Biotic and abiotic factors controlling respiration rates of above- and belowground woody debris of Fagus crenata and Quercus crispula in Japan. PLOS ONE 10, e0145113, https://doi.org/10.1371/journal.pone.0145113 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jones, D. & O’Hara, K. Variation in carbon fraction, density, and carbon density in conifer tree tissues. Forests 9, 430, https://doi.org/10.3390/f9070430 (2018).Article 

Google Scholar 
Jones, D. A. & O’Hara, K. L. Carbon density in managed coast redwood stands: implications for forest carbon estimation. Forestry 85, 99–110, https://doi.org/10.1093/forestry/cpr063 (2012).Article 

Google Scholar 
Jones, D. A. & O’Hara, K. L. The influence of preparation method on measured carbon fractions in tree tissues. Tree Physiology 36, 1177–1189, https://doi.org/10.1093/treephys/tpw051 (2016).CAS 
Article 
PubMed 

Google Scholar 
Joosten, R. & Schulte, A. Possible effects of altered growth behaviour of Norway Spruce (Picea abies) on carbon accounting. Climatic Change 55, 115–129, https://doi.org/10.1023/a:1020227806137 (2002).CAS 
Article 

Google Scholar 
Joosten, R., Schumacher, J., Wirth, C. & Schulte, A. Evaluating tree carbon predictions for beech (Fagus sylvatica L.) in western Germany. Forest Ecology and Management 189, 87–96, https://doi.org/10.1016/j.foreco.2003.07.037 (2004).Article 

Google Scholar 
Kim, C., Yoo, B., Jung, S. & Lee, K. Allometric equations to assess biomass, carbon and nitrogen content of black pine and red pine trees in southern Korea. iForest – Biogeosciences and Forestry 10, 483–490, https://doi.org/10.3832/ifor2164-010 (2017).Article 

Google Scholar 
Kort, J. & Turnock, R. Carbon reservoirs and biomass in Canadian prairie shelterbelts. Agroforestry Systems 44, 175-186, https://doi.org/10.1023/a:1006226006785 (1998).Köster, K., Metslaid, M., Engelhart, J. & Köster, E. Dead wood basic density, and the concentration of carbon and nitrogen for main tree species in managed hemiboreal forests. Forest Ecology and Management 354, 35–42, https://doi.org/10.1016/j.foreco.2015.06.039 (2015).Article 

Google Scholar 
Kraenzel, M., Castillo, A., Moore, T. & Potvin, C. Carbon storage of harvest-age teak (Tectona grandis) plantations, Panama. Forest Ecology and Management 173, 213–225, https://doi.org/10.1016/s0378-1127(02)00002-6 (2003).Article 

Google Scholar 
Laiho, R. & Laine, J. Tree stand biomass and carbon content in an age sequence of drained pine mires in southern Finland. Forest Ecology and Management 93, 161–169, https://doi.org/10.1016/s0378-1127(96)03916-3 (1997).Article 

Google Scholar 
Laiho, R. & Prescott, C. E. The contribution of coarse woody debris to carbon, nitrogen, and phosphorus cycles in three Rocky Mountain coniferous forests. Canadian Journal of Forest Research 29, 1592–1603, https://doi.org/10.1139/x99-132 (1999).Article 

Google Scholar 
Lambert, R. L., Lang, G. E. & Reiners, W. A. Loss of mass and chemical change in decaying boles of a subalpine Balsam Fir forest. Ecology 61, 1460–1473, https://doi.org/10.2307/1939054 (1980).Article 

Google Scholar 
Laughlin, D. C., Leppert, J. J., Moore, M. M. & Sieg, C. H. A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Functional Ecology 24, 493–501, https://doi.org/10.1111/j.1365-2435.2009.01672.x (2010).Article 

Google Scholar 
Li, X. et al. Biomass and carbon storage in an age-sequence of Korean Pine (Pinus koraiensis) plantation forests in central Korea. Journal of Plant Biology 54, 33–42, https://doi.org/10.1007/s12374-010-9140-9 (2011).ADS 
CAS 
Article 

Google Scholar 
Lombardi, F. et al. Investigating biochemical processes to assess deadwood decay of Beech and Silver Fir in Mediterranean mountain forests. Annals of Forest Science 70, 101–111, https://doi.org/10.1007/s13595-012-0230-3 (2013).Article 

Google Scholar 
Lutter, R., Tullus, A., Kanal, A., Tullus, T. & Tullus, H. The impact of former land-use type to above- and below-ground C and N pools in short-rotation hybrid aspen (Populus tremula L. × P. tremuloides Michx.) plantations in hemiboreal conditions. Forest Ecology and Management 378, 79–90, https://doi.org/10.1016/j.foreco.2016.07.021 (2016).Article 

Google Scholar 
Mahmood, H. et al. Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill Zone of Bangladesh. Journal of Forestry Research 31, 1235–1245, https://doi.org/10.1007/s11676-019-00881-5 (2020).CAS 
Article 

Google Scholar 
Maiti, R., Gonzalez Rodriguez, H. & Kumari, A. Wood density of ten native trees and shrubs and its possible relation with a few wood chemical compositions. American Journal of Plant Sciences 07, 1192–1197, https://doi.org/10.4236/ajps.2016.78114 (2016).CAS 
Article 

Google Scholar 
Mäkinen, H., Hynynen, J., Siitonen, J. & Sievänen, R. Predicting The decomposition of Scots Pine, Norway Spruce, and Birch stems in Finland. Ecological Applications 16, 1865–1879, https://doi.org/10.1890/1051-0761(2006)016[1865:ptdosp]2.0.co;2 (2006).Article 
PubMed 

Google Scholar 
Manuella, S. et al. Chemical transformations in downed logs and snags of mixed boreal species during decomposition. Canadian Journal of Forest Research 43, 785–798, https://doi.org/10.1139/cjfr-2013-0086 (2013).CAS 
Article 

Google Scholar 
Martin, A. R. & Thomas, S. C. Size-dependent changes in leaf and wood chemical traits in two Caribbean rainforest trees. Tree Physiology 33, 1338–1353, https://doi.org/10.1093/treephys/tpt085 (2013).CAS 
Article 
PubMed 

Google Scholar 
Martin, A. R., Thomas, S. C. & Zhao, Y. Size-dependent changes in wood chemical traits: a comparison of neotropical saplings and large trees. AoB PLANTS 5, plt039–plt039, https://doi.org/10.1093/aobpla/plt039 (2013).CAS 
Article 
PubMed Central 

Google Scholar 
Medlyn, B. E. et al. Effects of elevated [CO2] on photosynthesis in European forest species: a meta-analysis of model parameters. Plant, Cell & Environment 22, 1475–1495, https://doi.org/10.1046/j.1365-3040.1999.00523.x (1999).CAS 
Article 

Google Scholar 
Moreira, A. B., Gregoire, T. G. & Do Couto, H. T. Z. Wood density and carbon concentration of coarse woody debris in native forests, Brazil. Forest Ecosystems 6, https://doi.org/10.1186/s40663-019-0177-z (2019).Morhart, C., Sheppard, J. P., Schuler, J. K. & Spiecker, H. Above-ground woody biomass allocation and within tree carbon and nutrient distribution of wild cherry (Prunus avium L.) – a case study. Forest Ecosystems 3, https://doi.org/10.1186/s40663-016-0063-x (2016).Northup, B. K., Zitzer, S. F., Archer, S., Mcmurtry, C. R. & Boutton, T. W. Above-ground biomass and carbon and nitrogen content of woody species in a subtropical thornscrub parkland. Journal of Arid Environments 62, 23–43, https://doi.org/10.1016/j.jaridenv.2004.09.019 (2005).ADS 
Article 

Google Scholar 
Palviainen, M. & Finér, L. Decomposition and nutrient release from Norway spruce coarse roots and stumps – a 40-year chronosequence study. Forest Ecology and Management 358, 1–11, https://doi.org/10.1016/j.foreco.2015.08.036 (2015).Article 

Google Scholar 
Peri, P. L., Gargaglione, V., Martínez Pastur, G. & Lencinas, M. V. Carbon accumulation along a stand development sequence of Nothofagus antarctica forests across a gradient in site quality in Southern Patagonia. Forest Ecology and Management 260, 229–237, https://doi.org/10.1016/j.foreco.2010.04.027 (2010).Article 

Google Scholar 
Pompa-García, M. & Jurado, E. Carbon concentration in structures of Arctostaphylos pungens HBK: an alternative CO2 sink in forests. Phyton 84, 385-389 (2016).Pompa-García, M., Sigala-Rodríguez, J., Jurado, E. & Flores, J. Tissue carbon concentration of 175 Mexican forest species. iForest – Biogeosciences and Forestry 10, 754–758, https://doi.org/10.3832/ifor2421-010 (2017).Article 

Google Scholar 
Pompa-García, M. & Yerena-Yamalliel, J. I. Concentration of carbon in Pinus cembroides Zucc: potential source of global warming mitigation. Revista Chapingo Serie Ciencias Forestales y del Ambiente 20, 169–175, https://doi.org/10.5154/r.rchscfa.2014.04.014 (2014).Article 

Google Scholar 
Preston, C. M., Trofymow, J. A. & Flanagan, L. B. Decomposition, δ13C, and the “lignin paradox”. Canadian Journal of Soil Science 86, 235–245, https://doi.org/10.4141/s05-090 (2006).CAS 
Article 

Google Scholar 
Preston, C. M., Trofymow, J. A. & Nault, J. R. Decomposition and change in N and organic composition of small-diameter Douglas-fir woody debris over 23 years. Canadian Journal of Forest Research 42, 1153-1167 (2012).CAS 
Article 

Google Scholar 
Preston, C. M., Trofymow, J. A., Niu, J. & Fyfe, C. A. PMAS-NMR spectroscopy and chemical analysis of coarse woody debris in coastal forests of Vancouver Island. Forest Ecology and Management 111, 51–68, https://doi.org/10.1016/s0378-1127(98)00307-7 (1998).Article 

Google Scholar 
Rana, R., Langenfeld-Heyser, R., Finkeldey, R. & Polle, A. FTIR spectroscopy, chemical and histochemical characterisation of wood and lignin of five tropical timber wood species of the family of Dipterocarpaceae. Wood Science and Technology 44, 225–242, https://doi.org/10.1007/s00226-009-0281-2 (2010).CAS 
Article 

Google Scholar 
Ray, R., Majumder, N., Chowdhury, C. & Jana, T. K. Wood chemistry and density: an analog for response to the change of carbon sequestration in mangroves. Carbohydrate Polymers 90, 102–108, https://doi.org/10.1016/j.carbpol.2012.05.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
Rodrigues, D. P., Hamacher, C., Estrada, G. C. D. & Soares, M. L. G. Variability of carbon content in mangrove species: effect of species, compartments and tidal frequency. Aquatic Botany 120, 346–351, https://doi.org/10.1016/j.aquabot.2014.10.004 (2015).CAS 
Article 

Google Scholar 
Sakai, Y., Ugawa, S., Ishizuka, S., Takahashi, M. & Takenaka, C. Wood density and carbon and nitrogen concentrations in deadwood of Chamaecyparis obtusa and Cryptomeria japonica. Soil Science and Plant Nutrition 58, 526–537, https://doi.org/10.1080/00380768.2012.710526 (2012).CAS 
Article 

Google Scholar 
Sandström, F., Petersson, H., Kruys, N. & Ståhl, G. Biomass conversion factors (density and carbon concentration) by decay classes for dead wood of Pinus sylvestris, Picea abies and Betula spp. in boreal forests of Sweden. Forest Ecology and Management 243, 19–27, https://doi.org/10.1016/j.foreco.2007.01.081 (2007).Article 

Google Scholar 
Sanquetta, M. N. I., Sanquetta, C. R., Mognon, F., Corte, A. P. D. & Maas, G. C. B. Wood density and carbon content in young teak individuals from Pará, Brazil. Científica 44, 608, https://doi.org/10.15361/1984-5529.2016v44n4p608-614 (2016).Article 

Google Scholar 
Schwendenmann, L. & Mitchell, N. Carbon accumulation by native trees and soils in an urban park, Auckland. New Zealand Journal of Ecology 38(20), 213–220 (2014).Setälä, H., Marshall, V. G. & Trofymow, J. A. Influence of micro- and macro-habitat factors on collembolan communities in Douglas-fir stumps during forest succession. Applied Soil Ecology 2, 227–242, https://doi.org/10.1016/0929-1393(95)00053-9 (1995).Article 

Google Scholar 
Sohrabi, H., Bakhtiarvand-Bakhtiari, S. & Ahmadi, K. Above- and below-ground biomass and carbon stocks of different tree plantations in central Iran. Journal of Arid Land 8, 138–145, https://doi.org/10.1007/s40333-015-0087-z (2016).Article 

Google Scholar 
Telmo, C., Lousada, J. & Moreira, N. Proximate analysis, backwards stepwise regression between gross calorific value, ultimate and chemical analysis of wood. Bioresource Technology 101, 3808–3815, https://doi.org/10.1016/j.biortech.2010.01.021 (2010).CAS 
Article 
PubMed 

Google Scholar 
Thomas, A. L. et al. Carbon and nitrogen accumulation within four black walnut alley cropping sites across Missouri and Arkansas, USA. Agroforestry Systems 94, 1625–1638, https://doi.org/10.1007/s10457-019-00471-8 (2020).Article 

Google Scholar 
Thomas, S. C. & Malczewski, G. Wood carbon content of tree species in Eastern China: interspecific variability and the importance of the volatile fraction. Journal of Environmental Management 85, 659–662, https://doi.org/10.1016/j.jenvman.2006.04.022 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tolunay, D. Carbon concentrations of tree components, forest floor and understorey in young Pinus sylvestris stands in north-western Turkey. Scandinavian Journal of Forest Research 24, 394–402, https://doi.org/10.1080/02827580903164471 (2009).Article 

Google Scholar 
Tramoy, R., Sebilo, M., Nguyen Tu, T. T. & Schnyder, J. Carbon and nitrogen dynamics in decaying wood: paleoenvironmental implications. Environmental Chemistry 14, 9, https://doi.org/10.1071/en16049 (2017).CAS 
Article 

Google Scholar 
Van Geffen, K. G., Poorter, L., Sass-Klaassen, U., Van Logtestijn, R. S. P. & Cornelissen, J. H. C. The trait contribution to wood decomposition rates of 15 Neotropical tree species. Ecology 91, 3686–3697, https://doi.org/10.1890/09-2224.1 (2010).Article 
PubMed 

Google Scholar 
Wang, G. et al. Variations in the live biomass and carbon pools of Abies georgei along an elevation gradient on the Tibetan Plateau, China. Forest Ecology and Management 329, 255–263, https://doi.org/10.1016/j.foreco.2014.06.023 (2014).Article 

Google Scholar 
Wang, X. W., Weng, Y. H., Liu, G. F., Krasowski, M. J. & Yang, C. P. Variations in carbon concentration, sequestration and partitioning among Betula platyphylla provenances. Forest Ecology and Management 358, 344–352, https://doi.org/10.1016/j.foreco.2015.08.029 (2015).Article 

Google Scholar 
Watzlawick, L. F. et al. Teores de carbono em espécies da floresta ombrófila mista e efeito do grupo ecológico. Cerne 20, 613–620, https://doi.org/10.1590/01047760201420041492 (2014).Article 

Google Scholar 
Weber, J. C. et al. Variation in growth, wood density and carbon concentration in five tree and shrub species in Niger. New Forests 49, 35–51, https://doi.org/10.1007/s11056-017-9603-7 (2018).Article 

Google Scholar 
Weggler, K., Dobbertin, M., Jüngling, E., Kaufmann, E. & Thürig, E. Dead wood volume to dead wood carbon: the issue of conversion factors. European Journal of Forest Research 131, 1423–1438, https://doi.org/10.1007/s10342-012-0610-0 (2012).Article 

Google Scholar 
Widagdo, F. R. A., Xie, L., Dong, L. & Li, F. Origin-based biomass allometric equations, biomass partitioning, and carbon concentration variations of planted and natural Larix gmelinii in northeast China. Global Ecology and Conservation 23, e01111, https://doi.org/10.1016/j.gecco.2020.e01111 (2020).Article 

Google Scholar 
Wu, H. et al. Tree functional types simplify forest carbon stock estimates induced by carbon concentration variations among species in a subtropical area. Scientific Reports 7, https://doi.org/10.1038/s41598-017-05306-z (2017).Xing, Z. et al. Carbon and biomass partitioning in balsam fir (Abies balsamea). Tree Physiology 25, 1207–1217, https://doi.org/10.1093/treephys/25.9.1207 (2005).Article 
PubMed 

Google Scholar 
Yang, F.-F. et al. Dynamics of coarse woody debris and decomposition rates in an old-growth forest in lower tropical China. Forest Ecology and Management 259, 1666–1672, https://doi.org/10.1016/j.foreco.2010.01.046 (2010).Article 

Google Scholar 
Yeboah, D., Burton, A. J., Storer, A. J. & Opuni-Frimpong, E. Variation in wood density and carbon content of tropical plantation tree species from Ghana. New Forests 45, 35–52, https://doi.org/10.1007/s11056-013-9390-8 (2014).Article 

Google Scholar 
Ying, J., Weng, Y., Oswald, B. P. & Zhang, H. Variation in carbon concentrations and allocations among Larix olgensis populations growing in three field environments. Annals of Forest Science 76, https://doi.org/10.1007/s13595-019-0877-0 (2019).Yuan, J., Cheng, F., Zhu, X., Li, J. & Zhang, S. Respiration of downed logs in pine and oak forests in the Qinling Mountains, China. Soil Biology and Biochemistry 127, 1–9, https://doi.org/10.1016/j.soilbio.2018.09.012 (2018).CAS 
Article 

Google Scholar 
Zabek, L. M. & Prescott, C. E. Biomass equations and carbon content of aboveground leafless biomass of hybrid poplar in Coastal British Columbia. Forest Ecology and Management 223, 291–302, https://doi.org/10.1016/j.foreco.2005.11.009 (2006).Article 

Google Scholar 
Zhang, H., Jiang, Y., Song, M., He, J. & Guan, D. Improving understanding of carbon stock characteristics of Eucalyptus and Acacia trees in southern China through litter layer and woody debris. Scientific Reports 10, https://doi.org/10.1038/s41598-020-61476-3 (2020).Zhang, Q., Wang, C., Wang, X. & Quan, X. Carbon concentration variability of 10 Chinese temperate tree species. Forest Ecology and Management 258, 722–727, https://doi.org/10.1016/j.foreco.2009.05.009 (2009).Article 

Google Scholar 
Zheng, H. et al. Variation of carbon storage by different reforestation types in the hilly red soil region of southern China. Forest Ecology and Management 255, 1113–1121, https://doi.org/10.1016/j.foreco.2007.10.015 (2008).Article 

Google Scholar 
Zhou, L. et al. Tissue-specific carbon concentration, carbon stock, and distribution in Cunninghamia lanceolata (Lamb.) Hook plantations at various developmental stages in subtropical China. Annals of Forest Science 76, https://doi.org/10.1007/s13595-019-0851-x (2019).Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92, https://doi.org/10.1038/nature12872 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Muscarella, R. et al. The global abundance of tree palms. Global Ecology and Biogeography 29, 1495–1514, https://doi.org/10.1111/geb.13123 (2020).Article 

Google Scholar 
Du, H. et al. Mapping global bamboo forest distribution using multisource remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 1458–1471 (2018).ADS 
Article 

Google Scholar 
Iwashita, D. K., Litton, C. M. & Giardina, C. P. Coarse woody debris carbon storage across a mean annual temperature gradient in tropical montane wet forest. Forest Ecology and Management 291, 336–343, https://doi.org/10.1016/j.foreco.2012.11.043 (2013).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Wikström, N., Savolainen, V. & Chase, M. W. Evolution of the angiosperms: calibrating the family tree. Proceedings of the Royal Society of London. Series B: Biological Sciences 268, 2211–2220, https://doi.org/10.1098/rspb.2001.1782 (2001).Article 
PubMed 
PubMed Central 

Google Scholar 
Gastauer, M. & Meira Neto, J. A. A. Updated angiosperm family tree for analyzing phylogenetic diversity and community structure. Acta Botanica Brasilica 31, 191–198, https://doi.org/10.1590/0102-33062016abb0306 (2017).Article 

Google Scholar  More

Land Use Land Cover taxonomyThe classification schema or “taxonomy” for Dynamic World, shown in Table 1, was determined after a review of global LULC maps, including the USGS Anderson classification system18, ESA Land Use and Coverage Area frame Survey (LUCAS) land cover modalities19, MapBiomas classification20, and GlobeLand30 land cover types13. The Dynamic World taxonomy maintains a close semblance to the land use classes presented in the IPCC Good Practice Guidance (forest land, grassland, cropland, wetland, settlement, and other)21 to ensure easier application of the resulting data for estimating carbon stocks and greenhouse gas emissions. Unlike single-pixel labels, which are usually defined in terms of percent cover thresholds, the Dynamic World taxonomy was applied using “dense” polygon-based annotations such that LULC labels are applied to areas of relatively homogenous cover types with similar colors and textures.Table 1 Dynamic World Land Use Land Cover (LULC) classification taxonomy.Full size tableTraining dataset collectionOur modeling approach relies on semi-supervised deep learning and requires spatially dense (i.e., ideally wall-to-wall) annotations. To collect a diverse set of training and evaluation data, we divided the world into three regions: the Western Hemisphere (160°W to 20°W), Eastern Hemisphere-1 (20°W to 100°E), and Eastern Hemisphere-2 (100°E to 160°W). We further divided each region by the 14 RESOLVE Ecoregions biomes22. We collected a stratified sample of sites for each biome per region based on NASA MCD12Q1 land cover for 20174. Given the availability of higher-resolution LULC maps in the United States and Brazil, we used the NLCD 201610 and MapBiomas 201720 LULC products respectively in place of MODIS products for stratification in these two countries.At each sample location, we performed an initial selection of Sentinel-2 images from 2019 scenes based on image cloudiness metadata reported in the Sentinel-2 tile’s QA60 band. We further filtered scenes to remove images with many masked pixels. We finally extracted individual tiles of 510 × 510 pixels centered on the sample sites from random dates in 2019. Tiles were sampled in the UTM projection of the source image and we selected one tile corresponding to a single Sentinel-2 ID number and single date.Further steps were then taken to obtain an “as balanced as possible” training dataset with respect to the LULC classifications from the respective LULC products. In particular, for each Dynamic World LULC category contained within a tile, the tile was labeled to be high, medium, or low in that category. We then selected an approximately equal number of tiles with high, medium or low category labels for each category.To achieve a large dataset of labeled Sentinel-2 scenes, we worked with two groups of annotators. The first group included 25 annotators with previous photo-interpretation and/or remote sensing experience. The expert group labeled approximately 4,000 image tiles (Fig. 1a), which were then used to train and measure the performance and accuracy of a second “non-expert” group of 45 additional annotators who labeled a second set of approximately 20,000 image tiles (Fig. 1b). A final validation set of 409 image tiles were held back from the modeling effort and used for evaluation as described in the Technical Validation section. Each image tile in the validation set was annotated by three experts and one non-expert to facilitate cross-expert and expert/non-expert QA comparisons.Fig. 1Global distribution of annotated Sentinel-2 image tiles used for model training and periodic testing (neither including 409 validation tiles). (a) 4,000 tiles interpreted by a group of 25 experts (b) 20,000 tiles interpreted by a group of 45 non-experts. Hexagons represent approximately 58,500 km2 areas and shading corresponds to the count of annotated tile centroids per hexagon.Full size imageAll Dynamic World annotators used the Labelbox platform23, which provides a vector drawing tool to mark the boundaries of feature classes directly over the Sentinel-2 tile (Fig. 2). We instructed both expert and non-expert annotators to use dense markup instead of single pixel labels with a minimum mapping unit of 50 × 50 m (5 × 5 pixels). For water, trees, crops, built area, bare ground, snow & ice, and cloud, this was a fairly straightforward procedure at the Sentinel-2 10 m resolution since these feature classes tend to appear in fairly homogenous agglomerations. Shrub & scrub and flooded vegetation classes proved to be more challenging as they tended not to appear as homogenous features (e.g. mix of vegetation types) and have variable appearance. Annotators used their best discretion in these situations based on the guidance provided in our training material (i.e. descriptions and examples in Table 1). In addition to the Sentinel-2 tile, annotators had access to a matching high-resolution satellite image via Google Maps and ground photography via Google Street View from the image center point. We also provided the date and center point coordinates for each annotation. All annotators were asked to label at least 70% of a tile within 20 to 60 minutes and were allowed to skip some tiles to best balance their labeling accuracy with their efficiency.Fig. 2Sentinel-2 tile and example reference annotation provided as part of interpreter training. This example was used to illustrate the Flooded vegetation class, which is distinguished by small “mottled” areas of water mixed with vegetation near a riverbed. Also note that some areas of the tile are left unlabeled.Full size imageImage preprocessingWe prepared Sentinel-2 imagery in a number of ways to accommodate both annotation and training workflows. An overview of the preprocessing workflow is shown in Fig. 3.Fig. 3Training inputs workflow. Annotations created using Sentinel-2 Level 2 A Surface Reflectance imagery are paired with masked and normalized Sentinel-2 Level 1 C Top of Atmosphere imagery, and inputs are augmented to create training inputs used for modeling. Cloud and shadow masking involves a three-step process that combines the Sentinel-2 Cloud Probability (S2C) product with the Cloud Displacement Index (CDI), which is used to correct over-masking of bright non-cloud targets” and directional distance transform (DDT), which is used to remove the expected path of shadows based on sun-sensor geometry.Full size imageFor training data collection, we used the Sentinel-2 Level-2A (L2A) product, which provides radiometrically calibrated surface reflectance (SR) processed using the Sen2Cor software package24. This advanced level of processing was advantageous for annotation, as it attempts to remove inter-scene variability due to solar distance, zenith angle, and atmospheric conditions. However, systematically produced Sentinel-2 SR products are currently only available from 2017 onwards. Therefore, for our modeling approach, we used the Level-1C (L1C) product, which has been generated since the beginning of the Sentinel-2 program in 2015. The L1C product represents Top-of-Atmosphere (TOA) reflectance measurements and is not subject to a change in processing algorithm in the future. We note that for any L2A image, there is a corresponding L1C image, allowing us to directly map annotations performed using L2A imagery to the L1C imagery used in model training. All bands except for B1, B8A, B9, and B10 were kept, with all bands bilinearly upsampled to 10 m for both training and inference.In addition to our preliminary cloud filtering in training image selection, we adopted and applied a novel masking solution that combines several existing products and techniques. Our procedure is to first take the 10 m Sentinel-2 Cloud Probability (S2C) product available in Earth Engine25 and join it to our working set of Sentinel-2 scenes such that each image is paired with the corresponding mask. We compute a cloud mask by thresholding S2C using a cloud probability of 65% to identify pixels that are likely obscured by cloud cover. We then apply the Cloud Displacement Index (CDI) algorithm26 and threshold the result to produce a second cloud mask, which is intersected with the S2C mask to reduce errors of commission by removing bright non-cloud targets based on Sentinel-2 parallax effects. We finally intersect this sub-cirrus mask with a threshold on the Sentinel-2 cirrus band (B10) using the thresholding constants proposed for the CDI algorithm26, and take a morphological opening of this as our cloudy pixel mask. This mask is computed at 20 m resolution.In order to remove cloud shadows, we extend the cloudy pixel mask 5 km in the direction opposite the solar azimuthal angle using the scene level metadata “SOLAR_AZIMUTH_ANGLE” and a directional distance transform (DDT) operation in Earth Engine. The final cloud and shadow mask is resampled to 100 m to decrease both the data volume and processing time. The resulting mask is applied to Sentinel-2 images used for training and inference such that unmasked pixels represent observations that are likely to be cloud- and shadow-free.The distribution of Sentinel-2 reflectance values are highly compressed towards the low end of the sensor range, with the remainder mostly occupied by high return phenomena like snow and ice, bare ground, and specular reflection. To combat this imbalance, we introduce a normalization scheme that better utilizes the useful range of Sentinel-2 reflectance values for each band. We first log-transform the raw reflectance values to equalize the long tail of highly reflective surfaces, then remap percentiles of the log-transformed values to points on a sigmoid function. The latter is done to bound on (0, 1) without truncation, and condenses the extreme end members of reflectances to a smaller range.To account for an annotation skill differential between the non-expert and expert groups, we one-hot encode the labeled pixels, and smooth them according to the confidence in a binary label of the individual annotator (expert/non-expert): this is effectively linearly interpolating the distributions per-pixel from their one-hot encoding (i.e. a vector of binary variables for each class label) to uniform probability. We used 0.2 for experts, and 0.3 for non-experts (i.e. ~82% confidence on the true class for experts and ~73% confidence on the true class for the non-expert. We note that these values approximately mirror the Non-Expert to Expert Consensus agreement as discussed in the Technical Validation section). This is akin to standard label-smoothing27,28, with the addition that the degree of smoothing is associated with annotation confidence.We generate a pair of weights for each pixel in an augmented example designed to compensate for class imbalance across the training set and weight high-frequency spatial features at the inputs during “synthesis” (discussed further in the following section). We also include a weight per pixel designed to attenuate labels in the center of labeled polygons where human annotators often missed small details using a simple edge finding kernel.We finally perform a series of augmentations (random rotation and random per-band contrasting) to our input data to improve generalizability and performance of our model. These augmentations are applied four times to each example to yield our final training dataset of examples paired with class distributions, masks, and weights (Fig. 3).Model trainingOur broad approach to transferring the supervised label data to a system that could be applied globally was to train a Fully Convolutional Neural Network (FCNN)29. Conceptually, this approach transforms pre-processed Sentinel-2 optical bands to a discrete probability distribution of the classes in our taxonomy on the basis of spatial context. This is done per-image with the assumption that sufficient spatial and spectral context is available to recover one of our taxonomic labels at a pixel. There are a few notable benefits to such an approach: namely that given the generalizability of modern deep neural networks, it is possible, as we will show, to produce a single model that achieves acceptable agreement with hand-digitized expert annotations globally. Furthermore, since model outputs are generated from a single image and a single model, it is straightforward to scale as each Sentinel-2 L1C image need only be observed once.Although applying CNN modeling, including FCNN, to recover LULC is not a new idea30,31,32, we introduce a number of novel innovations that achieve state-of-the-art performance on LULC globally with a neural network architecture almost 100x smaller than architectures used for semantic segmentation or regression of ground-level camera imagery (specifically compared to U-Net33 and DeepLab v3+34 architectures). Our approach also leverages weak supervision by way of a synthesis pathway: this pathway includes a replica of the labeling model architecture that learns a mapping from estimated probabilities back to the input reflectances, in a way, a reverse LULC classifier that offers both multi-tasking and a constraint to overcome deficiencies in human labeling (Fig. 4).Fig. 4Training protocol used to recover the labeling model. The bottom row shows the progression from a normalized Sentinel-2 L1C image, to class probabilities, to synthesized Sentinel-2. The dashed red and blue arrows show how the labeling model is optimized with respect to both the class probability and synthesis pathway, and the synthesis model is optimized only with respect to the synthesized imagery. The example image is retrieved from Earth Engine using ee.Image(‘GOOGLE/DYNAMICWORLD/V1/20190517T083601_20190517T083604_T37UET’).Full size imageNear real-time inferenceUsing Earth Engine in combination with Cloud AI Platform, it is possible to handle enormous quantities of satellite data and apply custom image processing and classification methods using a simple scaling paradigm (Fig. 5). To generate our NRT products, we apply the normalization described earlier to the raw Sentinel-2 L1C imagery and pass all normalized bands except B1, B8A, B9 and B10 after bilinear upscaling to ee.Model.predictImage. This output is then masked using our cloud mask derived from the unnormalized L1C image. Creation of these images is triggered automatically when new Sentinel-2 L1C and S2C images are available. The NRT collection is continuously updated with new results. For a full Sentinel-2 tile (roughly 100 km x 100 km), predictions are completed on the order of 45 minutes. In total, we evaluate ~12,000 Sentinel-2 scenes per day, processing half on average due to a filter criteria on the CLOUDY_PIXEL_PERCENTAGE metadata of 35%. A new Dynamic World LULC image is processed approximately every 14.4 s.Fig. 5Near-Real-Time (NRT) prediction workflow. Input imagery is normalized following the same protocol used in training and the trained model is applied to generate land cover predictions. Predicted results are masked to remove cloud and cloud shadow artifacts using Sentinel-2 cloud probabilities (S2C), the Cloud Displacement Index (CDI) and a directional distance transform (DDT), then added to the Dynamic World image collection.Full size image More

Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050. The 2012 Revision (FAO, 2012).Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).CAS 
Article 

Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).CAS 
Article 

Google Scholar 
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).CAS 
Article 

Google Scholar 
Fan, M. S. et al. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. J. Exp. Bot. 63, 13–24 (2012).CAS 
Article 

Google Scholar 
Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).CAS 
Article 

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).CAS 
Article 

Google Scholar 
Porter, J. R. et al. Food Security and Food Production Systems (Cambridge Univ. Press, 2014).Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency: recent trends and future directions. Environ. Res. Lett. 8, 044041 (2013).Article 

Google Scholar 
Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 7, 5875–5895 (2015).Article 

Google Scholar 
Wall, D. & Six, J. Give soils their due. Science 347, 695 (2015).CAS 
Article 

Google Scholar 
Ray, D. K. et al. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).CAS 
Article 

Google Scholar 
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).CAS 
Article 

Google Scholar 
Nelson, G. C. et al. Climate Change: Impact on Agriculture and Costs of Adaptation (International Food Policy Research Institute, 2009).Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).Article 

Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
Article 

Google Scholar 
Schlenker, W., Hanemann, M. & Fisher, A. Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 95, 395–406 (2005).Article 

Google Scholar 
Piao, S. L. et al. The impacts of climate change on water resources and agriculture in China. Nature 467, 43–51 (2010).CAS 
Article 

Google Scholar 
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).CAS 
Article 

Google Scholar 
Ramankutty, N. et al. The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Glob. Ecol. Biogeogr. 11, 377–392 (2002).Article 

Google Scholar 
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).CAS 
Article 

Google Scholar 
Lobell, D. B. & Burke, M. B. On the use of statistical models to predict crop yield responses to climate change. Agr. For. Meteorol. 150, 1443–1452 (2010).Article 

Google Scholar 
Auffhammer, M. & Schlenker, W. Empirical studies on agricultural impacts and adaptation. Energy Econ. 46, 555–561 (2014).Article 

Google Scholar 
Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).CAS 
Article 

Google Scholar 
Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).CAS 
Article 

Google Scholar 
Basso, B. et al. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agr. Environ. Lett. 3, 180026 (2018).Mϋller, C. et al. Implication of climate mitigation for future agricultural production. Environ. Res. Lett. 10, 125004 (2015).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H. O. et al.) (Cambridge Univ. Press, 2022).Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).CAS 
Article 

Google Scholar 
Cui, Z. L. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–368 (2018).CAS 
Article 

Google Scholar 
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).Article 
CAS 

Google Scholar 
Müller, C. et al. Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).Jamieson, P. D., Porter, J. R. & Wilson, D. R. A test of the computer simulation model ARC-WHEAT on wheat crops grown in New Zealand. Field Crops Res. 27, 337–350 (1991).Article 

Google Scholar 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).CAS 
Article 

Google Scholar 
Xiong, W. et al. The Impacts of Climate Change on Chinese Agriculture—Phase II National Level Study Final Report (AEA Group, 2008).Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).Article 

Google Scholar 
Tao, F. et al. Global warming, rice production, and water use in China: developing a probabilistic assessment. Agr. For. Meteorol. 148, 94–110 (2008).Article 

Google Scholar 
Xiong, W. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat. Food 1, 63–69 (2020).Article 

Google Scholar 
Fernandez-Illescas, C. P., Porporato, A., Laio, F. & Rodriguez-Iturbe, I. The ecohydrological role of soil texture in a water-limited ecosystem. Water Resour. Res. 37, 2863–2872 (2001).Article 

Google Scholar 
Wang, E. L. et al. Capacity of soils to buffer impact of climate variability and value of seasonal forecasts. Agr. For. Meteorol. 149, 38–50 (2009).Article 

Google Scholar 
Vereecken, H. et al. Modeling soil processes: review, key challenges, and new perspectives. Vadose Zone J. 15, 1–57 (2016).Myers, R. J. K. et al. in The Biological Management of Tropical Soil Fertility (eds Woomer, P.I. & Swift, M.J.) Ch. 4 (Wiley, 1994).Smith, P. & Gregory, P. J. Climate change and sustainable food production. P. Nutr. Soc. 72, 21–28 (2013).Article 

Google Scholar 
Khasawneh, F. E., Sample, E. C. & Kamprath, E. J. The Role of Phosphorus in Agriculture (American Society of Agronomy, 1980).FAOSTAT (Statistics Division of the Food and Agriculture Organization of the United Nations, 2006); http://www.fao.org/faostat/en/#homeFan, M. S. et al. Plant-based assessment of inherent soil productivity and contributions to China’s cereal crop yield increase since 1980. PLoS ONE 8, e74617 (2013).CAS 
Article 

Google Scholar 
Liu, X. & Chen, F. Farming System in China (China Agriculture Press, 2005).Chen, X. P. in Fertilization Technology Highlights, (ed. Zhang, F. S) Ch. 6 (Chinese Agricultural Univ. Press, 2006).Zhang, F. et al. Integrated nutrient management for food security and environmental quality in China. Adv. Agron. 116, 1–40 (2012).CAS 
Article 

Google Scholar 
Bünemann, E. K. et al. Soil quality—a critical review. Soil Biol. Biochem. 120, 105–125 (2018).Article 
CAS 

Google Scholar 
National Soil Survey Office. Chinese Soil (China Agriculture Press, 1998) .Jiang, R. F. & Cui, J. Y. in Fertilization Technology Highlights, (ed. Zhang, F. S.) Ch. 5 (China Agricultural Univ. Press, 2006).Cramer, W. P. & Solomon, A. M. Climatic classification and future global redistribution of agricultural land. Clim. Res. 3, 97–110 (1993).Article 

Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data 38, 367–378 (2002).Article 

Google Scholar 
Buston, P. M. & Elith, J. Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis. J. Anim. Ecol. 80, 528–538 (2011).Article 

Google Scholar 
Friedman, J. H. & Meulman, J. J. Multiple additive regression trees with application in epidemiology. Stat. Med. 22, 1365–1381 (2003).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).Yang, J. M., Yang, J. Y., Liu, S. & Hoogenboom, G. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric. Syst. 127, 81–89 (2014).Article 

Google Scholar 
Loague, K. & Green, R. E. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contamin. Hydro. 7, 51–73 (1991).CAS 
Article 

Google Scholar 
Akinremi, O. O. et al. Evaluation of LEACHMN under Dryland conditions. I. Simulation of water and solute transport. Can. J. Soil Sci. 85, 223–232 (2005).Article 

Google Scholar 
Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 35, 103–114 (2011).Article 

Google Scholar 
Deng, N. et al. Closing yield gaps for rice self-sufficiency in China. Nat. Commun. 10, 1725 (2019).Article 
CAS 

Google Scholar 
Correndo, A. A. et al. Assessing the uncertainty of maize yield without nitrogen fertilization. Field Crops Res. 260, 107985 (2021).Article 

Google Scholar 
Rattalino Edreira, J. I. et al. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773–779 (2021).Article 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
Article 

Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
IPCC Climate Change 2014: Climate Change: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).Article 

Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).Article 

Google Scholar 
Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).Article 

Google Scholar 
China Agriculture Yearbook (China Agriculture Press, 2005). More

Chloroplast and repetitive nuclear DNA enrichment in the sedaDNA extractsTo the best of our knowledge, we generated the first large-scale target enriched dataset using sedaDNA extracted from sediments of multiple lakes. Sequencing of two datasets produced 325.5 million (M) quality-filtered paired-end DNA sequences. The first target enriched dataset, targeting both the chloroplast and a set of nuclear genes of Larix on 64 sedaDNA extracts and 19 negative controls from seven lake sediment records resulted in 324 M quality-filtered paired-end sequences. The second target enriched dataset, targeting only the set of nuclear genes of Larix on four samples and two negative controls from an additional lake (Lake CH12) resulted in 1.5 M sequences. Quality-filtering of an additional published target enriched dataset29, targeting the Larix chloroplast genome on the same CH12 samples as applied for the second dataset, added another 54 M sequences.For the chloroplast enrichment, 390 thousand (K) sequences (1%) were classified as Larix at the genus or species level. The average coverage of bait regions was 19% at a mean sequence depth of 0.8. Sequencing of 19 library and extraction blank (negative control) samples resulted in 597 K paired-end sequences, of which 58% quality-filtered and deduplicated sequences remained. Of these, 38% were classified, with 0.03% of them (463 sequences) corresponding to the genus Larix. Negative controls from library preparation resulted in no to very few (0 to 5) sequences mapping to the Larix chloroplast reference genome. Negative controls from DNA extractions, which were in several cases pooled to one library, showed a low number of sequences mapped to Larix (0 to 94 sequences, except 237 sequences in one case). Excluding all sequences in negative controls from the sample analysis had no impact on the patterns resulting from the analysis of sample data. Detailed results and evaluation of negative controls are included in the Supplementary Information (Fig. S5) and Supplementary Data 1 and 2. Samples of all lake records with sufficient sequence coverage showed damage patterns typical of ancient DNA (see Supplementary Data 3).These results are comparable to the results obtained by Schulte et al.29, where 36% of quality-filtered sequences were classified as Viridiplantae with 9% assigned to Larix. In contrast to29, we raised the confidence threshold of taxonomic classification (a parameter defining the number of k-mers needed to produce a match against a taxon in the database), which drastically reduced the number of classified sequences, but increased the confidence in the analysis36.To analyze the enrichment obtained by the nuclear gene bait set, taxonomic classification was repeated using a plant genome database including available Pinaceae genomes. The classification resulted in 716 K sequences assigned to Larix, increasing the previous results by 325 K sequences. However, almost no sequences were mapped against the targeting baits (a maximum of five sequences for some samples). A closer inspection of unmapped sequences assigned to Larix revealed a high content of repetitive DNAs. More specifically, taxonomically classified Larix sequences could be assembled to EulaSat1, the most abundant satellite repeat in the nuclear genome of Larix32,37. This short repeat with a 173 bp long motif is arranged in large arrays of tandemly repeated motifs and is exclusively present in larches32. Analysis of modern L. sibirica, and L. gmelinii (western and eastern range) genomes reveals that EulaSat1 occurs in all species, contributing to 0.62% (L. sibirica), 2.52% (western range L. gmelinii), and 2.39% (eastern range L. gmelinii), of the genomes, respectively (Fig. S2). A comparison of the sequence proportions mapping to the repeat motif in the different datasets of Lake CH12 showed a specific enrichment of the repeat motif by the nuclear gene hybridization probe set (Fig. S3).In total, 17 K sequences mapped to the repeat motif of EulaSat1. The abundance of all sequences mapped per sample is in agreement with the abundance of sequences mapped to the chloroplast genome, confirming the general history of forest development (Fig. 2). Analysis of the nucleotide frequencies in the repeat motif showed a high constancy over all samples (Fig. S4). This suggests high conservation of the EulaSat1 motif in Siberian larches over time and space. Although satellite repeats are reported to have a high sequence turnover, for larches it has been shown that repeat profiles between two geographically well-separated species—the European larch (L. decidua) and the Japanese larch (L. kaempferi)—are very similar32. The main satellite in all larches, EulaSat1, is believed to have greatly multiplied after the split of Larix from Pseudotsuga32. Given the ongoing hybridization between the three Siberian larch species, it is not surprising to find a consistent pattern of nucleotide frequencies in all samples.Fig. 2: Comparison of target enrichment with available DNA metabarcoding and pollen datasets.From left to right: Larix-classified sequence counts mapping to (1) the Larix chloroplast and (2) the EulaSat1 satellite repeat motif, (3) percentage of Larix counts in metabarcoding data, (4) percentage of Larix pollen in pollen assemblages. All data from this study, except metabarcoding data from lakes CH1213 and Bolshoye Shchuchye55 and all pollen data except for several samples of Lake Kyutyunda which were produced in this study56,57,71. Pollen data of Lake Lama and the Holocene part of Lake Kyutyunda are based on parallel sediment cores PG1111 and PG2022, respectively. No available data are marked with crosses, asterisk marks a single Larix pollen grain found in the Bolshoye Shchuchye sediments.Full size imageOff-target sequences in target enriched datasets have already been demonstrated to be useful for the analysis of high-copy DNA such as ribosomal DNA or plastomes34,38,39. A recent study on five modern sedges showed that target enriched sequencing data originally targeting a set of gene exons can also be used to study the repetitive sequence fraction and even infer phylogenetic relationships based on repetitive sequence abundance35. Another study showed that also sequence similarities between homologous repeat motifs can be used to reconstruct phylogenetic relationships among closely related taxa40,41. In the case of Larix satellite EuLaSat1 in our study, no change in nucleotide frequencies, neither related to locations nor in time, could be detected. However, our results show that the off-target fraction in target enriched sedaDNA datasets can hold valuable information and that repeat motifs in more diverse taxon groups could even be a target for enrichment. Specifically enriching for repeat motifs in sedaDNA extracts could enable the study of satellite repeat evolution as well as giving additional information on species abundance and phylogeography.In the two target enriched datasets, sequences taxonomically classified to the genus Larix and mapping to the chloroplast and to the repeat sequence, respectively, show similar patterns of abundance (see Fig. 2). Compared with published metabarcoding and pollen data from the same locations, the Larix abundance patterns can be globally reproduced, underpinning the notion that sequence abundances in target enriched data can be used as good estimates of plant abundances. For older parts of the lake records, target enriched data show Larix where metabarcoding data were unable to detect a clear signal (see Fig. 2, lakes Billyakh, Bolshoye Shchuchye, Kyutyunda, and Lama). This shows that target enrichment is superior to metabarcoding when analyzing one taxonomic group in-depth, as it is less prone to errors by DNA degradation, which can impede primer binding if the molecule becomes too short. Also, independent of age, rare taxa mostly need multiple PCR replicates to be detected by metabarcoding42,43. Target enrichment, however, is more sensitive in identifying one focal taxon group, as the total target length can be much larger (e.g., a complete organellar genome) than for metabarcoding, and the DNA damage patterns are put to use to authenticate ancient DNA. Also, it is limited by molecule length only by the applied threshold in the bioinformatic analysis, for which we used 30 base pairs (bp) as opposed to a minimum of 85 bp molecule length for the Larix metabarcoding marker (for the plant-specific trnL g/h marker44). Similarly, compared to traditional pollen analysis, target enrichment is more accurate at tracing a specific target group, as it is not dependent on pollen productivity. Especially in the case of Larix, pollen productivity is low and preservation poor, resulting in rare findings of its pollen in the sediments22,45. This could explain why for Lake Bolshoye Shchuchye, only a single Larix pollen grain was retrieved throughout the core, whereas target enrichment and metabarcoding show a strong signal in the Holocene sediments (last ~12 ka BP). Target-enriched data also records signals in MIS 2 sediments, however, sequence counts are extremely low, and as it is the only record, where both of the other proxies fail to report a signal, it should be interpreted with caution.A wider pre-glacial distribution of L. sibirica
Chloroplast genomes of L. gmelinii and L. sibirica differ at 157 positions, which can be used to differentiate species in target enriched sedaDNA29. Here, we applied this approach to lake sediment records, which are distributed across Siberia (Fig. 1) and have time ranges back to MIS3, and thereby were able to track species composition in space and time for wide parts of the species ranges.In lakes Billyakh and Kyutyunda, ca. 1500 km east of L. sibirica current range (Fig. 1), we found evidence for a wider distribution of L. sibirica around 32 and 34 ka BP in MIS3 (Fig. 3). Billyakh is situated in the western part of the Verkhoyansk Mountains, and Kyutyunda on the Central Siberian Plateau. Both lakes have low counts of Larix DNA sequences in their oldest samples dated to 51 ka BP (Billyakh) and 38 ka BP (Kyutyunda) with variants of L. gmelinii, but there is a sudden rise in variants attributed to L. sibirica at 34 ka BP (Billyakh) and 32 ka BP (Kyutyunda), which persists in the following samples, but strongly decreases in younger samples (Fig. 3). The rise in the L. sibirica DNA sequence variants coincides with a peak in sequence counts for Lake Kyutyunda. These signals suggest a rapid invasion of L. sibirica into the ranges of L. gmelinii in climatically favorable times and a local depletion or extinction of L. sibirica during the following harsher climates. Lake Billyakh pollen data suggest a moister and warmer climate around 50–30 ka BP than in the latter part of the Last Glacial associated with the MIS3 Interstadial in Siberia46.Fig. 3: Percentage and sequence counts at variable positions along Larix chloroplast genome assigned to species.Left: Alignment of Larix-classified DNA sequences against the chloroplast genome at the 157 variable positions between the species. For each position, the percentage of sequences assigned to a single species is displayed. Each row represents one sample named according to the calibrated age before present. Gray background indicates no coverage at the respective position. Right: Total number of sequences assigned to each of the species per sample.Full size imageStrong support for a wider pre-glacial distribution of L. sibirica comes from genetic analyses which show that it is genetically close to L. olgensis, today occurring on the Korean Peninsula and adjacent areas of China and Russia27,47. It is assumed that the L. sibirica-L. olgensis complex used to share a common range, which was disrupted and displaced when the better cold-adapted L. gmelinii expanded south and southwest during the more continental climatic conditions of the Pleistocene47,48. Furthermore, modern and ancient genetic studies suggest that the L. sibirica zone was recently invaded by L. gmelinii from the east in the hybridization zone of the species, as the climate cooled after the mid-Holocene thermal maximum13,23. Today, pure stands of L. sibirica do not form a continuous habitat, but occur in netted islands5 and morphological features of L. sibirica can be found in populations of L. gmelinii located at least a hundred kilometers east of the closest L. sibirica populations49. Macrofossil findings of L. sibirica in Scandinavia dated to the early Holocene, point to the capability of rapid long-distance jump dispersal of this species50. Fossil L. sibirica cones dated to the end of the Pliocene and in the Pleistocene have also been found far east of its current range in several river valleys including Kolyma, Aldan, and Omolon, and even in the basin of the Sea of Okhotsk9. These indicate long-distance seed dispersal by rivers which may also have assisted in successful establishment since the active-layer depth is deeper close to rivers51,52. As mentioned earlier, L. sibirica is sensitive to permafrost and waterlogged soils. A warmer phase with a deeper thawed layer above the permafrost could have enabled L. sibirica to spread and establish in regions that today are part of the geographic range of L. gmelinii, as L. sibirica is reported to have higher growth rates than L. gmelinii13.
Larix gmelinii formed northern LGM refugia across SiberiaThe possible survival of Larix in high latitude glacial refugia during the LGM is still under discussion4,53 although more and more evidence is reported in favor of the existence of such refugia17,20,21. The question of which of the Larix species formed these populations has hitherto been unanswered, as both pollen and established metabarcoding markers are not able to distinguish between species in the genus Larix, and findings of fossilized cones identifiable to species are rare. By enriching sedaDNA extracts for chloroplast genome sequences, we are, to the best of our knowledge, for the first time, able to distinguish between L. sibirica and L. gmelinii in glacial refugial populations.From Lake Lama, located at the western margin of the Putorana Plateau (Taymyr Peninsula), we obtained a continuous record extending from 23 ka BP to today with varying sequence counts with minima around 18–17 ka BP and 13 ka BP. All samples prior to the Holocene show variations predominantly assigned to L. gmelinii (Fig. 3). Our results suggest a local survival of L. gmelinii in the vicinity of Lake Lama throughout the LGM, which is supported by low numbers of Larix pollen detected through this period. Both target enriched sequence data and pollen indicate an increase from ca. 11 ka BP54. Sparse Larix pollen in the bottom part of the record could be an indication of a possible refugial population (Fig. 2; ref. 54).In Bolshoye Shchuchye, the westernmost lake of the study, situated in the Polar Ural Mountains, all Pleistocene samples show similarly a dominance of L. gmelinii sequence variations (Fig. 3). However, sequence counts for some samples are extremely low and samples from 18 and 10 ka BP had so low counts of mapped DNA sequences that none of the variable positions between the species was covered. Although sequences mapped to the satellite repeat of Larix also showed a Pleistocene signal, this was not repeated in pollen or metabarcoding (Fig. 2) which instead indicates a treeless arctic-alpine flora for the late Pleistocene55,56. Especially for the sample of 20.4 ka, Larix sequence counts are extremely low and new investigations would be needed to confirm a local presence of Larix during the LGM.The record of Lake Billyakh situated in the western Verkhoyansk Mountain Range, likewise shows extremely low counts of sequences mapped to the reference for a range of samples with no sequences covering the studied variable sites (45, 42, and 15 ka BP, 11–56 sequences mapped to non-variable sites). However, the pollen record for the same core shows a quasi-continuous record of Larix with a gap only occurring during the early LGM46 (25–22 ka BP, Fig. 2). Considering the known short-distance dispersal ability and poor preservation of Larix pollen, this strongly supports the presumed existence of a local glacial refugium at Lake Billyakh during that time20. Our samples also show a low but steady presence of Larix throughout the rest of the record, thus making glacial survival probable. The sample closest to the LGM (24 ka BP) indicates a clear dominance of L. gmelinii type variations.The only exception to this general pattern is the record from Lake Kyutyunda, which is located on the Central Siberian Plateau west of the Verkhoyansk Mountain Range. In this record, LGM samples have extremely low counts but show variations assigned to L. sibirica and not to L. gmelinii as in the other lakes. In addition, the preceding sample dated to the MIS3 interstadial shows L. sibirica variation. A possible explanation is that relics of L. sibirica survived during the LGM, but were unable to spread after climate warming, possibly due to genetic depletion or later local extinction. The presence of reworked sediment material can also not be excluded, as suggested by reworked pollen in the record57.In conclusion, our data show almost exclusively L. gmelinii variation for samples covering the most severe LGM climate conditions. This is in agreement with the ecological characteristics describing the species as adapted to extreme cold. In contrast to L. sibirica, it can grow in dwarf forms and propagate clonally and potentially survive thousands of years of adverse climatic conditions58.Postglacial colonization history—differences among larch speciesOf great interest in the Larix history is not only the location and extent of possible high latitude glacial refugia but also if and to what extent these refugia contributed to the recolonization of Siberia after the LGM. Northern refugial populations could have functioned as kernels of postglacial population spread and recolonization, or spreading could have been driven by populations that survived in southern refugia. There are only a few studies on modern populations that report evidence for possible recolonization scenarios of Larix23,27,28. Here, we show that patterns differ between L. sibirica and L. gmelinii.In the western part of our study region, two lakes are situated in the current distribution range of L. sibirica (Figs. 1, 4): Lake Bolshoye Shchuchye in the Polar Ural Mountains and Lake Lama on the Taymyr Peninsula. Despite this, both lakes show L. gmelinii for all Pleistocene samples, and a strong signal of L. sibirica variants only in the Holocene samples, with ages of 5.1 ka BP in Lake Bolshoye Shchuchye and 9.7 ka BP in Lake Lama (Fig. 3). The peak in L. sibirica also coincides with a peak of sequence counts in the respective sample, with a Larix pollen peak in Lake Lama sediments54, and metabarcoding for Lake Bolshoye Shchuchye55. This points to a migration of L. sibirica in its current northern area of northern distribution in the course of climate warming during the early Holocene, whereas glacial refugial populations were consisting of L. gmelinii. Although the local survival of L. gmelinii around Lake Bolshoye Shchuchye remains uncertain due to extremely low sequence counts, it is clear that L. sibirica did not form a refugial population at this site.Fig. 4: Percentage of DNA sequences assigned to references displayed on the geographical locations of the lakes investigated.Samples in the same time frame are averaged. Lake names and current species ranges are annotated in the middle plots. Colors indicate current species distribution (adapted from Semerikov and Lascoux72). The base map is done with ggmap73, map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.Full size imageA range-wide genetic study of L. sibirica analyzing chlorotypes and mitotypes of individuals23 found strong indications for rapid colonization of the West Siberian Plains from populations originating from the foothills of the Sayan Mountains in the south, close to the border of Mongolia, with only limited contribution from local populations. According to our results, the local populations could have been L. gmelinii populations, while the rapid invasion could have been L. sibirica.In the eastern range of the study region, in the current range of L. gmelinii, namely at lakes Emanda, Satagay, and Malaya Chabyda, genetic variations throughout the records are less pronounced. Of the three lake records, only that from Lake Emanda reaches back beyond the LGM, but with a sampling gap for the time of the LGM. Therefore, it remains uncertain whether populations survived the LGM locally, or whether they were invaded or replaced by populations coming from the south with Holocene warming. The restricted variations throughout the record, however, hint at stable populations, which is supported by scarce pollen findings (Fig. 2).Our data suggest that postglacial recolonization of L. sibirica was not started from high latitude glacial refugia, but from southern populations. In contrast, northern glacial populations of L. gmelinii could have potentially enhanced rapid dispersal after the LGM in their current area of distribution.Environment likely plays a more important role than historical factorsThe current boundaries of boreal Larix species arranged from west to east suggest a possible strong influence of the historical species distribution on the current distribution, whereas the gradient of increasing continental climate towards the east assumes a strong influence on the environment. By tracking species distribution in the past, spanning the time of the strongly adverse climate of the LGM, we can give hitherto unprecedented insights into species distribution history.Several lines of evidence suggest a strong influence of the environment on species distribution: (1) Signals for L. sibirica appeared in its current area of distribution as late as the Holocene warming, whereas cold Pleistocene samples are dominated by L. gmelinii type variation; (2) in lakes far east of its modern range, signals of variation typical for L. sibirica coincide with peaks in sequence counts (29 ka BP, Lake Billyakh; 32 ka BP Lake Kyutyunda), which point to more forested vegetation around the lakes and consequently a more favorable climate at that time; and (3) samples dated to the severely cold LGM are dominated by variations of the L. gmelinii type.This is in accordance with the different ecological characteristics described for the species. L. sibirica is sensitive to permafrost and only occurs outside of the zone of continuous permafrost5. In addition, L. sibirica achieves substantially higher growth rates and longer growth periods than L. gmelinii9,13 and can also produce more than twice as many seeds5. This potentially gives L. sibirica the ability to quickly react to climate change and outcompete the other species when the climate becomes more favorable.In contrast, L. gmelinii is adapted to extremely low soil and air temperatures and is able to grow on permafrost with very shallow thaw depths. It’s distribution almost completely coincides with continuous permafrost5, and even a restriction to permafrost areas is discussed as it does not grow well in field trials on warmer soils or where there is a small temperature gradient between air and soil9. Due to this ecology, L. gmelinii is more likely to survive in a high latitude refugium, even during the severe continental climate of the LGM, which was most probably connected to continuous permafrost of low active-layer depths.A study combining mitochondrial barcoding on sedaDNA and a modeling approach on Larix distribution in the Taymyr region around Lake CH12 concluded that the distributions of L. gmelinii and L. sibirica are most strongly influenced by stand density and thus by competition between the species, with L. gmelinii outcompeting L. sibirica at high stand densities13. As our study includes sediment cores reaching further back in time, we see a different trend. Instead of L. gmelinii, it was L. sibirica, which dominated samples with high sequence counts, suggesting high stand density and a more favorable climate. A possible explanation for the different outcomes is the use of different organelle genomes. Epp et al.13 used a marker representing the mitochondrial genome, which is known to introgress more rapidly and as a consequence might show a long past species history59,60.Our findings have potentially important implications for the projections of vegetation-climate feedback. A warming climate in conjunction with a greater permafrost thaw depth could enable the replacement of L. gmelinii by L. sibirica. In contrast to L. gmelinii, L. sibirica is not known to stabilize permafrost thus potentially further promoting permafrost thaw and with it the release of greenhouse gases, creating positive feedback on global warming11. On the other hand, the substantially higher growth rates of L. sibirica in comparison to L. gmelinii would increase carbon sequestration, thus mitigating global warming13. This shows the importance of understanding species-specific reactions to climate change, which can result in great shifts in distribution. Target enrichment applied on sedaDNA is able to reveal the impact of past climate change on populations and the increasing availability of modern reference genomes will further enhance its value of information. More