Ecology

1.
Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 1–9 (2016).
2.
Seebens, H. et al. Global rise in emerging alien species results from increased accessibility of new source pools. Proc. Natl Acad. Sci. USA 115, E2264–E2273 (2018).
CAS  PubMed  Article  Google Scholar 

3.
Seebens, H. et al. Global trade will accelerate plant invasions in emerging economies under climate change. Glob. Chang. Biol. 21, 4128–4140 (2015).
ADS  PubMed  Article  Google Scholar 

4.
Simberloff, D. et al. Impacts of biological invasions: what’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).
PubMed  Article  Google Scholar 

5.
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144 (2010).
Article  Google Scholar 

6.
Van Kleunen, M., Dawson, W., Schlaepfer, D., Jeschke, J. M. & Fischer, M. Are invaders different? A conceptual framework of comparative approaches for assessing determinants of invasiveness. Ecol. Lett. 13, 947–958 (2010).
PubMed  Google Scholar 

7.
Enserink, M. Biological invaders sweep in. Science 285, 1834–1836 (1999).
CAS  Article  Google Scholar 

8.
Hulme, P. E. Phenotypic plasticity and plant invasions: is it all Jack? Funct. Ecol. 22, 3–7 (2008).
Article  Google Scholar 

9.
Murray, B. R., Thrall, P. H., Gill, A. M. & Nicotra, A. B. How plant life-history and ecological traits relate to species rarity and commonness at varying spatial scales. Austral Ecol. 27, 291–310 (2002).
Article  Google Scholar 

10.
Davidson, A. M., Jennions, M. & Nicotra, A. B. Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis. Ecol. Lett. 14, 419–431 (2011).
PubMed  Article  Google Scholar 

11.
Bazin, É., Mathé-Hubert, H., Facon, B., Carlier, J. & Ravigné, V. The effect of mating system on invasiveness: Some genetic load may be advantageous when invading new environments. Biol. Invasion. 16, 875–886 (2014).
Article  Google Scholar 

12.
Zheng, Y. et al. Are invasive plants more competitive than native conspecifics? Patterns vary with competitors. Sci. Rep. 5, 1–8 (2015).
Google Scholar 

13.
Callaway, R. M. & Aschehoug, E. T. Invasive plants versus their new and old neighbors: a mechanism for exotic invasion. Science 290, 521–523 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

14.
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).
PubMed  Article  Google Scholar 

15.
Gallagher, R. V., Beaumont, L. J., Hughes, L. & Leishman, M. R. Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia. J. Ecol. 98, 790–799 (2010).
Article  Google Scholar 

16.
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Syst. 33, 181–233 (2002).
Article  Google Scholar 

18.
Hölldobler, Bert, E. O. W. The Ants. (Havard University Press, 1990).

19.
Meurisse, N., Rassati, D., Hurley, B. P., Brockerhoff, E. G. & Haack, R. A. Common pathways by which non-native forest insects move internationally and domestically. J. Pest Sci. 92, 13–27 (2018).
Article  Google Scholar 

20.
Bertelsmeier, C., Luque, G. M., Hoffmann, B. D. & Courchamp, F. Worldwide ant invasions under climate change. Biodivers. Conserv. 24, 117–128 (2015).
Article  Google Scholar 

21.
Fitzpatrick, M. C., Weltzin, J. F., Sanders, N. J. & Dunn, R. R. The biogeography of prediction error: why does the introduced range of the fire ant over-predict its native range? Glob. Ecol. Biogeogr. 16, 24–33 (2007).
Article  Google Scholar 

22.
Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 1–8 (2016).

23.
Bertelsmeier, C., Ollier, S., Liebhold, A. & Keller, L. Recent human history governs global ant invasion dynamics. Nat. Ecol. Evol. 1, 0184 (2017).
PubMed  PubMed Central  Article  Google Scholar 

24.
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
PubMed  Article  Google Scholar 

25.
Essl, F. et al. Socioeconomic legacy yields an invasion debt. Proc. Natl Acad. Sci. USA 108, 203–207 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

26.
Rouget, M. et al. Invasion debt-quantifying future biological invasions. Divers. Distrib. 22, 445–456 (2016).
Article  Google Scholar 

27.
Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional Areas. Biodivers. Inform. 2, 0–10 (2005).
Article  Google Scholar 

28.
Keane, R. M. & Crawley, M. J. Exotic plant invasions and the enemy release hypothesis. Trends Ecol. Evol. 17, 164–170 (2002).
Article  Google Scholar 

29.
Shea, K. & Chesson, P. Community ecology theory as a framework for biological invasions. Trends Ecol. Evol. 163, 170–176 (2002).
Article  Google Scholar 

30.
Bocsi, T. et al. Plants’ native distributions do not reflect climatic tolerance. Divers. Distrib. 22, 615–624 (2016).
Article  Google Scholar 

31.
Bolnick, D. I. et al. Ecological release from interspecific competition leads to decoupled changes in population and individual niche width. Proc. R. Soc. B Biol. Sci. 277, 1789–1797 (2010).
Article  Google Scholar 

32.
Torres, U. et al. Using niche conservatism information to prioritize hotspots of invasion by non-native freshwater invertebrates in New Zealand. Divers. Distrib. 24, 1802–1815 (2018).
Article  Google Scholar 

33.
Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).
Article  Google Scholar 

34.
Godefroid, M., Rasplus, J. Y. & Rossi, J. P. Is phylogeography helpful for invasive species risk assessment? The case study of the bark beetle genus Dendroctonus. Ecography 39, 1197–1209 (2016).
Article  Google Scholar 

35.
Bujan, J., Roeder, K. A., Yanoviak, S. P. & Kaspari, M. Seasonal plasticity of thermal tolerance in ants. Ecology 101, 1–6 (2020).
Article  Google Scholar 

36.
Bujan, J. & Kaspari, M. Nutrition modifies critical thermal maximum of a dominant canopy ant. J. Insect Physiol. 102, 1–6 (2017).
CAS  PubMed  Article  Google Scholar 

37.
Alexander, J. M. & Edwards, P. J. Limits to the niche and range margins of alien species. Oikos 119, 1377–1386 (2010).
Article  Google Scholar 

38.
Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).
PubMed  Article  Google Scholar 

39.
Tingley, R., Vallinoto, M., Sequeira, F. & Kearney, M. R. Realized niche shift during a global biological invasion. Proc. Natl Acad. Sci. USA 111, 10233–10238 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

40.
Medley, K. A. Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Glob. Ecol. Biogeogr. 19, 122–133 (2010).
Article  Google Scholar 

41.
Kolbe, J. J. et al. Genetic variation increases during biological invasion by a Cuban lizard. Nature 431, 177–181 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Angetter, L. S., Lotters, S. & Rodder, D. Climate niche shift in invasive species: the case of the brown anole. Biol. J. Linn. Soc. 104, 943–954 (2011).
Article  Google Scholar 

43.
Colautti, R. I. & Lau, J. A. Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Mol. Ecol. 24, 1999–2017 (2015).
PubMed  Article  Google Scholar 

44.
Bertelsmeier, C. & Keller, L. Bridgehead effects and role of adaptive evolution in invasive populations. Trends Ecol. Evol. 33, 527–534 (2018).
PubMed  Article  Google Scholar 

45.
Srivastava, V., Lafond, V. & Griess, V. C. Species distribution models (SDM): applications, benefits and challenges in invasive species management. CAB Rev. 14, 1–13 (2019).

46.
Pili, A. N., Tingley, R., Sy, E. Y., Diesmos, M. L. L. & Diesmos, A. C. Niche shifts and environmental non-equilibrium undermine the usefulness of ecological niche models for invasion risk assessments. Sci. Rep. 10, 1–18 (2020).
Article  CAS  Google Scholar 

47.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
PubMed  PubMed Central  Article  Google Scholar 

48.
Kirchhof, S. et al. Thermoregulatory behavior and high thermal preference buffer impact of climate change in a Namib Desert lizard. Ecosphere 8, e02033 (2017).

49.
Woods, H. A., Dillon, M. E. & Pincebourde, S. The roles of microclimatic diversity and of behavior in mediating the responses of ectotherms to climate change. J. Therm. Biol. 54, 86–97 (2015).
PubMed  Article  Google Scholar 

50.
Chapman, D. S., Scalone, R., Štefanić, E. & Bullock, J. M. Mechanistic species distribution modeling reveals a niche shift during invasion. Ecology 98, 1671–1680 (2017).
PubMed  Article  Google Scholar 

51.
Janicki, J., Narula, N., Ziegler, M., Guénard, B. & Economo, E. P. Visualizing and interacting with large-volume biodiversity data using client-server web-mapping applications: The design and implementation of antmaps.org. Ecol. Inform. 32, 185–193 (2016).
Article  Google Scholar 

52.
Guénard, B., Weiser, M. D., Gómez, K., Narula, N. & Economo, E. P. The Global Ant Biodiversity Informatics (GABI) database: Synthesizing data on the geographic distribution of ant species (Hymenoptera: Formicidae). Myrmecological N. 24, 83–89 (2017).
Google Scholar 

53.
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Article  Google Scholar 

54.
Pagad, S., Genovesi, P., Carnevali, L., Scalera, R. & Clout, M. IUCN SSC invasive species specialist group: Invasive alien species information management supporting practitioners, policy makers and decision takers. Manag. Biol. Invasion. 6, 127–135 (2015).
Article  Google Scholar 

55.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

56.
Dray, S. & Dufour, A.-B. The ade4 Package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

57.
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
Article  Google Scholar 

58.
Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Article  Google Scholar 

59.
Schoener, T. W. The Anolis Lizards of Bimini: resource partitioning in a complex fauna were invaded by anoline lizards. Ecol. Soc. Am. 49, 704–726 (1968).
Google Scholar 

60.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).
PubMed  Article  Google Scholar 

61.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

62.
Bates, O. K., Ollier, S. & Bertelsmeier, C. Smaller climatic niche shifts in invasive than non-invasive alien ant species. GitHub. https://doi.org/10.5281/zenodo.4041296 (2020).

63.
Team, R. C. R.: A Language and Environment for Statistical Computing. (2019). More

1.
Trathan, P. N. & Hill, S. L. in Biology and Ecology of Antarctic krill (ed. Siegel, V.) 321–350 (Springer, 2016).
2.
Atkinson, A. et al. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep Sea Res. Pt. 1. 56, 727–740 (2009).
Article  Google Scholar 

3.
Bar-On, Y. N., Philips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  Article  Google Scholar 

4.
Atkinson, A. et al. Sardine cycles, krill declines, and locust plagues: revisiting ‘wasp-waist’ food webs. Trends Ecol. Evol. 29, 309–316 (2014).
Article  Google Scholar 

5.
Cavan, E. L. et al. The importance of Antarctic krill in biogeochemical cycles. Nat. Commun. 10, 4742 (2019).
CAS  Article  Google Scholar 

6.
Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish. Fish 11, 203–209 (2010).
Article  Google Scholar 

7.
Schmidt, K. et al. Zooplankton gut passage mobilizes lithogenic iron for ocean productivity. Curr. Biol. 26, 2667–2673 (2016).
CAS  Article  Google Scholar 

8.
Nicol, S. & Foster, J. in Biology and Ecology of Antarctic krill 387–421 (Springer, 2016).

9.
Kawaguchi, S & Nicol, S. in Fisheries and Aquaculture Vol. 9. (eds Lovrich, G. & Thiel, M.) 137–158, https://doi.org/10.1093/oso/9780190865627.003.0006. (Oxford University Press, 2020).

10.
Turner, J. & Overland, J. Contrasting climate change in the two polar regions. Polar Res. 26, 146–164 (2009).
Article  Google Scholar 

11.
Rogers, A. D. et al. Antarctic futures: an assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the southern ocean. Annul Rev. Mar. Sci 12, 87–120 (2019).
Article  Google Scholar 

12.
Kawaguchi, S., Nicol, S. & Press, A. J. Direct effects of climate change on the Antarctic krill fishery. Fisheries Manag. Ecol. 16, 424–427 (2009).
Article  Google Scholar 

13.
Trathan, P. N. et al. Krill biomass in the Atlantic. Nature 367, 201–202 (1995).
Article  Google Scholar 

14.
Constable, A. J. & de la Mare, W. K. A generalised yield model for evaluating the yield and the long-term status of fish stocks under conditions of uncertainty. CCAMLR Sci. 3, 31–54 (1996).
Google Scholar 

15.
Hill, S. L. et al. Is current management of the Antarctic krill fishery in the Atlantic sector of the Southern Ocean precautionary? CCAMLR Sci. 23, 31–51 (2016).
Google Scholar 

16.
Hewitt, R. P. et al. Options for allocating the precautionary catch limit of krill among small-scale management units in the Scotia Sea. CCAMLR Sci 11, 81–97 (2004).
Google Scholar 

17.
Watters, G. M., Hill, S. L., Hinke, J. T., Matthews, J. & Reid, K. Decision-making for ecosystem-based management: evaluating options for a krill fishery with an ecosystem dynamics model. Ecol. Appl. 23, 710–725 (2013).
CAS  Article  Google Scholar 

18.
Trathan, P. N. et al. Managing fishery development in sensitive ecosystems: identifying penguin habitat use to direct management in Antarctica. Ecosphere 9, e02392 (2018).
Article  Google Scholar 

19.
Watters, G. M., Hinke, J. T. & Reiss, C. Long-term observations from Antarctica demonstrate that mismatched scales of fisheries management and predator-prey interaction lead to erroneous conclusions about precaution. Sci. Rep. 10, 2314 (2020).
CAS  Article  Google Scholar 

20.
Reiss, C. S., Cossio, A. M., Loeb, V. & Demer, D. A. Variations in the biomass of Antarctic krill (Euphausia superba) around the South Shetland Islands, 1996–2006. ICES J. Mar. Sci. 65, 497–508 (2008).
Article  Google Scholar 

21.
Fielding, S. et al. Interannual variability in Antarctic krill (Euphausia superba) density at South Georgia, Southern Ocean: 1997–2013. ICES J. Mar. Sci. 71, 2578–2588 (2014).
Article  Google Scholar 

22.
Brierley, A. S. & Reid, K. Krill and the diversity of science and society: An introduction to the Third International Symposium on Krill. J. Crust. Biol. 38, 651–655 (2018).
Google Scholar 

23.
Report of the Thirty-Sixth Meeting of the Scientific Committee of the Commission for the Conservation of Antarctic Marine Living Resources. (CCAMLR, Hobart, Australia, 2017).

24.
SC-CCAMLR Report of the thirty-eight Meeting of the Scientific Committee, of the Commission for the Conservation of Antarctic Marine Living Resources. (CCAMLR Hobart, Australia, 2019).

25.
Spiridonov, V. Spatial and temporal variability in reproductive timing of Antarctic krill (Euphausia superba Dana). Polar Biol. 15, 161–174 (1995).
Article  Google Scholar 

26.
Siegel, V. Distribution and population dynamics of Euphausia superba: summary of recent findings. Polar Biol. 29, 1–22 (2005).
Article  Google Scholar 

27.
Schmidt, K., Atkinson, A., Venables, H. & Pond, D. W. Early spawning of Antarctic krill in the Scotia Sea is fueled by ‘superfluous’ feeding on non-ice associated phytoplankton blooms. Deep Sea Res. II 59, 159–172 (2012).
Article  Google Scholar 

28.
Ross, R. B. & Quetin, L. B. Energetic cost to develop to the first feeding stage of Euphausia superba Dana and the effect of delays in food availability. J. Exp. Mar. Biol. Ecol. 133, 103–127 (1989).
Article  Google Scholar 

29.
Meyer, B. et al. The winter pack-ice zone provides a sheltered but food-poor habitat for larval Antarctic krill. Nat. Ecol. Evol 1, 1853–1861 (2017).
Article  Google Scholar 

30.
Brierley, A. S., Demer, D. A., Hewitt, R. P. & Watkins, J. L. Concordance of inter-annual fluctuations in densities of krill around South Georgia and Elephant Islands: biological evidence of same year teleconnections across the Scotia Sea. Mar. Biol. 134, 675–681 (1999).
Article  Google Scholar 

31.
Reiss, C. S. in Biology and Ecology of Antarctic krill 101–144 (Springer, 2016).

32.
Quetin, L. B., Ross, R. M., Fritsen, C. H. & Vernet, M. Ecological responses of Antarctic krill to environmental variability: can we predict the future? Ant. Sci. 19, 253–266 (2007).
Article  Google Scholar 

33.
Saba, G. K. et al. Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula. Nat. Commun. 5, 4318 (2014).
CAS  Article  Google Scholar 

34.
Murphy, E. J. et al. Spatial and temporal operation of the Scotia Sea ecosystem: a review of large-scale links in a krill centered food web. Phil. Trans. R. Soc. B 362, 113–148 (2007).
CAS  Article  Google Scholar 

35.
Kinzey, D. et al. Selectivity and two biomass measures in an age-based assessment of Antarctic krill (Euphausia superba). Fish. Res. 168, 72–84 (2015).
Article  Google Scholar 

36.
Siegel, V. & Loeb, V. et al. Recruitment of Antarctic krill (Euphausia superba) and possible causes for its variability. Mar. Ecol. Prog. Ser. 123, 45–56 (1995).
Article  Google Scholar 

37.
Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Oceanogr. 134, 93–122 (2015).
Article  Google Scholar 

38.
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).
Article  Google Scholar 

39.
Thorpe, S. E., Tarling, G. A. & Murphy, E. J. Circumpolar patterns in Antarctic krill larval recruitment: an environmentally driven model. Mar. Ecol. Prog. Ser. 613, 77–96 (2019).
Article  Google Scholar 

40.
Ryabov, A. B. et al. Competition-induced starvation drives large-scale population cycles in Antarctic krill. Nat. Ecol. Evol 1, 1–8 (2017).
Article  Google Scholar 

41.
Makarov, R. R. & Menshenina, L. L. On the distribution of euphausiid larvae in the Antarctic waters. Okeanologija Akademija Nauk SSSR. Okeanograficeskaja Komissija, Moskva 29, 825–831 (1989).
Google Scholar 

42.
Perry, F. A. et al. Habitat partitioning in Antarctic krill: spawning hotspots and nursery areas. PLoS ONE 14, e0219325 (2019).
CAS  Article  Google Scholar 

43.
Siegel, V & Watkins, J. N. in Biology and Ecology of Antarctic krill 21–100 (Springer, 2016).

44.
Hofmann, E. E. & Hüsrevoğlu, Y. S. A circumpolar modelling study of habitat control of Antarctic krill (Euphausia superba) reproductive success. Deep-Sea Res II 50, 3121–3142 (2003).
Article  Google Scholar 

45.
King, M. Fisheries Biology, Assessment and Management 341 (Fishing News Books, Blackwell Science, Oxford, 1995).

46.
Rombolá, E. R. et al. Variability of euphausiid larvae densities during the 2011, 2012, and 2014 summer seasons in the Atlantic sector of the Antarctic. Polar Sci. 19, 86–93 (2019).
Article  Google Scholar 

47.
Conroy, J. A., Reiss, C. S., Gleiber, M. R. & Steinberg, D. K. Linking Antarctic krill larval supply and recruitment along the Antarctic Peninsula. Integr. Comp. Biol. https://doi.org/10.1093/icb/icaa111 (2020).
Article  Google Scholar 

48.
Siegel, V. et al. Distribution and abundance of Antarctic krill (Euphausia superba) along the Antarctic Peninsula. Deep Sea Res. I 77, 63–74 (2013).
Article  Google Scholar 

49.
Lumpkin, R. & Centurioni, L. Global Drifter Program quality-controlled 6-hour interpolated data from ocean surface drifting buoys. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/7ntx-z961 (2019)

50.
Siegel, V. in Antarctic Ocean and Resources Variability 219–230 (Springer, 1988).

51.
Trathan, P. N. et al. Spatial variability of Antarctic krill in relation to mesoscale hydrography. Mar. Ecol. Prog. Ser. 98, 61–71 (1993).
Article  Google Scholar 

52.
Jazdzewski, K. et al. Biological and populational studies on krill near South Shetland Islands, Scotia Sea and South Georgia in the summer 1976. Polskie Archiwum Hydrobiologii 25, 607–631 (1978).
Google Scholar 

53.
Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16 (2017).
CAS  Article  Google Scholar 

54.
Piñones, A. et al. Modeling the remote and local connectivity of Antarctic krill populations along the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 481, 69–92 (2013).
Article  Google Scholar 

55.
Taki, K., Hayashi, T. & Naganobu, M. Characteristics of seasonal variation in diurnal vertical migration and aggregation of Antarctic krill (Euphausia superba) in the Scotia Sea, using Japanese fishery data. CCAMLR Sci. 12, 163–172 (2005).
Google Scholar 

56.
Barlow, K. E. et al. Are penguins and seals in competition for Antarctic krill at South Georgia? Mar. Biol. 140, 205–213 (2002).
Article  Google Scholar 

57.
Reid, K., Trathan, P. N., Croxall, J. P. & Hill, H. J. Krill caught by predators and nets: differences between species and techniques. Mar. Ecol. Prog. Ser. 140, 13–20 (1996).
Article  Google Scholar 

58.
Jackson, J. A. et al. Global diversity and oceanic divergence of humpback whales (Megaptera novaeangliae). Proc. Roy. Soc. B-Biol. Sci 281, 20133222 (2014).
Article  Google Scholar 

59.
Herr, H. et al. Horizontal niche partitioning of humpback and fin whales around the West Antarctic Peninsula: evidence from a concurrent whale and krill survey. Polar Biol. 39, 799–818 (2016).
Article  Google Scholar 

60.
Viquerat, S. & Herr, H. Mid-summer abundance estimates of fin whales Balaenoptera physalus around the South Orkney Islands and Elephant Island. ESR 32, 515–524 (2017).
Article  Google Scholar 

61.
Zerbini, A. N. et al. Assessing the recovery of an Antarctic predator form historical exploitation. Roy. Soc. Open Sci 6, 190368 (2019).
Article  Google Scholar 

62.
Reid, K. et al. Widening the net: spatio-temporal variability in the krill population structure across the Scotia Sea. Deep-Sea Res. II 51, 1275–1287 (2004).
Article  Google Scholar 

63.
Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).
CAS  Article  Google Scholar 

64.
Hill, S. L., Trathan, P. H. & Agnew, D. J. The risk to fishery performance associated with spatially resolved management of Antarctic krill (Euphausia superba) harvesting. ICES J. Mar. Sci 66, 2148–2154 (2009).
Article  Google Scholar 

65.
Tarling, G. A., Ward, P. & Thorpe, S. E. Spatial distributions of Southern Ocean mesozooplankton communities have been resilient to long-term surface warming. Global Change Biol. https://doi.org/10.1111/gcb.13834 (2017).

66.
Stammerjohn, S. S., Massom, R. A., Rind, D. & Martinson, D. G. Regions of rapid sea ice change: an inter-hemispheric seasonal comparison. Geophys. Res. Lett. 39, L06501 (2012).
Article  Google Scholar 

67.
Henley, S. F. et al. Variability and change in the west Antarctic Peninsula marine system: research priorities and opportunities. Prog. Oceanogr. https://doi.org/10.1016/j.pocean.2019.03.003 (2019).

68.
Turner, J. et al. Absence of 21st century warming on Antarctic Peninsula consistent with natural variability. Nature. 535, 411–415 (2016).
CAS  Article  Google Scholar 

69.
Swart, N. C. & Fyfe, J. C. Observed and simulated changes in the Southern Hemisphere surface westerly wind-stress. Geophys. Res. Letters 39, L16711 (2012).
Article  Google Scholar 

70.
Datwyler, C. et al. Teleconnection stationality, variability and trends of the Southern Annular Mode (SAM) during the last millennium. Clim. Dyn. 51, 2321–2339 (2017).
Article  Google Scholar 

71.
Stammerjohn, S. E. et al. Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability. J. Geophys. Res. 113, C03S90 (2008).
Article  Google Scholar 

72.
SC-CCAMLR Report of the twenty-ninth Meeting of the Scientific Committee, of the Commission for the Conservation of Antarctic Marine Living Resources. (CCAMLR Hobart, Australia, 2010).

73.
Cox, M. J. et al. No evidence for a decline in the density of Antarctic krill Euphausia superba Dana, 1850, in the Southwest Atlantic sector between 1976 and 2016. J. Crust. Biol. 38, 656–661 (2018).
Google Scholar 

74.
Loeb, V. et al. Effects of sea ice extent and krill or salp dominance on the Antarctic food web. Nature 387, 897–900 (1997).
CAS  Article  Google Scholar 

75.
Trivelpiece, W. Z. et al. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. Proc. Natl Acad. Sci. USA 108, 7625–7628 (2011).
CAS  Article  Google Scholar 

76.
Huang, T. et al. Relative changes in krill abundanceiInferred from Antarctic Fur Seal. PLoS ONE 6, e27331 (2011).
CAS  Article  Google Scholar 

77.
Atkinson, A., Siegel, V., Pakhomov, E. A. & Rothery, P. Long term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).
CAS  Article  Google Scholar 

78.
Forcada, J. & Hoffman, J. I. Climate change selects for heterozygosity in a declining fur seal population. Nature 511, 462–465 (2014).
CAS  Article  Google Scholar 

79.
McMahon, K. W. et al. Divergent trophic responses of sympatric penguin species to historic anthropogenic exploitation and recent climate change. Proc. Natl Acad Sci. USA 116, 25721–25727 (2019).
CAS  Article  Google Scholar 

80.
Hill, S. L., Atkinson, A., Pakhomov, E. A. & Siegel, V. Evidence for a decline in the population density of Antarctic krill Euphausia superba Dana 1850, still stands: A comment on Cox et al. J. Crust. Biol 39, 316–322 (2019).
Article  Google Scholar 

81.
Cox, M. J. et al. Clarifying trends in the density of Antarctic krill Euphausia superba Dana, 1850 in the South Atlantic. A response to Hill et al. J. Crust. Biol. 39, 323–327 (2019).
Article  Google Scholar 

82.
Hill, S. L. et al. Reference points for predators will progress ecosystem‐based management of fisheries. Fish. Fish. 21, 368–378 (2020).
Article  Google Scholar 

83.
Fuentes, V. et al. Glacial melting: an overlooked threat to Antarctic krill. Sci. Reps 6, 27234 (2016).
CAS  Article  Google Scholar 

84.
Flores et al. The response of Antarctic krill to climate change: Implications for management and research priorities. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).
Article  Google Scholar 

85.
Ross, R. M. et al. Palmer LTER: Patterns of distribution of five dominant zooplankton species in the epipelagic zone west of the Antarctic Peninsula, 1993–2004. Deep Sea Res. II 55, 2086–2105 (2008).
Article  Google Scholar 

86.
Loeb, V. J. et al. ENSO and variability of the Antarctic Peninsula pelagic marine ecosystem. Ant. Sci. 21, 135–148 (2009).
Article  Google Scholar 

87.
Beaugrand, G. & Kirby, R. R. How do marine pelagic species respond to climate change? Theories and observations. Annu. Rev. Mar. Sci. 10, 169–197 (2018).
Article  Google Scholar 

88.
Tarling, G. A. & Thorpe, S. E. Oceanic swarms of Antarctic krill perform satiation sinking. Proc. R. Soc. B 284, 20172015 (2017).
Article  CAS  Google Scholar 

89.
Hill, S. L., Phillips, T. & Atkinson, A. Potential climate change effects on the habitat of Antarctic krill in the Weddell Quadrant of the Southern Ocean. PLoS ONE 8, e72246 (2013).
CAS  Article  Google Scholar 

90.
Piñones, A. & Fedorov, A. V. Projected changes of Antarctic krill habitat by the end of the 21st century. Geophys. Res. Lett. 43, 8580–8589 (2016).
Article  Google Scholar 

91.
Murphy, E. J. et al. Restricted regions of enhanced growth of Antarctic krill in the circumpolar Southern Ocean. Sci. Reps 7, 6963 (2017).
Article  CAS  Google Scholar 

92.
Atkinson, A. et al. Natural growth rates in Antarctic krill (Euphausia superba): II. Predictive models based on food, temperature, body length, sex, and maturity stage. Limnol. Oceanogr. 51, 973–987 (2006).
Article  Google Scholar 

93.
Kawaguchi, S. et al. Risk maps for Antarctic krill under projected Southern Ocean acidification. Nat. Clim. Change 3, 343–347 (2013).
Article  CAS  Google Scholar 

94.
Kawaguchi, S. & Nicol, S. Learning about Antarctic krill from the fishery. Ant. Sci. 19, 219–230 (2007).
Article  Google Scholar 

95.
Warner, A. J., Hays, G. C. & Hays, G. Sampling by the Continuous Plankton Recorder survey. Prog. Oceanogr. 34, 237–256 (1994).
Article  Google Scholar 

96.
Petersen, W. FerryBox systems: State-of-the-art in Europe and future development. J. Mar. Syst. 140 A, 4–12 (2014).
Article  Google Scholar 

97.
Brierley, A. S. et al. Use of moored acoustic instruments to measure short-term variability in abundance of Antarctic krill. Limnol. Oceanogr.: Methods 4, 18–29 (2006).
Article  Google Scholar 

98.
Guihen, D. et al. An assessment of the use of ocean gliders to undertake acoustic measurements of zooplankton: the distribution and density of Antarctic krill (Euphausia superba) in the Weddell Sea. Limnol. Oceanogr.: Methods 12, 373–389 (2014).
Article  Google Scholar 

99.
Meinig, C. et al. Public private partnerships to advance regional ocean observing capabilities: A Saildrone and NOAA-PMEL case study and future considerations to expand to global scale observing. Front. Mar. Sci. 6, 448 (2019).
Article  Google Scholar 

100.
Park, Y. H. & Durand, I. Altimetry-derived Antarctic circumpolar current fronts. SEANOE. https://doi.org/10.17882/59800 (2019).

101.
Park, Y.-H. et al. Observations of the Antarctic Circumpolar Current over the Udintsev Fracture Zone, the narrowest choke point in the Southern Ocean. J. Geophys. Res.: Oceans. 124 https://doi.org/10.1029/2019JC015024 (2019)

102.
Ikeda, T. Development of the larvae of the Antarctic krill (Euphausia superba Dana) observed in the laboratory. J. Exp. Mar. Biol. Ecol. 75, 107–117 (1984).
Article  Google Scholar 

103.
Tarling, G. A. et al. Growth and shrinkage in Antarctic krill Euphausia superba is sex-dependent. Mar. Ecol. Prog. Ser. 547, 61–78 (2016).
Article  Google Scholar 

104.
Guinet, C. et al. Calibration procedures and first dataset of Southern Ocean chlorophyll a profiles collected by elephant seals equipped with a newly developed CTD-fluorescence tags. Earth Syst. Sci. Data 5, 15–29 (2013).
Article  Google Scholar 

105.
Thiebot, J-B et al. Jellyfish and other gelata as food for four penguin species—insights from predator-borne videos. Front. Ecol. Environ. https://doi.org/10.1002/fee.1529 (2017).

106.
Watanabe, Y. Y. & Takahashi, A. Linking animal-borne video to accelerometers reveals prey capture variability. Proc Natl Acad. Sci. USA 10, 2199–2204 (2013).
Article  Google Scholar  More

1.
Fu, B. et al. Three Gorges Project: efforts and challenges for the environment. Prog. Phys. Geog. 34, 741–754 (2010).
Article  Google Scholar 
2.
Yuan, X. et al. The littoral zone in the Three Gorges Reservoir, China: challenges and opportunities. Environ. Sci. Pollut. R. 20, 7092–7102 (2013).
Article  Google Scholar 

3.
Xu, X., Tan, Y. & Yang, G. Environmental impact assessments of the Three Gorges Project in China: issues and interventions. Earth Sci. Rev. 124, 115–125 (2013).
ADS  Article  Google Scholar 

4.
Zhang, Q. & Lou, Z. The environmental changes and mitigation actions in the Three Gorges Reservoir region China. Environ. Sci. Policy 14, 1132–1138 (2011).
Article  Google Scholar 

5.
Huang, Y. et al. Nutrient estimation by HJ-1 satellite imagery of Xiangxi Bay, Three Gorges Reservoir China. Environ. Earth Sci. 75, 633 (2016).
Article  CAS  Google Scholar 

6.
Willison, J. H. M., Li, R. & Yuan, X. Conservation and ecofriendly utilization of wetlands associated with the Three Gorges Reservoir. Environ. Sci. Pollut. R. 20, 6907–6916 (2013).
Article  Google Scholar 

7.
Liu, L., Liu, D., Johnson, D. M., Yi, Z. & Huang, Y. Effects of vertical mixing on phytoplankton blooms in Xiangxi Bay of Three Gorges Reservoir: implications for management. Water Res. 46, 2121–2130 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Ren, C., Wang, L., Zheng, B., Qian, J. & Ton, H. Ten-year change of total phosphorous pollution in the Min River, an upstream tributary of the Three Gorges Reservoir. Environ. Earth Sci. 75, 1015 (2016).
Article  CAS  Google Scholar 

9.
Li, C., Zhong, Z., Geng, Y. & Schneider, R. Comparative studies on physiological and biochemical adaptation of Taxodium distichum and Taxodium ascendens seedlings to different soil water regimes. Plant Soil. 329, 481–494 (2010).
CAS  Article  Google Scholar 

10.
Schoonover, J. E., Williard, K. W., Zaczek, J. J., Mangun, J. C. & Carver, A. D. Agricultural sedmient reduction by giant cane and forests riparian buffers. Water Air Soil Poll. 169, 303–315 (2006).
ADS  CAS  Article  Google Scholar 

11.
Wang, C., Li, C., Wei, H., Xie, Y. & Han, W. Effects of long-term periodic submergence on photosynthesis and growth of Taxodium distichum and Taxodium ascendens saplings in the hydro-fluctuation zone of the Three Gorges Reservoir of China. PLoS ONE 11, e162867 (2016).
Google Scholar 

12.
Yang, F., Wang, Y. & Chan, Z. Perspectives on screening winter-flood-tolerant woody species in the riparian protection forests of the three gorges reservoir. PLoS ONE 9, e108725 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Ye, C., Cheng, X., Zhang, Y., Wang, Z. & Zhang, Q. Soil nitrogen dynamics following short-term revegetation in the water level fluctuation zone of the Three Gorges Reservoir China. Ecol. Eng. 38, 37–44 (2012).
Article  Google Scholar 

14.
Capon, S. J. et al. Riparian ecosystems in the 21st century: hotspots for climate change adaptation?. Ecosystems 16, 359–381 (2013).
Article  Google Scholar 

15.
Gregory, S. V., Swanson, F. J., McKee, W. A. & Cummins, K. W. An ecosystem perspective of riparian zones. Bioscience 41, 540–551 (1991).
Article  Google Scholar 

16.
Zhang, M. et al. Leaf litter traits predominantly control litter decomposition in streams worldwide. Glob. Ecol. Biogeogr. 28, 1469–1486 (2019).
Article  Google Scholar 

17.
Ferreira, V., Encalada, A. C. & Graça, M. A. S. Effects of litter diversity on decomposition and biological colonization of submerged litter in temperate and tropical streams. Freshw. Sci. 31, 945–962 (2012).
Article  Google Scholar 

18.
Jabiol, J. & Chauvet, E. Fungi are involved in the effects of litter mixtures on consumption by shredders. Freshw. Biol. 57, 1667–1677 (2012).
Article  Google Scholar 

19.
Yang, Z., Liu, D., Ji, D. & Xiao, S. Influence of the impounding process of the Three Gorges Reservoir up to water level 172.5 m on water eutrophication in the Xiangxi Bay. Sci. China Technol. Sci. 53, 1114–1125 (2010).
ADS  CAS  Article  Google Scholar 

20.
Berglund, S. L. & Ågren, G. I. When will litter mixtures decompose faster or slower than individual litters? A model for two litters. Oikos 121, 1112–1120 (2012).
Article  Google Scholar 

21.
De Marco, A., Meola, A., Maisto, G., Giordano, M. & Virzo De Santo, A. Non-additive effects of litter mixtures on decomposition of leaf litters in a Mediterranean maquis. Plant Soil 344, 305–317 (2011).
CAS  Article  Google Scholar 

22.
Gartner, T. B. & Cardon, Z. G. Decomposition dynamics in mixed-species leaf litter. Oikos 104, 230–246 (2004).
Article  Google Scholar 

23.
Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).
PubMed  Article  Google Scholar 

24.
Schimel, J. P. & Hättenschwiler, S. Nitrogen transfer between decomposing leaves of different N status. Soil Biol. Biochem. 39, 1428–1436 (2007).
CAS  Article  Google Scholar 

25.
Lecerf, A. et al. Incubation time, functional litter diversity, and habitat characteristics predict litter-mixing effects on decomposition. Ecology 92, 160–169 (2011).
Article  Google Scholar 

26.
Wu, D., Li, T. & Wan, S. Time and litter species composition affect litter-mixing effects on decomposition rates. Plant Soil. 371, 355–366 (2013).
CAS  Article  Google Scholar 

27.
Swan, C. M., Healey, B. & Richardson, D. C. The role of native riparian tree species in decomposition of invasive tree of heaven (Ailanthus altissima) leaf litter in an urban stream. Ecoscience 15, 27–35 (2008).
Article  Google Scholar 

28.
Leroy, C. J. & Marks, J. C. Litter quality, stream characteristics and litter diversity influence decomposition rates and macroinvertebrates. Freshw. Biol. 51, 605–617 (2006).
Article  Google Scholar 

29.
Xie, Y., Xie, Y., Hu, C., Chen, X. & Li, F. Interaction between litter quality and simulated water depth on decomposition of two emergent macrophytes. J. Limnol. 75, 36–43 (2015).
Google Scholar 

30.
Sun, Z., Mou, X. & Liu, J. S. Effects of flooding regimes on the decomposition and nutrient dynamics of Calamagrostis angustifolia litter in the Sanjiang Plain of China. Environ. Earth Sci. 66, 2235–2246 (2012).
Article  Google Scholar 

31.
Wang, C., Xie, Y., Ren, Q. & Li, C. Leaf decomposition and nutrient release of three tree species in the hydro-fluctuation zone of the Three Gorges Dam Reservoir China. Environ. Sci. Pollut. R. 25, 23261–23275 (2018).
CAS  Article  Google Scholar 

32.
Xiao, L., Zhu, B., Nsenga Kumvimba, M. & Jiang, S. Plant soaking decomposition as well as nitrogen and phosphorous release in the water-level fluctuation zone of the Three Gorges Reservoir. Sci. Total Environ. 592, 527–534 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Bray, S. R., Kitajima, K. & Mack, M. C. Temporal dynamics of microbial communities on decomposing leaf litter of 10 plant species in relation to decomposition rate. Soil Biol. Biochem. 49, 30–37 (2012).
CAS  Article  Google Scholar 

35.
Graça, M. A. S. et al. A conceptual model of litter breakdown in low order streams. Int. Rev. Hydrobiol. 100, 1–12 (2015).
Article  CAS  Google Scholar 

36.
Lecerf, A., Risnoveanu, G., Popescu, C., Gessner, M. O. & Chauvet, E. Decomposition of diverse litter mixtures in streams. Ecology 88, 219–227 (2007).
Article  Google Scholar 

37.
Martínez, A., Larrañaga, A., Pérez, J., Descals, E. & Pozo, J. Temperature affects leaf litter decomposition in low-order forest streams: field and microcosm approaches. FEMS Microbiol. Ecol. 87, 257–267 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

38.
Kelley, R. H. & Jack, J. D. Leaf litter decomposition in an ephemeral karst lake (Chaney Lake, Kentucky, U.S.A). Hydrobiologia 482, 41–47 (2002).
Article  Google Scholar 

39.
Austin, A. T. & Vitousek, P. M. Precipitation, decomposition and litter decomposability of Metrosideros polymorpha in native forests on Hawai’i. J. Ecol. 88, 138–139 (2000).
Article  Google Scholar 

40.
Taylor, A. R., Schröter, D., Pflug, A. & Wolters, V. Response of different decomposer communities to the manipulation of moisture availability: potential effects of changing precipitation patterns. Glob. Change Biol. 10, 1313–1324 (2004).
ADS  Article  Google Scholar 

41.
Xie, Y., Xie, Y., Xiao, H., Chen, X. & Li, F. Controls on litter decomposition of emergent macrophyte in dongting lake wetlands. Ecosystems 20, 1383–1389 (2017).
CAS  Article  Google Scholar 

42.
Fernandes, I., Seena, S., Pascoal, C. & Cássio, F. Elevated temperature may intensify the positive effects of nutrients on microbial decomposition in streams. Freshw. Biol. 59, 2390–2399 (2014).
CAS  Article  Google Scholar 

43.
Ferreira, V. & Chauvet, E. Synergistic effects of water temperature and dissolved nutrients on litter decomposition and associated fungi. Glob. Change Biol. 17, 551–564 (2011).
ADS  Article  Google Scholar 

44.
Liu, C. et al. Mixing litter from deciduous and evergreen trees enhances decomposition in a subtropical karst forest in southwestern China. Soil Biol. Biochem. 101, 44–54 (2016).
Article  CAS  Google Scholar 

45.
Wu, F. et al. Admixture of alder (Alnus formosana) litter can improve the decomposition of eucalyptus (Eucalyptus grandis) litter. Soil Biol. Biochem. 73, 115–121 (2014).
CAS  Article  Google Scholar 

46.
Kominoski, J. S. et al. Nonadditive effects of leaf litter species diversity on breakdown dynamics in a Detritus-based stream. Ecology 88, 1167–1176 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Sanpera-Calbet, I. S. I. S., Lecerf, A. & Chauvet, E. Leaf diversity influences in-stream litter decomposition through effects on shredders. Freshw. Biol. 54, 1671–1682 (2009).
Article  Google Scholar 

48.
Ostrofsky, M. L. A comment on the use of exponential decay models to test nonadditive processing hypotheses in multispecies mixtures of litter. J. N. Am. Benthol. Soc. 26, 23–27 (2007).
Article  Google Scholar 

49.
Zanne, A. E. et al. A deteriorating state of affairs: how endogenous and exogenous factors determine plant decay rates. J. Ecol. 103, 1421–1431 (2015).
CAS  Article  Google Scholar 

50.
Hieber, M. & Gessner, M. O. Contribution of stream detrivores, fungi, and bacteria to leaf breakdown based on biomass estimates. Ecology 83, 1026–1038 (2002).
Article  Google Scholar 

51.
Schindler, M. H. & Gessner, M. O. Functional leaf traits and biodiversity effects on litter decomposition in a stream. Ecology 90, 1641–1649 (2009).
PubMed  Article  Google Scholar 

52.
Gessner, M. O. & Chauvet, E. Importance of stream microfungi in controlling breakdown rates of leaf litter. Ecology 75, 1807–1817 (1994).
Article  Google Scholar 

53.
Sommaruga, R., Crosa, D. & Mazzeo, N. Study on the Decomposition of Pistia stratiotes L. (Araceae) in Cisne Reservoir, Uruguay. Hydrobiologia 78, 263–272 (1993).
CAS  Google Scholar 

54.
Fraser, L. H., Carty, S. M. & Steer, D. A test of four plant species to reduce total nitrogen and total phosphorus from soil leachate in subsurface wetland microcosms. Bioresour. Technol. 94, 185–192 (2004).
CAS  PubMed  Article  Google Scholar 

55.
Ball, B. A., Bradford, M. A. & Hunter, M. D. Nitrogen and phosphorus release from mixed litter layers is lower than predicted from single species decay. Ecosystems 12, 87–100 (2009).
CAS  Article  Google Scholar 

56.
Olson, J. S. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44(2), 322–331 (1963).
Article  Google Scholar  More

Plant material
Z. japonica used in this study was collected from Fangchenggang, Guangxi, China.
DNA extraction and primer design
Leaves of Z. japonica were used as materials to extract genomic DNA from young leaves that had grown well. A MiniBEST Plant Genomic DNA Extraction Kit (TaKaRa, 9768) was used to extract genomic DNA from the leaves of Z. japonica following the manufacturer’s instructions. Based on the full-length cDNA sequence of ZjFes1 obtained by RACE18, three identical and high annealing temperature specific primers (SP Primer) were designed, and four specifically designed degenerate primers, AP1, AP2, AP3 and AP4, were used for thermal asymmetric interlaced PCR (TAIL-PCR). Typically, at least one of these degenerate primers can react with specific primers by TAIL-PCR based on the difference of annealing temperature, and the flanking sequence of known sequence can be obtained by three nested PCR reactions. Because the length obtained in one experiment cannot meet the experimental requirements, we continue to acquire the flanking sequence according to the sequence information obtained in the first genome walking. Four genome walkings were conducted. Twelve SP Primers were designed. DNAMAN software was used to combine the four fragments described above into a consensus sequence by combining overlapping fragments. Specific primers were designed to amplify 2 kb sequences according to the results (Table 1), and the experimental results were verified.
Table 1 PCR primer sequences.
Full size table

Cloning and construction of the plant expression vector and sequence analysis of promoter
The full-length promoter sequence was amplified using high fidelity polymerase 2 × TransStart FastPfu PCR SuperMix (-dye) (TRANSGEN BIOTECH, AS221-01) using the DNA of Z. japonica as a template following the manufacturer’s instructions. The PCR products were detected using 1% gel electrophoresis. The results showed that the size of the bands was the same as that of the target fragments, and the PCR products were recovered using a MiniBEST Agarose Gel DNA Extraction Kit Ver. 4.0 (TaKaRa, 9762). The pCXGUS-P plasmid is a vector designed to detect the activity of plant promoters. The promoter activity is detected by the dyeing intensity of GUS. We used XcmI to digest the empty vector to obtain T vector. After recovery, the product was recombined with T vector, and then the recombinant vector was transformed into E. coli DH5α Competent Cells (TaKaRa, 9057) following the manufacturer’s instructions. The positive samples identified by PCR were verified by sequencing at the Guangzhou Sequencing Department of Invitrogen. The sequencing results were compared using DNAMAN software. The plasmid was extracted from the correct bacterial solution and designated pZjFes1::GUS. The sequence analysis of cis-acting elements that could possibly be found in the promoter was performed using the plant-CARE online prediction database (plant cis-acting regulatory element, https://bioinformatics.psb.ugent.be/webtools/plantcare/html/)20.
Agrobacterium-mediated genetic transformation of pZjFes1::GUS into Arabidopsis thaliana
The fusion vector pZjFes1::GUS was transformed into Agrobacterium Rhizobium strain GV3101 chemically competent cells (Biomed, BC304) using the freeze–thaw method following the manufacturer’s instructions. Transgenic plants of A. thaliana were obtained by floral dipping. Plants in nutrient soil were cultured to form a large number of immature flower clusters. The monoclone of A. tumefaciens GV3101 was selected and inoculated in liquid LB medium containing kanamycin and rifampicin (50 µg/mL). The monoclone was cultured overnight at 200 rpm and 28 °C. A volume of 2 mL bacterial solution was transferred to a 500 mL flask culture (containing 200 mL liquid LB with 50 µg/mL kanamycin and rifampicin added) and was cultured overnight at 200 rpm and 28 °C. The next day, the OD600 of Agrobacterium solution was 1.8–2.0. The solution was centrifuged at 5000 rpm for 15 min at 4 °C. The supernatant was discarded, and the precipitate of A. tumefaciens was resuspended in 1/2 volume (100 mL) osmotic medium (1/2 Murashige-Skoog, 5% sucrose, 0.5 g/L MES, 10 µg/mL 6-BA, 200 µl/L Silwet L-77, and 150 µM acetyleugenone, pH 5.7), resulting in an OD600 of approximately 1.6. The bacterial solution was adsorbed on the transformed plants using the floral dip method (5 min), wrapped with film to keep it fresh, and cultured overnight, followed by the removal of the film. The plants were cultured until the seeds were ripe, and they were harvested. A mixed disinfectant consisting of 70% ethanol and 30% bleaching water was used to soak the seeds for 3 min, suspend them continuously, and wash them three times with anhydrous ethanol. The dried seeds were evenly dispersed on the surface of solid screening medium containing hygromycin (25 µg/mL). After stratification at 4 °C for 2 days, the seeds were germinated in a light incubator and cultured for 2 weeks at 21 °C and 16 h light/8 h darkness. The development of seedlings and length of roots were used to determine whether they were transformants.
GUS dyeing and activity analysis
The expression of GUS reporter gene in Arabidopsis tissues was determined using a GUS staining kit (Solarbio, G3060) following the manufacturer’s instructions. The seedlings, leaves, flowers and siliques to be dyed were immersed in GUS dye solution and incubated overnight at 37 °C. The chlorophyll was removed with 75% ethanol until the background color disappeared completely. The results were documented by photography using a Canon 60d camera.
The material needed to determine Gus enzyme activity was frozen rapidly with liquid nitrogen, and then ground into powder by ball mill. The extraction buffer solution (50 mM NaH2PO4 (pH 7.0), 10 mM EDTA, 0.1% Triton X-100, 0.1 (w / v) sodium dodecyl sulfonate, 10 mM β-mercaptoethanol) were added to extract protein. After centrifugation at 4 °C, 12,000 r/min for 10 min, the supernatant was taken as protein extract. The protein concentration was determined by Bradford method. 4-MUG, the substrate of GUS reaction, was added and reacted at 37 °C for 30 min. Fluorescence measurement was carried out under the condition of 365 nm excitation light and 455 nm emission light. Three independent biological repeats were conducted. Finally, the GUS enzyme activity value was calculated according to the relative change of product in unit time. More

1.
Westerling, A. L. Increasing western us forest wildfire activity: sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B 371, 20150178 (2016).
Article  Google Scholar 
2.
Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western united states, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
ADS  Article  Google Scholar 

3.
Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region—spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33 (2006).

4.
Littell, J. S., McKenzie, D., Peterson, D. L. & Westerling, A. L. Climate and wildfire area burned in western US ecoprovinces, 1916–2003. Ecol. Appl. 19, 1003–1021 (2009).
PubMed  Article  Google Scholar 

5.
Abatzoglou, J. T. & Kolden, C. A. Relationships between climate and macroscale area burned in the western United States. Int. J. Wildland Fire 22, 1003–1020 (2013).
Article  Google Scholar 

6.
Kelly, R. et al. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proc. Natl. Acad. Sci. 110, 13055–13060 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. 113, 11770–11775 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Williams, A. P. & Abatzoglou, J. T. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr. Clim. Change Rep. 2, 1–14 (2016).
Article  Google Scholar 

9.
Seager, R. et al. Climatology, variability, and trends in the us vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).
ADS  Article  Google Scholar 

10.
Radeloff, V. C. et al. Rapid growth of the us wildland–urban interface raises wildfire risk. Proc. Natl. Acad. Sci. 115, 3314–3319 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Fried, J. S. et al. Predicting the effect of climate change on wildfire behavior and initial attack success. Clim. Change 87, 251–264 (2008).
Article  Google Scholar 

12.
Agee, J. K. & Skinner, C. N. Basic principles of forest fuel reduction treatments. For. Ecol. Manag. 211, 83–96 (2005).
Article  Google Scholar 

13.
Schwilk, D. W. et al. The national fire and fire surrogate study: effects of fuel reduction methods on forest vegetation structure and fuels. Ecol. Appl. 19, 285–304 (2009).
PubMed  Article  Google Scholar 

14.
Whitehead, R. et al. Effect of a spaced thinning in mature lodgepole pine on within-stand microclimate and fine fuel moisture content. In Andrews, P. L., & Butler, B. W., comps. Fuels Management-How to Measure Success: Conference Proceedings. 28–30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, vol. 41, 523–536 (2006).

15.
Whitehead, R. J. et al. Effect of commercial thinning on within-stand microclimate and fine fuel moisture conditions in a mature lodgepole pine stand in southeastern British Columbia. Canadian Forest Service, Canadian Wood Fibre Centre. British Columbia, Information Report, FI-X-004 (2008).

16.
Parsons, R. A. et al. Modeling thinning effects on fire behavior with standfire. Ann. For. Sci. 75, 7 (2018).
Article  Google Scholar 

17.
Kalies, E. L. & Kent, L. L. Y. Tamm review: Are fuel treatments effective at achieving ecological and social objectives? A systematic review. For. Ecol. Manag. 375, 84–95 (2016).
Article  Google Scholar 

18.
Banerjee, T. Impacts of forest thinning on wildland fire behavior. Forests 11, 918 (2020).
Article  Google Scholar 

19.
Syifa, M., Panahi, M. & Lee, C.-W. Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: the case of the camp fire wildfire in California, USA. Remote Sensing 12, 623 (2020).
ADS  Article  Google Scholar 

20.
Storey, M. A., Price, O. F., Sharples, J. J. & Bradstock, R. A. Drivers of long-distance spotting during wildfires in south-eastern Australia. Int. J. Wildland Fire (2020).

21.
Arienti, M. C., Cumming, S. G. & Boutin, S. Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthropogenic linear features, fire weather, and management. Can. J. For. Res. 36, 3155–3166 (2006).
Article  Google Scholar 

22.
Van Wagner, C. E. Fire Behaviour Mechanisms in a Red Pine Plantation: Field and Laboratory Evidence, vol. 1229 (Ministry of Forestry and Rural Development, 1968).

23.
Wagner, C. V. Conditions for the start and spread of crown fire. Can. J. For. Res. 7, 23–34 (1977).
Article  Google Scholar 

24.
Graham, R. T., Harvey, A. E., Jain, T. B. & Tonn, J. R. Effects of thinning and similar stand treatments on fire behavior in western forests. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-463 (1999).

25.
Graham, R. T., McCaffrey, S. & Jain, T. B. Science basis for changing forest structure to modify wildfire behavior and severity. The Bark Beetles, Fuels, and Fire Bibliography 167 (2004).

26.
Varner, M. & Keyes, C. R. Fuels treatments and fire models: errors and corrections. Fire Manag. Today 69, 47–50 (2009).
Google Scholar 

27.
Amiro, B., Stocks, B., Alexander, M., Ana, F. & Wotton, B. Fire, climate change, carbon and fuel management in the Canadian boreal forest. Int. J. Wildland Fire 10, 405–4 (2001).
Article  Google Scholar 

28.
Pollet, J. & Omi, P. N. Effect of thinning and prescribed burning on crown fire severity in ponderosa pine forests. Int. J. Wildland Fire 11, 1–10 (2002).
Article  Google Scholar 

29.
Peterson, D. L. et al. Forest structure and fire hazard in dry forests of the western United States. Gen. Tech. Rep. PNW-GTR-628. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station. 30 p 628 (2005).

30.
Stephens, S. L. & Moghaddas, J. J. Experimental fuel treatment impacts on forest structure, potential fire behavior, and predicted tree mortality in a california mixed conifer forest. For. Ecol. Manag. 215, 21–36 (2005).
Article  Google Scholar 

31.
Safford, H. D., Schmidt, D. A. & Carlson, C. H. Effects of fuel treatments on fire severity in an area of wildland-urban interface, angora fire, lake Tahoe basin, California. For. Ecol. Manag. 258, 773–787 (2009).
Article  Google Scholar 

32.
Stephens, S. L. et al. Fire treatment effects on vegetation structure, fuels, and potential fire severity in western us forests. Ecol. Appl. 19, 305–320 (2009).
PubMed  Article  Google Scholar 

33.
Hudak, A. et al. Review of fuel treatment effectiveness in forests and rangelands and a case study from the 2007 megafires in central Idaho USA (no. rmrs-gtr-252). Fort Collins, CO: Rocky Mountain Research Station Publishing Services (2011).

34.
Waldrop, T. A. & Goodrick, S. L. Introduction to prescribed fires in southern ecosystems. Science Update SRS-054. Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station. 80 p. 54, 1–80 (2012).

35.
Martinson, E. J. & Omi, P. N. Fuel treatments and fire severity: a meta-analysis. Res. Pap. RMRS-RP-103WWW. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 38, p. 103 (2013).

36.
Kennedy, M. C. & Johnson, M. C. Fuel treatment prescriptions alter spatial patterns of fire severity around the wildland–urban interface during the Wallow Fire, Arizona, USA. For. Ecol. Manag. 318, 122–132 (2014).
Article  Google Scholar 

37.
Barnett, K., Parks, S. A., Miller, C. & Naughton, H. T. Beyond fuel treatment effectiveness: characterizing interactions between fire and treatments in the US. Forests 7, 237 (2016).
Article  Google Scholar 

38.
Just, M. G., Hohmann, M. G. & Hoffmann, W. A. Where fire stops: vegetation structure and microclimate influence fire spread along an ecotonal gradient. Plant Ecol. 217, 631–644 (2016).
Article  Google Scholar 

39.
Veenendaal, E. M. et al. On the relationship between fire regime and vegetation structure in the tropics. New Phytol. 218, 153–166 (2018).
PubMed  Article  PubMed Central  Google Scholar 

40.
Bessie, W. & Johnson, E. The relative importance of fuels and weather on fire behavior in subalpine forests. Ecology 76, 747–762 (1995).
Article  Google Scholar 

41.
Rothermel, R. C. A mathematical model for predicting fire spread in wildland fuels. Res. Pap. INT-115. Ogden, UT: US Department of Agriculture, Intermountain Forest and Range Experiment Station. 40 p. 115 (1972).

42.
Hoffman, C. M. et al. Surface fire intensity influences simulated crown fire behavior in lodgepole pine forests with recent mountain pine beetle-caused tree mortality. For. Sci. 59, 390–399 (2012).
Article  Google Scholar 

43.
Keyes, C. & Varner, J. Pitfalls in the silvicultural treatment of canopy fuels. Fire Management Today (2006).

44.
Moon, K., Duff, T. & Tolhurst, K. Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation. Fire Saf. J. 105, 320–329 (2016).
Article  Google Scholar 

45.
Beer, T. The interaction of wind and fire. Boundary-Layer Meteorol.https://doi.org/10.1007/BF00183958 (1991).
ADS  Article  Google Scholar 

46.
Cheney, N., Gould, J. & Catchpole, W. The influence of fuel, weather and fire shape variables on fire-spread in grasslands. Int. J. Wildland Fire 3, 31–44 (1993).
Article  Google Scholar 

47.
Cochrane, M. A. Fire science for rainforests. Nature 421, 913 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

48.
Fulé, P. Z., McHugh, C., Heinlein, T. A. & Covington, W. W. Potential fire behavior is reduced following forest restoration treatments (Technical Report 2001).

49.
Fulé, P. Z., Crouse, J. E., Roccaforte, J. P. & Kalies, E. L. Do thinning and/or burning treatments in western USA ponderosa or Jeffrey pine-dominated forests help restore natural fire behavior?. For. Ecol. Manag. 269, 68–81 (2012).
Article  Google Scholar 

50.
Contreras, M. A., Parsons, R. A. & Chung, W. Modeling tree-level fuel connectivity to evaluate the effectiveness of thinning treatments for reducing crown fire potential. For. Ecol. Manag. 264, 134–149 (2012).
Article  Google Scholar 

51.
White, D. L., Waldrop, T. A. & Jones, S. M. Forty years of prescribed burning on the santee fire plots: effects on understory vegetation. Gen. Tech. Rep. SE-69. Asheville, NC: US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. pp. 51–59 (1990).

52.
Davies, G., Domenech-Jardi, R., Gray, A. & Johnson, P. Vegetation structure and fire weather influence variation in burn severity and fuel consumption during peatland wildfires. Biogeosciences 12, 15737–15762 (2016).
Article  Google Scholar 

53.
Keeley, J. E. & Syphard, A. D. Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires. Fire Ecol. 15, 24 (2019).
Article  Google Scholar 

54.
Hiers, J. K. et al. Fine dead fuel moisture shows complex lagged responses to environmental conditions in a saw palmetto (Serenoa repens) flatwoods. Agric. For. Meteorol. 266, 20–28 (2019).
ADS  Article  Google Scholar 

55.
Finney, M. A. et al. Role of buoyant flame dynamics in wildfire spread. Proc. Natl. Acad. Sci. 112, 9833–9838 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

56.
Reisner, J., Wynne, S., Margolin, L. & Linn, R. Coupled atmospheric-fire modeling employing the method of averages. Mon. Weather Rev. 128, 3683–3691 (2000).
ADS  Article  Google Scholar 

57.
Mell, W., Maranghides, A., McDermott, R. & Manzello, S. L. Numerical simulation and experiments of burning douglas fir trees. Combust. Flame 156, 2023–2041 (2009).
CAS  Article  Google Scholar 

58.
Morvan, D. Physical phenomena and length scales governing the behaviour of wildfires: a case for physical modelling. Fire Technol. 47, 437–460 (2011).
Article  Google Scholar 

59.
Parsons, R. A., Mell, W. E. & McCauley, P. Linking 3d spatial models of fuels and fire: effects of spatial heterogeneity on fire behavior. Ecol. Model. 222, 679–691 (2011).
Article  Google Scholar 

60.
Parsons, R. et al. STANDFIRE: An IFT-DSS module for spatially explicit, 3d fuel treatment analysis (Technical Report 2015).

61.
Hoffman, C. M., Linn, R., Parsons, R., Sieg, C. & Winterkamp, J. Modeling spatial and temporal dynamics of wind flow and potential fire behavior following a mountain pine beetle outbreak in a lodgepole pine forest. Agric. For. Meteorol. 204, 79–93 (2015).
ADS  Article  Google Scholar 

62.
Hoffman, C. et al. Evaluating crown fire rate of spread predictions from physics-based models. Fire Technol. 52, 221–237 (2016).
Article  Google Scholar 

63.
Pimont, F. et al. Modeling fuels and fire effects in 3d: model description and applications. Environ. Model. Softw. 80, 225–244 (2016).
Article  Google Scholar 

64.
Pimont, F., Dupuy, J.-L., Linn, R. R., Parsons, R. & Martin-StPaul, N. Representativeness of wind measurements in fire experiments: lessons learned from large-eddy simulations in a homogeneous forest. Agric. For. Meteorol. 232, 479–488 (2017).
ADS  Article  Google Scholar 

65.
Pimont, F., Dupuy, J.-L., Linn, R. R. & Dupont, S. Impacts of tree canopy structure on wind flows and fire propagation simulated with FIRETEC. Ann. For. Sci. 68, 523 (2011).
Article  Google Scholar 

66.
Linn, R. R., Sieg, C. H., Hoffman, C. M., Winterkamp, J. L. & McMillin, J. D. Modeling wind fields and fire propagation following bark beetle outbreaks in spatially-heterogeneous Pinyon–Juniper woodland fuel complexes. Agric. For. Meteorol. 173, 139–153 (2013).
ADS  Article  Google Scholar 

67.
Kiefer, M. T., Heilman, W. E., Zhong, S., Charney, J. J. & Bian, X. Mean and turbulent flow downstream of a low-intensity fire: influence of canopy and background atmospheric conditions. J. Appl. Meteorol. Climatol. 54, 42–57 (2015).
ADS  Article  Google Scholar 

68.
Clements, C. B. et al. Observing the dynamics of wildland grass fires: fireflux—a field validation experiment. Bull. Am. Meteorol. Soc. 88, 1369–1382 (2007).
ADS  Article  Google Scholar 

69.
Clements, C. B., Zhong, S., Bian, X., Heilman, W. E. & Byun, D. W. First observations of turbulence generated by grass fires. J. Geophys. Res. Atmos. 113, D22 (2008).
Article  Google Scholar 

70.
Seto, D., Clements, C. B. & Heilman, W. E. Turbulence spectra measured during fire front passage. Agric. For. Meteorol. 169, 195–210. https://doi.org/10.1016/j.agrformet.2012.09.015 (2013).
ADS  Article  Google Scholar 

71.
Heilman, W. E. et al. Observations of fire-induced turbulence regimes during low-intensity wildland fires in forested environments: implications for smoke dispersion. Atmos. Sci. Lett. 16, 453–460 (2015).
ADS  Article  Google Scholar 

72.
Clements, C. B. et al. The fireflux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread. Int. J. Wildland Fire 28, 308–326 (2019).
Article  Google Scholar 

73.
Banerjee, T. & Katul, G. Logarithmic scaling in the longitudinal velocity variance explained by a spectral budget. Phys. Fluids 25, 125106 (2013).
ADS  Article  CAS  Google Scholar 

74.
Heilman, W. E. et al. Atmospheric turbulence observations in the vicinity of surface fires in forested environments. J. Appl. Meteorol. Climatol. 56, 3133–3150 (2017).
ADS  Article  Google Scholar 

75.
Keeley, J. E. & Zedler, P. H. Large, high-intensity fire events in southern California shrublands: debunking the fine-grain age patch model. Ecol. Appl. 19, 69–94 (2009).
PubMed  Article  Google Scholar 

76.
Jin, Y. et al. Contrasting controls on wildland fires in southern California during periods with and without Santa Ana winds. J. Geophys. Res. Biogeosciences 119, 432–450 (2014).
ADS  Article  Google Scholar 

77.
Hiers, J. K., O’Brien, J. J., Will, R. E. & Mitchell, R. J. Forest floor depth mediates understory vigor in xeric pinus palustris ecosystems. Ecol. Appl. 17, 806–814 (2007).
PubMed  Article  Google Scholar 

78.
Parresol, B. R., Shea, D. & Ottmar, R. Creating a fuels baseline and establishing fire frequency relationships to develop a landscape management strategy at the savannah river site. In Andrews, P. L. & Butler, B. W., comps Fuels Management-How to Measure Success: Conference Proceedings. 28–30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, vol. 41, pp 351–366 (2006).

79.
Sackett, S. S. & Haase, S. M. Fuel loadings in southwestern ecosystems of the United States. United States Department of Agriculture, Forest Service General Technical Report 187–192 (1996).

80.
Bigelow, S. W. & North, M. P. Microclimate effects of fuels-reduction and group-selection silviculture: implications for fire behavior in Sierran mixed-conifer forests. For. Ecol. Manag. 264, 51–59 (2012).
Article  Google Scholar 

81.
Faiella, S. M. & Bailey, J. D. Fluctuations in fuel moisture across restoration treatments in semi-arid ponderosa pine forests of northern Arizona, USA. Int. J. Wildland Fire 16, 119–127 (2007).
Article  Google Scholar 

82.
Estes, B. L., Knapp, E. E., Skinner, C. N. & Uzoh, F. C. Seasonal variation in surface fuel moisture between unthinned and thinned mixed conifer forest, northern California, USA. Int. J. Wildland Fire 21, 428–435 (2012).
Article  Google Scholar 

83.
Pook, E. & Gill, A. Variation of live and dead fine fuel moisture in pinus radiata plantations of the Australian-capital-territory. Int. J. Wildland Fire 3, 155–168 (1993).
Article  Google Scholar 

84.
Weatherspoon, C. P. & Skinner, C. Fire-silviculture relationships in sierra forests. Sierra nevada ecosystem project: final report to congress 2, 1167–1176 (1996).

85.
Countryman, C. Old-growth conversion also converts fire climate. US Forest Service Fire Control Notes 17, 15–19 (1955).
Google Scholar 

86.
Linn, R. R. A transport model for prediction of wildfire behavior. Technical Report, Los Alamos National Lab., NM (United States) (1997).

87.
Linn, R., Winterkamp, J., Colman, J. J., Edminster, C. & Bailey, J. D. Modeling interactions between fire and atmosphere in discrete element fuel beds. Int. J. Wildland Fire 14, 37–48 (2005).
Article  Google Scholar 

88.
Linn, R. R. & Cunningham, P. Numerical simulations of grass fires using a coupled atmosphere-fire model: basic fire behavior and dependence on wind speed. J. Geophys. Res. Atmos. 110, D13 (2005).
Article  Google Scholar  More

Human gut microbiome data and preprocessing
The publicly available data that we re-analyzed here were generated by David et al.32 accessible on the European Nucleotide Archive (ENA) under the accession number ERP006059, and by Hsiao et al.31 on the NCBI Short Read Archive (SRA) under the accession number PRJEB6358. The downloaded reads were trimmed with V-xtractor version 2.146 a HMM scan based method of isolating variable regions from 16S rRNA sequences) to ensure the amplicon sequences could be aligned across consistent fractions of the 16S rRNA variable regions. Trimmed reads were then clustered into OTUs using usearch v9.2.6447 with a minimum cluster size of two. Representative sequences from each OTU were classified using mothur v1.36.148 and the RDP reference 16S rRNA sequences v1649.
Prochlorococcus data
Data from Malstrom et al.33 was obtained from the Biological and Chemical Oceanography Data Management Office (https://www.bco-dmo.org), accession number 3381.
Mapper
Conceptually, the Mapper algorithm accepts as input a matrix of distances or dissimilarities between data, and aims to represent the shape of the distribution of data points in high-dimensional phase space as an undirected graph. In this graph, vertices represent neighborhoods of phase space spanned by subsets of adjacent data points, and edges represent connectivity between neighborhoods. In brief, it does this by dividing the data into overlapping subsets that are similar according to the output of at least one filter function that assigns a scalar value to each data point, performing local clustering on each subset, and representing the result as an undirected graph, where each vertex represents a local cluster of data points, and edges between vertices represent at least one shared data point between clusters.
Distance matrix
We interpreted microbiome relative abundances to be probability distributions, and thus used the square root of the Jensen-Shannon divergence as a metric50. However, it is important to note that any other metric can be used in place of the Jensen-Shannon distance, such as the Aitchison distance51, calculated from centered10 or isometric12 log-transformed relative abundances.
Filter functions and binning
For the filter functions used by Mapper to bin data points, we performed principal coordinate analysis (PCoA, also known as classical multidimensional scaling) in two dimensions on the pairwise distance matrix, and used the ranked values of principal coordinates (PCo) 1 and 2 as the first and second filter values for Mapper, following Rizvi et al.28. PCo ranks are an appropriate filter for our purposes, as it assigns similar filter values to points that are relatively close together in the original phase space. We wish to note that while PCoA leads to loss of information, the following local clustering step is performed using subsets of distances from the original distance matrix, and is thus not affected. The data points were then binned by overlapping intervals of the two ranked principal coordinates. For hyperparameters specifying these bins and their overlaps, see Table 1.
Table 1 Hyperparameters used to generate the Mapper representation of each data set.
Full size table

Local clustering
The algorithm first performs hierarchical clustering from all pairwise distances between data points within a bin of filter values. Then, it creates a histogram of branch lengths using a predefined number of bins, and uses the first empty bin in the histogram as a cutoff value, separating the hierarchical tree into single-linkage clusters. The algorithm thus finds a separation of length scales within each neighborhood of phase space represented by a bin of the filter values. We used the default number of histogram bins, 10, for each data set (Table 1).
Creating the undirected Mapper graph
The final output is produced by representing each local cluster of data points as a vertex, and drawing an edge between each pair of vertices that share at least one data point. When plotting, the size of each vertex represents the number of data points therein. Layout and visualization of the Mapper graph may be performed with any graph layout algorithm; we used the Fruchterman-Reingold force-directed layout algorithm52. It is important to note that the visualized shape of the Mapper graph depends on the algorithm used, and may not be deterministic. When performing a Mapper analysis, one should rely on the connectivity of the graph rather than the overall shape.
Selection of hyperparameters
The Mapper algorithm is relatively new, and there are currently no standard protocols to optimize the values of the hyperparameters. For our purposes, it was important that the algorithm achieved a sufficiently high resolution in partitioning data, but also adequately represented connections between regions of phase space. We thus used the following heuristic to set the number of intervals and percent overlap for each data set.
1.
The largest vertex in the resultant Mapper graph should represent no more than ≈10% of the total number of data points in the set;

2.
the number of connected components representing only one data point should be minimized.

We acknowledge that a heuristic determination of appropriate hyperparameter values leaves much to be desired; as such, we recommend future in-depth theoretical explorations of how the Mapper output depends on the choice of hyperparameters.
Density estimation
We estimated the inverse density for each vertex by calculating the k-nearest neighbors (kNN) distance53 for each constituent data point i.
We first define the k-neighborhood N(k)i of a point i, to be the set of k nearest neighbors of i, choosing k equal to 10% of the number of samples in each data set, rounded to the nearest integer. Then the kNN distance of point i is defined as:

$${rm{kNN}}(i,k)=frac{{sum }_{jin N{(k)}_{i}}{d}_{ij}}{k}$$
(1)

where dij is the distance between points i and j.
For a vertex V representing n points, we define its inverse density as

$${D}_{{rm{inv}}}(V)=frac{{sum }_{iin V}{rm{kNN}}(i,k)}{{n}^{2}}$$
(2)

The n2 term in the denominator compensates for the differing sizes of vertices. Finally, we invert the inverse density to obtain the estimated density:

$$D(V)=frac{1}{{D}_{{rm{inv}}}}$$
(3)

State assignment
We then defined states as topological features of the density surrounding local maxima of D. We designated each vertex with higher D than its neighbors to be a local maximum of the potential. Connected vertices tied for maximum D were each assigned to be a local maximum. To approximate a gradient, we converted the undirected Mapper graph to a directed graph, with each edge pointing from the vertex with lower D to the one with higher D. For each non-maximum vertex, we found the graph distance dg to each local maximum constrained by edge direction. We defined the state Bx of a maximum Vx as the set of vertices V with uniquely shortest graph distance to Vx:

$$Vin {B}_{x},{rm{if}},{d}_{g}(V,{V}_{x}), More

NATURE PODCAST
14 October 2020

A high pressure experiment reveals the world’s first room-temperature superconductor, and a method to target ecosystem restoration.

Nick Howe &

Search for this author in:

Shamini Bundell

Search for this author in:

Hear all the latest from the world of science, brought to you by Nick Howe and Shamini Bundell.
Your browser does not support the audio element.
Download MP3

In this episode:
00:44 Room-temperature superconductivity
For decades, scientists have been searching for a material that superconducts at room temperature. This week, researchers show a material that appears to do so, but only under pressures close to those at the centre of the planet. Research Article: Snider et al.; News: First room-temperature superconductor puzzles physicists
08:26 Coronapod
The Coronapod team revisit mask-use. Does public use really control the virus? And how much evidence is enough to turn the tide on this ongoing debate? News Feature: Face masks: what the data say
19:37 Research Highlights
A new method provides 3D printed materials with some flexibility, and why an honest post to Facebook may do you some good. Research Highlight: A promising 3D-printing method gets flexible; Research Highlight: Why Facebook users might want to show their true colours
22:11 The best way to restore ecosystems
Restoring degraded or human-utilised landscapes could help fight climate change and protect biodiversity. However, there are multiple costs and benefits that need to be balanced. Researchers hope a newly developed algorithm will help harmonise these factors and show the best locations to target restoration. Research Article: Strassburg et al.; News and Views: Prioritizing where to restore Earth’s ecosystems
28:40 Briefing Chat
We discuss some highlights from the Nature Briefing. This time, a 44 year speed record for solving a maths problem is beaten… just, and an ancient set of tracks show a mysterious journey. Quanta: Computer Scientists Break Traveling Salesperson Record; The Conversation: Fossil footprints: the fascinating story behind the longest known prehistoric journey
Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.
Other links
Don’t miss our latest YouTube video – Science under Trump: Four key moments
Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

Latest on:

An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Related Articles More

The aim of this study was to evaluate the kinetic of daily soil ingestion by growing bulls at tether-grazing when they received a very high sward and a large grazing area in which they stay during 11 days (no stake moving).
Use of herbage resource
The average sward height was 17.6 ± 0.3 cm (mean ± s.e.m.) along the 11 days of measurements from D2 to D12. Nevertheless, pregrazing sward height (at D2) was quite high with 49.2 ± 0.9 cm and significantly higher from those on D3 to D12 (14.4 ± 0.1 cm; P  More