Ecology

1.Bai, X. J., Wang, B. R., An, S. S., Zeng, Q. C. & Zhang, H. X. Response of forest species to C:N:P in the plant–litter–soil system and stoichiometric homeostasis of plant tissue during afforestation on the Loess Plateau, China. CATENA 183, 104186 (2019).CAS 
Article 

Google Scholar 
2.Zhao, X. N., Wu, P. T., Gao, X. D. & Persaud, N. Soil quality indicators in relation to land use and topography in a small catchment on the Loess Plateau of China. Land Degrad. Dev. 26(1), 54–61 (2015).Article 

Google Scholar 
3.Penuelas, J., Sardans, J., Rivas-Ubach, A. & Janssens, I. A. The human-induced imbalance between C, N, and P in Earth’s life system. GCB Bioenergy 18(1), 3–6 (2012).
Google Scholar 
4.Zhao, Z. P. et al. Effects of chemical fertilizer combined with organic manure on Fuji apple quality, yield and soil fertility in apple orchard on the Loess Plateau of China. Int. J. Agric. Biol. Eng. 7(2), 45–55 (2014).CAS 

Google Scholar 
5.Treseder, K. K. & Vitousek, P. M. Effects of soil nutrient availability on investment in acquisition of N and P in Havaiian rain forests. Ecology 82(4), 946–954 (2001).Article 

Google Scholar 
6.Vitousek, P. M. Nutrient cycling and nutrient use efficiency. Am. Nat. 119(4), 553–573 (1984).Article 

Google Scholar 
7.Zhong, Y. Q. W., Yan, W. M., Xu, X. B. & Shangguan, Z. P. Influence of nitrogen fertilization on wheat, and soil carbon, nitrogen and phosphorus stoichiometry characteristics. Int. J. Agric. Biol. 17, 1179–2118 (2015).CAS 
Article 

Google Scholar 
8.Cui, Q., Lü, X. T., Wang, Q. B. & Han, X. G. Nitrogen fertilization and fire act independently on foliar stoichiometry in a temperate steppe. Plant Soil 334, 209–219 (2010).CAS 
Article 

Google Scholar 
9.Louis, A. S. et al. Decadal changes in soil carbon and nitrogen under a range of irrigation and phosphorus fertilizer treatments. Soil Sci. Soc. Am. J. 77(1), 246–256 (2012).
Google Scholar 
10.Ostertag, R. Foliar nitrogen and phosphorus accumulation responses after fertilization: An example from nutrient-limited Hawaiian forests. Plant Soil 334, 85–98 (2010).ADS 
CAS 
Article 

Google Scholar 
11.Hu, Q. J., Sheng, M. Y., Bai, Y. X., Jie, Y. & Xiao, H. L. Response of C, N, and P stoichiometry characteristics of Broussonetia papyrifera to altitude gradients and soil nutrients in the karst rocky ecosystem, SW China. Plant Soil https://doi.org/10.1007/s11104-020-04742-7 (2020).Article 

Google Scholar 
12.Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, 2002).
Google Scholar 
13.Zhang, G. Q., Zhang, P., Peng, S. Z., Chen, Y. M. & Cao, Y. The coupling of leaf, litter, and soil nutrients in warm temperate forests in northwestern China. Sci. Rep. 7(1), 11754 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Pang, Y. et al. The linkages of plant, litter and soil C:N:P stoichiometry and nutrient stock in different secondary mixed forest types in the Qinling Mountains, China. PeerJ 8(4), e9274 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Heyburn, J., Mckenzie, P., Crawlwy, M. J. & Fornara, D. A. Effects of grassland management on plant C:N:P stoichiomtry: Implications for soil elment cycling and storage. Ecosphere 8(10), e01963 (2017).Article 

Google Scholar 
16.Sun, X. et al. Initial responses of grass litter tissue chemistry and N:P stoichiometry to varied N and P input rates and ratios in Inner Mongolia. Agric. Ecosyst. Environ. 252, 114–125 (2018).CAS 
Article 

Google Scholar 
17.Ding, F. et al. Opposite effects of nitrogen fertilization and plastic film mulching on crop N and P stoichiometry in a temperate agroecosystem. J. Plant Ecol. 12(4), 682–692 (2019).Article 

Google Scholar 
18.Ye, Y. S. et al. Carbon, nitrogen and phosphorus accumulation and partitioning, and C:N:P stoichiometry in late-season rice under different water and nitrogen managements. PLoS ONE 9(7), e101776 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Sistla, S. A., Appling, A. P., Lewandowska, A. M., Taylor, B. N. & Wolf, A. A. Stoichiometric flexibility in response to fertilization along gradients of environmental and organismal nutrient richness. Oikos 124(7), 949–959 (2015).CAS 
Article 

Google Scholar 
20.Ladanai, S., Ågren, G. I. & Olsson, B. A. Relationships between tree and soil properties in Picea abies and Pinus sylvestris forests in Sweden. Ecosystems 13(2), 302–316 (2010).CAS 
Article 

Google Scholar 
21.Lu, J. Y. et al. Leaf resorption and stoichiometry of N and P of 1, 2 and 3 year-old alfalfa under one-time P fertilization. Soil Till. Res. 197, 104481 (2020).Article 

Google Scholar 
22.Lu, J. Y., Yang, M., Liu, M. G., Lu, Y. X. & Yang, H. M. Nitrogen and phosphorus fertilizations alter nitrogen, phosphorus and potassium resorption of alfalfa in the Loess Plateau of China. J. Plant Nutr. 42(18), 2234–2246 (2019).CAS 
Article 

Google Scholar 
23.Jiang, H. M., Jiang, J. P., Jia, Y., Li, F. M. & Xu, J. Z. Soil carbon pool and effects of soil fertility in seeded alfalfa fields on the semi-arid Loess Plateau in China. Soil Biol. Biochem. 38(8), 2350–2358 (2006).CAS 
Article 

Google Scholar 
24.Gu, Y. J. et al. Alfalfa forage yield, soil water and P availability in response to plastic film mulch and P fertilization in a semiarid environment. Field Crop Res. 215, 94–103 (2018).Article 

Google Scholar 
25.Herbert, D. A., Williams, M. & Rastetter, E. B. A model analysis of N and P limitaiton on carbon accumulation in Amazonian secondary forest after alternate land-use abandonment. Biogeochemistry 65, 121–150 (2003).CAS 
Article 

Google Scholar 
26.Zhang, L. X., Bai, Y. F. & Han, X. G. Differential responses of N:P stoichiometry of Leymus chinensis and Carex korshinskyi to N additions in a steppe ecosystem in Nei Mongol. Acta Bot. Sin. 46, 259–270 (2004).
Google Scholar 
27.Stewart, J. R., Kennedy, G. J., Landes, R. D. & Dawson, J. Foliar-nitrogen and phosphorus resorption patterns differ among nitrogen-fixing and nonfixing temperate-deciduous trees and shrubs. Int. J. Plant Sci. 169(4), 495–502 (2008).CAS 
Article 

Google Scholar 
28.Vance, C. P., Uhde-Stone, C. & Allan, D. L. Phosphorus acquisition and use: Critical adaptations by plant for securing a non renewable resource. New Phytol. 157, 423–447 (2003).CAS 
PubMed 
Article 

Google Scholar 
29.Han, W. X., Fang, J. Y., Guo, D. L. & Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol. 168(2), 377–385 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Ma, H. M. et al. Moderate clipping stimulates over-compensatory growth of Leymus chinensis under saline-alkali stress throuth high allocation of biomass and nitrogen to shoots. Plant Growth Regul. 92, 95–106 (2020).CAS 
Article 

Google Scholar 
31.Sophie, Z. B. et al. The application of ecological stoichiometry to plant–microbial-soil organic matter transformations. Ecol. Monogr. 85(2), 133–155 (2015).Article 

Google Scholar 
32.Schmitt, A., Pausch, J. & Kuzyakov, Y. C and N allocation in soil under ryegrass and alfalfa extimated by 13C and 15N labelling. Plant Soil 368, 581–590 (2013).CAS 
Article 

Google Scholar 
33.Koerselman, W. & Meuleman, A. F. The vegetation N:P ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).Article 

Google Scholar 
34.Tian, H. G., Chen, G. S., Zhang, C., Melillo, J. M. & Hall, C. A. S. Pattern and variation of C:N:P ratios in China’s soils: A synthesis of observational data. Biogeochemistry 98, 139–151 (2010).CAS 
Article 

Google Scholar 
35.Ding, X. Q. et al. Establishing P fertilization reconmendation index of different vegetables by STP with the “3414” field experiments in South China. Int. J. Agric. Biol. 16, 603–608 (2014).CAS 

Google Scholar 
36.Suo, Y. Y. et al. Local-scale determinants of elemental stoichiometry of soil in an old-growth temperate forest. Plant Soil 408, 401–414 (2016).CAS 
Article 

Google Scholar 
37.Qiu, W. H., Liu, J. S., Li, B. Y. & Wang, Z. H. N2O and CO2 emissions from a dryland wheat cropping system with long-term N fertilization and their relationships with soil C, N and bacterial community. Environ. Sci. Pollut. Res. 27, 8673–8683 (2020).CAS 
Article 

Google Scholar 
38.Appelhans, S. C., Barbagelata, P. A., Melchiori, R. J. M. & Boem, F. G. Assessing soil P fractions changes with long-term phosphorus fertilization related to crop yield of soybean and maize. Soil Use Manag. 36(3), 524–535 (2020).Article 

Google Scholar 
39.Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. New Phytol. 193, 696–704 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Chen, X. D. et al. Soil alkaline phosphatase activity and bacterial phoD gene abundance and diversity under long-term nitrogen and manure inputs. Geoderma 349, 36–44 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Van Huysen, T. L., Perakis, S. S. & Harmon, M. K. Decomposition drives convergence of forest litter nutrient stoichiometry following phosphorus addition. Plant Soil 406(1–2), 1–14 (2016).Article 
CAS 

Google Scholar 
42.Li, M. et al. Role of plant species and soil phosphorus concentrations in determining phosphorus: Nutrient stoichiometry in leaves and fine roots. Plant Soil 445, 231–242 (2019).Article 
CAS 

Google Scholar 
43.Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in fresh water, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).PubMed 
Article 

Google Scholar 
44.Shaver, G. R. & Melillo, J. M. Nutrient budgets of marsh plant: Efficiency concepts and relation to availability. Ecology 65, 1491–1510 (1984).Article 

Google Scholar 
45.De Vos, B., Van Meirvenne, M., Quataert, P. & Muys, B. Predictive quality of pedotransfer functions for estimating bulk density of forest soils. Soil Sci. Soc. Am. J. 69(2), 500–510 (2005).Article 

Google Scholar  More

1.Hobbs, R. J. et al. Restoration ecology: the challenge of social values and expectations. Front. Ecol. Environ. 2, 43–38 (2004).Article 

Google Scholar 
2.Harris, J. A., Hobbs, R. J., Higgs, E. & Aronson, J. C. Ecological restoration and global climate change. Restor. Ecol. 14, 170–176 (2006).3.Aronson, J. C. & Vallejo, R. in Restoration Ecology: The New Frontier (eds. van Andel, J. & Aronson, J. C.) (John Wiley & Sons, 2009).4.Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Plaza, C. et al. Soil resources and element stocks in drylands to face global issues. Sci. Rep. 8, 13788 (2018).6.Aronson, J., Goodwin, N., Orlando, L., Eisenberg, C. & Cross, A. T. A world of possibilities: six restoration strategies to support the United Nation’s Decade on Ecosystem Restoration. Restor. Ecol. 28, 730–736 (2020).Article 

Google Scholar 
7.Drylands and Land Degradation (IUCN, 2017).8.Bainbridge, D. A. A Guide for Desert and Dryland Restoration: New Hope for Arid Lands (Island Press, 2012).9.Millennium Ecosystem Assessment Findings (Millennium Ecosystem Assessment, 2005).10.Reynolds, J. F., Maestre, F. T., Kemp, P. R., Stafford-Smith, D. M. & Lambin, E. in Terrestrial Ecosystems in a Changing World (eds. Canadell, J. G., Pataki, D. E. & Pitelka, L. F.) 247–257 (Springer, 2007); https://doi.org/10.1007/978-3-540-32730-1_2011.Hoover, D. L. et al. Traversing the wasteland: a framework for assessing ecological threats to drylands. BioScience 70, 35–47 (2020).Article 

Google Scholar 
12.Hardegree, S. P., Jones, T. A., Roundy, B. A., Shaw, N. L. & Monaco, T. A. in Conservation Benefits of Rangeland Practices 171–213 (United States Department of Agriculture, 2011).13.James, J. J., Svejcar, T. J. & Rinella, M. J. Demographic processes limiting seedling recruitment in arid grassland restoration. J. Appl. Ecol. 48, 961–969 (2011).Article 

Google Scholar 
14.Okin, G. S. et al. Connectivity in dryland landscapes: shifting concepts of spatial interactions. Front. Ecol. Environ. 13, 20–27 (2015).Article 

Google Scholar 
15.Svejcar, L. N. & Kildisheva, O. A. The age of restoration: challenges presented by dryland systems. Plant Ecol. 218, 1–6 (2017).Article 

Google Scholar 
16.Safriel, U. et al. Dryland Systems. Ecosystems and Human Well-being: Current State and Trends.: Findings of the Condition and Trends Working Group 623–662 (Millennium Ecosystem Assessment, 2005).17.Ward, D. The Biology of Deserts (Oxford Univ. Press, 2016).18.Li, Y., Chen, Y. & Li, Z. Dry/wet pattern changes in global dryland areas over the past six decades. Glob. Planet. Change 178, 184–192 (2019).Article 

Google Scholar 
19.Prăvălie, R., Bandoc, G., Patriche, C. & Sternberg, T. Recent changes in global drylands: evidences from two major aridity databases. Catena 178, 209–231 (2019).Article 

Google Scholar 
20.Yao, J. et al. Accelerated dryland expansion regulates future variability in dryland gross primary production. Nat. Commun. 11, 1665 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Aridity Zones and Dryland Populations: An Assessment of Population Levels in the World’s Drylands with Reference to Africa (UNSO/UNDP, 1997); http://digitallibrary.un.org/record/43231222.van den Berg, L. & Kellner, K. Restoring degraded patches in a semi-arid rangeland of South Africa. J. Arid. Environ. 61, 497–511 (2005).Article 

Google Scholar 
23.Valkó, O. et al. Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds. Nat. Conserv. 24, 65–80 (2018).Article 

Google Scholar 
24.Louhaichi, M., Clifton, K. & Hassan, S. Direct seeding of Salsola vermiculata for rehabilitation of degraded arid and semi-arid rangelands. Range Manag. Agrofor. 35, 182–187 (2014).
Google Scholar 
25.Pérez, D. R., González, F., Ceballos, C., Oneto, M. E. & Aronson, J. Direct seeding and outplantings in drylands of Argentinean Patagonia: estimated costs, and prospects for large-scale restoration and rehabilitation. Restor. Ecol. 27, 1105–1116 (2019).Article 

Google Scholar 
26.Kiehl, K., Kirmer, A., Donath, T. W., Rasran, L. & Hölzel, N. Species introduction in restoration projects: evaluation of different techniques for the establishment of semi-natural grasslands in Central and Northwestern Europe. Basic Appl. Ecol. 11, 285–299 (2010).Article 

Google Scholar 
27.Miguel, M. F., Butterfield, H. S. & Lortie, C. J. A meta-analysis contrasting active versus passive restoration practices in dryland agricultural ecosystems. PeerJ 8, e10428 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Kildisheva, O. A., Erickson, T. E., Merritt, D. J. & Dixon, K. W. Setting the scene for dryland recovery: an overview and key findings from a workshop targeting seed-based restoration. Restor. Ecol. 24, S36–S42 (2016).Article 

Google Scholar 
29.Lewandrowski, W., Erickson, T. E., Dixon, K. W. & Stevens, J. C. Increasing the germination envelope under water stress improves seedling emergence in two dominant grass species across different pulse rainfall events. J. Appl. Ecol. 54, 997–1007 (2017).CAS 
Article 

Google Scholar 
30.Ladouceur, E. & Shackelford, N. The power of data synthesis to shape the future of the restoration community and capacity. Restor. Ecol. 29, e13251 (2020).
Google Scholar 
31.Temperton, V. M., Baasch, A., von Gillhaussen, P. & Kirmer, A. in Foundations of Restoration Ecology (eds. Palmer, M. A., Zedler, J. B. & Falk, D. A.) 245–270 (Island Press/Center for Resource Economics, 2016); https://doi.org/10.5822/978-1-61091-698-1_932.Hulvey, K. B. & Aigner, P. A. Using filter-based community assembly models to improve restoration outcomes. J. Appl. Ecol. 51, 997–1005 (2014).Article 

Google Scholar 
33.van Wilgen, B. W. The evolution of fire and invasive alien plant management practices in fynbos. S. Afr. J. Sci. 105, 335–342 (2009).
Google Scholar 
34.Arianoutsoua, M. & Vilà, M. Fire and invasive plant species in the Mediterranean Basin. Isr. J. Ecol. Evol. 58, 195–203 (2012).
Google Scholar 
35.Leger, E. A. & Baughman, O. W. What seeds to plant in the Great Basin? Comparing traits prioritized in native plant cultivars and releases with those that promote survival in the field. Nat. Areas. J. 35, 54–68 (2015).Article 

Google Scholar 
36.Porensky, L. M., Vaughn, K. J. & Young, T. P. Can initial intraspecific spatial aggregation increase multi-year coexistence by creating temporal priority? Ecol. Appl. 22, 927–936 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
37.FAOSTAT Statistical Database (Food and Agriculture Organization of the United Nations, 1997).38.Balazs, K. R. et al. The right trait in the right place at the right time: matching traits to environment improves restoration outcomes. Ecol. Appl. 30, e02110 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Knutson, K. C. et al. Long-term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. J. Appl. Ecol. 51, 1414–1424 (2014).Article 

Google Scholar 
40.Brown, C. S. & Bugg, R. L. Effects of established perennial grasses on introduction of native forbs in California. Restor. Ecol. 9, 38–48 (2001).Article 

Google Scholar 
41.Porensky, L. M. et al. Arid old-field restoration: native perennial grasses suppress weeds and erosion, but also suppress native shrubs. Agric. Ecosyst. Environ. 184, 135–144 (2014).Article 

Google Scholar 
42.Hardegree, S. P. et al. Hydrothermal assessment of temporal variability in seedbed microclimate. Rangel. Ecol. Manag. 66, 127–135 (2013).Article 

Google Scholar 
43.Copeland, S. M. et al. Long-term trends in restoration and associated land treatments in the southwestern United States. Restor. Ecol. 26, 311–322 (2018).Article 

Google Scholar 
44.Abella, S. R., Craig, D. J., Smith, S. D. & Newton, A. C. Identifying native vegetation for reducing exotic species during the restoration of desert ecosystems. Restor. Ecol. 20, 781–787 (2012).Article 

Google Scholar 
45.Mulroy, T. W. & Rundel, P. W. Annual plants: adaptations to desert environments. BioScience 27, 109–114 (1977).Article 

Google Scholar 
46.Leger, E. A., Goergen, E. M. & Forbis de Queiroz, T. Can native annual forbs reduce Bromus tectorum biomass and indirectly facilitate establishment of a native perennial grass? J. Arid. Environ. 102, 9–16 (2014).Article 

Google Scholar 
47.Gutiérrez, J. R., Arancio, G. & Jaksic, F. M. Variation in vegetation and seed bank in a Chilean semi-arid community affected by ENSO 1997. J. Veg. Sci. 11, 641–648 (2000).Article 

Google Scholar 
48.Venable, D. L. Bet hedging in a guild of desert annuals. Ecology 88, 1086–1090 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Baskin, C. C. Seed ecology: a diverse and vibrant field of study. Seed Sci. Res. 27, 61–64 (2017).Article 

Google Scholar 
50.Padilla, F. M., Ortega, R., Sánchez, J. & Pugnaire, F. I. Rethinking species selection for restoration of arid shrublands. Basic Appl. Ecol. 10, 640–647 (2009).Article 

Google Scholar 
51.SER International Primer on Ecological Restoration (SER, 2004).52.The Plant List (WFO, 2013).53.Seed Information Database (Royal Botanic Gardens, Kew, 2019).54.Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).Article 

Google Scholar 
55.USDA, NRCS. The PLANTS Database (National Plant Data Team, 2020).56.Western Australian Herbarium. FloraBase—the Western Australian Flora (Department of Biodiversity, Conservation and Attractions, 1998).57.Fick, S. E. & Hijmans, R. J. Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).58.Trabucco, A. & Zomer, R. J. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database, v3 (CGIAR Consortium for Spatial Information, 2019).59.Barrow, C. J. World atlas of desertification (United Nations Environment Programme). Land Degrad. Dev. 3, 249–249 (1992).Article 

Google Scholar 
60.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).62.Crawley, M. J. in The R Book 569–591 (Wiley, 2007).63.Wortley, L., Hero, J.-M. & Howes, M. Evaluating ecological restoration success: a review of the literature. Restor. Ecol. 21, 537–543 (2013).Article 

Google Scholar  More

1.Bennett, S. C. The ontogeny of Pteranodon and other pterosaurs. Paleobiology 19, 92–106 (1993).Article 

Google Scholar 
2.Bennett, S. C. A statistical study of Rhamphorhynchus from the Solnhofen Limestone of Germany: Year-classes of a single large species. J. Paleontol. 69, 569–580 (1995).Article 

Google Scholar 
3.Bennett, S. C. Year-classes of pterosaurs from the Solnhofen Limestone of Germany: Taxonomic and systematic implications. J. Vertebr. Paleontol. 16, 432–444 (1996).Article 

Google Scholar 
4.Bennett, S. C. New smallest specimen of the pterosaur Pteranodon and ontogenetic niches in pterosaurs. J. Paleontol. 92, 254–271 (2018).Article 

Google Scholar 
5.Kellner, A. W. A. Comments on Triassic pterosaurs with discussion about ontogeny and description of new taxa. An. Acad. Bras. Ciênc. 87, 669–689 (2015).PubMed 
Article 

Google Scholar 
6.Chiappe, L. M., Codorniú, L., Grellet-Tinner, G. & Rivarola, D. Argentinian unhatched pterosaur fossil. Nature 432, 571–572 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Wang, X. & Zhou, Z. Pterosaur embryo from the Early Cretaceous. Nature 429, 521 (2004).Article 
CAS 

Google Scholar 
8.Manzig, P. C. et al. Discovery of a rare pterosaur bone bed in a Cretaceous desert with insights on ontogeny and behavior of flying reptiles. PLoS ONE 9, e100005. https://doi.org/10.1371/journal.pone.0100005 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Wang, X. et al. Sexually dimorphic tridimensionally preserved pterosaurs and their eggs from China. Curr. Biol. 24, 1323–1330 (2014).CAS 
PubMed 
Article 

Google Scholar 
10.Codorniú, L., Chiappe, L. & Rivarola, D. Neonate morphology and development in pterosaurs: evidence from a ctenochasmatid embryo from the Early Cretaceous of Argentina. In New Perspectives on Pterosaur Palaeobiology Vol. 455 (eds Hone, D. W. E. et al.) 83–94 (Geological Society London Special Publications, 2018).11.Wang, X. et al. Egg accumulation with 3D embryos provides insight into the life history of a pterosaur. Science 358, 1197–1201 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Unwin, D. M. The Pterosaurs from Deep Time (Pi Press, 2005).13.Prondvai, E., Stein, K., Ősi, A. & Sander, M. P. Life history of Rhamphorhynchus inferred from bone histology and the diversity of pterosaurian growth strategies. PLoS ONE 7, e31392. https://doi.org/10.1371/journal.pone.0031392 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Heij, C. J., Rompas, C. F. E. & Moeliker, C. W. The biology of the Mollucan megapode Eulipoa wallacei (Aves, Galliformes, Megapodiidae) on Haruku and other Mollucan Islands; part 2. Deinsea 3, 1–120 (1997).
Google Scholar 
15.Jackson, B. E., Segre, P. & Dial, K. P. Precocial development of locomotor performance in a ground-dwelling bird (Alectoris chukar): Negotiating a three-dimensional terrestrial environment. Proc. R. Soc. B 276, 3457–3466 (2009).PubMed 
Article 

Google Scholar 
16.Healey, C. Dispersal of newly hatched orange-footed scrubfowl Megapodius reinwardt. Emu 94, 220–221 (1994).Article 

Google Scholar 
17.Starck, J. M. Structural variants and invariants in avian embryonic and postnatal development. Oxford Ornithol. Ser. 8, 59–88 (1998).
Google Scholar 
18.Chinsamy, A., Codorniú, L. & Chiappe, L. Developmental growth patterns of the filter-feeder pterosaur, Pterodaustro guinazui. Biol. Lett. 4, 282–285 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Hone, D. W. E., Ratcliffe, J. M., Riskin, D. K., Hermanson, J. W. & Reisz, R. R. Unique near isometric ontogeny in the pterosaur Rhamphorhynchus suggests hatchlings could fly. Lethaia 54, 106–112 (2020).Article 

Google Scholar 
20.Habib, M. B. Comparative evidence for quadrupedal launch in pterosaurs. Zitteliana B28, 159–166 (2008).
Google Scholar 
21.Codorniú, L. & Chiappe, L. M. Early juvenile pterosaurs (Pterodactyloidea: Pterodaustro guinazui) from the Lower Cretaceous of central Argentina. Can. J. Earth Sci. 41, 9–18 (2004).ADS 
Article 

Google Scholar 
22.Kellner, A. W. A. Pterosaur phylogeny and comments on the evolutionary history of the group. In Evolution and Palaeobiology of Pterosaurs Vol. 217 (eds Buffetaut, E. & Mazin, J.-M.) 105–137 (Geol. Soc. Spec. Publ, 2003).23.Wang, X., Kellner, A. W. A., Zhou, Z. & Campos, D. D. A. Discovery of a rare arboreal forest-dwelling flying reptile (Pterosauria, Pterodactyloidea) from China. Proc. Natl. Acad. Sci. USA 105, 1983–1987 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
24.Andres, B., Clark, J. & Xu, X. The earliest pterodactyloid and the origin of the group. Curr. Biol. 24, 1011–1016 (2014).CAS 
PubMed 
Article 

Google Scholar 
25.Witton, M. P. Pterosaurs: Natural History, Evolution, Anatomy (Princeton University Press, 2013).26.Hone, D. W. E., Farke, A. A. & Wedel, M. J. Ontogeny and the fossil record: What, if anything, is an adult dinosaur?. Biol. Lett. 12, 20150947 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Campione, N. E., Brink, K. S., Freedman, E. A., McGarrity, C. T. & Evans, D. C. ‘Glishades ericksoni’, an indeterminate juvenile hadrosaurid from the Two Medicine Formation of Montana: Implications for hadrosauroid diversity in the latest Cretaceous (Campanian-Maastrichtian) of western North America. Palaeobio. Palaeoenv. 93, 65–75 (2013).
Google Scholar 
28.Wellnhofer, P. & Kellner, A. W. A. The skull of Tapejara wellnhoferi Kellner (Reptilia, Pterosauria) from the Lower Cretaceous Santana Formation of the Araripe Basin, Northeastern Brazil. Mitteilungen der Bayerischen Staatssammlung für Paläontologie und Historische Geologie 31, 89–106 (1991).
Google Scholar 
29.Unwin, D. M. On the phylogeny and evolutionary history of pterosaurs. In Evolution and Palaeobiology of Pterosaurs Vol. 217 (eds Buffetaut, E. & Mazin, J.-M.) 139–190 (Geol. Soc. Spec. Publ, 2003).30.Kellner, A. W. A. New information on the Tapejaridae (Pterosauria, Pterodactyloidea) and discussion of the relationships of this clade. Ameghiniana 41, 521–534 (2004).
Google Scholar 
31.Lü, J. et al. A new species of Huaxiapterus (Pterosauria: Pterodactyloidea) from the Lower Cretaceous of Western Liaoning, China with comments on the systematics of tapejarid pterosaurs. Acta Geol. Sin. 80, 315–326 (2006).
Google Scholar 
32.Eck, K., Elgin, R. & Frey, E. On the osteology of Tapejara wellnhoferi KELLNER 1989 and the first occurrence of a multiple specimen assemblage from the Santana Formation, Araripe Basin, NE-Brazil. Swiss J. Paleontol. 130, 277–296 (2011).Article 

Google Scholar 
33.Bennett, S. C. Sexual dimorphism in Pteranodon and other pterosaurs, with comments on cranial crests. J. Vertebr. Paleontol. 12, 422–434 (1992).Article 

Google Scholar 
34.Tomkins, J. L., LeBas, N. R., Witton, M. P., Martill, D. M. & Humphries, S. Positive allometry and the prehistory of sexual selection. Am. Nat. 176, 141–148 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Pinheiro, F. L. & Rodrigues, T. Anhanguera taxonomy revisited: Is our understanding of Santana Group pterosaur diversity biased by poor biological and stratigraphic control?. PeerJ 5, e3285. https://doi.org/10.7717/peerj.3285 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Li, J. J., Lü, J. & Zhang, B. K. A new sinopterid pterosaur from the Mesozoic of western Liaoning Province, China. Acta Palaeontologica Sinica 42, 442–447 (2003).
Google Scholar 
37.Bennett, S. C. Juvenile specimens of the pterosaur Germanodactylus cristatus, with a review of the genus. J. Vertebr. Paleontol. 26, 872–878 (2006).Article 

Google Scholar 
38.Bennett, S. C. New information on body size and cranial display structures of Pterodactylus antiquus, with a revision of the genus. Palaeontol. Z. 87, 269–289 (2013).Article 

Google Scholar 
39.Bennett, S. C. Soft tissue preservation of the cranial crest of the pterosaur Germanodactylus from Solnhofen. J. Vertebr. Paleontol. 22, 43–48 (2002).Article 

Google Scholar 
40.Wang, X. & Zhou, Z. A new pterosaur (Pterodactyloidea, Tapejaridae) from the Early Cretaceous Jiufotang Formation of western Liaoning, China and its implications for biostratigraphy. Chin. Sci. Bull. 48, 16–23 (2003).Article 

Google Scholar 
41.Jouve, S. Description of the skull of a Ctenochasma (Pterosauria) from the latest Jurassic of eastern France, with a taxonomic revision of European Tithonian Pterodactyloidea. J. Vertebr. Paleontol. 24, 542–554 (2004).Article 

Google Scholar 
42.McGuire, J. A. Allometric prediction of locomotor performance: An example from Southeast Asian flying lizards. Am. Nat. 161, 337–349 (2003).PubMed 
Article 

Google Scholar 
43.McGuire, J. A. & Dudley, R. The biology of gliding in flying lizards (genus Draco) and their fossil and extant analogs. Integr. Comp. Biol. 51, 983–990 (2011).PubMed 
Article 

Google Scholar 
44.Witton, M. P. A new approach to determining pterosaur body mass and its implications for pterosaur flight. Zitteliana B28, 143–158 (2008).
Google Scholar 
45.Henderson, D. M. Pterosaur body mass estimates from three-dimensional mathematical slicing. J. Vertebr. Paleontol. 30, 768–785 (2010).Article 

Google Scholar 
46.Witton, M. P. Flight performance and lifestyle of Dimorphodon macronyx. Flugsaurier 2015 Portsmouth abstract volume, 57–60 (2015).47.Martin, E. G. & Palmer, C. A novel method of estimating pterosaur skeletal mass using computed tomography scans. J. Vertebr. Paleontol. 34, 1466–1469 (2014).Article 

Google Scholar 
48.Martin-Silverstone, E. et al. Exploring the relationship between skeletal mass and total body mass in birds. PLoS ONE 10, e0141794. https://doi.org/10.1371/journal.pone.0141794 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Elgin, R., Hone, D. W. E. & Frey, E. The extent of the pterosaur flight membrane. Acta Palaeontol. Pol. 56, 99–111 (2011).Article 

Google Scholar 
50.Pennycuick, C. J. Modelling the Flying Bird (Academic, 2008).51.Witton, M. P. & Habib, M. B. On the size and flight diversity of giant pterosaurs, the use of birds as pterosaur analogues and comments on pterosaur flightlessness. PLoS ONE 5, e13982. https://doi.org/10.1371/journal.pone.0013982 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Bennett, S. C. New interpretation of the wings of the pterosaur Rhamphorhynchus muensteri based on the Zittel and Marsh specimens. J. Paleont. 1, 1–25 (2016).
Google Scholar 
53.Palmer, C. & Dyke, G. J. Biomechanics of the unique pterosaur pteroid. P. Roy. Soc. B 277, 1121–1127 (2010).
Google Scholar 
54.Currey, J. D. Bones: Structure and Mechanics (Princeton University Press, 2002).55.Vernes, K. Gliding performance of the Northern flying squirrel (Glaucomys sabrinus) in mature mixed forest of eastern Canada. J. Mammal. 82, 1026–1033 (2001).Article 

Google Scholar 
56.Socha, J. J. Gliding flight in the paradise tree snake. Nature 418, 603–604 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
57.Jackson, S. M. Gliding Mammals of the World (Csiro Publishing, 2012).58.Alexander, D. E. Nature’s Flyers: Birds, Insects, and the Biomechanics of Flight (JHU Press, 2004).59.Socha, J. J., Jafari, F., Munk, Y. & Byrnes, G. How animals glide: From trajectory to morphology. Can. J. Zoo. 93, 901–924 (2015).Article 

Google Scholar 
60.Biewener, A. A. Bone strength in small mammals and bipedal birds: Do safety factors change with body size?. J. Exp. Biol. 98, 289–301 (1982).CAS 
PubMed 
Article 

Google Scholar 
61.Currey, J. D. & Alexander, R. M. The thickness of the walls of tubular bones. J. Zool. 206, 453–468 (1985).Article 

Google Scholar 
62.Habib, M. Constraining the air giants: Limits on size in flying animals as an example of constraint-based biomechanical theories of form. Biol. Theory 8, 245–252 (2013).Article 

Google Scholar 
63.Vidovic, S. U. & Martill, D. M. Pterodactylus scolopaciceps Meyer, 1860 (Pterosauria, Pterodactyloidea) from the Upper Jurassic of Bavaria, Germany: The problem of cryptic pterosaur taxa in early ontogeny. PLoS ONE 9, e110646. https://doi.org/10.1371/journal.pone.0110646 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Grigg, G. & Kirshner, D. Biology and Evolution of Crocodylians (CSIRO Publishing, 2015).65.Alerstam, T., Rosén, M., Bäckman, J., Ericson, P. G. & Hellgren, O. Flight speeds among bird species: Allometric and phylogenetic effects. PLoS Biol. 5, e197. https://doi.org/10.1371/journal.pbio.0050197 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Dial, K. P. & Jackson, B. E. When hatchlings outperform adults: locomotor development in Australian brush turkeys (Alectura lathami, Galliformes). Proc. R. Soc. B 278, 1610–1616 (2010).PubMed 
Article 

Google Scholar 
67.Rayner, J. M. Form and function in avian flight. Curr. Ornithol. 5, 1–66 (1988).
Google Scholar 
68.Marden, J. H. From damselflies to pterosaurs: How burst and sustainable flight performance scale with size. Am. J. Physiol. Reg. I 266, R1077–R1084 (1994).CAS 
Article 

Google Scholar 
69.Tobalske, B. W., Altshuler, D. L. & Powers, D. R. Take-off mechanics in hummingbirds (Trochilidae). J. Exp. Biol. 207, 1345–1352 (2004).PubMed 
Article 

Google Scholar 
70.Unwin, D. M. & Deeming, D. C. Pterosaur eggshell structure and its implications for pterosaur reproductive biology. Zitteliana B28, 199–207 (2008).
Google Scholar 
71.Unwin, D. M. & Martill, D. M. Pterosaurs of the Crato formation. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 475–524 (Cambridge University Press, 2007).72.Lü, J. et al. An egg-adult association, gender, and reproduction in pterosaurs. Science 331, 321–324 (2011).ADS 
PubMed 
Article 
CAS 

Google Scholar 
73.Naish, D. & Witton, M. P. Neck biomechanics indicate that giant Transylvanian azhdarchid pterosaurs were short-necked arch predators. PeerJ 5, e2908. https://doi.org/10.7717/peerj.2908 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Wellnhofer, P. The Illustrated Encyclopedia of Pterosaurs (Crescent Books, 1991).75.Witton, M. P. & Naish, D. A reappraisal of azhdarchid pterosaur functional morphology and paleoecology. PLoS ONE 3, e2271. https://doi.org/10.1371/journal.pone.0002271 (2008).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Díaz, S. et al. Pervasive human-driven decline of life on earth points to the need for transformative change. Science 366, eaax3100 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Sánchez-Bayo, F. & Wyckhuys, K. A. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

Google Scholar 
3.Simmons, B. I. et al. Worldwide insect declines: An important message, but interpret with caution. Ecol. Evol. 9, 3678–3680 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Valtonen, A. et al. Long-term species loss and homogenization of moth communities in Central Europe. J. Anim. Ecol. 86, 730–738 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
5.van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
6.Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Thomas, C., Jones, T. H. & Hartley, S. E. “Insectageddon”: A call for more robust data and rigorous analyses. Glob. Change Biol. 25,1891–1892 (2019).ADS 
Article 

Google Scholar 
8.Enkhtur, K., Boldgiv, B. & Pfeiffer, M. Diversity and distribution patterns of geometrid moths (Geometridae, Lepidoptera) in Mongolia. Diversity 12, 186 (2020).Article 

Google Scholar 
9.Pullaiah, T. Global Biodiversity: Volume 1: Selected Countries in Asia (CRC Press, 2018).Book 

Google Scholar 
10.Knyazev, S. A., Makhov, I. A., Matov, A. Y. & Yakovlev, R. V. Check-list of Macroheterocera (Insecta, Lepidoptera) collected in 2019 in Mongolia by Russian entomological expeditions. Ecol. Montenegrina 38, 186–204 (2020).Article 

Google Scholar 
11.Ustjuzhanin, P., Kovtunovich, V. & Yakovlev, R. Alucitidae (Lepidoptera), a new family for the Mongolian fauna. Nota Lepidopterol. 39, 61 (2016).Article 

Google Scholar 
12.Volynkin, A. V. & Gyulai, P. A new species of Athaumasta Hampson, 1906 (Lepidoptera, Noctuidae, Bryophilinae) from the Altai Mountains of Mongolia and China. Zootaxa 4508, 594–600 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Saldaitis, A. Review of the genus Kerzhnerocossus Yakovlev, 2011 (Lepidoptera: Cossidae) with descriptions of two new species from Russia and Mongolia. Zootaxa 4294, 389–394 (2017).Article 

Google Scholar 
14.Yakovlev, R. V. & Doroshkin, V. V. Hyles svetlana Shovkoon, 2010 (Lepidoptera: Sphingidae)—new species for Mongolian fauna and new records of Hawk-moths in Western Mongolia. Russian Entomological Journal. 26(3), 263–266 (2017).Article 

Google Scholar 
15.Volynkin, A. V., Titov, S. V. & Černila, M. Anarta insolita umay, a new subspecies from Russian Altai and Mongolia, with re-characterization of Anarta insolita uigurica (Hacker, 1998) (Lepidoptera, Noctuidae, Noctuinae). Ecol. Montenegrina 35, 115–122 (2020).Article 

Google Scholar 
16.Gershenson, Z. S. New Records of Yponomeutoid Moths (Lepidoptera, Yponomeutidae, Argyrestiidae Ypsolophidae, Plutelliidae) from the Palaearctic Region. Vestnik  Zoologii 50(1), 23–30 (2016).17.GBIF.org. GBIF Occurrence Download data. https://doi.org/10.15468/dl.h5ebh7 (2021).18.Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).Article 

Google Scholar 
19.Daniel, B., Francois, G. & Legendre, P. Numerical Ecology with R (Springer, 2011).MATH 

Google Scholar 
20.Jurasinski, G., Retzer, V. & Beierkuhnlein, C. Inventory, differentiation, and proportional diversity: A consistent terminology for quantifying species diversity. Oecologia 159, 15–26 (2009).ADS 
PubMed 
Article 

Google Scholar 
21.Bachand, M. et al. Species indicators of ecosystem recovery after reducing large herbivore density: Comparing taxa and testing species combinations. Ecol. Indic. 38, 12–19 (2014).Article 

Google Scholar 
22.Enkhtur, K., Pfeiffer, M., Lkhagva, A. & Boldgiv, B. Response of moths (Lepidoptera: Heterocera) to livestock grazing in Mongolian rangelands. Ecol. Indic. 72, 667–674 (2017).Article 

Google Scholar 
23.Baselga, A., Gómez-Rodríguez, C. & Lobo, J. M. Historical legacies in world amphibian diversity revealed by the turnover and nestedness components of beta diversity. PLoS ONE 7, e32341 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).Article 

Google Scholar 
25.Whittaker, R. J., Nogués-Bravo, D. & Araújo, M. B. Geographical gradients of species richness: A test of the water-energy conjecture of Hawkins et al. (2003) using European data for five taxa. Glob. Ecol. Biogeogr. 16, 76–89 (2007).Article 

Google Scholar 
26.Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Ahlborn, J. et al. Climate–grazing interactions in Mongolian rangelands: Effects of grazing change along a large-scale environmental gradient. J. Arid Environ. 173, 104043 (2020).ADS 
Article 

Google Scholar 
28.Bai, Y. et al. Positive linear relationship between productivity and diversity: Evidence from the Eurasian Steppe. J. Appl. Ecol. 44, 1023–1034 (2007).Article 

Google Scholar 
29.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Anderson, M. J. et al. Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Tuomisto, H. A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33, 2–22 (2010).Article 

Google Scholar 
32.Hoffmann, S. et al. Remote sensing of β-diversity: Evidence from plant communities in a semi-natural system. Appl. Veg. Sci. 22, 13–26 (2019).Article 

Google Scholar 
33.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
34.Fontana, V. et al. Species richness and beta diversity patterns of multiple taxa along an elevational gradient in pastured grasslands in the European Alps. Sci. Rep. 10, 1–11 (2020).Article 
CAS 

Google Scholar 
35.Pfeiffer, M., Dulamsuren, C., Jäschke, Y. & Wesche, K. Grasslands of China and Mongolia:Spatial Extent, Land Use and Conservation. In Grasslands of the World: Diversity, Management and Conservation. (CRC Press, 2018).36.Pfeiffer, M., Dulamsuren, C. & Wesche, K. Grasslands and Shrublands of Mongolia. In Reference Module in Earth Systems and Environmental Sciences. 759–772 (Elsevier, 2019).37.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).PubMed 
Article 

Google Scholar 
38.Kraft, N. J. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linn. Soc. 28, 65–82 (1986).Article 

Google Scholar 
40.Wang, Y., Ding, P., Chen, S. & Zheng, G. Nestedness of bird assemblages on urban woodlots: Implications for conservation. Landsc. Urban Plan. 111, 59–67 (2013).Article 

Google Scholar 
41.Hylander, K., Nilsson, C., Gunnar Jonsson, B. & Göthner, T. Differences in habitat quality explain nestedness in a land snail meta-community. Oikos 108, 351–361 (2005).Article 

Google Scholar 
42.Osório, N. C., Cunha, E. R., Tramonte, R. P., Mormul, R. P. & Rodrigues, L. Habitat complexity drives the turnover and nestedness patterns in a periphytic algae community. Limnology 20, 297–307 (2019).Article 
CAS 

Google Scholar 
43.St. Pierre, J. I. & Kovalenko, K. E. Effect of habitat complexity attributes on species richness. Ecosphere 5, 1–10 (2014).Article 

Google Scholar 
44.Wright, D. H. & Reeves, J. H. On the meaning and measurement of nestedness of species assemblages. Oecologia 92, 416–428 (1992).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Paknia, O., Grundler, M. & Pfeiffer, M. Species richness and niche differentiation of darkling beetles (Coleoptera: Tenebrionidae) in Mongolian steppe ecosystems. In Steppe Ecosyst. Biol. Divers. Manag. Restor. 47–72 (Nova Sci. Publ.,2013).46.Rabl, D., Gottsberger, B., Brehm, G., Hofhansl, F. & Fiedler, K. Moth assemblages in Costa Rica rain forest mirror small-scale topographic heterogeneity. Biotropica 52, 288–301 (2020).Article 

Google Scholar 
47.McGeachie, W. J. The effects of moonlight illuminance, temperature and wind speed on light-trap catches of moths. Bull. Entomol. Res. 79, 185–192 (1989).Article 

Google Scholar 
48.Antão, L. H., Pöyry, J., Leinonen, R. & Roslin, T. Contrasting latitudinal patterns in diversity and stability in a high-latitude species-rich moth community. Glob. Ecol. Biogeogr. 29, 896–907 (2020).Article 

Google Scholar 
49.Steiner, A. Die Nachtfalter Deutschlands: ein Feldführer: sämtliche nachtaktiven Großschmetterlinge in Lebendfotos und auf Farbtafeln (Bugbook Publishing, 2014).
Google Scholar 
50.Spalding, A., Young, M. & Dennis, R. L. The importance of host plant-habitat substrate in the maintenance of a unique isolate of the Sandhill Rustic: Disturbance, shingle matrix and bare ground indicators. J. Insect Conserv. 16, 839–846 (2012).Article 

Google Scholar 
51.Betzholtz, P.-E. & Franzen, M. Mobility is related to species traits in noctuid moths. Ecol. Entomol. 36, 369–376 (2011).Article 

Google Scholar 
52.Soininen, J., Heino, J. & Wang, J. A meta-analysis of nestedness and turnover components of beta diversity across organisms and ecosystems. Glob. Ecol. Biogeogr. 27, 96–109 (2018).Article 

Google Scholar 
53.Holt, R. D. & Hoopes, M. F. Food web dynamics in a metacommunity context. In Metacommunities. Spat. Dyn. Ecol. Communities (ed. Holyoak, M.) 68–94 (Univ. of Chicago Press, 2005).54.Robinson GS, Ackery PR, Kitching IJ, Beccaloni GW, Hernández LM. HOSTS—a database of the World’s Lepidopteran hostplants https://www.nhm.ac.uk/our-science/data/hostplants (2010).55.Moreno, C., Cianciaruso, M. V., Sgarbi, L. F. & Ferro, V. G. Richness and composition of tiger moths (Erebidae: Arctiinae) in a Neotropical savanna: Are heterogeneous habitats richer in species?. Nat. Conserv. 12, 138–143 (2014).Article 

Google Scholar 
56.von Wehrden, H., Hanspach, J., Kaczensky, P., Fischer, J. & Wesche, K. Global assessment of the non-equilibrium concept in rangelands. Ecol. Appl. 22, 393–399 (2012).Article 

Google Scholar 
57.Ashton, L. A. et al. Altitudinal patterns of moth diversity in tropical and subtropical Australian rainforests. Austral. Ecol. 41, 197–208 (2016).Article 

Google Scholar 
58.Liu, Y. Y. et al. Changing climate and overgrazing are decimating Mongolian steppes. PLoS ONE 8, e57599 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lang, B. et al. Grazing effects on intraspecific trait variability vary with changing precipitation patterns in Mongolian rangelands. Ecol. Evol. 10(2),678-691 (2020).60.Brehm, G. A new LED lamp for the collection of nocturnal Lepidoptera and a spectral comparison of light-trapping lamps. Nota Lepidopterol. 40, 87 (2017).Article 

Google Scholar 
61.Brehm, G. & Axmacher, J. C. A comparison of manual and automatic moth sampling methods (Lepidoptera: Arctiidae, Geometridae) in a rain forest in Costa Rica. Environ. Entomol. 35, 757–764 (2006).Article 

Google Scholar 
62.Rennwald, E. & Rodeland, E. Lepiforum: Bestimmung von Schmetterlingen (Lepidoptera) und ihren Präimaginalstadien. http://www.lepiforum.de (2002).63.Knyazev, S. A. Electronic atlas of Lepidoptera in Omsk region. http://omflies.ru/ (2017).64.Yang, M. et al. The first mitochondrial genome of the family Epicopeiidae and higher-level phylogeny of Macroheterocera (Lepidoptera: Ditrysia). Int. J. Biol. Macromol. 136, 123–132 (2019).CAS 
PubMed 
Article 

Google Scholar 
65.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
66.Mongolian Statistical Information Service. Livestock. http://1212.mn/stat.aspx?LIST_ID=976_L10_1 (2020).67.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan (2019).68.Linlin Yan. ggvenn: Draw Venn Diagram by ‘ggplot2’. R package version 0.1.8. https://CRAN.R-project.org/package=ggvenn (2021).69.Baselga, A. et al. betapart: Partitioning Beta Diversity into Turnover and Nestedness Components. R package version 1.5.2. https://CRAN.R-project.org/package=betapart (2020).70.Crawley, M. J. The R Book (Wiley, 2012).MATH 
Book 

Google Scholar 
71.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar  More

1.Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. U. S. A. 114, E6089–E6096 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Briggs, J. C. Marine extinctions and conservation. Mar. Biol. 158, 485–488 (2011).Article 

Google Scholar 
3.Heupel, M. R., Knip, D. M., Simpfendorfer, C. A. & Dulvy, N. K. Sizing up the ecological role of sharks as predators. Mar. Ecol. Prog. Ser. 495, 291–298 (2014).ADS 
Article 

Google Scholar 
4.TRAFFIC East Asia. Shark product trade in Hong Kong and mainland China and implementation of the CITES shark listings. TRAFFIC East Asia (2004).5.Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–34 (2014).Article 

Google Scholar 
7.Dwyer, R. G. et al. Individual and population benefits of marine reserves for reef sharks. Curr. Biol. 30, 480-489.e5 (2020).CAS 
PubMed 
Article 

Google Scholar 
8.Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 1–6 (2013).Article 

Google Scholar 
9.Cabral, R. B. et al. A global network of marine protected areas for food. Proc. Natl. Acad. Sci. U. S. A. 117, 28134–28139 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Camhi, M. D., Fordham, S. V. & Fowler, S. L. Domestic and International Management for Pelagic Sharks. in Sharks of the Open Ocean: Biology, Fisheries and Conservation (eds. M. D. Camhi, E. K. Pikitch & E. A. Babcock) 418–444 (Blackwell, 2009). https://doi.org/10.1002/9781444302516.ch34.11.Schiller, L., Alava, J. J., Grove, J., Reck, G. & Pauly, D. The demise of Darwin’s fishes: evidence of fishing down and illegal shark finning in the Galápagos Islands. Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 431–446 (2015).Article 

Google Scholar 
12.Feitosa, L. M. et al. DNA-based identification reveals illegal trade of threatened shark species in a global elasmobranch conservation hotspot. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
13.Reck, G. Development of the Galápagos Marine Reserve. in The Galapagos Marine Reserve. Social and Ecological Interactions in the Galapagos Islands (ed. Denkinger J, V. L.) 139‒158 (Springer, 2014).14.PNG (Parque Nacional Galápagos). Barco chino deberá pagar 6 millones por daño ambiental dispone Sala de lo Penal. https://www.galapagos.gob.ec/barco-chino-debera-pagar-6-millones-por-dano-ambiental-dispone-sala-de-lo-penal/ (2017).15.Fiscalía General del Estado Ecuatoriano. Boletín de Prensa FGE N. 096-DC-2019: Corte Nacional aceptó recurso de casación por delito contra la flora y fauna silvestres en Galápagos. https://www.fiscalia.gob.ec/corte-nacional-acepto-recurso-de-casacion-por-delito-contra-la-flora-y-fauna-silvestres-en-galapagos/ (2019).16.D’Afflisio, E., Braca, P., Millefiori, L. M. & Willett, P. Maritime Anomaly Detection Based on Mean-Reverting Stochastic Processes Applied to a Real-World Scenario. in 2018 21st International Conference on Information Fusion, FUSION 2018 1171–1177 (Institute of Electrical and Electronics Engineers Inc., 2018). https://doi.org/10.23919/ICIF.2018.8455854.17.Cutlip, K. Our Data Suggests Transhippment Involved in Refrigerated Cargo Vessel Just Sentenced to $5.9 Million and Jail Time for Carrying Illegal Sharks. https://globalfishingwatch.org/impacts/policy-compliance/transhippment-involved-in-reefer-sentenced-for-carrying-illegal-sharks/ (2017).18.Compagno, L., Dando, M. & Fowler, S. Sharks of the World (Princeton University Press, 2005).
Google Scholar 
19.Bradley, D. et al. Leveraging satellite technology to create true shark sanctuaries. Conserv. Lett. 12, 1–8 (2019).Article 

Google Scholar 
20.Cardeñosa, D. et al. Species composition of the largest shark fin retail-market in mainland China. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
21.IATTC. Resolution C-11-10. Resolution on the conservation of oceanic whitetip sharks caught in association with fisheries in the Antigua convention area. (IATCC, 2011).22.Gonzalez-Pestana, A., Kouri J., C. & Velez-Zuazo, X. Shark fisheries in the Southeast Pacific: A 61-year analysis from Peru. F1000Research 3, 164 (2014).23.Martínez-Ortiz, J., Aires-Da-silva, A. M., Lennert-Cody, C. E. & Maunderxs, M. N. The ecuadorian artisanal fishery for large pelagics: Species composition and spatio-temporal dynamics. PLoS ONE 10, 1–29 (2015).Article 
CAS 

Google Scholar 
24.Bustamante, C. & Bennett, M. B. Insights into the reproductive biology and fisheries of two commercially exploited species, shortfin mako (Isurus oxyrinchus) and blue shark (Prionace glauca), in the south-east Pacific Ocean. Fish. Res. 143, 174–183 (2013).Article 

Google Scholar 
25.Hinton, M. G. et al. Stock Status Indicators for Fisheries of the Eastern Pacific Ocean. INTER-AMERICAN TROPICAL TUNA COMISSION, 19, 142–182 (2011).26.Duffy, L. M., Lennert-Cody, C. E., Olson, R. J., Minte-Vera, C. V. & Griffiths, S. P. Assessing vulnerability of bycatch species in the tuna purse-seine fisheries of the eastern Pacific Ocean. Fish. Res. 219, 105316 (2019).Article 

Google Scholar 
27.Clarke, S. C., Harley, S. J., Hoyle, S. D. & Rice, J. S. Population trends in Pacific Oceanic sharks and the utility of regulations on shark finning. Conserv. Biol. 27, 197–209 (2013).PubMed 
Article 

Google Scholar 
28.Martinez Ortiz, J. et al. Abundancia estacional de Tiburones desembarcados en Manta-Ecuador. EPESPO-PMRC, 9–27 (2007).29.Román-Verdesoto, M. Updated summary regarding hammerhead sharks caught in the tuna fisheries in the Eastern Pacific Ocean 6th Meeting of the Scientific Advisory Committee IATTC. (2015).30.IATTC. Resolution C-16-06: Conservation Measures for Shark Species, with Special Emphasis on the Silky Shark (Carcharhinus falciformis), for the years 2017, 2018, and 2019. (IATTC, 2016).31.Alava, J. J. Massive Chinese Fleet Jeopardizes Threatened Shark Species around the Galápagos Marine Reserve and Waters off Ecuador. Int. J. Fish. Sci. Res. 1, 8–10 (2017).
Google Scholar 
32.El Universo. Se detectan tres flotas pesqueras chinas cerca de Galápagos . https://Www.Eluniverso.Com/Noticias/2019/03/21/Nota/7244318/Se-Detectan-Tres-Flotas-Pesqueras-Chinas-Cerca-Galapagos (2019).33.El Universo. Armada del Ecuador detecta flota pesquera con 260 barcos en las cercanías de Galápagos. https://www.eluniverso.com/noticias/2020/07/16/nota/7908768/armada-ecuador-detecta-flota-pesquera-260-barcos-cercanias. (2020).34.El Universo. Varios barcos chinos, que integran la flota extranjera que pesca cerca de Ecuador, estarían emitiendo ‘falsas coordenadas’; aparecen en Nueva Zelanda. https://www.eluniverso.com/noticias/2020/08/06/nota/7932429/flota-china-pesquera-galapagos-ecuador-nueva-zelanda-ecuador#cxrecs_s. (2020)35.Stuff. Chinese vessels off Galapagos ‘cloaking’ in New Zealand. https://www.stuff.co.nz/environment/122339295/chinese-vessels-off-galapagos-cloaking-in-new-zealand. (2020).36.Mas, F., Forselledo, R. & Domingo, A. Length-length relationships for six pelagic shark species. Collect. Vol. Sci. Pap. ICCAT 70, 2441–2450 (2014).
Google Scholar 
37.D’Alberto, B. M. et al. Age, growth and maturity of oceanic whitetip shark (Carcharhinus longimanus) from Papua New Guinea. Mar. Freshw. Res. 68, 1118–1129 (2017).Article 

Google Scholar 
38.Oshitani, S., Nakano, H. & Tanaka, S. Age and growth of the silky shark Carcharhinus falciformis from the Pacific Ocean. Fish. Sci. 69, 456–464 (2003).CAS 
Article 

Google Scholar 
39.Joung, S. J., Chen, N. F., Hsu, H. H. & Liu, K. M. Estimates of life history parameters of the oceanic whitetip shark, Carcharhinus longimanus, in the Western North Pacific Ocean. Mar. Biol. Res. 12, 758–768 (2016).Article 

Google Scholar 
40.Naylor, G. J. P. et al. A DNA sequencebased approach to the identification of shark and ray species and its implications for global elasmobranch diversity and parasitology. Bull. Am. Museum Nat. Hist. 21, 1–262 (2012).
Google Scholar 
41.Peñafiel, N., Flores, D. M., Rivero De Aguilar, J., Guayasamin, J. M. & Bonaccorso, E. A cost-effective protocol for total DNA isolation from animal tissue. Neotrop. Biodivers. 5, 69–74 (2019).42.Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2018).Article 
CAS 

Google Scholar 
44.Maddison, W. P. & Maddison, D. R. Mesquite: A modular system for evolutionary Mesquite installation for evolutionary analysis. (2003).45.Aparicio-Puerta, E. et al. SRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res. 47, W530–W535 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
47.Trifinopoulos, J., Nguyen, L. T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Seki, T., Taniuchi, T., Nakano, H. & Shimizu, M. Age, growth and reproduction of the oceanic Whitetip shark from the Pacific Ocean. Fish. Sci. 64, 14–20 (1998).CAS 
Article 

Google Scholar 
50.Bergman, B. Reefer Fined $5.9 Million for Endangered Catch in Galapagos Recently Rendezvoused with Chinese Longliners. https://skytruth.org/2017/08/galapagos-reefer-fined-5-9-million/ (2017).51.Romero-Caicedo, A. F., Galván-Magaña, F. & Martínez-Ortiz, J. Reproduction of the pelagic thresher shark Alopias pelagicus in the equatorial Pacific. J. Mar. Biol. Assoc. U. K. 94, 1501–1507 (2014).Article 

Google Scholar 
52.Chen, C., Liu, K. & Chang, Y. Reproductive biology of the bigeye thresher shark, Alopias superciliosus (Lowe, 1839) (Chondrichthyes: Alopiidae), in the northwestern Pacific. Ichthyol. Res. 44, 227–236 (1997).Article 

Google Scholar 
53.Bradley, D. et al. Growth and life history variability of the grey reef shark (Carcharhinus amblyrhynchos) across its range. PLoS ONE 12, 1–20 (2017).
Google Scholar 
54.Holmes, B. J. et al. Age and growth of the tiger shark Galeocerdo cuvier off the east coast of Australia. J. Fish Biol. 87, 422–448 (2015).CAS 
PubMed 
Article 

Google Scholar 
55.Nakano, H Stevens, J. The biology and ecology of the blue shark, Prionace glauca. in Sharks of the open ocean: Biology, fisheries and conservation (Vol. 1) (ed. Camhi, Merry D Pikitch, E K Babcock, E. A.) 140‒151 (Blackwell Scientific Publications, 2008).56.Gubanov, Y. E. The reproduction of some species of pelagic sharks from the equatorial zone of the Indian Ocean. J. Ichthyol. 18, 781–792 (1978).
Google Scholar 
57.Fahmi & Sumadhiharga, K. Size, sex and length at maturity of four common sharks caught from Western Indonesia. Mar. Res. Indones. 32, 7–19 (2007).58.Nava, P. N. & Márquez-Farías, J. F. Talla de madurez del tiburón martillo, Sphyrna zygaena, capturado en el Golfo de California. Hidrobiologica 24, 129–135 (2014).
Google Scholar 
59.Saïdi, B., Bradaï, M. N. & Bouaïn, A. Reproductive biology of the smooth-hound shark Mustelus mustelus (L.) in the Gulf of Gabès (south-central Mediterranean Sea). J. Fish Biol. 72, 1343–1354 (2008). More

1.Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).CAS 

Google Scholar 
2.Zhang, J. et al. Effect of drought on agronomic traits of rice and wheat: a meta-analysis. Int. J. Environ. Res. Public Health 15, 839 (2018).3.Hirasawa, T., in Genetic Improvement of Rice for Water-Limited Environments (eds Ito, O, O’Toole, J. C. & Hardy, B.) 89–98 (International Rice Research Institute, 1999).4.Pandey, V. & Shukla, A. Acclimation and tolerance strategies of rice under drought stress. Rice Sci. 22, 147–161 (2015).
Google Scholar 
5.Compant, S., van der Heijden, M. G. A. & Sessitsch, A. Climate change effects on beneficial plant-microorganism interactions. FEMS Microbiol. Ecol. 73, 197–214 (2010).CAS 

Google Scholar 
6.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).
Google Scholar 
7.Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).PubMed 
PubMed Central 

Google Scholar 
8.Santos-Medellín, C., Edwards, J., Liechty, Z., Nguyen, B. & Sundaresan, V. Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. mBio 8, e00764-17 (2017).PubMed 
PubMed Central 

Google Scholar 
9.Naylor, D., DeGraaf, S., Purdom, E. & Coleman-Derr, D. Drought and host selection influence bacterial community dynamics in the grass root microbiome. ISME J. https://doi.org/10.1038/ismej.2017.118 (2017).10.Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1717617115 (2018).11.Edwards, J. A. et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 16, e2003862 (2018).PubMed 
PubMed Central 

Google Scholar 
12.Zhang, J. et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci. China Life Sci. 61, 613–621 (2018).
Google Scholar 
13.Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. USA 115, E4284–E4293 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Liechty, Z. et al. Comparative analysis of root microbiomes of rice cultivars with high and low methane emissions reveals differences in abundance of methanogenic archaea and putative upstream fermenters. mSystems 5, e00897-19 (2020).PubMed 
PubMed Central 

Google Scholar 
15.Rong, X. & Huang, Y. Taxonomic evaluation of the Streptomyces griseus clade using multilocus sequence analysis and DNA–DNA hybridization, with proposal to combine 29 species and three subspecies as 11 genomic species. Int. J. Syst. Evol. Microbiol. 60, 696–703 (2010).CAS 

Google Scholar 
16.Lin, L. & Xu, X. Indole-3-acetic acid production by endophytic Streptomyces sp. En-1 isolated from medicinal plants. Curr. Microbiol. 67, 209–217 (2013).CAS 

Google Scholar 
17.Legault, G. S., Lerat, S., Nicolas, P. & Beaulieu, C. Tryptophan regulates thaxtomin A and indole-3-acetic acid production in Streptomyces scabiei and modifies its interactions with radish seedlings. Phytopathology 101, 1045–1051 (2011).CAS 

Google Scholar 
18.Guo, J. et al. Seed-borne, endospheric and rhizospheric core microbiota as predictor for plant functional traits across rice cultivars are dominated by deterministic processes. New Phytol. https://doi.org/10.1111/nph.17297 (2021).19.de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).PubMed 
PubMed Central 

Google Scholar 
20.de Vries, F. T. & Shade, A. Controls on soil microbial community stability under climate change. Front. Microbiol. 4, 265 (2013).PubMed 
PubMed Central 

Google Scholar 
21.Borken, W. & Matzner, E. Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Glob. Change Biol. 15, 808–824 (2009).
Google Scholar 
22.Lueders, T. & Friedrich, M. W. Effects of amendment with ferrihydrite and gypsum on the structure and activity of methanogenic populations in rice field soil. Appl. Environ. Microbiol. 68, 2484–2494 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Linquist, B. A. et al. Reducing greenhouse gas emissions, water use, and grain arsenic levels in rice systems. Glob. Change Biol. 21, 407–417 (2015).
Google Scholar 
24.Speirs, L. B. M., Rice, D. T. F., Petrovski, S. & Seviour, R. J. The phylogeny, biodiversity, and ecology of the chloroflexi in activated sludge. Front. Microbiol. 10, 2015 (2019).PubMed 
PubMed Central 

Google Scholar 
25.Thomas, S. H. et al. The mosaic genome of Anaeromyxobacter dehalogenans strain 2CP-C suggests an aerobic common ancestor to the delta-proteobacteria. PLoS ONE 3, e2103 (2008).PubMed 
PubMed Central 

Google Scholar 
26.Yang, T. H., Coppi, M. V., Lovley, D. R. & Sun, J. Metabolic response of Geobacter sulfurreducens towards electron donor/acceptor variation. Microb. Cell Fact. 9, 90 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Keller, K. L. & Wall, J. D. Genetics and molecular biology of the electron flow for sulfate respiration in desulfovibrio. Front. Microbiol. 2, 135 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. https://doi.org/10.1038/s41564-018-0129-3 (2018).29.Williams, A. & de Vries, F. T. Plant root exudation under drought: implications for ecosystem functioning. New Phytol. 225, 1899–1905 (2020).
Google Scholar 
30.Vries, F. T. et al. Changes in root-exudate-induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol. 224, 132–145 (2019).PubMed 
PubMed Central 

Google Scholar 
31.Casartelli, A. et al. Exploring traditional aus-type rice for metabolites conferring drought tolerance. Rice 11, 9 (2018).PubMed 
PubMed Central 

Google Scholar 
32.Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. https://doi.org/10.1038/ismej.2017.85 (2017).33.Kang, D.-J. & Futakuchi, K. Effect of moderate drought-stress on flowering time of interspecific hybrid progenies (Oryza sativa L. × Oryza glaberrima Steud.). J. Crop Sci. Biotechnol. 22, 75–81 (2019).
Google Scholar 
34.Guo, X. et al. Host-associated quantitative abundance profiling reveals the microbial load variation of root microbiome. Plant Commun. 1, 100003 (2020).PubMed 
PubMed Central 

Google Scholar 
35.Varoquaux, N. et al. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1907500116 (2019).36.Li, P. et al. Physiological and transcriptome analyses reveal short-term responses and formation of memory under drought stress in rice. Front. Genet. 10, 55 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A. & Dufresne, A. The importance of the microbiome of the plant holobiont. New Phytol. 206, 1196–1206 (2015).PubMed 
PubMed Central 

Google Scholar 
38.Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
Google Scholar 
39.Shade, A. & Stopnisek, N. Abundance-occupancy distributions to prioritize plant core microbiome membership. Curr. Opin. Microbiol. 49, 50–58 (2019).
Google Scholar 
40.Suralta, R. R. et al. Plasticity in nodal root elongation through the hardpan triggered by rewatering during soil moisture fluctuation stress in rice. Sci. Rep. 8, 4341 (2018).PubMed 
PubMed Central 

Google Scholar 
41.Hamedi, J. & Mohammadipanah, F. Biotechnological application and taxonomical distribution of plant growth promoting actinobacteria. J. Ind. Microbiol. Biotechnol. 42, 157–171 (2015).CAS 

Google Scholar 
42.Vurukonda, S. S. K. P., Vardharajula, S., Shrivastava, M. & SkZ, A. Enhancement of drought stress tolerance in crops by plant growth promoting rhizobacteria. Microbiol. Res. 184, 13–24 (2016).
Google Scholar 
43.Aznar, A. & Dellagi, A. New insights into the role of siderophores as triggers of plant immunity: what can we learn from animals? J. Exp. Bot. 66, 3001–3010 (2015).CAS 

Google Scholar 
44.Viaene, T., Langendries, S., Beirinckx, S., Maes, M. & Goormachtig, S. Streptomyces as a plant’s best friend? FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw119 (2016).45.Meena, K. K. et al. Abiotic stress responses and microbe-mediated mitigation in plants: the omics strategies. Front. Plant Sci. 8, 172 (2017).PubMed 
PubMed Central 

Google Scholar 
46.Mukamuhirwa, A. et al. Effect of intermittent drought on grain yield and quality of rice (Oryza sativa L.) grown in Rwanda. J. Agro Crop Sci. 206, 252–262 (2020).CAS 

Google Scholar 
47.Fleta-Soriano, E. & Munné-Bosch, S. Stress memory and the inevitable effects of drought: a physiological perspective. Front. Plant Sci. 7, 143 (2016).PubMed 
PubMed Central 

Google Scholar 
48.Ding, Y., Fromm, M. & Avramova, Z. Multiple exposures to drought ‘train’ transcriptional responses in Arabidopsis. Nat. Commun. 3, 740 (2012).
Google Scholar 
49.de la Fuente Cantó, C. et al. An extended root phenotype: the rhizosphere, its formation and impacts on plant fitness. Plant J. 103, 951–964 (2020).
Google Scholar 
50.Kittas, C., Bartzanas, T. & Jaffrin, A. Temperature gradients in a partially shaded large greenhouse equipped with evaporative cooling pads. Biosyst. Eng. 85, 87–94 (2003).
Google Scholar 
51.Edwards, J. et al. Soil domestication by rice cultivation results in plant–soil feedback through shifts in soil microbiota. Genome Biol. 20, 221 (2019).PubMed 
PubMed Central 

Google Scholar 
52.Edwards, J., Santos-Medellín, C. & Sundaresan, V. Extraction and 16S rRNA sequence analysis of microbiomes associated with rice roots. Bio. Protoc. 8, e2884 (2018).53.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).CAS 

Google Scholar 
54.Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. 13, 31 (2012).CAS 

Google Scholar 
55.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Weimer, B. C. 100K Pathogen Genome Project. Genome Announc. 5, e00594-17 (2017).59.Kong, N. et al. Draft genome sequences of 1,183 Salmonella strains from the 100K Pathogen Genome Project. Genome Announc. 5, e00518–17 (2017).PubMed 
PubMed Central 

Google Scholar 
60.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 

Google Scholar 
63.Medema, M. H. et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 39, W339–W346 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018); https://www.R-project.org/65.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
68.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 

Google Scholar 
69.Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Oksanen, J. et al. vegan: Community Ecology Package (2018).71.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).72.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 13 (2017).
Google Scholar 
73.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: estimated marginal means, aka least-squares means. R package v.1, 3 (R Foundation for Statistical Computing, 2018).74.Kassambara, A. Rstatix: pipe-friendly framework for basic statistical tests. R package v.0.6.0 (R Foundation for Statistical Computing, 2020).75.Graves, S., Piepho, H.-P., Selzer, L. & Dorai-Raj, S. multcompView: visualizations of paired comparisons. R package v.0.1-7 (R Foundation for Statistical Computing, 2015).76.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Liaw, A. & Wiener, M. Classification and regression by randomForest. R. News 2, 18–22 (2002).
Google Scholar 
78.Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Our analyses of marine invertebrate communities at a regional scale and at two local sites revealed that taxonomic density (i.e. species density) was a sensitive index of marine sediment quality. However, although Hill–Simpson diversity and Pielou evenness were shown to respond to sediment variability in the regional dataset, they could be insensitive or respond falsely when a low number of individuals was observed, and when more than one community co-existed at a local site. These results from two local sites should serve as a point of caution when using diversity indices. Although these indices can provide a good understanding of how communities respond to sediment degradation, it is important to understand how these indices collapse when there is a small number of individuals observed or when the data span multiple co-existing communities. This emphasises the need for better strategies for the ecological assessment of sediment quality based on diversity indices at the local scale in marine areas.The analyses of the regional dataset show that WC had a larger impact than other variables on the taxonomic density of benthic invertebrate communities, although grain size and organic matter content are also thought to affect benthic invertebrate richnesse.g.17,18. The high contribution of WC likely reflects its physical effects on sediment structure. The optimal range of WC for the burrowing activity of benthic invertebrates is between around 25% WC at the densest (i.e. hardest) and around 40% WC at the loosest (i.e. softest) packing of sediment19,20. A WC value exceeding the upper limit of this optimal range could indicate sediment that is too soft for the burrowing activity of benthic invertebrates; this may explain the negative effect of WC on taxonomic density observed in this study.The relatively high standard deviation of random effects as compared against the effect size of WC in GLMMs suggests that unmeasured variables had a strong effect on taxonomic density. Salinity21 and anthropogenic impacts, such as dredging and trawling13, are well-known factors that could affect the diversity of benthic invertebrates. However, they were not considered in the regional dataset, because these factors are site- and sampling-location specific, and therefore it was impossible to identify which factors needed to be measured prior to investigation. Our study highlights one advantage of GLMMs, which is the ability to show the effects of these unmeasured factors. The effect size of WC was almost as large as that of the random effects in the low-frequency group (Table 1), which suggests that rare invertebrates were more sensitive to sediment degradation in this group, and that this sensitivity contributed to the overall response of taxonomic density.The analyses of the regional dataset also showed that an increase in WC caused a decrease in Hill–Simpson diversity and Pielou evenness in the reliable data. This result is consistent with previously identified responses to sediment degradation6. Because the low values of these indices occurred in communities with a few dominant species, this suggests that the benthic community was dominated by a few species in soft sediments (i.e. where WC was high).Similarly, increasing WC was associated with a significant decline in species density at the two local sites (Table 2), and the trend was significant for both reliable and unreliable data. This suggests that WC can be an indicator of benthic invertebrate species density at the local scale. However, it is likely that the trend in species density was not only caused by the effect of sediment softness (as is suggested by our analysis of the regional dataset) but also by other factors. One such factor is anoxia, which has been observed from August to October in the water column above the sediment in Nagoya Port at locations where no individuals were sampled (i.e., N5, N9, N10, and N12)22. Similarly, high organic-carbon and trace-metal concentrations have been reported in our study area23. These factors could have co-occurred with high WC, and thereby contributed to the decline in species density observed in our study. Because spatial correlations between variables tend to occur at local scales24, it is difficult to identify factors that affect species density at this scale. Species density is itself a sensitive indicator; however, if alternatives are needed, parameters that explain variations in species density, such as WC, are recommended for use as a representative variable in local assessment.WC did not always have a significant negative effect on Hill–Simpson diversity or Pielou evenness at the local scale (Table 2). The significant negative effect of WC on Hill–Simpson diversity identified in the reliable data from Nagoya Port indicates that community structure was dominated by a few species at higher WC, which mirrors the results obtained from the regional dataset. However, WC had no significant effect on Pielou evenness, and even the effect on Hill–Simpson diversity was only significant once locations that have different coexisting community structures (i.e., the Fujimae tidal flat, N8, and N20) were excluded from the analysis. These results mean that these diversity indices are not as sensitive to changes in WC as species density. Conversely, we found questionable significant negative effects of WC on both Hill–Simpson diversity and Pielou evenness when unreliable data were included in the analysis (Table 2). The low values of these indices obtained at high WC likely reflect artefacts in the unreliable data (Fig. 5c, d).It is important to find and exclude coexisting communities when analysing the effects of sediment degradation on indices of community structure (i.e., Hill–Simpson diversity and Pielou evenness) in a target community. In Matsunaga Bay, the river-mouth community on the intertidal flat was found to have a distinct sediment-particle-size composition compared to other communities in the bay based on multivariate analysis (Fig. 3c). In addition, because the polychaete Simplisetia erythraeensis that dominated the river-mouth community can be found in brackish environments (WoRMS: http://www.marinespecies.org/), low salinity (which was not measured in this study) may be a distinguishing feature of this location. Therefore, environmental characteristics such as sediment particle size, salinity, and the location of the intertidal flat likely underlie the spatial variability of community structure in this bay.Whereas we were able to predict the spatial variability of community structure prior to field sampling in Matsunaga Bay, this was not true in Nagoya Port. Our a priori expectation was that the benthic community on the Fujimae intertidal flat would have a distinct structure because of its location; although this was borne out by the data, we were unable to predict that there would also be distinct community structures at N8 and N20 because of the complex spatial patterning of benthic communities in this area. The explanatory variables we selected (salinity, C/N, WC, and D50) explained less than 11% of the total variance in community structure. This weak explanatory power indicates that unmeasured environmental variables may underlie the complex spatial patterning of benthic communities observed in our study, which is typical of the complexity often found in urbanised marine areas13.Although our results demonstrate that excluding distinct coexisting communities from the overall data is important when analysing species density (Fig. 5b) and Hill–Simpson diversity (Fig. 5c), such communities can be difficult to distinguish prior to field sampling. Therefore, post-hoc multivariate analysis is needed to distinguish between a target community and other communities. In addition, because diversity indices are affected by both species composition and the proportions of individuals in each taxon, the use of multiple distances between sampling points is recommended to assess how communities differ across space.The unreliability of Hill–Simpson diversity and Pielou evenness values calculated from small sample sizes can be explained by a theoretical framework for the effective number of species9. The effective number of species, which reflects the number of dominant species14, is predicted to decline or remain unchanged in response to low species density in cases where taxonomic density has a sensitive negative response (Fig. 6a). However, the effective number of species can be underestimated when there is a small sample size (Fig. 6b). This suggests that the questionable negative responses of Hill–Simpson diversity and Pielou evenness (which is calculated from the Shannon index) (Table 2) likely do not reflect real changes in community structure in Nagoya Port, but instead are caused by an artefact that negatively correlates with sediment degradation. However, low Pielou evenness was rarely associated with unreliable data in our study (Appendix S2). Pielou evenness tended to be high, approaching 1.0, in unreliable data from the regional dataset (Supplementary Fig. S2) and Matsunaga Bay (Supplementary Fig. S4). This bias can be explained as a possible result of small sample size. Our results should serve as a warning that false or insensitive responses in Hill–Simpson diversity and Pielou evenness may occur if sample size is insufficient to estimate these indices accurately.Figure 6Two mechanisms that can affect the effective number of species (which can be estimated with Hill–Simpson diversity). (a) The effective number of species becomes lower at low species density with sufficient sample size. When the degradation of sediment quality (SQ degradation) affects species density but not the density of individuals, the effective number of species decreases as a real response to community structure. However, as shown in (b), the effective number of species also becomes lower at small sample size n. When SQ degradation affects the density of individuals, the effective number of species might not reflect a real response in community structure.Full size imageIn this study, we used a sample size of 50 individuals as the threshold between reliable and unreliable data. Although a sample-size threshold can be useful when judging whether a sample accurately reflects community structure, the specific value we used was not based on any scientific evidence. In fact, our datasets included several data points classified as “reliable” that were not sufficiently saturated in Hill–Simpson diversity (Supplementary Figs. S2a, S4a, and S5a). Sample coverage is an index that standardises the number of taxa observed by the completeness of the sample15,25. The sample coverage of the reliable data was close to 1.0 (complete) and greater than that of the unreliable data in all three datasets used in this study. Although the rarefaction curve is a more direct way to show the estimation accuracy of Hill–Simpson diversity, the simplicity of the sample coverage index (as compared to drawing a rarefaction curve) is an advantage when judging data reliability. In addition, sample coverage is useful when plotting the degree of accuracy in two-dimensional figures, as was done in this study.When the number of individuals observed, n, is not sufficient to estimate the indices of community structure accurately, we can use an extrapolation technique that provides more reliable estimates by doubling the number of individuals observed to 2n9. Although we did not use this technique in this study because our objective was to explore how small sample sizes affect assessments of marine sediment quality, this technique is a useful solution for practical assessment when the number of individuals observed is not sufficient.In conclusion, our results show that species density responds sensitively to sediment degradation. By contrast, indices of community structure (i.e. Hill–Simpson diversity and Pielou evenness) were insensitive at the local scale because of masking by multiple coexisting communities, and sometimes produced misleading results because of inaccuracies associated with small sample sizes. Because indices for community structure provide a good understanding of how communities respond to sediment degradation, which cannot be provided by species density, ecological approaches using these indices have merits for assessing sediment quality because they are more realistic under field conditions3 and because they reduce uncertainties26,27. The potential for misleading and insensitive results must be avoided to keep from diluting these merits. We recommend that these diversity indices for community structure be used in local assessments only if it is possible to obtain a sufficient sample size for accurate estimation, and if co-existing communities can be differentiated before field sampling or by post-hoc analysis through sampling at multiple distances. More

1.Bell P. Eutrophication and coral reefs—some examples in the Great Barrier Reef lagoon. Water Res. 1992;26:553–68.CAS 
Article 

Google Scholar 
2.Odum HT, Odum EP. Trophic structure and productivity of a windward coral reef community on Eniwetok Atoll. Ecol Monogr. 1955;25:291–320.Article 

Google Scholar 
3.Ainsworth TD, Krause L, Bridge T, Torda G, Raina JB, Zakrzewski M, et al. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts. ISME J. 2015;9:2261–74.CAS 
Article 

Google Scholar 
4.Ceh J, Kilburn MR, Cliff JB, Raina JB, van Keulen M, Bourne DG. Nutrient cycling in early coral life stages: Pocillopora damicornis larvae provide their algal symbiont (Symbiodinium) with nitrogen acquired from bacterial associates. Ecol Evol. 2013;3:2393–400.Article 

Google Scholar 
5.Fine M, Loya Y. Endolithic algae: an alternative source of photoassimilates during coral bleaching. Proc R Soc B. 2002;269:1205–10.PubMed 
PubMed Central 
Article 

Google Scholar 
6.Benavides M, Bednarz VN, Ferrier-Pagès C. Diazotrophs: overlooked key players within the coral symbiosis and tropical reef ecosystems? Front Mar Sci. 2017;4:2261–17.Article 

Google Scholar 
7.Cardini U, Bednarz VN, van Hoytema N, Rovere A, Naumann MS, Al-Rshaidat MMD, et al. Budget of primary production and dinitrogen fixation in a highly seasonal Red Sea coral reef. Ecosystems. 2016;19:771–85.Article 

Google Scholar 
8.Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Bednarz VN, van de Water JAJM, Rabouille S, Maguer JF, Grover R, Ferrier-Pagès C. Diazotrophic community and associated dinitrogen fixation within the temperate coral Oculina patagonica. Environ Microbiol. 2018;21:480–95.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
10.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Davey M, Holmes G, Johnstone R. High rates of nitrogen fixation (acetylene reduction) on coral skeletons following bleaching mortality. Coral Reefs. 2007;27:227–36.Article 

Google Scholar 
12.Lesser MP, Falcón LI, Rodríguez-Román A, Enríquez S, Hoegh-Guldberg O, Iglesias-Prieto R. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser. 2007;346:143–52.CAS 
Article 

Google Scholar 
13.Olson ND, Ainsworth TD, Gates RD, Takabayashi M. Diazotrophic bacteria associated with Hawaiian Montipora corals: diversity and abundance in correlation with symbiotic dinoflagellates. J Exp Mar Biol Ecol. 2009;371:140–6.CAS 
Article 

Google Scholar 
14.Benavides M, Houlbrèque F, Camps M, Lorrain A, Grosso O, Bonnet S. Diazotrophs: a non-negligible source of nitrogen for the tropical coral Stylophora pistillata. J Exp Biol. 2016;219:2608–12.PubMed 
PubMed Central 

Google Scholar 
15.Cardini U, Bednarz V, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B. 2015;282:20152257.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Grover R, Ferrier-Pagès C, Maguer JF, Ezzat L, Fine M. Nitrogen fixation in the mucus of Red Sea corals. J Exp Biol. 2014;217:3962–3.PubMed 
PubMed Central 

Google Scholar 
17.Mohr W, Großkopf T, Wallace DWR, LaRoche J. Methodological underestimation of oceanic nitrogen fixation rates. PLoS ONE. 2010;5:e12583.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Montoya JP, Voss M, Kahler P, Capone DG. A simple, high-precision, high-sensitivity tracer assay for N2 fixation. Appl Environ Microbiol. 1996;62:986–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Bednarz VN, Grover R, Maguer JF, Fine M, Ferrier-Pagès C. The assimilation of diazotroph-derived nitrogen by scleractinian corals depends on their betabolic status. MBio. 2017;8:e02058-16.20.Meunier V, Bonnet S, Pernice M, Benavides M, Lorrain A, Grosso O, et al. Bleaching forces coral’s heterotrophy on diazotrophs and Synechococcus. ISME J. 2019;13:2882–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Cardini U, van Hoytema N, Bednarz VN, Rix L, Foster RA, Al-Rshaidat MMD, et al. Microbial dinitrogen fixation in coral holobionts exposed to thermal stress and bleaching. Environ Microbiol. 2016;18:2620–33.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Moisander PH, Beinart RA, Hewson I, White AE, Johnson KS, Carlson CA, et al. Unicellular cyanobacterial distributions broaden the oceanic N2 fixation domain. Science. 2010;327:1512–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Sangsawang L, Casareto BE, Ohba H, Vu HM, Meekaew A, Suzuki T, et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R Soc Open Sci. 2017;4:171201.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.PubMed 
Article 
PubMed Central 

Google Scholar 
25.D’Angelo C, Wiedenmann J. Impacts of nutrient enrichment on coral reefs: new perspectives and implications for coastal management and reef survival. Curr Opin Environ Sustain. 2014;7:82–93.Article 

Google Scholar 
26.Capone DG, O’Neil JM, Zehr J, Carpenter EJ. Basis for diel variation in nitrogenase activity in the marine planktonic Cyanobacterium Trichodesmium thiebautii. Appl Environ Microbiol. 1990;56:3532–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Mulholland MR, Ohki K, Capone DG. Nutrient controls on nitrogen uptake and metabolism by natural populations and cultures of Trichodesmium (Cyanobacteria). J Phycol. 2001;37:1001–9.CAS 
Article 

Google Scholar 
28.Mulholland MR, Bernhardt PW, Widner BN, Selden CR, Chappell PD, Clayton S, et al. High rates of N2 fixation in temperate, western North Atlantic coastal waters expand the realm of marine diazotrophy. Global Biogeochem Cycles. 2019;33:826–40.CAS 
Article 

Google Scholar 
29.Wen Z, Lin W, Shen R, Hong H, Kao SJ, Shi D. Nitrogen fixation in two coastal upwelling regions of the Taiwan Strait. Sci Rep. 2017;7:1–10.Article 
CAS 

Google Scholar 
30.Grosse J, Bombar D, Doan HN, Nguyen LN, Voss M. The Mekong River plume fuels nitrogen fixation and determines phytoplankton species distribution in the South China Sea during low and high discharge season. Limnol Oceanogr. 2010;55:1668–80.CAS 
Article 

Google Scholar 
31.Mills MM, Turk-Kubo KA, Dijken GL, Henke BA, Harding K, Wilson ST, et al. Unusual marine cyanobacteria/haptophyte symbiosis relies on N2 fixation even in N-rich environments. ISME J. 2020;14:2395–406.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Henke BA, Turk-Kubo KA, Bonnet S, Zehr JP. Distributions and abundances of sublineages of the N2fixing cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) in the New Caledonian coral lagoon. Front Microbiol. 2018;9:554.PubMed 
PubMed Central 
Article 

Google Scholar 
33.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 
Article 

Google Scholar 
34.Knapp AN. The sensitivity of marine N2 fixation to dissolved inorganic nitrogen. Front Microbiol. 2012;3:374.CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:1490–7.Article 
CAS 

Google Scholar 
36.Erler DV, Shepherd BO, Linsley BK, Nothdurft LD, Hua Q, Lough JM. Has nitrogen supply to coral reefs in the South Pacific Ocean changed over the past 50 thousand years? Paleoceanogr Paleoclimatol. 2019;34:567–79.Article 

Google Scholar 
37.Pratte ZA, Richardson LL, Mills DK. Microbiota shifts in the surface mucopolysaccharide layer of corals transferred from natural to aquaria settings. J Invertebr Pathol. 2015;125:42–4.PubMed 
Article 
PubMed Central 

Google Scholar 
38.Kooperman N, Ben-Dov E, Kramarsky-Winter E, Barak Z, Kushmaro A. Coral mucus-associated bacterial communities from natural and aquarium environments. FEMS Microbiol Lett. 2007;276:106–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Kirchman DL. Growth rates of microbes in the oceans. Annu Rev Mar Sci. 2016;8:285–309.Article 

Google Scholar 
40.Hu SK, Campbell V, Connell P, Gellene AG, Liu Z, Terrado R, et al. Protistan diversity and activity inferred from RNA and DNA at a coastal ocean site in the eastern North Pacific. FEMS Microbiol Ecol. 2016;92:fiw050.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
41.Campbell BJ, Kirchman DL. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 2013;7:210–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Massana R, Gobet A, Audic S, Bass D, Bittner L, Boutte C, et al. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ Microbiol. 2015;17:4035–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bauman AG, Hoey AS, Dunshea G, Feary DA, Low J, Todd PA. Macroalgal browsing on a heavily degraded, urbanized equatorial reef system. Sci Rep. 2017;7:1–8.CAS 
Article 

Google Scholar 
45.Januchowski-Hartley FA, Bauman AG, Morgan KM, Seah JCL, Huang D, Todd PA. Accreting coral reefs in a highly urbanized environment. Coral Reefs. 2020;39:717–31.Article 

Google Scholar 
46.Klawonn I, Lavik G, Böning P, Marchant HK, Dekaezemacker J, Mohr W, et al. Simple approach for the preparation of 15−15N2-enriched water for nitrogen fixation assessments: evaluation, application and recommendations. Front Microbiol. 2015;6:769.PubMed 
PubMed Central 
Article 

Google Scholar 
47.Pupier CA, Bednarz VN, Grover R, Fine M, Maguer JF, Ferrier-Pagès C. Divergent capacity of scleractinian and soft corals to assimilate and transfer diazotrophically derived nitrogen to the reef environment. Front Microbiol. 2019;10:1860.PubMed 
PubMed Central 
Article 

Google Scholar 
48.Bombar D, Paerl RW, Anderson R, Riemann L. Filtration via conventional glass fiber filters in 15N2 tracer assays fails to capture all nitrogen-fixing prokaryotes. Front Mar Sci. 2018;5:e00929–11.Article 

Google Scholar 
49.R Core Team. R: a language and environment for statistical computing. 2019. https://www.R-project.org/.50.Hansen HP, Koroleff F. Determination of nutrients. In: Grasshoff K, Kremling K, Ehrhardt M, editors. Methods of seawater analysis. Weinheim, Germany: Wiley; 1999. p. 159–228.51.Morgan KM, Moynihan MA, Sanwlani N, Switzer AD. Light limitation and depth-variable sedimentation drives vertical reef compression on turbid coral reefs. Front Mar Sci. 2020;7:571256.Article 

Google Scholar 
52.Comeau AM, Li WKW, Tremblay JÉ, Carmack EC, Lovejoy C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE. 2011;6:e27492.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Kwong WK, del Campo J, Mathur V, Vermeij MJA, Keeling PJ. A widespread coral-infecting apicomplexan with chlorophyll biosynthesis genes. Nature. 2019;568:103–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Comeau AM, Douglas GM, Langille MGI. Microbiome helper: a custom and streamlined workflow for microbiome research. mSystems. 2017;2:e00127-16.55.Weiler BA. Bacterial Communities in tissues and surficial mucus of the cold-water coral Paragorgia arborea. Front Mar Sci. 2018;5:378.Article 

Google Scholar 
56.Gaby JC, Buckley DH. A comprehensive evaluation of PCR primers to amplify the nifH gene of nitrogenase. PLoS ONE. 2012;7:e42149.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Bower SM, Carnegie RB, Goh B, Jones SRM, Lowe GJ, Mak MWS. Preferential PCR amplification of parasitic protistan small subunit rDNA from metazoan tissues. J Eukaryot Microbiol. 2004;51:325–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Piredda R, Tomasino MP, D’Erchia AM, Manzari C, Pesole G, Montresor M, et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean long term ecological research site. FEMS Microbiol Ecol. 2016;93:fiw200.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
59.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research. 2016;5:1492.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.McLaren MR. Silva SSU taxonomic training data formatted for DADA2 (Silva version 138) [Data set]. Zenodo; 2020. https://doi.org/10.5281/zenodo.3731176.63.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–604.CAS 
PubMed 
Article 

Google Scholar 
64.Heller P, Tripp JH, Turk-Kubo K, Zehr JP. ARBitrator: a software pipeline for on-demand retrieval of auto-curated nifH sequences from GenBank. Bioinformatics. 2014;30:2883–90.CAS 
PubMed 
Article 

Google Scholar 
65.Moynihan MA. moyn413/nifHdada2: nifH dada2 reference database, v1.1.0. Zenodo; 2020. https://doi.org/10.5281/zenodo.3964214.66.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:1–11.Article 
CAS 

Google Scholar 
67.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
68.Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23:127–8.CAS 
PubMed 
Article 

Google Scholar 
69.Bell PRF, Elmetri I, Lapointe BE. Evidence of large-scale chronic eutrophication in the Great Barrier Reef: quantification of chlorophyll a thresholds for sustaining coral reef communities. Ambio. 2013;43:361–76.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Conti-Jerpe IE, Thompson PD, Wong CWM, Oliveira NL, Duprey NN, Moynihan MA, et al. Trophic strategy and bleaching resistance in reef-building corals. Sci Adv. 2020;6:eaaz5443.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Ferrier-Pagès C, Witting J, Tambutté E, Sebens KP. Effect of natural zooplankton feeding on the tissue and skeletal growth of the scleractinian coral Stylophora pistillata. Coral Reefs. 2003;22:229–40.Article 

Google Scholar 
72.Ferrier-Pagès C, Hoogenboom M, Houlbrèque F. The role of plankton in coral trophodynamics. In: Dubinsky Z, Stambler N, editors. Coral reefs: an ecosystem in transition. Dordrecht, The Netherlands: Springer; 2011. p. 215–29.73.Pernice M, Raina JB, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 
Article 

Google Scholar 
74.Huggett MJ, Apprill A. Coral microbiome database: integration of sequences reveals high diversity and relatedness of coral-associated microbes. Environ Microbiol Rep. 2019;11:372–85.PubMed 
Article 

Google Scholar 
75.Méheust R, Castelle CJ, Carnevali PBM, Farag IF, He C, Chen LX, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Nomata J, Mizoguchi T, Tamiaki H, Fujita Y. A second nitrogenase-like enzyme for bacteriochlorophyll biosynthesis: reconstitution of chlorophyllide a reductase with purified X-protein (BchX) and YZ-protein (BchY-BchZ) from Rhodobacter capsulatus. J Biol Chem. 2006;281:15021–8.CAS 
PubMed 
Article 

Google Scholar 
77.Suzuki JY, Bauer CE. Light-independent chlorophyll biosynthesis: involvement of the chloroplast gene chlL (frxC). Plant Cell. 1992;4:929–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Bednarz VN, Naumann MS, Cardini U, van Hoytema N, Rix L, Al-Rshaidat MMD, et al. Contrasting seasonal responses in dinitrogen fixation between shallow and deep-water colonies of the model coral Stylophora pistillata in the northern Red Sea. PLoS ONE. 2018;13:e0199022.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
79.Weber L, González Díaz P, Armenteros M, Apprill A. The coral ecosphere: a unique coral reef habitat that fosters coral–microbial interactions. Limnol Oceanogr. 2019;64:2373–88.CAS 
Article 

Google Scholar 
80.Bourne DG, Munn CB. Diversity of bacteria associated with the coral Pocillopora damicornis from the Great Barrier Reef. Environ Microbiol. 2005;7:1162–74.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Pernice M, Meibom A, Van Den Heuvel A, Kopp C, Domart-Coulon I, Hoegh-Guldberg O, et al. A single-cell view of ammonium assimilation in coral–dinoflagellate symbiosis. ISME J. 2012;6:1314–24.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Kopp C, Pernice M, Domart-Coulon I, Djediat C, Spangenberg JE, Alexander D, et al. Highly dynamic cellular-level response of symbiotic coral to a sudden increase in environmental nitrogen. MBio. 2013;4:e00052-13.83.Magnusson SH, Fine M, Kühl M. Light microclimate of endolithic phototrophs in the scleractinian corals Montipora monasteriata and Porites cylindrica. Mar Ecol Prog Ser. 2007;332:119–28.Article 

Google Scholar 
84.Schlichter D, Zscharnack B, Krisch H. Transfer of photoassimilates from endolithic algae to coral tissue. Naturwissenschaften. 1995;82:561–4.CAS 
Article 

Google Scholar 
85.Kemp DW, Colella MA, Bartlett LA, Ruzicka RR, Porter JW, Fitt WK. Life after cold death: reef coral and coral reef responses to the 2010 cold water anomaly in the Florida Keys. Ecosphere. 2016;7:e01373.86.Fine M, Roff G, Ainsworth TD, Hoegh-Guldberg O. Phototrophic microendoliths bloom during coral “white syndrome”. Coral Reefs. 2006;25:577–81.Article 

Google Scholar 
87.Fine M, Oren U, Loya Y. Bleaching effect on regeneration and resource translocation in the coral Oculina patagonica. Mar Ecol Prog Ser. 2002;234:119–25.Article 

Google Scholar 
88.Littman RA, Willis BL, Pfeffer C, Bourne DG. Diversities of coral-associated bacteria differ with location, but not species, for three acroporid corals on the Great Barrier Reef. FEMS Microbiol Ecol. 2009;68:152–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Dunphy CM, Gouhier TC, Chu ND, Vollmer SV. Structure and stability of the coral microbiome in space and time. Sci Rep. 2019;9:1–13.
Google Scholar 
90.Le Campion-Alsumard T, Golubic S, Hutchings P. Microbial endoliths in skeletons of live and dead corals: Porites lobata (Moorea, French Polynesia). Mar Ecol Prog Ser. 1955;117:149–57.Article 

Google Scholar 
91.Yang SH, Tandon K, Lu CY, Wada N, Shih CJ, Hsiao SSY, et al. Metagenomic, phylogenetic, and functional characterization of predominant endolithic green sulfur bacteria in the coral Isopora palifera. Microbiome. 2019;7:1–13.Article 

Google Scholar 
92.Yost DM, Wang LH, Fan TY, Chen CS, Lee RW, Sogin E, et al. Diversity in skeletal architecture influences biological heterogeneity and Symbiodinium habitat in corals. Zoology. 2013;116:262–9.PubMed 
Article 
PubMed Central 

Google Scholar 
93.Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Jacques TG, Pilson MEQ. Experimental ecology of the temperate scleractinian coral Astrangia danae I. Partition of respiration, photosynthesis and calcification between host and symbionts. Mar Biol. 1983;60:167–78.Article 

Google Scholar 
95.Shashar N, Stambler N. Endolithic algae within corals-life in an extreme environment. J Exp Mar Biol Ecol. 1992;163:277–86.CAS 
Article 

Google Scholar 
96.Risk MJ, Muller HR. Porewater in coral heads: evidence for nutrient regeneration. Limnol Oceanogr. 1983;28:1004–8.Article 

Google Scholar 
97.Ferrer LM, Szmant AM. Nutrient regeneration by the endolithic community in coral skeletons. In: Proceedings of the 6th International Coral Reef Symposium. 2. Townsville, Australia: AIMS; 1988. p. 1–4.98.Raymond J, Siefert JL, Staples CR, Blankenship RE. The natural history of nitrogen fixation. Mol Biol Evol. 2004;21:541–54.CAS 
Article 

Google Scholar 
99.Gaby JC, Buckley DH. A global census of nitrogenase diversity. Environ Microbiol. 2011;13:1790–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Gaby JC, Rishishwar L, Valderrama-Aguirre LC, Green SJ, Valderrama-Aguirre A, Jordan IK, et al. Diazotroph community characterization via a high-throughput nifH amplicon sequencing and analysis pipeline. Appl Environ Microbiol. 2018;84:e01512-17.101.Liang J, Yu K, Wang Y, Huang X, Huang W, Qin Z, et al. Diazotroph diversity associated with scleractinian corals and its relationships with environmental variables in the South China Sea. Front Physiol. 2020;11:615.PubMed 
PubMed Central 
Article 

Google Scholar 
102.Marcelino VR, Morrow KM, van Oppen MJH, Bourne DG, Verbruggen H. Diversity and stability of coral endolithic microbial communities at a naturally high pCO2 reef. Molecular Ecology. 2017;26:5344–57.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Leggat WP, Camp EF, Suggett DJ, Heron SF, Fordyce AJ, Gardner S, et al. Rapid coral decay is associated with marine heatwave mortality events on reefs. Curr Biol. 2019;29:2723–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Chen YH, Yang SH, Tandon K, Lu CY, Chen HJ, Shih CJ, et al. A genomic view of coral-associated Prosthecochloris and a companion sulfate-reducing bacterium. bioRxiv. 2019. https://doi.org/10.1101/2019.12.20.883736.105.Weiler BA, Verhoeven JTP, Dufour SC. Bacterial communities in tissues and surficial mucus of the cold-water coral Paragorgia arborea. Front Mar Sci. 2018;5:378.Article 

Google Scholar 
106.Tiedje J. Ecology of denitrification and dissimilatory nitrate reduction to ammonium. In: Zehnder J, editor. Environmental microbiology of anaerobes. NY: John Wiley and Sons; 1988. p. 179–244.107.Becker CC, Brandt M, Miller C, Apprill A. Stony coral tissue loss disease biomarker bacteria identified in corals and overlying waters using a rapid field-based sequencing approach. bioRxiv. 2021. https://doi.org/10.1101/2021.02.17.431614.108.Parker KE, Ward JO, Eggleston EM, Fedorov E, Parkinson JE, Dahlgren CP, et al. Characterization of a thermally tolerant Orbicella faveolata reef in Abaco, The Bahamas. Coral Reefs. 2020;39:675–85.Article 

Google Scholar 
109.Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:1–9.Article 
CAS 

Google Scholar 
110.Kim BH, Ramanan R, Cho DH, Oh HM, Kim HS. Role of Rhizobium, a plant growth promoting bacterium, in enhancing algal biomass through mutualistic interaction. Biomass Bioenerg. 2014;69:95–105.CAS 
Article 

Google Scholar 
111.Wu Z, Yang X, Lin S, Lee WH, Lam PKS. Isolation and characterization of a Rhizobium bacterium associated with the toxic dinoflagellate Gambierdiscus balechii. bioRxiv. 2019. https://doi.org/10.1101/789107.112.Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogenfixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
113.Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Shashar N, Cohen Y, Loya Y, Sar N. Nitrogen fixation (acetylene reduction) in stony corals: evidence for coral-bacteria interactions. Mar Ecol Prog Ser. 1994;111:259–64.CAS 
Article 

Google Scholar 
115.Bednarz VN, Cardini U, van Hoytema N, Al-Rshaidat M, Wild C. Seasonal variation in dinitrogen fixation and oxygen fluxes associated with two dominant zooxanthellate soft corals from the northern Red Sea. Mar Ecol Prog Ser. 2015;519:141–52.Article 

Google Scholar  More