Ecology

Soil pH, biomass and Cd content of peanutSoil pHFigure 1 shows that, in this study, application of slaked lime significantly increased soil pH in nearly all growth stages (p  C1200  > C900  > C600  > C300  > C0. Among the soil characteristics, soil pH is considered as an important index that impact Cd uptake by crops, since pH can obviously affect the speciation and solubility of Cd in soil liquids15. The use of slaked lime can neutralize excessive H+ concentrations in soil solutions and decrease Cd solubility33, but there were no observable differences among the different growth stages.Figure 1Effects of slaked lime application on soil pH values. The values are means (± SD) of three replicates. Bar groups with different capital letters indicate significant differences (p  More

Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Luong, A. D., Dewulf, J. & De Laender, F. Quantifying the primary biotic resource use by fisheries: A global assessment. Sci. Total Environ. 719, 137352 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Pauly, D. How the global fish market contributes to human micronutrient deficiencies. Nature 574, 41–42 (2019).ADS 
CAS 
PubMed 

Google Scholar 
FAO. The State of World Fisheries and Aquaculture 2020 (FAO, 2020). https://doi.org/10.4060/ca9229en.Book 

Google Scholar 
Shin, Y.-J., Rochet, M.-J., Jennings, S., Field, J. G. & Gislason, H. Using size-based indicators to evaluate the ecosystem effects of fishing. ICES J. Mar. Sci. 62, 384–396 (2005).
Google Scholar 
Perry, A. L. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Novaglio, C., Smith, A. D. M., Frusher, S. & Ferretti, F. Identifying historical baseline at the onset of exploitation to improve understanding of fishing impacts. Aquat. Conserv. Mar. Freshwat. Ecosyst. 30, 475–485 (2020).
Google Scholar 
Nagelkerken, I. & Connell, S. D. Global alteration of ocean ecosystem functioning due to increasing human CO2 emissions. Proc. Natl. Acad. Sci. 112, 13272–13277 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagelkerken, I., Goldenberg, S. U., Ferreira, C. M., Ullah, H. & Connell, S. D. Trophic pyramids reorganize when food web architecture fails to adjust to ocean change. Science 832, 829–832 (2020).ADS 

Google Scholar 
Lemoine, N. P. & Burkepile, D. E. Temperature-induced mismatches between consumption and metabolism reduce consumer fitness. Ecology 93, 2483–2489 (2012).PubMed 

Google Scholar 
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Moore, J. K. et al. Sustained climate warming drives declining marine biological productivity. Science 359, 1139–1143 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Ullah, H., Nagelkerken, I., Goldenberg, S. U. & Fordham, D. A. Climate change could drive marine food web collapse through altered trophic flows and cyanobacterial proliferation. PLoS Biol. 16, e2003446 (2018).PubMed 
PubMed Central 

Google Scholar 
Wing, S. R., Durante, L. M., Connolly, A. J., Sabadel, A. J. M. & Wing, L. C. Overexploitation and decline in kelp forests inflate the bioenergetic costs of fisheries. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13448 (2021).Article 

Google Scholar 
Maureaud, A. et al. Global change in the trophic functioning of marine food webs. PLoS One 12, e0182826 (2017).PubMed 
PubMed Central 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
PubMed 

Google Scholar 
Chown, S. L. Marine food webs destabilized. Science 369, 770–771 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Saporiti, F. et al. Longer and less overlapping food webs in anthropogenically disturbed marine ecosystems: Confirmations from the past. PLoS One 9, 1–13 (2014).
Google Scholar 
Gilby, B. L. et al. Human actions alter tidal marsh seascapes and the provision of ecosystem services. Estuaries Coasts https://doi.org/10.1007/s12237-020-00830-0 (2020).Article 

Google Scholar 
Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 11609 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Durante, L. M., Beentjes, M. P. & Wing, S. R. Shifting trophic architecture of marine fisheries in New Zealand: Implications for guiding effective ecosystem-based management. Fish Fish. 21, 813–830 (2020).
Google Scholar 
Shears, N. T. & Bowen, M. M. Half a century of coastal temperature records reveal complex warming trends in western boundary currents. Sci. Rep. 7, 1–9 (2017).CAS 

Google Scholar 
Wing, S. R. & Wing, E. Prehistoric fisheries in the Caribbean. Coral Reefs 20, 1–8 (2001).
Google Scholar 
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Irwin, G. & Walrond, C. ‘When was New Zealand first settled?—Extinction and decline’. Te Ara—the Encyclopedia of New Zealand 8 (2016). http://www.teara.govt.nz/en/when-was-new-zealand-first-settled/page-7. Accessed 4 June 2019.Johnson, D. & Haworth, J. Hooked—The Sory of New Zealand Fishing Industry (Hazard Press, 2004).
Google Scholar 
Urlich, S. C. & Handley, S. J. From ‘clean and green’ to ‘brown and down’: A synthesis of historical changes to biodiversity and marine ecosystems in the Marlborough Sounds, New Zealand. Ocean Coast. Manage. 198, 105349 (2020).
Google Scholar 
Ramos, R. & González-Solís, J. Trace me if you can: The use of intrinsic biogeochemical markers in marine top predators. Front. Ecol. Environ. 10, 258–266 (2012).
Google Scholar 
Graham, D. H. Food of fishes of Otago Harbour and Adjacent Sea. R. Soc. N. Z. 20, 421–436 (1939).
Google Scholar 
Hanchet, S. Diet of spiny dogfish, Squalus acanthias Linnaeus, on the east coast, South Island, New Zealand. J. Fish Biol. 39, 313–323 (1991).
Google Scholar 
Connell, A., Dunn, M. & Forman, J. Diet and dietary variation of New Zealand hoki Macruronus novaezelandiae. NZ J. Mar. Freshw. Res. 44, 289–308 (2010).
Google Scholar 
Forman, J. & Dunn, M. The influence of ontogeny and environment on the diet of lookdown dory, Cyttus traversi. NZ J. Mar. Freshw. Res. 44, 329–342 (2010).
Google Scholar 
Horn, P. L., Forman, J. S. & Dunn, M. R. Dietary partitioning by two sympatric fish species, red cod (Pseudophycis bachus) and sea perch ( Helicolenus percoides), on Chatham Rise, New Zealand. Mar. Biol. Res. 8, 624–634 (2012).
Google Scholar 
Fisheries New Zealand. Fisheries Assessment Plenary, May 2020: Stock Assessments and Stock Status (2020).Ladds, M., Pinkerton, M. H., Jones, E., Durante, L. & Dunn, M. Relationship between morphometrics and trophic levels in deep-sea fishes. Mar. Ecol. Prog. Ser. 637, 225–235 (2020).ADS 

Google Scholar 
Durante, L. M. et al. Oceanographic transport along frontal zones forms carbon, nitrogen, and oxygen isoscapes on the east coast of New Zealand : Implications for ecological studies. Cont. Shelf Res. 216, 1–15 (2021).
Google Scholar 
Funes, M., Irigoyen, A. J., Trobbiani, G. A. & Galván, D. E. Stable isotopes reveal different dependencies on benthic and pelagic pathways between Munida gregaria ecotypes. Food Webs 17, 1–9 (2018).
Google Scholar 
Zeldis, J. R. & Jillett, J. B. Aggregation of pelagic Munida gregaria (Fabricius) (Decapoda, Anomura) by coastal fronts and internal waves. J. Plankton Res. 4, 839–857 (1982).
Google Scholar 
Durante, L. M., Beentjes, M. P. & Wing, S. R. Decadal changes in exploited fish communities and their relationship with temperature, fisheries exploitation, and ecological traits in New Zealand waters. NZ J. Mar. Freshw. Res. 10, 1–27 (2021).
Google Scholar 
Prugh, L. R. et al. The rise of the mesopredator. Bioscience 59, 779–791 (2009).
Google Scholar 
Chiswell, S. M. & Sutton, P. J. H. Relationships between long-term ocean warming, marine heat waves and primary production in the New Zealand region. NZ J. Mar. Freshw. Res. https://doi.org/10.1080/00288330.2020.1713181 (2020).Article 

Google Scholar 
Thomsen, M. S. et al. Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Front. Mar. Sci. 6, 1–10 (2019).
Google Scholar 
Pinkerton, M. H. et al. Changes to the food-web of the Hauraki Gulf during the period of human occupation: A mass-balance model approach. New Zealand Aquatic Environment and Biodiversity Report No. 160. (2015).Garrison, L. Fishing effects on spatial distribution and trophic guild structure of the fish community in the Georges Bank region. ICES J. Mar. Sci. 57, 723–730 (2000).
Google Scholar 
Link, J. S. & Garrison, L. P. Changes in piscivory associated with fishing induced changes to the finfish community on Georges Bank. Fish. Res. 55, 71–86 (2002).
Google Scholar 
Wainright, S. C., Fogarty, M. J., Greenfield, R. C. & Fry, B. Long-term changes in the Georges Bank food web: Trends in stable isotopic compositions of fish scales. Mar. Biol. 115, 481–493 (1993).
Google Scholar 
Udy, J. A. et al. Regional differences in supply of organic matter from kelp forests drive trophodynamics of temperate reef fish. Mar. Ecol. Prog. Ser. 621, 19–32 (2019).ADS 

Google Scholar 
Koenigs, C., Miller, R. & Page, H. Top predators rely on carbon derived from giant kelp Macrocystis pyrifera. Mar. Ecol. Prog. Ser. 537, 1–8 (2015).ADS 
CAS 

Google Scholar 
Clark, M. R., Anderson, O. F., Chris Francis, R. I. C. & Tracey, D. M. The effects of commercial exploitation on orange roughy (Hoplostethus atlanticus) from the continental slope of the Chatham Rise, New Zealand, from 1979 to 1997. Fish. Res. 45, 217–238 (2000).
Google Scholar 
Fenaughty, J. M. & Bagley, N. M. WJ Scott New Zealand Trawling Survey—South Island East Coast. Technical Report 157. (1981).Brodeur, R. & Pearcy, W. Effects of environmental variability on trophic interactions and food web structure in a pelagic upwelling ecosystem. Mar. Ecol. Prog. Ser. 84, 101–119 (1992).ADS 

Google Scholar 
Tam, J., Purca, S., Duarte, L. O., Blaskovic, V. & Espinoza, P. Changes in the diet of hake associated with El Niño 1997–1998 in the northern Humboldt Current ecosystem. Adv. Geosci. 6, 63–67 (2006).
Google Scholar 
Murphy, R. J., Pinkerton, M. H., Richardson, K. M., Bradford-Grieve, J. M. & Boyd, P. W. Phytoplankton distributions around New Zealand derived from SeaWiFS remotely-sensed ocean colour data. NZ J. Mar. Freshw. Res. 35, 343–362 (2001).
Google Scholar 
Zeldis, J. Ecology of Munida gregaria (Decapoda, Anomura) distribution and abundance, population dynamics and fisheries. Mar. Ecol. Prog. Ser. 22, 77–99 (1985).ADS 

Google Scholar 
Williams, B. G. The effect of the environment on the morphology of Munida Gregaria (Fabricius) (Decapoda, Anomura). Crustaceana 24, 197–210 (1973).
Google Scholar 
Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P. & Peterson, C. H. Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science 315, 1846–1850 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Udy, J. A. et al. Organic matter derived from kelp supports a large proportion of biomass in temperate rocky reef fish communities: Implications for ecosystem-based management. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1503–1519 (2019).
Google Scholar 
Jackson, J. B. C. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 

Google Scholar 
Kirby, R. R., Beaugrand, G. & Lindley, J. A. Synergistic effects of climate and fishing in a marine ecosystem. Ecosystems 12, 548–561 (2009).
Google Scholar 
MacGibbon, D. J., Beentjes, M. P., Lyon, W. L. & Ladroit, Y. Inshore trawl survey of Canterbury Bight and Pegasus Bay, April–June 2018 (KAH1803). New Zealand Fisheries Assessment Report 2019/03. (2019).Stevens, W. D., O’Driscoll, R. L., Ballara, S. L. & Schimel, A. C. G. Trawl survey of hoki and middle-depth species on the Chatham Rise, January 2018 (TAN1801). New Zealand Fisheries Assessment Report 2018/41. (2018).Durante, L. M., Sabadel, A. J. M., Frew, R. D., Ingram, T. & Wing, S. R. Effects of fixatives on stable isotopes of fish muscle tissue: Implications for trophic studies on preserved specimens. Ecol. Appl. 30, 1–16 (2020).
Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).
Google Scholar 
Post, D. M. et al. Getting to the fat of the matter: Models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152, 179–189 (2007).ADS 
PubMed 

Google Scholar 
Verburg, P. The need to correct for the Suess effect in the application of δ13C in sediment of autotrophic Lake Tanganyika, as a productivity proxy in the Anthropocene. J. Paleolimnol. 37, 591–602 (2007).ADS 

Google Scholar 
Keeling, C. D. The Suess effect: 13Carbon-14Carbon interrelations. Environ. Int. 2, 229–300 (1979).CAS 

Google Scholar 
Sabadel, A., Durante, L. & Wing, S. Stable isotopes of amino acids from reef fishes uncover Suess and nitrogen enrichment effects on local ecosystems. Mar. Ecol. Prog. Ser. 647, 149–160 (2020).ADS 
CAS 

Google Scholar 
Eide, M., Olsen, A., Ninnemann, U. S. & Eldevik, T. A global estimate of the full oceanic 13C Suess effect since the preindustrial. Glob. Biogeochem. Cycles 31, 492–514 (2017).ADS 
CAS 

Google Scholar 
McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, 1–26 (2016).
Google Scholar 
Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 

Google Scholar 
Whiteman, J. P., Smith, E. A. E., Besser, A. C. & Newsome, S. D. A guide to using compound-specific stable isotope analysis to study the fates of molecules in organisms and ecosystems. Diversity 11, 1–18 (2019).
Google Scholar 
Hilton, G. M. et al. A stable isotopic investigation into the causes of decline in a sub-Antarctic predator, the rockhopper penguin. Glob. Change Biol. 12, 611–625 (2006).ADS 

Google Scholar 
Lorrain, A. et al. Nitrogen and carbon isotope values of individual amino acids: A tool to study foraging ecology of penguins in the Southern Ocean. Mar. Ecol. Prog. Ser. 391, 293–306 (2009).ADS 
CAS 

Google Scholar 
Quillfeldt, P. & Masello, J. F. Compound-specific stable isotope analyses in Falkland Islands seabirds reveal seasonal changes in trophic positions. BMC Ecol. 20, 1–12 (2020).
Google Scholar 
Sabadel, A. J. M., Woodward, E. M. S., Van Hale, R. & Frew, R. D. Compound-specific isotope analysis of amino acids: A tool to unravel complex symbiotic trophic relationships. Food Webs 6, 9–18 (2016).
Google Scholar 
Styring, A. K. et al. Practical considerations in the determination of compound-specific amino acid δ15N values in animal and plant tissues by gas chromatography-combustion-isotope ratio mass spectrometry, following derivatisation to their N-acetylisopropyl e. Rapid Commun. Mass Spectrom. 26, 2328–2334 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Coplen, T. B. Guidelines and recommended terms for expression of stable-isotope-ratio and gas-ratio measurement results. Rapid Commun. Mass Spectrom. 25, 2538–2560 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Phillips, D. L. & Gregg, J. W. J. W. Uncertainty in source partitioning using stable isotopes. Oecologia 127, 171–179 (2001).ADS 
PubMed 

Google Scholar 
Jack, L. & Wing, S. R. Individual variability in trophic position and diet of a marine omnivore is linked to kelp bed habitat. Mar. Ecol. Prog. Ser. 443, 129–139 (2011).ADS 
CAS 

Google Scholar 
McCutchan, J. H., Lewis, W. M., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).CAS 

Google Scholar 
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).PubMed 

Google Scholar 
McMahon, K. W., Thorrold, S. R., Elsdon, T. S. & Mccarthy, M. D. Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnol. Oceanogr. 60, 1076–1087 (2015).ADS 
CAS 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).PubMed 

Google Scholar 
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48 (2007).PubMed 

Google Scholar 
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).CAS 

Google Scholar 
Anderson, M., Gorley, R. N. & Clarke, K. R. PERMANOVA + for PRIMER: Guide to Software and Statistical Methods. 1, 1:218 (2008).Mullan, A. Influence of Southern Oscillation on New Zealand Weather. In Proceedings of Western Pacific International Meeting and Workshop on TOGA-COARE (1996).Francis, M. P., Hurst, R. J., McArdle, B. H., Bagley, N. W. & Anderson, O. F. New Zealand demersal fish assemblages. Environ. Biol. Fishes 65, 215–234 (2002).
Google Scholar 
Beentjes, M. P., Bull, B., Hurst, R. J. & Bagley, N. W. Demersal fish assemblages along the continental shelf and upper slope of the east coast of the South Island, New Zealand. NZ J. Mar. Freshw. Res. 36, 197–223 (2002).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2020).SAS Institute. JMP. (2018).Clarke, K. R. & Gorley, R. N. PRIMER v6: User Manual/Tutorial. (PRIMER-E, 2006). More

In recent years there has been an increasing concern regarding the global decline of pollinators and pollination services1,2,3. Recent studies estimate that over 87% of the flowering plant species rely on biotic pollination4. Pollination is a mutualistic interaction, and plants provide pollinators with various rewards, including nectar, oil, or excess pollen to feed upon5,6. Although bees are the most well-known pollinator group, pollination can be performed by a wide variety of species, including mammals, birds, reptiles, and other insects.Plant-pollinator interactions are among the key processes that generate and maintain biodiversity7,8. The coevolutionary processes involved in animal pollination have helped maintain the structure and function of entire communities and species’ networks. Wild plant species and natural ecosystems provide several products and services, including nutrient cycling, medicine, food, a source of pollinators for domesticated crops, and alternative food and shelter sources for agricultural pollinators9. However, the complex web of interactions and the large number of species involved (ca. 400,000 species globally) makes it challenging to estimate pollinators’ value in natural ecosystems, particularly when the life history of so many pollinator species remains little studied and understood10.Pollinators also provide highly valuable ecosystem services to crops11,12. More than 70% of the world’s crops depend directly on insect pollination, making pollination key to food security11,13. The European honeybee (Apis mellifera) is likely the most economically important pollinator of crops worldwide13,14. Honeybees are adaptable, easy to manage, and cost-efficient. However, in recent years, ‘colony collapse’ caused by several factors, including parasitic mites and the excessive use of pesticides and herbicides, have led to a decline in managed honeybee colonies in many parts of the world15,16,17. Similarly, habitat loss and fragmentation have detrimental effects on both native and commercial pollinators. In degraded habitats, pollinators struggle to find resources and nesting sites18,19,20.In Chile, pollination represents a multimillion-dollar business. Between January and October 2020, the export of Chilean fruit reached USD 4.149 million, while fresh vegetables generated USD 347 million during the same period21. Although agricultural pollinators have been well studied, native pollinators remain largely unknown. With over 460 species of native bees in Chile, approximately 70% are endemic; researchers have only begun to understand the relationships between native plants and their pollinators22,23,24. Also, managed honeybees and bumblebees introduced to Chile for crop pollination are highly invasive and easily leave croplands to forage in neighbouring native ecosystems25,26, competing directly with native pollinators for the ever-diminishing resources in native grasslands and forests posing a threat to Chile’s unique ecoregions25,27.Because of the importance of pollination in the maintenance of biodiversity and the economic benefits of agricultural crop production, there is an urgent need to understand the causes behind the current decline in pollinator species. In this sense, collating and reviewing existing information on pollinators and making this information easily accessible in the form of a user-friendly database is of immeasurable value. In this study, we compiled the information available about pollination and pollinators (sensu lato) for Chile, aiming to understand plant-pollinator interactions, identify knowledge and geographic gaps, and provide a baseline from which to carry out further studies. We aimed to make a datasheet with a format that was adaptable to different regions and other countries by allowing it to be easily understood, easy to access and find and aiming to avoid duplicity of data. This study represents the first systematic effort to compile the available information on pollination and pollinators for Chile. This pollination catalogue for Chile adds to other international efforts of systematising this information as, for example, the Catalogue of Afrotropical Bees28 and the CPC Plant Pollinators Database29. More

Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science (New York, N.Y.) 320, 1034–1039 (2008).ADS 
CAS 

Google Scholar 
Jørgensen, B. B. & Kasten, S. in Marine Geochemistry, edited by H. D. Schulz & M. Zabel (Springer, 2006), 271–309.Broman, E., Sjöstedt, J., Pinhassi, J. & Dopson, M. Shifts in coastal sediment oxygenation cause pronounced changes in microbial community composition and associated metabolism. Microbiome 5, 96 (2017).PubMed 
PubMed Central 

Google Scholar 
Billerbeck, M. et al. Surficial and deep pore water circulation governs spatial and temporal scales of nutrient recycling in intertidal sand flat sediment. Mar. Ecol. Prog. Ser. 326, 61–76 (2006).ADS 
CAS 

Google Scholar 
Booth, J. M., Fusi, M., Marasco, R., Mbobo, T. & Daffonchio, D. Fiddler crab bioturbation determines consistent changes in bacterial communities across contrasting environmental conditions. Sci. Rep. 9, 3749 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Torti, A., Lever, M. A. & Jørgensen, B. B. Origin, dynamics, and implications of extracellular DNA pools in marine sediments. Mar. Genom. 24(Pt 3), 185–196 (2015).
Google Scholar 
Starke, R., Pylro, V. S. & Morais, D. K. 16S rRNA gene copy number normalization does not provide more reliable conclusions in metataxonomic surveys. Microb. Ecol. 81, 535–539 (2021).CAS 
PubMed 

Google Scholar 
Blazewicz, S. J., Barnard, R. L., Daly, R. A. & Firestone, M. K. Evaluating rRNA as an indicator of microbial activity in environmental communities: Limitations and uses. ISME J. 7, 2061–2068 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
de Vrieze, J., Pinto, A. J., Sloan, W. T. & Ijaz, U. Z. The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool. Microbiome 6, 63 (2018).PubMed 
PubMed Central 

Google Scholar 
Zhang, Y., Zhao, Z., Dai, M., Jiao, N. & Herndl, G. J. Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea. Mol. Ecol. 23, 2260–2274 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meyer, K. M., Petersen, I. A. B., Tobi, E., Korte, L. & Bohannan, B. J. M. Use of RNA and DNA to identify mechanisms of bacterial community homogenization. Front. Microbiol. 10, 2066 (2019).PubMed 
PubMed Central 

Google Scholar 
Petro, C., Starnawski, P., Schramm, A. & Kjeldsen, K. U. Microbial community assembly in marine sediments. Aquat. Microb. Ecol. 79, 177–195 (2017).
Google Scholar 
Walsh, E. A. et al. Relationship of bacterial richness to organic degradation rate and sediment age in subseafloor sediment. Appl. Environ. Microbiol. 82, 4994–4999 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dinsdale, E. A. et al. Microbial ecology of four coral atolls in the Northern Line Islands. PLoS ONE 3, e1584 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Schmitt, S. et al. Salinity, microbe and carbonate mineral relationships in brackish and hypersaline lake sediments: A case study from the tropical Pacific coral atoll of Kiritimati. Depositional Rec. 5, 212–229 (2019).
Google Scholar 
Schneider, D., Arp, G., Reimer, A., Reitner, J. & Daniel, R. Phylogenetic analysis of a microbialite-forming microbial mat from a hypersaline lake of the Kiritimati atoll, Central Pacific. PLoS ONE 8, e66662 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, B. et al. Sediment microbial communities and their potential role as environmental pollution indicators in Xuande Atoll, South China Sea. Front. Microbiol. 11, 1011 (2020).PubMed 
PubMed Central 

Google Scholar 
Galand, P. E. et al. Phylogenetic and functional diversity of Bacteria and Archaea in a unique stratified lagoon, the Clipperton atoll (N Pacific). FEMS Microbiol. Ecol. 79, 203–217 (2012).CAS 
PubMed 

Google Scholar 
Stoddart, D. R. The conservation of Aldabra. Geogr. J. 134, 471 (1968).
Google Scholar 
Farrow, G. E. & Brander, K. M. Tidal studies on Aldabra. Phil. Trans. R. Soc. Lond. B 260, 93–121 (1971).ADS 

Google Scholar 
Gaillard, C., Bernier, P. & Gruet, Y. L. lagon d’Aldabra (Seychelles, Océan indien), un modèle pour le paléoenvironnement de Cerin (Kimméridgien supérieur, Jura méridional, France). Geobios 27, 331–348 (1994).
Google Scholar 
Hamylton, S., Spencer, T. & Hagan, A. B. Spatial modelling of benthic cover using remote sensing data in the Aldabra lagoon, western Indian Ocean. Mar. Ecol. Prog. Ser. 460, 35–47 (2012).ADS 

Google Scholar 
Braithwaite, C. J. R. Last interglacial changes in sea level on Aldabra, western Indian Ocean. Sedimentology 67, 3236–3258 (2020).
Google Scholar 
Haverkamp, P. J. et al. Giant tortoise habitats under increasing drought conditions on Aldabra Atoll—Ecological indicators to monitor rainfall anomalies and related vegetation activity. Ecol. Ind. 80, 354–362 (2017).
Google Scholar 
Hughes, R. N. & Gamble, J. C. A quantitative survey of the biota of intertidal soft substrata on Aldabra Atoll, Indian Ocean. Phil. Trans. R. Soc. Lond. B 279, 327–355 (1977).ADS 

Google Scholar 
Braithwaite, C., Casanova, J., Frevert, T. & Whitton, B. A. Recent stromatolites in landlocked pools on Aldabra, Western Indian Ocean. Palaeogeogr. Palaeoclimatol. Palaeoecol. 69, 145–165 (1989).
Google Scholar 
Potts, M. & Whitton, B. A. Nitrogen fixation by blue-green algal communities in the intertidal zone of the lagoon of Aldabra Atoll. Oecologia 27, 275–283 (1977).ADS 
CAS 
PubMed 

Google Scholar 
Potts, M. & Whitton, B. A. Vegetation of the intertidal zone of the lagoon of Aldabra, with particular reference to the photosynthetic prokaryotic communities. Proc. R. Soc. Lond. B. 208, 13–55 (1980).ADS 

Google Scholar 
Meyers, P. A. Preservation of elemental and isotopic source identification of sedimentary organic matter. Chem. Geol. 114, 289–302 (1994).ADS 
CAS 

Google Scholar 
Choi, A., Lee, K., Oh, H.-M., Feng, J. & Cho, J.-C. Litoricola marina sp. nov.. Int. J. Syst. Evolut. Microbiol. 60, 1303–1306 (2010).CAS 

Google Scholar 
Durham, B. P. et al. Draft genome sequence of marine alphaproteobacterial strain HIMB11, the first cultivated representative of a unique lineage within the Roseobacter clade possessing an unusually small genome. Stand Genom. Sci. 9, 632–645 (2014).
Google Scholar 
Boehm, A. B., Yamahara, K. M. & Sassoubre, L. M. Diversity and transport of microorganisms in intertidal sands of the California coast. Appl. Environ. Microbiol. 80, 3943–3951 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Probandt, D., Eickhorst, T., Ellrott, A., Amann, R. & Knittel, K. Microbial life on a sand grain: From bulk sediment to single grains. ISME J. 12, 623–633 (2018).PubMed 

Google Scholar 
Wong, H. L., Smith, D.-L., Visscher, P. T. & Burns, B. P. Niche differentiation of bacterial communities at a millimeter scale in Shark Bay microbial mats. Sci. Rep. 5, 15607 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dupraz, C., Visscher, P. T., Baumgartner, L. K. & Reid, R. P. Microbe-mineral interactions: Early carbonate precipitation in a hypersaline lake (Eleuthera Island, Bahamas). Sedimentology 51, 745–765 (2004).ADS 
CAS 

Google Scholar 
Diaz, M. R., Piggot, A. M., Eberli, G. P. & Klaus, J. S. Bacterial community of oolitic carbonate sediments of the Bahamas Archipelago. Mar. Ecol. Prog. Ser. 485, 9–24 (2013).ADS 

Google Scholar 
Cui, H., Yang, K., Pagaling, E. & Yan, T. Spatial and temporal variation in enterococcal abundance and its relationship to the microbial community in Hawaii beach sand and water. Appl. Environ. Microbiol. 79, 3601–3609 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Petriglieri, F., Nierychlo, M., Nielsen, P. H. & McIlroy, S. J. In situ visualisation of the abundant Chloroflexi populations in full-scale anaerobic digesters and the fate of immigrating species. PLoS ONE 13, e0206255 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wietz, M., Gram, L., Jørgensen, B. & Schramm, A. Latitudinal patterns in the abundance of major marine bacterioplankton groups. Aquat. Microb. Ecol. 61, 179–189 (2010).
Google Scholar 
Wemheuer, B. et al. Impact of a phytoplankton bloom on the diversity of the active bacterial community in the southern North Sea as revealed by metatranscriptomic approaches. FEMS Microbiol. Ecol. 87, 378–389 (2014).CAS 
PubMed 

Google Scholar 
Heywood, K. J., Stevens, D. P. & Bigg, G. R. Eddy formation behind the tropical island of Aldabra. Deep Sea Res. Part I 43, 555–578 (1996).
Google Scholar 
Pérez-Cataluña, A. et al. Revisiting the taxonomy of the genus Arcobacter: Getting order from the chaos. Front. Microbiol. 9, 2077 (2018).PubMed 
PubMed Central 

Google Scholar 
Revsbech, N. P. & Jørgensen, B. B. Microelectrodes: Their Use in Microbial Ecology. In Advances in Microbial Ecology (ed. Marshall, K. C.) 293–352 (Springer, 1989).
Google Scholar 
Watson, J. et al. Reductively debrominating strains of Propionigenium maris from burrows of bromophenol-producing marine infauna. Int. J. Syst. Evol. Microbiol. 50(Pt 3), 1035–1042 (2000).CAS 
PubMed 

Google Scholar 
Sasi, J. T. S., Rahul, K., Ramaprasad, E. V. V., Sasikala, C. & Ramana, C. V. Arcobacter anaerophilus sp. nov., isolated from an estuarine sediment and emended description of the genus Arcobacter. Int. J. Syst. Evolut. Microbiol. 63, 4619–4625 (2013).
Google Scholar 
Rinke, C. et al. High genetic similarity between two geographically distinct strains of the sulfur-oxidizing symbiont ‘Candidatus Thiobios zoothamnicoli’. FEMS Microbiol. Ecol. 67, 229–241 (2009).CAS 
PubMed 

Google Scholar 
Vartoukian, S. R., Palmer, R. M. & Wade, W. G. The division “Synergistes”. Anaerobe 13, 99–106 (2007).CAS 
PubMed 

Google Scholar 
Janssen, P. H. & Liesack, W. Succinate decarboxylation by Propionigenium maris sp. nov., a new anaerobic bacterium from an estuarine sediment. Arch. Microbiol. 164, 29–35 (1995).CAS 
PubMed 

Google Scholar 
Shiozaki, T. et al. Nitrification and its influence on biogeochemical cycles from the equatorial Pacific to the Arctic Ocean. ISME J. 10, 2184–2197 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
González-Domenech, C. M., Martínez-Checa, F., Béjar, V. & Quesada, E. Denitrification as an important taxonomic marker within the genus Halomonas. Syst. Appl. Microbiol. 33, 85–93 (2010).PubMed 

Google Scholar 
Farmer, J. J., Michael, J. J., Brenner, F. W., Cameron, D. N. & Birkhead, K. M. The Book. In Bergey’s Manual of Systematics of Archaea and Bacteria (eds Whitman, W. B. et al.) 1–79 (Wiley, 2016).
Google Scholar 
Ventosa, A. & Haba, R. R. in Bergey’s Manual of Systematics of Archaea and Bacteria, edited by W. B. Whitman, et al. (Wiley, 2015), 1–16.Lloyd, K. G. Time as a microbial resource. Environ. Microbiol. Rep. 13, 18–21 (2021).PubMed 

Google Scholar 
Holguin, G., Vazquez, P. & Bashan, Y. The role of sediment microorganisms in the productivity, conservation, and rehabilitation of mangrove ecosystems: An overview. Biol. Fertil. Soils 33, 265–278 (2001).CAS 

Google Scholar 
Nanca, C. L., Neri, K. D., Ngo, A. C. R., Bennett, R. M. & Dedeles, G. R. Degradation of polycyclic aromatic hydrocarbons by moderately halophilic bacteria from Luzon salt beds. J. Health Pollut. 8, 180915 (2018).PubMed 
PubMed Central 

Google Scholar 
Bird, J. T. et al. Uncultured microbial phyla suggest mechanisms for multi-thousand-year subsistence in Baltic Sea sediments. MBio 10, 1002 (2019).
Google Scholar 
Moulton, O. M. et al. Microbial associations with macrobiota in coastal ecosystems: Patterns and implications for nitrogen cycling. Front. Ecol. Environ. 14, 200–208 (2016).
Google Scholar 
Park, S., Park, J.-M., Kang, C.-H. & Yoon, J.-H. Aestuariispira insulae gen. nov., sp. nov., a lipolytic bacterium isolated from a tidal flat. Int. J. Syst. Evol. Microbiol. 64, 1841–1846 (2014).CAS 
PubMed 

Google Scholar 
Evans, M. V. et al. Members of Marinobacter and Arcobacter influence system biogeochemistry during early production of hydraulically fractured natural gas wells in the Appalachian Basin. Front. Microbiol. 9, 2646 (2018).PubMed 
PubMed Central 

Google Scholar 
Wilhelm, R. C. Following the terrestrial tracks of Caulobacter – redefining the ecology of a reputed aquatic oligotroph. ISME J. 12, 3025–3037 (2018).PubMed 
PubMed Central 

Google Scholar 
Suzuki, D., Ueki, A., Amaishi, A. & Ueki, K. Desulfopila aestuarii gen. nov., sp. nov., a Gram-negative, rod-like, sulfate-reducing bacterium isolated from an estuarine sediment in Japan. Int. J. Syst. Evol. Microbiol. 57, 520–526 (2007).CAS 
PubMed 

Google Scholar 
Dawson, K. S., Scheller, S., Dillon, J. G. & Orphan, V. J. Stable isotope phenotyping via cluster analysis of nanoSIMS data as a method for characterizing distinct microbial ecophysiologies and sulfur-cycling in the environment. Front. Microbiol. 7, 774 (2016).PubMed 
PubMed Central 

Google Scholar 
Fadhlaoui, K. et al. Fusibacter fontis sp. nov., a sulfur-reducing, anaerobic bacterium isolated from a mesothermic Tunisian spring. Int. J. Syst. Evol. Microbiol. 65, 3501–3506 (2015).CAS 
PubMed 

Google Scholar 
Kjeldsen, K. U. et al. Diversity of sulfate-reducing bacteria from an extreme hypersaline sediment, Great Salt Lake (Utah). FEMS Microbiol. Ecol. 60, 287–298 (2007).CAS 
PubMed 

Google Scholar 
Schneider, D., Wemheuer, F., Pfeiffer, B. & Wemheuer, B. Extraction of total DNA and RNA from marine filter samples and generation of a cDNA as universal template for marker gene studies. Methods Mol. Biol. Clifton N J 1539, 13–22 (2017).CAS 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 

Google Scholar 
Berkelmann, D., Schneider, D., Hennings, N., Meryandini, A. & Daniel, R. Soil bacterial community structures in relation to different oil palm management practices. Sci. Data 7, 421 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
von Hoyningen-Huene, A. J. E. et al. Bacterial succession along a sediment porewater gradient at Lake Neusiedl in Austria. Sci. data 6, 163 (2019).
Google Scholar 
Tange, O. Gnu parallel-the command-line power tool. login: The USENIX Mag. 36, 42–47 (2011).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34, i884–i890 (2018).
Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate Illumina paired-end read merger. Bioinformatics (Oxford, England) 30, 614–620 (2014).CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 17, 10 (2011).
Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing (2016).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 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 

Google Scholar 
SILVAngs. SILVAngs – rDNA-based microbial community analysis using next-generation sequencing (NGS) data – user guide. Available at https://www.arb-silva.de/fileadmin/silva_databases/sngs/SILVAngs_User_Guide.pdf (2017).McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: How I learned to stop worrying and love the ome-ome. GigaScience 1, 7 (2012).PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree – tree figure drawing tool (2018).R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).RStudio Team. RStudio: integrated development for R (RStudio Inc., 2021).Chen, L. et al. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ 6, e4600 (2018).PubMed 
PubMed Central 

Google Scholar 
Pereira, M. B., Wallroth, M., Jonsson, V. & Kristiansson, E. Comparison of normalization methods for the analysis of metagenomic gene abundance data. BMC Genom. 19, 274 (2018).
Google Scholar 
Andersen, K. S., Kirkegaard, R. H., Karst, S. M. & Albertsen, M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data (2018).Oksanen, J. et al. vegan: Community ecology package (2018).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics (Oxford, England) 26, 1463–1464 (2010).CAS 

Google Scholar 
Harrel Jr, F. E., with contributions from Charles Dupont and many others. Hmisc: Harrell Miscellaneous (2021).Wei, T. & Simko, V. R package “corrplot”: Visualization of a Correlation (2021).de Cáceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 

Google Scholar 
Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Esri Inc. ArcGIS Desktop (Esri Inc., 2019).Inkscape Developers. Inkscape (2020).Fussmann, D. et al. Authigenic formation of Ca–Mg carbonates in the shallow alkaline Lake Neusiedl, Austria. Biogeosciences 17, 2085–2106 (2020).ADS 
CAS 

Google Scholar 
Parkhurst, D. L. & Appelo, C. A. in U.S. Geological Survey Techniques and Methods (2013), Vol. 6, pp. 2328–7055. More

Plant materialRice (Oryza sativa L.) plants used for the experiment were from the collection of Taiwan Agricultural Research Institute. The rice variety (Tainan No. 11) used in this study has enhanced resistance to rice blast. The use of plant materials complies with international, national, and/or institutional guidelines and legislation.Field areaThis study was carried out in experimental rice fields under low-external-input and conventional farming in central Taiwan (23.5859 N, 120.4083 E; 8.0 ha). The annual average temperature ranged from 23 to 25 °C, the annual average relative humidity ranged from 75 to 92%, and the annual rainfall ranged from 1020 to 2873 mm year−1 (average data between 2006 and 2016 measured at a nearby weather station; Fig. 1). The experimental paddy plots were defined by considering the typical dimensions of the agricultural fields in Taiwan (0.5 to 1.0 ha). A long-term experiment was conducted from 2006 to 2016 to study the effects of different agronomic management on biodiversity, productivity, and environment, including traceability system, soil fertility, nitrogen leaching, production costs, disease incidence and severity, the abundance of pest and beneficial insects, and weed dynamics.The treatments consisted of conventional farming with high chemical fertilizer input (CF) and low-external-input farming with low fertilizer input (LF). In the CF farming, we followed the fertilizer recommendations that are constructed to meet the nutrient requirements of the crop. In the LF farming, the chemical fertilizers were largely reduced compared to the recommendation (see next paragraph for the details). The experiment was conducted as a randomized complete block design (RCBD) with four replicates. In agricultural experiments, the RCBD is a standard procedure by grouping experimental units into blocks. For example, the design can control variation in the experiments by considering spatial influences and adjusting the effects of target factors in fields. Each experimental unit consisted of a 0.58 ± 0.16 ha of the area of the field. Additional nutrient management in the LF system includes (1) nitrogen-fixing and cover crops, (2) manure and compost applications, (3) plant and soil nutrient analyses for adjusting fertilization, and (4) reduced tillage. Soil-available potassium gradually decreased during the 10-year study period in the area of the LF system. Over the study period, the LF system achieved the similar level of crop production as that of the CF system (Fig. S1).In our study area, there were two growing seasons within a year: one in the first half of the year (from February to June) and one in the second half (from August to December). The ground fertilizers were applied before rice seedlings were transplanted, followed by additional fertilizations during the tillering and boosting stages. The total amount of fertilizers used for the CF system included 140–180 and 120–140 kg N ha−1, 70–72 and 60 kg P2O5 ha−1, and 85 and 60 kg K2O ha−1 for the first and second seasons, respectively. For the LF system, 100 and 80 kg N ha−1, 30 and 30 kg P2O5 ha−1, and 30 and 30 kg K2O ha−1 were applied in the first and second seasons, respectively. The larger amount of fertilizers for the first season was due to its longer duration. For each rice growing season, fungicides were applied to both farming systems once during the boosting stage. During the fungicide application, a 10% mixture of Cartap plus Probenazole or 6% probenazole for rice blast (both 30 kg ha−1) and 1.5% Furametpyr for sheath blight (20 kg ha−1) were used.Rice disease monitoringThe major rice disease (rice blast; Fig. S2) was monitored biweekly in the CF and LF systems over the two growing seasons per year, with each growing season including (in chronological order) the tillering, flowering, and maturing stages. There was a total of 123 occasions during our study. The plants were disease free when planted out. When the lesion of the rice blast began to appear in the fields from the tillering stage to the maturing stage, the effects of the two treatments (CF and LF systems) in the paddy fields on the disease incidence of rice blast (caused by Pyricularia oryzae) were investigated. For each plot (or experimental unit), the incidence of rice disease was randomly examined at 5 points and for 25 plexuses (i.e., each derived from one primary tiller) per point. The disease incidence was quantified as the percentage of infected plexuses that were determined based on the presence of infected leaves.The area under the disease progress curve (AUDPC) was used to quantify disease incidences over time, and the relative AUDPC ((RAUDPC)) was used because of unequal sampling duration in the growing seasons during our study period. For each plot (or experimental unit), we used the (RAUDPC) to summarize the incidences of disease during each growing season as follows:$$RAUDPC=frac{sum_{i=1}^{n-1}frac{{y}_{i}+{y}_{i+1}}{2}times left({t}_{i+1}-{t}_{i}right)}{100 times left({t}_{n}-{t}_{1}right)},$$
(1)
where ({y}_{i}) and ({t}_{i}) are the disease incidence (%) and time (day) at the (i)th observation, respectively, and (n) is the total number of observations.Bayesian modelingWe built a mechanistic model that was applied to assess the interplay within a network of relationships among weather fluctuation, farming system, and disease incidence in the paddy fields. The model describes how (1) temperature and relative humidity together influence the development of primary inoculum, (2) rainfall detaches the fungal spores on the host tissues, and (3) rainfall and wind catch the airborne spores onto the leaf area. These environmental processes determine the disease incidence in the model. In addition, this model considers that farming systems can suppress or accelerate disease incidence. By fitting our model to the observed incidence, Bayesian inference was used as the parameter estimation technique for the models. In addition, we tested the alternative mechanistic hypotheses based on a model-selection criterion and cross vaidation (see subsequent paragraphs).With a linearity assumption, the incidences of disease (RAUDPC) were modeled as an inverse-logit function of the progress rate of the development of primary inoculum ((IP) with values between 0 and 1) and the net catchment of the airborne spores by rainfall and wind ((CT) with values between 0 and 1; when subtracting the detachment of spores by rainfall from the host tissue) as follows:$$RAUDPC=invLogitleft({a}_{f}+{b}_{1}bullet logitleft(avg_IPright)+{b}_{2}bullet logitleft(avg_IPbullet avg_CTright)right),$$
(2)
where ({a}_{f}), ({b}_{1}), and ({b}_{2}) describe the constant baseline for different farming systems ((f) = the CF or LF system), the direct effect size of (avg_IP), and the mediating effect size of (avg_CT) through (IP), respectively. The two parameters ((avg_IP) and (avg_CT)) are averaged (IP) and (CT) during the growing season, respectively (see below for details). The effect sizes ({b}_{1}) and ({b}_{2}) have values more than zero. The constant baseline allows the management-specific acting in the model when they can influence the disease incidence.The process rate (IP) was simulated as a function of the temperature response ((fleft(Tright)) with values between 0 and 1) and hourly air relative humidity ((RH,) %) as follows20:$$IP= left{begin{array}{ll}0& mathrm{if}, RH 0) are the steepness and midpoint parameters to control the portion of spores caught by the wind, respectively.The Bayesian framework ‘Stan’49 and its R interface ‘RStan’50 were used to construct and fit the models. There were two competing models: either considering the difference between the CF and LF systems by not fixed to the same values of the constant baseline ({a}_{f}) in Formula (2) or not. For each model, four Markov Chain Monte Carlo (MCMC) chains (for numerical approximations of Bayesian inference) ran, each with 5,000 iterations, and the first half of the iterations of each chain were discarded as burn-in. The R-hat statistic of each parameter approaches a value of 1, indicating model convergence. With a total of 2,000 samples, collected as one sample for every 5 iterations for each chain, the model parameters and their posterior distribution were estimated. To compare the two competing models, we calculated the widely applicable information criterion (WAIC) using the R package ‘loo’51. The best model was determined based on the lowest WAIC. By using the same R package, we also performed the approximate leave-one-out cross-validation (LOO-CV) to estimate the predictive ability of the two Bayesian models. Here, we used the expected log predictive density (ELPD) to be the predictive performance.Compliance with ethical standardsThe authors declare that they have no conflict of interest. This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors. More

Sentinel-1 data selectionThe Copernicus Sentinel-1 mission was launched by the European Space Agency (ESA) in 2014 with the Sentinel-1A satellite, complemented with the second Sentinel-1B satellite in 2016. Each satellite has a 12-days repeat cycle. Continuity of the Sentinel-1 mission has been approved by ESA until 2030 and replacement satellites will be launched. The satellites operate in different acquisition modes over different parts of the globe. Land masses are covered primarily by the Interferometric Wide-Swath Mode (IW) with a 250 km swath width across-track. Single-look-complex (SLC) Level 1.1 data are required for interferometric processing. Along-track, Sentinel-1 data are sliced into consecutive frames (slices) of about 250 km length. Data are distributed via ESA’s Scientific Sentinel-1 Hub, which is mirrored at NASA’s Alaska Satellite Facility DAAC (ASF-DAAC). During production, Sentinel-1 SLC data were accessed on the ASF-DAAC data repository which resides on Amazon’s AWS S3 bucket in region us-west-2.Sentinel-1 satellites cover various parts of Earth in ascending and descending flight direction in a total of 175 relative orbits. ESA’s flight plan has some areas covered every six days and in both flight directions, predominantly over Europe. For the production of this data set, Sentinel-1 SLC frames were selected from all available scenes acquired between December 1st 2019 and November 30th 2020. Over the one-year timeframe, a maximum of 30 to 31 acquisitions at 12-days repeat, and 60 to 61 acquisitions at 6-days repeat intervals can be expected. The following selection criteria were applied consecutively to achieve global coverage with uniform distribution of acquisitions across seasons (Fig. 1):

Global descending data (Fig. 1a) were selected where the one-year stack size had at least 25 acquisitions.

Spatial gaps were filled with ascending data (Fig. 1a) where the one-year stack size had at least 25 acquisitions.

For spatial consistency, over conterminous North America north of Panama, preference was given to ascending data where both ascending and descending data existed with stack sizes over 25 acquisitions.

For stack sizes less than 25 acquisitions, preference was given to the flight direction with the larger number of acquisitions.

Remaining gaps were filled with data from the flight direction available.

Fig. 1Flight direction, polarization mode, and InSAR stack sizes of 6- and 12-days repeat coverage of Sentinel-1 data acquired between December 1st 2019 and November 30th 2020 selected for processing.Full size imageArctic and Antarctic regions are typically covered with polarization modes of horizontal transmit (HH single- or HH/HV dual-polarization). Figure 1b shows the global distribution of the processed data in horizontal and vertical polarization transmit modes, respectively. Table 1 summarizes the number of selected scenes in the two flight directions and various polarization modes. The total number of processed Sentinel-1 SLC frames came to ~205,000 scenes with a total raw input data volume of about 850 Terabytes. Figure 1c,d show the spatial distribution of the final scene selection with the number of 6- and 12-days repeat-pass image pairs. Consistent 6-days repeat coverage with about sixty image pairs from either ascending or descending orbits could be processed over Europe, the coastal areas of Greenland and Antarctica, and some smaller areas around the world; note that in some regions (e.g., India, interior Greenland, Northern Canada, Eastern China) 6-days repeat coverage was available in certain seasons only (Fig. 1c). A consistent coverage with 12-days repeat-pass imagery, instead, could be processed almost globally with the nominal maximum of about thirty repeat-pass pairs in areas where only one satellite, Sentinel-1A or Sentinel-1B, acquired data in all but few areas above 60° N in Canada, Greenland, or Russia (Fig. 1d). In some small areas in the Midwestern United States, the Khabarovsk region in Far-Eastern Russia, or in the Northern Sahara, neither Sentinel-1A nor Sentinel-1B acquire data in IW mode, leading to small gaps in the final data set.Table 1 Number of Sentinel-1 Single Look Complex scenes processed.Full size tableProcessing approachThe overall processing workflow was developed based on the interferometric processing software developed by GAMMA Remote Sensing and geared towards efficient processing in the Amazon Web Services (AWS) cloud environment utilizing Earth Big Data LLC’s cloud scaling solutions. The workflow is divided into three main blocks as illustrated in Fig. 2. Sentinel-1A and -1B acquire data along 175 relative orbits/orbital tracks. Blocks 1 and 2 were processed on a per relative orbit basis; block 3 was initiated after blocks 1 and 2 had been completed for all relative orbits.Fig. 2Implementation of the Sentinel-1 interferometric processor in the AWS cloud environment.Full size imageProcessing Block 1For each SLC of a given relative orbit, processing block 1 entailed:

1.

Conversion of SLC image files to a GAMMA specific format. Each Sentinel-1 SLC, covering an area of ~250 × 250 km, consists of six SLC image files (one SLC image file for each of the three sub-swaths in co- (VV or HH) and cross-polarizations (VH or HV).

2.

Compensation of the SLC amplitudes for the noise equivalent sigma zero (NESZ).

3.

The orbit state vectors provided with the original Sentinel-1 SLCs were updated with the precision state vectors (AUX_POEORB) distributed by the Sentinel-1 payload data ground segment 20 days after data take with a precision (3σ) generally of the order of 1 cm (target requirement  More

Jardat, P. & Lansade, L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans. Anim. Cogn. https://doi.org/10.1007/s10071-021-01557-6 (2021).Article 
PubMed 

Google Scholar 
Knolle, F., Goncalves, R. P. & Jennifer Morton, A. Sheep recognize familiar and unfamiliar human faces from two-dimensional images. R. Soc. Open Sci. 4, 171228 (2017).Nawroth, C. & McElligott, A. G. Human head orientation and eye visibility as indicators of attention for goats (Capra hircus). PeerJ 5, e3073 (2017).Albuquerque, N. et al. Dogs recognize dog and human emotions. Biol. Lett. 12, 20150883 (2016).Article 

Google Scholar 
Albuquerque, N., Guo, K., Wilkinson, A., Resende, B. & Mills, D. S. Mouth-licking by dogs as a response to emotional stimuli. Behav. Processes 146, 42–45 (2018).Article 

Google Scholar 
Quaranta, A., D’ingeo, S., Amoruso, R. & Siniscalchi, M. Emotion recognition in cats. Animals 10, 1107 (2020).Sabiniewicz, A., Tarnowska, K., Świątek, R., Sorokowski, P. & Laska, M. Olfactory-based interspecific recognition of human emotions: Horses (Equus ferus caballus) can recognize fear and happiness body odour from humans (Homo sapiens). Appl. Anim. Behav. Sci. 230, 105072 (2020).Smith, A. V., Proops, L., Grounds, K., Wathan, J. & McComb, K. Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus). Biol. Lett. 12, 20150907 (2016).Article 

Google Scholar 
Smith, A. V. et al. Domestic horses (Equus caballus) discriminate between negative and positive human nonverbal vocalisations. Sci. Rep. 8, 13052 (2018).ADS 
Article 

Google Scholar 
Nakamura, K., Takimoto-Inose, A. & Hasegawa, T. Cross-modal perception of human emotion in domestic horses (Equus caballus). Sci. Rep. 8, 8660 (2018).ADS 
Article 

Google Scholar 
Trösch, M. et al. Horses categorize human emotions cross-modally based on facial expression and non-verbal vocalizations. Animals 9, 862 (2019).Article 

Google Scholar 
Sankey, C., Henry, S., André, N., Richard-Yris, M. A. & Hausberger, M. Do horses have a concept of person? PLoS One 6, e18331 (2011).Trösch, M., Bertin, E., Calandreau, L., Nowak, R. & Lansade, L. Unwilling or willing but unable: can horses interpret human actions as goal directed?. Anim. Cogn. 23, 1035–1040 (2020).Article 

Google Scholar 
Warmuth, V. et al. Reconstructing the origin and spread of horse domestication in the Eurasian steppe. Proc. Natl. Acad. Sci. 109, 8202–8206 (2012).ADS 
CAS 
Article 

Google Scholar 
VanDierendonck, M. C. & Goodwin, D. Social contact in horses: implications for human-horse interactions. in The human-animal relationship. Forever and a day (eds. de Jonge, F. H. & van den Bos, R.) 65–81 (Royal van Gorcum, 2005).Saint-Georges, C. et al. Motherese in Interaction: At the Cross-Road of Emotion and Cognition? (A Systematic Review). PLoS ONE 8, 78103 (2013).ADS 
Article 

Google Scholar 
Benjamin, A. & Slocombe, K. ‘Who’s a good boy?!’ Dogs prefer naturalistic dog-directed speech. Anim. Cogn. 21, 353–364 (2018).Article 

Google Scholar 
Ben-Aderet, T., Gallego-Abenza, M., Reby, D. & Mathevon, N. Dog-directed speech: Why do we use it and do dogs pay attention to it?. Proc. R. Soc. B Biol. Sci. 284, 20162429 (2017).Article 

Google Scholar 
Jeannin, S., Gilbert, C., Amy, M. & Leboucher, G. Pet-directed speech draws adult dogs’ attention more efficiently than Adult-directed speech. Sci. Rep. 7, 4980 (2017).ADS 
Article 

Google Scholar 
Lesch, R. et al. Talking to dogs: Companion animal-directed speech in a stress test. Animals 9, 417 (2019).Article 

Google Scholar 
Lansade, L. et al. Horses are sensitive to baby talk : Pet-directed speech facilitates communication with humans in a pointing task and during grooming. Anim. Cogn. 5, 999–1006 (2021).Article 

Google Scholar 
Schachner, A. & Hannon, E. E. Infant-Directed Speech Drives Social Preferences in 5-Month-Old Infants. Dev. Psychol. 47, 19–25 (2011).Article 

Google Scholar 
Fernald, A. Approval and Disapproval: Infant Responsiveness to Vocal Affect in Familiar and Unfamiliar Languages. Child Dev. 64, 657–674 (1993).CAS 
Article 

Google Scholar 
Slonecker, E. M., Simpson, E. A., Suomi, S. J. & Paukner, A. Who’s my little monkey? Effects of infant-directed speech on visual retention in infant rhesus macaques. Dev. Sci. 21, 12519 (2018).Article 

Google Scholar 
Kaplan, P. S., Goldstein, M. H., Huckeby, E. R. & Cooper, R. P. Habituation, sensitization, and infants’ responses to motherse speech. Dev. Psychobiol. 28, 45–57 (1995).CAS 
Article 

Google Scholar 
Lansade, L. et al. Facial expression and oxytocin as possible markers of positive emotions in horses. Sci. Rep. 8, 14680 (2018).ADS 
Article 

Google Scholar 
Hausberger, M. et al. Mutual interactions between cognition and welfare: The horse as an animal model. Neurosci. Biobehav. Rev. 107, 540–559 (2019).CAS 
Article 

Google Scholar 
Fortin, M. et al. Emotional state and personality influence cognitive flexibility in horses (Equus caballus). J. Comp. Psychol. 132, 130–140 (2018).Article 

Google Scholar 
Trösch, M. et al. Horses feel emotions when they watch positive and negative horse–human interactions in a video and transpose what they saw to real life. Anim. Cogn. 23, 643–653 (2020).Article 

Google Scholar 
Forkman, B., Boissy, A., Meunier-Salaün, M. C., Canali, E. & Jones, R. B. A critical review of fear tests used on cattle, pigs, sheep, poultry and horses. Physiol. Behav. 92, 340–374 (2007).CAS 
Article 

Google Scholar 
Lansade, L., Bouissou, M. F. & Erhard, H. W. Fearfulness in horses: A temperament trait stable across time and situations. Appl. Anim. Behav. Sci. 115, 182–200 (2008).Article 

Google Scholar 
Stomp, M. et al. An unexpected acoustic indicator of positive emotions in horses. PLoS One 13, e0197898 (2018).Briefer, E. F. et al. Segregation of information about emotional arousal and valence in horse whinnies. Sci. Rep. 5, 9989 (2015).ADS 
Article 

Google Scholar 
Briefer, E. F., Tettamanti, F. & McElligott, A. G. Emotions in goats: Mapping physiological, behavioural and vocal profiles. Anim. Behav. 99, 131–143 (2015).Article 

Google Scholar 
Mendl, M., Burman, O. H. P. & Paul, E. S. An integrative and functional framework for the study of animal emotion and mood. in Proceedings of the Royal Society B: Biological Sciences vol. 277 2895–2904 (Royal Society, 2010).Siniscalchi, M., D’Ingeo, S. & Quaranta, A. Orienting asymmetries and physiological reactivity in dogs’ response to human emotional faces. Learn. Behav. 46, 574–585 (2018).Article 

Google Scholar 
Munsters, C. C. B. M., Visser, K. E. K., van den Broek, J. & Sloet van Oldruitenborgh-Oosterbaan, M. M. The influence of challenging objects and horse-rider matching on heart rate, heart rate variability and behavioural score in riding horses. Vet. J. 192, 75–80 (2012).Siniscalchi, M., D’Ingeo, S., Minunno, M. & Quaranta, A. Communication in dogs. Animals 8, 131 (2018).Article 

Google Scholar 
Call, J., Hare, B., Carpenter, M. & Tomasello, M. ‘Unwilling’ versus ‘unable’: Chimpanzees’ understanding of human intentional action. Dev. Sci. 7, 488–498 (2004).Article 

Google Scholar 
Kaminski, J., Schulz, L. & Tomasello, M. How dogs know when communication is intended for them. Dev. Sci. 15, 222–232 (2012).Article 

Google Scholar 
Pongrácz, P., Szapu, J. S. & Faragó, T. Cats (Felis silvestris catus) read human gaze for referential information. Intelligence 74, 43–52 (2019).Article 

Google Scholar 
Pongrácz, P. & Onofer, D. L. Cats show an unexpected pattern of response to human ostensive cues in a series of A-not-B error tests. Anim. Cogn. 23, 681–689 (2020).Article 

Google Scholar 
Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428-1432.e4 (2018).CAS 
Article 

Google Scholar 
Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163 (2016).Article 

Google Scholar 
von Borell, E. et al. Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals—A review. Physiol. Behav. 92, 293–316 (2007).Article 

Google Scholar  More

Brutsaert, W. Hydrology: An Introduction (Cambridge Univ. Press, 2005).Philip, J. Plant water relations: some physical aspects. Annu. Rev. Plant Physiol. 17, 245–268 (1966).
Google Scholar 
Ghezzehei, T. A., Sulman, B., Arnold, C. L., Bogie, N. A. & Berhe, A. A. On the role of soil water retention characteristic on aerobic microbial respiration. Biogeosciences 16, 1187–1209 (2019).
Google Scholar 
Boyer, J. Differing sensitivity of photosynthesis to low leaf water potentials in corn and soybean. Plant Physiol. 46, 236–239 (1970).
Google Scholar 
Jarvis, P. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Phil. Trans. R. Soc. Lond. B 273, 593–610 (1976).
Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).
Google Scholar 
Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Biol. 40, 19–36 (1989).
Google Scholar 
Whalley, W., Ober, E. & Jenkins, M. J. J. Measurement of the matric potential of soil water in the rhizosphere. J. Exp. Biol. 64, 3951–3963 (2013).
Google Scholar 
Yu, H., Yang, P. & Lin, H. Spatiotemporal patterns of soil matric potential in the Shale Hills Critical Zone Observatory. Vadose Zone J. https://doi.org/10.2136/vzj2014.11.0167 (2015).Campbell, G. S. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci. 117, 311–314 (1974).
Google Scholar 
van Genuchten, M. T. A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).
Google Scholar 
Dorigo, W. et al. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 15, 1675–1698 (2011).Scott, B. L. et al. New soil property database improves Oklahoma Mesonet soil moisture estimates. J. Atmos. Ocean. Technol. 30, 2585–2595 (2013).
Google Scholar 
Campbell, G. S. Soil water potential measurement: an overview. Irrig. Sci. 9, 265–273 (1988).
Google Scholar 
Van Looy, K. et al. Pedotransfer functions in Earth system science: challenges and perspectives. Rev. Geophys. 55, 1199–1256 (2017).
Google Scholar 
Clapp, R. B. & Hornberger, G. M. Empirical equations for some soil hydraulic properties. Water Resour. Res. 14, 601–604 (1978).
Google Scholar 
Cosby, B., Hornberger, G., Clapp, R. & Ginn, T. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20, 682–690 (1984).
Google Scholar 
Zhang, Y. & Schaap, M. G. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). J. Hydrol. 547, 39–53 (2017).
Google Scholar 
Fatichi, S. et al. Soil structure is an important omission in Earth system models. Nat. Commun. 11, 522 (2020).
Google Scholar 
Ghezzehei, T. A. & Albalasmeh, A. A. Spatial distribution of rhizodeposits provides built-in water potential gradient in the rhizosphere. Ecol. Modell. 298, 53–63 (2015).
Google Scholar 
Leung, A. K., Garg, A. & Ng, C. W. W. Effects of plant roots on soil-water retention and induced suction in vegetated soil. Eng. Geol. 193, 183–197 (2015).
Google Scholar 
Caplan, J. S. et al. Decadal-scale shifts in soil hydraulic properties as induced by altered precipitation. Sci. Adv. 5, eaau6635 (2019).
Google Scholar 
Peña-Sancho, C., López, M., Gracia, R. & Moret-Fernández, D. Effects of tillage on the soil water retention curve during a fallow period of a semiarid dryland. Soil Res. 55, 114–123 (2017).
Google Scholar 
Stoof, C. R., Wesseling, J. G. & Ritsema, C. J. Effects of fire and ash on soil water retention. Geoderma 159, 276–285 (2010).
Google Scholar 
Gutmann, E. & Small, E. The effect of soil hydraulic properties vs. soil texture in land surface models. Geophys. Res. Lett. 32, L02402 (2005).
Google Scholar 
Weihermüller, L. et al. Choice of pedotransfer functions matters when simulating soil water balance fluxes. J. Adv. Model. Earth Syst. 13, e2020MS002404 (2021).
Google Scholar 
Shi, Y., Davis, K. J., Zhang, F. & Duffy, C. J. Evaluation of the parameter sensitivities of a coupled land surface hydrologic model at a critical zone observatory. J. Hydrometeorol. 15, 279–299 (2014).
Google Scholar 
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J. & Yu, X. J. Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: a multivariate real-data experiment. Adv. Water Res. 83, 421–427 (2015).
Google Scholar 
Shi, Y. et al. Simulating high‐resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model. Hydrol. Process. 29, 4624–4637 (2015).
Google Scholar 
Sobol, I. M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55, 271–280 (2001).
Google Scholar 
Boucher, O. et al. Presentation and evaluation of the IPSL‐CM6A‐LR climate model. J. Adv. Model. Earth Syst. 12, e2019MS002010 (2020).
Google Scholar 
Lurton, T. et al. Implementation of the CMIP6 forcing data in the IPSL‐CM6A‐LR model. J. Adv. Model. Earth Syst. 12, e2019MS001940 (2020).
Google Scholar 
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).
Google Scholar 
Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).
Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).
Google Scholar 
Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Satellite‐based assessment of land surface energy partitioning–soil moisture relationships and effects of confounding variables. Water Resour. Res. 55, 10657–10677 (2019).
Google Scholar 
Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. N. Phytol. 218, 1430–1449 (2018).
Google Scholar 
Baldocchi, D. D., Xu, L. & Kiang, N. How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oak–grass savanna and an annual grassland. Agric. For. Meteorol. 123, 13–39 (2004).
Google Scholar 
Trugman, A. T., Anderegg, L. D., Shaw, J. D. & Anderegg, W. R. Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition. Proc. Natl Acad. Sci. USA 117, 8532–8538 (2020).
Google Scholar 
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol. 178, 719–739 (2008).
Google Scholar 
Martínez-Vilalta, J. et al. Towards a statistically robust determination of minimum water potential and hydraulic risk in plants. New Phytol. 232, 404–417 (2021).Taiz, L., Zeiger, E., Møller, I. M. & Murphy, A. Plant Physiology and Development 6th edn (Sinauer Associates, 2015).Scholander, P. F., Bradstreet, E. D., Hemmingsen, E. & Hammel, H. Sap pressure in vascular plants: negative hydrostatic pressure can be measured in plants. Science 148, 339–346 (1965).
Google Scholar 
Martínez‐Vilalta, J., Poyatos, R., Aguadé, D., Retana, J. & Mencuccini, M. A new look at water transport regulation in plants. N. Phytol. 204, 105–115 (2014).
Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).
Google Scholar 
Matheny, A. M. et al. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere https://doi.org/10.1890/es15-00170.1 (2015).Wood, J. D., Knapp, B. O., Muzika, R.-M., Stambaugh, M. C. & Gu, L. The importance of drought–pathogen interactions in driving oak mortality events in the Ozark Border Region. Environ. Res. Lett. 13, 015004 (2018).
Google Scholar 
Hinckley, T. M., Lassoie, J. P. & Running, S. W. Temporal and spatial variations in the water status of forest trees. For. Sci. 24, a0001–z0001 (1978).
Google Scholar 
Marks, C. O. & Lechowicz, M. J. The ecological and functional correlates of nocturnal transpiration. Tree Physiol. 27, 577–584 (2007).
Google Scholar 
O’Keefe, K. & Nippert, J. B. Drivers of nocturnal water flux in a tallgrass prairie. Funct. Ecol. 32, 1155–1167 (2018).
Google Scholar 
Donovan, L., Linton, M. & Richards, J. Predawn plant water potential does not necessarily equilibrate with soil water potential under well-watered conditions. Oecologia 129, 328–335 (2001).
Google Scholar 
Kannenberg, S. A. et al. Opportunities, challenges and pitfalls in characterizing plant water‐use strategies. Funct. Ecol. 36, 24–37 (2022).Oliveira, R. S. et al. Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems. New Phytol. 230, 904–923 (2021).Feng, X. et al. Beyond isohydricity: the role of environmental variability in determining plant drought responses. Plant Cell Environ. 42, 1104–1111 (2019).
Google Scholar 
Guo, J. S., Hultine, K. R., Koch, G. W., Kropp, H. & Ogle, K. Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub. N. Phytol. 225, 713–726 (2020).
Google Scholar 
Hochberg, U., Rockwell, F. E., Holbrook, N. M. & Cochard, H. Iso/anisohydry: a plant–environment interaction rather than a simple hydraulic trait. Trends Plant Sci. 23, 112–120 (2018).
Google Scholar 
Novick, K. A., Konings, A. G. & Gentine, P. Beyond soil water potential: an expanded view on isohydricity including land–atmosphere interactions and phenology. Plant Cell Environ. 42, 1802–1815 (2019).
Google Scholar 
McCulloh, K. A. et al. A dynamic yet vulnerable pipeline: integration and coordination of hydraulic traits across whole plants. Plant Cell Environ. 42, 2789–2807 (2019).
Google Scholar 
Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
Google Scholar 
Mirfenderesgi, G., Matheny, A. M. & Bohrer, G. Hydrodynamic trait coordination and cost–benefit trade‐offs throughout the isohydric–anisohydric continuum in trees. Ecohydrology 12, e2041 (2019).
Google Scholar 
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M. & Guan, K. Diversity in plant hydraulic traits explains seasonal and inter‐annual variations of vegetation dynamics in seasonally dry tropical forests. N. Phytol. 212, 80–95 (2016).
Google Scholar 
De Kauwe, M. G. et al. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe. Biogeosciences 12, 7503–7518 (2015).
Google Scholar 
Meinzer, F. C. et al. Converging patterns of uptake and hydraulic redistribution of soil water in contrasting woody vegetation types. Tree Physiol. 24, 919–928 (2004).
Google Scholar 
Scott, R. L., Cable, W. L. & Hultine, K. R. The ecohydrologic significance of hydraulic redistribution in a semiarid savanna. Water Resour. Res. 44, W02440 (2008).
Google Scholar 
Tyree, M. T. & Ewers, F. W. The hydraulic architecture of trees and other woody plants. N. Phytol. 119, 345–360 (1991).
Google Scholar 
Johnson, D. M. et al. A test of the hydraulic vulnerability segmentation hypothesis in angiosperm and conifer tree species. Tree Physiol. 36, 983–993 (2016).
Google Scholar 
Lehto, T. & Zwiazek, J. J. Ectomycorrhizas and water relations of trees: a review. Mycorrhiza 21, 71–90 (2011).
Google Scholar 
Bezerra-Coelho, C. R., Zhuang, L., Barbosa, M. C., Soto, M. A. & Van Genuchten, M. T. Further tests of the HYPROP evaporation method for estimating the unsaturated soil hydraulic properties. J. Hydrol. Hydromech. 66, 161–169 (2018).
Google Scholar 
Wullschleger, S., Dixon, M. & Oosterhuis, D. Field measurement of leaf water potential with a temperature‐corrected in situ thermocouple psychrometer. Plant Cell Environ. 11, 199–203 (1988).
Google Scholar 
Holtzman, N. M. et al. L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand. Biogeosciences 18, 739–753 (2021).
Google Scholar 
Nagy, R. C. et al. Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community. Ecosphere 12, e03833 (2021).
Google Scholar 
Novick, K. A. et al. The AmeriFlux network: a coalition of the willing. Agric. For. Meteorol. 249, 444–456 (2018).
Google Scholar 
Baldocchi, D. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).
Google Scholar 
Poyatos, R. et al. Global transpiration data from sap flow measurements: the SAPFLUXNET database. Earth Syst. Sci. Data 13, 2607–2649 (2021).Jackson, T. & Schmugge, T. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).
Google Scholar 
Konings, A. G., Rao, K. & Steele‐Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. N. Phytol. 223, 1166–1172 (2019).
Google Scholar 
Konings, A. G. et al. Detecting forest response to droughts with global observations of vegetation water content. Glob. Change Biol. https://doi.org/10.1111/gcb.15872 (2021).Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeosci. 122, 3031–3046 (2017).
Google Scholar 
Simunek, J., Van Genuchten, M. T. & Sejna, M. The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media (Dept Environ. Sci. Univ. California Riverside, 2005).Naylor, S., Letsinger, S., Ficklin, D., Ellett, K. & Olyphant, G. A hydropedological approach to quantifying groundwater recharge in various glacial settings of the mid‐continental USA. Hydrol. Process. 30, 1594–1608 (2016).
Google Scholar 
Urbanski, S. et al. Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J. Geophys. Res. Biogeosci. 112, G02020 (2007).
Google Scholar 
Thum, T. et al. Parametrization of two photosynthesis models at the canopy scale in a northern boreal Scots pine forest. Tellus B 59, 874–890 (2007).
Google Scholar 
Ardö, J., Mölder, M., El-Tahir, B. A. & Elkhidir, H. A. M. Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan. Carbon Balance Manage. 3, 7 (2008).
Google Scholar 
Roman, D. T. et al. The role of isohydric and anisohydric species in determining ecosystem-scale response to severe drought. Oecologia 179, 641–654 (2015).
Google Scholar 
Fu, C. et al. Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites. Hydrol. Earth Syst. Sci. 20, 2001–2018 (2016).
Google Scholar 
Liang, J. et al. Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements. Geosci. Model Dev. 12, 1601–1612 (2019).Herman, J. & Usher, W. SALib: an open-source Python library for sensitivity analysis. J. Open Source Softw. https://doi.org/10.21105/joss.00097 (2017). More