Philippa Kaur

Genetic structure of the parental populationBased on the lnPD and ΔK values obtained using STRUCTURE, we identified two genetic groups within the DHS Acer population (Supplementary Fig. S1). The q value from STRUCTURE analysis represents the proportion of ancestral origin28 (Fig. 2a). Among the 70 individual trees, 72.9% were assigned a q value smaller than 0.1 or larger than 0.9, thereby signifying a typical bimodal distribution (Fig. 2b). Individuals with q value greater than 0.9 and consistent genetic origin from the NEA region were defined as the NEA lineage (hereafter “NEA-DHS”), whereas those with values less than 0.1 and with consistent genetic origin from the SEA region were defined as the SEA lineage (hereafter “SEA-DHS”). Individuals with intermediate q value between 0.1 and 0.9 were defined as hybrid genetic types (hereafter “Hybrid-DHS”). Accordingly, we identified 27 SEA-DHS (38.6%), 24 NEA-DHS (34.3%), and 19 Hybrid-DHS (27.1%) (Fig. 2b).Figure 2Genetic structure of the parental and offspring population. (a) Bar plots illustrating the genetic composition of the adult (leaf) and offspring (fruit) populations in the Daheishan National Nature Reserve (DHS). Each individual is represented by a line partitioned into color segments corresponding to its ancestral proportion. Red color represents the ancestral proportion of Southern East Asia lineage. Green color represents the ancestral proportion of Northern East Asia lineage. Black lines in bar plots of leaf population separate individuals with ancestral proportion (q value) bigger than 0.9 or smaller than 0.1 from hybrids (0.1  0.5) produced by the SEA-DHS were obtained from a single tree, which was identified as SEA-DHS based on the DHS-only dataset, although it was indicated to be Hybrid-DHS based on the whole-range dataset. The Hybrid-DHS maternal trees produced 17.6% pure SEA-DHS seeds, 57.6% pure NEA-DHS seeds, and 24.7% hybrid seeds.Flowering phenologyThe sexual system of Acer has four phenotypes: duodichogamous, protogynous, protandrous, and male31. Hence, there are three functional sex types: (1) “Male I” flowers open earlier than “Female” flowers, with mature stamens, no style, and ovary; (2) “Female” flowers have mature pistils, short filaments, and indehiscence anthers; (3) “Male II” flowers open later than “Female” flowers, with mature stamens, ovaries, and separated stigmas. Duodichogamy is characterized by “Male I,” “Female,” and “Male II” types; protandry by “Male I” and “Female” types; and protogyny by “Female” and “Male II” types31.During the flowering season, we monitored a total of 10,074 flowers produced by 29 trees (Fig. 2d), among which one tree (SEA-DHS) was protandrous, four trees (three Hybrid-DHS and one NEA-DHS) were protogynous, and the remaining 24 trees were duodichogamous. We observed that the blooming phenology of SEA-DHS and NEA-DHS differed significantly to most assessed phenological indices, with a single exception being a marginally significant difference in the peak blooming time of Male I (Table 1). Compared with NEA-DHS, SEA-DHS were characterized by significantly later flowering phenology, with Male I commencement and cessation of blooming being on average two and three days later, respectively. Similarly, the commencement, peak, and cessation of Female occurred later by averages of 4, 4, and 5 days, respectively, whereas those of Male II occurred later by 5, 4, and 5 days, respectively. Furthermore, the duration of blooming was significantly longer in the SEA-DHS group than in the NEA-DHS group by three days. In the case of Hybrid-DHS, the values obtained for all assessed phenological indices were intermediate between those of the two parental types. Among these, the values of the six indices differed significantly from one or the other parental types, with the majority (5/6) differing from those of the SEA-DHS. Thus, phenologically, Hybrid-DHS appeared to be closer to NEA-DHS.Table 1 Flowering phenology of SEA-DHS, Hybrid-DHS, and NEA-DHS.Full size tableHowever, despite the differing phenology of the SEA-DHS and NEA-DHS, we observed instances of overlap in the blooming periods of male or female flowers in one genetic type with those of flowers of the opposite sex in another genetic type. For example, the peak of Female among NEA-DHS (11.67 ± 0.67) was found to coincide with the peak of Male I (11.44 ± 1.06; p = 0.879) in SEA-DHS. Similarly, Female blooming in the SEA-DHS peaked (16.11 ± 1.09) just 1 d after the peak of Male II (15.50 ± 0.43) in the NEA-DHS (p = 0.667), which at this time still retained an abundance of male flowers in bloom. In contrast, we detected no overlapping phenology with respect to the blooming of Male I of NEA-DHS or Male II of SEA-DHS with the Female in another genetic type.Morphological variation of leaves and fruitLeaves Among the eight leaf indices, all except InfectionRatio were significantly different between lineages. Generally, the leaves of NEA-DHS were found to have seven lobes, whereas those of SEA-DHS were typically five lobed (Lobes#), thereby contributing to significantly larger leaves in NEA-DHS than in SEA-DHS (TotalArea). Furthermore, NEA-DHS leaves had shorter and wider central lobes (CentralLength and CentralWidth), as well as an earlier and narrower inflection of the central lobes (InflectionLength and InflectionWidth), compared with those of SEA-DHS (Table 2). Six indices had correlation coefficients of less than 0.7, which were used for principal component analysis (PCA) analysis (Supplementary Table S2). The first two axes of the PCA were found to explain 63.7% of the variation in leaf morphology (Fig. 3a), with InflectionLength, CentralLength, and CentralRatio contributing the most to the first axis (38.2%), whereas TotalArea contributed the most to the second axis (25.5%) (Supplementary Table S3). The leaves of SEA-DHS and NEA-DHS plants were largely clustered in separate groups (Fig. 3a). However, all indices were continuous variables with large overlaps between the lineages (Table 2). For example, NEA-DHS had a significantly larger leaf area (21.06–88.70 cm2) than SEA-DHS (11.34–70.09 cm2). The shape of the central lobe is another major leaf trait that distinguishes between the two species. NEA-DHS had a shorter and wider central lobe (CentralRatio:0.67–2.49), while SEA-DHS had a longer and narrower central lobe (CentralRatio:0.9–3.46).Table 2 Morphological variation in the leaves and fruits of Acer trees in the Daheishan National Nature Reserve.Full size tableFigure 3Morphological variation in the leaves (a) and fruits (b) of southern and northern East Asia lineages of the Acer species complex in the Daheishan National Nature Reserve based on principal component analysis. SEA-DHS: Southern East Asia lineage of the Acer species complex in the DHS; NEA-DHS: Northern East Asia lineage of the Acer species complex in the DHS; Hybrid-DHS: hybrids between SEA-DHS and NEA-DHS lineages.Full size imageWith regard to Hybrid-DHS, the leaves were morphologically intermediate between those of the two parental types (Fig. 3a), as were the values of the assessed morphological trait indices (Table 2).Fruits 11 indices of fruits were significantly different between lineages. NEA-DHS tend to be characterized by smaller fruits (FruitLength and FruitWidth), seeds (SeedLength, SeedWidth and JunctionWidth), and fruit wings (WingLength and WingWidth). Moreover, the seed wings of NEA-DHS fruits are typically oriented at an obtuse angle, whereas those of SEA-DHS fruits tend to be aligned at a right angle (FruitAngle). The length ratio of the wing and seed (Wing:Seed) was larger in NEA-DHS than in SEA-DHS (1.24 vs 1.06, respectively, Table 2). Eight indices had correlation coefficients of less than 0.7, which were retained for PCA analysis (Supplementary Table S4). The first two axes of the PCA explained 58.4% of the variation in fruit morphology (Fig. 3b), with JunctionWidth and SeedLength contributing the most to the first axis (35.1%), whereas SeedRatio and WingRatio contributed the most to the second axis (23.3%) (Supplementary Table S3). The fruits of SEA-DHS and NEA-DHS plants were largely clustered in separate groups, with most fruits of SEA-DHS having negative values in Axis 1, while most fruits of NEA-DHS having positive values (Fig. 3b). Both JunctionWidth and SeedLength in Axis 1 reflect the size of the seed. NEA-DHS had smaller seed (SeedLength: 0.63–1.21 cm, SeedWidth:0.43–0.75 cm), while larger seed in SEA-DHS (SeedLength:0.79–1.49 cm, SeedWidth:0.49–0.93 cm). All indices were continuous variables with large overlaps between the lineages (Table 2).The morphology of Hybrid-DHS fruits was generally intermediate between that of the two parental types (Fig. 3b), as reflected in the values of the different morphological traits. The exceptions in this regard were FruitLength, WingLength, as well as two ratio indices (SeedRatio and WingRatio), with hybrid trees typically producing longer fruit with longer fruit wings (Table 2).Ecological niche divergence between NEA and SEAWe found a positive correlation between q value from Structure analysis and altitude (Pearson’s r = 0.83, p  670 m), whereas SEA-DHS was clustered at the foothill ( More

Clapham, P. J. Encyclopedia of Marine Mammals 489–492 (Elsevier, 2018).Book 

Google Scholar 
Calambokidis, J. et al. Movements and population structure of humpback whales in the North Pacific. Mar. Mamm. Sci. 17, 769–794. https://doi.org/10.1111/j.1748-7692.2001.tb01298.x (2001).Article 

Google Scholar 
Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endangered Species Res. 32, 551–567. https://doi.org/10.3354/esr00822 (2017).Article 

Google Scholar 
Darling, J. D. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in the western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566 (2005).Article 

Google Scholar 
Marcondes, M. C. C. et al. The Southern Ocean Exchange: Porous boundaries between humpback whale breeding populations in southern polar waters. Sci. Rep. 11, 23618. https://doi.org/10.1038/s41598-021-02612-5 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Witteveen, B. H., Foy, R. J., Wynne, K. M. & Tremblay, Y. Investigation of foraging habits and prey selection by humpback whales (Megaptera novaeangliae) using acoustic tags and concurrent fish surveys. Mar. Mamm. Sci. 24, 516–534. https://doi.org/10.1111/j.1748-7692.2008.00193.x (2008).Article 

Google Scholar 
Barendse, J. et al. Migration redefined? Seasonality, movements and group composition of humpback whales Megaptera novaeangliae off the west coast of South Africa. Afr. J. Mar. Sci. 32, 1–22 (2010).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One 12, e0172002. https://doi.org/10.1371/journal.pone.0172002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barendse, J. et al. Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching. Afr. J. Mar. Sci. 33, 353–373 (2011).Article 

Google Scholar 
Schall, E. et al. Multi-year presence of humpback whales in the Atlantic sector of the Southern Ocean but not during El Niño. Commun. Biol. 4, 1–7. https://doi.org/10.1038/s42003-021-02332-6 (2021).Article 

Google Scholar 
Amaral, A. R. et al. Population genetic structure among feeding aggregations of humpback whales in the Southern Ocean. Mar. Biol. 163, 1–13. https://doi.org/10.1007/s00227-016-2904-0 (2016).Article 

Google Scholar 
Schall, E. et al. Humpback whale song recordings suggest common feeding ground occupation by multiple populations. Sci. Rep. 11, 1–13. https://doi.org/10.1038/s41598-021-98295-z (2021).ADS 
CAS 
Article 

Google Scholar 
International Whaling Commission. Annex H: Report of the Sub-Committee on Other Southern Hemisphere Whale Stocks. (2016).Payne, R. & Guinee, L. N. Humpback whale (Megaptera novaeangliae) songs as an indicator of “stocks”. Commun. Behav. Whales 20, 333–358 (1983).
Google Scholar 
Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Indic. 89, 455–465. https://doi.org/10.1016/j.ecolind.2018.02.030 (2018).Article 

Google Scholar 
Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818. https://doi.org/10.1111/brv.12309 (2017).Article 
PubMed 

Google Scholar 
Garland, E. C. et al. Humpback Whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS One 8, e79422. https://doi.org/10.1371/journal.pone.0079422 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148. https://doi.org/10.1111/j.1748-7692.1989.tb00328.x (1989).Article 

Google Scholar 
Van Opzeeland, I. C. et al. Towards collective circum-antarctic passive acoustic monitoring: The southern ocean hydrophone network (SOHN). Polarforschung 83, 47–61 (2013).
Google Scholar 
Gridley, T., Silva, M., Wilkinson, C., Seakamela, S. & Elwen, S. H. Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa. J. Acoust. Soc. Am. 143, 298–304 (2018).ADS 
Article 

Google Scholar 
Ross-Marsh, E., Elwen, S. H., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics 30, 163–179 (2021).Article 

Google Scholar 
Garland, E. C. et al. Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations. Conserv. Biol. 29, 1198–1207. https://doi.org/10.1111/cobi.12492 (2015).Article 
PubMed 

Google Scholar 
Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 91, 101–114 (2014).Article 

Google Scholar 
El-Gabbas, A., Van Opzeeland, I., Burkhardt, E. & Boebel, O. Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean. Divers. Distrib. 27, 1536–1552. https://doi.org/10.1111/ddi.13300 (2021).Article 

Google Scholar 
Schall, E. et al. Large-scale spatial variabilities in the humpback whale acoustic presence in the Atlantic sector of the Southern Ocean. R. Soc. Open Sci. 7, 201347. https://doi.org/10.1098/rsos.201347 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
International Whaling Commission. Report of the scientific committee. Annex G. Report of the sub-committee on comprehensive assessment of southern hemisphere humpback whales. Appenix4. Initial alternative hypotheses for the distribution of humpack breeding stocks on the feeding grounds. Report of the International Whlaing Commission 48, 181 (1998).International Whaling Commission. Report on the workshop on the comprehensive assessment of Southern Hemisphere humpback whales. J. Cetacean Res. Manage. Spec. Issue 3, 1–50 (2011).
Google Scholar 
Winn, H. E. & Winn, L. K. Song of Humpback Whale Megaptera-Novaeangliae in West-Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/Bf00395631 (1978).Article 

Google Scholar 
Payne, K. & Payne, R. Large-scale changes over 19 years in songs of Humpback Whales in Bermuda. Z. Tierpsychol. 68, 89–114 (1985).Article 

Google Scholar 
Thomisch, K. et al. Temporal patterns in the acoustic presence of baleen whale species in a presumed breeding area off Namibia. Mar. Ecol. Prog. Ser. 620, 201–214 (2019).ADS 
Article 

Google Scholar 
Buchan, S. J., Stafford, K. M. & Hucke-Gaete, R. Seasonal occurrence of southeast Pacific blue whale songs in southern Chile and the eastern tropical Pacific. Mar. Mamm. Sci. 31, 440–458. https://doi.org/10.1111/mms.12173 (2015).Article 

Google Scholar 
Ross-Marsh, E., Elwen, S., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics https://doi.org/10.1080/09524622.2019.1710254 (2020).Article 

Google Scholar 
Cholewiak, D. M., Sousa-Lima, R. S. & Cerchio, S. Humpback whale song hierarchical structure: Historical context and discussion of current classification issues. Mar. Mamm. Sci. 29, E312–E332. https://doi.org/10.1111/mms.12005 (2013).Article 

Google Scholar 
M_Map: A Mapping Package for MATLAB v. 1.4m. (2020).Raven Pro: Interactive sound analysis software. Version 1.6 ([Ithaca (NY)]: The Cornell Lab of Ornithology. Accessed 1 Mar 2018 (2022).Schall, E., Roca, I. & Van Opzeeland, I. Acoustic metrics to assess humpback whale song unit structure from the Atlantic sector of the Southern ocean. J. Acoust. Soc. Am. 149, 4649–4658. https://doi.org/10.1121/10.0005315 (2021).ADS 
Article 
PubMed 

Google Scholar 
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302. https://doi.org/10.2307/1932409 (1945).Article 

Google Scholar 
R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Suzuki, R., Terada, Y. & Shimodaira, H. pvclust: Hierarchical clustering with P-values via multiscale bootstrap resampling. R package version 2.2-0 (2019).Kohonen, T. Median strings. Pattern Recogn. Lett. 3, 309–313. https://doi.org/10.1016/0167-8655(85)90061-3 (1985).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: Tests using humpback whale song. Behaviour 149, 1413–1441. https://doi.org/10.1163/1568539x-00003032 (2012).Article 

Google Scholar 
Van der Loo, M. P. The stringdist package for approximate string matching. R J. 6, 111–122 (2014).Article 

Google Scholar 
Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetacean Res. Manage. Spec. Issue 3, 113–118. https://doi.org/10.3354/meps313295 (2011).Article 

Google Scholar 
Rosenbaum, H. C., Maxwell, S. M., Kershaw, F. & Mate, B. Long-range movement of Humpback Whales and their overlap with anthropogenic activity in the South Atlantic Ocean. Conserv. Biol. 28, 604–615. https://doi.org/10.1111/cobi.12225 (2014).Article 
PubMed 

Google Scholar 
Reisinger, R. R. et al. Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of Southern Ocean humpback whales. Remote Sens. 13, 2074. https://doi.org/10.3390/rs13112074 (2021).ADS 
Article 

Google Scholar 
Garland, E. C. & McGregor, P. K. Cultural transmission, evolution, and revolution in vocal displays: Insights from bird and whale song. Front. Psychol. 11, 2387. https://doi.org/10.3389/fpsyg.2020.544929 (2020).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups”—a novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One https://doi.org/10.1371/journal.pone.0172002 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating east Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244. https://doi.org/10.3354/meps11551 (2015).ADS 
Article 

Google Scholar 
Riekkola, L., Andrews-Goff, V., Friedlaender, A., Zerbini, A. N. & Constantine, R. Longer migration not necessarily the costliest strategy for migrating humpback whales. Aquat. Conserv. Mar. Freshwat. Ecosyst. 30, 937–948. https://doi.org/10.1002/aqc.3295 (2020).Article 

Google Scholar 
Torres, L. G. A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar. Mamm. Sci. 33, 1170–1193. https://doi.org/10.1111/mms.12426 (2017).Article 

Google Scholar 
Horton, T. W. et al. Straight as an arrow: Humpback whales swim constant course tracks during long-distance migration. Biol. Lett. 7, 674–679. https://doi.org/10.1098/rsbl.2011.0279 (2001).Article 

Google Scholar 
Au, W. W. L. et al. Acoustic properties of humpback whale songs. J. Acoust. Soc. Am. 120, 1103–1110. https://doi.org/10.1121/1.2211547 (2006).ADS 
Article 
PubMed 

Google Scholar 
Dunlop, R. A., Cato, D. H., Noad, M. J. & Stokes, D. M. Source levels of social sounds in migrating humpback whales (Megaptera novaeangliae). J. Acoust. Soc. Am. 134, 706–714. https://doi.org/10.1121/1.4807828 (2013).ADS 
Article 
PubMed 

Google Scholar 
Cheeseman, T. et al. Advanced image recognition: A fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. https://doi.org/10.1007/s42991-021-00180-9 (2021).Article 

Google Scholar 
Felix, F. et al. A new case of interoceanic movement of a humpback whale in the Southern Hemisphere: The El Nino Link. Aquat. Mamm. 46, 578–584. https://doi.org/10.1578/AM.46.6.2020.578 (2020).Article 

Google Scholar 
Pomilla, C. & Rosenbaum, H. C. Against the current: An inter-oceanic whale migration event. Biol. Lett. 1, 476–479 (2005).Article 

Google Scholar 
Stevick, P. T. et al. A quarter of a world away: Female humpback whale moves 10,000 km between breeding areas. Biol. Lett. 7, 299–302. https://doi.org/10.1098/rsbl.2010.0717 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111–120. https://doi.org/10.1641/0006-3568(2006)056[0111:Kcasie]2.0.Co;2 (2006).Article 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103. https://doi.org/10.1038/nature02996 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
Article 

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

Google Scholar 
Marrari, M., Daly, K. L. & Hu, C. Spatial and temporal variability of SeaWiFS chlorophyll a distributions west of the Antarctic Peninsula: Implications for krill production. Deep Sea Res. Part II 55, 377–392. https://doi.org/10.1016/j.dsr2.2007.11.011 (2008).ADS 
Article 

Google Scholar 
Sremba, A. L., Hancock-Hanser, B., Branch, T. A., LeDuc, R. L. & Baker, C. S. Circumpolar diversity and geographic differentiation of mtDNA in the critically endangered Antarctic Blue Whale (Balaenoptera musculus intermedia). PLoS One 7, e32579. https://doi.org/10.1371/journal.pone.0032579 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bortolotto, G. A., Danilewicz, D., Andriolo, A., Secchi, E. R. & Zerbini, A. N. Whale, whale, everywhere: Increasing abundance of Western South Atlantic Humpback Whales (Megaptera novaeangliae) in their wintering grounds. PLoS One 11, e0164596. https://doi.org/10.1371/journal.pone.0164596 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Félix, F., Castro, C. & Laake, J. L. Abundance and survival estimates of the southeastern Pacific humpback whale stock from 1991–2006 photo-identification surveys in Ecuador. J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi.303 (2020).Article 

Google Scholar 
Ward, E., Zerbini, A. N., Kinas, P. G., Engel, M. H. & Andriolo, A. Estimates of population growth rates of humpback whales (Megaptera novaeangliae) in the wintering grounds off the coast of Brazil (Breeding Stock A). J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi3.323 (2020).Article 

Google Scholar 
Seyboth, E. et al. Influence of krill (Euphausia superba) availability on humpback whale (Megaptera novaeangliae) reproductive rate. Mar. Mammal Sci. https://doi.org/10.1111/mms.12805 (2021).Article 

Google Scholar 
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).ADS 
Article 

Google Scholar 
Santora, J. A., Reiss, C. S., Loeb, V. J. & Veit, R. R. Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Eupahusia superba suggests size-dependent predation. Mar. Ecol. Prog. Ser. 405, 255–269 (2010).ADS 
Article 

Google Scholar 
Friedlaender, A. S., Lawson, G. L. & Halpin, P. N. Evidence of resource partitioning between humpback and minke whales around the western Antarctic Peninsula. Mar. Mamm. Sci. 25, 402–415 (2009).Article 

Google Scholar 
Reid, K., Brierley, A. S. & Nevitt, G. A. An initial examination of relationships between the distribution of whales and antarctic krill Euphausia superba at South Georgia. J. Cetacean Res. Manage. 2, 143–149 (2000).
Google Scholar 
Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish Fish. 11, 203–209. https://doi.org/10.1111/j.1467-2979.2010.00356.x (2010).Article 

Google Scholar 
Teschke, K., Pehlke, H., Deininger, M., Jerosch, K. & Brey, T. Scientific background document in support of the development of a CCAMLR MPA in the Weddell Sea (Antarctica)-Version 2016. (2016).Teschke, K. et al. Planning marine protected areas under the CCAMLR regime—the case of the Weddell Sea (Antarctica). Mar. Policy 124, 104370 (2021).Article 

Google Scholar  More

China economy-wide material flow identification: system boundary, processes, and materialsThe first step is to define an economy, i.e., the economic (rather than geographical) territory of a country in which the activities and transactions of producer and consumer units are resident. Additionally, the period is a total of thirty-one years, from 1990 to 2020, for the following reasons: (1) statistics before 1990 are of poor quality and are insufficient to allow us to conduct analyses; and (2) so far, statistics have just recently been updated to cover the year of 2020. Furthermore, the analytical framework (hereinafter referred to as China EW-MFA) is developed to explore material utilisation and its environmental consequences within China’s economy.The general structure of China EW-MFA is depicted in Fig. 1, which comprises seven processes. (1) Input of extracted resources: domestic natural resources are extracted from the environment to the economy through human-controlled means. (2) Output of domestic processed materials: after being processed by manufacturers, materials are released from the economy into the environment in the form of by-products and residues, which can be classified by their destinations (i.e., air, land, and water) and pathways (dissipative use and losses). (3) Input and (4) output by cross-border trade: by imports and exports, materials are transported between China’s economy and the economies of the rest of the world. (5) Input and (6) output of balancing items (BI): sometimes, materials identified in the output processes are not considered by inputs, which needs to be balanced. For example, the utilisation of fossil energy materials by combustion causes the emission of carbon dioxide (CO2) into the air, which is identified as system output, but requirements of oxygen (O2) as system input are not counted. (7) Additions to the system: within the economy, materials would have been added to the economy in the form of buildings, infrastructures, durable goods, and household appliances, which are referred to as the net additions to stock (NAS).Fig. 1The general structure of China EW-MFA. To note, white data cells can be obtained directly from official statistics, whereas grey cells are estimated.Full size imageThe last step is to specify the materials concerned in each process. Four types (in blue boxes in Fig. 1) of natural materials are extracted and input into the economy in China, i.e., harvested biomass (33 items), mined metal ores (28 items), quarried non-metallic minerals (155 items), and mined fossil energy materials (6 items in 3 classes). Materials (green boxes) released into the air are greenhouse gases (e.g., CO2, methane (CH4), dinitrogen oxide (N2O)), air pollutants (e.g., particulate matter 10 (PM10), black carbon (BC)), and toxic contaminants of mercury (Hg) in divalent, gaseous elemental, and particulate forms. Those released into the water are inorganic matters (of nitrogen (N), phosphorus (P), Arsenic (As), and four heavy metals of lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr)) and organic matters of cyanide, petroleum, and volatile phenol. Materials released into the land are waste disposal in uncontrolled landfills, which are illegal in China. Some materials are dissipated by application, for example, fertilisers, compost, sewage sludge being applied to agricultural land, and pesticides being used to cultivate crops. Some would be unintentionally dissipated from abrasion, corrosion, erosion, and leakages. Materials (in red boxes) are BI, which includes the input of O2 and output of water vapour in the fossil energy material combustion process, the input of O2 and output of water vapour and CO2 in the respiration process of human and cultivated livestock, input and output of water in imported and exported beverages, and the output of water from domestically extracting crops.There are some messages needed to be mentioned: (1) Material of water is not included since its flow volume is more substantial than others, which needs to be independently analysed; (2) Activities of foreign tourists, cross-border transfer of emissions through natural media, etc. are excluded. (3) To be clear, we refer to a data cell as a specific flow process of a specific substance in a specific year, e.g., the number of cereals domestically extracted in 2020.Data acquisition: sources and collectionBased on our China EW-MFA, we first analyse accessibility, reliability, completeness, rules of redistribution, etc., for each data source (yellow boxes in Fig. 1), including China national database, China rural statistical yearbooks, USGS mineral yearbooks, etc. The complete list of data sources and descriptions are presented in Table 1. Then, we store the originally retrieved data source files in a semi- or unstructured format (e.g., CSV, PDF). Next, we manually collect these statistics and reorganise them according to China EW-MFA material types and processes. However, only a tiny part of retrieved statistics can be applied directly, as specified in black colour in Fig. 1.Table 1 Data sources and descriptions.Full size tableData compilation: parameter localisation and data estimationA few inconsistencies in statistics were noticed, which would result in data incompleteness. For example, the domestic extraction of vegetables has been accounted for and published since 1995, before which statistics are unavailable. The domestically harvested timber has been measured in the volume unit of cubic metres, which needs to be converted into the mass unit via density conversion factor. Therefore, acquired statistics have to be estimated, which are specified in grey colour in Fig. 1. The following section elaborates on each data cell’s estimation methods, localised parameters, references, etc. In our uploaded data files, the original statistics, data sources, and compilation methods (using formulas) are all implemented, as explained in the Data Records Section.

The input of natural resources by domestic extraction

Vegetables in crops: Statistics of vegetable production (WVegetables)16 during 1990–1994 are unavailable, which is estimated based on the relationship between the production yield (PYield) and areas (AVegetables), as shown in Eq. 1. Here, PYield is assumed to remain constant at 27.04 thousand tonnes per thousand hectares from 1990 to 1995, derived by dividing vegetable production (257,267 thousand tonnes) by areas (9,515 thousand hectares) in 1995.$${W}_{Vegetables}={P}_{Yield}times {A}_{Vegetables}$$
(1)

Nuts in crops: One of them is chestnuts. The chestnut production in 2020 is unavailable, which is assumed to be the same as in 2019.

Crop residues in biomass residues: They are referred to as that harvested production of crops that do not reach the market to be sold but are instead employed as raw materials for commercial purposes such as energy generation and livestock husbandry. This number (Wcrop residues) can be calculated by first determining the number of crop residues available from primary crop production (Wcrop) and the harvest factor (Pharvest factor), and then using the recovery rate (Precovery rate) to determine the number of crop residues used by the economy, as shown in Eq. 2. These parameters have been localized by previous studies17,18, which are adopted in this study, i.e., wheat (1.1 for Pharvest factor and 0.463 for Precovery rate), maize (1.2, 0.463), rice (0.9, 0.463), sugar cane (0.5, 0.9), beetroots (0.7, 0.9), tuber (0.5, 0.463), pulse (1.2, 0.7), cotton (3.4, 0.463), fibre crops (1.8, 0.463), silkworm cocoons (1.8, 0.463), and oil-bearing crops (1.8, 0.463).$${W}_{cropresidues}={W}_{crop}times {P}_{harvestfactor}times {P}_{recoveryrate}$$
(2)

Roughage of grazed biomass and fodder crops in biomass residues: In China, the grazed biomass for roughage includes annual forage and perennial forage, whereas fodder crops comprise straw feed, processed straw feed, and all other fodder crops. However, information19 on grazed biomass production is only accessible from 2006 to 2018, whereas fodder crop statistics are only available from 2015 to 2017. Equation 3 and Eq. 4 can be used to estimate unavailable statistics. To note, we assume that China’s domestic roughage supply structure has remained unaltered, which has two meanings. The proportion of total domestic roughage production (WDomestic production) in requirement (WRoughage requirement) has remained constant, while the proportion (PSupply fraction) of grazed biomass and fodder crop in domestic roughage production has been unchanged. The requirement (WRoughage requirement) is determined by the quantity of livestock (QLivestock) and their annual feeding amount (PAnnual intake). PAnnual intake (in tonnes per head per year) has been localised for each type of livestock4, with 4.5 for live cattle and buffaloes, 0.5 for sheep and goats, 3.7 for horses, and 2.2 for mules and asses.$${W}_{Roughagerequirement}={Q}_{Livestock}times {P}_{Annualintake}$$
(3)
$${W}_{Domesticproduction}={W}_{Roughagerequirement}times {P}_{Supplyfraction}$$
(4)

Timber in wood: As illustrated in Eq. 5, wood production16 is reported in volume units of cubic metres (VTimber), which need to be converted into mass units (WTimber) via density (PDensity). The parameter PDensity is assumed to be 0.58 tonnes per cubic metre, calculated by averaging 0.52 for coniferous types and 0.64 for non-coniferous ones4.$${W}_{Timber}={V}_{Timber}times {P}_{Density}$$
(5)

Non-ferrous metals in metal ores: Non-ferrous metal statistics are derived from two sources. China statistics20 are measured in gross ore (WMetal ores in gross ore) but are only available from 1999 to 2017, whereas the USGS statistics21 cover the period of 1990 to 2020 but they are measured in metal or concentrate content (WMetal ores in other units). Therefore, USGS statistics need to be converted with an empirical unit conversion factor (PUnit conversion factor) before being applied to estimate unavailable statistics reported by China, as shown in Eq. 6. Conversion factors are localised for each non-ferrous metal in each year from 2000 to 2017 by using USGS statistics divided by China statistics and then averaged after removing the highest value and the lowest value (i.e., trimmed mean). This factor could capture the general relationship between statistics from two separate sources, which can be used in other long time-series studies on resource management on a particular element in China.$${W}_{Metaloresingrossore}={W}_{Metaloresinotherunits}/{P}_{Unitconversionfactor}$$
(6)

Non-metallic minerals: The official China-specific information on non-metallic mineral domestic production is available between 1999 and 201720, the rest of which could be estimated from USGS statistics (1990–2020)21. Also, two differences in reporting standards are observed resulting from the material coverages and reporting units. China statistics contain eighty-eight materials in mineral ores, whereas the USGS only includes twenty in the concentrate unit. Therefore, a conversion factor is developed in this estimation, as shown in Eq. 7. This conversion factor is applied to the total amount of non-metallic mineral production, which is assumed to have been constant from 1990 to 1999 at 11.38% (1999) and 12.56% (2017) from 2017 to 2020.$${W}_{Mineralsingrossore}={W}_{Mineralsinotherunits}/{P}_{Conversionfactor}$$
(7)

Coal in fossil energy materials: Coal, mined in China, includes raw coal, peat, stone coal, and oil shale. Except for raw coal, statistics for the rest are only available from 1999 to 201720. The unavailable data (WOther coals) is estimated using Eq. 8 under the assumption that the structure of the coal supply in China barely changes. That is, the proportion (PSupply fraction) of peat, stone coal, and oil shale in raw coal production (WRaw coal) remains constant, so the 1999 proportion is applied to all years before that (earlier years of 1990–1998), while the 2017 proportion is used to the recent years between 2018 and 2020. For example, PSupply fraction for oil shale production was assumed to be 0.014% during 1990–1999, calculated by dividing raw coal production (1,250,000) by oil shale production (179) in 1999. PSupply fraction in the earlier and the recent years are 0.007% and 0.001% for peat, 0.203% and 0.031% for stone coal, and 0.014% and 0.067% for oil shale.

$${W}_{Othercoals}={W}_{Rawcoal}/{P}_{Supplyfraction}$$
(8)

The output of processed materials by release

Materials released into the air: In China, thirteen materials are released into the air, as shown in Fig. 1. The emission of sulphur dioxide (SO2) is reported in China environmental statistical yearbooks22,23, while the rest is specified in the EDGAR24. However, in EDGAR, statistics for recent years have not yet been updated, which are estimated with the value in the most recent year in our database. For example, nitrous oxide (NOx) records are only available for the years prior to 2016, with 26,365 thousand tonnes in 2015 and 26,837 in 2014. As a result of the observed decreasing trend in NOx emissions, NOx emission data for 2016–2020 is estimated to be 26,000 thousand tonnes. This estimate may be subjective due to constraints, but it would be aligned with European statistics, allowing for international comparisons. Data can be updated after the EDGAR statistics have been updated.

Materials released into the water: Ten principal materials have been found in China wastewater (both industrial and municipal) that are nitrogen (N), phosphorus (P), organic pollutants of petroleum, volatile phenol and cyanide, heavy metals of mercury (Hg), lead (Pb), cadmium (C·d), and the hexavalent chromium (Cr6+), and arsenic (As). Many statistics22,23 have been of poor quality (e.g., inconsistent material coverages between years). Given that the statistics of pollutants in industrial wastewater cover more periods and contain fewer abnormal observations, the total material emissions can be approximated from those of industrial wastewater. Equations 9 and 10 show the estimation processes. The materials in industrial wastewater (WIndustrial materials) are first identified using material mass concentration (PConcentration) and the weight of industrial wastewater (WIndustrial wastewater), and then the materials in total wastewater (WTotal materials) are identified using the proportion (PContribution) of materials in industrial wastewaters (WIndustrial materials) to the total. The assumption is that PConcentration and PContribution change gradually between years, which enables to use linear interpolation method to estimate unavailable parameters. Consider cyanide: its PConcentration was 23.61 (1‰ ppm) in 2005 and 37.31 in 2002, which was assumed to be 28.18 in 2004 and 32.74 in 2003. PConcentration was assumed to be 100% throughout the years for cyanide because all cyanide emissions in China are driven by industrial wastewater discharges. Later, the total material emissions can be derived by dividing the industrial wastewater mass by PConcentration.$${W}_{Industrialmaterials}={W}_{Industrialwastewater}times {P}_{Concentration}$$
(9)
$${W}_{Totalmaterials}={W}_{Industrialmaterials},/,{P}_{Contribution}$$
(10)

Materials released to the land: This is zero because uncontrolled landfills are illegal in China.

Materials dissipated by organic fertiliser use: In China, manure is the primary organic fertiliser, which is excreted by pigs, dairy cows, calves, sheep, horses, asses, mules, camels, chickens, and other animals. As shown in Eq. 11, the manure production (WManure) is estimated through the amounts of raised livestock (QLivestock, heads), the weight of daily manure production (PManure production, kilograms per head per day), the number of days they are raised (PFeeding period, in days per year), and the moisture content of their manure (PDry matter, %) for each type of animal. These parameters are region-specific, which have been localised by Chinese scholars25,26,27 and listed in Table 2.$${W}_{Manure}={Q}_{Livestock}times {P}_{Manureproduction}times {P}_{Feedingperiod}times {P}_{Drymatter}$$
(11)
Table 2 Localised parameters for animal manure production.Full size table

Materials dissipated by mineral fertiliser use: The mineral fertilisers used in China are four types, i.e., nitrogen (N), phosphorus (P), potash (K), and compound. Their usage (WFertiliser usage) is measured in nutrient mass (WNutrient materials), which needs to be converted into the gross mass by dividing their nutrient content (PNutrient content). Equation 12 shows the estimation. This parameter of PNutrient content is localised by the Ministry of Agriculture and Rural Affairs of China28 as 29%, 22%, 35%, and 44% for N- bearing, P- bearing, K-bearing, and compound fertilisers, respectively.$${W}_{Fertiliserusage}={W}_{Nutrientmaterials}/{P}_{Nutrientcontent}$$
(12)

Materials dissipated by sewage sludge: Sewage sludge is the residue generated by municipal wastewater treatment. As demonstrated in Eq. 13, its dissipative use (Wss, dissipation) is the untreated amount of production (Wss, production), represented by the parameter of Pss, dissipation rate. Sewage sludge production (Wss, production) statistics are only available for the years 2006–202029, and data for the remaining years can be estimated using Eq. 14 and Eq. 15. In Eq. 14, Pss, production rate represents the relationship between sewage sludge production (Wss, production, 2006–2020) and wastewater treatment (Www, treatment, 2002–2020), and in Eq. 15, Pww, treatment efficiency represents the relationship between the quantity of treated wastewater (Www, treatment, 2002–2020) and the treatment capacity (Www, treatment capacity, 1990–2020). In this estimation, three assumptions are made. The first is to estimate Www, treatment, Pww, treatment efficiency is assumed to be unchanged at 63% during 1990–2001, given it has been increasing from 63% in 2002 to ~80% in recent years. The second is that, in order to estimate Wss, production, Pss, production rate is assumed to be unchanged at 3.5 between 1990 and 2005, suggesting 3.5 tonnes of sewage sludge are generated by processing 10,000 cubic metres of wastewater. This assumption is determined by that Pss, production rate is approximately 3.5 during 2006–2010 while declines sharply and stabilises at around two during 2011–2020. The last is, to estimate the Wss,dissipation, Pss,dissipation rate is assumed to be 5% between 1990 and 2005, given it has been around 5% during 2006–2020.$${W}_{ss,dissipation}={W}_{ss,production}times {P}_{ss,dissipationrate}$$
(13)
$${W}_{ss,production}={W}_{ww,treatment}times {P}_{ss,productionrate}$$
(14)
$${W}_{ww,treatment}={W}_{ww,treatmentcapacity}times {P}_{ww,treatmentefficiency}$$
(15)

Materials dissipated by composting: Composting is a natural process that uses microbes to turn organic materials into other products, which are then used for fertilising and entering the environment. In China, composting has been used to treat two materials: feces and municipal waste, whose quantities (WComposting) were only available from 2003 to 201029. The unavailable data can be estimated using Eq. 16. The dry weight of materials treated by composting (WComposting) is proportionally related to the fresh weight of all treated materials (WTotal), the proportion treated by composting (PComposting rate), and the dry content (PDry matter). Considering that China’s composting capacity has been declining since 2001 due to the implementation of waste incineration power generation technologies30, Pcomposting rate is assumed to be the same as it was in 2003 (9.5%) between 1990 and 2002, and 1.5% in 2010 between 2011 and 2020. The parameter of PDry matter is 50%4.$${W}_{Composting}={W}_{Total}times {P}_{Compostingrate}times {P}_{Drymatter}$$
(16)

The input and output by cross-border trade. Statistics of imports and exports have been gathered since 1962 and stored in the UN Comtrade database31. However, the data quality issue of outliers, and missing values, especially in weight, is reportedly identified. In our previous work, we addressed these issues, and an improved database32 is provided. Details about our estimation methods can be found in publications33,34,35. As UN Comtrade lists 5,039 different commodity types (in 6-digit HS0 commodity code), yet only 18 material types are specified in the China EW-MFA, UN Comtrade statistics need to be aligned to the China EW-MFA framework. Therefore, we compared each commodity and each material type between them and established a correspondence table to map UN Comtrade commodity types onto our EW-MFA material types. For example, non-ferrous metal materials of China EW-MFA include commodities, such as copper ores and concentrates (260300 HS0 code), silver powder (710610), manganese, articles thereof, and waste or scrap (811100), etc., whereas biomass residues include cereal straw and husks (121300), lucerne meal and pellets (121410), and other fodder and forage products (121410). This correspondence table between HS0 and EW-MFA classification for imports and exports is provided in Supplementary File 1.

The input of balancing items

O2 required for combustion: In BI, requirements for materials can be abstracted as equalling exogenous demands minus intrinsic supplies (Eq. 17). Three parts (two demands and one supply) are considered for O2 requirements by the combustion process: (1) demanding exogenous oxygen to oxidise elements (e.g., carbon, sulphur, nitrogen, etc., except for hydrogen) released into the air, (2) demanding exogenous oxygen to oxidise the hydrogen embedded in fossil energy materials, and (3) providing intrinsic oxygen embedded in fossil energy materials. The first part can be estimated via Eq. 18 by multiplying air emissions (WEmissions) of CO2, N2O, NOx, CO, and SO2 by their oxygen content (POxygen content). For the second (Eq. 19), the oxygen demand is estimated based on the principle of mass balance by converting the hydrogen amount of domestically utilised fossil energy materials (WFossil fuel materials × PHydrogen content) via molar mass conversion factor (PMass conversion factor). PMass conversion factor equals 7.92, derived by the molar mass of one oxygen (16 g/mol) divided by that of two hydrogen atoms (2 × 1.01 g/mol). The last is the intrinsic supplies from fossil fuel materials, which is identified via Eq. 20 by multiplying the domestically utilised amount of fossil fuel materials (WFossil fuel materials) by their oxygen content (POxygen content). The parameters in this estimation are presented in Table 3. As a footnote here, the domestically utilised amount is referred to as the domestic material consumption (DMC), which equals domestic extraction (DE) plus imports (IM) and minus exports (EX).$${W}_{Requirements}={W}_{Demands}-{W}_{Supplies}$$
(17)
$${W}_{Demands}={W}_{Emissions}times {P}_{Oxygencontent}$$
(18)
$${W}_{Demands}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(19)
$${W}_{Supplies}={W}_{Fossilfuelmaterials}times {P}_{Oxygencontent}$$
(20)
Table 3 Parameters related to combustion processes4.Full size table

O2 required for respiration: O2 is required by the metabolic activities of living organisms, the majority of which are humans and livestock. Bacteria are another sort of organism, which are not included in this estimation because their O2 requirements are too small to be quantified. The respiration-required O2 is related to the total quantity (QOrganisms) and their respiration activity by organism types, as shown in Eq. 21. The respiration activity is represented by the respiration requirement coefficient (PRespiration requirement coefficient), which is the average quantity of O2 that each organism utilises to maintain the metabolic activity, as listed in Table 4.$${W}_{Demands}={Q}_{Organisms}times {P}_{Respirationrequirementcoefficient}$$
(21)
Table 4 Parameters related to respiration processes4.Full size table

Water required for the domestic production of exported beverages: The exported beverages are produced domestically using domestically extracted materials, especially a large amount of water. The weight of water is considered in the output by cross-border trade but is not included in the domestic extraction input. The resulted imbalance can be identified by specifying the water weight in beverages, i.e., multiplying the traded beverage weight (WMaterials) by a parameter of the water content (PWater content), as given in Eq. 22. Fruit and vegetable juices (2009 in HS0 code) and beverages (code 22) are covered in the improved UN Comtrade database32, with PWater content of 85% for the first and 90% for the latter4.

$${W}_{Water}={W}_{Materials}times {P}_{Watercontent}$$
(22)

The output of balancing items.

Water vapour from combustion: Water vapour emissions by domestically combusting fossil fuel materials are contributed by two paths. The direct evaporation of embedded water is the first path (Eq. 23), which can be derived by multiplying the DMC of fossil fuel materials by their moisture content (PMoisture content). The PMoisture content for each type of fossil fuel material is listed in Table 3. The other is the generation of water vapour during hydrogen oxidation, which can be calculated by converting the oxidised weight of hydrogen to the water weight using the molar mass conversion factor (PMass conversion factor), as given in Eq. 24. PMass conversion factor equals 8.92 by dividing the molar mass of water (18.02 g/mol) by that of two hydrogen atoms (2 × 1.01 g/mol).$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Moisturecontent}$$
(23)
$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(24)

Water vapour and CO2 from respiration: Respiration activities of organisms will produce water vapour and CO2, whose estimation is similar to that of O2 requirements. As shown in Eq. 25, the respiration-caused gas emissions are related to the number of organisms (QOrganisms) and the respiration activity by organism types. The latter is represented by the parameter of respiration emission coefficient (PRespiration emission coefficient), which is specified in Table 4 for water vapour and CO2 for each type of organism.$${W}_{Emissions}={Q}_{Organisms}times {P}_{Respirationemissioncoefficient}$$
(25)

Water from imported beverages: The estimation approach is the same as water by the domestic production of exported beverages, as described in Eq. 16.

Water in biomass products: Usually, the input of biomass products by domestic extraction16 has been measured in fresh weight, but their corresponding output29 by sewage sludge, composting, etc., are in dry weight, leading to an imbalance in water weight. The water weight in biomass products is calculated by multiplying their domestic extraction amount in fresh weight (WBiomass) by a parameter of moisture content at harvest (PMoisture content), as shown in Eq. 26. The values of PMoisture content by biomass products are presented in Table 5.Table 5 The moisture content at harvest for each biomass product4.Full size table

$${W}_{Water}={W}_{Biomass}times {P}_{Moisturecontent}$$
(26)
Material flow quantificationThe above attempts have quantified material inputs and outputs by flows and presented a detailed profile of material utilisation for each material in China’s economy. In order to depict the economy in a more general way, EW-MFA indicators are assessed by aggregating flows by materials or periods as below.

Domestic extraction (DE): is referred to as natural materials that are extracted from the domestic environment and are used in the domestic economy, i.e., the total input of natural materials by extraction.

Domestic processed output (DPO): is referred to as materials that are released to the domestic environment after being processed in the domestic economy, i.e., the total output of processed materials by release.

Import (IM): is referred to as all goods (in the form of raw materials, semi-finished materials, and final products) that originated from other economies and are further used in the domestic economy. It is calculated as the sum of all imported goods.

Export (EX): is referred to as all goods that originated from the domestic economy and are transported to other economies to be used. It is calculated as the sum of all exported goods.

Domestic material input (DMI): is referred to as materials that originated from the domestic environment by extraction and other economies and are available (to be used or to be stored) for the domestic economy. It is calculated as the sum of DE plus IM, as shown in Eq. 27.$$DMI=DE+IM$$
(27)

Domestic material consumption (DMC): is referred to as materials that are directly used in the domestic economy after parts of them are exported to other economies. It is calculated as the difference between DMI and EX.

Physical trade balance (PTB): is referred to as a surplus or deficit of materials for the domestic economy. It is calculated as the difference between IM and EX.

Net additions to stock (NAS): is referred to as materials that remain in the domestic economy. It is calculated by taking BI items into account, as shown in Eq. 28.

$$NAS=DMC+B{I}_{in}-DPO-B{I}_{out}$$
(28) More

Reaka-Kudla, M. L. The global biodiversity of coral reefs: a comparison with rain forests. Biodivers. II. Underst. Prot. Our Biol. Resour. 2, 551 (1997).
Google Scholar 
Connell, J. H. Population ecology of reef-building corals. in Biology and Geology of Coral Reefs (eds. Jones, O. A. & Endean, R.) 205–245 (Academic Press, 1973). doi:https://doi.org/10.1016/B978-0-12-395526-5.50015-8.Berumen, M. L. et al. The status of coral reef ecology research in the Red Sea. Coral Reefs 32, 737–748 (2013).ADS 
Article 

Google Scholar 
Hughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).Article 
PubMed 

Google Scholar 
Edmunds, P. J. & Riegl, B. Urgent need for coral demography in a world where corals are disappearing. Mar. Ecol. Prog. Ser. 635, 233–242 (2020).ADS 
Article 

Google Scholar 
Pisapia, C. et al. Projected shifts in coral size structure in the Anthropocene. Adv Mar Biol 87, 31–60 (2020).Article 
PubMed 

Google Scholar 
Meesters, E. et al. Colony size-frequency distributions of scleractinian coral populations: spatial and interspecific variation. Mar. Ecol. Prog. Ser. 209, 43–54 (2001).ADS 
Article 

Google Scholar 
Riegl, B. et al. Demographic mechanisms of reef coral species winnowing from communities under increased environmental stress. Front. Mar. Sci. 4, 344 (2017).Article 

Google Scholar 
Pisapia, C., Burn, D. & Pratchett, M. Changes in the population and community structure of corals during recent disturbances (February 2016-October 2017) on Maldivian coral reefs. Sci. Rep. 9, 1–12 (2019).CAS 
Article 

Google Scholar 
Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. Long-term shifts in the colony size structure of coral populations along the Great Barrier Reef. Proc. R. Soc. B 287, 20201432 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
Lachs, L. et al. Linking population size structure, heat stress and bleaching responses in a subtropical endemic coral. Coral Reefs 40, 777–790 (2021).Article 

Google Scholar 
McClanahan, T., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).Article 

Google Scholar 
Grimsditch, G. et al. Variation in size frequency distribution of coral populations under different fishing pressures in two contrasting locations in the Indian Ocean. Mar. Environ. Res. 131, 146–155 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bak, R. P. & Meesters, E. H. Coral population structure: the hidden information of colony size-frequency distributions. Mar. Ecol. Prog. Ser. 162, 301–306 (1998).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. & Jackson, J. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).Article 

Google Scholar 
Soong, K. Colony size as a species character in massive reef corals. Coral Reefs 12, 77–83 (1993).ADS 
Article 

Google Scholar 
Bak, R. P. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).Article 

Google Scholar 
Adjeroud, M., Pratchett, M. S., Kospartov, M. C., Lejeusne, C. & Penin, L. Small-scale variability in the size structure of scleractinian corals around Moorea, French Polynesia: patterns across depths and locations. Hydrobiologia 589, 117–126 (2007).Article 

Google Scholar 
Adjeroud, M., Mauguit, Q. & Penin, L. The size-structure of corals with contrasting life-histories: A multi-scale analysis across environmental conditions. Mar. Environ. Res. 112, 131–139 (2015).CAS 
Article 
PubMed 

Google Scholar 
Bauman, A. G. et al. Variation in the size structure of corals is related to environmental extremes in the Persian Gulf. Mar. Environ. Res. 84, 43–50 (2013).CAS 
Article 
PubMed 

Google Scholar 
Smith, L., Devlin, M., Haynes, D. & Gilmour, J. A demographic approach to monitoring the health of coral reefs. Mar. Pollut. Bull. 51, 399–407 (2005).CAS 
Article 
PubMed 

Google Scholar 
Lowe, R. J. & Falter, J. L. Oceanic forcing of coral reefs. Annu. Rev. Mar. Sci. 7, 43–66 (2015).ADS 
Article 

Google Scholar 
Thornborough, K., Davies, P. Reef flats. Encycl. Mod. Coral Reefs 869–876 (2011).Camp, E. F. et al. The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci. 5, 4 (2018).Article 

Google Scholar 
Bellwood, D. R. et al. The role of the reef flat in coral reef trophodynamics: Past, present, and future. Ecol. Evol. 8, 4108–4119 (2018).PubMed Central 
Article 
PubMed 

Google Scholar 
Pineda, J. et al. Two spatial scales in a bleaching event: Corals from the mildest and the most extreme thermal environments escape mortality. Limnol. Oceanogr. https://doi.org/10.4319/lo.2013.58.5.1531 (2013).Article 

Google Scholar 
Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Riegl, B., Berumen, M. & Bruckner, A. Coral population trajectories, increased disturbance and management intervention: A sensitivity analysis. Ecol. Evol. https://doi.org/10.1002/ece3.519 (2013).Article 
PubMed Central 
PubMed 

Google Scholar 
Loya, Y. The red sea coral Stylophora pistillata is an r strategist. Nature https://doi.org/10.1038/259478a0 (1976).Article 
PubMed 

Google Scholar 
Lozano-Cortés, D. F. & Berumen, M. L. Colony size-frequency distribution of pocilloporid juvenile corals along a natural environmental gradient in the Red Sea. Mar. Pollut. Bull. 105, 546–552 (2016).Article 
CAS 
PubMed 

Google Scholar 
Ellis, J. et al. Cross shelf benthic biodiversity patterns in the Southern Red Sea. Sci. Rep. 7, 1–14 (2017).Article 
CAS 

Google Scholar 
Furby, K. A., Bouwmeester, J. & Berumen, M. L. Susceptibility of central Red Sea corals during a major bleaching event. Coral Reefs 32, 505–513 (2013).ADS 
Article 

Google Scholar 
Monroe, A. A. et al. In situ observations of coral bleaching in the central Saudi Arabian Red Sea during the 2015/2016 global coral bleaching event. PLoS ONE 13, e0195814 (2018).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
Davis, K. et al. Observations of the thermal environment on Red Sea platform reefs: A heat budget analysis. Coral Reefs 30, 25–36 (2011).ADS 
Article 

Google Scholar 
Liu, G. et al. Reef-scale thermal stress monitoring of coral ecosystems: new 5-km global products from NOAA Coral Reef Watch. Remote Sens. 6, 11579–11606 (2014).ADS 
Article 

Google Scholar 
Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Change Biol. https://doi.org/10.1111/gcb.15148 (2020).Article 

Google Scholar 
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
Morais, J., Morais, R. A., Tebbett, S. B., Pratchett, M. S. & Bellwood, D. R. Dangerous demographics in post-bleach corals reveal boom-bust versus protracted declines. Sci. Rep. 11, 1–7 (2021).Article 
CAS 

Google Scholar 
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology https://doi.org/10.2307/2265514 (1996).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata. 2. Synchronization in breeding and seasonality of planulae shedding. Mar. Ecol. Prog. Ser. 1, 145–152 (1979).ADS 
Article 

Google Scholar 
Komsta, L. & Novomestky, F. Moments, cumulants, skewness, kurtosis and related tests. R Package Version 14, (2015).Anderson, M., Gorley, R. & Clarke, K. PERMANOVA+ for PRIMER: guide to software and statistical methods. Primer-E Plymouth UK (2008).Meziere, Z. et al. Stylophora under stress: A review of research trends and impacts of stressors on a model coral species. Sci. Total Environ. 816, 151639 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata 1. Gonads and planulae. Mar. Ecol. Prog. Ser. 1, 133–144 (1979).ADS 
Article 

Google Scholar 
Nishikawa, A., Katoh, M. & Sakai, K. Larval settlement rates and gene flow of broadcast-spawning (Acropora tenuis) and planula-brooding (Stylophora pistillata) corals. Mar. Ecol. Progress Ser. https://doi.org/10.3354/meps256087 (2003).Article 

Google Scholar 
Monroe, A. Genetic differentiation across multiple spatial scales of the Red Sea of the corals Stylophora pistillata and Pocillopora verrucosa. M.S. thesis, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (2015).Gouezo, M. et al. Relative roles of biological and physical processes influencing coral recruitment during the lag phase of reef community recovery. Sci. Rep. 10, 1–12 (2020).ADS 
Article 
CAS 

Google Scholar 
Boco, S. R., Cabansag, J. B. P., Jamodiong, E. A. & Ticzon, V. S. Size-frequency distributions of scleractinian coral (Porites spp.) colonies inside and outside a marine reserve in Leyte Gulf, central Philippines. Reg. Stud. Mar. Sci. 35, 101147 (2020).
Google Scholar 
River, G. F. & Edmunds, P. J. Mechanisms of interaction between macroalgae and scleractinians on a coral reef in Jamaica. J. Exp. Mar. Biol. Ecol. 261, 159–172 (2001).Article 
PubMed 

Google Scholar 
Kuffner, I. B. et al. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar. Ecol. Prog. Ser. 323, 107–117 (2006).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lewis, J. B. Abundance, distribution and partial mortality of the massive coral Siderastrea siderea on degrading coral reefs at Barbados West Indies. Mar. Pollut. Bull. 34, 622–627 (1997).CAS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Coral colony tissue damage in six species of reef-building corals: partial mortality in relation with depth and surface area. J. Sea Res. 37, 131–144 (1997).ADS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Partial mortality in three species of reef-building corals and the relation with colony morphology. Bull. Mar. Sci. 58, 838–852 (1996).
Google Scholar 
Graham, J. & Van Woesik, R. The effects of partial mortality on the fecundity of three common Caribbean corals. Mar. Biol. 160, 2561–2565 (2013).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Intraspecific competitive networks in the Red Sea coral Stylophora pistillata. Coral Reefs https://doi.org/10.1007/BF00571193 (1983).Article 

Google Scholar 
Takabayashi, M. & Hoegh-Guldberg, O. Ecological and physiological differences between two colour morphs of the coral Pocillopora damicornis. Mar. Biol. 123, 705–714 (1995).Article 

Google Scholar 
Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne ‘ohe Bay, O ‘ahu, Hawai ‘i. Coral Reefs 37, 423–430 (2018).ADS 
Article 

Google Scholar 
Gochfeld, D., Ansley, M., Ankisetty, S. & Aeby, G. Antibacterial chemical resistance to disease in the Hawaiian coral Montipora capitata. Planta Med. 80, CL31 (2014).Article 

Google Scholar 
Shore-Maggio, A., Callahan, S. M. & Aeby, G. S. Trade-offs in disease and bleaching susceptibility among two color morphs of the Hawaiian reef coral Montipora capitata. Coral Reefs 37, 507–517 (2018).ADS 
Article 

Google Scholar 
Dove, S. G., Takabayashi, M. & Hoegh-Guldberg, O. Isolation and partial characterization of the pink and blue pigments of pocilloporid and acroporid corals. Biol. Bull. 189, 288–297 (1995).CAS 
Article 
PubMed 

Google Scholar 
Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs https://doi.org/10.1007/s00338-020-01917-7 (2020).Article 

Google Scholar  More

Tracing the symptomology of predation through macroscopic plaquesA culture bioassay (Expanded Microcoleus Mortality Assay, or EMMA) (Fig. 1 and see Materials and Methods) based on the capacity of a soil to induce complete mortality in the foundational biocrust cyanobacterium Microcoleus vaginatus helped us trace the pathogen detected in biocrust production facilities to the development of cm-sized plaques, or zones of cyanobacterial clearing, in natural biocrusts. These plaques were revealed to the naked eye (Fig. 2) when the soil was wet (i.e., after a rain event), as impacted areas would fail to green up by the migration of cyanobacteria to the surface21, enabling us to detect and quantify them with relative ease. Soil samples obtained from such plaques (n = 30) from different sites (n = 6; Table S1) in the US Southwest were invariably EMMA + , and the pathogens always filterable with pore sizes 0.45–1 µm but not larger, and always insensitive to the eukaryotic inhibitor cycloheximide, indicating the agent’s prokaryotic nature and small size, while paired samples from asymptomatic areas just outside the plaques were always EMMA- (Table S2). These end-point EMMA solutions never gave rise to cyanobacterial re-growth upon further incubation and maintained its infectivity of fresh cyanobacterial cultures for up to 6 months. A one-time, small-scale sampling across a plaque at intervals of 2 mm using microcoring22 showed that the boundary of the visible plaque demarcated exactly the end of infectivity, samples 0–2 mm outside the plaque proving non-infective. Further, inoculation of healthy, natural biocrusts with EMMA + suspensions resulted in the local development of biocrust plaques, and soil from these plaques was itself EMMA + , in partial fulfillment of Koch’s postulates. Yet, standard microbiological plating failed to yield any isolates that were EMMA + (we tested 30 unique isolates), even though standard plating with similar isolation efforts can successfully cultivate a large portion of heterotrophs from biocrusts23.Fig. 1: EMMA bioassay (Expanded Microcoleus Mortality Assay), used to study biocrust pathogens.a Typical visual progression of a positive EMMA inoculated with soil or culture to be tested, as used to test for pathogenicity to Microcoleus vaginatus PPC 9802 in the field and in enrichments. b Typical degradation of cyanobacterial biomass during an EMMA displayed through electron microscopy: healthy Microcoleus vaginatus PPC 9802 filaments (top) display abundant photosynthetic membranes (white arrows), peptidoglycan cross-walls (yellow arrows) and carboxysomes (green arrow). As infection proceeds (downwards), patent degradation of intracellular structures follows, leaving only cellular ghosts in the form of peptidoglycan wall remnants (yellow arrows), including the characteristically enlarged peptidoglycan “bumper” of terminal cells (red arrow). Intracellular bacilloid bacteria can sometimes be observed (blue arrow). Cyanobacterial cultures lose all viability. Scale bars = 1 µm. n = 250 images from 4 independent experiments. c Assay modification used in flow cytometry/cell sorting, showing enrichments positive for predation in the top two rows and those negative for predation below. d Test and controls in EMMA to ensure prokaryotic nature of the disease agent.Full size imageFig. 2: Symptomology in nature: biocrust plaques.Main: Macroscopic view of a soil surface colonized by cyanobacterial biocrusts and impacted by multiple plaques as taken after a rain in a quadrat used for field surveys. Insert: Close-up of a single plaque, showing well-demarcated boundaries and a typical central area of new cyanobacterial colonization.Full size imageCultivation, identification, and salient genomic traits of the cyanobacterial pathogenTo study these organisms, we turned to enrichment of pathogen/prey co-cultures based on repeated passages through EMMA and differential size filtration combined with dilution-to-extinction approaches, followed by purification with flow cytometry/cell sorting. The process was monitored by 16S rRNA gene amplicon sequencing, and eventually yielded a highly enriched co-culture of the cyanobacterium with a genetically homogenous (one single Amplicon Sequence Variant) population that made up more than 80% of reads (Fig. 3 a, b). We name the organism represented by this ASV Candidatus Cyanoraptor togatus. That it corresponds indeed to the predator is supported by the fact that of the 17 ASV’s detected in the final enrichment, only 10 were consistently detected at all infectious stages in the process and, among these, only our candidate ASV steadily increased in relative abundance through the enrichment process (Fig. 3 a, b). This final enrichment of C. togatus, LGM-1, constitutes the basis for downstream biological and molecular analyses. Its ASV was most similar to little-known members of the family Chitinophagaceae in the phylum Bacteroidetes. LGM-1’s genome was sequenced and assembled into a single 3.3 Mb contig with 1,781 putative and 1,328 hypothetical genes (Table S3), though most proteins had low identity (Fig. 4: Compiled paired ratios of functional parameters and compositional (relative) abundance in biocrusts across plaque boundaries (circles), red bars denoting the medians for each group of ratios, and bar background color denoting the p-values that the median is significantly different from unity (Wilcoxon paired ratio two-sided tests), where gray is non-significant (p  >  0.1), light orange is 0.05   > p   p  More

Statistical characteristics of rill cross-sectional asymmetry (RCA)The rill cross-sectional asymmetry (RCA) is a key parameter in describing rill morphology and includes the asymmetry ratio of the width (Aw) and the asymmetry ratio of the area (Aa). It reflects the differences in certain aspects of natural conditions resulting in inconsistent development speeds on both sides of a rill cross-section. The cross-section was classified as left-biased if Aw, Aa < 0, quasi-symmetrical if Aw, Aa = 0, and right skewed if Aw, Aa > 0. The left/right deflection reflects that erosion on the right happened faster than on the left, so the slope on the left is not as steep as on the right. The results of this study show that asymmetry is a common phenomenon in the cross-section of a rill. The Aw ranged from − 1.77 to 1.97, with an average value of − 0.034. There were 374 cross-sections whose RCA was less than or equal to 0, meaning that 53% of the cross-sections were right-biased. The Aa ranged from − 1.81 to 1.71, with an average of − 0.046. There were 374 cross-sections with an RCA of less than or equal to 0, meaning that 53% of the cross-sections were right-biased (Fig. 1).Figure 1Statistical characteristics of the rill cross-sectional asymmetry (RCA).Full size imageFigure 2 shows that there are four Aw groups in the interval (− 1.7, − 1.5), 53 groups in the interval (− 1.5, − 1.0), 144 groups in the interval (− 1.0, − 0.5), 173 groups in the interval (− 0.5, 0), 174 groups in the interval (0, 0.5), 120 groups in the interval (0.5, 1.0), 39 groups in the interval (1.0, 1.5), and five groups in the interval (1.5, 2). The Aa has 15 groups in the interval (− 1.8, − 1.5), 63 groups in the interval (− 1.5, − 1.0), 130 groups in the interval (− 1.0, − 0.5), 166 groups in the interval (− 0.5, 0), 161 groups in the interval (0, 0.5), 110 groups in the interval (0.5, 1.0), 53 groups in the interval (1.0, 1.5), and 14 groups in the interval (1.5, 2). The RCA of most cross-sections is concentrated in the interval (− 0.5, 0.5). This interval of Aw contains 491 cross-sections, accounting for 68.96% of the total. There are 470 cross-sections in this interval of Aa, accounting for 66.01% of the total. This indicates that, although the rill cross-section exhibits some asymmetry, the difference between both sides of the section is small (Fig. 2).Figure 2Distribution characteristics of the RCA.Full size imageThe influence of a single topographic factor on the RCACorrelation analyses of the Aw, Aa, and the slope difference on both sides (B), rill length (L), rill slope length (I), rill head catchment area (A), difference between the catchment areas of both sides (R), rill bending coefficient (K), and location of the section angle of turning of the rill (J) were carried out. The results show that the main factors that have a significant linear correlation with the Aw and the Aa are B (p < 0.01), with correlation coefficients of 0.32 and 0.22, respectively (Fig. 3). That is, the greater the difference in slope between the two sides, the more asymmetric the rill cross-section. R also has a significant linear correlation with the Aw (p < 0.05), with a correlation coefficient of 0.07. This means that the greater the difference in the catchments between the left and right sides of the rill, the greater the asymmetry of the rill cross-section. However, other topographic factors have no significant correlation with the RCA.Figure 3Correlation between rill cross-sectional asymmetry (RCA) and topographic factors.Full size imageB is the difference in slope between the left and right sides of the rill cross-section catchment area. The closer B gets to 0, the smaller the difference in slope between the left and right sides of the rill cross-section catchment area. When the catchment area slope on the right side of the cross-section is greater than that on the left side, B < 0; and when the catchment area slope on the left side of the cross-section is greater than that on the right side, B > 0. Grouping B reveals that the average RCA increases as B increases (Fig. 4). When B is (− 30, − 20), Aw is − 0.48 and Aa is − 0.38; when B is (− 20, − 10), Aw is − 0.36 and Aa is − 0.31; when B is (− 10, 0), Aw is − 0.23 and Aa is − 0.22; when B is (0, 10), Aw is 0.21 and Aa is 0.16; when B is (10, 20), Aw is 0.47 and Aa is 0.40; and when B is (20, 40), Aw is 0.31 and Aa is 0.13. These are relatively low values because this group only has two sets of cross-sections which cannot represent the characteristics of interval B. The sign of the RCA is the same as the sign of B. The directionality of the RCA is significantly affected by B. When the slope of the left catchment area is large, RCA > 0, and the rill cross-section appears to be left-biased; when the slope of the right catchment area is large, RCA < 0, and the cross-section appears to the righ-biased.Figure 4The asymmetry of different B values.Full size imageThe influence of multiple topographic factors on the RCAIn order to explore the influence of multiple topographic factors on the RCA, principal component analysis (PCA) was used to extract the main feature components of the topographic data. The PCA results show that the nine topographic factors can be reflected by two principal components at 61.84% (characteristic value: 3.117+1.211=4.328 variables) (Table 1). Therefore, the analysis of the first two principal components could reflect most of the information from all the data.Table 1 Calculation results of topographic factor principal component analysis (PCA).Full size tableThe contribution rate of the first principal component is 44.534%. The characteristic is that the factor variables have high positive loads for the four factors L, I, A, and K. L has the largest contribution rate at 88.5%, followed by A, I, and K, at 87.5%, 81.1%, and 60.2%, respectively. Therefore, the first component represents the rill slope and rill shape.The contribution rate of the second principal component is 17.303%. The characteristic is that the factor variables have high positive loads for the three factors B, J, and R. B has the largest contribution rate at 83.5%, followed by J and R, at 57.4% and 55.7%, respectively. Therefore, the second component represents the effect of the difference between the two sides of the rill.Based on the correlation between the topographic factors and the RCA of a rill cross-section in the Yuanmou dry-hot valley area, the following was observed: asymmetry in rill cross-sections is ubiquitous. The distribution range of Aw is − 1.77 to 1.97, the average value is − 0.034, and the cross-section that is right-biased accounts for 53%. A correlation analysis of the RCA and seven topographic factors shows that B has a significant positive correlation with the Aw and Aa (p < 0.01), the average RCA increases as B increases, and the directionality of the RCA is affected by B. When B > 0, RCA > 0, and the rill cross-section appears to the left; when B < 0, RCA < 0, and the cross-section appears to the right. The difference in catchment area between the sides has a significant linear correlation with the Aw (p < 0.05). Other single topographic factors have no significant correlation with the RCA. Principal component analysis and calculations show that the first principal component represents the influence of the rill slope surface and rill shape on the rill cross-sectional asymmetry. The contribution rate is 44.534%, which is characterized by a high positive load on the L, I, A, and K factors. The second principal component represents the effect of the difference between the two sides of the rill. The contribution rate is 44.534%, which is characterized by a high positive load on the B, J, and R factors. More

Ecological networks: Linking structure to dynamics in food webs. (Oxford University Press, 2006).Adaptive food webs: Stability and transitions of real and model ecosystems. (Cambridge University Press, 2018).Pimm, S. L. Food Webs (Springer, 1982).Book 

Google Scholar 
Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. (Cambridge University Press, 2017). doi:https://doi.org/10.1017/9781316871867.da Mata, A. S. Complex Networks: A Mini-review. Braz. J. Phys. 50, 658–672 (2020).ADS 
Article 

Google Scholar 
Zhang, W. Fundamentals of Network Biology. (World Scientific (Europe), 2018). https://doi.org/10.1142/q0149.Reichman, O. J., Jones, M. B. & Schildhauer, M. P. Challenges and opportunities of open data in ecology. Science 331, 703–705 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. situating ecology as a big-data science: Current advances, challenges, and solutions. Bioscience 68, 563–576 (2018).Article 

Google Scholar 
Osawa, T. Perspectives on biodiversity informatics for ecology. Ecol. Res. 34, 446–456 (2019).Article 

Google Scholar 
Shin, N. et al. Toward more data publication of long-term ecological observations. Ecol. Res. 35, 700–707 (2020).Article 

Google Scholar 
Pringle, R. M. & Hutchinson, M. C. Resolving food-web structure. Annu. Rev. Ecol. Evol. Syst. 51, 55–80 (2020).Article 

Google Scholar 
Derocles, S. A. P. et al. Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. in Advances in Ecological Research vol. 58 1–62 (Elsevier, 2018).Vacher, C. et al. Learning ecological networks from next-generation sequencing data. in Advances in Ecological Research vol. 54, 1–39 (Elsevier, 2016).Evans, D. M., Kitson, J. J. N., Lunt, D. H., Straw, N. A. & Pocock, M. J. O. Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems. Funct. Ecol. 30, 1904–1916 (2016).Article 

Google Scholar 
Pocock, M. J. O. et al. A vision for global biodiversity monitoring with citizen science. in Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).Sultana, M. & Storch, I. Suitability of open digital species records for assessing biodiversity patterns in cities: A case study using avian records. J. Urban Ecol. 7, juab014 (2021).Article 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400 (2016).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).Article 

Google Scholar 
Fontaine, C. et al. The ecological and evolutionary implications of merging different types of networks: Merging networks with different interaction types. Ecol. Lett. 14, 1170–1181 (2011).PubMed 
Article 

Google Scholar 
Martinson, H. M. & Fagan, W. F. Trophic disruption: A meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems. Ecol. Lett. 17, 1178–1189 (2014).PubMed 
Article 

Google Scholar 
Marczak, L. B., Thompson, R. M. & Richardson, J. S. Meta-analysis: Trophic level, Habitat, and productivity shape the food web effects of resource subsidies. Ecology 88, 140–148 (2007).PubMed 
Article 

Google Scholar 
McCary, M. A., Mores, R., Farfan, M. A. & Wise, D. H. Invasive plants have different effects on trophic structure of green and brown food webs in terrestrial ecosystems: A meta-analysis. Ecol. Lett. 19, 328–335 (2016).PubMed 
Article 

Google Scholar 
Cirtwill, A. R., Stouffer, D. B. & Romanuk, T. N. Latitudinal gradients in biotic niche breadth vary across ecosystem types. Proc. R. Soc. B Biol. Sci. 282, 20151589 (2015).Article 
CAS 

Google Scholar 
Fortuna, M. A., Ortega, R. & Bascompte, J. The Web of Life. ArXiv14032575 Q-Bio (2014).Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evol. 3, 919–927 (2019).PubMed 
Article 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
Article 

Google Scholar 
Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peng, J. et al. Linking ecosystem services and circuit theory to identify ecological security patterns. Sci. Total Environ. 644, 781–790 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Su, Y. et al. Modeling the optimal ecological security pattern for guiding the urban constructed land expansions. Urban For. Urban Green. 19, 35–46 (2016).Article 

Google Scholar 
Kowarik, I. Novel urban ecosystems, biodiversity, and conservation. Environ. Pollut. 159, 1974–1983 (2011).CAS 
PubMed 
Article 

Google Scholar 
Di Marco, M., Watson, J. E. M., Venter, O. & Possingham, H. P. Global biodiversity targets require both sufficiency and efficiency. Conserv. Lett. 9, 395–397 (2016).Article 

Google Scholar 
Kim, K.-H. & Pauleit, S. Landscape character, biodiversity and land use planning: The case of Kwangju City Region, South Korea. Land Use Policy 24, 264–274 (2007).Article 

Google Scholar 
Young, J. et al. Towards sustainable land use: Identifying and managing the conflicts between human activities and biodiversity conservation in Europe. Biodivers. Conserv. 14, 1641–1661 (2005).Article 

Google Scholar 
Dardonville, M., Urruty, N., Bockstaller, C. & Therond, O. Influence of diversity and intensification level on vulnerability, resilience and robustness of agricultural systems. Agric. Syst. 184, 102913 (2020).Article 

Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J. & Ellison, A. M. Ecological network metrics: Opportunities for synthesis. Ecosphere 8, e01900 (2017).Article 

Google Scholar 
Newman, M. E. J. Networks. (Oxford University Press, 2018).Levine, S. Several measures of trophic structure applicable to complex food webs. J. Theor. Biol. 83, 195–207 (1980).ADS 
Article 

Google Scholar 
Guimarães, P. R. The structure of ecological networks across levels of organization. Annu. Rev. Ecol. Evol. Syst. 51, 433–460 (2020).Article 

Google Scholar 
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Jordán, F., Benedek, Z. & Podani, J. Quantifying positional importance in food webs: A comparison of centrality indices. Ecol. Model. 205, 270–275 (2007).Article 

Google Scholar 
Jordán, F., Liu, W. & Davis, A. J. Topological keystone species: Measures of positional importance in food webs. Oikos 112, 535–546 (2006).Article 

Google Scholar 
Jordán, F., Okey, T. A., Bauer, B. & Libralato, S. Identifying important species: Linking structure and function in ecological networks. Ecol. Model. 216, 75–80 (2008).Article 

Google Scholar 
Jiang, L. Determination of keystone species in CSM food web: A topological analysis of network structure. Netw. Biol. 5, 13 (2015).
Google Scholar 
Abarca-Arenas, L. G., Franco-Lopez, J., Peterson, M. S., Brown-Peterson, N. J. & Valero-Pacheco, E. Sociometric analysis of the role of penaeids in the continental shelf food web off Veracruz. Mexico Based By-catch Fish. Res. 87, 46–57 (2007).
Google Scholar 
Abascal-Monroy, I. M. et al. Functional and structural food web comparison of Terminos Lagoon, Mexico in Three Periods (1980, 1998, and 2011). Estuaries Coasts 39, 1282–1293 (2016).Article 

Google Scholar 
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Windsor, F. M. et al. Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks. J. Appl. Ecol. 58, 2770–2782 (2021).Article 

Google Scholar 
Klaise, J. & Johnson, S. The origin of motif families in food webs. Sci. Rep. 7, 16197 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Estrada, E. Characterization of topological keystone species. Ecol. Complex. 4, 48–57 (2007).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Impacts on stream food webs of native and exotic forest: An intercontinental comparison. Ecology 84, 145–161 (2003).Article 

Google Scholar 
Bascompte, J., Melian, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dunne, J. A. et al. The roles and impacts of human hunter-gatherers in North Pacific marine food webs. Sci. Rep. 6, 21179 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gauzens, B., Legendre, S., Lazzaro, X. & Lacroix, G. Food-web aggregation, methodological and functional issues. Oikos 122, 1606–1615 (2013).Article 

Google Scholar 
Patonai, K. & Jordán, F. Aggregation of incomplete food web data may help to suggest sampling strategies. Ecol. Model. 352, 77–89 (2017).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Is resolution the solution?: The effect of taxonomic resolution on the calculated properties of three stream food webs. Freshw. Biol. 44, 413–422 (2000).Article 

Google Scholar 
Abarca-Arenas, L. G. & Ulanowicz, R. E. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296 (2002).Article 

Google Scholar 
Jordán, F. & Osváth, G. The sensitivity of food web topology to temporal data aggregation. Ecol. Model. 220, 3141–3146 (2009).Article 

Google Scholar 
European Commission. Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions: EU Biodiversity Strategy for 2030 Bringing nature back into our lives. Preprint at https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0380 (2020).European Parliament. European Parliament resolution of 9 June 2021 on the EU Biodiversity Strategy for 2030: Bringing nature back into our lives (P9_TA(2021)0277). Preprint at https://www.europarl.europa.eu/doceo/document/TA-9-2021-0277_EN.html (2021).Felson, A. J. & Ellison, A. M. Designing (for) Urban Food Webs. Front. Ecol. Evol. 9, 582041 (2021).Article 

Google Scholar 
Warren, P. et al. Urban food webs: Predators, prey, and the people who feed them. Bull. Ecol. Soc. Am. 87, 387–393 (2006).Article 

Google Scholar 
De Montis, A., Ganciu, A., Cabras, M., Bardi, A. & Mulas, M. Comparative ecological network analysis: An application to Italy. Land Use Policy 81, 714–724 (2019).Article 

Google Scholar 
Poisot, T. et al. Mangal—making ecological network analysis simple. Ecography 39, 384–390 (2016).Article 

Google Scholar 
Morris, Z. B., Weissburg, M. & Bras, B. Ecological network analysis of urban–industrial ecosystems. J. Ind. Ecol. 25, 193–204 (2021).Article 

Google Scholar 
Chamberlain, S. A. & Szöcs, E. taxize: Taxonomic search and retrieval in R. F1000 Research 2, 191 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkX. in Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008).Scotti, M. & Jordán, F. Relationships between centrality indices and trophic levels in food webs. Community Ecol. 11, 59–67 (2010).Article 

Google Scholar 
Gouveia, C., Móréh, Á. & Jordán, F. Combining centrality indices: Maximizing the predictability of keystone species in food webs. Ecol. Indic. 126, 107617 (2021).Article 

Google Scholar 
Allesina, S. & Pascual, M. Googling Food Webs: Can an Eigenvector Measure Species’ Importance for Coextinctions?. PLoS Comput. Biol. 5, e1000494 (2009).ADS 
MathSciNet 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Patro, S. G. K. & Sahu, K. K. Normalization: A preprocessing stage. https://doi.org/10.48550/ARXIV.1503.06462(2015).Reback, J. et al. pandas-dev/pandas: Pandas 1.2.3. (Zenodo, 2021). 10.5281/ZENODO.4572994.Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).Article 

Google Scholar 
Waskom, M. et al. mwaskom/seaborn: v0.11.1 (December 2020). (Zenodo, 2020). 10.5281/ZENODO.4379347.Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).Article 

Google Scholar 
Gao, P. & Kupfer, J. A. Uncovering food web structure using a novel trophic similarity measure. Ecol. Inform. 30, 110–118 (2015).Article 

Google Scholar 
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: Two levels of group detection in food webs. J. R. Soc. Interface 12, 20141176 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Rudiger, P. et al. holoviz/holoviews: Version 1.14.2. (Zenodo, 2021). 10.5281/ZENODO.4581995.Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar  More

Healthy foods are green foodsAn analysis of 57,000 foods reveals which have the best and worst environmental impacts. A team of researchers used an algorithm to estimate how much of each ingredient was in thousands of products sold in major UK supermarket chains. The scientists then gave food items an environmental-impact score out of 100 — with 100 being the worst — by combining the impacts of the ingredients in 100 grams of each product. They considered several factors, including greenhouse-gas emissions and land use.Healthier foods tended to have low environmental impacts, the team found. Products containing lamb and beef — such as ready-made meat pies — had the most serious environmental impact. The lowest-impact foods tended to be made with plants and included bread products, fruits, vegetables, grains and sugar-rich drinks. There were some notable exceptions: both nuts and seafood had a good nutrition score but relatively high environmental impacts.

Source: M. Clark et al. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).

COVID-19 reboundAfter the COVID-19 antiviral Paxlovid began to be used in late 2021, researchers noticed that some people experience a rebound in the virus and symptoms after taking the drug. Two recent studies suggest that it is surprisingly common for SARS-CoV-2 to return in untreated cases of COVID-19 too. To determine the frequency of rebound, Jonathan Li, a physician-scientist at Brigham and Women’s Hospital in Boston, Massachusetts, and his team analysed data from hundreds of people who were randomized to receive a placebo in a large-scale trial of COVID-19 antibody drugs. More than one-quarter of participants who were infected with SARS-CoV-2 reported a rebound in their symptoms, according to the study, which has not yet been peer reviewed. “The main take-home message is that recovery from COVID-19 is not going to be a linear process,” Li says.

Source: Deo, R. et al. Preprint at medRxiv https://doi.org/10.1101/2022.08.01.22278278 (2022).

How trees grow in a warmer worldThis chart shows how the early arrival of spring, due to climate change, affects the growth of trees and the amount of carbon they sequester. In a paper in Nature, researchers investigated the consequences of an early start to the growing season in deciduous forests. Leaf emergence is followed by carbon uptake through the process of photosynthesis. Over time, carbon can be captured for long-term sequestration if it contributes to the radial growth of stems or to wood formation. The areas under the curves represent annual growth in terms of: the amount of carbon captured by leaves (top curves, brown); annual radial growth (lower left curve, blue); and increase in woody biomass (lower right curve, red).The authors report that the early arrival of spring, which shifts the margins of the growing season (lighter curves), had little impact on the final annual tree-ring width or the amount of woody biomass produced, whereas high temperatures in summer had a negative effect on radial growth (dotted curve). Other studies (plotted here as dotted curves) indicate that high temperatures and related drought can suppress carbon capture and woody-biomass production.The study provides evidence that warmer springs have advanced the leaf emergence of temperate deciduous forests but have not substantially increased their wood production. This suggests that the extra uptake of carbon dioxide does not contribute to sustainable carbon sequestration in the trunks of long-lived trees, as our News & Views article explains. More