Ecology

Ice environment
Physicochemical analyses of individual ice blocks were conducted to observe eventual differences that could be attributed to spatially related gradual freezing–melting and fresh ice deposition, and to characterize the habitat that enables long-term survival of ice microbiota. All ice samples contained low concentrations of salts, indicating that they originated from recent clean snow. Concentrations of anions in the upper layers, Ice-1 and Ice-2, were similar. However, the bottom layer Ice-3 had distinctly higher electrical conductivity (EC), hardness and alkalinity, less nitrate, and more sulphate. This could indicate that this ice stratum includes a higher proportion of percolation water, which contains more ions than rain and snow as shown by the differences between the percolation water from the cave Planinska jama (that was used for preparing growth media) and the ice, as shown in Table 1. Total organic carbon (TOC) concentrations in the ice were in a range typical of karst streams22, and above the minimum values reported for surface streams, i.e. 0.1–36.6 mg/l23, indicating a significant input of organic matter for the underground ecosystem. TOC indicates an available in situ source of carbon for the ice microbiome. Nitrogen expressed as nitrate did not exhibit high values in ice samples (Table 1). In this respect, a parallel can be drawn with karst sediments, where microbes are commonly limited more by carbon and phosphorus than by nitrogen24.
Table 1 Characteristics of ice samples from Paradana.
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Besides EC and temperature, pH and dissolved oxygen are additionaly two influential parametres that can affect the abundance and taxonomic structure of microbial communities. pH was found to drive the shift in the community structure not only in habitats such as freshwater, marine sediments or soils but also in cold habitats as Antarctic soils25. In the current samples, the pH effect on the microbial community structure is less evident because all the values are rather similar (Table 1). Cave ice habitats with incoming waterflow are probably not oxygen depleted; on the contrary, for example in Antarctic lakes, glacial meltwater inflow is responsible for oxygen supersaturation26.
Isotopically, the Ice-3 stratum was significantly lighter than the stratum represented by Ice-1 and Ice-2 (Table 1). Correlation of δ2H and deuterium excess did not indicate any effect of kinetic fractionation during water freezing. Thus, intersection of the freezing-line determined by stable isotopes in samples Ice-1 to Ice-3 (δ2H = 6.48δ18O + 2.88) with the local meteoric-water line (LMWL) constructed for the precipitation station at Postojna (Supplementary Fig. S1) (δ2H = 7.95 δ18O + 12.13), provided the δ18O value − 6.3‰ for the original water before freezing. It represents relatively enriched water, but such a value is not uncommon in daily precipitation in Slovenia27. The ice lake in Paradana is presumably formed by the refreezing of water from melting snow accumulated during the winter months20, with some contribution of water dripping from the cave ceiling. November and December 2015 had only a few days with precipitation in Postojna (5 and 4, respectively). However, January and February 2016 had 12 and 20 days with precipitation and monthly totals were high, 152 mm and 312 mm, respectively. The air temperature data adjusted for the elevation difference between Postojna and the Trnovski gozd karst plateau (about 600 m) indicate that about one third of the precipitation in January and one half in February probably fell as snow. The rest was probably a mixture of solid and liquid precipitation, but heavy rains could have occurred as well (e.g. about 55.5 mm of precipitation was measured in Postojna on February 8–9, with mean daily air temperatures between 8 °C and 9 °C). Isotopic composition of precipitation varied significantly between and also during individual events. It is known that snow cover can preserve the isotopic composition of the original snowfalls for long periods28. However, individual snowfalls can mix at the entrance of the cave and the isotopic composition of snow accumulated in the cave can also be influenced by thaws caused by temporary increases of air temperature or rainfall. The isotopic composition of snowmelt water that eventually refreezes in the cave is therefore the result of many processes. Further research with better temporal and spatial resolution of samples and sampling of snowmelt water would be needed to improve knowledge on the dynamics and sources of ice formation. LMWLs known from the literature for other precipitation stations in Slovenia, i.e. Kozina, Portorož and Ljubljana that are given in Supplementary Fig. S1 provided δ18O values for the original water, which we consider too high (− 3.0‰ for LMWL from Portorož, − 3.8‰ for LMWL from Ljubljana and − 5,1‰ for LMWL from Kozina). Postojna is the closest precipitation station to the Paradana and the data on isotopic composition of precipitation cover the period of ice sampling (Supplementary Fig. S2). Therefore, the LMWL at Postojna could be the best representation of the isotopic composition of precipitation supplying water to the Paradana Ice Cave (after considering the elevation difference between the two sites, which is about 600 m).
When analysed in more detail, results obtained using the approach described above (to calculate the isotopic composition of the water that formed the sampled ice) also revealed the sensitivity of the constructed LMWL, the length of data series and extreme values. This is illustrated by records of isotopically very light precipitation in November and December 2015 (δ18O − 17.6‰ and − 14.2 δ18O, respectively). Although such isotopically light precipitation occurred in just two of the 27 months of the observation period, the two values changed the LMWL intercept significantly. However, because they did occur, they cannot be disregarded in the LMWL construction. Daily precipitation data indicate that in both cases monthly values were influenced dominantly by precipitation that fell during just one day (precipitation on those days represented almost the entire monthly precipitation). The LMWL intercept at Postojna without those two months would be 8.3, i.e. closely similar to values in Ljubljana and Kozina. Long-term data from Ljubljana show that the δ18O value of monthly precipitation was lower than − 16.0‰ (values around − 14.0‰ were quite abundant until 1986 and after 2004) in only 5 months in the years 1981–2010. Thus, precipitation with notable isotopically light values, as observed in Postojna between 21 and 23 November 2015 (92% of the precipitation fell on 22 November) appears to be rare in the study area. Nevertheless, it was observed, and it influenced the intercept of LMWL significantly.
It is worth noting that the δ18O values of Ice-1 and Ice-2 are higher than those reported for the Paradana Ice Cave by Carey et al.20. Deuterium excess is also significantly higher than the mean value reported for samples from different depths of ice by Carey et al.20. The difference in δ18O values could be related to different sampling sites. Carey et al.20 sampled the wall ice, whereas the samples collected during this study represent the frozen lake. Investigation of the difference in deuterium levels would be especially interesting. It could point at the input (either by overland flow from the cave entrance or by percolation from the vadose zone) of water from the autumn/winter months, with precipitation from the Eastern Mediterranean air masses having particularly high d-excess (up to 22‰). The Western Mediterranean air masses have d-excess of about 14‰, whereas air masses from the Atlantic have values of only about 10‰29. Late autumn to early winter precipitation in Slovenia (October to December) regularly exhibits high d-excess27. Unfortunately, the available data are insufficient to support analysis of the reason for high deuterium excess of the ice in detail. Study samples also display far lower concentrations of chloride, sulphate and nitrate than samples collected by Carey et al.20.
Concentration of microbes in cave ice
The upper ice stratum represented by Ice-1 and Ice-2 had comparable microbial load expressed in total ATP concentration and total cell counts, whereas the Ice-3 block exhibited significantly higher values (Table 1). Interestingly, the total cell counts of microorganisms in the ice samples was similar (4.67 × 104–15.15 × 104) to that recorded in the Pivka River (SW Slovenia) at the ponor connecting to the karst underground, i.e. 4.29 × 104–12.38 × 104, 30. A large proportion (51.0–85.4%) of entrapped microbes in the ice were viable, showing that they were able to survive ice formation and melting, or even several freezing–melting cycles. A relatively high cell viability can be linked to the availability of compatible solutes, indicated by correspondingly high TOC (Table 1). Not only do sugars and polyols increase microbial resistance to freezing, they can also be used inside the cell as carbon and nitrogen sources31. Higher concentration of salts in Ice-3 block was accompanied by the highest total cell counts and percentage of viable cells (Table 1). In ice from Scărişoara Cave total cell counts varied from 0.84 × 103 to 3.14 × 104 cells/ml with corresponding viability from 28.2 to 84.9%, but no correlation was observed between the ice age (0–13,000 years BP) or depth (0–25 m) and the total number of cells or viability14.
The media types used in this study differed in their ability to stimulate the growth of colonies. In general, nutrient-poor media and low temperatures resulted in higher colony counts in all samples. This phenomenon has been reported previously in cave microbiology, but was not correlated with phylogenetic diversity of microbes obtained on the growth media32. After 28 days of incubation, samples grown on the oligotrophic medium with percolation water (PWA) and cultivated at 10 °C produced the highest colony counts (Table 2). In context this indicates that cave percolation water contains soluble compounds that are not present in tap water and which support the growth of cave-ice microorganisms. With respect to individual samples, the highest colony counts were found in the Ice-3 sample, i.e., 167.37‰ of all cell biomass, determined by flow cytometry (Table 2), and this sample also contained the highest concentration of nutrients (Table 1). Cultivable anaerobic bacteria and fungi were detected in all the ice samples (Table 2).
Table 2 Colony counts (colony-forming units—CFU/ml) and their proportion to total cell counts determined by flow cytometry (‰) at different cultivation conditions and media.
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Communities in the ice blocks differed in the representation of r-strategists, with their predominance in the Ice-1, and a big difference between Ice-1 and Ice-2, the two ice samples from the same stratum. Interestingly, a more-uniform community structure in terms of r-strategists was displayed in ice block Ice-2–Ice-3 (Table 1). R-strategists commonly dominate in uncrowded and unstable habitats where resources are temporarily abundant and available; with development of a community, r-strategists are gradually replaced by the slow-growing equilibrium K-strategists33.
Cultivation on different media showed that the ice contained metabolically diverse microorganisms, aerobic and anaerobic bacteria and fungi. Two species of yellow-green algae were also recovered in cultures from samples Ice-2 and Ice-3. The two cultivated species, Chloridella glacialis and Ellipsoidion perminimum (for identification see Supplementary Fig. S3), were also found in green ice from Antarctica34. It is known from results of previous studies that algae in ice can survive and even grow under such adverse conditions34,35,36. They can also be well adapted to low light and low water temperature; for example they can thrive under ice- and snow-cover where the available photosynthetic photon flux density is only around the photosynthetic compensation point37. In these terms, and particularly in ice caves with available light, algae and cyanobacteria should not be overlooked as an important part of the ice microbial community. Interestingly, in Himalayan-type glaciers, the algae-rich layers in ice cores were suggested as providing accurate boundary markers of annual layers38. It remains unclear whether algae can be applied similarly as boundary markers in cave ice. Their existence is already known from some caves, for example in Hungary in a small ice cave colonizing surfaces of the ice39, Romania in Scarişoara Ice Cave at the ice/water interface40 and in New Mexico, USA, in Zuni Ice Cave giving the distinctive greenish patina of the layered ice35.
Bacterial community structure
Previous study of ice from the Paradana Ice Cave showed that it probably originates from local rainfall that reaches the cave as drip water after dissolving bedrock while percolating from the surface, and from snow that includes dust particles20. Thus, the largely impacted cave ice in Paradana has different sources, each bringing along a diverse and adaptable microbiota. 16S metagenomic analysis was conducted to describe the taxonomic composition of bacteria found in different ice blocks. Quality filtration of sequence readings gave a total number of 120,381 sequences in the three studied samples (Table 3). The number of operational taxonomic units (OTUs) varied from 185 in Ice-2 to 304 in Ice-1. This pattern was in alignment with values of alpha diversity parameters: extrapolated richness (Chao1), abundance-based coverage estimator (ACE) and Shannon index (Table 3). The rarefaction curves indicated that the diversity had been sampled sufficiently (Supplementary Fig. S4).
Table 3 Number of reads, OTUs, taxon richness and diversity indexes for cave ice samples.
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A Venn diagram of the distribution of 441 distinct OTUs found in the three studied samples is presented in Fig. 1. Observations showed that 119 OTUs (28.3%) occurred in all three samples and can be interpreted as “a core microbiome”. Three of these OTUs dominated microbial communities in individual samples (relative abundance range 14.5–56.5%) and corresponded to the members of the genera Pseudomonas, Lysobacter, and Sphingomonas, as discussed below. These were followed in abundance by Polaromonas, Flavobacterium, Rhodoferax, Nocardioides, and Pseudonocardia (relative abundance range 3.3–6.9%). Another 35 OTUs had relative abundance above 0.5% and the remaining 76 OTUs had relative abundance below 0.5%. The unique OTUs probably contribute to the variability due to internal variations within the ice block caused by incoming snow or the freezing of percolation water. For example, samples Ice-2 and Ice-3 were cut from the same ice block in a vertical ice profile, but differed in their content of dark, particulate, organic inclusions.
Figure 1

Prokaryotic OTU distribution in cave ice. The Venn diagram indicates the number of distinct and shared OTUs in ice samples Ice-1, Ice-2 and Ice-3.

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Members of 29 bacterial phyla were detected in the cave ice microbiome (Fig. 2, Supplementary Fig. S5). All samples were dominated by Proteobacteria, with relative abundances of 79.1% in Ice-2, 65.5% in Ice-3 and 55.9% in Ice-1.
Figure 2

Relative abundance of phyla in the cave-ice samples. Phyla with relative abundance  1% of phylotypes in at least one sample and corresponded to Firmicutes, Cyanobacteria and Gemmatimonadetes. Phototrophic bacterial phylotypes belonging to Cyanobacteria were recovered from all three samples. They represented 1.3% of phylotypes in sample Ice-1, but only 0.6% and 0.3% in samples Ice-2 and Ice-3 respectively, from where algae, C. glacialis and E. perminimum, were obtained via cultivation.
Phyla whose relative abundance was less than 1% were grouped together and classified as “Rare phyla”. These phyla comprised 2.2%, 1.5% and 1.2% of Ice-1, Ice-2, and Ice-3, respectively. Their relative abundance is presented in Supplementary Fig. S5.
Among the 31 classes detected in this study, members of Gammaproteobacteria were most abundant and represented 20.1% (Ice-1), 45.3% (Ice-2) and 42.5% (Ice-3) of total detected phylotypes (Fig. 3A). This proteobacterial group was also most abundant in the ice from Scărişoara Cave14. Actinobacteria represented the second most abundant group of phylotypes, with its relative abundances declining from 30.8% in Ice-1 to 26.2% in Ice-3 and 11.7% in Ice-2. Other notably abundant classes were Alpha- and Betaproteobacteria, whose abundances ranged from 9.6 to 26.3% and from 6.9 to 12.3%, respectively.
Figure 3

Heat-map analysis of the relative abundance of members of cave-ice prokaryotic communities at class (A) and genus (B) levels in Ice-1, Ice-2 and Ice-3. Phylotypes whose relative abundances at class level were  More

Construction of RUGs from rumen sequencing data
We produced 979G of Illumina sequencing data from 4 cows, 2 sheep, 4 red deer and 2 reindeer samples, then performed a metagenomic assembly of single samples and a co-assembly of all samples. This created a set of 391 dereplicated genomes (99% ANI (average nucleotide identity)) with estimated completeness ≥ 80% and estimated contamination ≤ 10% (Fig. 1). 284 of these genomes were produced from the single-sample assemblies and 107 were produced from the co-assemblies. 172 genomes were > 90% complete with contamination  90% complete with More

Tested animals
Altogether 73 individuals from seven species of subterranean rodents differing in body mass, phylogenetic relatedness, and sociality were studied (Table 1). All animals were adult non-breeders, or their breeding history was unknown in solitary species, but none of them showed signs of recent breeding, which may theoretically influence measured parameters. For the purpose of this study, we used the following taxa. African mole-rats (Bathyergidae): the social Ansell’s mole-rat Fukomys anselli (Burda, Zima, Scharff, Macholán & Kawalika 1999) occupies the miombo in a small area near Zambia’s capital Lusaka; another social species of the genus Fukomys is named here as Fukomys “Nsanje” because founders of the breeding colony were captured near town Nsanje in south Malawi. Although we used name Fukomys darlingi (Thomas 1895) for mole-rats from this population in previous studies (e.g.38,49), its taxonomic status is still not resolved; the social common mole-rat Cryptomys hottentotus hottentotus (Lesson, 1826) occurs in mesic and semi-arid regions of southern Africa; the solitary Cape dune mole-rat Bathyergus suillus (Schreber, 1782) inhabits sandy soils along the south-western coast of South Africa; and the solitary Cape mole-rat Georychus capensis (Pallas, 1778) occupies mesic areas of the South Africa50. In addition, we studied the social coruro Spalacopus cyanus (Molina, 1782) (Octodontidae) occupying various habitats in Chile51; and the solitary Upper Galilee Mountains blind mole rat Nannospalax galili (Nevo, Ivanitskaya & Beiles 2001) (Spalacidae) from Israel52. Further information about the species including number of individuals used in the study, their physiology and ecology is shown in Table 1.
All experiments were done on captive animals. Georychus capensis, C. hottentotus, and B. suillus, were captured about four months before the experiment, and kept in the animal facility at the University of Pretoria, South Africa (temperature: 23 °C; humidity: 40–60%, photoperiod: 12L:12D). The animals were housed in plastic boxes with wood shavings used as a bedding. Cryptomys hottentotus and G. capensis were fed with sweet potatoes; B. suillus with sweet potatoes, carrots, and fresh grass. Fukomys anselli, F. “Nsanje”, N. galili, and S. cyanus were kept for at least three years in captivity (or born in captivity) before the experiment in the animal facility at the University of South Bohemia in České Budějovice, Czech Republic (temperature: African mole-rats 25 °C, N. galili and S. cyanus 23 °C; humidity: 40–50%, photoperiod: 12L:12D). The animals were kept in terraria with peat as a substrate and fed with carrots, potatoes, sweet potatoes, beetroot, apple, and rodent dry food mix ad libitum.
Experimental design
We measured Tb and Ts in all species at six Tas (10, 15, 20, 25, 30 and 35 °C). Each individual of all species was measured only once in each Ta. Measurements were conducted in temperature controlled experimental rooms in České Budějovice and Pretoria. Each animal was tested on two experimental days.
The animals were placed in the experimental room individually in plastic buckets with wood shavings as bedding. On the first day, the experimental procedure started at Ta 25 °C. They spent 60 min of initial habituation in the first Ta after which Tb and Ts were measured as described in the following paragraphs. The Ta was then increased to 30 °C and 35 °C, respectively. After the experimental room reached the focal Ta, the animals were left minimally 30 min in each Ta to acclimate, and the measurements were repeated. Considering their relatively small body size, tested animals were very likely in thermal equilibrium after this period because mammals of a comparable body mass are thermally equilibrated after similar period of acclimation53,54,55,56. On the second day, the procedure was repeated with the initial Ta 20 °C and decreasing to 15 °C and 10 °C, respectively. The time span between the measurements of the same individual in different Ta was at least 150 min. Between experimental days, the animals were kept at 25 °C in the experimental room (individuals of social species were housed together with their family members).
Body temperature measurements
We used two sets of equipment to measure animal Tb and Ts. In B. suillus, G. capensis, and C. hottentotus, Tb was measured by intraperitoneally injected PIT tags ( More

1.
IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).
2.
IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2019).

3.
Adoption of the Paris Agreement FCCC/CP/2015/10/Add.1 (UNFCCC, 2015).

4.
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. New anthropogenic land use estimates for the Holocene: HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).
Article  Google Scholar 

5.
Griscom, B. W. et al. National mitigation potential from natural climate solutions in the tropics. Philos. Trans. R. Soc. B 375, 20190126 (2020).
CAS  Article  Google Scholar 

6.
Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).
Article  Google Scholar 

7.
Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).
CAS  Article  Google Scholar 

8.
Hansis, E., Davis, S. J. & Pongratz, J. Relevance of methodological choices for accounting of land use change carbon fluxes. Glob. Biogeochem. Cycles 29, 1230–1246 (2015).
CAS  Article  Google Scholar 

9.
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
CAS  Article  Google Scholar 

10.
IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Eggleston, S. et al.) (IGES, 2006).

11.
IPCC. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Buendia, E. C. et al.) (IPCC, 2019).

12.
Grassi, G. et al. Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change 8, 914–920 (2018).
CAS  Article  Google Scholar 

13.
Lee, D., Llopis, P., Waterworth, R., Roberts, G. & Pearson, T. Approaches to REDD+ Nesting: Lessons Learned from Country Experiences (World Bank, 2018).

14.
Streck, C. et al. Options for Enhancing REDD+ Collaboration in the Context of Article 6 of the Paris Agreement (Meridian Institute, 2017).

15.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  Article  Google Scholar 

16.
World Database on Protected Areas User Manual (UNEP, 2016); https://www.protectedplanet.net/en/resources/wdpa-manual

17.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
CAS  Article  Google Scholar 

18.
Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).
CAS  Article  Google Scholar 

19.
PRODES Deforestation (INPE, 2019); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes

20.
Ogle, S. M.et al. Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change. Carbon Balance Manag. 13, 9 (2018).

21.
Pearson, T. R., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).
Article  Google Scholar 

22.
Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).
Article  Google Scholar 

23.
Van Der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).
Article  Google Scholar 

24.
Kirschbaum, M. U., Zeng, G., Ximenes, F., Giltrap, D. L. & Zeldis, J. R. Towards a more complete quantification of the global carbon cycle. Biogeosciences 16, 831–846 (2019).

25.
Global Forest Observations Initiative. Integration of Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests 2nd edn (FAO, 2016).

26.
Potapov, P. et al. Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000–2017 Landsat time-series. Remote Sens. Environ. 232, 111278 (2019).
Article  Google Scholar 

27.
Federici, S., Lee, D. & Herold, M. Forest Mitigation: A Permanent Contribution to the Paris Agreement? (Climate and Land Use Alliance, 2017).

28.
Romijn, E. et al. Assessing change in national forest monitoring capacities of 99 tropical countries. Ecol. Manag. 352, 109–123 (2015).
Article  Google Scholar 

29.
Cook-Patton, S. Mapping potential carbon capture from global natural forest regrowth. Nature 585, 545–550 (2020).
CAS  Article  Google Scholar 

30.
The Global Stocktake (UNFCCC, 2015); https://unfccc.int/topics/science/workstreams/global-stocktake-referred-to-in-article-14-of-the-paris-agreement

31.
Austin, K. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).
Article  Google Scholar 

32.
Gaveau, D. L. et al. Four decades of forest persistence, clearance and logging on Borneo. PLoS ONE 9, e101654 (2014).
Article  Google Scholar 

33.
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
Article  Google Scholar 

34.
Gunarso, P., Hartoyo, M., Agus, F. & Killeen, T. in Reports from the Technical Panels of the 2nd Greenhouse Gas Working Group of the Roundtable on Sustainable Palm Oil (eds Killeen, T. J. & Goon, J.) 29–64 (RSPO, 2013).

35.
Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159 (2011).
Article  Google Scholar 

36.
Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12, 40–45 (2019).
CAS  Article  Google Scholar 

37.
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).
CAS  Article  Google Scholar 

38.
Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).
Article  Google Scholar 

39.
Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).
Article  Google Scholar 

40.
IPCC Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (eds Hiraishi, T. et al.) (IPCC, 2014).

41.
Methodological Tool: Estimation of Carbon Stocks and Change in Carbon Stocks in Dead Wood and Litter in A/R CDM Project Activities (UNFCCC, 2013); https://cdm.unfccc.int/methodologies/ARmethodologies/tools/ar-am-tool-12-v3.0.pdf

42.
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).
Article  Google Scholar 

43.
Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environ. Res. Lett. 13, 055002 (2018).
Article  Google Scholar 

44.
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).
Article  Google Scholar 

45.
Global Ecological Zones for FAO Forest Reporting: 2010 Update (FAO, 2012).

46.
Brus, D. et al. Statistical mapping of tree species over Europe. Eur. J. Res. 131, 145–157 (2012).
Article  Google Scholar 

47.
Del Lungo, A., Ball, J. & Carle, J. Global Planted Forests Thematic Study: Results and Analysis (FAO, 2006); http://www.fao.org/forestry/12139-03441d093f070ea7d7c4e3ec3f306507.pdf

48.
Portugal National Greenhouse Gas Inventory submitted to the UNFCCC, 1990–2018 (UNFCCC, 2020).

49.
Harris, N. L., Goldman, E. D. & Gibbes, S. Spatial Database on Planted Trees Version 1.0 https://www.wri.org/publication/spatialdatabase-planted-trees (WRI, 2019).

50.
Smith, J. E., Heath, L. S., Skog, K. E. & Birdsey, R. A. Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States General Technical Report (USDA, Forest Service, 2006); https://doi.org/10.2737/NE-GTR-343

51.
Ruefenacht, B. et al. Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing 74, 1379–1388 (2008).
Article  Google Scholar 

52.
Pan, Y. et al. Age structure and disturbance legacy of North American forests. Biogeosciences 8, 715–732 (2011) .
Article  Google Scholar 

53.
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).
Article  Google Scholar 

54.
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).
Article  Google Scholar 

55.
Roman-Cuesta, R. M. et al. Hotspots of gross emissions from the land use sector: patterns, uncertainties, and leading emission sources for the period 2000–2005 in the tropics. Biogeosciences 13, 4253–4269 (2016).
CAS  Article  Google Scholar 

56.
Carter, S. et al. Agriculture-driven deforestation in the tropics from 1990–2015: emissions, trends and uncertainties. Environ. Res. Lett. 13, 014002 (2017).
Article  Google Scholar  More

1.
Srinivasarao, C. et al. Grain yield and carbon sequestration potential of post monsoon sorghum cultivation in Vertisols in the semi arid tropics of central India. Geoderma 175–176, 90–97 (2012).
ADS  Article  CAS  Google Scholar 
2.
Yadav, R. K., Singh, S. P., Lal, D. & Kumar, A. Fodder production and soil health with conjunctive use of saline and good quality water in ustipsamments of a semi-arid region. L. Degrad. Dev. 18, 153–161 (2007).
Article  Google Scholar 

3.
Visser, S., Keesstra, S., Maas, G., de Cleen, M. & Molenaar, C. Soil as a basis to create enabling conditions for transitions towards sustainable land management as a Key to Achieve the SDGs by 2030. Sustainability 11, 6792 (2019).
Article  Google Scholar 

4.
Keesstra, S. et al. Soil-related Sustainable Development Goals: Four concepts to make land degradation neutrality and restoration work. Land 7, 133 (2018).
Article  Google Scholar 

5.
Sharma, B. R. & Minhas, P. S. Strategies for managing saline/alkali waters for sustainable agricultural production in South Asia. Agric. Water Manag. 78, 136–151 (2005).
Article  Google Scholar 

6.
Basak, N., Barman, A., Sundha, P. & Rai, A. K. Recent trends in soil salinity appraisal and management. In Soil Analysis: Recent Trends and Applications (eds Rakshit, A. et al.) 143–162 (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-2039-6_9.
Google Scholar 

7.
Liu, T. et al. Soil environment and growth adaptation strategies of Amorpha fruticosa as affected by mulching in a moderately saline wasteland. Land Degrad. Dev. 31, 2672–2683. https://doi.org/10.1002/ldr.3612 (2020).

8.
Yu, K. & Wang, G. Long-term impacts of shrub plantations in a desert–oasis ecotone: accumulation of soil nutrients, salinity, and development of herbaceour layer. Land Degrad. Dev. 29, 2681–2693 (2018).
Article  Google Scholar 

9.
Chauhan, C. P. S., Singh, R. B. & Gupta, S. K. Supplemental irrigation of wheat with saline water. Agric. Water Manag. 95, 253–258 (2008).
Article  Google Scholar 

10.
Qadir, M. et al. Productivity enhancement of salt-affected environments through crop diversification. Land Degrad. Dev. 19, 429–453 (2008).
Article  Google Scholar 

11.
Kumar, S. et al. Forage Production Technology for Arable Lands 48 (Indian Grassland and Fodder Research Institute, Jhansi 284 003, Uttar Pradesh, India. Technology Bulletin no.1/2012, 2012).

12.
Maas, E. V. & Grattan, S. R. Crop yields as affected by salinity. In Handbook of Plant and Crop Stress (ed. Pessarakli, M.) 55–108 (Marcel Dekker, New York, 1999).
Google Scholar 

13.
Kumar, S., Agrawal, R. K., Dixit A. K., Rai, A. K. & Rai, S. K. Forage crops and their management 60 (Indian Grassland and Fodder Research Institute, Jhansi 284 003, Uttar Pradesh, India. Technology Bulletin no. 2/2012, 2012).

14.
Jiang, J., Huo, Z., Feng, S. & Zhang, C. Effect of irrigation amount and water salinity on water consumption and water productivity of spring wheat in Northwest China. Field Crop. Res. 137, 78–88 (2012).
Article  Google Scholar 

15.
Nagaz, K., Masmoudi, M. M. & Mechlia, N. B. Impacts of irrigation regimes with saline water on carrot productivity and soil salinity. J. Saudi Soc. Agric. Sci. 11, 19–27 (2012).
CAS  Google Scholar 

16.
Mosaffa, H. R. & Sepaskhah, A. R. Performance of irrigation regimes and water salinity on winter wheat as influenced by planting methods. Agric. Water Manag. 216, 444–456 (2019).
Article  Google Scholar 

17.
Purakayastha, T. J. et al. Soil health card development for efficient soil management in Haryana, India. Soil Tillage Res. 191, 294–305 (2019).
Article  Google Scholar 

18.
Grigg, A. H., Sheridan, G. J., Pearce, A. B. & Mulligan, D. R. The effect of organic mulch amendments on the physical and chemical properties and revegetation success of a saline-sodic minespoil from central Queensland, Australia. Soil Res. 44, 97–105 (2006).
CAS  Article  Google Scholar 

19.
Wang, Q. et al. The effects of no-tillage with subsoiling on soil properties and maize yield: 12-year experiment on alkaline soils of Northeast China. Soil Tillage Res. 137, 43–49 (2014).
Article  Google Scholar 

20.
Basak, N. & Mandal, B. Soil quality management through carbon farming under intensive agriculture systems. Indian J. Fertil. 12, 54–64 (2019).
Google Scholar 

21.
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Change Biol. 21, 973–985 (2015).
ADS  Article  Google Scholar 

22.
de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl. Acad. Sci. USA 110, 14296–14301 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

23.
Anonymous. Vision 2050 31 (ICAR-Central Soil Salinity Research Institute, Karnal, India, 2015). www.cssri.res.in.

24.
Singh, A. Managing the salinization and drainage problems of irrigated areas through remote sensing and GIS techniques. Ecol. Indic. 89, 584–589 (2018).
Article  Google Scholar 

25.
Yao, R., Yang, J., Gao, P., Zhang, J. & Jin, W. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil Tillage Res. 128, 137–148 (2013).
Article  Google Scholar 

26.
Napoli, M., Marta, A. D., Zanchi, C. A. & Orlandini, S. Assessment of soil and nutrient losses by runoff under different soil management practices in an Italian hilly vineyard. Soil Tillage Res. 168, 71–80 (2017).
Article  Google Scholar 

27.
Sharma, D. P., Rao, K. V. G. K., Singh, K. N., Kumbhare, P. S. & Oosterbaan, R. J. Conjunctive use of saline and non-saline irrigation waters in semi-arid regions. Irrig. Sci. 15, 25–33 (1994).
Article  Google Scholar 

28.
Sharma, D. P., Singh, K. N. & Kumbhare, P. S. Reuse of agricultural drainage water for crop production. J. Indian Soc. Soil Sci. 49, 483–488 (2001).
Google Scholar 

29.
Heimsath, A. M., Dietrich, W. E., Nishiizumi, K. & Finkel, R. C. The soil production function and landscape equilibrium. Nature 388, 358–361 (1997).
ADS  CAS  Article  Google Scholar 

30.
Giacomini, S. J., Recous, S., Mary, B. & Aita, C. Simulating the effects of N availability, straw particle size and location in soil on C and N mineralization. Plant Soil 301, 289–301 (2007).
CAS  Article  Google Scholar 

31.
Nawaz, A., Farooq, M., Lal, R., Rehman, A. & Rehman, H. Comparison of conventional and conservation rice-wheat systems in Punjab, Pakistan. Soil Tillage Res. 169, 35–43 (2017)

32.
Rai, A. K., Bhardwaj, R., Sureja, A. K. & Bhattacharyya, D. Effect of pine needles on inorganic nitrogen pools of soil treated with fertilizers and manure under cabbage crop. Range Manag. Agrofor. 32, 118–123 (2011).
Google Scholar 

33.
Dong, Q., Yang, Y., Yu, K. & Feng, H. Effects of straw mulching and plastic film mulching on improving soil organic carbon and nitrogen fractions, crop yield and water use efficiency in the Loess Plateau, China. Agric. Water Manag. 201, 133–143 (2018).
Article  Google Scholar 

34.
Wade, J. et al. Improved soil biological health increases corn grain yield in N fertilized systems across the Corn Belt. Sci. Rep. 10, 3917 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Mitran, T., Mani, P. K., Bandyopadhyay, P. K. & Basak, N. Effects of organic amendments on soil physical attributes and aggregate-associated phosphorus under long-term rice-wheat cropping. Pedosphere 28, 823–832 (2018).
Article  Google Scholar 

36.
Sundha, P., Basak, N., Rai, A. K., Yadav, R. K. & Sharma, D. K. N and P release pattern in saline-sodic soil amended with gypsum and municipal solid waste compost. J. Soil Salin. Water Qual. 9, 145–155 (2017).
Google Scholar 

37.
Margenot, A. J. et al. Can conservation agriculture improve phosphorus (P) availability in weathered soils? Effects of tillage and residue management on soil P status after 9 years in a Kenyan Oxisol. Soil Tillage Res. 166, 157–166 (2017).
Article  Google Scholar 

38.
Deubel, A., Hofmann, B. & Orzessek, D. Long-term effects of tillage on stratification and plant availability of phosphate and potassium in a loess chernozem. Soil Tillage Res. 117, 85–92 (2011).
Article  Google Scholar 

39.
Wei, K., Chen, Z., Zhu, A., Zhang, J. & Chen, L. Application of 31P NMR spectroscopy in determining phosphatase activities and P composition in soil aggregates influenced by tillage and residue management practices. Soil Tillage Res. 138, 35–43 (2014).
Article  Google Scholar 

40.
Domínguez, R., Campillo, C. D., Pena, F. & Delgado, A. Effect of soil properties and reclamation practices on phosphorus dynamics in reclaimed calcareous marsh soils from the Guadalquivir Valley, SW Spain. Arid Land Res. Manag. 15, 203–221 (2001).
Article  Google Scholar 

41.
Ghosh, S., Wilson, B., Ghoshal, S., Senapati, N. & Mandal, B. Organic amendments influence soil quality and carbon sequestration in the Indo-Gangetic plains of India. Agric. Ecosyst. Environ. 156, 134–141 (2012).
Article  Google Scholar 

42.
Ding, Z. et al. The integrated effect of salinity, organic amendments, phosphorus fertilizers, and deficit irrigation on soil properties, phosphorus fractionation and wheat productivity. Sci. Rep. 10, 2736 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Mitran, T., Mani, P. K., Basak, N., Biswas, S. & Mandal, B. Organic amendments influence on soil biological indices and yield in rice-based cropping system in Coastal Sundarbans of India. Commun. Soil Sci. Plant Anal. 48, 170–185 (2017).
CAS  Article  Google Scholar 

44.
Salinas-Garcı́a, J. R. et al. Tillage effects on microbial biomass and nutrient distribution in soils under rain-fed corn production in central-western Mexico. Soil Tillage Res. 66, 143–152 (2002).
Article  Google Scholar 

45.
Basak, N., Datta, A., Mitran, T., Mandal, B. & Mani, P. K. Impact of organic and mineral inputs onto soil biological and metabolic activities under a long-term rice-wheat cropping system in sub-tropical Indian Inceptisols. J. Environ. Biol. 37, 83–89 (2016).
PubMed  PubMed Central  Google Scholar 

46.
Dixit, A. K. et al. Soil properties, crop productivity and energetics under different tillage practices in fodder sorghum + cowpea–wheat cropping system. Arch. Agron. Soil Sci. 65, 492–506. https://doi.org/10.1080/03650340.2018.1507024 (2019).
CAS  Article  Google Scholar 

47.
Rai, A. K., Bhardwaj, R. & Sureja, A. K. Effect of mixing pine needles litters on soil biological properties and phosphorus availability in soil amended with fertilizers and manures. Commun. Soil Sci. Plant Anal. 48, 1052–1058. https://doi.org/10.1080/00103624.2017.1323096 (2017).
CAS  Article  Google Scholar 

48.
Wichern, F., Islam, M. R., Hemkemeyer, M., Watson, C. & Joergensen, R. G. Organic amendments alleviate salinity effects on soil microorganisms and mineralisation processes in aerobic and anaerobic paddy rice soils. Front. Sustain. Food Syst. 4, 30 (2020).
Article  Google Scholar 

49.
Meena, M. D. et al. Effects of municipal solid waste compost, rice-straw compost and mineral fertilisers on biological and chemical properties of a saline soil and yields in a mustard–pearl millet cropping system. Soil Res. 54, 958–969 (2016).
CAS  Article  Google Scholar 

50.
Yan, N. & Marschner, P. Response of microbial activity and biomass to increasing salinity depends on the final salinity, not the original salinity. Soil Biol. Biochem. 53, 50–55 (2012).
CAS  Article  Google Scholar 

51.
Gao, Y. et al. Effects of salinization and crude oil contamination on soil bacterial community structure in the Yellow River Delta region, China. Appl. Soil Ecol. 86, 165–173 (2015).
Article  Google Scholar 

52.
Liang, Y. et al. Organic manure stimulates biological activity and barley growth in soil subject to secondary salinization. Soil Biol. Biochem. 37, 1185–1195 (2005).
CAS  Article  Google Scholar 

53.
Yuan, B.-C., Li, Z.-Z., Liu, H., Gao, M. & Zhang, Y.-Y. Microbial biomass and activity in salt affected soils under arid conditions. Appl. Soil Ecol. 35, 319–328 (2007).
Article  Google Scholar 

54.
Paul, E. A. & Clark, F. E. Soil Microbiology and Biochemistry (Academic Press, San Diego, 1989).
Google Scholar 

55.
Bünemann, E. K. et al. Soil quality—a critical review. Soil Biol. Biochem. 120, 105–125 (2018).
Article  CAS  Google Scholar 

56.
Silva, A. & Stocker, L. What is a transition? Exploring visual and textual definitions among sustainability transition networks. Glob. Environ. Change 50, 60–74 (2018).
Article  Google Scholar 

57.
Minhas, P. S. & Gupta, R. K. Quality of Irrigation Water: Assessment and Management 123 (Indian Council of Agricultural Research, New Delhi, 1992).
Google Scholar 

58.
Gelaye, K. K., Zehetner, F., Loiskandl, W. & Klik, A. Effects of soil texture and groundwater level on leaching of salt from saline fields in Kesem irrigation scheme, Ethiopia. Soil Water Res. 14, 221–228 (2019).
CAS  Article  Google Scholar 

59.
Cerdà, A., Rodrigo-Comino, J., Giménez-Morera, A. & Keesstra, S. D. An economic, perception and biophysical approach to the use of oat straw as mulch in Mediterranean rainfed agriculture land. Ecol. Eng. 108, 162–171 (2017).
Article  Google Scholar 

60.
Rodrigo-Comino, J., Davis, J., Keesstra, S. D. & Cerdà, A. Updated measurements in vineyards improves accuracy of soil erosion rates. Agron. J. 110, 411–417 (2018).
Article  Google Scholar 

61.
Kumar, A. et al. Impact of water deficit (salt and drought) stress on physiological, biochemical and yield attributes on wheat (Triticum aestivum) varieties. Indian J. Agric. Sci. 88, 1624–1632 (2018).
CAS  Google Scholar 

62.
Soil Survey Division Staff. Soil Survey Manual (United States Department of Agriculture, Washington. Handbook no 18, 1993).

63.
Richards, L. A. Diagnosis and Improvement of Saline and Alkali Soils 160 (Government Printing Office (Superindent of Documents), Washington, DC, 1954).

64.
Jackson, M. L. Soil Chemical Analysis 498 (Printice Hall of India Pvt Ltd., New Delhi, 1967).
Google Scholar 

65.
Subbiah, B. V. & Asija, G. L. A rapid procedure for assessment of available nitrogen in rice soils. Curr. Sci. 25, 259–260 (1956).
CAS  Google Scholar 

66.
Voroney, R. P. & Paul, E. A. Determination of kC and kNin situ for calibration of the chloroform fumigation-incubation method. Soil Biol. Biochem. 16, 9–14 (1984).
CAS  Article  Google Scholar 

67.
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).
CAS  Article  Google Scholar 

68.
Dick, R. P., Breakwell, D. P. & Turco, R. F. Soil Enzyme activities and biodiversity measurements as integrative microbiological indicators. Methods Assess. Soil Qual. https://doi.org/10.2136/sssaspecpub49.c15 (1997).
Article  Google Scholar 

69.
Andrews, S. S. & Carroll, C. R. Designing a soil quality assessment tool for sustainable agroecosystem management. Ecol. Appl. 11, 1573–1585 (2001).
Article  Google Scholar 

70.
Mandal, B., Basak, N., Singha, R. S. & Biswas, S. Soil health measurement techniques. In Soil Health: Concept, Status and Monitoring (eds. Katyal, J. C. et al.) 1–98 (Indian Society of Soil Science, New Delhi. Bulletin no. 30, 2016). More

1.
Zoumis T, Schmidt A, Grigorova L, Calmano W. Contaminants in sediments: remobilisation and demobilisation. Sci Total Environ. 2001;266:195–202.
CAS  PubMed  Article  Google Scholar 
2.
SØNdergaard M, Jeppesen E, Lauridsen TL, Skov C, Van Nes EH, Roijackers R, et al. Lake restoration: successes, failures and long-term effects. J Appl Ecol. 2007;44:1095–105.
Article  CAS  Google Scholar 

3.
Zhao CS, Yang Y, Yang ST, Xiang H, Wang F, Chen X, et al. Impact of spatial variations in water quality and hydrological factors on the food-web structure in urban aquatic environments. Water Res. 2019;153:121–33.
CAS  PubMed  Article  Google Scholar 

4.
Wang C, Zhai W, Shan B. Oxygen microprofile in the prepared sediments and its implication for the sediment oxygen consuming process in a heavily polluted river of China. Environ Sci Pollut Res Int. 2016;23:8634–43.
CAS  PubMed  Article  Google Scholar 

5.
Liu B, Han RM, Wang WL, Yao H, Zhou F. Oxygen microprofiles within the sediment-water interface studied by optode and its implication for aeration of polluted urban rivers. Environ Sci Pollut Res Int. 2017;24:9481–94.
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Rysgaard S, Risgaard-Petersen N, Sloth NP, Jensen K, Nielsen LP. Oxygen regulation of nitrification and denitrification in sediments. Limnol Oceanogr. 2003;39:1643–52.
Article  Google Scholar 

7.
Broman E, Sachpazidou V, Pinhassi J, Dopson M. Oxygenation of hypoxic coastal Baltic Sea sediments impacts on chemistry, microbial community composition, and metabolism. Front Microbiol. 2017;8:2453–2453.
PubMed  PubMed Central  Article  Google Scholar 

8.
Zheng B, Wang L, Liu L. Bacterial community structure and its regulating factors in the intertidal sediment along the Liaodong Bay of Bohai Sea, China. Microbiol Res. 2014;169:585–92.
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Yu P, Wang J, Chen J, Guo J, Yang H, Chen Q. Successful control of phosphorus release from sediments using oxygen nano-bubble-modified minerals. Sci Total Environ. 2019;663:654–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Papageorgiou N, Kalantzi I, Karakassis I. Effects of fish farming on the biological and geochemical properties of muddy and sandy sediments in the Mediterranean Sea. Mar Environ Res. 2010;69:326–36.
CAS  PubMed  Article  Google Scholar 

11.
Pfeffer C, Larsen S, Song J, Dong MD, Besenbacher F, Meyer RL, et al. Filamentous bacteria transport electrons over centimetre distances. Nature. 2012;491:218–21.
CAS  PubMed  Article  Google Scholar 

12.
Nielsen LP, Risgaard-Petersen N. Rethinking sediment biogeochemistry after the discovery of electric currents. Annu Rev Mar Sci. 2015;7:425–42.
Article  Google Scholar 

13.
Burdorf LDW, Tramper A, Seitaj D, Meire L, Hidalgo-Martinez S, Zetsche E-M, et al. Long-distance electron transport occurs globally in marine sediments. Biogeosciences. 2017;14:683–701.
CAS  Article  Google Scholar 

14.
Sandfeld T, Marzocchi U, Petro C, Schramm A, Risgaard-Petersen N. Electrogenic sulfide oxidation mediated by cable bacteria stimulates sulfate reduction in freshwater sediments. ISME J. 2020;14:1233–46.
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Muller H, Bosch J, Griebler C, Damgaard LR, Nielsen LP, Lueders T, et al. Long-distance electron transfer by cable bacteria in aquifer sediments. ISME J. 2016;10:2010–9.
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Malkin SY, Rao AM, Seitaj D, Vasquez-Cardenas D, Zetsche EM, Hidalgo-Martinez S, et al. Natural occurrence of microbial sulphur oxidation by long-range electron transport in the seafloor. ISME J. 2014;8:1843–54.
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Rao AMF, Malkin SY, Hidalgo-Martinez S, Meysman FJR. The impact of electrogenic sulfide oxidation on elemental cycling and solute fluxes in coastal sediment. Geochim et Cosmochim Acta. 2016;172:265–86.
CAS  Article  Google Scholar 

18.
Marzocchi U, Palma E, Rossetti S, Aulenta F, Scoma A. Parallel artificial and biological electric circuits power petroleum decontamination: the case of snorkel and cable bacteria. Water Res. 2020;173:115520.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Kjeldsen KU, Schreiber L, Thorup CA, Boesen T, Bjerg JT, Yang T, et al. On the evolution and physiology of cable bacteria. Proc Natl Acad Sci USA. 2019;116:19116–25.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Schauer R, Risgaard-Petersen N, Kjeldsen KU, Bjerg JJT, Jorgensen BB, Schramm A, et al. Succession of cable bacteria and electric currents in marine sediment. ISME J. 2014;8:1314–22.
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Burdorf LDW, Malkin SY, Bjerg JT, van Rijswijk P, Criens F, Tramper A, et al. The effect of oxygen availability on long-distance electron transport in marine sediments. Limnol Oceanogr. 2018;63:1799–816.
CAS  Article  Google Scholar 

22.
Zhou J, Deng Y, Luo F, He Z, Yang Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. mBio. 2011;2:e00122-11.
Article  Google Scholar 

23.
Faust K, Raes J. Microbial interactions: from networks to model. Nat Rev Microbiol. 2012;10:538–50.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. mBio. 2010;1:e00169–110.
PubMed  PubMed Central  Google Scholar 

25.
Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.
PubMed  Article  CAS  PubMed Central  Google Scholar 

26.
Tu Q, Yan Q, Deng Y, Michaletz ST, Buzzard V, Weiser MD, et al. Biogeographic patterns of microbial co-occurrence ecological networks in six American forests. Soil Biol Biochem. 2020;148:107897.
CAS  Article  Google Scholar 

27.
Hu A, Ju F, Hou L, Li J, Yang X, Wang H, et al. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environ Microbiol. 2017;19:4993–5009.
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Deng Y, Jiang YH, Yang Y, He Z, Luo F, Zhou J. Molecular ecological network analyses. BMC Bioinform. 2012;13:113.
Article  Google Scholar 

29.
Kruskal JB. Nonmetric multidimensional scaling: a numerical method. Psychometrika. 1964;29:115–29.
Article  Google Scholar 

30.
Guo X, Feng J, Shi Z, Zhou X, Yuan M, Tao X, et al. Climate warming leads to divergent succession of grassland microbial communities. Nat Clim Change. 2018;8:813–8.
Article  Google Scholar 

31.
Legendre P, Legendre LF. Numerical ecology. 3rd ed. Oxford, UK: Elsevier; 2012.

32.
van den Wollenberg AL. Redundancy analysis an alternative for canonical correlation analysis. Psychometrika. 1977;42:207–19.
Article  Google Scholar 

33.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018. https://www.R-project.org/.

34.
Goslee SC, Urban DL. The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw. 2007;22:1–19.
Article  Google Scholar 

35.
Luo Y, Hui D, Zhang D. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology. 2006;87:53–63.
PubMed  Article  Google Scholar 

36.
Scholz VV, Meckenstock RU, Nielsen LP, Risgaard-Petersen N. Cable bacteria reduce methane emissions from rice-vegetated soils. Nat Commun. 2020;11:1878.
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Risgaard-Petersen N, Kristiansen M, Frederiksen RB, Dittmer AL, Bjerg JT, Trojan D, et al. Cable bacteria in freshwater sediments. Appl Environ Microbiol. 2015;81:6003–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Coates JD, Anderson RT, Lovley DR. Oxidation of polycyclic aromatic hydrocarbons under sulfate-reducing conditions. Appl Environ Microbiol. 1996;62:1099–101.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Coates JD, Chakraborty R, McInerney MJ. Anaerobic benzene biodegradation—a new era. Res Microbiol. 2002;153:621–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Matturro B, Cruz Viggi C, Aulenta F, Rossetti S. Cable bacteria and the bioelectrochemical snorkel: the natural and engineered facets playing a role in hydrocarbons degradation in marine sediments. Front Microbiol. 2017;8:952.
PubMed  PubMed Central  Article  Google Scholar 

41.
Huisingh J, McNeill JJ, Matrone G. Sulfate reduction by a Desulfovibrio species isolated from sheep rumen. Appl Microbiol. 1974;28:489–97.
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Gupta A, Dutta A, Sarkar J, Panigrahi MK, Sar P. Low-abundance members of the Firmicutes facilitate bioremediation of soil impacted by highly acidic mine drainage from the Malanjkhand Copper Project, India. Front Microbiol. 2018;9:2882–2882.
PubMed  PubMed Central  Article  Google Scholar 

43.
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682–682.
PubMed  PubMed Central  Article  Google Scholar 

44.
Coates JD, Councell T, Ellis DJ, Lovley DR. Carbohydrate oxidation coupled to Fe(III) reduction, a novel form of anaerobic metabolism. Anaerobe. 1998;4:277–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Caccavo F Jr., Lonergan DJ, Lovley DR, Davis M, Stolz JF, McInerney MJ. Geobacter sulfurreducens sp. nov., a hydrogen- and acetate-oxidizing dissimilatory metal-reducing microorganism. Appl Environ Microbiol. 1994;60:3752–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Loesche WJ. Oxygen sensitivity of various anaerobic bacteria. Appl Microbiol. 1969;18:723–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Duncan SH, Louis P, Thomson JM, Flint HJ. The role of pH in determining the species composition of the human colonic microbiota. Environ Microbiol. 2009;11:2112–22.
PubMed  Article  PubMed Central  Google Scholar 

48.
Borin S, Brusetti L, Mapelli F, D’Auria G, Brusa T, Marzorati M, et al. Sulfur cycling and methanogenesis primarily drive microbial colonization of the highly sulfidic Urania deep hypersaline basin. Proc Natl Acad Sci USA. 2009;106:9151–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Yun Y, Wang H, Man B, Xiang X, Zhou J, Qiu X, et al. The relationship between pH and bacterial communities in a single karst ecosystem and its implication for soil acidification. Front Microbiol. 2016;7:1955–1955.
PubMed  PubMed Central  Article  Google Scholar 

50.
Sohn JH, Kwon KK, Kang JH, Jung HB, Kim SJ. Novosphingobium pentaromativorans sp. nov., a high-molecular-mass polycyclic aromatic hydrocarbon-degrading bacterium isolated from estuarine sediment. Int J Syst Evol Microbiol. 2004;54:1483–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Rodriguez-Conde S, Molina L, González P, García-Puente A, Segura A. Degradation of phenanthrene by Novosphingobium sp. HS2a improved plant growth in PAHs-contaminated environments. Appl Microbiol Biotechnol. 2016;100:10627–36.
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Sha S, Zhong J, Chen B, Lin L, Luan T. Novosphingobium guangzhouense sp. nov., with the ability to degrade 1-methylphenanthrene. Int J Syst Evolut Microbiol. 2017;67:489–97.
CAS  Article  Google Scholar 

53.
Ghosal D, Ghosh S, Dutta TK, Ahn Y. Current state of knowledge in microbial degradation of polycyclic aromatic hydrocarbons (PAHs): a review. Front Microbiol. 2016;7:1369.
PubMed  PubMed Central  Google Scholar 

54.
Yan Z, Zhang Y, Wu H, Yang M, Zhang H, Hao Z, et al. Isolation and characterization of a bacterial strain Hydrogenophaga sp. PYR1 for anaerobic pyrene and benzo[a]pyrene biodegradation. RSC Adv. 2017;7:46690–8.
CAS  Article  Google Scholar 

55.
Weiss JV, Rentz JA, Plaia T, Neubauer SC, Merrill-Floyd M, Lilburn T, et al. Characterization of neutrophilic Fe(II)-oxidizing bacteria isolated from the rhizosphere of wetland plants and description of Ferritrophicum radicicola gen. nov. sp. nov., and Sideroxydans paludicola sp. nov. Geomicrobiol J. 2007;24:559–70.
CAS  Article  Google Scholar 

56.
Lenchi N, Inceoğlu O, Kebbouche-Gana S, Gana ML, Llirós M, Servais P, et al. Diversity of microbial communities in production and injection waters of Algerian oilfields revealed by 16S rRNA gene Amplicon 454 pyrosequencing. PLoS ONE. 2013;8:e66588.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Nogales B, Moore ER, Llobet-Brossa E, Rossello-Mora R, Amann R, Timmis KN. Combined use of 16S ribosomal DNA and 16S rRNA to study the bacterial community of polychlorinated biphenyl-polluted soil. Appl Environ Microbiol. 2001;67:1874–84.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Xu P, Xiao E, Zeng L, He F, Wu Z. Enhanced degradation of pyrene and phenanthrene in sediments through synergistic interactions between microbial fuel cells and submerged macrophyte Vallisneria spiralis. J Soils Sediment. 2019;19:2634–49.
CAS  Article  Google Scholar 

59.
Singleton DR, Jones MD, Richardson SD, Aitken MD. Pyrosequence analyses of bacterial communities during simulated in situ bioremediation of polycyclic aromatic hydrocarbon-contaminated soil. Appl Microbiol Biotechnol. 2013;97:8381–91.
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Lu XY, Zhang T, Fang HH. Bacteria-mediated PAH degradation in soil and sediment. Appl Microbiol Biotechnol. 2011;89:1357–71.
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Wang C, Huang Y, Zhang Z, Wang H. Salinity effect on the metabolic pathway and microbial function in phenanthrene degradation by a halophilic consortium. AMB Express. 2018;8:67.
PubMed  PubMed Central  Article  CAS  Google Scholar 

62.
Dastgheib SM, Amoozegar MA, Khajeh K, Shavandi M, Ventosa A. Biodegradation of polycyclic aromatic hydrocarbons by a halophilic microbial consortium. Appl Microbiol Biotechnol. 2012;95:789–98.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Vasquez-Cardenas D, van de Vossenberg J, Polerecky L, Malkin SY, Schauer R, Hidalgo-Martinez S, et al. Microbial carbon metabolism associated with electrogenic sulphur oxidation in coastal sediments. ISME J. 2015;9:1966–78.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Wasmund K, Cooper M, Schreiber L, Lloyd KG, Baker BJ, Petersen DG, et al. Single-cell genome and group-specific dsrAB sequencing implicate marine members of the class Dehalococcoidia (phylum Chloroflexi) in sulfur cycling. mBio. 2016;7:e00266-16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Liang B, Wang L-Y, Mbadinga SM, Liu J-F, Yang S-Z, Gu J-D, et al. Anaerolineaceae and Methanosaeta turned to be the dominant microorganisms in alkanes-dependent methanogenic culture after long-term of incubation. AMB Express. 2015;5:117–117.
PubMed  Article  CAS  PubMed Central  Google Scholar 

66.
Logan BE, Rossi R, Ragab AA, Saikaly PE. Electroactive microorganisms in bioelectrochemical systems. Nat Rev Microbiol. 2019;17:307–19.
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Pisciotta JM, Zaybak Z, Call DF, Nam J-Y, Logan BE. Enrichment of microbial electrolysis cell biocathodes from sediment microbial fuel cell bioanodes. Appl Environ Microbiol. 2012;78:5212–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Wang B, Zhang H, Yang Y, Xu M. Diffusion and filamentous bacteria jointly govern the spatiotemporal process of sulfide removal in sediment microbial fuel cells. Chem Eng J. 2021;405:126680.
CAS  Article  Google Scholar 

69.
Li X, Li Y, Zhang X, Zhao X, Sun Y, Weng L, et al. Long-term effect of biochar amendment on the biodegradation of petroleum hydrocarbons in soil microbial fuel cells. Sci Total Environ. 2019;651:796–806.
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Malvankar NS, King GM, Lovley DR. Centimeter-long electron transport in marine sediments via conductive minerals. ISME J. 2015;9:527–31.
CAS  PubMed  Article  Google Scholar 

71.
Bjerg JT, Boschker HTS, Larsen S, Berry D, Schmid M, Millo D, et al. Long-distance electron transport in individual, living cable bacteria. Proc Natl Acad Sci USA. 2018;115:5786–91.
CAS  PubMed  Article  Google Scholar 

72.
Meysman FJR, Cornelissen R, Trashin S, Bonné R, Martinez SH, van der Veen J, et al. A highly conductive fibre network enables centimetre-scale electron transport in multicellular cable bacteria. Nat Commun. 2019;10:4120.
PubMed  PubMed Central  Article  CAS  Google Scholar 

73.
Teske A. Cable bacteria, living electrical conduits in the microbial world. Proc Natl Acad Sci USA. 2019;116:18759.
CAS  PubMed  Article  Google Scholar 

74.
Risgaard-Petersen N, Revil A, Meister P, Nielsen LP. Sulfur, iron-, and calcium cycling associated with natural electric currents running through marine sediment. Geochim et Cosmochim Acta. 2012;92:1–13.
CAS  Article  Google Scholar 

75.
Risgaard-Petersen N, Damgaard LR, Revil A, Nielsen LP. Mapping electron sources and sinks in a marine biogeobattery. J Geophys Res Biogeosci. 2014;119:1475–86.
CAS  Article  Google Scholar  More

In this study, we showed that the expression of cry and cp4-epsps genes in wild cotton altered the secretion of EFN, the associations with different ant species, and the levels of herbivore damage on target plants. Wcry constantly maintained a high production of EFN, regardless of the MeJA treatment, but nectar production was minimal in Wcp4-epsps. These changes in nectar inducibility seem to modify the composition of ant communities, foster the dominance of the generalist and defensive species C. planatus in Bt plants and the presence of ants without defensive role, M. ebeninum, in the herbicide tolerant genotype, while W plants had both defending species (C. planatus, C. rectangularis aulicus and P. gracilis) and invasive ant species (P. longicornis) in the same proportion. Furthermore, herbivore damage and its associated ant community were different according to the introgressed transgene.
Wild and introgressed cotton do not display phenotypic equivalence in natural conditions
In general, it has been assumed that introgressed and wild genotypes should display similar phenotypes in the absence of the selection agents targeted by transgenes. However, when we compared the control group and the three genotypes, we registered different nectar secretion patterns among them (Fig. 1). Similar results have been registered in populations of bt rice and glyphosate-tolerant sunflowers living in natural conditions where introgressed plants are different from their wild relatives5.
Transgene expression modified indirect induced defences in wild cotton
Most plants are able to induce responses after herbivore damage and/or phytohormone exogenous application (i.e. jasmonic acid, JA; methyl jasmonate, MeJA; and salicylic acid, SA)11,28,29. However, unlike wild plants without transgenes, individuals with transgenes were not sensitive to the induction treatment with MeJA for increasing their EFN production (Fig. 1). These results contrast with previous reports on cultivated varieties, such as Bt and glyphosate-resistant (cp4-epsps), in which direct defences such as gossypol terpenoids (160%), hemigossypolone (160%), helicoids 1|4 (213%) and indirect defenses, such as volatile compounds (VOCs) (171.2%) and extrafloral nectar (EFN) (133%), were reported to increase in plants sprinkled with JA and MeJA21,28,29,30.
The inability of plants with transgenes to have the production of extrafloral nectar induced in them was related to different processes dependent on the identity of the transgenes in question. Whereas Wcry control plants had a high EFN production equivalent to the induced state of W plants, EFN production in Wcp4-epsps plants was inhibited. Contrasting these findings with results obtained under controlled conditions (i.e. greenhouse and crop conditions)3,21, we suggest that EFN production is linked to genotypes with transgenes and abiotic stress in the coastal dunes, because transgenes are connected to main metabolic pathways that respond to stressful conditions21.
Wild cotton with cp4-epsps
In the absence of herbicides acting as a selection agent, wild plants with cp4-epsps exhibited large differences compared to wild plants without them. Their low nectar production ( > 8 µg/mL) (Fig. 1) could be linked to the crosstalk between the jasmonate and the salicylate (SA) pathways (Fig. 4, orange and purple section). In G. hirsutum and other species, SA signalling has been proven to negatively affect JA signalling (e.g. Zea mays, Solanum lycopersicum, Nicotiana tabacum and Arabidopsis thaliana)31,32,33: therefore, we suggest an interference between the SA and JA pathways given previous reports that an over-expression of the cp4-epsps gene modifies the second part of the shikimate pathway (post-chorismate), which leads to the synthesis of essential amino acids as phenylalanine, tryptophan, or tyrosine, the latter being a precursor of benzoic acid BE, and SA34,35 (Fig. 4, purple section). This evidence highlights that hidden crosstalk effects among different metabolic pathways can scale up and modify plant phenotypes (e.g. extrafloral nectar production).
Figure 4

A diagram illustrating how the expression of cry (A) and cp4-epsps (B) in absence of their selection agent (pests and glyphosate) can affect the extrafloral nectar production. The extrafloral nectar (EFN) production is an induced defence that can be triggered by foliar herbivory, mechanical damage, and exogenous application of phytohormones (i.e. jasmonic acid, methyl jasmonate, and salicylic acid). These factors activate the octadecanoid pathway, and therefore, the production of extrafloral nectar, (A) aqua rectangle. The (C) section is an example of this reaction in a wild cotton plant (without transgenes). After damage, the key genes (yellow mesh) of the octadecanoid pathway are activated and produce extrafloral nectar. Another scenario is when the wild cotton expresses cry genes (A section), in this case, the key genes of the octadecanoid pathway interact synergistically with the cry transgene (green mesh). This triggers an over-expression of the production of EFN (aqua thick arrow), switching from inducible to constitutive responses. When the plants express cp4-epsps (B section), the production of extrafloral nectar is reduced or inhibited. A possible answer is an over-expression of the epsps gene (gold curve arrow), that increased production of salicylic acid which creates a crosstalk between shikimate and octadecanoid pathways (black cross-talk arrow). When the shikimate pathway is activated, the principal inducible defence is the production of volatile organic compounds (VOCs) (pink rectangle).

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Wild cotton with cry
Wild cotton plants with cry genes continuously produced EFN as a constitutive defence (Fig. 1), in equivalent quantities as the induced state of W plants. EFN production is regulated by the octadecanoid signalling pathway, which can be activated by herbivore damage, mechanical damage, and phytohormones, such as JA and MeJA21,28 (Fig. 4, green section). However, for cotton, a specific elicitor is not necessary36. Four key genes for the synthesis of JA and MeJA have been described: AOS, AOC, HPL, and COI137. In Bt maize, studies comparing GM corn and its isogenic lines report an increase of 24% in phenols and 63% of DIMBOA (2,4-dihidroxi-7-metoxi-1,4-benzoxazin-3-ona; natural defences against lepidopteran herbivores)11. This is consistent with observations of a synergy between maize direct defences and Bt genes, after exogenous applications of JA (Fig. 4, orange section). Considering the latter, we suggest that Wcry cotton may present a similar response, as the genes activating the JA pathway are GhAOS and GhCOI1 (homologs to maize JA biosynthesis genes: ZmAOS and ZmCOI1), in addition to Ghppo1, which confers natural resistance to lepidopteran pest, such as H. armigera38. The interaction of cry with other genes could modify the production of EFN in Wcry plants.
Effect of the transgenes’ expression on ants associated to wild cotton
We identified eight species of ants harvesting EFN (Table 2), but with distinctive communities as a function of the plant genotype. This result suggests that the change in quantity, and possibly the composition and quality of EFN, can influence the ant community associated with G. hirsutum39,40,41.
Changes in plant reward production could potentially compromise the attraction of natural enemies of herbivores42. In our study, the availability of EFN was modified. Although species richness was the same as in W plants (Table 2), the most abundant ant species associated with Wcp4-epsps plants, M. ebeninum, is considered a generalist species. Moreover, due to the lack of aggressive behaviour, this species does not represent an effective biotic defence43. The high abundance of this non-defensive species could be associated with the greater herbivore damage observed in Wcp4-epsps plants (Fig. 2). In contrast, W or Wcry plants showed a greater abundance of more aggressive ant species such as C. planatus, C. rectangulatus, and P. brunneus and significantly less herbivore damage.
In Wcry cotton, the community of patrolling ants was mainly dominated by C. planatus, in both treatments (control and induction). Interestingly, although the amount of nectar did not vary between treatments, the abundance of ants was significantly different. The dominance of a single ant species could have benefited the plants with increased indirect defence, reducing herbivore damage and promoting a greater seed production per plant, as described in Turnera ulmifolia44, Schomburgkia tibicinis45, and Opuntia stricta42. However, considering the aggressive and dominant behaviour of C. planatus, there may be ecological costs through antagonistic relationships with pollinators. Ants can interrupt pollination and affect plant fitness25,46,47. The outcome of these mutualistic and antagonistic interactions requires further study.
Effects of transgenes on herbivore damage
Considering that the type of mutualism that cotton sustains with ants is defensive, we suggest that the change we observed in the composition of ants is likely to have influenced herbivore damage in the different genotypes, which in turn has the potential to reduce fitness as shown by other studies of cotton48,49,50. However, a study carried out on wild upland cotton reported that plants tolerate intermediate levels of leaf damage inflicted by leaf-chewing insects ( More

Sample collection
Algae were sampled from August 27th to September 21st (2017) from seven populations also collected for Bonthond et al. [28], including three native populations; Akkeshi (Japan), Soukanzan (Japan), Rongcheng (China); and four non-native populations; Pleudihen-sur-Rance (France), Nordstrand (Germany), Cape Charles Beach (Viriginia) and Tomales Bay (California, Fig. 1, Table S1). Individuals fixed to hard substratum (see [30]) were sampled at least a meter apart from one another and stored in separate plastic bags. As A. vermiculophyllum has a complex, haplodiplontic life-cycle only diploids were included in the experiment. Life-cycle stages were identified in the field with a dissecting microscope or post-hoc by microsatellite genotyping [31]. After transport in coolers and storage at 4 °C in the lab, bags with algae were shipped to Germany, arriving within 4–6 days after collection. In the climate room (15 °C), individuals were transferred to separate transparent aquaria with transparent lids, containing 1.75 L artificial seawater (ASW) prepared from tap water and 24 gL−1 artificial sea salt without CaCO3 (high CaCO3 concentrations increase disease risk, Weinberger data unpublished) and exposed to 12 h of light per day (86.0 µmol m−2s−1 at the water surface). Aquaria were moderately aerated with aeration stones. Per population, four diploid individuals were acclimated over 31–32 days to climate room conditions prior to starting the experiment. Water was exchanged weekly with new ASW enriched with 2 mL Provasoli-Enrichment Solution (PES; [32]). At the start of the experiment, wet weight was recorded and individuals were divided into two parts of ~10 g each and placed into two plastic tanks with 1.75 L water and 2 mL PES (Fig. 1).
Fig. 1: Schematic overview of the sampling design and experimental process.

Algae were collected from native populations Rongcheng (ron), Soukanzan (sou) and Akkeshi (akk) and non-native populations Tomales Bay (tmb), Cape Charles Beach (ccb), Pleudihen-sur-Rance (fdm) and Nordstrand (nor). In the climate room algae were acclimated for 5 weeks and divided into two thalli. One of the thalli was treated for three days with an antibiotic mixture after which both groups were monitored for six weeks, during which the treated algae received inoculum with each water change. Microbiota samples were taken in the field (tfield), directly after disturbance (t0) and after 1, 2, 4 and 6 weeks (t1, t2, t4 and t6).

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Experimental setup
To rigorously disturb the microbial community, one of each of the pairs of aquaria containing the same algal individual was treated with a combination of antibiotics, aiming to increase the effectivity (10 mgL−1 ampicillin, 10 mgL−1 streptomycin, 10 mgL−1 chloramphenicol) and the other (control) remained untreated. All experimental work was conducted with disposable gloves and sterilized equipment, to minimize contamination. After three days, the water was removed from all tanks (treated and control) and the wet weight was recorded for all algae. All individuals were rinsed with one 1.75 L volume ASW and re-incubated in 1.75 L ASW. Subsequently, both groups received new ASW with 2 mL PES weekly and individuals treated with antibiotics received also 2 mL inoculum. The inoculum was prepared from individuals of all 7 populations, following the procedure to remove epibiota as described in Bonthond et al. [28]. Briefly, apical fragments of 1 g were separated from the thallus and transferred to 50 mL tubes containing 15 ± 1 glass beads (3 mm) and 15 mL ASW and vortexed for 6 min to separate epibiota from the algal tissue. In total, 8 samples were prepared from one individual per population. The resulting suspensions were pooled and mixed with glycerol (20% final glycerol concentration), aliquoted in 50 mL tubes and stored at −20 °C. For each water exchange, a new aliquot was defrosted at room temperature and added to the water of treated algae. Wet weight was recorded weekly with water exchanges. Before weighing the individual on aluminum foil, it was dipped twice on a separate aluminum foil sheet, to reduce attached water in a systematic way. Endo- and epiphytic microbiota were sampled in the field (tfield, [28]), at the start of the experiment (t0), after one week (t1), two weeks (t2), four weeks (t4) and six weeks (t6, Fig. 1). To equalize acclimation times across populations the experiment was stacked into five groups (Table S2). At each sampling moment, 0.5 or 1 g of tissue was separated from all individuals with sterilized forceps and epibiota were extracted similarly to the preparation of the inoculum. The resulting suspension was filtered through 0.2 µm pore size PCTA filters. Both the filters and the remaining tissue were preserved at −20 °C.
DNA extraction and amplicon sequencing
Tissue samples were defrosted, rinsed with absolute ethanol and DNA free water to remove hydro- and moderately lipophilic cells and molecules from the surface and cut to fragments with sterilized scissors. DNA was then extracted from these fragments (endobiota) and from preserved filters (epibiota) using the ZYMO Fecal/soil microbe kit (D6102; ZYMO-Research, Irvine, CA, USA), following the manufacturer’s protocol. Although this method to separate endo- and epibiota was shown to resolve distinct communities [28], tightly attached epiphytic cells may not be completely removed from the surface and detectable in endophytic samples as well. Two 16S-V4 amplicon libraries, over which the samples were divided in a balanced manner, were prepared as in Bonthond et al. [28], following the two-step PCR strategy from Gohl et al. [33], using the same set of 16S-V4 target primers and indexing primers. The libraries were sequenced on the Illumina MiSeq platform (2×300 PE) at the Max-Planck-Institute for Evolutionary Biology (Plön, Germany), including four negative DNA extraction controls and four negative and positive PCR controls (mock communities; ZYMO-D6311). The fastq files were de-multiplexed (0 mismatches). Relevant field samples from Bonthond et al. [28] were combined with the new dataset and assembled, quality filtered and classified altogether with Mothur v1.43.0 [34] using the SILVA-alignment release 132 [35]. Sequences were clustered within 3% dissimilarity into OTUs using the opticlust algorithm. Mitochondrial, chloroplast, eukaryotic and unclassified sequences were removed. To prepare the community matrix we discarded singleton OTUs (in the full dataset), samples with More