Ecology

1.
Wood, T. J. & Goulson, D. The environmental risks of neonicotinoid pesticides: a review of the evidence post 2013. Environ. Sci. Pollut. Res. Int. 24, 17285–17325. https://doi.org/10.1007/s11356-017-9240-x (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 
2.
Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480. https://doi.org/10.1146/annurev-ento-011019-025151 (2020).
CAS  Article  PubMed  Google Scholar 

3.
Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).
Article  Google Scholar 

4.
Gallai, N., Salles, J.-M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821. https://doi.org/10.1016/j.ecolecon.2008.06.014 (2009).
Article  Google Scholar 

5.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809. https://doi.org/10.1371/journal.pone.0185809 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971. https://doi.org/10.1016/j.cub.2019.06.069 (2019).
CAS  Article  PubMed  Google Scholar 

7.
Casida, J. E. & Durkin, K. A. Neuroactive insecticides: targets, selectivity, resistance, and secondary effects. Annu. Rev. Entomol. 58, 99–117. https://doi.org/10.1146/annurev-ento-120811-153645 (2013).
CAS  Article  PubMed  Google Scholar 

8.
Popp, J., Pető, K. & Nagy, J. Pesticide productivity and food security. A review. . Agron. Sustain. Dev. 33, 243–255. https://doi.org/10.1007/s13593-012-0105-x (2013).
Article  Google Scholar 

9.
Casida, J. E. Neonicotinoids and other insect nicotinic receptor competitive modulators: progress and prospects. Annu. Rev. Entomol. 63, 125–144. https://doi.org/10.1146/annurev-ento-020117-043042 (2018).
CAS  Article  PubMed  Google Scholar 

10.
Matsuda, K., Ihara, M. & Sattelle, D. B. Neonicotinoid insecticides: molecular targets, resistance, and toxicity. Annu. Rev. Pharmacol. Toxicol. 60, 241–255. https://doi.org/10.1146/annurev-pharmtox-010818-021747 (2020).
CAS  Article  PubMed  Google Scholar 

11.
Goulson, D. REVIEW: an overview of the environmental risks posed by neonicotinoid insecticides. J. Appl. Ecol. 50, 977–987. https://doi.org/10.1111/1365-2664.12111 (2013).
Article  Google Scholar 

12.
Eng, M. L., Stutchbury, B. J. M. & Morrissey, C. A. A neonicotinoid insecticide reduces fueling and delays migration in songbirds. Science 365, 1177. https://doi.org/10.1126/science.aaw9419 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

13.
Yamamuro, M. et al. Neonicotinoids disrupt aquatic food webs and decrease fishery yields. Science (New York, N.Y.) 366, 620. https://doi.org/10.1126/science.aax3442 (2019).
ADS  CAS  Article  Google Scholar 

14.
Han, W., Tian, Y. & Shen, X. Human exposure to neonicotinoid insecticides and the evaluation of their potential toxicity: an overview. Chemosphere 192, 59–65. https://doi.org/10.1016/j.chemosphere.2017.10.149 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

15.
Nauen, R., Ebbinghaus-Kintscher, U., Salgado, V. L. & Kaussmann, M. Thiamethoxam is a neonicotinoid precursor converted to clothianidin in insects and plants. Pestic. Biochem. Physiol. 76, 55–69. https://doi.org/10.1016/S0048-3575(03)00065-8 (2003).
CAS  Article  Google Scholar 

16.
EFSA. Peer review of the pesticide risk assessment of the active substance thiacloprid. Eur. Food Saf. Auth. J. 17, e05595 https://doi.org/10.2903/j.efsa.2019.5595 (2019).
Article  Google Scholar 

17.
Nicholls, E. et al. Monitoring neonicotinoid exposure for bees in rural and peri-urban areas of the U.K. during the transition from pre- to post-moratorium. Environ. Sci. Technol. 52, 9391–9402. https://doi.org/10.1021/acs.est.7b06573 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

18.
Wintermantel, D. et al. Neonicotinoid-induced mortality risk for bees foraging on oilseed rape nectar persists despite EU moratorium. Sci. Total Environ. 704, 135400. https://doi.org/10.1016/j.scitotenv.2019.135400 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

19.
Cressey, D. Fears for bees as UK lifts insecticide ban. Nature News. https://www.nature.com/news/fears-for-bees-as-uk-lifts-insecticide-ban-1.18052 (2015).

20.
Lamsa, J., Kuusela, E., Tuomi, J., Juntunen, S. & Watts, P. C. Low dose of neonicotinoid insecticide reduces foraging motivation of bumblebees. Proc. R. Soc. B 285, 20180506 https://doi.org/10.1098/rspb.2018.0506 (2018).
Article  PubMed  PubMed Central  Google Scholar 

21.
Tasman, K., Rands, S. A. & Hodge, J. J. The neonicotinoid insecticide imidacloprid disrupts bumblebee foraging rhythms and sleep. iScience 23, 101827 https://doi.org/10.2139/ssrn.3586989 (2020).
Article  PubMed  PubMed Central  Google Scholar 

22.
Palmer, M. J. et al. Cholinergic pesticides cause mushroom body neuronal inactivation in honeybees. Nat. Commun. 4, 1634. https://doi.org/10.1038/ncomms2648 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Aso, Y. et al. Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila. eLife 3, e04580. https://doi.org/10.7554/eLife.04580 (2014).
Article  PubMed  PubMed Central  Google Scholar 

24.
Barnstedt, O. et al. Memory-relevant mushroom body output synapses are cholinergic. Neuron 89, 1237–1247. https://doi.org/10.1016/j.neuron.2016.02.015 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Helfrich-Förster, C. Sleep in insects. Annu. Rev. Entomol. 63, 69–86. https://doi.org/10.1146/annurev-ento-020117-043201 (2018).
CAS  Article  PubMed  Google Scholar 

26.
Peng, Y. C. & Yang, E. C. Sublethal dosage of imidacloprid reduces the microglomerular density of honey bee mushroom bodies. Sci. Rep. 6, 19298. https://doi.org/10.1038/srep19298 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Smith, D. B. et al. Insecticide exposure during brood or early-adult development reduces brain growth and impairs adult learning in bumblebees. Proc. R. Soc. B 287, 20192442. https://doi.org/10.1098/rspb.2019.2442 (2020).
CAS  Article  PubMed  Google Scholar 

28.
Andrione, M., Vallortigara, G., Antolini, R. & Haase, A. Neonicotinoid-induced impairment of odour coding in the honeybee. Sci. Rep. 6, 38110. https://doi.org/10.1038/srep38110 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Chouhan, N. S., Wolf, R., Helfrich-Förster, C. & Heisenberg, M. Flies remember the time of day. Curr. Biol. 25, 1619–1624. https://doi.org/10.1016/j.cub.2015.04.032 (2015).
CAS  Article  PubMed  Google Scholar 

30.
Flyer-Adams, J. G. et al. Regulation of olfactory associative memory by the circadian clock output signal Pigment-dispersing factor (PDF). J. Neurosci. 40, 9066–9077https://doi.org/10.1523/JNEUROSCI.0782-20.2020 (2020).
Article  PubMed  PubMed Central  Google Scholar 

31.
Zwaka, H. et al. Context odor presentation during sleep enhances memory in honeybees. Curr. Biol. 25(21), 869–2874. https://doi.org/10.1016/j.cub.2015.09.069 (2015).
CAS  Article  Google Scholar 

32.
Seugnet, L., Suzuki, Y., Donlea, J. M., Gottschalk, L. & Shaw, P. J. Sleep deprivation during early-adult development results in long-lasting learning deficits in adult Drosophila. Sleep 34, 137–146. https://doi.org/10.1093/sleep/34.2.137 (2011).
Article  PubMed  PubMed Central  Google Scholar 

33.
Tackenberg, M. C. et al. Neonicotinoids disrupt circadian rhythms and sleep in honey bees. Sci. Rep. 10, 17929. https://doi.org/10.1038/s41598-020-72041-3 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Helfrich-Forster, C. et al. The extraretinal eyelet of Drosophila: development, ultrastructure, and putative circadian function. J. Neurosci. 22, 9255–9266. https://doi.org/10.1523/JNEUROSCI.22-21-09255.2002 (2002).
Article  PubMed  PubMed Central  Google Scholar 

35.
Muraro, N. I. & Ceriani, M. F. Acetylcholine from visual circuits modulates the activity of arousal neurons in Drosophila. J. Neurosci. 35, 16315. https://doi.org/10.1523/JNEUROSCI.1571-15.2015 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
McCarthy, E. V. et al. Synchronized bilateral synaptic inputs to Drosophila melanogaster neuropeptidergic rest/arousal neurons. J. Neurosci. 31, 8181–8193. https://doi.org/10.1523/jneurosci.2017-10.2011 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Parisky, K. M. et al. PDF cells are a GABA-responsive wake-promoting component of the Drosophila sleep circuit. Neuron 60, 672–682. https://doi.org/10.1016/j.neuron.2008.10.042 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Ly, S., Pack, A. I. & Naidoo, N. The neurobiological basis of sleep: Insights from Drosophila. Neurosci. Biobehav. Rev. 87, 67–86. https://doi.org/10.1016/j.neubiorev.2018.01.015 (2018).
Article  PubMed  PubMed Central  Google Scholar 

39.
Wegener, C., Hamasaka, Y. & Nassel, D. R. Acetylcholine increases intracellular Ca2+ via nicotinic receptors in cultured PDF-containing clock neurons of Drosophila. J. Neurophysiol. 91, 912–923. https://doi.org/10.1152/jn.00678.2003 (2004).
CAS  Article  PubMed  Google Scholar 

40.
Renn, S. C., Park, J. H., Rosbash, M., Hall, J. C. & Taghert, P. H. A pdf neuropeptide gene mutation and ablation of PDF neurons each cause severe abnormalities of behavioral circadian rhythms in Drosophila. Cell 99, 791–802. https://doi.org/10.1016/s0092-8674(00)81676-1 (1999).
CAS  Article  PubMed  Google Scholar 

41.
Schlichting, M., Menegazzi, P., Rosbash, M. & Helfrich-Förster, C. A distinct visual pathway mediates high-intensity light adaptation of the circadian clock in Drosophila. J. Neurosci. 39, 1621. https://doi.org/10.1523/JNEUROSCI.1497-18.2018 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Lelito, K. & Shafer, O. Reciprocal cholinergic and GABAergic modulation of the small ventrolateral pacemaker neurons of Drosophila’s circadian clock neuron network. J. Neurophysiol. 107, 2096–2108. https://doi.org/10.1152/jn.00931.2011 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Nitabach, M. N. et al. Electrical hyperexcitation of lateral ventral pacemaker neurons desynchronizes downstream circadian oscillators in the fly circadian circuit and induces multiple behavioral periods. J. Neurosci. 26, 479–489. https://doi.org/10.1523/jneurosci.3915-05.2006 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Cao, G. & Nitabach, M. N. Circadian control of membrane excitability in Drosophila melanogaster lateral ventral clock neurons. J. Neurosci. 28, 6493–6501. https://doi.org/10.1523/jneurosci.1503-08.2008 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Fernández, M. P., Berni, J. & Ceriani, M. F. Circadian remodeling of neuronal circuits involved in rhythmic behavior. PLoS Biol. 6, e69. https://doi.org/10.1371/journal.pbio.0060069 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

46.
Park, J. H. et al. Differential regulation of circadian pacemaker output by separate clock genes in Drosophila. Proc. Natl. Acad. Sci. 97, 3608. https://doi.org/10.1073/pnas.97.7.3608 (2000).
ADS  CAS  Article  PubMed  Google Scholar 

47.
Martelli, F. et al. Low doses of the neonicotinoid insecticide imidacloprid induce ROS triggering neurological and metabolic impairments in Drosophila. Proc. Natl. Acad. Sci. 117(41), 25840–25850. https://doi.org/10.1073/pnas.2011828117 (2020).
CAS  Article  PubMed  Google Scholar 

48.
Numata, H., Miyazaki, Y. & Ikeno, T. Common features in diverse insect clocks. Zool. Lett. 1, 10. https://doi.org/10.1186/s40851-014-0003-y (2015).
Article  Google Scholar 

49.
Farris, S. & Sinakevitch, I. Development and evolution of the insect mushroom bodies: towards the understanding of conserved developmental mechanisms in a higher brain center. Arthropod Struct. Dev. 32, 79–101. https://doi.org/10.1016/S1467-8039(03)00009-4 (2003).
Article  PubMed  Google Scholar 

50.
Jones, A. K. & Sattelle, D. B. In Insect Nicotinic Acetylcholine Receptors (ed Thany, S. H.) 25–43 (Springer New York, 2010).

51.
Homem, R. A. et al. Evolutionary trade-offs of insecticide resistance—the fitness costs associated with target-site mutations in the nAChR of Drosophila melanogaster. Mol. Ecol. 29, 2661–2675. https://doi.org/10.1111/mec.15503 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Blacquiere, T., Smagghe, G., van Gestel, C. A. & Mommaerts, V. Neonicotinoids in bees: a review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992. https://doi.org/10.1007/s10646-012-0863-x (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Stanley, D. A. & Raine, N. E. Bumblebee colony development following chronic exposure to field-realistic levels of the neonicotinoid pesticide thiamethoxam under laboratory conditions. Sci. Rep. 7, 8005. https://doi.org/10.1038/s41598-017-08752-x (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

54.
Whitehorn, P. R., O’Connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351. https://doi.org/10.1126/science.1215025 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

55.
Williamson, S. M., Willis, S. J. & Wright, G. A. Exposure to neonicotinoids influences the motor function of adult worker honeybees. Ecotoxicology 23, 1409–1418. https://doi.org/10.1007/s10646-014-1283-x (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Wright, G. A., Softley, S. & Earnshaw, H. Low doses of neonicotinoid pesticides in food rewards impair short-term olfactory memory in foraging-age honeybees. Sci. Rep. 5, 15322. https://doi.org/10.1038/srep15322 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Malik, B. R. & Hodge, J. J. Drosophila adult olfactory shock learning. J. Vis. Exp. 90, 50107. https://doi.org/10.3791/50107 (2014).
CAS  Article  Google Scholar 

58.
Hodge, J. J. & Stanewsky, R. Function of the Shaw potassium channel within the Drosophila circadian clock. PLoS ONE 3, e2274–e2274. https://doi.org/10.1371/journal.pone.0002274 (2008).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Moffat, C. et al. Neonicotinoids target distinct nicotinic acetylcholine receptors and neurons, leading to differential risks to bumblebees. Sci. Rep. 6, 24764. https://doi.org/10.1038/srep24764 (2016).
ADS  Article  PubMed  PubMed Central  Google Scholar 

60.
Busto, G. U., Cervantes-Sandoval, I. & Davis, R. L. Olfactory learning in Drosophila. Physiology 25, 338–346. https://doi.org/10.1152/physiol.00026.2010 (2010).
CAS  Article  PubMed  Google Scholar 

61.
Lyons, L. C. & Roman, G. Circadian modulation of short-term memory in Drosophila. Learn. Mem. 16, 19–27. https://doi.org/10.1101/lm.1146009 (2009).
Article  PubMed  PubMed Central  Google Scholar 

62.
Depetris-Chauvin, A. et al. Adult-specific electrical silencing of pacemaker neurons uncouples the molecular oscillator from circadian outputs. Curr. Biol. 21, 1783–1793. https://doi.org/10.1016/j.cub.2011.09.027 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Baz, E.-S., Wei, H., Grosshans, J. & Stengl, M. Calcium responses of circadian pacemaker neurons of the cockroach Rhyparobia maderae to acetylcholine and histamine. J. Comp. Physiol. A. 199, 365–374. https://doi.org/10.1007/s00359-013-0800-3 (2013).
CAS  Article  Google Scholar 

64.
Sheeba, V. et al. Large ventral lateral neurons modulate arousal and sleep in Drosophila. Curr. Biol. 18, 1537–1545. https://doi.org/10.1016/j.cub.2008.08.033 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

65.
Thany, S. H. Insect Nicotinic Acetylcholine Receptors (Springer, New York, 2011).
Google Scholar 

66.
Gill, R. J. & Raine, N. E. Chronic impairment of bumblebee natural foraging behaviour induced by sublethal pesticide exposure. Funct. Ecol. 28, 1459–1471. https://doi.org/10.1111/1365-2435.12292 (2014).
Article  Google Scholar 

67.
Bloch, G., Bar-Shai, N., Cytter, Y. & Green, R. Time is honey: circadian clocks of bees and flowers and how their interactions may influence ecological communities. Phil. Trans. R. Soc. B 372, 20160256. https://doi.org/10.1098/rstb.2016.0256 (2017).
CAS  Article  Google Scholar 

68.
van Alphen, B., Yap, M. H. W., Kirszenblat, L., Kottler, B. & van Swinderen, B. A dynamic deep sleep stage in Drosophila. J. Neurosci. 33, 6917. https://doi.org/10.1523/JNEUROSCI.0061-13.2013 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Buhl, E., Higham, J. P. & Hodge, J. J. L. Alzheimer’s disease-associated tau alters Drosophila circadian activity, sleep and clock neuron electrophysiology. Neurobiol. Dis. 130, 104507. https://doi.org/10.1016/j.nbd.2019.104507 (2019).
CAS  Article  PubMed  Google Scholar 

70.
Levine, J. D., Funes, P., Dowse, H. B. & Hall, J. C. Signal analysis of behavioral and molecular cycles. BMC Neurosci. 3, 1. https://doi.org/10.1186/1471-2202-3-1 (2002).
Article  PubMed  PubMed Central  Google Scholar 

71.
Faville, R., Kottler, B., Goodhill, G. J., Shaw, P. J. & van Swinderen, B. How deeply does your mutant sleep? Probing arousal to better understand sleep defects in Drosophila. Sci. Rep. 5, 8454. https://doi.org/10.1038/srep08454 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
Donelson, N. C. et al. High-resolution positional tracking for long-term analysis of Drosophila sleep and locomotion using the “tracker” program. PLoS ONE 7, e37250. https://doi.org/10.1371/journal.pone.0037250 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

73.
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676. https://doi.org/10.1038/nmeth.2019 (2012).
CAS  Article  Google Scholar  More

Determination of the mtDNA D-loop haplotypes of indigenous chicken breeds and red junglefowl in Thailand
We determined the nucleotide sequences of the 780 bp fragments of the mtDNA D-loop region, including the hypervariable segment I, in 125 individuals from 10 indigenous chicken breeds (a list of breeds is shown in Table 1), and 279 red junglefowls from two subspecies (G. g. gallus and G. g. spadiceus) within 12 populations in Thailand. A total of 44 haplotypes with 62 variable sites, consisting of 26 singletons and 36 parsimony informative sites were identified (Supplementary Tables S1, S2; accession No. LC542982 to LC543385). Table 2 summarizes the details of the haplotypes found in the 10 indigenous chicken breeds and 12 red junglefowl populations, and Fig. 1 shows the composition of each haplogroup of indigenous chickens and two subspecies of red junglefowl. Forty-four haplotypes were temporally classified into eight common haplogroups; A, B, C, D, E, F, H, and J (Supplementary Figs. S1, S2), according to Liu et al.4 and Miao et al.5. In the present study, we treated haplogroups C and D as one unit (CD) as they were not clearly separated. In addition, haplogroup J was closely related to haplogroup CD. Haplogroups A, B, and E were predominant in the Thai indigenous chickens (Table 2), and their frequencies were almost the same (Fig. 1). Haplogroup CD was predominant in the G. g. spadiceus population, but rare in indigenous chickens. In the G. g. gallus population, the haplogroups B, CD, and E were detected at almost the same frequency; however, haplogroup A was not detected. The frequency of haplogroup J, which was mainly found in the Si Sa Ket population, was much higher in G. g. gallus compared with indigenous chicken and G. g. spadiceus populations (Fig. 1). BT, NK-W, NK-B, LHK, CH, PHD, Decoy, fighting chicken, and seven red junglefowl populations (Huai Sai [Ggg], Huai Sai [Ggs], Sa Kaeo, Chanthaburi, Khao Kho, Chaiyaphum, and Khok Mai Rua) each exhibited breed- or population-specific haplotypes (Supplementary Table S2): Hap_04 (BT); Hap_07 and 08 (NK-W); Hap_09 (NK-B); Hap_10 and Hap_11 (LHK); Hap_14 (CH); Hap_17 (PHD); Hap_18 to 20 (Decoy); Hap_29 to 33 (fighting chicken); Hap_21 (Huai Sai [Ggg]); Hap_22 (Huai Sai [Ggs]); Hap_24 and 25 (Sa Kaeo); Hap_27 and 28 (Chanthaburi); Hap_36 (Khao Kho); Hap_38 and 39 (Chaiyaphum); and Hap_43 and 44 (Khok Mai Rua). Twenty-nine out of 44 D-loop haplotypes which had not been previously deposited in GenBank, were newly identified in the present study.
Table 1 List of indigenous chicken breeds and red junglefowl populations examined in the present study.
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Table 2 Summary of haplogroups of mtDNA D-loop sequences and haplotypes that were found in 10 indigenous chicken breeds and 12 populations of two Gallus gallus subspecies in Thailand and their distribution.
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Figure 1

Composition ratio of haplogroups of the mtDNA D-loop sequences in chickens indigenous to Thailand, G. g. spadiceus, and G. g. gallus. The haplogroup names were conformed to those described by Miao et al.5 The numbers in parentheses indicate the number of individuals examined.

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The topologies of the Bayesian tree and the maximum-likelihood (ML) tree based on the HKY + G + I model of evolution, which were selected as the best-fit substitution model, were fundamentally similar. Although the Bayesian posterior probability of the internal nodes and the ML bootstrap values were relatively low due to the short internal branches (multifurcations) of the phylogenetic trees, the haplogroups A, B, F–I, K, Y, and Z were supported by a Bayesian posterior probability of greater than 0.97 (Supplementary Figs. S1, S2). Both The trees revealed that the D-loop sequences obtained in this study could be classified into six haplogroups: A, B, CD, E, F, and J, and a complex group of rare haplogroups (H, I, K, W, and X), except for an unclassified haplotype, Hap_38.
Six haplotypes from seven indigenous and two red junglefowl populations (LHK, CH, PHD, KP, BT, Decoy, fighting chicken, Huai Sai [Ggs], and Petchaburi) belonged to haplogroup A (Fig. 2a). Seven haplotypes from seven indigenous chicken breeds and five red junglefowl populations (LHK, CH, PHD, KP, Decoy, fighting chicken, DT, Sa Kaeo, Huai Sai [Ggg], Huai Sai [Ggs], Khao Kho, and Petchaburi) were classified into haplogroup B (Fig. 2b). Haplogroup CD contained 12 haplotypes, which were identified in two Thai indigenous chicken breeds (PHD and fighting chicken) and seven red junglefowl populations (Chanthaburi, Khok Mai Rua, Chaing Rai, Huai Sai [Ggs], Khao Kho, Chaiyaphum, and Huai Yang Pan) (Fig. 2c). Eight haplotypes in four indigenous chicken breeds (CH, BT, NK-W, and NK-B) and four red junglefowl populations (Roi Et, Khok Mai Rua, Chaiyaphum, and Huai Yang Pan) belonged to haplogroup E (Fig. 2e). Haplogroup F contained one haplotype, which was only found in two indigenous chicken breeds (LHK and PHD) (Fig. 2f). Eight haplotypes of haplogroup J (Hap_10, Hap_11, Hap_24 to 26, Hap_34, Hap_40, and Hap_42) were found in two indigenous chicken breeds (LHK and fighting chicken) and seven red junglefowl populations (Sa Kaeo, Chabthaburi, Si Saket, Roi Et, Khok Mai Rua, Chaing Rai, and Huai Yang Pan) (Fig. 2c). Only one haplotype of haplogroup H (Hap_05) was detected in two indigenous chicken breeds (BT and fighting chicken) (Fig. 2d). Hap_38, which was found in three individuals of the Chaiyaphum population, did not belong to any known haplogroups; however, the haplotype was more closely related to haplogroup CD than the other haplogroups (Fig. 2c; Supplementary Figs. S1, S2).
Figure 2

Locations of mtDNA D-loop haplotypes of Thai red junglefowl and indigenous chicken populations in the global chicken population network. (a) Haplogroup A. (b) Haplogroup B. (c) Haplogroups CD, Y, Z, J, and an unclassified haplotype, Hap_38. (d) Haplogroups H, I, K, X, and W. (e) Haplogroup E. (f) Haplogroup F. Haplotypes that were found in the present study and representative haplotypes reported by Miao et al.5 are shown by magenta and yellow circles, respectively. Black nodes are the inferred intermediate haplotypes. The number of bars on the lines, which link haplotypes, represent the number of nucleotide substitutions that occurred between the haplotypes for comparison.

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Divergence times for each haplotype were determined using BEAST analysis (Supplementary Table S2) and were 0.24–0.45 kilo years ago (KYA) for haplogroup A, 0.15–0.39 KYA for haplogroup B, and 0.14–0.37 KYA for haplogroup CD; the haplotypes of haplogroup E exhibited a wide range of divergent times, ranging from 0.12 to 0.70 KYA (0.41 KYA on average). One haplotype in haplogroups F and H had possibly diverged at 0.33 and 0.34 KYA, respectively. The divergence times of haplotypes in haplogroup J ranged from 0.10 to 0.60 KYA. Hap_38 exhibited a markedly earlier divergence time, which was estimated to be approximately 12,000 years ago (Supplementary Table S2; Supplementary Fig. S1).
Genetic diversity of mtDNA D-loop sequences
The number of D-loop haplotypes in each population (H) ranged from 1 (G. g. gallus population at Huai Sai and Si Sa Ket) to 10 (fighting chicken) (Table 3). Among the Thai indigenous chicken breeds, LHK, CH, PHD, KP, BT, Decoy and fighting chicken exhibited relatively higher genetic diversity (pi, 0.005 for KP and Decoy to 0.009 for LHK, PHD, and fighting chicken; Theta-w, 2.86 for KP to 8.30 for fighting chickens) than in NK-W, NK-B, and DT (pi, 0.001 for NK-W, NK-B, and DT; Theta-w, 0.35 for NK-B to 0.71 for NK-W) (Table 3). With regard to the red junglefowl populations, all populations excluding Si Sa Ket and Petchaburi exhibited similar levels of genetic diversity. The Petchaburi population had two haplotypes, and the genetic diversity was relatively low. The low genetic diversity in the Si Sa Ket population was attributed to the fact that all 30 examined individuals shared only one haplotype. Seven out of the 10 indigenous chicken breeds (LHK, CH, PHD, Decoy, fighting chicken, NK-W, and DT) exhibited negative Tajima’s D values, suggesting that the chickens were bred under purifying selection within each population; however, even though the Tajima’s D values of all populations were not statistically significant (p > 0.05).
Table 3 Genetic diversity of indigenous chicken breeds and red junglefowl populations estimated using of mtDNA D-loop sequences and 28 microsatellite markers.
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Phylogenetic relationships among mtDNA D-loop haplotypes in red junglefowl in Asia
Haplogroups A, B, CD, E, and J were frequently identified in red junglefowl in Thailand (Fig. 1; Supplementary Table S2). Haplotypes from Thailand, Vietnam, Laos, and Myanmar were located in internal nodes of the haplogroup A, B, CD, and E, and haplotypes from China were derived from the haplotypes in Southeast Asia (Fig. 3). Haplogroup J exclusively consisted of haplotypes from Thailand, Vietnam, and Cambodia. In haplogroup F, haplotypes from Cambodia exhibited the ancestral haplotypes of Chinese red junglefowl. Three haplotypes of red junglefowl from Indonesia were observed in haplogroup K. A small number of haplotypes from the other rare haplogroups G and W to Z were only observed in Chinese red junglefowl.
Figure 3

Median-joining haplotype network of mtDNA D-loop sequences of red junglefowl. The haplotypes are approximately subdivided into 12 haplogroups, A, B, CD, E, F, G, J, K, W, X, Y, and Z, and unclassified haplotypes (U) in this network, according to the haplotype classification by Miao et al.5 and the present study. The sizes of circles indicate relative frequencies of haplotypes, and the number of bars on the lines, which link haplotypes, represent the number of nucleotide substitutions that occurred between the haplotypes for comparison. Black nodes are the inferred intermediate haplotypes. The geographic origins of haplotypes or subspecies names are shown using circles with different colours. The numbers in parentheses after the location or subspecies names indicate the numbers of sequences used for analyses. Detailed information on the sequences obtained from the database are listed in Supplementary Table S7.

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Genetic characteristics of indigenous chicken breeds and red junglefowl estimated by 28 microsatellite DNA markers
Two-hundred and ninety-eight red junglefowls in two subspecies (G. g. gallus and G. g. spadiceus) from 12 populations and 138 chickens from 10 indigenous chicken breeds, were used for the genetic diversity analyses using 28 microsatellite markers (Table 1; Supplementary Table S3). The allelic richness (AR) values ranged from 1.40 for MCW103 to 1.93 for MCW0014 (1.77 on average) (Supplementary Table S3). Na ranged from 2.14 for MCW0103 to 7.59 for LEI0192 (2.86 on average). FIS varied from – 0.08 for LEI0166 to 0.54 for MCW0014 (0.05 on average). The FST and FIT values fell within the 0.07 (MCW0098) to 0.31 (MCW0247) range and 0.09 (MCW0123) to 0.63 (MCW0014) range, respectively (FST = 0.17, FIT = 0.22 on average). Two markers, MCW0222 and MCW0014, showed the null allele frequency across all populations (NAF) higher than 0.2 (Supplementary Table S3). Looking at each population, null allele frequencies higher than 0.2 were detected in seven, three, and three populations for MCW0014, MCW0222, and LEI0192, respectively, and in less than one or two populations for the other 11 markers (Supplementary Table S4).
Significant departures from Hardy–Weinberg equilibrium were observed for LEI0234, MCW0014, and MCW0123 in more than 10 populations (p  More

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