Ecology

Data
Data were collected in 2015 through a survey deployed as part of the Feed the Future Buena Milpa project (http://www.cimmyt.org/project-profile/buena-milpa/). The project’s goal was to reduce food insecurity and malnutrition by fostering sustainable, resilient, and innovative maize-based farming systems in the WHG.
We conducted the survey, with support from local researchers and agronomy students of the University of San Carlos (USAC), in summer of 2015 in 989 maize-growing farm households in 64 communities of 16 municipalities of the WHG. The number of surveys at the departmental level was: Totonicapán (226), Quiche (350), Quetzaltenango (187) and Huehuetenango (226) (See Supplementary Table 1 and Supplementary Fig. 1 for details). Criteria to select communities were: location within the targeted municipalities in the Buena Milpa project, the active work of project partners within the communities, and their distribution within four selected watersheds that included farming systems at different altitudes, ranging from 1400 to 3200 masl. For household selection, enumerators walked radial transects to survey household members, choosing only households that explicitly agreed to participate and had agricultural land on at least part of which maize was grown. The survey was a closed questionnaire with 139 questions in 18 sections including 5 project themes: milpa-maize germplasm improvement, natural resource conservation in farming system, farming system diversification, agricultural innovation systems and social inclusion.
Milpa diversity and extent
We used survey findings regarding the previous year’s crop production (2014) for all 989 surveyed households to understand the importance and diversity of the milpa system. The 989 households reported crop production on a total of 1,541 plots, 1,324 of which included maize. The other 217 plots grew potato (85 plots), coffee (50), vegetables (31), bean (19), fruit trees (12), faba bean (7), forestry trees (6), pea (2), oats, wheat, etc. Given the study’s focus on smallholder farmers, we discarded other 119 plots that had unrealistically large values for plot size or maize production levels for the region (i.e. more than ten times the average plot size or calculated maize yield,  > 2 ha and  > 11 ton ha−1 year−1).
For the resulting 1205 plots we constructed a tree depicting maize-system diversity in the WHG, with each node indicating the main cropping associations. The tree was nested, so the first node displays all maize plots, with successive splits for monocrop vs intercropped maize and with crop names presented according to how often they are grown (overall: maize  > common beans  > potatoes  > squash  > faba beans  > fruit trees  > vegetables). So, in plots where maize is grown with potatoes and common beans, the association is termed maize-bean-potatoes.
Milpa and food security
We assessed maize yield differences for monocropping and intercropping to detect a yield penalty or advantage for maize (see below), including survey information only for households from which we had available yield data for all crops. In the survey, total production for each crop was recorded regardless of the number of plots on which it was grown and therefore, for 398 households (40.2% of the sample) that had more than one plot under maize or any of the milpa crops, it was impossible to calculate the yield and had to be discarded from the analysis due to a lack of available plot-level information. The results were further screened for complete crop information and unrealistic values on crop production levels (i.e. ten times higher than the average yield for the different crops—See Supplemental Table 2), resulting in a usable sample of 368 plots.
For statistical analysis, only those crop combinations with a sample size equal to or greater than 9 plots were selected, resulting in 357 plots with, in descending order, the following numbers of 300 plots sown to each cropping combination: maize monocrop (163), maize-bean (109), maize-bean-squash (30), maize-bean/faba (12), maize-potato (13), maize-squash (11), maize-bean-potato (10), maize-faba (9). Other crop combinations with very low sample sizes were maize-squash-faba (4), maize-potato-faba (3), maize-bean-squash-faba (2), maize-bean-potato-faba (1) and maize-bean-potato-squash (1), making it difficult to include them in further statistical analyses.
Maize yields for plots under monocropped maize (163) were first compared to maize yields from intercropped plots (194). To choose the most appropriate statistical test, we checked if the outcome variable, maize yield, met the assumptions required for a parametric test. Although we had independent samples, large sample sizes, and homogeneous variances (F = 1.1809, p-value = 0.267), maize yield proved to be non-normally distributed after Shapiro-Wilks normality test was significant (W = 0.911, p-value  1]{ }}}{{text{N}}}{ } times {overline{text{s}}},{[18]}$$

where (N) is the number of nutrients considered (14), (s_{i}) is the fraction of potentially nourished male adults by a given nutrient, and (overline{s}) is the average of potentially nourished persons for all nutrients. To calculate s we multiplied the yield of each crop reported in the survey (kg ha−1) by the nutrient composition of each crop, using the content per 100 g of the 14 different nutrients for each crop, as per the INCAP Food Composition Table for Central America32 and using the INCAP RDA for an adult male33 (See Supplementary Tables 3 and 4). All values were calculated per hectare and year. PNA levels across cropping associations were also compared. First, we checked if PNA data met the assumptions required for a parametric test. PNA variances proved to be non-homogeneous (F = 14.5, p-value =   More

1.
Lynam, C. P. et al. Have jellyfish in the Irish Sea benefited from climate change and overfishing?. Glob. Change Biol. 17, 767–782 (2011).
ADS  Article  Google Scholar 
2.
Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. 110, 1000–1005 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Lynam, C. P., Hay, S. J. & Brierley, A. S. Jellyfish abundance and climatic variation: Contrasting responses in oceanographically distinct regions of the North Sea, and possible implications for fisheries. J. Mar. Biol. Assoc. 85, 435–450 (2005).
Article  Google Scholar 

4.
Lynam, C. P. et al. Jellyfish overtake fish in a heavily fished ecosystem. Curr. Biol. 16, R492-493 (2006).
CAS  PubMed  Article  Google Scholar 

5.
Hays, G. C., Doyle, T. K. & Houghton, J. D. R. A paradigm shift in the trophic importance of jellyfish?. Trends Ecol. Evol. 33, 874–884 (2018).
PubMed  Article  Google Scholar 

6.
Gibbons, M. J. & Richardson, A. J. Patterns of jellyfish abundance in the North Atlantic. In Jellyfish Blooms: Causes, Consequences, and Recent Advances: Proceedings of the Second International Jellyfish Blooms Symposium, held at the Gold Coast, Queensland, Australia, 24–27 June, 2007 (eds. Pitt, K. A. & Purcell, J. E.) 51–65 (Springer Netherlands, 2009). https://doi.org/10.1007/978-1-4020-9749-2_4.

7.
Attrill, M. J., Wright, J. & Edwards, M. Climate-related increases in jellyfish frequency suggest a more gelatinous future for the North Sea. Limnol. Oceanogr. 52, 480–485 (2007).
ADS  Article  Google Scholar 

8.
Brodeur, R. D., Sugisaki, H. & Hunt, G. L. Jr. Increases in jellyfish biomass in the Bering Sea: Implications for the ecosystem. Mar. Ecol. Prog. Ser. 233, 89–103 (2002).
ADS  Article  Google Scholar 

9.
Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing down marine food webs. Science 279, 860–863 (1998).
ADS  CAS  PubMed  Article  Google Scholar 

10.
Purcell, J. E. & Arai, M. N. Interactions of pelagic cnidarians and ctenophores with fish: A review. Hydrobiologia 451, 27–44 (2001).
Article  Google Scholar 

11.
Robinson, K. L. et al. Jellyfish, forage fish, and the world’s major fisheries. Oceanography 27, 104–115 (2014).
Article  Google Scholar 

12.
Uye, S. Blooms of the giant jellyfish Nemopilema nomurai: A threat to the fisheries sustainability of the East Asian Marginal Seas. Plankton Benthos Res. 3, 125–131 (2008).
Article  Google Scholar 

13.
Wright, R. M., Le Quéré, C., Buitenhuis, E., Pitois, S. & Gibbons, M. Unique role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model. Biogeosci. Discuss. https://doi.org/10.5194/bg-2020-136 (2020).
Article  Google Scholar 

14.
Jackson, J. B. C. Ecological extinction and evolution in the brave new ocean. Proc. Natl. Acad. Sci. 105, 11458–11465 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

15.
Kintner, A. & Brierley, A. S. Cryptic hydrozoan blooms pose risks to gill health in farmed North Atlantic salmon (Salmo salar). J. Mar. Biol. Assoc. 99, 539–550 (2019).
Article  Google Scholar 

16.
Flynn, B. A. et al. Temporal and spatial patterns in the abundance of jellyfish in the northern Benguela upwelling ecosystem and their link to thwarted pelagic fishery recovery. Afr. J. Mar. Sci. 34, 131–146 (2012).
Article  Google Scholar 

17.
Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study. Glob. Biogeochem. Cycles 34, e2020GB006704 (2020).
ADS  CAS  Article  Google Scholar 

18.
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

19.
Hays, G. C., Richardson, A. J. & Robinson, C. Climate change and marine plankton. Trends Ecol. Evol. 20, 337–344 (2005).
PubMed  Article  Google Scholar 

20.
Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the northeast Atlantic. Science 305, 1609–1612 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Suikkanen, S. et al. Climate change and eutrophication induced shifts in northern summer plankton communities. PLoS ONE 8, e66475 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Wiafe, G., Yaqub, H. B., Mensah, M. A. & Frid, C. L. J. Impact of climate change on long-term zooplankton biomass in the upwelling region of the Gulf of Guinea. ICES J. Mar. Sci. 65, 318–324 (2008).
Article  Google Scholar 

23.
Reid, P. C., Colebrook, J. M., Matthews, J. B. L. & Aiken, J. The Continuous Plankton Recorder: Concepts and history, from Plankton Indicator to undulating recorders. Prog. Oceanogr. 58, 117–173 (2003).
ADS  Article  Google Scholar 

24.
Edwards, M. et al. Plankton, jellyfish and climate in the North-East Atlantic. MCCIP Sci. Rev. 2020, 322–353. https://doi.org/10.14465/2020.arc15.plk (2020).
Article  Google Scholar 

25.
ICES. Report of the Working Group on the Celtic Seas Ecoregion (WGCSE), 11–19 May 2011, Copenhagen, Denmark. (2011).

26.
Bartolino, V. et al. Herring assessment working group for the area south of 62° N (HAWG). (2019) https://doi.org/10.17895/ices.pub.5460.

27.
ICES Advice Book 5. https://www.ices.dk/sites/pub/Publication%20Reports/Advice/2007/may/her-nirs.pdf (2007).

28.
Beaugrand, G. The North Sea regime shift: Evidence, causes, mechanisms and consequences. Prog. Oceanogr. 60, 245–262 (2004).
ADS  Article  Google Scholar 

29.
Gregory, B., Christophe, L. & Martin, E. Rapid biogeographical plankton shifts in the North Atlantic Ocean. Glob. Change Biol. 15, 1790–1803 (2009).
ADS  Article  Google Scholar 

30.
deYoung, B. et al. Regime shifts in marine ecosystems: Detection, prediction and management. Trends Ecol. Evol. 23, 402–409 (2008).
PubMed  Article  Google Scholar 

31.
Bastian, T. et al. Large-scale sampling reveals the spatio-temporal distributions of the jellyfish Aurelia aurita and Cyanea capillata in the Irish Sea. Mar. Biol. 158, 2639–2652 (2011).
Article  Google Scholar 

32.
Houghton, J. D. R., Doyle, T. K., Davenport, J. & Hays, G. C. Developing a simple, rapid method for identifying and monitoring jellyfish aggregations from the air. Mar. Ecol. Prog. Ser. 314, 159–170 (2006).
ADS  Article  Google Scholar 

33.
Bastian, T., Lilley, M. K. S., Beggs, S. E., Hays, G. C. & Doyle, T. K. Ecosystem relevance of variable jellyfish biomass in the Irish Sea between years, regions and water types. Estuar. Coast. Shelf Sci. 149, 302–312 (2014).
ADS  Article  Google Scholar 

34.
Heckerman, D., Geiger, D. & Chickering, D. M. Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995).
MATH  Google Scholar 

35.
Milns, I., Beale, C. M. & Smith, V. A. Revealing ecological networks using Bayesian network inference algorithms. Ecology 91, 1892–1899 (2010).
PubMed  Article  Google Scholar 

36.
Mitchell, E. G. & Neutel, A.-M. Feedback spectra of soil food webs across a complexity gradient, and the importance of three-species loops to stability. Theor. Ecol. 5, 153–159 (2012).
Article  Google Scholar 

37.
Olff, H. et al. Parallel ecological networks in ecosystems. Philos. Trans. R. Soc. B Biol. Sci. 364, 1755–1779 (2009).
Article  Google Scholar 

38.
Mitchell, E. G., Whittle, R. & Griffths, H. J. Benthic ecosystem cascade effects in Antarctica using Bayesian network inference. Commun. Biol. 3, 582 (2020).
PubMed  PubMed Central  Article  Google Scholar 

39.
Yu, J., Smith, V. A., Wang, P. P., Hartemink, A. J. & Jarvis, E. D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 3594–3603 (2004).
CAS  PubMed  Article  Google Scholar 

40.
Yu, J., Smith, V. A., Wang, P. P., Hartemink, E. J. & Jarvis, E. D. Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. In Prof. of Int. (2002).

41.
Smith, V. A., Yu, J., Smulders, T. V., Hartemink, A. J. & Jarvis, E. D. Computational inference of neural information flow networks. PLOS Comput. Biol. 2, e161 (2006).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Mitchell, E. G. & Butterfield, N. J. Spatial analyses of Ediacaran communities at Mistaken Point. Paleobiology 44, 40–57 (2018).
Article  Google Scholar 

43.
Mitchell, E. G., Durden, J. M. & Ruhl, H. A. First network analysis of interspecific associations of abyssal benthic megafauna reveals potential vulnerability of abyssal hill community. Prog. Oceanogr. 187, 102401 (2020).
Article  Google Scholar 

44.
Mitchell, E. G. & Harris, S. Mortality, population and community dynamics of the glass sponge dominated community “The Forest of the Weird” from the RIDGE seamount, Johnston Atoll, Pacific Ocean. Front. Mar. Sci. 7, 872 (2020).
Article  Google Scholar 

45.
Reid, P. C., Borges, M. D. F. & Svendsen, E. A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery. Fish. Res. 50, 163–171 (2001).
Article  Google Scholar 

46.
Brierley, A. S. et al. Acoustic observations of jellyfish in the Namibian Benguela. Mar. Ecol. Prog. Ser. 210, 55–66 (2001).
ADS  Article  Google Scholar 

47.
Brierley, A. S. et al. Towards the acoustic estimation of jellyfish abundance. Mar. Ecol. Prog. Ser. 295, 105–111 (2005).
ADS  Article  Google Scholar 

48.
MacLennan, D. N. & Simmonds, E. J. Fisheries Acoustics (Springer, Berlin, 2013).
Google Scholar 

49.
Planque, B. & Fromentin, J. Calanus and environment in the eastern North Atlantic. I. Spatial and temporal patterns of C. finmarchicus and C. helgolandicus. Mar. Ecol. Prog. Ser. 134, 101–109 (1996).
ADS  Article  Google Scholar 

50.
Batten, S. D. et al. CPR sampling: The technical background, materials and methods, consistency and comparability. Prog. Oceanogr. 58, 193–215 (2003).
ADS  Article  Google Scholar 

51.
Richardson, A. J. et al. Using continuous plankton recorder data. Prog. Oceanogr. 68, 27–74 (2006).
ADS  Article  Google Scholar 

52.
John, E. H. et al. Continuous plankton records stand the test of time: Evaluation of flow rates, clogging and the continuity of the CPR time-series. J. Plankton Res. 24, 941–946 (2002).
Article  Google Scholar 

53.
Beaugrand, G., Ibañez, F., Lindley, J. A. & Reid, P. C. Diversity of calanoid copepods in the North Atlantic and adjacent seas: Species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195 (2002).
ADS  Article  Google Scholar 

54.
Yu, J. Developing Bayesian Network Inference Algorithms to Predict Causal Functional Pathways in Biological Systems (Duke University, Durham, 2005).
Google Scholar 

55.
Chickering, D. M. Learning Bayesian Networks is NP-Complete. In Learning from Data: Artificial Intelligence and Statistics V (eds. Fisher, D. & Lenz, H.-J.) 121–130 (Springer, Berlin, 1996). https://doi.org/10.1007/978-1-4612-2404-4_12.

56.
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, (2003).

57.
Jones, P. D., Jonsson, T. & Wheeler, D. Extension to the North Atlantic oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland. Int. J. Climatol. 17, 1433–1450 (1997).
Article  Google Scholar 

58.
Mitchell, E. G. Functional programming through deep time: Modeling the first complex ecosystems on earth. ACM SIGPLAN Not. 46, 28–31 (2011).
MATH  Article  Google Scholar 

59.
Jones, S. P. Haskell 98 Language and Libraries: The Revised Report (Cambridge University Press, Cambridge, 2003).
Google Scholar 

60.
Friedman, N., Geiger, D. & Goldszmidt, M. Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997).
MATH  Article  Google Scholar 

61.
Dickey-Collas, M., Nash, R. D. M. & Brown, J. The location of spawning of Irish sea herring (Clupea harengus). J. Mar. Biol. Assoc. 81, 713–714 (2001).
Article  Google Scholar 

62.
Nash, R. D. M. & Geffen, A. J. Seasonal and interannual variation in abundance of Calanus finmarchicus (Gunnerus) and Calanus helgolandicus (Claus) in inshore waters (west coast of the Isle of Man) in the central Irish Sea. J. Plankton Res. 26, 265–273 (2004).
Article  Google Scholar 

63.
Hurrell, J. W. & Deser, C. North Atlantic climate variability: The role of the North Atlantic Oscillation. J. Mar. Syst. 78, 28–41 (2009).
Article  Google Scholar 

64.
Brotz, L., Cheung, W. W. L., Kleisner, K., Pakhomov, E. & Pauly, D. Increasing jellyfish populations: trends in Large Marine Ecosystems. In Jellyfish Blooms IV: Interactions with Humans and Fisheries (eds. Purcell, J. et al.) 3–20 (Springer Netherlands, 2012). https://doi.org/10.1007/978-94-007-5316-7_2.

65.
Purcell, J. E. Jellyfish and ctenophore blooms coincide with human proliferations and environmental perturbations. Annu. Rev. Mar. Sci. 4, 209–235 (2012).
ADS  Article  Google Scholar 

66.
Pitt, K. A., Lucas, C. H., Condon, R. H., Duarte, C. M. & Stewart-Koster, B. Claims that anthropogenic stressors facilitate jellyfish blooms have been amplified beyond the available evidence: A systematic review. Front. Mar. Sci. 5, 451 (2018).
Article  Google Scholar 

67.
Sanz-Martín, M. et al. Flawed citation practices facilitate the unsubstantiated perception of a global trend toward increased jellyfish blooms. Glob. Ecol. Biogeogr. 25, 1039–1049 (2016).
Article  Google Scholar 

68.
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: Robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Article  Google Scholar 

69.
Gardner, M. R. & Ashby, W. R. Connectance of large dynamic (cybernetic) systems: Critical values for stability. Nature 228, 784–784 (1970).
ADS  CAS  PubMed  Article  Google Scholar 

70.
Lawton, J. H. & Brown, V. K. Redundancy in ecosystems. In Biodiversity and Ecosystem Function (eds. Schulze, E.-D. & Mooney, H. A.) 255–270 (Springer, Berlin, 1994). https://doi.org/10.1007/978-3-642-58001-7_12.

71.
Thébault, E. & Loreau, M. Trophic interactions and the relationship between species diversity and ecosystem stability. Am. Nat. 166, E95–E114 (2005).
PubMed  Article  Google Scholar 

72.
Wang, S. & Loreau, M. Biodiversity and ecosystem stability across scales in metacommunities. Ecol. Lett. 19, 510–518 (2016).
PubMed  PubMed Central  Article  Google Scholar 

73.
Van Voris, P., O’Neill, R. V., Emanuel, W. R. & Shugart, H. H. Functional complexity and ecosystem stability. Ecology 61, 1352–1360 (1980).
Article  Google Scholar 

74.
Graham, W. M. & Kroutil, R. M. Size-based prey selectivity and dietary shifts in the jellyfish, Aurelia aurita. J. Plankton Res. 23, 67–74 (2001).
Article  Google Scholar 

75.
Marques, R., Bonnet, D., Carré, C., Roques, C. & Darnaude, A. M. Trophic ecology of a blooming jellyfish (Aurelia coerulea) in a Mediterranean coastal lagoon. Limnol. Oceanogr. n/a.

76.
Anninsky, B. E., Finenko, G. A., Datsyk, N. A. & Kıdeyş, A. E. Trophic ecology and assessment of the predatory impact of the Moon jellyfish Aurelia aurita (Linnaeus, 1758) on zooplankton in the Black Sea (2020) https://doi.org/10.21411/cbm.a.96dd01aa.

77.
Widmer, C. L., Fox, C. J. & Brierley, A. S. Effects of temperature and salinity on four species of northeastern Atlantic scyphistomae (Cnidaria: Scyphozoa). Mar. Ecol. Prog. Ser. 559, 73–88 (2016).
ADS  Article  Google Scholar 

78.
Watson, D. I. & Barnes, D. K. A. Temporal and spatial components of variability in benthic recruitment, a 5-year temperate example. Mar. Biol. 145, 201–214 (2004).
Article  Google Scholar 

79.
Arrhenius, F. & Hansson, S. Food consumption of larval, young and adult herring and sprat in the Baltic Sea. Mar. Ecol. Prog. Ser. 96, 125–137 (1993).
ADS  Article  Google Scholar 

80.
Williams, R., Conway, D. V. P. & Hunt, H. G. The role of copepods in the planktonic ecosystems of mixed and stratified waters of the European shelf seas. Hydrobiologia 292, 521–530 (1994).
Article  Google Scholar 

81.
Gowen, R. J., Mills, D. K., Trimmer, M. & Nedwell, D. B. Production and its fate in two coastal regions of the Irish Sea: The influence of anthropogenic nutrients. Mar. Ecol. Prog. Ser. 208, 51–64 (2000).
ADS  Article  Google Scholar 

82.
Scorrano, S., Aglieri, G., Boero, F., Dawson, M. N. & Piraino, S. Unmasking Aurelia species in the Mediterranean Sea: An integrative morphometric and molecular approach. Zool. J. Linn. Soc. 180, 243–267 (2017).
Google Scholar 

83.
Haussermann, V., Dawson, M. N. & Forsterra, G. First record of the moon jellyfish, Aurelia for Chile. Spixana 32, 3–7 (2009).
Google Scholar  More

Study area
The field work was carried out at the Sturt Meadows calcrete aquifer (28°41′ S 120°58′ E) located on Sturt Meadows pastoral station, Western Australia, ~ 42 km from the settlement of Leonora (833 km northeast of Perth, see Fig. 1a). The study area is a calcrete aquifer lying in the Raeside paleodrainages in the Yilgarn region of Western Australia (Fig. 1a). The vegetation of the area is Acacia woodlands, primarily Acacia aneura (F.Muell. ex Benth.), and is subjected to combined grazing pressure from domestic stock, feral animals and macropods. The aquifer is accessible through a bore grid comprising 115 bore holes of between 5 and 11 m depth (Fig. 1b).
Figure 1

Map of the Sturt Meadows calcrete. (a) Location within the Yilgarn craton region and detailed paleodrainages/cacretes in the area and (b) the grid map showing the location of the boreholes sampled for stygofauna together with probe samples, water samples (in light blue) and the combination of both. Map was produced in ArcGIS Desktop 10.679 and edited in Adobe Illustrator 25.080.

Full size image

Three sampling campaigns were carried out, two of them (LR1: 26/07/2017 and LR2: 07/11/2017) corresponding to low rainfall periods24 and one during the wet season (high rainfall, HR; two consecutive days of sampling collection: 17-18/03/2018) (Supplementary Fig. 1). The well-studied stygofaunal community of the area is composed of 11 main stygofaunal taxa belonging to five Classes: Oligochaeta (family Tubificidae (Vejdovský 1884)), subcohort Hydrachnidia, Maxillopoda (two species of harpacticoids: Novanitocrella cf. aboriginesi (Karanovic, 2004), Schizopera cf. austindownsi (Karanovic, 2004) and four species of cyclopoids: Halicyclops kieferi (Karanovic, 2004), Halicyclops cf. ambiguous (Kiefer, 1967), Schizopera slenderfurca (Karanovic & Cooper, 2012) and Fierscyclops fiersi (De Laurentiis et al., 2001)), Malacostraca: Amphipoda (species Scutachiltonia axfordi (King, 2012), Yilgarniella sturtensis (King, 2012) and Stygochiltonia bradfordae (King, 2012)) and Insecta: Coleoptera: Dytiscidae (species Paroster macrosturtensis (Watts & Humphreys 2006), Paroster mesosturtensis (Watts & Humphreys 2006) and Paroster microsturtensis (Watts & Humphreys 2006) and respective larvae).
Field work procedures and sample preparation
Given the sensitivity of the hydrological dynamics in shallow calretes23,25, extensive water extraction along the bores was avoided, and preliminary tests on the bores with the highest water depth were carried out to quantify potential risk of dewatering the calcrete. During the field campaigns LR2 and HR, 20 water samples in total (two samples for stable isotope analysis on DOC (Dissolved Organic Carbon) and DIC (Dissolved Inorganic Carbon), three samples for radiocarbon analysis on DOC, one sample for radiocarbon analysis on DIC, and two samples for stable isotope and radiocarbon analyses on POC (Particulate Organic Carbon)) were collected from bores D13 and W4 (Fig. 1b), which are representative of the two main geological conformations of the area—calcrete (W4) and clay (D13) (Supplementary Fig. 2)—and host stable hydrological and biotic conditions7. Water samples were collected using a submersible centrifugal pump (GEOSub 12 V Purging Pump) after wells were purged of three well-volumes and stabilisation of in-field parameters was observed, according to the methodology in Bryan et al.26.
Samples for 14CDIC analysis were filtered through 0.45 μm filters and collected in 1 L high density poly-ethylene (HDPE) bottles. δ13CDIC samples were filtered through 0.2 μm filters, collected in 12 mL glass vials (Exetainers) and refrigerated after sampling. δ 13CDOC samples were filtered through 0.2 μm filters, collected in 60 mL HDPE bottles and frozen after sampling.14CDOC samples were filtered through 0.2 μm filters, collected in 3 L HDPE bottles and frozen after sampling.
In order to investigate 14C and δ 13C content of POC, two extra liters were collected from the same bores (D13, W4) and kept frozen (− 20 °C) until further analyses. 14CPOC δ13CPOC samples were then filtered through pre-combusted GF/F filters (12 h at 450 °C), washed with 1.2 N HCl to remove any inorganic carbon, and subsequently dried at 60 °C for 24 h. All samples were closed with sealing tape after collection to limit atmospheric exchange and kept in darkness.
Temperature, pH, ORP, salinity, DO and depth were measured in situ using a portable Hydrolab Quanta Multi‐Probe Meter across 30 bores during LR1, LR2 and HR23 (presented in Supplementary Table 8). Adult and larval stygofaunal specimens were collected from the same bores by hauling a weighted plankton net (mesh 100 μm27) five times through the water column (Fig. 1b). All biological samples were kept frozen (− 20 °C) in darkness until laboratory processing. Individual organisms were counted and identified (and consequently separated) to the lowest taxonomic level via optical microscopy and reference to specific taxonomic keys. Plant material, sediment samples and fauna were each separated during the sorting in the laboratory and each taxon pooled according to sampling campaign (LR1, LR2 or HR) and subsequently washed with Milli-Q water to remove surface impurities from their bodies. Sediment samples were soaked in acid (0.1 N HCl) to remove inorganic carbon, and together with the other samples were then oven dried at 60 °C overnight and ground until obtaining a homogeneous fine powder and stored at − 20 °C until further analyses.
Previous investigations on the ecological niche trends at Sturt Meadows indicated that all stygofauna characterize similar niche occupations under low rainfall regimes (LR1 and LR2)23. Stygofaunal specimens from the two low rainfall sampling events were combined to form sample LR to address the competing requirements between isotopic detection limits, analytical replicates and cost, while maintaining the main taxonomic and biological classifications7.
Bulk isotope and 14C analyses
Water δ13CDIC and δ13CPOC isotopic ratios were analysed by Isotope Ratio Mass Spectrometer—WABC at The University of Western Australia using a GasBench II coupled with a Delta XL Mass Spectrometer (Thermo-Fisher Scientific)—and the results, with a precision of ± 0.10‰, were reported as ‰ deviation from the NBS19 and NSB18 international carbonate standard28.
δ13CDOC isotopic ratios of waters were analysed via Liquid Chromatography Isotope Ratio Mass Spectrometer (LC-IRMS) at the La Trobe Institute for Molecular Sciences (LIMS, La Trobe University, Melbourne, Australia) comprising an Accela 600 pump connected to a Delta V Plus Isotope Ratio Mass Spectrometer via a Thermo Scientific LC Isolink (Thermo Scientific).
C and N bulk SIA on homogenised samples of sediment, roots, stygofauna and copepods (cyclopoids and harpacticoids) were performed at the Australian Nuclear Science and Technology Organisation (ANSTO, Sydney, Australia). Samples were loaded into tin capsules and analysed with a continuous flow isotope ratio mass spectrometer (CF-IRMS), model Delta V Plus (Thermo Scientific Corporation, U.S.A.), interfaced with an elemental analyser (Thermo Fisher Flash 2000 HT EA, Thermo Electron Corporation, USA) following the procedure published by Mazumder et al.29.
For radiocarbon analyses, samples (sediment, roots, copepods, ants, stygofauna) were treated with 1 M HCl for 2 h to remove all possible carbonate contamination. These pre-treated samples together with 14CPOC, 14CDOC and 14CDIC samples were subjected to CO2 extraction and graphitization following the methodology published by Hua et al.30. 14C content of samples was determined by means of the Accelerator Mass Spectrometry (AMS) at ANSTO.
Carbon CSIA
Carbon CSIA followed the procedure described in Saccò et al.7. Samples of roots and stygofaunal specimens were hydrolysed under vacuum with 0.5 to 1 mL of amino acid-free 6 M HCl (Sigma-Aldrich) at 110 °C for 24 h. The protein hydrolysates were dried overnight in a rotary vacuum concentrator and stored in a freezer. Prior to analysis, the samples were dissolved in Milli-Q water and 10 μL of 1-mmol solution of 2-aminoisobutyric acid (Sigma-Aldrich) as internal standard. The sample stock had a concentration of approximately 8 to 10 mg/mL, which was further diluted as needed. Single amino acid carbon isotope analysis was carried out at the La Trobe Institute for Molecular Sciences (LIMS, La Trobe University, Melbourne, Australia) using an Accela 600 pump connected to a Delta V Plus Isotope Ratio Mass Spectrometer via a Thermo Scientific LC Isolink (Thermo Scientific).
The amino acids were separated using a mixed mode (reverse phase/ion exchange) Primesep A column (2.1 × 250 mm, 100 °C, 5 μm, SIELC Technologies) following the chromatographic method described in Mora et al.31 after Smith et al.32. Mobile phases are those described in Mora et al.33. Percentage of Phases B and C in the conditioning run, as well as flow rate of the analytical run and timing of onset of 100% Phase C were adjusted as needed. Samples were injected onto the column in the 15 μL—partial loop or no waste—injection mode, and measured in duplicate or triplicate.
To elucidate carbon flows through the stygofaunal community we focused on the essential amino acids Valine (Val), Phenylalanine (Phe) and Arginine (Arg), as these compounds must be integrated through diet and cannot be synthetised internally by the fauna14,34. In addition, to distinguish between terrestrial and aquatic carbon sources, the ratio between Val and Phe signals (δ13CVal-Phe), a widely employed index in archaeology and freshwater biology35, was calculated for roots, water mites, aquatic worms, amphipods and beetles (larvae and adults).
Microbial taxonomic and functional gene analyses
Consumer amphipods (Scutachiltonia axfordi (AM1), Yilgarniella sturtensis (AM2), S. bradfordae (AM3)), cyclopoids and harpacticoids, together with predator stygobiotic beetles (Paroster macrosturtensis (B), P. mesosturtensis (M) and P. microsturtensis (S)) (see Saccò el at 7. for further details on the trophic characterisation of the stygofaunal community at Sturt Meadows), were used for gut microbiome bacterial 16S metabarcoding and microbial functional analysis. A total of 16 AM1, 16 AM2, 16 AM3, 20 cyclopoids and 20 harpaticoids and 20 of each one of the three Paroster species (B, M and S), were sorted into duplicates of stygobiotic pools of 3–5 individuals from both LR and HR events for DNA extraction. Prior to DNA extraction stygobitic animals (3–5 individuals per pool; n = 40) were placed in a petri dish containing ultrapure water and UV sterilized in a UV oven for 10 min to eliminate any bacterial species that may be contained on the exoskeleton as this study targeted the gut microbiome. Immediately post-UV treatment, the animals were placed into tissue lysis tubes with 180 μL tissue lysis buffer (ATL) and 20 μL Proteinase K and homogenised using Minilys tissue homogeniser (ThermoFisher Scientific, Australia) at high speed for 30 s. Lysis tubes, inclusive of two laboratory controls, were incubated at 56 °C using an agitating heat block (Eppendorf ThermoStat C, VWR, Australia) for 5 h.
Following the incubation, the analytical procedure was adapted from Saccò et al.22 and DNA extraction was carried out using DNeasy Blood and Tissue Kit (Qiagen; Venlo, Netherlands) and eluted off the silica column in 30–50 μL AE buffer. The quality and quantity of DNA extracted from each stygobitic pool was measured using quantitative PCR (qPCR), targeting the bacterial 16S gene. PCR reactions were used to assess the quality and quantity of the DNA target of interest via qPCR (Applied Biosystems [ABI], USA) in 25 μL reaction volumes consisting of 2 mM MgCl2 (Fisher Biotec, Australia), 1 × PCR Gold Buffer (Fisher Biotec, Australia), 0.4 μM dNTPs (Astral Scientific, Australia), 0.1 mg bovine serum albumin (Fisher Biotec, Australia), 0.4 μM of each primer (Bact16S_515F and Bact16S_806R36,37), and 0.2 μL of AmpliTaq Gold (AmpliTaq Gold, ABI, USA), and 2 μL of template DNA (Neat, 1/10, 1/100 dilutions). The cycling conditions were: initial denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 52 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min.
DNA extracts that successfully yielded DNA of sufficient quality, free of inhibition, as determined by the initial qPCR screen (detailed above), were assigned a unique 6–8 bp multiplex identifier tag (MID-tag) with the bacterial 16S primer set. Independent MID-tag qPCR for each stygobiotic pool were carried out in 25 μL reactions containing 1 × PCR Gold Buffer, 2.5 mM MgCl2, 0.4 mg/mL BSA, 0.25 mM of each dNTP, 0.4 μM of each primer, 0.2 μL AmpliTaq Gold and 2–4 μL of DNA as determined by the initial qPCR screen. The cycling conditions for qPCR using the MID-tag primer sets were as described above. MID-tag PCR amplicons were generated in duplicate and the library was pooled in equimolar ratio post-PCR for DNA sequencing. The final library was size selected (160–600 bp) using Pippin Prep (Sage Sciences, USA) to remove any MID-tag primer-dimer products that may have formed during amplification. The final library concentration was determined using a QuBitTM 4 Fluorometer (Thermofischer, Australia) and sequenced using a 300 cycle V2 kit on an Illumina MiSeq platform (Illumina, USA).
MID-tag bacterial 16S sequence reads obtained from the MiSeq were sorted (filtered) back to the stygobitic pool based on the MID-tags assigned to each DNA extract using Geneious v10.2.538. MID-tag and primer sequences were trimmed from the sequence reads allowing for no mismatch in length or base composition.
Filtered reads were then input into a containerised workflow comprising USEARCH39 and BLASTN40, which was run on a high-throughput HPC cluster at Pawsey supercomputing centre. The fastx-uniques, unoise3 (with minimum abundance of 8) and otutab commands of USEARCH were applied to generate unique sequences, ZOTUs (zero-radius Operational Taxonomic Units) and abundance table, respectively. The ZOTUs were compared against the nucleotide database using the following parameters in BLASTN: perc_identity ≥ 94, evalue ≤ 1e−3, best_hit_score_edge 0.05, best_hit_overhang 0.25, qcov_hsp_perc 100, max_target_seqs = 5. An in-house Python script was used to assign the ZOTUs to their lowest common ancestor (LCA)41. The threshold for dropping a taxonomic assignment to LCA was set to perc_identity ≥ 96 and the difference between the % of identity of the two hits when their query coverage is equal was set to 1. Results on the microbial taxonomic diversity detected in ground water samples from a previous study on carbon inputs in the aquifer22 were incorporated in this work to allow qualitative comparison with the stygofaunal gut diversity.
To investigate functional activity involved in carbon cycling, the 16S metabarcoding data were fed to the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) software package to generate predicted metagenome profiles42. These profiles were clustered into Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologs (KOs)43 and MetaCyc pathway abundances44 focusing on the relative abundances of four carbon metabolisms: carbon fixation in prokaryotes, carbohydrates, lipids and amino acid metabolisms. Relative abundance of pathways linked with methane, nitrogen and sulfur metabolisms were also investigated.
Statistical analyses
The Phyloseq package in R45,46 was used to plot the ZOTU abundance for the styfofaunal specimens at the order level under LR and HR periods. The Statistical Analysis of Metagenomic Profiles (STAMP) bioinformatics software package was used to carry out Principal Components Analysis (PCA) to assess the differences between functional genomic fingerprints based on the KEGG orthologs function prediction between copepods (C and H) and amphipods (AM1, AM2 and AM3), and determine statistically significant results from the PICRUSt2 output47. Clustering patterns according to rainfall periods (LR and HR) and major consumers taxonomic groups (cyclopoids, harpacticoids and amphipods) were assessed through Permutational multivariate analysis of variance (PERMANOVA, R-package46 ‘vegan’) and pairwise post hoc pairwise multilevel comparisons48.
For comparison of potential shifts in abundances of microbial metabolic pathways within groundwater samples, copepods and amphipods across rainfall periods, analysis of variance (ANOVA) was performed on the abundance data (two replicates per each group) on the predicted pathways depicting carbon fixation, carbohydrate, lipid, amino acid, methane, nitrogen and sulfur metabolisms. ANOVAs coupled with Tukey’s HSD pairwise comparisons (R-package46 ‘stats’) were employed to inspect significant differences between bulk SIA (δ13C and δ15N) and essential amino acid (δ13CPhe, δ13CArg, δ13CVal and δ13CVal-Phe) data from the stygofaunal taxa within the different rainfall conditions (LR and HR). PERMANOVAs (R-package46 ‘vegan’) were also performed to investigate the potential clustering trends within the stygofaunal taxa across rainfall periods from the combination of radiocarbon (Δ14C) and carbon SIA (δ13C) isotopic fingerprints.
SIMM (Stable Isotope Mixing Models, R-package46 ‘simmr’) were used to estimate dietary proportions of copepods and amphipods within a Bayesian framework. Due to the lack of species-specific trophic discrimination factors for stygofauna, we employed the widely accepted values of 3.4 ± 2‰ for nitrogen and 0.5 ± 1‰ for carbon49. Markov chain Monte Carlo (MCMC) algorithms were used for simulating posterior distributions in SIMM, and MCMC convergence was evaluated using the Gelman-Rubin diagnostic by using 1.1 as a threshold value for analysis validation. More

Interseasonal VGC dominates peak-to-late-season growth
We first examined the VGC effect at the seasonal scale, using satellite-derived Normalized Difference Vegetation Index (NDVI, see “Methods”) for the 1982–2016 period. We defined the dormancy season (DS) and three periods of the growing season, i.e., EGS, peak growing season (PGS), and late growing season (LGS), based on phenological metrics (see “Methods”). The partial autocorrelation calculated for NDVI time series of two consecutive seasons, after factoring out concurrent and preceding climatic impacts, provides an estimate of the interseasonal VGC effects (see “Methods”). At the hemispheric scale, our analyses show a significant (p  More

State variables
The first step in describing the macroscopic properties of the swarm is to define a set of state variables that fully characterizes the state of the system. The equation of state then links these state variables in a functional relation. In classical thermodynamics, a complete set of state variables is given by the conjugate pairs of pressure P and volume V, temperature T and entropy S, and, if the number of particles is not fixed, chemical potential μ and number of particles N. We use an analogous set of state variables here to characterize swarms. The most straightforward state variable to define is the number of individuals N, which is given simply by the number of midges in the swarm at a given time (note that midges that are not swarming simply sit on the walls or floor of the laboratory enclosure). The volume V of the swarm can be straightforwardly defined and computed as the volume of the convex hull enclosing all the midges. Note that while N and V are not independently controllable quantities, the ratio N/V is empirically approximately constant in large swarms24, meaning that the “thermodynamic” limit (that is, (N to infty) and (V to infty) with (frac{N}{V} to rho)) is approached in our swarms25. In typical swarming events, N changes on a time scale that is very slow compared to the swarm dynamics; thus, a chemical potential is not needed to describe the instantaneous state of the swarm. Note, though, that since the number of midges varies between measurements that may be separated by many days, N remains a relevant state variable for capturing swarm-to-swarm variability.
The remaining three state variables are somewhat more subtle, but can be defined by building on previous work. It has been explicitly shown26 that a virial relation based on the kinetic energy and an effective potential energy holds for laboratory swarms of Chironomus riparius. For particles moving in a potential, this virial relation can be used to define a pressure26. As we have shown previously, swarming midges behave as if they are trapped in a harmonic potential well that binds them to the swarm, with a spring constant k(N) that depends on the swarm size24,26 (Fig. 1b). The difference between the kinetic energy and this harmonic potential energy thus allows us to compute a pressure4,26,27, which is conceptually similar to the swim pressure defined in other active systems28. The virial theorem thus provides a link between kinetic energy, potential energy, and a field that plays the role of a pressure, when coupled with the observation that individual midges to a good approximation behave as if they are moving in a harmonic potential24,26. We can write this virial pressure P (per unit mass, assuming a constant mass per midge) as

$$P = frac{1}{3NV}mathop sum limits_{i = 1}^{N} left( {v_{i}^{2} – frac{1}{2}langle krangle r_{i}^{2} } right),$$

where N is the number of midges in the swarm, V is the swarm volume, vi is the velocity of midge i, ri is its distance from the swarm centre of mass, and (langle krangle = langle – {mathbf{a}}_{i} cdot hat{mathbf{r}}_{i} / r_{i} rangle) is the effective spring constant of the emergent potential well that binds midges to the swarm. In this expression, ai is the acceleration of midge i, (hat{mathbf{r}}_{i}) is the unit vector pointing from a midge towards the instantaneous centre of mass of the swarm (defined as (1/Nsumnolimits_{i = 1}^{N} {{mathbf{r}}_i })) and averages are taken over the individuals in the swarm. This spring constant depends on the swarm size N (Fig. 1b). We note that we have previously simply used the directly computed potential energy (- langle {mathbf{a}}_{i} cdot {mathbf{r}}_{i} rangle) to define the pressure4,27; here, we instead average the potential terms and fit them to a power law in N (Fig. 1b) to mitigate the contribution of spurious instantaneous noise in the individual positions that would be enhanced by differentiating them twice to compute accelerations. We use this power law to determine the spring constant k instantaneously at each time step.
The results from the two methods for computing the pressure are similar and consistent, but the method we use here is less prone to noise. Physically, this pressure P can be interpreted as the additional spatially variable energy density required to keep the midges bound to the swarm given that their potential energy varies in space but their mean velocity (and therefore kinetic energy) does not. Thus, compared to a simple passive particle moving in a harmonic well, midges have more kinetic energy than expected at the swarm edges; this pressure compensates for the excess kinetic energy. This pressure should be viewed as a manifestation of the active nature of the midges (similar to a swim pressure28), since the kinetic energy is an active property of each individual midge and the potential energy is an emergent property of the swarm.
We can define a Shannon-like entropy S via its definition in terms of the joint probability distributions of position and velocity. This entropy is defined as

$$S = – mathop smallint limits_{ – infty }^{infty } p(x,;v)log_{2} p(x,;v)dxdv,$$

where p(x,v) is the joint probability density function (PDF) of midge position and velocity. S here is measured in bits, as it is naturally an information entropy. Empirically, we find that the position and velocity PDFs are nearly statistically independent for all components and close to Gaussian, aside from the vertical component of the position (Fig. 1c–f). However, the deviation from Gaussianity in this component (which occurs because of the symmetry breaking due to the ground) does not significantly affect the estimate of the entropy; thus, we approximate it as Gaussian as well. Making these approximations, we can thus analytically write the (extensive) entropy as

$$S = frac{3N}{{ln 2}}ln left( {2pi esigma_{x} sigma_{v} } right),$$

where (sigma_{x}) and (sigma_{v}) are the standard deviations of the midge positions and velocities, respectively. In practice, we calculated (sigma_{v}) by averaging the instantaneous root-mean-square values of all three velocity components rather than a time-averaged value; the difference between these components was always less than 10%. This expression makes it more clear why the Gaussian approximation for the vertical component of the position is reasonable here: only the mean and variance of the PDFs are required to compute the entropy, and these low moments are very similar for the true data and the Gaussian estimate.
Although there is no obvious definition of temperature for a swarm, we can define one starting from the entropy, since temperature (when scaled by a Boltzmann constant) can be defined as the increase in the total physical energy of the system due to the addition of a single bit of entropy. Given our definitions, adding a single bit of entropy (that is, setting (S to S + 1)) for constant (sigma_{x}) and N (that is, a swarm of fixed number and spatial size) is equivalent to setting (sigma_{v} to 2^{1/(3N)} sigma_{v} .) Adding this entropy changes the total energy of the system by an amount

$$frac{3}{2}sigma_{v}^{2} Nleft( {2^{frac{2}{3N}} – 1} right) equiv k_{B}^{*} T,$$

which we thus define as the temperature (k_{B}^{*} T). Even though this temperature is nominally a function of the swarm size N, it correctly yields an intensive temperature as expected in the limit of large N, as the explicit N-dependence vanishes in that limit since (lim_{n to infty } k_{B}^{*} T = sigma_{v}^{2} ln 2). In practice, this limit is achieved very rapidly: we find that this temperature is nearly independent of N for N larger than about 20, consistent with our earlier results on the effective “thermodynamic limit” for swarms25. The effective Boltzmann constant (k_{B}^{*}) is included here to convert between temperature and energy, though we note that we cannot set its value, as there is no intrinsically preferred temperature scale.
Equipartition
With these definitions in hand, we can evaluate the suitability of these quantities for describing the macroscopic state of midge swarms. First, we note that proper state variables ought to be independent of the swarm history; that is, they ought to describe only the current state of the system rather than the protocol by which that state was prepared. Although this property is difficult to prove incontrovertibly, none of the definitions of our state variables have history dependence. We further find that when these state variables are modulated (see below), their correlation times are very short, lending support to their interpretation as true state variables. We can also compare the relationships between these state variables and the swarm behaviour to what would be expected classically. In equilibrium thermodynamics, for example, temperature is connected to the number of degrees of freedom (d.o.f.) in a system via equipartition, such that each d.o.f. contributes an energy of (frac{1}{2}k_{B}^{*} T). We can write the total energy E of a swarm as the sum of the kinetic energy (E_{k} (t) = frac{1}{2}v^{2}) and potential energy (E_{p} (t) = frac{1}{2}k(N)r(t)^{2}) for all the individuals, where r is the distance of a midge to the swarm centre of mass, v is the velocity of a midge, and k(N) is the effective spring constant. Surprisingly, even though individual midges are certainly not in equilibrium due to their active nature, we find that the total energy is linear in both T and N (Fig. 2a), and that there is no apparent anisotropy, suggesting that equipartition holds for our swarms. This result is highly nontrivial, especially given that our definition of T does not contain the spring constant k(N), which is only determined empirically from our data. Moreover, the slope of the (E/k_{B}^{*} T) curve is well approximated as (9/2)N, implying that each midge has 9 effective d.o.f. (or 6 after discounting the factor of ({text{ln}}2) in our definition of (k_{B}^{*} T)) These d.o.f. can be identified as 3 translational and 3 potential modes, given that the potential well in which the midges reside is three-dimensional. These results demonstrate the surprising applicability of equilibrium thermodynamics for describing the macroscopic state of swarms29.
Figure 2

Equipartition and the equation of state. (a) The total energy of the system (E) normalized by (k_{B}^{*} T) as a function of swarm size (blue) along with the kinetic energy (E_{k}) (yellow) and potential energy (E_{p}) (blue). The total normalized energy of the system is well approximated by (9/2)N (black dashed line), indicating that each individual midge contributes ((9/2)k_{B}^{*} T) to E and thus has 9 degrees of freedom (6 after discounting the factor of ({text{ln}}2) in our definition of (k_{B}^{*} T)). The deviations from that behaviour for the largest swarms can be attributed to a growing uncertainty in the energy due to the smaller number of experiments with such large swarms. (b) A portion of our ensemble of data of the measured pressure (blue). The yellow line is the reconstruction of the pressure from our equation of state. The inset shows a zoomed-in portion of the data to highlight the quality of the reconstruction. (c) PDF of the pressure for our entire data ensemble23. The statistics of the directly measured pressure (blue) and reconstructed pressure from the equation of state have nearly identical statistics for the full dynamic range of the signal.

Full size image

Equation of state
The fundamental relation in any thermodynamic system is the equation of state that expresses how the state variables co-vary. Equations of state are thus the foundation for the design and control of thermodynamic systems, because they describe how the system will respond when a subset of the state variables are modulated. Any equation of state can be written in the form (P = f(V, ;T,;N)) for some function f. Although the form of f is a priori unknown, it can typically be written as a power series in V, T, and N, in the spirit of a virial expansion. We fit the equation of state to our data assuming the functional form

$$P = f(V, ;k_{B}^{*} T,;N) = c_{4} V^{{c_{1} }} (k_{B}^{*} T)^{{c_{2} }} N^{{c_{3} }},$$

and using nonlinear least-squares regression. We chose to fit to the pressure for convenient analogy with a thermodynamic framework, but any other variable would have been an equivalent possibility. We note that when fitting, we normalized all the state variables by their root-mean-square values so that they were all of the same order of magnitude. These normalization pre-factors do not change the exponents, but are instead simply absorbed into (c_{4}). Thus, to leading order, we assume (P = f(V,;k_{B}^{*} T,;N) propto V^{{c_{1} }} (k_{B}^{*} T)^{{c_{2} }} N^{{c_{3} }}) and fit this relation to the swarm pressure (Fig. 2b,c), obtaining c1 = − 1.7, c2 = 2, and c3 = 1, with uncertainties on the order of 1%. Although the expression for the pressure does depend on three parameters in a nonlinear fashion, the resulting estimates for these parameters are remarkably stable and consistent across all measurements. Hence, we arrive at the equation of state (PV^{1.7} propto N(k_{B}^{*} T)^{2} .)
This equation of state reveals aspects of the nature of swarms, particularly when compared with the linear equation of state for an ideal gas (where (PV = Nk_{B} T)). In both cases, for example, to maintain a fixed pressure and volume, smaller systems need to be hotter; but this requirement is less severe for swarms since the temperature is squared, meaning that midges have to speed up less than ideal gas molecules do. Likewise, to maintain a fixed temperature, volume expansion must be counteracted by a reduction in pressure; but midges must lower the pressure more than a corresponding ideal gas, which is reflective of the decrease of the swarm spring constant with size.
Thermodynamic cycling
Beyond such reasoning, however, the true power of an equation of state in thermodynamics lies in specifying how the state variables will change when some are varied but the system remains in the same state, such as in an engine. To demonstrate that our equation of state similarly describes swarms, it is thus necessary to drive them away from their natural state. Although it is impossible to manipulate the state variables directly in this system of living organisms as one would do with a mechanical system, we have shown previously that time-varying acoustic30 and illumination27 stimulation lead to macroscopic changes in swarm behaviour. Here we therefore build on these findings and use interlaced illumination changes and acoustic signals to drive swarms along four distinct paths in pressure–volume space, analogous to a thermodynamic engine cycle. The stimulation protocol is sketched in Fig. 3a. The “on” state of the acoustic signal is telegraph noise (see Experimental details), while the “off” state is completely quiet. The illumination signal simply switches between two different steady light levels. Switching between the four states of “light-high and sound-on,” “light-high and sound-off,” “light-low and sound-off,” and “light-low and sound-on” with a 40-s period (Fig. 3a) produces the pressure–volume cycle shown in Fig. 3b. We suspect that the loops in the cycle stem from the swarm’s typical “startle” response after abrupt changes in environmental conditions, followed by a rapid relaxation to a steady state27,30.
Figure 3

Thermodynamic cycling of a midge swarm with (langle Nrangle = 27). Schematic of the perturbation cycle showing the illumination (solid) and sound (dashed) signal timings. The symbols indicate the switching points identified in (b). (b,c) Phase-averaged swarm behaviour during the perturbation cycle plotted in the pressure–volume phase plane for (b) the pressure signal as measured and (c) as reconstructed using our equation of state. (leftlangle {,} rightrangle_{phi }) denotes a phase average of a quantity over a full cycle. The four different states of the perturbation signal are indicated. The data has been averaged using a moving 3.5-s window for clarity. The swarm behaviour moves in a closed loop in this phase plane during this cycling, as would be expected for an engine in equilibrium thermodynamics, and the equation of state holds throughout even though it was developed only for unperturbed swarms. (d) Phase-averaged pressure (langle Prangle_{phi }) of the swarm during a continuous cycle through the four light and sound states. The blue line shows the directly measured pressure and the yellow line shows the reconstruction using the equation of state.

Full size image

In addition to the pressure and volume, we can also measure the other state variables as we perturb the swarms. Given that we do not observe any evidence of a phase transition, we would expect that our equation of state, if valid, should hold throughout this cycle. To check this hypothesis, we used the measured V, T, and N values during unperturbed experiments along with the equation of state to predict the scaling exponents, and in turn the pressure P. We then use these baseline, unperturbed exponents and V, T, and N during the interlaced perturbations to predict a pressure P. This pressure prediction matches the measured signal exceptionally well (Fig. 3c,d) even though the equation of state was formulated only using data from unperturbed swarms, highlighting the quality of this thermodynamic analogy. Although we might expect that a strong enough perturbation might lead to qualitatively different behaviour (if the swarm went through the analog of a phase transition31), our results give strong support to the hypothesis that our equation of state should hold for any perturbation that does not drive such a transition. More

1.
Chapin, F. S., Folke, C. & Kofinas, G. P. A framework for understanding change. In Principles of Ecosystem Stewardship (eds Folke, C. et al.) (Springer, Berlin, 2009).
Google Scholar 
2.
Ellis, E. C. & Ramankutty, N. Putting people in the map: Anthropogenic biomes of the world. Front. Ecol. Environ. 6, 439–447 (2008).
Article  Google Scholar 

3.
Conover, M. R. Resolving Human–Wildlife Conflicts: The Science of Wildlife Damage Management (Lewis Publishers CRC Press, Boca Raton, 2002).
Google Scholar 

4.
Madhusudan, M. D. Living amidst large wildlife: Livestock and crop depredation by large mammals in the interior villages of Bhadra Tiger Reserve, South India. Environ. Manag. 31, 0466–0475 (2003).
CAS  Article  Google Scholar 

5.
Aryal, A., Brunton, D. H., Ji, W., Barraclough, R. K. & Raubenheimer, D. Human–carnivore conflict: Ecological and economical sustainability of predation on livestock by snow leopard and other carnivores in the Himalaya. Sustain. Sci. 9, 321–329 (2014).
Article  Google Scholar 

6.
Bhatnagar, Y. V., Wangchuk, R., Prins, H. H., Van Wieren, S. E. & Mishra, C. Perceived conflicts between pastoralism and conservation of the kiang Equus kiang in the Ladakh trans-Himalaya, India. Environ. Manag. 38, 934–941 (2006).
Article  Google Scholar 

7.
Hernández, F., Corcoran, D., Graells, G., Roos, C. & Downey, M. C. Rancher perspectives of a livestock-wildlife conflict in Southern Chile. Rangelands 39, 56–63 (2017).
Article  Google Scholar 

8.
Michalk, D. L. et al. Sustainability and future food security—A global perspective for livestock production. Land Degrad. Dev. 30, 561–573 (2019).
Article  Google Scholar 

9.
Hilker, T., Natsagdorj, E., Waring, R. H., Lyapustin, A. & Wang, Y. Satellite observed widespread decline in Mongolian grasslands largely due to overgrazing. Glob. Change Biol. 20, 418–428 (2014).
ADS  Article  Google Scholar 

10.
Zheng, Z., Feng, C., Ye, S., Diao, Z. & Lü, S. Ecological pressures on grassland ecosystems and their conservation strategies in Northern China. Chin. J. Popul. Resourc. Environ. 13, 87–91 (2015).
Article  Google Scholar 

11.
Wen, L. et al. Effect of degradation intensity on grassland ecosystem services in the alpine region of Qinghai-Tibetan Plateau, China. PLoS ONE 8, e58432 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Krausman, P. R. et al. Livestock grazing, wildlife habitat, and rangeland values. Rangelands 31, 15–19 (2009).
Article  Google Scholar 

13.
Bestelmeyer, B. T., Estell, R. E. & Havstad, K. M. Big questions emerging from a century of rangeland science and management. Rangel. Ecol. Manag. 65, 543–544 (2012).
Article  Google Scholar 

14.
Galt, D., Molinar, F., Navarro, J., Joseph, J. & Holechek, J. L. Grazing capacity and stocking rate. Rangel. Arch. 22, 7–11 (2000).
Google Scholar 

15.
Hobbs, N. T. & Swift, D. M. Estimates of habitat carrying capacity incorporating explicit nutritional constraints. J. Wildl. Manag. 49, 814–822 (1985).
Article  Google Scholar 

16.
Vallentine, J. F. Grazing Management 2nd edn. (Academic Press, New York, 2001).
Google Scholar 

17.
Mckeon, G. M. et al. Climate change impacts on northern Australian rangeland livestock carrying capacity: A review of issues. Rangel. J. 31, 1–29 (2009).
Article  Google Scholar 

18.
Yu, L., Zhou, L., Liu, W. & Zhou, H. Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of alpine grasslands in Golog Prefecture, China. Pedosphere 20, 342–351 (2010).
Article  Google Scholar 

19.
Chen, J. Review of canceling herds and returning to grassland policy in Sanjiangyuan Region in Qinghai Province: Based on survey of Maduo County. Natl. Res. Qinghai 19, 110–115 (2008).
Google Scholar 

20.
Qin, D. Ecological Protection and Sustainable Development of Three-River-Source Area (Science Press, London, 2014).
Google Scholar 

21.
Zhao, L., Li, Q. & Zhao, X. Multi-functionality and management of grassland in the Sanjiangyuan region. Resour. Sci. 42, 78–86 (2020).
Google Scholar 

22.
Olson, D. M. & Dinerstein, E. The Global 200: A representation approach to conserving the Earth’s most biologically valuable ecoregions. Conserv. Biol. 12, 502–515 (1998).
Article  Google Scholar 

23.
Li, J. et al. Global priority conservation areas in the face of 21st century climate change. PLoS ONE 8, 54839 (2013).
ADS  Article  CAS  Google Scholar 

24.
Zhou, H., Zhou, L., Liu, W., Zhao, X. & Lai, D. Causes of grassland degradation and sustainable development of animal husbandry in Maduo County, Qinghai Province. Grassl. China 25, 63–67 (2003).
Google Scholar 

25.
Xu, J., Chen, J., Hu, Y. & Zhao, Z. Research on the status and the dynamic of grassland degradation in Maduo County Qinghai Province. Pratacult. Sci. 28, 359–364 (2011).
Google Scholar 

26.
NDRCC. Three-River-Source National Park Master Plan. https://www.ndrc.gov.cn/xxgk/zcfb/ghwb/201801/t20180117_962245.html. (National Development and Reform Commission of China, 2018).

27.
Fu, M. et al. Functional zoning and space management of three-river-source national park. J. Geog. Sci. 29, 2069–2084 (2019).
Article  Google Scholar 

28.
Sun, J. et al. Reconsidering the efficiency of grazing exclusion using fences on the Tibetan Plateau. Sci. Bull. 65, 1405–1414 (2020).
Article  Google Scholar 

29.
Tuanmu, M. et al. Effects of payments for ecosystem services on wildlife habitat recovery. Conserv. Biol. 30, 827–835 (2016).
PubMed  Article  PubMed Central  Google Scholar 

30.
Watzold, F. & Drechsler, M. Spatially uniform versus spatially heterogeneous compensation payments for biodiversity-enhancing land-use measures. Environ. Resour. Econ. 31, 73–93 (2005).
Article  Google Scholar 

31.
Hu, Z., Kong, D. & Jin, L. Grassland eco-compensation: Rate differentiations of “reward for balanced grazing” and its reasons. China Popul. Resour. Environ. 25, 152–159 (2015).
Google Scholar 

32.
Lu, C., Xie, G. & Xiao, Y. Ecological Compensation and the cost of wildlife conservation: Chang Tang Grasslands, Tibet. J. Resour. Ecol. 3, 20–25 (2012).
Article  Google Scholar 

33.
Sitters, J. et al. Herded cattle and wild grazers partition water but share forage resources during dry years in East African savannas. Biol. Cons. 142, 738–750 (2009).
Article  Google Scholar 

34.
Stokely, T. D. & Betts, M. G. Deer-mediated ecosystem service versus disservice depends on forest management intensity. J. Appl. Ecol. 57, 31–42 (2019).
Article  Google Scholar 

35.
Östman, Ö. et al. Estimating competition between wildlife and humans—a case of cormorants and coastal fisheries in the Baltic Sea. PLoS ONE 8, e83763 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Guerra, A. S. Wolves of the Sea: Managing human-wildlife conflict in an increasingly tense ocean. Marine Policy 99, 369–373 (2018).
Article  Google Scholar 

37.
Xin, Y. et al. The evaluation of carrying capacity of grassland in Qinghai. Qinghai Pratacult. 20, 13–22 (2011).
Google Scholar 

38.
Du, J., Wang, G. & Li, Y. Rate and causes of degradation of alpine grassland in the source regions of the Yangtze and Yellow River during the last 45 years. Acta Pratacult. Sin. 24, 5–15 (2015).
Google Scholar 

39.
Yu, L. et al. Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of alpine grasslands in Golog Prefecture, China. Pedosphere 20, 342–351 (2010).
Article  Google Scholar 

40.
Lü, X. et al. Spatio-temporal changes of grassland production based on MODIS NPP in the Three-River Source Region from 2006 to 2015. J. Nat. Resour. 32, 1857–1868 (2017).
Google Scholar 

41.
Knapp, A. K. et al. Resolving the Dust Bowl paradox of grassland responses to extreme drought. Proc. Natl. Acad. Sci. 117, 22249–22255 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Wu, X. et al. Predicting the shift of threatened ungulates’ habitats with climate change in Altun Mountain National Nature Reserve of the Northwestern Qinghai-Tibetan Plateau. Clim. Change 142, 331–344 (2017).
Article  Google Scholar 

43.
Maduo County Local Records Compilation Committee. Maduo County Local Record (Qinghai Ethnic Publishing House, Qinghai, 2011).
Google Scholar 

44.
Kondoh, H., Koizumi, T. & Ikeda, K. A geostatistical approach to spatial density distributions of sika deer (Cervus nippon). J. For. Res. 18, 93–100 (2013).
Article  Google Scholar 

45.
Norris, D. et al. How to not inflate population estimates? Spatial density distribution of white-lipped peccaries in a continuous Atlantic forest. Anim. Conserv. 14, 492–501 (2011).
Article  Google Scholar 

46.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Article  Google Scholar 

47.
Field, C. B., Randerson, J. T. & Malmström, C. M. Global net primary production: Combining ecology and remote sensing. Remote Sens. Environ. 51, 74–88 (1995).
ADS  Article  Google Scholar 

48.
Ali, I., Cawkwell, F., Dwyer, E., Barrett, B. & Green, S. Satellite remote sensing of grasslands: From observation to management. J. Plant Ecol. 9, 649–671 (2016).
Article  Google Scholar 

49.
Chen, A. et al. Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations. Glob. Change Biol. 2, 15373 (2020).
Article  Google Scholar 

50.
Department of Animal Husbandry. Calculation of reasonable rangeland carrying capacity of natural grassland (NY/T635–2015). (Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2015).

51.
Zhao, F., Lin, G. & Zhao, Z. The analysis of relationship between grassland and livestock based on MODIS vegetation index in Madoi in Qinghai. Heilongjiang Anim. Sci. Vet. Med. 1, 75–77 (2012).
Google Scholar 

52.
Yang, L. et al. Tick-defense grooming patterns of two sympatric Tibetan ungulates. J. Zool. 307, 242–248 (2019).
Article  Google Scholar  More

1.
Giller, K. E. et al. N2 Africa: Putting nitrogen fixation to work for smallholder farmers in Africa. In Agrological Intensification of Agricultural Systems in the African Highlands (eds Vanlauwe, B. et al.) 156–174 (Routledge, London, 2013).
Google Scholar 
2.
Goss, M. J., de Varennes, A., Smith, P. S. & Ferguson, J. A. Nitrogen fixation by soybean grown with different levels of mineral nitrogen and fertilizer replacement value for a following crop. Can. J. So. Sci. 82, 139–145 (2002).
Article  Google Scholar 

3.
Abdul-Aziz, A.L. Contribution of rhizobium and phosphorus fertilizer to biological nitrogen fixation and grain yield of soybean in the Tolon District. A Thesis Submitted to the Department of Crop and Soil Sciences, Faculty of Agriculture, Kwame Nkrumah University of Science and Technology, Kumasi, in partial fulfillment of the requirement of the degree of Master of Science in soil science (2013).

4.
Argaw, S. Evaluation of co-inoculation of Bradyrhizobium japonicum and phosphate solubilizing Pseudomonas spp. effect on soybean (Glycine max L. (Merri)) in Assossa area. J. Agric. Sci. Tech. 14, 213–224 (2012).
CAS  Google Scholar 

5.
Keneni, G. et al. Phenotypic diversity for symbio-agronomic characters in Ethiopia chickpea (Cicer arietinum L.) germplasm accessions. Afr. J. Biotechnol. 11(63), 12634–12651 (2012).
Google Scholar 

6.
Ouma, E. W., Asango, A. M., Maingi, J. & Njeru, M. Elucidating the potential of native rhizobial isolates to improve biological nitrogen fixation and growth of common bean and soybean in smallholder farming systems of Kenya. Int. J. Agron. 2016, 1–7 (2016).
Article  CAS  Google Scholar 

7.
Stajković, O. et al. Improvement of common bean growth by co-inoculation with Rhizobium and plant growth-promoting bacteria. Rom. Biotech. Lett. 16, 5919–5926 (2011).
Google Scholar 

8.
Chen, Y. X. et al. Faba bean (Vicia faba L.) nodulating rhizobia in Panxi, China, are diverse at species, plant growth promoting ability, and symbiosis related gene levels. Front. Microbiol. 9, 1–10 (2018).
Article  Google Scholar 

9.
Saἲdi, S., Chebil, S., Gtari, M. & Mhamdi, R. Characterization of root-nodule bacteria isolated from vicia faba and selection of plant growth promoting isolates. World J. Microbiol. Biotechnol. 29, 1099–1106 (2013).
Article  CAS  Google Scholar 

10.
Workalemahu, W. The effect of indigenous toot nodulating bacteria on nodulation and growth of faba bean (Vicia faba L.) in the low-input agricultural systems of Tigray highlands, northern Ethiopia. Momona Ethiop. J. Sci. 1(2), 30–43 (2009).
Article  Google Scholar 

11.
Sánchez-Cañizares, C. et al. Genomic diversity in the endosymbiotic bacterium Rhizobium leguminosarum. Genes. 9(60), 1–26 (2018).
Google Scholar 

12.
Laguerre, G., Louvrier, P., Allard, M. R. & Amarger, N. Compatibility of rhizobial genotypes within natural populations of Rhizobium leguminosarum biovar viciae for nodulation of host-legumes. Appl. Environ. Microbiol. 69(4), 2276–2283 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Sachs, J. L., Kembel, S. W., Lau, A. M. & Simms, E. L. In situphylogenetic structure and diversity of wild Bradyrhizobium communities. Appl. Environ. Microbiol. 75, 4727–4735 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Laguerre, G., Depret, G., Bourion, V. & Duc, G. Rhizobium leguminosarumbv. viciae genotypes interact with pea plants in developmental responses of nodules, roots and shoots. New Phytol. 176, 680–690 (2007).
PubMed  Article  Google Scholar 

15.
McKenzie, R. H. et al. Response of peat or rhizobia inoculation and start nitrogen in Alberta. Can. J. Plant Sci. 81, 637–643 (2001).
Article  Google Scholar 

16.
Siczek, A. & Lipiec, J. Impact of faba bean-seed rhizobial inoculation on microbial activity in the rhizosphere soil during growing season. Int. J. Mol. Sci. 17(784), 1–9 (2016).
Google Scholar 

17.
Downie, J. A. The roles of extracellular proteins, polysaccharides and signals in the interactions of rhizobia with legume roots. FEMS Microbiol. Rev. 34, 150–170 (2010).
CAS  PubMed  Article  Google Scholar 

18.
Fujita, H., Aoki, S. & Kawaguchi, M. Evolutionary dynamics of nitrogen fixation in the legume-rhizobia symbiosis. PLoS ONE 9, e93670 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Beltayef, H. et al. Statement of biological nitrogen fixation in snap bean under Mediterranean semi-arid conditions. Bulgarian J. of Agri. Sci. 24(2), 244–251 (2018).
Google Scholar 

20.
Pitkäjärvi, J. et al. Persistence, population dynamics and competitiveness for nodulation of marker gene tagged Rhizobium galegaestrains in field lysimeters in the Boreal climatic zone. FEMS Microbiol. Ecol. 2, 2 (2003).
Google Scholar 

21.
Bouyoucos, G. J. Hydrometer method improvement for making particle size analysis of soils. Agron. J. 54, 179–186 (1962).
Google Scholar 

22.
Black, G. R. & Hertge, K. H. Bulk density. In methods of soil analysis (ed. Klute, A.) 377–382 (SSSA, Madison, Wiscosin, 1986).
Google Scholar 

23.
Carter, M. R. & Gregorich, E. G. Soil Sampling and Methods of Analysis 2nd edn. (Canadian Soil Science Society, Boca Raton, 2008).
Google Scholar 

24.
Mclean, E. O. Aluminum. In Methods of Soil Analysis, Part 2 chemical methods (ed. Black, C. A.) 978–998 (America Sci. Agron, Madison, 1965).
Google Scholar 

25.
van Reeuwijk, L. P. Procedures for Soil Analysis 6th edn. (Technical Paper/International Soil Reference and Information Center, Wageningen, 2002).
Google Scholar 

26.
Jońca, Z. & Lewandowski, W. Verification of measurement capabilities of flame atomic spectrometry for the determination of sodium, potassium, magnesium, and calcium in natural fresh water part I. Comparison of recommended methods. Polish J. Environ. Studies 13(3), 275–280 (2004).
Google Scholar 

27.
Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).
ADS  CAS  Article  Google Scholar 

28.
Bremner, J. M. & Mulvaney, C. S. Nitrogen total in methods of soil analysis. In Chemical and Microbiological Properties (ed. Page, A. L.) (SSSA, Wiscosin, 1982).
Google Scholar 

29.
Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. A. Estimation of available phosphorous in soils by extraction with sodium bicarbonate. USDA Circ. 939, 1–19 (1954).
Google Scholar 

30.
Vincent, J. M. A manual for the Practical Study of Root-Nodule Bacteria. IBP Handbook No 15 (Blackwell Scientific Publications Ltd, Oxford, 1970).
Google Scholar 

31.
Woomer, P., Bennett, J. & Yost, R. Overcoming inflexibilities in most-probable-number-procedures. Agron. J. 82, 349–353 (1990).
Article  Google Scholar 

32.
Somasegaran, P. & Hoben, H. J. Handbook for Rhizobia: Methods in Legume-Rhizobium Technology (Springer Verlag, New York, 1994).
Google Scholar 

33.
Broughton, W. J. & Diworth, M. J. Control of leghemoglobin synthesis in snake beans. Biochem. J. 125, 1075–1080 (1971).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Guene, N. F. D., Diouf, A. & Gueye, M. Nodulation and nitrogen fixation of field grown common bean (Phaseolus vulgaris) as influenced by fungicide seed treatment. Afr. J. Biotechnol. 2(7), 198–201 (2003).
CAS  Article  Google Scholar 

35.
Ondieki, D. K., Nyaboga, E. N., Wagacha, J. M. & Mwaura, F. B. Morphological and genetic diversity of rhizobia nodulating cowpea (Vigna unguiculata L.) from agricultural soils of Lower Eastern Kenya. Int. J. Microbiol. 2017, 1–9 (2017).
Article  CAS  Google Scholar 

36.
Rice, W. A., Clyton, G. W., Lupwayi, N. Z. & Olsen, P. E. Evaluation of coated seeds as a Rhizobium delivery system for field pea. Can. J. Plant Sci. 81(1), 248–249 (2001).
Google Scholar 

37.
Beets, P. N., Pearce, S. H., Oliver, G. R. & Clinton, P. W. Root to shoot rations for deriving below-ground biomass of Pinus radiate standards. New Zealand J. Forestry Sci. 37(2), 267–288 (2007).
Google Scholar 

38.
SAS. SAS/STAT Software Syntax, Version 9.0. SAS Institute, Cary, NC. USA(2010).

39.
Raposeiras, R. et al. Rhizobium strains competitiveness on bean nodulation in Cerrado soils. Brasilia 41, 439–447 (2006).
Google Scholar 

40.
Ulzen, J. Optimizing legume-rhizobia symbiosis to enhance legume grain yield in smallholder farming system in Ghana. A thesis submitted to the Department of Crop and Soil Sciences, Faculty of Agriculture, Kwame Nkrumah University Science and Technology, Kumasi, in partial fulfillment of the requirements of the degree of doctor of philosophy in soil science(2018).

41.
Zengeni, R., Mpepereki, S. & Giller, K. E. Manure and soil properties affect survival and persistence of soybean nodulation rhizobia in smallholder soils of Zimbabwe. Agric. Ecosyst. Environ. Appl. Soil Eco. 32, 232–242 (2006).
Article  Google Scholar 

42.
Jida, M. & Assefa, F. Phenotypic diversity and plant growth promoting characteristics of Mesorhizobium species isolated from faba bean (Vicia faba L.) growing areas of Ethiopia. Afr. J. Biotechnol. 11, 7483–7493 (2012).
CAS  Google Scholar 

43.
Kawaletz, H., Molder, I., Terwei, P. A. A. & Ammer, S. Z. C. Pot experiments with woody species. A review. Forestry 87, 4 (2014).
Article  Google Scholar 

44.
Farid, M. & Navabi, A. N2 fixation ability of different dry bean genotypes. Can. J. Plant Sci. 95, 1243–1257 (2015).
CAS  Article  Google Scholar 

45.
Nkot, L. N. et al. Abundance of legume nodulating bacteria in soils of diverse land use systems in Cameroon. Univ. J. Plant Sci. 3(5), 97–108 (2015).
Article  Google Scholar 

46.
Solomon, T., Pant, L.M. & Angaw, T. Effects of inoculation by Bradyrhizobium japonicum strains on nodulation, nitrogen fixation, and yield of soybean (Glycine max L. Merill) varieties on Nitosols of Bako, western Ethiopia. International Scholarly Research Network ISRN Agron. 2012, Article ID 261475, Pp. 1–8 (2012).

47.
Adamu, A., Hailemariam, A., Assefa, F. & Bekele, E. Studies of Rhizobium inoculation and fertilizer treatment on growth and production of faba bean (Vicia faba L.) in some yield-depleted and yield-sustained regions of Semen Shoa. Ethiopia J. of Sci. 24, 197–211 (2001).
Google Scholar 

48.
Mohammadi, K., Sohrabi, Y., Heidari, G., Khalesro, S. & Majidi, M. Effective factors on biological nitrogen fixation. Afr. J. Agric. Res. 7, 1782–1788 (2012).
Google Scholar 

49.
Raven, J. A. Protein turnover and plant RNA and phosphorus requirement in relation to nitrogen fixation. Plant Sci. 189, 25–35 (2012).
Article  CAS  Google Scholar 

50.
Bello, S. K., Yusuf, A. A. & Cargele, M. Performance of cowpea as influenced by native strain of rhizobia, lime and phosphorus in Samaru, Nigeria. Symbiosis 75, 167–176 (2018).
CAS  PubMed  Article  Google Scholar 

51.
Talaat, N. B. & Abdallah, A. M. Response of faba bean (Vicia faba L.) to dual inoculation with Rhizobium and VA mycorrhiza under different levels of N and P fertilization. J. Appl. Sci. Res. 4, 1092–1102 (2008).
CAS  Google Scholar 

52.
Yoseph, T. & Worku, W. Effect of NP fertilizer rate and Bradyrhizobium inoculation on nodulation, N-uptake and crude protein content of soybean (Glycine Max (L) Merrill), at Jinka. Southern Ethiopia. J. Biol. Agric. Healthcare 4(6), 49–54 (2014).
Google Scholar 

53.
Hungria, M., Campo, R. J. & Mendes, I. C. Benefits of inoculation of the common bean (Phaseolus vulgaris) crop with efficient and competitive Rhizobium tropici strains. Biol. Fertl. Soils 39, 51–61 (2003).
Article  CAS  Google Scholar 

54.
Kellman, A.W. Rhizobium inoculation, cultivar and management effects on the growth, development and yield of common bean (Phaseolus vulgaris L.). A PhD thesis, Lincoln University, New Zealand(2008).

55.
Voisin, A. S., Salon, C. & Warembourg, F. R. Seasonal patterns of 13C partitioning between shoot and nodulated roots of N2-or nitrate fed- Pisum sativum (L). Ann. Bot. 91, 539–546 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Graham, P. H., Hungria, M. & Tlusty, B. Breeding for better nitrogen fixation in grain legumes: Where do the rhizobia fit in?. Crop Manag https://doi.org/10.1094/CM-2004-0301-02-RV (2004).
Article  Google Scholar 

57.
Minalku, A., Gebrekidan, H. & Assefa, F. Symbiotic effectiveness and characterization of Rhizobium strains of faba bean (Vicia faba L.) collected from eastern and western Hararghe highlands of Ethiopia. Ethiopian J. Nat. Resour 11(2), 223–244 (2009).
Google Scholar 

58.
Sindhu, S., Dua, S., Verma, M. K. & Khandewal, A. Growth promotion of legumes by inoculation of rhizosphere bacteria. In Microbes for Legume Improvement (eds Khan, M. S. et al.) (Springer-Verlag, Wien, 2010).
Google Scholar 

59.
Hemissi, I. et al. Effects of some Rhizobium strains on chickpea growth and biological control of Rhizoctonia solani. Afr. J. Micro. Res. 5(25), 4080–4090 (2011).
CAS  Google Scholar 

60.
Zahran, H. H. Rhizobia from Wild legumes: Diversity, taxonomy, ecology, nitrogen fixation and biotechnology. J. Biotechnol. 91, 143–153 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Zahir, Z. A., Arshad, M. & Frankenberger, J. R. Plant growth promoting Rhizobacteria: Applications and perspectives in agriculture. J. Adv. Agron. 81, 97–16 (2004).
CAS  Article  Google Scholar 

62.
Thilakarathna, M. S. et al. Evaluating the effectiveness of Rhizobium inoculants and micronutrients as technologies for Nepalese common bean smallholder farmers in the real-world context of highly variable hillside environments and indigenous farming practices. Agriculture 9(20), 1–17 (2019).
Google Scholar 

63.
Tena, W., Wolde-Meskel, E. & Walley, F. Symbiotic efficiency of native and exotic Rhizobium strains nodulating lentil (Lens culinaris Medik) in soils of southern Ethiopia. Agronomy 6, 1–11 (2016).
Article  CAS  Google Scholar 

64.
Cakmakci, R., Donmez, M. F. & Erdogan, U. The effect of plant growth promoting rhizobacteria on barley seedling growth, nutrient uptake, some soil properties, and bacterial counts. Turk. J. Agr. For. 31, 189–199 (2007).
CAS  Google Scholar 

65.
Kyei-Boahen, S., Giroux, C. & Walley, F. L. Fall vs Springrhizobial inoculation of chickpea. Can. J. Plant Sci. 85, 893–896 (2005).
Article  Google Scholar 

66.
Oğutcu, H., Algur, O. F., Elkoca, E. & Kantar, F. The determination of symbiotic effectiveness of Rhizobium strains isolated from wild chickpeas collected from high altitudes in Erzurum. Turkey J. Agric. Forestry 32, 241–248 (2008).
Google Scholar 

67.
Korir, H., Mungai, N. W., Thuita, M., Hamba, Y. & Masso, G. Co-inoculation effect of rhizobia and plant growth promoting rhizobacteria on common bean growth in a low phosphorus soil. Front. Plant Sci. 8, 141 (2017).
PubMed  PubMed Central  Article  Google Scholar 

68.
van de Werf, A. & Negal, O. S. Carbon allocation to shoots and roots in relation to nitrogen supply is mediated by cytokinins and sucrose: Opinion. Plant Soil 185, 21–32 (1996).
Article  Google Scholar 

69.
Koevoets, I. T., Venema, J. H., Elzegna, J. T. M. & Testerink, C. Roots withstanding their environment: Exploiting root system architecture responses to abiotic stress to improve crop tolerance. Front. Plant Sci. 7, 1335 (2016).
PubMed  PubMed Central  Article  Google Scholar 

70.
Ferreira, P. A. A., Bomfeti, C. A., Soares, B. L. & Moreira, F. M. S. Efficient nitrogen-fixing Rhizobium strains isolated from Amazonian soils are highly tolerant to acidity and aluminum. World J. Microbiol. Biotechnol. 28(5), 1947–1959 (2012).
Article  CAS  Google Scholar 

71.
Kawaka, F., Dida, M.M., Peter, A., Opala, P.A., Ombori, O., Maingi, J., Osoro, N., Muthini, M., Amoding, A., Mukaminega, D. & Muoma, J. Symbiotic efficiency of native rhizobia nodulating common bean (Phaseolus vulgaris L.) in soils of western Kenya. International Scholarly Research Notices (2014).

72.
Guo, Y. J., Ni, Y. & Huang, J. G. Effects of Rhizobium, arbuscular mycorrhiza and lime on nodulation, growth and nutrient uptake of lucerne in acid purplish soil in China. Trop. Grassl. 44, 109–114 (2010).
Google Scholar 

73.
Gicharu, G. K., Gitonga, N. M., Boga, H., Cheruiyot, R. C. & Maingi, J. M. Effect of inoculating selected climbing bean cultivars with different rhizobia strains on nitrogen fixation. Int. J. Microbiol. Res. 1(2), 25–31 (2013).
Google Scholar 

74.
Mothapo, N. V. et al. Cropping history affects nodulation and symbiotic efficiency of distinct hairy vetch (Vicia villosa Roth) genotypes with resident soil rhizobia. Biol. Fertil. Soils. 49(7), 871–879 (2013).
Article  Google Scholar 

75.
Oono, R. & Denison, R. F. Comparing symbiotic efficiency between swollen versus non-swollen RhizobialBacteriods. Plant Physiol. 154, 1541–1548 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Sharma, S., Upadhyay, R. G. & Sharma, C. R. Effect of Rhizobium inoculation and nitrogen on growth, dry matter accumulation and yield of black gram (Vigna mungo). Legume Res. 23(1), 64–66 (2000).
Google Scholar 

77.
Kantar, F., Elkoca, E., Ögütcü, H. & Algur, Ö. F. Chickpea yields in relation to Rhizobium inoculation from wild chickpea at high altitudes. J. Agron. Crop Sci. 189, 291–297 (2003).
Article  Google Scholar  More

1.
Anker, J. C. H. et al. Distance to pig farms as risk factor for community-onset livestock-associated MRSA CC398 infection in persons without known contact to pig farms: a nationwide study. Zoonoses Public Health 65, 352–360 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Broens, E. M., Graat, E. A. M., Van Der Wolf, P. J., Van De Giessen, A. W. & De Jong, M. C. M. Prevalence and risk factor analysis of livestock associated MRSA-positive pig herds in The Netherlands. Prev. Vet. Med. 102, 41–49 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Sørensen, A. I. V. Spread and control of livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) in Danish pig herds. PhD Thesis, March 2018 Anna Irene Vedel Sørensen. (2018).

4.
Danish Veterinary and Food Administration. Results of screening for livestock-associated MRSA in pigs in 2016. Fødevarestyrelsen. www.foedevarestyrelsen.dk/Nyheder/Aktuelt/Documents/MRSA%20ekspertgruppe%20-%20resultatene%20forekomst%20af%20husdyr-MRSA%20i%20svin%202016.pdf (2017).

5.
Schulz, J., Boklund, A., Toft, N. & Halasa, T. Drivers for livestock-associated methicillin-resistant staphylococcus aureus spread among Danish pig herds: a simulation study. Sci. Rep. 8, 1–11 (2018).
Article  CAS  Google Scholar 

6.
Borck Høg, B. et al. DANMAP 2016: use of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from food animals, food and humans in Denmark. Statens Serum Institut, National Veterinary Institute, Technical University of Denmark National Food Institute, Technical University of Denmark (2017).

7.
Borck Høg, B. et al. DANMAP 2017: use of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from food animals, food and humans in Denmark. Statens Serum Institut, National Veterinary Institute, Technical University of Denmark National Food Institute, Technical University of Denmark (2018).

8.
Larsen, J. et al. Emergence of livestock-associated methicillin-resistant staphylococcus aureus bloodstream infections in Denmark. Clin. Infect. Dis. 65, 1072–1076 (2017).
PubMed  PubMed Central  Article  Google Scholar 

9.
Gibbs, S. G., Green, C. F., Tarwater, P. M. & Scarpino, P. V. Airborne antibiotic resistant and nonresistant bacteria and fungi recovered from two swine herd confined animal feeding operations. J. Occup. Environ. Hyg. 1, 699–706 (2004).
PubMed  Article  PubMed Central  Google Scholar 

10.
Scarpino, P. V. & Quinn, H. Bioaerosol distribution patterns adjacent to two swine-growing-finishing housed confinement units in the American Midwest. J. Aerosol Sci. 29, 553–554 (1998).
ADS  Article  Google Scholar 

11.
Baldacchino, F., Muenworn, V., Desquesnes, M., Desoli, F. & Charoenviriyaphap, T. Transmission of pathogens by Stomoxys flies (Diptera, Muscidae): a review. Parasite 20, 26 (2013).
PubMed  PubMed Central  Article  Google Scholar 

12.
Nayduch, D. & Burrus, R. G. Flourishing in filth: house fly-microbe interactions across life history. Ann. Entomol. Soc. Am. 110, 6–18 (2017).
CAS  Article  Google Scholar 

13.
Bahrndorff, S., De Jonge, N., Skovgård, H. & Nielsen, J. L. Bacterial communities associated with houseflies (Musca domestica L.) sampled within and between farms. PLoS ONE 12, 1–15 (2017).
Article  CAS  Google Scholar 

14.
Park, R. et al. Microbial communities of the house fly Musca domestica vary with geographical location and habitat. Microbiome 7, 1–12 (2019).
CAS  Article  Google Scholar 

15.
Forsey, T. & Darougar, S. Transmission of chlamydiae by the housefly. Br. J. Ophthalmol. 65, 147–150 (1981).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Skovgård, H., Kristensen, K. & Hald, B. Retention of Campylobacter (Campylobacterales: Campylobacteraceae) in the house fly (Diptera: Muscidae). J. Med. Entomol. 48, 1202–1209 (2011).
PubMed  Article  Google Scholar 

17.
Mellor, P. S., Kitching, R. P. & Wilkinson, P. J. Mechanical transmission of capripox virus and African swine fever virus by Stomoxys calcitrans. Res. Vet. Sci. 43, 109–112 (1987).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Nayduch, D., Cho, H. & Joyner, C. Staphylococcus aureus in the house fly: temporospatial fate of bacteria and expression of the antimicrobial peptide defensin. J. Med. Entomol. 50, 171–178 (2013).
PubMed  PubMed Central  Article  Google Scholar 

19.
Espinosa-Gongora, C., Dahl, J., Elvstrøm, A., van Wamel, W. J. & Guardabassi, L. Individual predisposition to Staphylococcus aureus colonization in pigs on the basis of quantification, carriage dynamics, and serological profiles. Appl. Environ. Microbiol. 81, 1251–1256 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Friesen, K., Berkebile, D. R., Zhu, J. J. & Taylor, D. B. Laboratory rearing of stable flies and other Muscoid Diptera. J. Vis. Exp. 138, 1–7 (2018)
Google Scholar 

21.
Liu, C. M. et al. Staphylococcus aureus and the ecology of the nasal microbiome. Sci. Adv. 1, 5 (2015).
Google Scholar 

22.
Beresford, D. V. & Sutcliffe, J. F. Local infestation or long-distance migration? The seasonal recolonization of dairy farms by Stomoxys calcitrans (Diptera: Muscidae) in South Central Ontario, Canada. J. Econ. Entomol. 102, 788–798 (2009).
CAS  PubMed  Article  Google Scholar 

23.
Bailey, D. L., Whitfield, T. L. & Smittle, B. J. Flight and dispersal of the stable fly. J. Econ. Entomol. 66, 410–411 (1973).
Article  Google Scholar 

24.
Hogsette, J. A. & Ruff, J. P. Stable fly (Diptera: Muscidae) migration in northwest Florida. Environ. Entomol. 14, 170–175 (1985).
Article  Google Scholar 

25.
SPF-system. Landbrug and Fødevarer: Sundhedsstyringen. http://spfsus.dk/en (2020).

26.
Hansen, J. E. et al. LA-MRSA CC398 in dairy cattle and veal calf farms indicates spillover from pig production. Front. Microbiol. 10, 2733 (2019).
PubMed  PubMed Central  Article  Google Scholar 

27.
Singhal, N., Kumar, M., Kanaujia, P. K. & Virdi, J. S. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front. Microbiol. 6, 1–16 (2015).
Article  Google Scholar 

28.
IBM Corp. IBM SPSS Statistics for Windows v 25.0. Armonk, NY: IBM Corp. (2017).

29.
Clopper, C. J. & Pearson, E. S. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404 (1934).
MATH  Article  Google Scholar 

30.
Therneau, T. A Package for Survival Analysis in R. R package version 3. 1–11. https://cran.r-project.org/package=survival. (2020).

31.
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, New York, 2000).
Google Scholar 

32.
R Core Team. R: A language and environment for statistical computing. https://www.r-project.org/ (2020).

33.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

34.
Anonymous Kort and Matrikelstyrelsen. Beregnede adressekoordinater v 1.0 (2001).

35.
Dahlem, G. A. House fly: (musca domestica) in Encyclopedia of insects. In Encyclopedia Insects (eds Resh, V. H. & Cardé, R. T.) 469–470 (Elsevier, Amsterdam, 2009).
Google Scholar 

36.
Skovgård, H. & Nachman, G. Population dynamics of stable flies Stomoxys Calcitrans (Diptera: Muscidae) at an organic dairy farm in Denmark based on mark-recapture with destructive sub-sampling. Environ. Entomol. 41, 20–29 (2012).
PubMed  Article  PubMed Central  Google Scholar 

37.
Taylor, D. B. et al. Dispersal of stable flies (Diptera: Muscidae) from Larval development sites in a Nebraska landscape. Environ. Entomol. 39, 1101–1110 (2010).
CAS  PubMed  Article  Google Scholar 

38.
Quarterman, K., Kilpatrick, J. & Mathis, W. Fly dispersal in a rural area near Savannah, Georgia. J. Econ. Entomol. 47, 413–419 (1954).
Article  Google Scholar 

39.
Schoof, H. F., Siverly, R. E. & Jensen, J. A. House fly dispersion studies in metropolitan areas. J. Econ. Entomol. 45, 675–683 (1952).
CAS  Article  Google Scholar 

40.
Chakrabarti, S., Kambhampati, S. & Zurek, L. Assessment of house fly dispersal between rural and urban habitats in Kansas, USA. J. Kansas Entomol. Soc. 83, 172–188 (2010).
Article  Google Scholar 

41.
Gill, C., Bahrndorff, S. & Lowenberger, C. Campylobacter jejuni in Musca domestica: an examination of survival and transmission potential in light of the innate immune responses of the house flies. Insect Sci. 24, 584–598 (2017).
CAS  PubMed  Article  Google Scholar 

42.
Nayduch, D., Noblet, G. P. & Stutzenberger, F. J. Vector potential of houseflies for the bacterium Aeromonas caviae. Med. Vet. Entomol. 16, 193–198 (2002).
CAS  PubMed  Article  Google Scholar 

43.
Fasanella, A. et al. Evaluation of the house fly Musca domestica as a mechanical vector for an Anthrax. PLoS ONE 5, 4–8 (2010).
Article  CAS  Google Scholar 

44.
Baleba, S. B. S., Torto, B., Masiga, D., Weldon, C. W. & Getahun, M. N. Egg-laying decisions based on olfactory cues enhance offspring fitness in Stomoxys calcitrans L. (Diptera: Muscidae). Sci. Rep. 9, 1–13 (2019).
CAS  Article  Google Scholar 

45.
Zhu, J. J., Zhang, Q. H., Taylor, D. B. & Friesen, K. A. Visual and olfactory enhancement of stable fly trapping. Pest Manag. Sci. 72, 1765–1771 (2016).
CAS  PubMed  Article  Google Scholar 

46.
Rosen, K., Roesler, U., Merle, R. & Friese, A. Persistent and transient airborne MRSA colonization of piglets in a newly established animal model. Front. Microbiol. 9, 1–12 (2018).
Article  Google Scholar 

47.
Angen, Ø., Feld, L., Larsen, J., Rostgaard, K. & Skov, R. Transmission of methicillin-resistant staphylococcus aureus to human volunteers visiting a swine farm. Appl. Environ. Microbiol. 83, 1–10 (2017).
CAS  Article  Google Scholar 

48.
Feld, L., Bay, H., Angen, Ø., Larsen, A. R. & Madsen, A. M. Survival of LA-MRSA in dust from swine farms. Ann. Work Expo. Heal. 62, 147–156 (2018).
CAS  Article  Google Scholar 

49.
Madsen, A. M., Markouch, A., Frederiksen, M. W. & Tendal, K. Measurement of dust-borne MRSA in pig farms using different approaches. J. Appl. Microbiol. 126, 1580–1593 (2019).
CAS  PubMed  Article  Google Scholar 

50.
Hald, B., Sommer, H. M. & Skovgård, H. Use of fly screens to reduce Campylobacter spp. introduction in broiler houses. Emerg. Infect. Dis. 13, 1951–1953 (2007).
PubMed  PubMed Central  Article  Google Scholar  More