Philippa Kaur

1.
Clements, A. N. The Biology of Mosquitoes: Sensory Reception and Behaviour Vol. 2 (CAB1 Publishing, 1999).
2.
Diabaté, A. et al. Spatial distribution and male mating success of Anopheles gambiae swarms. BMC Evol. Biol. 11, 184 (2011).
PubMed  PubMed Central  Google Scholar 

3.
Assogba, B. S. et al. Characterization of swarming and mating behaviour between Anopheles coluzzii and Anopheles melas in a sympatry area of Benin. Acta Trop. 132, S53–S63 (2014).
PubMed  Google Scholar 

4.
Sawadogo, P. S. et al. Swarming behaviour in natural populations of Anopheles gambiae and An. coluzzii: review of 4 years survey in rural areas of sympatry, Burkina Faso (West Africa). Acta Trop. 132, S42–S52 (2014).
PubMed  Google Scholar 

5.
Zawada, J. W. et al. Molecular and physiological analysis of Anopheles funestus swarms in Nchelenge, Zambia. Malar. J. 17, 49 (2018).
PubMed  PubMed Central  Google Scholar 

6.
Achinko, D. et al. Swarming and mating activity of Anopheles gambiae mosquitoes in semi-field enclosures. Med. Vet. Entomol. 30, 14–20 (2016).
CAS  PubMed  Google Scholar 

7.
Kaindoa, E. W. et al. Swarms of the malaria vector Anopheles funestus in Tanzania. Malar. J. 18, 29 (2019).
PubMed  PubMed Central  Google Scholar 

8.
Lees, R. S. et al. Review: improving our knowledge of male mosquito biology in relation to genetic control programmes. Acta Trop. 132, S2–S11 (2013).
PubMed  Google Scholar 

9.
Howell, P. I. & Knols, B. G. J. Male mating biology. Malar. J. 8, S8 (2009).
PubMed  PubMed Central  Google Scholar 

10.
Jones, M. D. R., Gubbins, S. J. & Cubbin, C. M. Circadian flight activity in 4 sibling species of Anopheles-gambiae complex (Diptera, Culicidae). Bull. Entomol. Res. 64, 241–246 (1974).
Google Scholar 

11.
Rund, S. S. C., Lee, S. J., Bush, B. R. & Duffield, G. E. Strain- and sex-specific differences in daily flight activity and the circadian clock of Anopheles gambiae mosquitoes. J. Insect Physiol. 58, 1609–1619 (2012).
CAS  PubMed  Google Scholar 

12.
Charlwood, J. D. & Jones, M. D. R. Mating in the mosquito, Anopheles-gambiae s-l. II. Swarming behavior. Physiol. Entomol. 5, 315–320 (1980).
Google Scholar 

13.
Niang, A. et al. Semi-field and indoor setups to study malaria mosquito swarming behavior. Parasit. Vectors 12, 446 (2019).
PubMed  PubMed Central  Google Scholar 

14.
Marchand, R. P. Field observations on swarming and mating in Anopheles gambiae mosquitos in Tanzania. Neth. J. Zool. 34, 367–387 (1984).
Google Scholar 

15.
Fawaz, E. Y., Allan, S. A., Bernier, U. R., Obenauer, P. J. & Diclaro, J. W. Swarming mechanisms in the yellow fever mosquito: aggregation pheromones are involved in the mating behavior of Aedes aegypti. J. Vector Ecol. 39, 347–354 (2014).
PubMed  Google Scholar 

16.
Cabrera, M. & Jaffe, K. An aggregation pheromone modulates lekking behavior in the vector mosquito Aedes aegypti (Diptera: Culicidae). J. Am. Mosq. Control Assoc. 23, 1–10 (2007).
PubMed  Google Scholar 

17.
Gjullin, C. M., Whitfield, T. L. & Buckley, J. F. Male pheromones of Culex quinquefasciatus, C. tarsalis and C. pipiens that attract females of these species. Mosq. News 27, 382–387 (1967).
Google Scholar 

18.
Vas, G. & Vekey, K. Solid-phase microextraction: a powerful sample preparation tool prior to mass spectrometric analysis. J. Mass Spectrom. 39, 233–254 (2004).
CAS  PubMed  Google Scholar 

19.
Borg-Karlson, A. & Mozuraitis, R. Solid phase microextraction technique used for collecting volatiles released by individual signalling Phyllonorycter sylvella moths. Z. Naturforsch. C 51c, 599–602 (1996).
Google Scholar 

20.
Millar, J. G. & Sims, J. J. in Methods in Chemical Ecology: Chemical Methods Vol. 1 (eds Millar, J. G. & Haynes, K. F.) 1–37 (Kluwer Academic Publishers, 2000).

21.
Diabaté, A. et al. Mixed swarms of the molecular M and S forms of Anopheles gambiae (Diptera: Culicidae) in sympatric area from Burkina Faso. J. Med. Entomol. 43, 480–483 (2006).
PubMed  Google Scholar 

22.
Charlwood, J. D., Thompson, R. & Madsen, H. Observations on the swarming and mating behaviour of Anopheles funestus from southern Mozambique. Malar. J. 2, 2 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Dabire, K. R. et al. Assortative mating in mixed swarms of the mosquito Anopheles gambiae s.s. M and S molecular forms, in Burkina Faso, West Africa. Med. Vet. Entomol. 27, 298–312 (2013).
CAS  PubMed  Google Scholar 

24.
Sawadogo, S. P. et al. Differences in timing of mating swarms in sympatric populations of Anopheles coluzzii and Anopheles gambiae s.s. (formerly An. gambiae M and S molecular forms) in Burkina Faso, West Africa. Parasit. Vectors 6, 275 (2013).
PubMed  PubMed Central  Google Scholar 

25.
Pennetier, C., Warren, B., Dabire, K. R., Russell, I. J. & Gibson, G. “Singing on the wing” as a mechanism for species recognition in the malarial mosquito Anopheles gambiae. Curr. Biol. 20, 278–278 (2010).

26.
Somda, N. S. B. et al. Ecology of reproduction of Anopheles arabiensis in an urban area of Bobo-Dioulasso, Burkina Faso (West Africa): monthly swarming and mating frequency and their relation to environmental factors. PLoS ONE 13, e0205966 (2018).
Google Scholar 

27.
Cator, L. J., Ng’Habi, K. R., Hoy, R. R. & Harrington, L. C. Sizing up a mate: variation in production and response to acoustic signals in Anopheles gambiae. Behav. Ecol. 21, 1033–1039 (2010).
Google Scholar 

28.
Gibson, G., Warren, B. & Russell, I. J. Humming in tune: sex and species recognition by mosquitoes on the wing. J. Assoc. Res. Otolaryngol. 11, 527–540 (2010).
PubMed  PubMed Central  Google Scholar 

29.
Carlson, D. A. & Service, M. W. Differentiation between species of the Anopheles gambiae Giles complex (Diptera, Culicidae) by analysis of cuticular hydrocarbons. Ann. Trop. Med. Parasitol. 73, 589–592 (1979).
CAS  PubMed  Google Scholar 

30.
Carlson, D. A. & Service, M. W. Identification of mosquitos of Anopheles gambiae species complex-A and complex-B by analysis of cuticular components. Science 207, 1089–1091 (1980).
CAS  PubMed  Google Scholar 

31.
Milligan, P. J. M. et al. A study of the use of gas-chromatography of cuticular hydrocarbons for identifying members of the Anopheles gambiae (Diptera, Culicidae) complex. Bull. Entomol. Res. 83, 613–624 (1993).
CAS  Google Scholar 

32.
Caputo, B. et al. Comparative analysis of epicuticular lipid profiles of sympatric and allopatric field populations of Anopheles gambiae s.s. molecular forms and An. arabiensis from Burkina Faso (West Africa). Insect Biochem. Mol. Biol. 37, 389–398 (2007).
CAS  PubMed  Google Scholar 

33.
Tripet, F., Dolo, G., Traore, S. & Lanzaro, G. C. The “wingbeat hypothesis” of reproductive isolation between members of the Anopheles gambiae complex (Diptera: Culicidae) does not fly. J. Med. Entomol. 41, 375–384 (2004).
PubMed  Google Scholar 

34.
Simoes, P. M. V., Gibson, G. & Russell, I. J. Pre-copula acoustic behaviour of males in the malarial mosquitoes Anopheles coluzzii and Anopheles gambiae s.s. does not contribute to reproductive isolation. J. Exp. Biol. 220, 379–385 (2017).
PubMed  Google Scholar 

35.
Pombi, M. et al. Dissecting functional components of reproductive isolation among closely related sympatric species of the Anopheles gambiae complex. Evol. Appl. 10, 1102–1120 (2017).
PubMed  PubMed Central  Google Scholar 

36.
Lawniczak, M. K. N. et al. Widespread divergence between incipient Anopheles gambiae species revealed by whole genome sequences. Science 330, 512–514 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Wicker-Thomas, C. Evolution of insect pheromones and their role in reproductive isolation and speciation. Ann. Soc. Entomol. Fr. 47, 55–62 (2011).
Google Scholar 

38.
Francke, W. & Schulz, S. in Comprehensive Natural Products II: Chemistry and Biology Vol. 4 Chemical Ecology (eds Liu, H. W. & Mander, L.) 153–223 (Elsevier, 2010).

39.
El-Sayed, A. M. The Pherobase: Database of Pheromones and Semiochemicals (Pherobase, 2020); https://www.pherobase.com

40.
Blomquist, G. J. et al. Pheromone production in bark beetles. Insect Biochem. Mol. Biol. 40, 699–712 (2010).
CAS  PubMed  Google Scholar 

41.
Slodowicz, M., Ceriani-Nakamurakare, E., Carmaran, C. & Gonzalez-Audino, P. Sex pheromone component produced by microbial associates of the forest pest Megaplatypus mutatus. Entomol. Exp. Appl. 167, 231–240 (2019).
CAS  Google Scholar 

42.
Xiao, Z. J. & Lu, J. R. Generation of acetoin and its derivatives in foods. J. Agric. Food Chem. 62, 6487–6497 (2014).
CAS  PubMed  Google Scholar 

43.
de Boer, J. G. et al. Odours of Plasmodium falciparum-infected participants influence mosquito–host interactions. Sci. Rep. 7, 9283 (2017).
PubMed  PubMed Central  Google Scholar 

44.
Jha, S. K. Characterization of human body odor and identification of aldehydes using chemical sensor. Rev. Anal. Chem. 36, 20160028 (2017).
Google Scholar 

45.
Tchouassi, D. P. et al. Common host-derived chemicals increase catches of disease-transmitting mosquitoes and can improve early warning systems for Rift Valley fever virus. PLoS Negl. Trop. Dis. 7, e2007 (2013).
PubMed  PubMed Central  Google Scholar 

46.
Meijerink, J. et al. Identification of olfactory stimulants for Anopheles gambiae from human sweat samples. J. Chem. Ecol. 26, 1367–1382 (2000).
CAS  Google Scholar 

47.
Logan, J. G. et al. Arm-in-cage testing of natural human-derived mosquito repellents. Malar. J. 9, 239 (2010).
PubMed  PubMed Central  Google Scholar 

48.
Menger, D. J., Van Loon, J. J. A. & Takken, W. Assessing the efficacy of candidate mosquito repellents against the background of an attractive source that mimics a human host. Med. Vet. Entomol. 28, 407–413 (2014).
CAS  PubMed  Google Scholar 

49.
Nyasembe, V. O. et al. Development and assessment of plant-based synthetic odor baits for surveillance and control of malaria vectors. PLoS ONE 9, e89818 (2014).
PubMed  PubMed Central  Google Scholar 

50.
Jacob, J. W. et al. Independent and interactive effect of plant- and mammalian-based odors on the response of the malaria vector, Anopheles garnbiae. Acta Trop. 185, 98–106 (2018).
CAS  PubMed  Google Scholar 

51.
Vanickova, L., Canale, A. & Benelli, G. Sexual chemoecology of mosquitoes (Diptera, Culicidae): current knowledge and implications for vector control programs. Parasitol. Int. 66, 190–195 (2017).
PubMed  Google Scholar 

52.
Sawadogo, S. P. et al. Targeting male mosquito swarms to control malaria vector density. PLoS ONE 12, e0173273 (2017).
PubMed  PubMed Central  Google Scholar 

53.
Emami, S. N., Ranford-Cartwright, L. C. & Ferguson, H. M. The transmission potential of malaria-infected mosquitoes (An. gambiae-Keele, An.arabiensis-Ifakara) is altered by the vertebrate blood type they consume during parasite development. Sci. Rep. 7, 40520 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Hunt, R. H., Brooke, B. D., Pillay, C., Koekemoer, L. L. & Coetzee, M. Laboratory selection for and characteristics of pyrethroid resistance in the malaria vector Anopheles funestus. Med. Vet. Entomol. 19, 271–275 (2005).
CAS  PubMed  Google Scholar 

55.
Anderson, J. F. Histopathology of intersexuality in mosquitoes. J. Exp. Zool. 165, 475–484 (1967).
CAS  PubMed  Google Scholar 

56.
Oliva, C. F., Benedict, M. Q., Lemperiere, G. & Gilles, J. Laboratory selection for an accelerated mosquito sexual development rate. Malar. J. 10, 135 (2011).
PubMed  PubMed Central  Google Scholar 

57.
Brezolin, A. N. et al. Tools for detecting insect semiochemicals: a review. Anal. Bioanal. Chem. 410, 4091–4108 (2018).
CAS  PubMed  Google Scholar 

58.
AL-Khshemawee, H., Agarwal, M. & Ren, Y. Evaluation of stable isotope 13C6-glucose on volatile organic compounds in different stages of Mediterranean fruit fly (Medfly) Ceratitis capitate (Diptera: Tephritidae). Entomol. Ornithol. Herpetol. 6, 1–5 (2017).
Google Scholar 

59.
Hare, D. J. in Methods in Chemical Ecology: Bioassay Methods (eds Millar, J. G. & Haynes, K. F.) 212–270 (Kluwer Academic Publishers, 2000).

60.
Munhenga, G. et al. Mating competitiveness of sterile genetic sexing strain males (GAMA) under laboratory and semi-field conditions: steps towards the use of the sterile insect technique to control the major malaria vector Anopheles arabiensis in South Africa. Parasit. Vectors 9, 122 (2016).
PubMed  PubMed Central  Google Scholar 

61.
Spitzen, J. et al. A 3D analysis of flight behavior of Anopheles gambiae sensu stricto malaria mosquitoes in response to human odor and heat. PLoS ONE 8, e62995 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

62.
EthoVision XT Base + MAM and Track 3D module-2019 (Noldus, 2019). More

1.
Centner, T. J. & Heric, D. C. Anti-community state pesticide preemption laws prevent local governments from protecting people from harm. Int. J. Agric. Sustain. 17, 118–126. https://doi.org/10.1080/14735903.2019.1568814 (2019).
Article  Google Scholar 
2.
Lopez-Pacheco, I. Y. et al. Anthropogenic contaminants of high concern: Existence in water resources and their adverse effects. Sci. Total Environ. 690, 1068–1088. https://doi.org/10.1016/j.scitotenv.2019.07.052 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

3.
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358. https://doi.org/10.1371/journal.pcbi.1002358 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Rani, M., Shanker, U. & Jassal, V. Recent strategies for removal and degradation of persistent & toxic organochlorine pesticides using nanoparticles: A review. J. Environ. Manage. 190, 208–222. https://doi.org/10.1016/j.jenvman.2016.12.068 (2017).
CAS  Article  PubMed  Google Scholar 

5.
Arcuri, A. & Hendlin, Y. H. The chemical anthropocene: Glyphosate as a case study of pesticide exposures. King’s Law J. 30, 234–253. https://doi.org/10.1080/09615768.2019.1645436 (2019).
Article  Google Scholar 

6.
Agency, U. S. E. P. Atrazine—Background and Updates. https://www.epa.gov/ingredients-used-pesticide-products/atrazine-background-and-updates.

7.
Kastner, M. & Miltner, A. Application of compost for effective bioremediation of organic contaminants and pollutants in soil. Appl. Microbiol. Biotechnol. 100, 3433–3449. https://doi.org/10.1007/s00253-016-7378-y (2016).
CAS  Article  PubMed  Google Scholar 

8.
Kanissery, R. G. & Sims, G. K. Biostimulation for the enhanced degradation of herbicides in soil. Appl. Environ. Soil Sci. 2011, 10. https://doi.org/10.1155/2011/843450 (2011).
CAS  Article  Google Scholar 

9.
Viegas, C. A. et al. Evaluating formulation and storage of Arthrobacter aurescens strain TC1 as a bioremediation tool for terbuthylazine contaminated soils: Efficacy on abatement of aquatic ecotoxicity. Sci. Total Environ. 668, 714–722. https://doi.org/10.1016/j.scitotenv.2019.02.355 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

10.
Perez-Garcia, O., Lear, G. & Singhal, N. Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front. Microbiol. 7, 673. https://doi.org/10.3389/fmicb.2016.00673 (2016).
Article  PubMed  PubMed Central  Google Scholar 

11.
Nilsson, T., Rova, M. & Backlund, A. S. Microbial metabolism of oxochlorates: A bioenergetic perspective. Biochim. Biophys. Acta 1827, 189–197. https://doi.org/10.1016/j.bbabio.2012.06.010 (2013).
CAS  Article  PubMed  Google Scholar 

12.
Mani, D. & Kumar, C. Biotechnological advances in bioremediation of heavy metals contaminated ecosystems: An overview with special reference to phytoremediation. Int. J. Environ. Sci. Technol. 11, 843–872. https://doi.org/10.1007/s13762-013-0299-8 (2013).
CAS  Article  Google Scholar 

13.
Shah, J. & Dahanukar, N. Bioremediation of organometallic compounds by bacterial degradation. Indian J. Microbiol. 52, 300–304. https://doi.org/10.1007/s12088-011-0223-1 (2012).
CAS  Article  PubMed  Google Scholar 

14.
Megharaj, M., Ramakrishnan, B., Venkateswarlu, K., Sethunathan, N. & Naidu, R. Bioremediation approaches for organic pollutants: A critical perspective. Environ. Int. 37, 1362–1375. https://doi.org/10.1016/j.envint.2011.06.003 (2011).
CAS  Article  PubMed  Google Scholar 

15.
Nousiainen, A. O. et al. Bioremediation strategies for removal of residual atrazine in the boreal groundwater zone. Appl. Microbiol. Biotechnol. 99, 10249–10259. https://doi.org/10.1007/s00253-015-6828-2 (2015).
CAS  Article  PubMed  Google Scholar 

16.
Sagarkar, S. et al. Soil mesocosm studies on atrazine bioremediation. J. Environ. Manag. 139, 208–216. https://doi.org/10.1016/j.jenvman.2014.02.016 (2014).
CAS  Article  Google Scholar 

17.
Fan, X. & Song, F. Bioremediation of atrazine: Recent advances and promises. J. Soils Sediments 14, 1727–1737. https://doi.org/10.1007/s11368-014-0921-5 (2014).
CAS  Article  Google Scholar 

18.
Singh, B. & Singh, K. Microbial degradation of herbicides. Crit. Rev. Microbiol. 42, 245–261. https://doi.org/10.3109/1040841X.2014.929564 (2016).
CAS  Article  PubMed  Google Scholar 

19.
Adams, G. O., Fufeyin, P. T., Okoro, S. E. & Ehinomen, I. Bioremediation, biostimulation and bioaugmention: A review. Int. J. Environ. Bioremed. Biodegrad. 3, 28–39 (2015).
CAS  Google Scholar 

20.
Zhao, X., Bai, S., Li, C., Yang, J. & Ma, F. Bioaugmentation of atrazine removal in constructed wetland: Performance, microbial dynamics, and environmental impacts. Biores. Technol. 289, 121618. https://doi.org/10.1016/j.biortech.2019.121618 (2019).
CAS  Article  Google Scholar 

21.
Wang, Q. & Xie, S. Isolation and characterization of a high-efficiency soil atrazine-degrading Arthrobacter sp. strain. Int. Biodeteriorat. Biodegrad. 71, 61–66. https://doi.org/10.1016/j.ibiod.2012.04.005 (2012).
CAS  Article  Google Scholar 

22.
Fang, H., Lian, J., Wang, H., Cai, L. & Yu, Y. Exploring bacterial community structure and function associated with atrazine biodegradation in repeatedly treated soils. J. Hazard. Mater. 286, 457–465. https://doi.org/10.1016/j.jhazmat.2015.01.006 (2015).
CAS  Article  PubMed  Google Scholar 

23.
Zhang, Y. et al. Metabolic ability and individual characteristics of an atrazine-degrading consortium DNC5. J. Hazard Mater. 237–238, 376–381. https://doi.org/10.1016/j.jhazmat.2012.08.047 (2012).
CAS  Article  PubMed  Google Scholar 

24.
Strong, L. C., Rosendahl, C., Johnson, G., Sadowsky, M. J. & Wackett, L. P. Arthrobacter aurescens TC1 metabolizes diverse s-triazine ring compounds. Appl. Environ. Microbiol. 68, 5973–5980 (2002).
CAS  Article  Google Scholar 

25.
Sagarkar, S. et al. Monitoring bioremediation of atrazine in soil microcosms using molecular tools. Environ. Pollut. 172, 108–115. https://doi.org/10.1016/j.envpol.2012.07.048 (2013).
CAS  Article  PubMed  Google Scholar 

26.
Silva, V. P. et al. Evaluation of Arthrobacter aurescens strain TC1 as bioaugmentation bacterium in soils contaminated with the herbicidal substance terbuthylazine. PLoS ONE 10, e0144978 (2015).
Article  Google Scholar 

27.
Deutch, C. E., Bui, A. P. & Ho, T. Growth of Paenarthrobacter aurescens strain TC1 on atrazine and isopropylamine during osmotic stress. Ann. Microbiol. 68, 569–577. https://doi.org/10.1007/s13213-018-1364-9 (2018).
CAS  Article  Google Scholar 

28.
Xu, C. et al. Genome-scale metabolic model in guiding metabolic engineering of microbial improvement. Appl. Microbiol. Biotechnol. 97, 519–539. https://doi.org/10.1007/s00253-012-4543-9 (2013).
CAS  Article  PubMed  Google Scholar 

29.
Contador, C. A., Rodriguez, V., Andrews, B. A. & Asenjo, J. A. Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation. Antonie Van Leeuwenhoek 108, 1075–1090. https://doi.org/10.1007/s10482-015-0561-9 (2015).
CAS  Article  PubMed  Google Scholar 

30.
Mahadevan, R. & Lovley, D. R. The degree of redundancy in metabolic genes is linked to mode of metabolism. Biophys. J. 94, 1216–1220. https://doi.org/10.1529/biophysj.107.118414 (2008).
CAS  Article  PubMed  Google Scholar 

31.
Mahadevan, R. et al. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl. Environ. Microbiol. 72, 1558–1568. https://doi.org/10.1128/AEM.72.2.1558-1568.2006 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Oberhardt, M. A. et al. Harnessing the landscape of microbial culture media to predict new organism-media pairings. Nat. Commun. 6, 8493. https://doi.org/10.1038/ncomms9493 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Lee, J., Yun, H., Feist, A. M., Palsson, B. Ø & Lee, S. Y. Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-008-1654-4 (2008).
Article  PubMed  Google Scholar 

34.
Thiele, I., Vo, T. D., Price, N. D. & Palsson, B. O. Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol. 187, 5818–5830. https://doi.org/10.1128/JB.187.16.5818-5830.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Price, N. D., Papin, J. A., Schilling, C. H. & Palsson, B. O. Genome-scale microbial in silico models: The constraints-based approach. Trends Biotechnol. 21, 162–169. https://doi.org/10.1016/S0167-7799(03)00030-1 (2003).
CAS  Article  PubMed  Google Scholar 

36.
Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54. https://doi.org/10.1186/gb-2003-4-9-r54 (2003).
Article  PubMed  PubMed Central  Google Scholar 

37.
Fang, Y. et al. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model. J. Contam. Hydrol. 122, 96–103. https://doi.org/10.1016/j.jconhyd.2010.11.007 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Zomorrodi, A. R. & Segre, D. Synthetic ecology of microbes: Mathematical models and applications. J. Mol. Biol. 428, 837–861. https://doi.org/10.1016/j.jmb.2015.10.019 (2016).
CAS  Article  PubMed  Google Scholar 

39.
Biswas, K., Paul, D. & Sinha, S. N. Biological agents of bioremediation: A concise review. Microbiology 1, 39–43 (2015).
Google Scholar 

40.
Henson, M. A. Genome-scale modelling of microbial metabolism with temporal and spatial resolution. Biochem. Soc. Trans. 43, 1164–1171. https://doi.org/10.1042/BST20150146 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Cordova, L. T., Long, C. P., Venkataramanan, K. P. & Antoniewicz, M. R. Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium. Metab. Eng. 32, 74–81. https://doi.org/10.1016/j.ymben.2015.09.009 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
de la Torre, A. et al. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1). Microb. Cell Fact. 14, 188. https://doi.org/10.1186/s12934-015-0377-3 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Zou, W. et al. Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001. J. Biotechnol. 161, 42–48. https://doi.org/10.1016/j.jbiotec.2012.05.015 (2012).
CAS  Article  PubMed  Google Scholar 

44.
Pacheco, M. P., Bintener, T. & Sauter, T. Towards the network-based prediction of repurposed drugs using patient-specific metabolic models. EBioMedicine https://doi.org/10.1016/j.ebiom.2019.04.017 (2019).
Article  PubMed  PubMed Central  Google Scholar 

45.
Izallalen, M. et al. Geobacter sulfurreducens strain engineered for increased rates of respiration. Metab. Eng. 10, 267–275. https://doi.org/10.1016/j.ymben.2008.06.005 (2008).
CAS  Article  PubMed  Google Scholar 

46.
Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657. https://doi.org/10.1002/bit.10803 (2003).
CAS  Article  PubMed  Google Scholar 

47.
Ang, K. S., Lakshmanan, M., Lee, N. R. & Lee, D. Y. Metabolic modeling of microbial community interactions for health, environmental and biotechnological applications. Curr. Genom. 19, 712–722. https://doi.org/10.2174/1389202919666180911144055 (2018).
CAS  Article  Google Scholar 

48.
Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305–316. https://doi.org/10.1038/ismej.2010.117 (2011).
Article  PubMed  Google Scholar 

49.
Xu, X. et al. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. ISME J. 13, 494–508. https://doi.org/10.1038/s41396-018-0288-5 (2019).
CAS  Article  PubMed  Google Scholar 

50.
Faust, K. Microbial consortium design benefits from metabolic modeling. Trends Biotechnol. 37, 123–125. https://doi.org/10.1016/j.tibtech.2018.11.004 (2019).
CAS  Article  PubMed  Google Scholar 

51.
Mongodin, E. F. et al. Secrets of soil survival revealed by the genome sequence of Arthrobacter aurescens TC1. PLoS Genet. 2, e214. https://doi.org/10.1371/journal.pgen.0020214 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982. https://doi.org/10.1038/nbt.1672 (2010).
CAS  Article  PubMed  Google Scholar 

53.
Oh, Y. K., Palsson, B. O., Park, S. M., Schilling, C. H. & Mahadevan, R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282, 28791–28799. https://doi.org/10.1074/jbc.M703759200 (2007).
CAS  Article  PubMed  Google Scholar 

54.
Thiele, I. & Palsson, B. O. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121. https://doi.org/10.1038/nprot.2009.203 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

55.
Meyer, F. et al. The metagenomics RAST server—A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 9, 1–8. https://doi.org/10.1186/1471-2105-9-386 (2008).
CAS  Article  Google Scholar 

56.
Kanehisa, M. et al. Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42, D199-205. https://doi.org/10.1093/nar/gkt1076 (2014).
CAS  Article  Google Scholar 

57.
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 42, D26-31. https://doi.org/10.1093/nar/gkt1069 (2014).
CAS  Article  PubMed  Google Scholar 

58.
Apweiler, R. et al. UniProt: The Universal Protein knowledgebase. Nucleic Acids Res. 32, D115-119. https://doi.org/10.1093/nar/gkh131 (2004).
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Kundu, K. et al. Defining lower limits of biodegradation: Atrazine degradation regulated by mass transfer and maintenance demand in Arthrobacter aurescens TC1. ISME J. https://doi.org/10.1038/s41396-019-0430-z (2019).
Article  PubMed  PubMed Central  Google Scholar 

60.
Larocque, M., Chenard, T. & Najmanovich, R. A curated C. difficile strain 630 metabolic network: Prediction of essential targets and inhibitors. BMC Syst. Biol. 8, 117. https://doi.org/10.1186/s12918-014-0117-z (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

61.
Monk, J. M. et al. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 35, 904–908. https://doi.org/10.1038/nbt.3956 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

62.
Hucka, M. et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531. https://doi.org/10.1093/bioinformatics/btg015 (2003).
CAS  Article  PubMed  Google Scholar 

63.
Govantes, F. et al. Regulation of the atrazine-degradative genes in Pseudomonas sp. strain ADP. FEMS Microbiol. Lett. 310, 1–8. https://doi.org/10.1111/j.1574-6968.2010.01991.x (2010).
CAS  Article  PubMed  Google Scholar 

64.
Guo, Q., Zhang, J., Wan, R. & Xie, S. Impacts of carbon sources on simazine biodegradation by Arthrobacter strain SD3-25 in liquid culture and soil microcosm. Int. Biodeterior. Biodegrad. 89, 1–6 (2014).
CAS  Article  Google Scholar 

65.
Dsouza, M., Taylor, M. W., Turner, S. J. & Aislabie, J. Genomic and phenotypic insights into the ecology of Arthrobacter from Antarctic soils. BMC Genom. 16, 36. https://doi.org/10.1186/s12864-015-1220-2 (2015).
Article  Google Scholar 

66.
Deutch, C. E. L-Proline catabolism by the high G+C Gram-positive bacterium Paenarthrobacter aurescens strain TC1. Antonie Van Leeuwenhoek 112, 237–251. https://doi.org/10.1007/s10482-018-1148-z (2019).
CAS  Article  PubMed  Google Scholar 

67.
Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).
CAS  Article  Google Scholar 

68.
Zhang, Q. et al. Impacts of nitrogen and phosphorus on atrazine-contaminated soil remediation and detoxification by Arthrobacter sp. strain HB-5. Environ. Earth Sci. 71, 1465–1471 (2014).
CAS  Article  Google Scholar 

69.
Lachance, J. C. et al. BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput. Biol. 15, e1006971. https://doi.org/10.1371/journal.pcbi.1006971 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Reed, J. L., Famili, I., Thiele, I. & Palsson, B. O. Towards multidimensional genome annotation. Nat. Rev. Genet. 7, 130–141. https://doi.org/10.1038/nrg1769 (2006).
CAS  Article  PubMed  Google Scholar 

71.
Casida, J. E. & Bryant, R. J. The ABCs of pesticide toxicology: Amounts, biology, and chemistry. Toxicol. Res. (Camb.) 6, 755–763. https://doi.org/10.1039/c7tx00198c (2017).
CAS  Article  Google Scholar 

72.
King, Z. A. et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515-522. https://doi.org/10.1093/nar/gkv1049 (2016).
CAS  Article  PubMed  Google Scholar 

73.
Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731 (1994).
CAS  Article  Google Scholar 

74.
Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis?. Nat. Biotechnol. 28, 245–248. https://doi.org/10.1038/nbt.1614 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115. https://doi.org/10.1016/j.celrep.2014.03.070 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

76.
King, Z. A. et al. Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput. Biol. 11, e1004321. https://doi.org/10.1371/journal.pcbi.1004321 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar  More

Generation of Image Dataset and Preprocessing
In Summer 2019, we partnered with Hillsborough county mosquito control district in Florida to lay outdoor mosquito traps over multiple days. Each morning after laying traps, we collected all captured mosquitoes, froze them in a portable container and took them to the county lab, where taxonomists identified them for us. For this study, we utilized 23 specimens of Aedes aegypti and Aedes infirmatus, and 22 specimens of Aedes taeniorhynchus, Anopheles crucians, Anopheles quadrimaculatus, Anopheles stephensi, Culex coronator, Culex nigripalpus and Culex salinarius. We point out that specimens of eight species were trapped in the wild. The An. stephensi specimens alone were lab-raised whose ancestors were originally trapped in India.
Each specimen was then emplaced on a plain flat surface, and then imaged using one smartphone (among iPhone 8, 8 Plus, and Samsung Galaxy S8, S10) in normal indoor light conditions. To take images, the smartphone was attached to a movable platform 4 to 5 inches above the mosquito specimen, and three photos at different angles were taken. One directly above, and two at (45^{circ }) angles to the specimen opposite from each other. As a result of these procedures, we generated a total of 600 images. Then, 500 of these images were preprocessed to generate the training dataset, and the remaining 100 images were separated out for validation. For preprocessing, the images were scaled down to (1024 times 1024) pixels for faster training (which did not lower accuracy). The images were augmented by adding Gaussian blur and randomly flipping them from left to right. These methods are standard in image processing, which better account for variances during run-time execution. After this procedure, our training dataset increased to 1500 images.
Note here that all mosquitoes used in this study are vectors. Among these, Aedes aegypti is particularly dangerous, since it spreads Zika fever, dengue, chikungunya and yellow fever. This mosquito is also globally distributed now.
Our Deep Neural Network Framework based on Mask R-CNN
To address our goal of extracting anatomical components from a mosquito image, a straightforward approach is to try a mixture of Gaussian models to remove background from the image1,15. But this will only remove the background, without being able to extract anatomical components in the foreground separately. There are other recent approaches in the realm also. One technique is U-Net16, wherein semantic segmentation based on deep neural networks is proposed. However, this technique does not lend itself to instance segmentation (i.e., segmenting and labeling of pixels across multiple classes). Multi-task Network Cascade17 (MNC) is an instance segmentation technique, but it is prone to information loss, and is not suitable for images as complex as mosquitoes with multiple anatomical components. Fully Convolutional Instance-aware Semantic Segmentation18 (FCIS) is another instance segmentation technique, but it is prone to systematic errors on overlapping instances and creates spurious edges, which are not desirable. DeepMask19 developed by Facebook, extracts masks (i.e., pixels) and then uses Fast R-CNN20 technique to classify the pixels within the mask. This technique though is slow as it does not enable segmentation and classification in parallel. Furthermore, it uses selective search to find out regions of interest, which further adds to delays in training and inference.
In our problem, we have leveraged Mask R-CNN11 neural network architecture for extracting masks (i.e. pixels) comprising of objects of interest within an image which eliminates selective search, and also uses Regional Proposal Network (RPN)21 to learn correct regions of interest. This approach best suited for quicker training and inference. Apart from that, it uses superior alignment techniques for feature maps, which helps prevent information loss. The basic architecture is shown in Fig. 1. Adapting it for our problem requires a series of steps presented below.

Annotation for Ground-truth First, we manually annotate our training and validation images using VGG Image Annotator (VIA) tool22. To do so, we manually (and carefully) emplace bounding polygons around each anatomical component in our training and validation images. The pixels within the polygons and associated labels (i.e., thorax, abdomen, wing or leg) serve as ground truth. One sample annotated image is shown in Fig. 4.

Generate Feature Maps using CNN Then, we learn semantically rich features in the training image dataset to recognize the complex anatomical components of the mosquito. To do so, our neural network architecture is a combination of the popular Res-Net101 architecture with Feature Pyramid Networks (FPN)12. Very briefly, ResNet-10123 is a CNN with residual connections, and was specifically designed to remove vanishing gradients at later layers during training. It is relatively simple with 345 layers. Addition of a feature pyramid network to ResNet was attempted in another study, where the motivation was to leverage the naturally pyramidal shape of CNNs, and to also create a subsequent feature pyramid network that combines low resolution semantically strong features with high resolution semantically weak features using a top-down pathway and lateral connections12. This resulting architecture is well suited to learn from images at different scales from only minimal input image scales. Ensuring scale-invariant learning is specifically important for our problem, since mosquito images can be generated at different scales during run-time, due to diversity in camera hardware and human induced variations.

Emplacing anchors on anatomical components in the image In this step, we leverage the notion of Regional Proposal Network (RPN)21 and results from the previous two steps, to design a simpler CNN that will learn feature maps corresponding to ground-truthed anatomical components in the training images. The end goal is to emplace anchors (rectangular boxes) that enclose the detected anatomical components of interest in the image.

Classification and pixel-level extraction Finally, we align the feature maps of the anchors (i.e., region of interest) learned from the above step into fixed sized feature maps which serve as input to three branches to: (a) label the anchors with the anatomical component; (b) extract only the pixels within the anchors that represents an anatomical component; and (c) tighten the anchors for improved accuracy. All three steps are done in parallel.

Figure 4

Manual annotation of each anatomy (thorax, abdomen, wings, and legs) using VGG Image Annotator (VIA) tool.

Full size image

Loss functions
For our problem, recall that there are three specific sub-problems: labeling the anchors as thorax, abdomen, wings or leg; masking the corresponding pixels within each anchor; and a regressor to tighten anchors. We elaborate now on the loss functions used for these three sub-problems. We do so because, loss functions are a critical component during training and validation of deep neural networks to improve learning accuracy and avoid overfitting.
Labeling (or classification) loss For classifying the anchors, we utilized the Categorical Cross Entropy loss function, and it worked well. For a single anchor j, the loss is given by,

$$begin{aligned} CCE_j=-log(p), end{aligned}$$
(1)

where p is the model estimated probability for the ground truth class of the anchor.
Masking loss Masking is most challenging, considering the complexity in learning to detect only pixels comprising of anatomical components in an anchor. Initially, we experimented with the simple Binary Cross Entropy loss function. With this loss function, we noticed good accuracy for pixels corresponding to thorax, wings and abdomen. But, many pixels corresponding to legs were mis-classified as background. This is because of class imbalance highlighted in Fig. 5, wherein we see significantly larger number of background pixels, compared to number of foreground pixels for anchors (colored blue) emplaced around legs. This imbalance leads to poor learning for legs, because the binary class entropy loss function is biased towards the (much more, and easier to classify) background pixels.
Figure 5

After emplacement of anchors, we see significantly more background pixels than foreground pixels for anchors encompassing legs.

Full size image

To fix this shortcoming, we investigated another more recent loss function called focal loss24 which lowers the effect of well classified samples on the loss, and rather places more emphasis on the harder samples. This loss function hence prevents more commonly occurring background pixels from overwhelming the not so commonly occurring foreground pixels during learning, hence overcoming class imbalance problems. The focal loss for a pixel i is represented as,

$$begin{aligned} FL(i)=-(1-p)^gamma log (p), end{aligned}$$
(2)

where p is the model estimated probability for the ground truth class, and (gamma) is a tunable parameter, which was set as 2 in our model. With these definitions, it is easy to see that when a pixel is mis-classified and (p rightarrow 0), then the modulating factor ((1-p)^gamma) tends to 1 and the loss (log(p)) is not affected. However, when a pixel is classified correctly and when (p rightarrow 1), the loss is down-weighted. In this manner, priority during training is emphasized more on the hard negative classifications, hence yielding superior classification performance in the case of unbalanced datsets. Utilizing the focal loss gave us superior classification results for all anatomical components.
Regressor loss To tighten the anchors and hence improve masking accuracy, the loss function we utilized is based on the summation of Smooth L1 functions computed across anchor, ground truth and predicted anchors. Let (x, y) denote the top-left coordinate of a predicted anchor. Let (x_a) and (x^*) denote the same for anchors generated by the RPN, and the manually generated ground-truth. The notations are the same for the y coordinate, width w and height h of an anchor. We define several terms first, following which the loss function (L_{reg}) used in our architecture is presented.

$$begin{aligned} begin{array}{l} t_x^*=frac{(x^*-x_a)}{w_a},quad quad t_y^*=frac{(y^*-y_a)}{h_a},quad quad t_w^*=log (frac{w^*}{w_a}),quad quad t_h^*=log (frac{h^*}{h_a}),\ \ t_x=frac{(x-x_a)}{w_a},quad quad t_y=frac{(y-y_a)}{h_a},quad quad t_w=log (frac{w}{w_a}),~quad quad t_h=log (frac{h}{h_a}),\ \ smooth_{L_1}= {left{ begin{array}{ll} 0.5x^2 ,&{} text {if } |x|< 1\ |x| -0.5, &{} text {otherwise} end{array}right. } ~~~text {and} \ \ L_{reg}(t_i,t_{i}^{*})=sum _{iepsilon {{x,y,w,h}}}smooth_{L_1}(t_{i}^{*}-t_i).\ \ end{array} end{aligned}$$ (3) Hyperparameters For convenience, Table 5 lists values of critical hyperparameters in our finalized architecture. Table 5 Values of critical hyperparameters in our architecture. Full size table More

Study areas
The lotic exclosure experiment was conducted in Brogers Creek, a westerly flowing stream arising near the town of Nowra on the south coast of New South Wales, Australia (34°44′ S, 150°35′ E), an area with warm summers and cool winters. The stream winds through a steep valley surrounded by dairy farms, with riparian vegetation consisting of an undisturbed overstorey of river oaks (Casuarina cunninghamiana) with an understorey of sedges (Lomandra longifolia), introduced grasses and herbs. River oaks are the main source of litter input, dropping needle-like cladodes and small branches. The dairy farms contribute some organic matter as run-off, although no eutrophication was observed. The stream depth is 2 m maximum, but usually ≤ 1–1.5 m. The substratum is a mix of boulders, gravel, pebbles and cobbles, with silt and detritus in slow-flowing backwaters. A large population of platypuses was resident in the stream during the study, with over 78 individuals captured from August 1998 to August 2001, and individuals travelled its length while foraging44.
The lentic exclosure experiment was conducted in Lake Lea, a small (142 ha), shallow, relatively undisturbed sub-alpine dystrophic lake in north-western Tasmania (41°30′S, 146°5′E)45. Water depth is mostly 1–2 m, with one hole over 10 m deep. The lake substrate is mostly mud and sand, with some large areas of stone and rocky outcrops. Despite being relatively thinly vegetated, diverse macrophytes are present45 with extensive but patchy beds of submerged quillwort (Isoetes drumondii) that provide structural habitat and food for aquatic invertebrates. Platypuses and introduced brown trout (Salmo trutta) are the main vertebrate predators of invertebrates in the lake. We selected this lake as natural undisturbed freshwater lakes are rare in mainland Australia, and none have been studied with respect to platypus. Lake Lea, by contrast, has a large and well-studied population of platypuses32,33,34,46,47,48. Prior to our experiment, 52 individual platypuses were captured48. However, as Bethge48 did not sample the entire lake, the population probably exceeded 52 animals. The exclosure experiment was conducted in the north west of the lake, to avoid interference by anglers48.
Experimental design
Because of the paucity of exclosure experiments investigating the impacts of aquatic mammals on their prey, we briefly reviewed equivalent studies on terrestrial mammals to seek information on appropriate exclosure size, replication and design. Experiments excluding insectivorous mammals, although scant, have used sheet metal or nylon mesh as barrier materials, creating exclusion plots of 3 × 3 m40,41. These experiments used 3–4 exclusion plots and 3–4 control plots, and reported rapid increases in numbers of spiders40 and of large invertebrates41. In further experiments, Wise and Chen42 excluded all vertebrates from 50 m2 plots (n = 5 treatments, 5 controls), but detected no effect on densities of wolf spiders. A review of the effects of predator removal on terrestrial vertebrate prey found that 23 of 116 experiments used exclosures43. Of these, only 13 studies reported any replication, this ranged from 2 to 4 removal plots and an equal number of control plots in all cases43. The median size of plots was 2 ha, reflecting the larger spatial requirements of vertebrate compared to invertebrate prey. Despite the possibility that exclusion fences might affect prey, only two studies reported the use of procedural controls (i.e. sham fences)40,66, all others used open control plots to compare the effects of predator exclusion43.
Following this review, we ran an exclosure experiment in the stream from late summer through autumn 1999 and in the lake from late summer to autumn 2000. The experiments were designed to examine the impact of platypuses on the abundance, taxon richness and community structure of benthic invertebrates, as well as on sediment and epilithic algal biomass. We used three treatments in each of these two contrasting experimental systems: exclosure cages (− PLATYPUS) which prevented access by platypuses to the substrata; uncaged benthic areas (+ PLATYPUS) where platypuses had free access; and a procedural control to determine any cage effects (+ PLATYPUS control).
In the stream, we selected a large pool, ~ 100 m in length, bounded upstream and downstream by 10–20 m long riffles, and installed four mesh cages to exclude platypuses (− PLATYPUS treatment). As noted above, this level of replication is similar to, or greater than, that in most terrestrial exclosure experiments. All cages (1.2 m × 1.2 m, 30 cm high) were constructed of brown plastic Nylex® garden mesh (mesh dimension 5 × 5 cm). Five extra holes, 5 × 10 cm, vertically aligned, were cut in the mesh on all sides and at the top of the cages. These holes, and mesh size, while excluding platypuses, allowed access by invertebrates and fish, including adults of larger fish in the system—Australian bass (Macquaria novemaculeata), long-finned eels (Anguilla reinhardtii), and short-finned eels (Anguilla australis). As judged by the free movement of leaf litter and detritus in water through the cages, the cages had minimal or no effect on water current velocity. Four additional mesh cages of the same dimensions were installed as procedural controls (+ PLATYPUS control) but had 25 × 25 cm holes in the sides and top. These cages allowed free access by platypuses yet still approximated any influence of the cage structure on movements of platypuses, fish, and invertebrates. Plastic mesh was used to prevent any possible interference with platypus electroreception during feeding49. In addition to the mesh cages, four open, uncaged plots the same dimensions as the cages were marked on the open stream bed to serve as open treatments (+ PLATYPUS).
The cage mesh was secured to the substrate using metal stakes and rocks. To simulate this disturbance for all treatments, including the open treatment, rocks were similarly displaced. Cages were placed at the downstream end of the pool where current velocity was minimal, at least 2 m from the stream edges to avoid any systematic differences in current velocity due to the stream banks. Although treatments were confined to broadly the same area, and thus were exposed to similar environmental conditions, we stratified the placement of cages in water depths of 0.45–1.25 m to ensure more representative sampling of the environment. We also placed cages with opposite corners in line with stream flow to minimise leaf litter accumulation on the upstream edge. Treatment plots were ≥ 3 m apart; as the benthic prey of platypuses was expected to be largely sessile, this separation was considered sufficient to avoid spatial confounding. Within these constraints, cages were set in random locations, with assignment to treatment made at random. A single post driven into the substrate was used to mark locations of the + PLATYPUS treatment replicates.
Within each treatment replicate a sediment trap consisting of a plastic tube 10 cm high, with a 4.5 cm diameter opening, was fixed vertically to a stake ~ 20 cm inside the downstream corner of the cage. Sediment traps were used to collect benthic sediments disturbed and suspended by platypus foraging activities or other disturbances. Also, a pre-conditioned terracotta tile (20 cm2) was placed in the middle of each cage, or in the case of the + PLATYPUS treatment, about 20 cm upstream of the sediment tube/marker post to determine if platypuses had any direct or indirect effects on epilithic algae. If platypuses suppress algal-grazing herbivorous invertebrates, it is likely that algal abundance would vary differentially between treatments on the artificial tile substrates. Tiles were preconditioned by leaching them in the river for six weeks prior to the experiment, and any accumulated algae were removed before deployment.
The exclosure experiment in the lake was similar to that conducted in the stream, except that six replicates of each treatment were used rather than four. This increased statistical power to detect any treatment differences, given that the lake was expected to have lower invertebrate biomass compared with the stream. Treatment plots were again ≥ 3 m apart, set up on sites where the substrate was firm enough to support the cages, and treatments allocated randomly. Platypuses are larger in Tasmania than on mainland Australia, but still much smaller than the holes in the procedural control cages and thus able to readily pass through them. Brown trout (Salmo trutta) in the lake are 0.6–1 kg, but rarely reach this size (https://www.ifs.tas.gov.au/ifs/IFSDatabaseManager/WatersDatabase/lake-lea), so individuals could readily pass through all the exclosures.
Both experiments ran for six weeks before invertebrate sampling took place. Six weeks was deemed long enough for any potential effects of platypus foraging to be detected, especially as the late summer to autumn study period is when male platypuses attain their greatest body mass and condition and could be expected to forage most intensively29,44. Conversely, a more prolonged experimental period would have seen increasing damage to the exclosure structures from both water flow and human interference. We did not repeat the experiments in winter through spring to avoid disturbance to the platypus breeding season44. However, there is little or no seasonal variation in the composition of aquatic invertebrates between seasons, at least in the stream system29. This may suggest that similar results could be obtained at other times, although further experiments are needed to confirm this. At least 14 platypuses were known to have moved through the experimental stream pool over the study period, with some individuals visiting the open and control treatments, based on capture and radiotracking data44. In comparison, only five Australian bass were captured during extensive net sampling during the same period, suggesting that, during the course of the experiment, platypus abundance exceeded that of the most abundant large predatory fish in the pool44. Platypuses were probably present in much greater numbers in the pool than those identified, as platypuses in this system have large and overlapping linear home ranges44, and numbers were not monitored continuously during the experiment.
Ideally the experiments would have been replicated in multiple streams and lakes to increase the power and generality of our results, and to have been run across different seasons, but this was not logistically possible. We therefore interpret our results with caution and note that our conclusions are restricted to the sites and seasons that were studied.
Invertebrate, algal, and sediment sampling
Invertebrates were sampled by day in both systems using a Brooks suction sampler (Brooks67 (33 cm2 sampling area). Although 33 cm2 is relatively small, pilot studies suggested that this area would yield sufficient invertebrates to allow robust tests of our hypotheses. However, because we also expected small-scale spatial variation in the invertebrates, we took three sub-samples of invertebrates in each replicate cage. Suctioning for each sample took 60 s, with the sampler held firmly over the substrate. Samples were then preserved separately in 70% ethanol and transported to the laboratory for identification.
Invertebrates were sorted from the detritus under × 6–× 40 magnification, counted, and identified to genus where possible68. Exceptions, due to taxonomic impediments, were fly larvae of the families Chironomidae and Tipulidae, aquatic mites (Acarina), worms (Oligochaeta), flatworms (Dugesiidae), and members of the beetle family Scirtidae. Invertebrates were assigned to a trophic group (detritivore, herbivore, omnivore, predator) using published accounts36,50 and following our previous work29,44. These assignations are approximate as diets can vary between instars and locales. However, the categories were considered to be broadly useful in determining functional roles51 and thus for elucidating the role of platypuses in predator–prey and potentially trophic cascades in the study ecosystems. Leaf litter detritus from the stream samples was retained, dried and weighed, but these data are not presented as allochthonous leaf litter was not common in the lake, thus preventing direct comparisons44.
At the conclusion of both experiments, six weeks after exclosure establishment, algae were vigorously brushed from the tiles, washed into vials using stream or lake water and preserved using 2% Lugol’s iodine solution. In the laboratory, algae were filtered onto pre-weighed 0.45 μm filterpaper, dried at 60 °C to constant weight, and weighed to 0.0001 g. Sediment traps were collected and the material was transferred to a pre-weighed drying dish and dried to constant weight at 60 °C. The material was then weighed to 0.01 g precision.
Data analysis
All analyses focused on comparisons between the three treatments (i.e., + PLATYPUS, − PLATYPUS and + PLATYPUS control) within each experiment, but separately between the lotic and lentic systems. Two sets of analyses were undertaken for the two ecosystem datasets. Firstly, univariate comparisons were carried out to identify differences among means for the abundance and taxon richness of invertebrates and invertebrate trophic groups (hypotheses 1 and 2), algal biomass and sediment mass (hypotheses 3 and 4). Secondly, multivariate analyses were carried out to explore possible shifts in composition of the invertebrate community as a whole among treatments, separately in both systems (hypothesis 2). We did not formally compare the datasets observed in the lentic and lotic systems (hypothesis 5), but instead compared the effect sizes arising from the manipulation of platypus in each system.
Univariate data were subjected to Cochran’s test for homogeneity of variances52. Due to heterogeneity, invertebrate data were √-transformed, then analysed using a nested (hierarchical) one-factor analysis of variance (ANOVA), with treatments fixed, and replicates nested within treatments52. We took this approach to quantify replicate-within-treatment variance, rather than losing information by averaging across samples52. Algae and sediment data were also √-transformed and compared among treatments using a one-factor ANOVA. Tukey’s multiple comparison test was performed on each pair-wise comparison to identify sources of difference between treatments. Univariate tests were conducted using SYSTAT version 9 and Statistica 13.
The multivariate invertebrate community dataset was analysed using PRIMER, version 5. Community composition within each treatment was first assessed using a Bray–Curtis dissimilarity matrix. This distance measure is widely used in ecological studies, and is considered to be robust53,54 and useful in determining the underlying structure of biological communities. The matrix was then subjected to non-metric multidimensional scaling (nMDS), providing an ordination where the distance between samples reflects relative similarity in species composition. Data were square root transformed to down-weight the effects of the most common taxa and maintains the effects of the less common taxa29,55. An analysis of similarity (ANOSIM routine, PRIMER ver. 5) was performed on the dissimilarity matrices to test for differences between treatments. This permutation test uses a randomisation approach to generate significance levels to test a priori hypotheses about differences between groups of samples54,55. The SIMPER (Similarity Percentages) sub-routine in Primer ver. 555 was used to examine the contribution of each taxon to the average dissimilarity between all pairs of inter-group samples. This test does not have a statistical hypothesis-testing framework, but is useful in data exploration to indicate which ‘taxa’ are principally responsible for differences between a priori defined groups that differ in matrix structure55. SIMPER was used to determine which trophic groups contributed to dissimilarities between the + PLATYPUS and − PLATYPUS treatments. More

Field sampling
We defined a sampling universe based on the presence of non-zero estimated introduction risk in the contiguous United States7 within the range of salamander species known to be susceptible to Bsal2. Because of their presumed high susceptibility to Bsal2 and large ranges24, we targeted newts of the genera Notophthalmus and Taricha in the eastern and western U.S., respectively. Challenge trials for most of the diverse amphibian fauna in the U.S. were lacking when we designed the study, though we expected that some anuran species can serve as infection reservoirs of Bsal25,26,26. We therefore sampled other amphibian species (anurans and caudates) as well (Supplement).
We set a target of 10,000 samples across the United States. Our expectation was that if Bsal was introduced into the U.S. it would most likely be transmitted by an infected individual intentionally released. Site selection was therefore non-random as we sought sites that were generally accessible to the public or near areas frequented by visitors. Accordingly, we avoided remote areas that would be less prone to such an introduction. We defined a site as a waterbody, wetland, or group of proximate aquatic habitats that could reasonably be epidemiologically linked based on the transmission of Bsal by annual host movements or transport of infective stages via water. We aimed to capture 30 animals per site (N), which would result in 90% certainty of detecting Bsal when present, assuming a Bsal detection probability (p) of 0.75 on an infected individual27 and a presumed low prevalence value of 0.10. We recognize that a prevalence value lower than 0.10 may be possible, but it would have been prohibitively difficult to sample more individuals per site.

$$ Certainty = 1 – [theta *(1 – p) + (1 – theta )]^{N} $$
(1)

We captured target animals by hand, net, or trap. After capture, we handled each animal separately using disposable, powderless vinyl gloves and new, clean plastic bags to avoid cross contamination. All handling of animals was conducted in accordance with relevant guidelines and with appropriate collecting permits. All experimental protocols were approved by U.S. Geological Survey Institutional Animal Care and Use Committee. Appropriate permit numbers and information may be obtained from first author upon request. We rubbed rayon-tipped sterile swabs (MW-113, Medical Wire & Equipment, Corsham, England) over the plantar side of one front and one hind limb, the ventral tail surface of caudates, the dorsal side of the body, and the ventral surface of the body 5 times each28. We placed the swabs into sterile plastic vials with 20 μl of sterile deionized water. We recorded the snout-vent length, sex, and any visible signs of skin lesions for each individual. We collected two separate swabs from each animal, holding one in reserve to provide confirmation if Bsal was detected on the first swab. We chilled swabs immediately after field collection and subsequently froze them at ≤ − 20 °C within 3 days. Frozen swabs were sent to the U.S. Geological Survey’s National Wildlife Health Center in Madison, Wisconsin, for analysis.
Molecular methods
We extracted DNA from swabs as described by Hyatt et al.29 except that 125 μl of PrepMan® Ultra Sample Preparation Reagent (Applied Biosystems, Foster City, CA) and 100 mg of zirconium/silica beads (Biospec Products, Bartlesville, OK) were used so that the entire swab was immersed. The bead-beating steps were conducted using a FastPrep®-24 homogenizer (MP Biomedicals, Santa Ana, CA). We used a real-time TaqMan polymerase chain reaction (PCR) for detection of Bsal on the extracted DNA as described in Blooi et al.30,31,31. We ran reactions on the 7,500 fast real-time PCR system (Applied Biosystems, Foster City, CA) using QuantiFast Probe RT-PCR mastermix kit with ROX dye (Qiagen, Valencia, CA) and BSA as per the kit instructions. We used five microliters of the PrepMan® solution containing the extracted DNA as template for the PCR. We included a negative extraction control and a standard curve run in duplicate on each PCR plate. The standard curve consisted of five different concentrations of the target sequence for Bsal inserted into plasmids. The concentrations of the standards occurred at ten-fold dilutions ranging from 110–1,100,000 copies (0.5–5,000 fg DNA) per reaction (on some initial runs, the standard range was 11–110,000 copies per reaction). The threshold for signal detection was set at 5% of the maximum fluorescence of the standards run for that assay. We considered a positive detection of Bsal DNA if a detectable signal existed at 37 or fewer PCR cycles and no detection in all other cases. We calculated the efficiency of each run using standard curve amplification and repeated PCR plates with an efficiency of less than 90% or greater than 110%.
Data analyses
The probability of failing to detect a species given that it occurs is different than the probability of occurrence given non-detection32. We focused on this latter quantity and estimated the average probability of Bsal occurrence at sampled sites, given non-detection data, survey effort, and alternative hypotheses about the status of Bsal in the U.S. We defined occupancy as the probability of Bsal occurrence at the site level and prevalence as the probability of Bsal occurrence on an individual. Under this latter definition, prevalence included both infections and Bsal zoospores from the environment that might be detected on the skin of an infected individual. The probability of Bsal occurrence given non-detection was represented probabilistically as Pr(zi = 1|Σ(yij) = 0), where zi is the latent occupancy state for site i (zi = 1 for occupied sites and zi = 0 for unoccupied sites) and yij is the imperfectly observed pathogen status of a sampled individual j at site i. At occupied sites, observations were a product of the pathogen status of the individual (wj = 1 for pathogen positive individuals and wj = 0 for pathogen negative individuals) and the probability of detecting Bsal on infected individuals (p).
Using Bayes Theorem, the probability of Bsal occurrence at a single site i conditional on non-detection ((varphi_{i})) can be calculated using prior expectations about Bsal occupancy ((psi_{prior})) and prevalence ((theta_{prior})). In addition, the total number of individuals sampled at each site (N) and the total number of replicates collected per individual (K) were considered.

$$ begin{aligned} varphi_{i} = Pr {(}z_{i} = 1{|}sum y_{ij} = 0) & = frac{{Pr {(}sum y_{ij} = 0{|}z_{i} = 1){Pr}left( {z_{i} = 1} right)}}{{Pr {(}sum y_{ij} = 0{|}z_{i} = 1){Pr}left( {z_{i} = 1} right) + {Pr}left( {z_{i} = 0} right)}} \ & = frac{{left( {left( {1 – theta } right) + theta left( {1 – p} right)^{K} } right)^{N} psi }}{{left( {left( {1 – theta } right) + theta left( {1 – p} right)^{K} } right)^{N} psi + left( {1 – psi } right)}} \ end{aligned} $$
(2)

This equation yields the probability that an observation of Bsal non-detection came from an occupied site (i.e., a false negative), given survey effort (K, N) and prior expectations about pathogen detectability ((p)), occurrence ((psi)) and prevalence ((theta)).
Prior expectations were derived from four hypotheses about Bsal invasion in the United States using this probabilistic framework (Table 1), leading to different predictions about Bsal occurrence and prevalence. If Bsal is endemic to the U.S., we expect it to be widespread within suitable habitats (high (psi)). If Bsal invaded the U.S. recently, we expect it to be present at a small proportion of locations (low (psi)). This hypothesis is unlikely biologically given what we know about Bsal and invasive pathogens and it is only included here for theoretical completeness. We would expect that additional extensive sampling would fail to increase the posterior probability of this state of nature. In addition, and independent of occurrence rates, Bsal transmission within an infected population may vary. Reported Bsal prevalence values from field studies range across species, sites, and with time since invasion17,18,18. Therefore, we also consider two categories of site prevalence: a rapid transmission scenario where Bsal prevalence is high within infected populations (high (theta)), and a slow transmission scenario ((theta)) where Bsal prevalence is low within infected populations. To evaluate the probability that Bsal was present at any of our sampled sites given non-detection, we calculated Eq. (2) for each site across a range of occurrence ((psi ,)= 0.05–0.95) and prevalence ((theta ,)= 0.05–0.95) values and used the mean result of Eq. (2) ((hat{varphi })) from all our sampled sites as the metric to summarize the probability of Bsal presence in our sampling frame.
Table 1 Hypotheses concerning the arrival and occurrence of Batrachochytrium salamandrivorans within sites ((psi)) and populations ((theta)), given it occurs in the United States.
Full size table More

1.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
2.
Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics (Springer Science & Business Media, 2012).

3.
Lambers, H., Chapin, F. S. & Pons, T. L. Plant Physiological Ecology (Springer New York, 2008).

4.
Ögren, E. & Evans, J. R. Photosynthetic light-response curves. Planta 189, 182–190 (1993).
Google Scholar 

5.
Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).
Google Scholar 

6.
Walters, M. B. & Field, C. B. Photosynthetic light acclimation in two rainforest Piper species with different ecological amplitudes. Oecologia 72, 449–456 (1987).
CAS  PubMed  Google Scholar 

7.
Walters, R. G. & Horton, P. Acclimation of Arabidopsis thaliana to the light environment: changes in composition of the photosynthetic apparatus. Planta 195, 248–256 (1994).
CAS  Google Scholar 

8.
Bailey, S., Walters, R. G., Jansson, S. & Horton, P. Acclimation of Arabidopsis thaliana to the light environment: the existence of separate low light and high light responses. Planta 213, 794–801 (2001).
CAS  PubMed  Google Scholar 

9.
Sims, D. A. & Pearcy, R. W. Photosynthetic characteristics of a tropical forest understory herb, Alocasia macrorrhiza, and a related crop species, Colocasia esculenta grown in contrasting light environments. Oecologia 79, 53–59 (1989).
PubMed  Google Scholar 

10.
Poorter, H. et al. A meta-analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance. New Phytol. 223, 1073–1105 (2019).
CAS  PubMed  Google Scholar 

11.
Hikosaka, K. & Terashima, I. A model of the acclimation of photosynthesis in the leaves of C3 plants to sun and shade with respect to nitrogen use. Plant Cell Environ. 18, 605–618 (1995).
CAS  Google Scholar 

12.
Warren, C. R. & Adams, M. A. Distribution of N, Rubisco and photosynthesis in Pinus pinaster and acclimation to light. Plant Cell Environ. 24, 597–609 (2001).
CAS  Google Scholar 

13.
Laisk, A., Nedbal, L. & Govindjee (eds) Photosynthesis in silico (Springer Netherlands, 2009); https://doi.org/10.1007/978-1-4020-9237-4

14.
Baldocchi, D. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).
CAS  Google Scholar 

15.
Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of global change biology. Glob. Change Biol. 26, 242–260 (2020).
Google Scholar 

16.
Keenan, T. F. et al. Widespread inhibition of daytime ecosystem respiration. Nat. Ecol. Evol. 3, 407–415 (2019).
PubMed  PubMed Central  Google Scholar 

17.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).
PubMed  Google Scholar 

18.
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).
CAS  PubMed  Google Scholar 

19.
Smith, N. G. et al. Global photosynthetic capacity is optimized to the environment. Ecol. Lett. 22, 506–517 (2019).
PubMed  PubMed Central  Google Scholar 

20.
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e38345 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Bauerle, W. L. et al. Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. Proc. Natl Acad. Sci. USA 109, 8612–8617 (2012).
CAS  PubMed  Google Scholar 

23.
He, L. et al. Changes in the shadow: the shifting role of shaded leaves in global carbon and water cycles under climate change. Geophys. Res. Lett. 45, 5052–5061 (2018).
Google Scholar 

24.
Ögren, E. & Rosenqvist, E. On the significance of photoinhibition of photosynthesis in the field and its generality among species. Photosynth. Res. 33, 63–71 (1992).
PubMed  Google Scholar 

25.
Greer, D. H., Berry, J. A. & Björkman, O. Photoinhibition of photosynthesis in intact bean leaves: role of light and temperature, and requirement for chloroplast-protein synthesis during recovery. Planta 168, 253–260 (1986).
CAS  PubMed  Google Scholar 

26.
Öquist, G., Chow, W. S. & Anderson, J. M. Photoinhibition of photosynthesis represents a mechanism for the long-term regulation of photosystem II. Planta 186, 450–460 (1992).
PubMed  Google Scholar 

27.
Daniel, E. The temperature dependence of photoinhibition in leaves of Phaseolus vulgaris (L.). Influence of CO2 and O2 concentrations. Plant Sci. 124, 1–8 (1997).
CAS  Google Scholar 

28.
Tyystjärvi, E Photoinhibition of photosystem II. Int. Rev. Cell Mol. Biol. 300, 243–303 (2013).
PubMed  Google Scholar 

29.
Katul, G. G., Palmroth, S. & Oren, R. Leaf stomatal responses to vapour pressure deficit under current and CO2-enriched atmosphere explained by the economics of gas exchange. Plant Cell Environ. 32, 968–979 (2009).
CAS  PubMed  Google Scholar 

30.
Oren, R. et al. Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 22, 1515–1526 (1999).
Google Scholar 

31.
Laing, W. A. Temperature and light response curves for photosynthesis in kiwifruit (Actinidia chinensis) cv. Hayward. N. Zeal. J. Agric. Res. 28, 117–124 (1985).
Google Scholar 

32.
Leverenz, J. W. The effects of illumination sequence, CO2 concentration, temperature and acclimation on the convexity of the photosynthetic light response curve. Physiol. Plant 74, 332–341 (1988).
Google Scholar 

33.
Bazzaz, F. A. & Carlson, R. W. Photosynthetic acclimation to variability in the light environment of early and late successional plants. Oecologia 54, 313–316 (1982).
CAS  PubMed  Google Scholar 

34.
Valladares, F., Martinez-Ferri, E., Balaguer, L., Perez-Corona, E. & Manrique, E. Low leaf-level response to light and nutrients in Mediterranean evergreen oaks: a conservative resource-use strategy? New Phytol. 148, 79–91 (2000).
CAS  Google Scholar 

35.
Poorter, H. & Evans, J. R. Photosynthetic nitrogen-use efficiency of species that differ inherently in specific leaf area. Oecologia 116, 26–37 (1998).
PubMed  Google Scholar 

36.
Kattge, J., Knorr, W., Raddatz, T. & Wirth, C. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Glob. Change Biol. 15, 976–991 (2009).
Google Scholar 

37.
Murchie, E. H. & Horton, P. Acclimation of photosynthesis to irradiance and spectral quality in British plant species: chlorophyll content, photosynthetic capacity and habitat preference. Plant Cell Environ. 20, 438–448 (1997).
Google Scholar 

38.
Alter, P., Dreissen, A., Luo, F. L. & Matsubara, S. Acclimatory responses of Arabidopsis to fluctuating light environment: comparison of different sunfleck regimes and accessions. Photosynth. Res. 113, 221–237 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Slattery, R. A., Walker, B. J., Weber, A. P. M. & Ort, D. R. The impacts of fluctuating light on crop performance. Plant Physiol. 176, 990–1003 (2018).
CAS  PubMed  Google Scholar 

40.
Murchie, E. H., Hubbart, S., Chen, Y., Peng, S. & Horton, P. Acclimation of rice photosynthesis to irradiance under field conditions. Plant Physiol. 130, 1999–2010 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Thornton, P. E. & Zimmermann, N. E. An improved canopy integration scheme for a land surface model with prognostic canopy structure. J. Clim. 20, 3902–3923 (2007).
Google Scholar 

42.
Grant, R. F. et al. Interannual variation in net ecosystem productivity of Canadian forests as affected by regional weather patterns—a FLUXNET-Canada synthesis. Agric. Meteorol. 149, 2022–2039 (2009).
Google Scholar 

43.
Ryu, Y. et al. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob. Biogeochem. Cycles 25, GB4017 (2011).
Google Scholar 

44.
Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).
PubMed  PubMed Central  Google Scholar 

45.
Luo, X., Croft, H., Chen, J. M., He, L. & Keenan, T. F. Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. Glob. Change Biol. 25, 2499–2514 (2019).
Google Scholar 

46.
Pastorello, G. Z. et al. A new data set to keep a sharper eye on land–air exchanges. Eos 98, https://doi.org/10.1029/2017EO071597 (2017).

47.
Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010).
Google Scholar 

48.
Schaefer, K. et al. A model–data comparison of gross primary productivity: results from the North American Carbon Program site synthesis. J. Geophys. Res. Biogeosci. 117, G03010 (2012).
Google Scholar 

49.
Ricciuto, D. M. et al. NACP Site: Terrestrial Biosphere Model Output Data in Original Format (ORNL DAAC, 2013); https://doi.org/10.3334/ornldaac/1192

50.
Ricciuto, D. M. et al. NACP Site: Terrestrial Biosphere Model and Aggregated Flux Data in Standard Format (ORNL DAAC, 2013); https://doi.org/10.3334/ornldaac/1183

51.
Gonsamo, A. et al. Improved assessment of gross and net primary productivity of Canada’s landmass. J. Geophys. Res. Biogeosci. 118, 1546–1560 (2013).
Google Scholar 

52.
Wang, Q. et al. Simulation and scaling of temporal variation in gross primary production for coniferous and deciduous temperate forests. Glob. Change Biol. 10, 37–51 (2004).
Google Scholar 

53.
Chen, J., Liu, J., Cihlar, J. & Goulden, M. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).
CAS  Google Scholar 

54.
Luo, X. et al. Comparison of big-leaf, two-big-leaf, and two-leaf upscaling schemes for evapotranspiration estimation using coupled carbon–water modeling. J. Geophys. Res. Biogeosci. 123, 207–225 (2018).
Google Scholar 

55.
Xu, L. & Baldocchi, D. D. Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature. Tree Physiol. 23, 865–877 (2003).
PubMed  Google Scholar 

56.
Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).
PubMed  PubMed Central  Google Scholar 

57.
Fisher, J. B., Whittaker, R. J. & Malhi, Y. ET come home: potential evapotranspiration in geographical ecology. Glob. Ecol. Biogeogr. 20, 1–18 (2011).
Google Scholar 

58.
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).
CAS  PubMed  Google Scholar 

59.
Medlyn, B. E. et al. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 25, 1167–1179 (2002).
CAS  Google Scholar  More

1.
Wall, D. H. et al. Soil Ecology And Ecosystem Services. p. 406 (Oxford University Press, 2012).
2.
Blouin, M. et al. A review of earthworm impact on soil function and ecosystem services. Eur. J. Soil Sci. 64, 161–182 (2013).
Google Scholar 

3.
Baveye, P. C., Baveye, J. & Gowdy, J. Soil ‘Ecosystem’ services and natural capital: critical appraisal of research on uncertain ground. Front. Environ. Sci. Eng. China 4, 1–49 (2016).
Google Scholar 

4.
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).
ADS  CAS  PubMed  Google Scholar 

5.
Heemsbergen & Hal, V. Biodiversity effects on soil processes explained by interspecific functional dissimilarity biodiversity effects on soil processes explained by interspecific. Science 306, 8–10 (2004).
Google Scholar 

6.
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
ADS  CAS  PubMed  Google Scholar 

7.
Schuldt, A. et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Risch, A. C. et al. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. Nat. Commun. 9, 3684 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

9.
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
ADS  CAS  PubMed  Google Scholar 

10.
Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. USA 112, 201516684 (2015).
Google Scholar 

11.
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).
PubMed  Google Scholar 

12.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Song, D. et al. Large-scale patterns of distribution and diversity of terrestrial nematodes. Appl. Soil Ecol. 114, 161–169 (2017).
Google Scholar 

14.
Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).
PubMed  Google Scholar 

15.
Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. New Phytol. 211, 404–410 (2016).
PubMed  Google Scholar 

16.
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

17.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
ADS  CAS  PubMed  Google Scholar 

18.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

19.
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
ADS  CAS  PubMed  Google Scholar 

20.
Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).

21.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
ADS  CAS  PubMed  Google Scholar 

22.
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity-ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
ADS  PubMed  PubMed Central  Google Scholar 

23.
van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 21, 31–42 (2018).
PubMed  Google Scholar 

24.
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

25.
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat. Ecol. Evol. 2, 1042–1043 (2018).
PubMed  PubMed Central  Google Scholar 

26.
Wetzel, F. T. et al. Unlocking biodiversity data: prioritization and filling the gaps in biodiversity observation data in Europe. Biol. Conserv. 221, 78–85 (2018).
Google Scholar 

27.
Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. Biol. Sci. 280, 20122649 (2013).
PubMed  PubMed Central  Google Scholar 

28.
Eisenhauer, N., Bonn, A. & Guerra, C. A. Recognizing the quiet extinction of invertebrates. Nat. Commun. 10, 50 (2019).

29.
Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).
Google Scholar 

30.
Paleari, S. Is the European Union protecting soil? A critical analysis of Community environmental policy and law. Land Use Policy 64, 163–173 (2017).
Google Scholar 

31.
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).
PubMed  Google Scholar 

32.
Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z.-Q. & Bourne, P. E. Biodiversity data should be published, cited, and peer reviewed. Trends Ecol. Evol. 28, 454–461 (2013).
PubMed  Google Scholar 

33.
Bingham, H. C., Doudin, M. & Weatherdon, L. V. The biodiversity informatics landscape: elements, connections and opportunities. 3, e14059 (2017).

34.
Gibb, H. et al. A global database of ant species abundances. Ecology 98, 883–884 (2017).
PubMed  Google Scholar 

35.
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

36.
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 71, 711–730 (2017).
CAS  PubMed  Google Scholar 

37.
de Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272 (2010).
Google Scholar 

38.
Stewart, G. Meta-analysis in applied ecology. Biol. Lett. 6, 78–81 (2010).
PubMed  Google Scholar 

39.
Rillig, M. C. et al. Biodiversity research: data without theory—theory without data. Front. Ecol. Evol. 3, 20 (2015).
ADS  Google Scholar 

40.
Coleman, D. C., Callaham, M. A. & Crossley, D. A., Jr. Fundamentals of Soil Ecology. (Academic Press, 2017).

41.
Lavelle, P. & Spain, A. Soil Ecology. (Springer Science & Business Media, 2001).

42.
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
Google Scholar 

43.
Phillips, H. R. P. et al. 2019. Global distribution of earthworm diversity. Science https://doi.org/10.1126/science.aax4851 (2019).

44.
van den Hoogen J., et al. Soil nematode abundance and functional group composition at a global scale. Nature https://doi.org/10.1038/s41586-019-1418-6 (2019).

45.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
PubMed  Google Scholar 

46.
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (JRC and the Global Soil Biodiversity Initiative, 2016).

47.
Guenard, B., Weiser, M. D. & Gomez, K. The Global Ant Biodiversity Informatics (GABI) database: synthesizing data on the geographic distribution of ant species (Hymenoptera: Formicidae). Myrmecol. News 24, 83–89 (2017).

48.
Nielsen, U. N. et al. The enigma of soil animal species diversity revisited: the role of small-scale heterogeneity. PLoS ONE 5, e11567 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

49.
Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42, S3–S15 (2006).

50.
Evans, T. A., Dawes, T. Z., Ward, P. R. & Lo, N. Ants and termites increase crop yield in a dry climate. Nat. Commun. 2, 262–267 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

51.
Eisenhauer, N., Bowker, M. A., Grace, J. B. & Powell, J. R. From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology. Pedobiologia 58, 65–72 (2015).
Google Scholar 

52.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
ADS  CAS  PubMed  Google Scholar 

53.
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

54.
Fraser, L. H. et al. Plant ecology. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 349, 302–305 (2015).
ADS  CAS  PubMed  Google Scholar 

55.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

56.
Borgman, C. L., Wallis, J. C. & Enyedy, N. Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries. Int. J. Digit. Lib. 7, 17–30 (2007).
Google Scholar 

57.
Hampton, S. E. et al. Big data and the future of ecology. Front. Ecol. Environ. 11, 156–162 (2013).
Google Scholar 

58.
Pey, B. et al. Current use of and future needs for soil invertebrate functional traits in community ecology. Basic Appl. Ecol. 15, 194–206 (2014).
Google Scholar 

59.
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Chang. Biol. 21, 973–985 (2015).
ADS  PubMed  Google Scholar 

60.
Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).
ADS  PubMed  Google Scholar 

61.
Wheeler, Q. D., Raven, P. H. & Wilson, E. O. Taxonomy: impediment or expedient? Science 303, 285 (2004).
CAS  PubMed  Google Scholar 

62.
Delgado-Baquerizo, M. & Eldridge, D. J. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems 22, 1–12 (2019).
Google Scholar 

63.
Hursh, A. et al. The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale. Glob. Chang. Biol. 23, 2090–2103 (2017).
ADS  PubMed  Google Scholar 

64.
Wang, Q., Liu, S. & Tian, P. Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems. Glob. Chang. Biol. 24, 2841–2849 (2018).
ADS  PubMed  Google Scholar 

65.
Prăvălie, R. Drylands extent and environmental issues. A global approach. Earth-Sci. Rev. 161, 259–278 (2016).
ADS  Google Scholar 

66.
Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).
ADS  Google Scholar 

67.
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. (2018).

68.
Cowan, D. A. et al. Microbiomics of Namib Desert habitats. Extremophiles 24, 17–29 (2020).
CAS  PubMed  Google Scholar 

69.
Rutgers, M. et al. Mapping earthworm communities in Europe. Appl. Soil Ecol. 97, 98–111 (2016).
Google Scholar 

70.
Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99, 583–596 (2018).
PubMed  Google Scholar 

71.
Chen, S., Zou, J., Hu, Z., Chen, H. & Lu, Y. Global annual soil respiration in relation to climate, soil properties and vegetation characteristics: summary of available data. Agric. For. Meteorol. 198–199, 335–346 (2014).
ADS  Google Scholar 

72.
Zhang, D., Hui, D., Luo, Y. & Zhou, G. Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. J. Plant Ecol. 1, 85–93 (2008).
Google Scholar 

73.
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. USA 104, 5925–5930 (2007).
ADS  CAS  PubMed  Google Scholar 

74.
Cameron, E. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019)

75.
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).
ADS  CAS  PubMed  Google Scholar 

76.
Eisenhauer, N. et al. Plant diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition in a long-term grassland experiment. Proc. Natl Acad. Sci. USA 110, 6889–6894 (2013).
ADS  CAS  PubMed  Google Scholar 

77.
Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).
ADS  CAS  PubMed  Google Scholar 

78.
Menegotto, A. & Rangel, T. F. Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness. Nat. Commun. 9, 4713 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

79.
Eisenhauer, N. et al. Priorities for research in soil ecology. Pedobiologia 63, 1–7 (2017).
PubMed  PubMed Central  Google Scholar 

80.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed  PubMed Central  Google Scholar 

81.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
ADS  CAS  Google Scholar 

82.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).
ADS  CAS  Google Scholar 

83.
Titeux, N. et al. Biodiversity scenarios neglect future land-use changes. Glob. Chang. Biol. 22, 2505–2515 (2016).
ADS  PubMed  Google Scholar 

84.
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).
Google Scholar 

85.
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52 (2012).
ADS  Google Scholar 

86.
Kharin, V. V., Zwiers, F. W., Zhang, X. & Hegerl, G. C. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Clim. 20, 1419–1444 (2007).
ADS  Google Scholar 

87.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

88.
Bronselaer, B. et al. Change in future climate due to Antarctic meltwater. Nature 564, 53–58 (2018).
ADS  CAS  PubMed  Google Scholar 

89.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L. & Fichefet, T. Long-term climate change: projections, commitments and irreversibility. Chapter 12 (eds T. Stocker et al) 1029–1136 (Cambridge University Press, 2013).

90.
Delgado-Baquerizo, M., Eldridge, D. J., Hamonts, K. & Singh, B. K. Ant colonies promote the diversity of soil microbial communities. ISME J. https://doi.org/10.1038/s41396-018-0335-2 (2019).

91.
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).
ADS  CAS  PubMed  Google Scholar 

92.
Thomson, S. A. et al. Taxonomy based on science is necessary for global conservation. PLoS Biol. 16, e2005075 (2018).
PubMed  PubMed Central  Google Scholar 

93.
Drew, L. W. Are we losing the science of taxonomy? Bioscience 61, 942–946 (2011).
Google Scholar 

94.
Paknia, O., Sh., H. R. & Koch, A. Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity exploration. Organ. Divers. Evol. 15, 619–629 (2015).
Google Scholar 

95.
Prathapan, K. D. et al. When the cure kills-CBD limits biodiversity research. Science 360, 1405–1406 (2018).
ADS  CAS  PubMed  Google Scholar 

96.
Neumann, D. et al. Global biodiversity research tied up by juridical interpretations of access and benefit sharing. Org. Divers. Evol. 18, 1–12 (2017).
Google Scholar 

97.
Leimu, R. & Koricheva, J. What determines the citation frequency of ecological papers? Trends Ecol. Evol. 20, 28–32 (2005).
PubMed  Google Scholar 

98.
Hugerth, L. W. & Andersson, A. F. Analysing microbial community composition through amplicon sequencing: from sampling to hypothesis testing. Front. Microbiol. 8, 1561 (2017).
PubMed  PubMed Central  Google Scholar 

99.
Terrat, S. et al. Meta-barcoded evaluation of the ISO standard 11063 DNA extraction procedure to characterize soil bacterial and fungal community diversity and composition. Microb. Biotechnol. 8, 131–142 (2015).
CAS  PubMed  Google Scholar 

100.
Kõljalg, U., Larsson, K. H. & Abarenkov, K. UNITE: a database providing web‐based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166, 1063–1068 (2005).

101.
Mathieu, J., Caro, G. & Dupont, L. Methods for studying earthworm dispersal. Appl. Soil Ecol. 123, 339–344 (2018).
Google Scholar 

102.
Pauchard, N. Access and benefit sharing under the convention on biological diversity and its protocol: what can some numbers tell us about the effectiveness of the regulatory regime? Resources 6, 11 (2017).
Google Scholar 

103.
Saha, S., Saha, S. & Saha, S. K. Barriers in Bangladesh. Elife 7, e41926 (2018).

104.
Prathapan, K. D. & Rajan, P. D. Biodiversity access and benefit-sharing: weaving a rope of sand. Curr. Sci. 100, 290–293 (2011).
Google Scholar 

105.
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
ADS  PubMed  Google Scholar 

106.
Terrat, S. et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE 12, e0186766 (2017).
PubMed  PubMed Central  Google Scholar 

107.
Makiola, A., et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).

108.
Maestre, F. T. & Eisenhauer, N. Recommendations for establishing global collaborative networks in soil ecology. Soil Organ. 91, 73–85 (2019).
Google Scholar 

109.
Phillips, H. R. P. et al. Red list of a black box. Nat. Ecol. Evol. 1, 0103 (2017).
Google Scholar 

110.
Davison, J. et al. Microbial island biogeography: isolation shapes the life history characteristics but not diversity of root-symbiotic fungal communities. ISME J. https://doi.org/10.1038/s41396-018-0196-8 (2018).

111.
Overmann, J. Significance and future role of microbial resource centers. Syst. Appl. Microbiol. 38, 258–265 (2015).
PubMed  Google Scholar 

112.
Overmann, J. & Scholz, A. H. Microbiological research under the nagoya protocol: facts and fiction. Trends Microbiol. 25, 85–88 (2017).
CAS  PubMed  Google Scholar 

113.
Bockmann, F. A. et al. Brazil’s government attacks biodiversity. Science 360, 865 (2018).
ADS  CAS  PubMed  Google Scholar 

114.
Scbd-Unep. Nagoya Declaration on Biodiversity in Development Cooperation. 2 (UNEP, 2010).

115.
Perrings, C. et al. Ecosystem services for 2020. Science 330, 323–324 (2010).
ADS  CAS  PubMed  Google Scholar 

116.
Bond-Lamberty, B. & Thomson, A. A global database of soil respiration data. Biogeosci. Discuss. 7, 1321–1344 (2010).
ADS  Google Scholar 

117.
Bamforth, S. S. Interpreting soil ciliate biodiversity. Plant Soil 170, 159–164 (1995).
CAS  Google Scholar 

118.
Mathieu, J. EGrowth: a global database on intraspecific body growth variability in earthworm. Soil Biol. Biochem. 122, 71–80 (2018).
CAS  Google Scholar 

119.
Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns in belowground communities. Ecol. Lett. 12, 1238–1249 (2009).
PubMed  Google Scholar 

120.
Nelson, M. B., Martiny, A. C. & Martiny, J. B. H. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).
CAS  PubMed  Google Scholar 

121.
Chen, J., Yang, S. T., Li, H. W., Zhang, B. & Lv, J. R. Research on geographical environment unit division based on the method of natural breaks (Jenks). ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XL-4/W3, pp. 47–50 (2013).

122.
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
PubMed  PubMed Central  Google Scholar 

123.
Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).
Google Scholar 

124.
Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).
Google Scholar 

125.
Mallavan B. P., Minasny B., McBratney A. B., in Digital Soil Mapping. pp. 137–150 (Springer, Dordrecht, 2010).

126.
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).
PubMed  Google Scholar 

127.
Hengl, T. et al. SoilGrids1km–global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

128.
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar 

129.
Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 1–19 (2017).
Google Scholar 

130.
Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) | The Long Term Archive. Available at: https://lta.cr.usgs.gov/GMTED2010. Accessed on 6 December 2018.

131.
European Space Agency. ESA – Land Cover CCI – Product User Guide Version 2.0. (2017).

132.
Frostegård, Å., Tunlid, A. & Bååth, E. Microbial biomass measured as total lipid phosphate in soils of different organic content. J. Microbiol. Methods 14, 151–163 (1991).
Google Scholar 

133.
Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

134.
Eppo, P. M. Nematode extraction. EPPO Bull. 43, 471–495 (2013).
Google Scholar 

135.
ISO/FDIS. Soil Quality – Sampling Of Soil Invertebrates – Part 1: Hand-sorting And Extraction Of Earthworms. (ISO, 2018).

136.
ISO. Soil quality – Sampling Of Soil Invertebrates – Part 4: Sampling, Extraction And Identification Of Soil-Inhabiting Nematodes. (ISO, 09-2011).

137.
Hunter, P. A. DEAL for open access: The negotiations between the German DEAL project and publishers have global implications for academic publishing beyond just Germany. EMBO Rep. 19, e46317 (2018).

138.
Knapp, A. K. et al. Past, present, and future roles of long-term experiments in the LTER network. Bioscience 62, 377–389 (2012).

139.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
ADS  CAS  PubMed  Google Scholar 

140.
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 1–8 (2017).
Google Scholar 

141.
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).
ADS  CAS  PubMed  Google Scholar 

142.
Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: successes and aspirations. BMC Biol. 12, 69 (2014).
PubMed  PubMed Central  Google Scholar 

143.
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).
ADS  CAS  PubMed  Google Scholar 

144.
Darcy, J. L., Lynch, R. C., King, A. J., Robeson, M. S. & Schmidt, S. K. Global distribution of Polaromonas phylotypes–evidence for a highly successful dispersal capacity. PLoS ONE 6, e23742 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

145.
Hendershot, J. N., Read, Q. D., Henning, J. A., Sanders, N. J. & Classen, A. T. Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales. Ecology 98, 1757–1763 (2017).
PubMed  Google Scholar 

146.
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).
ADS  CAS  PubMed  Google Scholar 

147.
Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl Acad. Sci. USA 104, 11436–11440 (2007).
ADS  CAS  PubMed  Google Scholar 

148.
Neal, A. L. et al. Phylogenetic distribution, biogeography and the effects of land management upon bacterial non-specific Acid phosphatase Gene diversity and abundance. Plant Soil 427, 175–189 (2018).
CAS  PubMed  Google Scholar 

149.
Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 107 (2017).
PubMed  Google Scholar 

150.
Bates, S. T. et al. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917 (2011).
MathSciNet  CAS  PubMed  Google Scholar 

151.
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).
ADS  CAS  PubMed  Google Scholar 

152.
Kivlin, S. N., Hawkes, C. V. & Treseder, K. K. Global diversity and distribution of arbuscular mycorrhizal fungi. Soil Biol. Biochem. 43, 2294–2303 (2011).
CAS  Google Scholar 

153.
Pärtel, M. et al. Historical biome distribution and recent human disturbance shape the diversity of arbuscular mycorrhizal fungi. New Phytol. 216, 227–238 (2017).
PubMed  Google Scholar 

154.
Põlme, S. et al. Biogeography of ectomycorrhizal fungi associated with alders (Alnus spp.) in relation to biotic and abiotic variables at the global scale. New Phytol. 198, 1239–1249 (2013).
PubMed  Google Scholar 

155.
Sharrock, R. A. et al. A global assessment using PCR techniques of mycorrhizal fungal populations colonising Tithonia diversifolia. Mycorrhiza 14, 103–109 (2004).
CAS  PubMed  Google Scholar 

156.
Tedersoo, L. et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol. 21, 4160–4170 (2012).
PubMed  Google Scholar 

157.
Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe: arbuscular mycorrhizal fungal communities around the globe. J. Ecol. 94, 778–790 (2006).
Google Scholar 

158.
Öpik, M. et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol. 188, 223–241 (2010).
PubMed  Google Scholar 

159.
Stürmer, S. L., Bever, J. D. & Morton, J. B. Biogeography of arbuscular mycorrhizal fungi (Glomeromycota): a phylogenetic perspective on species distribution patterns. Mycorrhiza 28, 587–603 (2018).
PubMed  Google Scholar 

160.
Bates, S. T. et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 7, 652–659 (2013).
CAS  PubMed  Google Scholar 

161.
Lara, E., Roussel‐Delif, L. & Fournier, B. Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae. J. Biogeogr. 43, 520–532 (2016).

162.
Finlay, B. J., Esteban, G. F., Clarke, K. J. & Olmo, J. L. Biodiversity of terrestrial protozoa appears homogeneous across local and global spatial scales. Protist 152, 355–366 (2001).
CAS  PubMed  Google Scholar 

163.
Chao, A., C. Li, P., Agatha, S. & Foissner, W. A statistical approach to estimate soil ciliate diversity and distribution based on data from five continents. Oikos 114, 479–493 (2006).

164.
Foissner, W. Global soil ciliate (Protozoa, Ciliophora) diversity: a probability-based approach using large sample collections from Africa, Australia and Antarctica. Biodivers. Conserv. 6, 1627–1638 (1997).
Google Scholar 

165.
Nielsen, U. N. et al. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties: Global-scale patterns of soil nematode assemblage structure. Glob. Ecol. Biogeogr. 23, 968–978 (2014).
Google Scholar 

166.
Wu, T., Ayres, E., Bardgett, R. D., Wall, D. H. & Garey, J. R. Molecular study of worldwide distribution and diversity of soil animals. Proc. Natl Acad. Sci. USA 108, 17720–17725 (2011).
ADS  CAS  PubMed  Google Scholar 

167.
Robeson, M. S. et al. Soil rotifer communities are extremely diverse globally but spatially autocorrelated locally. Proc. Natl Acad. Sci. USA 108, 4406–4410 (2011).
ADS  CAS  PubMed  Google Scholar 

168.
Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Chang. Biol. 14, 2661–2677 (2008).

169.
Pachl, P. et al. The tropics as an ancient cradle of oribatid mite diversity. Acarologia 57, 309–322 (2016).
Google Scholar 

170.
Dahlsjö, C. A. L. et al. First comparison of quantitative estimates of termite biomass and abundance reveals strong intercontinental differences. J. Trop. Ecol. 30, 143–152 (2014).
Google Scholar 

171.
Briones, M. J. I., Ineson, P. & Heinemeyer, A. Predicting potential impacts of climate change on the geographical distribution of enchytraeids: a meta‐analysis approach. Glob. Chang. Biol. 13, 2252–2269 (2007).

172.
Silver, W. L. & Miya, R. K. Global patterns in root decomposition: comparisons of climate and litter quality effects. Oecologia 129, 407–419 (2001).
ADS  PubMed  Google Scholar 

173.
Zhang, T. ’an, Chen, H. Y. H. & Ruan, H. Global negative effects of nitrogen deposition on soil microbes. ISME J. 12, 1817–1825 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

174.
Sinsabaugh, R. L., Turner, B. L. & Talbot, J. M. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).

175.
Xu, M. & Shang, H. Contribution of soil respiration to the global carbon equation. J. Plant Physiol. 203, 16–28 (2016).
CAS  PubMed  Google Scholar 

176.
Raich, J. W. & Tufekciogul, A. Vegetation and soil respiration: correlations and controls. Biogeochemistry 48, 71–90 (2000).
CAS  Google Scholar 

177.
Wang, J., Chadwick, D. R., Cheng, Y. & Yan, X. Global analysis of agricultural soil denitrification in response to fertilizer nitrogen. Sci. Total Environ. 616-617, 908–917 (2018).
ADS  CAS  PubMed  Google Scholar 

178.
Rahmati, M. et al. Development and analysis of the Soil Water Infiltration Global database. Earth Syst. Sci. Data 10, 1237–1263 (2017).
ADS  Google Scholar 

179.
Serna-Chavez, H. M., Fierer, N. & van Bodegom, P. M. Global drivers and patterns of microbial abundance in soil: Global patterns of soil microbial biomass. Glob. Ecol. Biogeogr. 22, 1162–1172 (2013).
Google Scholar 

180.
Howison, R. A., Olff, H., Koppel, J. & Smit, C. Biotically driven vegetation mosaics in grazing ecosystems: the battle between bioturbation and biocompaction. Ecol. Monogr. 87, 363–378 (2017).

181.
Lehmann, A., Zheng, W. & Rillig, M. C. Soil biota contributions to soil aggregation. Nat. Ecol. Evol.1, 1–9 (2017).
Google Scholar 

182.
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).
Google Scholar 

183.
van Straaten Oliver, Z. R. T. A. & Bossio, D. Carbon, Land And Water: A Global Analysis Of The Hydrologic Dimensions Of Climate Change Mitigation Through Afforestation/reforestation. (IWMI, 2006). More

1.
van Valen, L. M. Resetting the Phanerozoic community evolution. Nature 307, 93–106 (1984).
Google Scholar 
2.
Alroy, J. Dynamics of origination and extinction in the marine fossil record. Proc. Natl Acad. Sci. USA 105, 11536–11542 (2008).
CAS  PubMed  Google Scholar 

3.
Foote, M. Origination and extinction through the Phanerozoic: a new approach. J. Geol. 111, 125–148 (2003).
Google Scholar 

4.
Gilinsky, N. L. Volatility and the Phanerozoic decline of background extinction intensity. Paleobiology 20, 445–458 (1994).
Google Scholar 

5.
Lieberman, B. S. & Melott, A. L. Declining volatility, a general property of disparate systems: from fossils, to stocks, to the stars. Palaeontology 56, 1297–1304 (2013).
Google Scholar 

6.
Knope, M. L., Bush, A. M., Frishkoff, L. O., Heim, N. A. & Payne, J. L. Ecologically diverse clades dominate the oceans via extinction resistance. Science 367, 1035–1038 (2020).
CAS  PubMed  Google Scholar 

7.
Janzen, D. H. On ecological fitting. Oikos 45, 308–310 (1985).
Google Scholar 

8.
Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).
PubMed  Google Scholar 

9.
Nielsen, S. N. & Müller, F. in Handbook of Ecosystem Theories and Management (eds Jørgensen, S. E. & Müller, F.) 195–216 (CRC Press, 2000).

10.
Hui, C. et al. Defining invasiveness and invasibility in ecological networks. Biol. Invasions 18, 971–983 (2016).
Google Scholar 

11.
Foote, M. et al. Rise and fall of species occupancy in Cenozoic fossil mollusks. Science 318, 1131–1134 (2007).
CAS  PubMed  Google Scholar 

12.
Foote, M. Symmetric waxing and waning of marine invertebrate genera. Paleobiology 33, 517–529 (2007).
Google Scholar 

13.
Liow, L. H. & Stenseth, N. C. The rise and fall of species: implications for macroevolutionary and macroecological studies. Proc. R. Soc. Lond. B 274, 2745–2752 (2007).
Google Scholar 

14.
Zliobaite, I., Fortelius, M. & Stenseth, N. C. Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, 92–95 (2017).
CAS  PubMed  Google Scholar 

15.
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).
PubMed  Google Scholar 

16.
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists EO Wilson Award address. Am. Nat. 175, 623–639 (2010).
PubMed  Google Scholar 

17.
Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).
CAS  PubMed  Google Scholar 

18.
Erwin, D. H. Novelty and innovation in the history of life. Curr. Biol. 25, R930–R940 (2015).
CAS  PubMed  Google Scholar 

19.
Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8, 4–15 (1982).
Google Scholar 

20.
Cooper, A. & Fortey, R. Evolutionary explosions and the phylogenetic fuse. Trends Ecol. Evol. 13, 151–156 (1998).
CAS  PubMed  Google Scholar 

21.
Jablonski, D. & Bottjer, D. J. in Major Evolutionary Radiations (eds Taylor, P. D. & Larwood, G. P.) 17–57 (Systematics Association, 1990).

22.
Jablonski, D. Approaches to macroevolution: 1. general concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).
PubMed  PubMed Central  Google Scholar 

23.
Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Natl Acad. Sci. USA 108, 15908–15913 (2011).
CAS  PubMed  Google Scholar 

24.
Kröger, B., Desrochers, A. & Ernst, A. The reengineering of reef habitats during the Great Ordovician Biodiversification Event. PALAIOS 32, 584–599 (2017).
Google Scholar 

25.
Robeck, H. E., Maley, C. C. & Donoghue, M. J. Taxonomy and temporal diversity patterns. Paleobiology 26, 171–187 (2000).
Google Scholar 

26.
Hendricks, J. R., Saupe, E. E., Myers, C. E., Hermsen, E. J. & Allmon, W. D. The generification of the fossil record. Paleobiology 40, 511–528 (2014).
Google Scholar 

27.
Wagner, P. J., Aberhan, M., Hendy, A. & Kiessling, W. The effects of taxonomic standardization on sampling-standardized estimates of historical diversity. Proc. R. Soc. B 274, 439–444 (2007).
PubMed  Google Scholar 

28.
Plotnick, R. E. & Wagner, P. J. Roundup of the usual suspects: common genera in the fossil record and the nature of the wastebasket taxa. Paleobiology 32, 126–146 (2006).
Google Scholar 

29.
Bambach, R. K., Bush, M. A. & Erwin, D. H. Autecology and the filling of ecospace: key metazoan radiations. Palaeontology 50, 1–22 (2007).
Google Scholar 

30.
Knope, M. L., Heim, N. A., Frishkoff, L. O. & Payne, J. L. Limited role of functional differentiation in early diversification of animals. Nat. Commun. 6, 6455 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Sepkoski, J. J. A kinetic model of Phanerozoic taxonomic diversity. III Post-Paleozoic families and mass extinctions. Paleobiology 10, 246–267 (1984).
Google Scholar 

32.
Alroy, J. The shifting balance of diversity among major marine animal groups. Science 329, 1191–1194 (2010).
CAS  PubMed  Google Scholar 

33.
Liow, L. H. & Nichols, J. D. in The Paleontological Society Short Course, October 30th 2010 (eds Alroy, J. & Hunt, G.) 81–94 (Cambridge Univ. Press, 2010).

34.
Kiessling, W. & Kocsis, Á. T. Adding fossil occupancy trajectories to the assessment of modern extinction risk. Biol. Lett. 12, 20150813 (2016).
PubMed  PubMed Central  Google Scholar 

35.
Fridley, J. D., Vandermast, D. B., Kuppinger, D. M., Manthey, M. L. & Peet, R. K. Co-occurrence based assessment of habitat generalists and specialists: a new approach for the measurement of niche width. J. Ecol. 95, 707–722 (2007).
Google Scholar 

36.
Hofmann, R., Tietje, M. & Aberhan, M. Diversity partitioning in Phanerozoic benthic marine communities. Proc. Natl Acad. Sci. USA 116, 79–83 (2019).
PubMed  Google Scholar 

37.
Bottjer, D. J., Hagadorn, J. W. & Dornbos, S. Q. The Cambrian substrate revolution. GSA Today 10, 1–7 (2000).
Google Scholar 

38.
Knoll, A. H. & Follows, M. J. A bottom-up perspective on ecosystem change in Mesozoic oceans. Proc. R. Soc. B 283, 20161755 (2016).
PubMed  Google Scholar 

39.
Bambach, R. K. Seafood through time: changes in biomass, energetics, and productivity in the marine ecosystem. Paleobiology 19, 372–397 (1993).
Google Scholar 

40.
Westrop, S. R. The life habits of the Ordovician illaenine trilobite Bumastoides. Lethaia 16, 15–24 (1983).
Google Scholar 

41.
O’Dea, A. & Jackson, J. Environmental change drove macroevolution in cupuladriid bryozoans. Proc. R. Soc. B 276, 3629–3634 (2009).
PubMed  Google Scholar 

42.
Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl Acad. Sci. USA 116, 7207 (2019).
PubMed  Google Scholar 

43.
Bush, A. M. & Bambach, R. K. Sustained Mesozoic–Cenozoic diversification of marine Metazoa: a consistent signal from the fossil record. Geology 43, 979–982 (2015).
Google Scholar 

44.
Leibold, M. A. & McPeek, M. A. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87, 1399–1410 (2006).
PubMed  Google Scholar 

45.
McPeek, M. A. The ecological dynamics of clade diversification and community assembly. Am. Nat. 172, E270–E284 (2008).
PubMed  Google Scholar 

46.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar 

47.
Wagner, P. J., Aberhan, M., Hendy, A. & W, K. The effects of taxonomic standardization on occurrence-based estimates of diversity. Proc. R. Soc. Lond. B 274, 439–444 (2007).
Google Scholar 

48.
Cohen, K. M., Harper, D. A. T. & Gibbard, P. L. ICS International Chronostratigraphic Chart 2018/08 (International Commission on Stratigraphy, IUGS, 2018); www.stratigraphy.org

49.
Nichols, J. D. & Pollock, K. H. Estimating taxonomic diversity, extinction rates, and speciation rates from fossil data using capture–recapture models. Paleobiology 9, 150–163 (1983).
Google Scholar 

50.
Connolly, S. R. & Miller, A. I. Joint estimation of sampling and turnover rates from fossil databases: capture–mark–recapture methods revisited. Paleobiology 27, 767–751 (2001).
Google Scholar 

51.
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52, 860–873 (1996).
Google Scholar 

52.
Pradel, R. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52, 703–709 (1996).
Google Scholar 

53.
Liow, L. H., Reitan, T. & Harnik, P. G. Ecological interactions on macroevolutionary time scales: clams and brachiopods are more than ships that pass in the night. Ecol. Lett. 18, 1030–1039 (2015).
PubMed  Google Scholar 

54.
Alroy, J. A more precise speciation and extinction rate estimator. Paleobiology 41, 633–639 (2015).
Google Scholar 

55.
Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).
Google Scholar 

56.
Cornette, J. L. & Lieberman, B. S. Random walks in the history of life. Proc. Natl Acad. Sci. USA 101, 187–191 (2004).
CAS  PubMed  Google Scholar 

57.
Fuller, W. A. Introduction to Statistical Time Series (Wiley, 2009).

58.
Manthey, M. & Fridley, J. D. Beta diversity metrics and the estimation of niche width via species co-occurrence data: reply to Zeleny. J. Ecol. 97, 18–22 (2009).
Google Scholar  More