Ecology

1.
Nisbet, E. G. et al. The age of Rubisco: the evolution of oxygenic photosynthesis. Geobiology 5, 311–335 (2007).
CAS  Google Scholar 
2.
Tabita, F. R. et al. Function, structure, and evolution of the RubisCO-like proteins and their RubisCO homologs. Microbiol. Mol. Biol. Rev. 71, 576–599 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

3.
Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E. & Scott, S. S. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships. J. Exp. Bot. 59, 1515–1524 (2007).
Google Scholar 

4.
Andrews, T. J. Catalysis by cyanobacterial ribulose-bisphosphate carboxylase large subunits in the complete absence of small subunits. J. Biol. Chem. 263, 12213–12219 (1988).
CAS  PubMed  Google Scholar 

5.
Morell, M. K., Wilkin, J. M., Kane, H. J. & Andrews, T. J. Side reactions catalyzed by ribulose-bisphosphate carboxylase in the presence and absence of small subunits. J. Biol. Chem. 272, 5445–5451 (1997).
CAS  PubMed  Google Scholar 

6.
Spreitzer, R. J. Role of the small subunit in ribulose-1,5-bisphosphate carboxylase/oxygenase. Arch. Biochem. Biophys. 414, 141–149 (2003).
CAS  PubMed  Google Scholar 

7.
Joshi, J., Mueller-Cajar, O., Tsai, Y.-C. C., Hartl, F. U. & Hayer-Hartl, M. Role of small subunit in mediating assembly of red-type form I rubisco. J. Biol. Chem. 290, 1066–1074 (2015).
CAS  PubMed  Google Scholar 

8.
Liu, C. et al. Coupled chaperone action in folding and assembly of hexadecameric Rubisco. Nature 463, 197–202 (2010).
CAS  PubMed  Google Scholar 

9.
Grabsztunowicz, M., Górski, Z., Luciński, R. & Jackowski, G. A reversible decrease in ribulose 1,5-bisphosphate carboxylase/oxygenase carboxylation activity caused by the aggregation of the enzyme’s large subunit is triggered in response to the exposure of moderate irradiance-grown plants to low irradiance. Physiol. Plant. 154, 591–608 (2015).
CAS  PubMed  Google Scholar 

10.
Kusian, B. & Bowien, B. Organization and regulation of cbb CO2 assimilation genes in autotrophic bacteria. FEMS Microbiol. Rev. 21, 135–155 (1997).
CAS  PubMed  Google Scholar 

11.
Tabita, F. R. Microbial ribulose 1,5-bisphosphate carboxylase/oxygenase: a different perspective. Photosynth. Res. 60, 1–28 (1999).
CAS  Google Scholar 

12.
Whitney, S. M. & Andrews, T. J. The gene for the ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) small subunit relocated to the plastid genome of tobacco directs the synthesis of small subunits that assemble into Rubisco. Plant Cell 13, 193–205 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Bryant, D. A. & Liu, Z. in Advances in Botanical Research (ed. Beatty, J. T.) 99–150 (Academic Press, 2013).

14.
Shih, P. M., Ward, L. M. & Fischer, W. W. Evolution of the 3-hydroxypropionate bicycle and recent transfer of anoxygenic photosynthesis into the Chloroflexi. Proc. Natl Acad. Sci. USA 114, 10749–10754 (2017).
CAS  PubMed  Google Scholar 

15.
Ward, L. M., Hemp, J., Shih, P. M., McGlynn, S. E. & Fischer, W. W. Evolution of phototrophy in the Chloroflexi phylum driven by horizontal gene transfer. Front. Microbiol. 9, 260 (2018).
PubMed  PubMed Central  Google Scholar 

16.
Fischer, W. W., Hemp, J. & Johnson, J. E. Evolution of oxygenic photosynthesis. Annu. Rev. Earth Planet. Sci. 44, 647–683 (2016).
CAS  Google Scholar 

17.
Roy, H. Rubisco assembly: a model system for studying the mechanism of chaperonin action. Plant Cell 1, 1035–1042 (1989).
CAS  PubMed  PubMed Central  Google Scholar 

18.
Hayer-Hartl, M. From chaperonins to Rubisco assembly and metabolic repair. Protein Sci. 26, 2324–2333 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

19.
Aigner, H. et al. Plant RuBisCo assembly in E. coli with five chloroplast chaperones including BSD2. Science 358, 1272–1278 (2017).
CAS  PubMed  Google Scholar 

20.
Wilson, R. H. & Hayer-Hartl, M. Complex chaperone dependence of Rubisco biogenesis. Biochemistry 57, 3210–3216 (2018).
CAS  PubMed  Google Scholar 

21.
Saschenbrecker, S. et al. Structure and function of RbcX, an assembly chaperone for hexadecameric Rubisco. Cell 129, 1189–1200 (2007).
CAS  PubMed  Google Scholar 

22.
Gunn, L. H., Valegård, K. & Andersson, I. A unique structural domain in Methanococcoides burtonii ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) acts as a small subunit mimic. J. Biol. Chem. 292, 6838–6850 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Goloubinoff, P., Christeller, J. T., Gatenby, A. A. & Lorimer, G. H. Reconstitution of active dimeric ribulose bisphosphate carboxylase from an unfolded state depends on two chaperonin proteins and Mg-ATP. Nature 342, 884–889 (1989).
CAS  PubMed  Google Scholar 

24.
Parry, M. A. J., Keys, A. J. & Gutteridge, S. Variation in the specificity factor of C3 higher plant Rubiscos determined by the total consumption of ribulose-P2. J. Exp. Bot. 40, 317–320 (1989).
CAS  Google Scholar 

25.
Tcherkez, G. G. B., Farquhar, G. D. & Andrews, T. J. Despite slow catalysis and confused substrate specificity, all ribulose bisphosphate carboxylases may be nearly perfectly optimized. Proc. Natl Acad. Sci. USA 103, 7246–7251 (2006).
CAS  PubMed  Google Scholar 

26.
Flamholz, A. I. et al. Revisiting trade-offs between Rubisco kinetic parameters. Biochemistry 58, 3365–3376 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Yamada, T. & Sekiguchi, Y. Cultivation of uncultured Chloroflexi subphyla: significance and ecophysiology of formerly uncultured Chloroflexi ‘subphylum i’ with natural and biotechnological relevance. Microbes Environ. 24, 205–216 (2009).
PubMed  Google Scholar 

28.
Hemp, J., Ward, L. M., Pace, L. A. & Fischer, W. W. Draft genome sequence of Ornatilinea apprima P3M-1, an anaerobic member of the Chloroflexi class Anaerolineae. Genome Announc. 3, e01353-15 (2015).

29.
Ward, L. M., Hemp, J., Pace, L. A. & Fischer, W. W. Draft genome sequence of Leptolinea tardivitalis YMTK-2, a mesophilic anaerobe from the Chloroflexi class Anaerolineae. Genome Announc. 3, e01356-15 (2015).

30.
Alonso, H., Blayney, M. J., Beck, J. L. & Whitney, S. M. Substrate-induced assembly of Methanococcoides burtonii d-ribulose-1,5-bisphosphate carboxylase/oxygenase dimers into decamers. J. Biol. Chem. 284, 33876–33882 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Knott, G. J. et al. Structural basis for AcrVA4 inhibition of specific CRISPR-Cas12a. eLife 8, e49110 (2019).

32.
Duff, A. P., Andrews, T. J. & Curmi, P. M. The transition between the open and closed states of Rubisco is triggered by the inter-phosphate distance of the bound bisphosphate. J. Mol. Biol. 298, 903–916 (2000).
CAS  PubMed  Google Scholar 

33.
Newman, J., Branden, C. I. & Jones, T. A. Structure determination and refinement of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301. Acta Crystallogr. D. Biol. Crystallogr. 49, 548–560 (1993).
CAS  PubMed  Google Scholar 

34.
Lu, Z., Zhao, Z. & Fu, B. Efficient protein alignment algorithm for protein search. BMC Bioinf. 11, S34 (2010).
Google Scholar 

35.
Cleland, W. W., Andrews, T. J., Gutteridge, S., Hartman, F. C. & Lorimer, G. H. Mechanism of Rubisco: the carbamate as general base. Chem. Rev. 98, 549–562 (1998).
CAS  PubMed  Google Scholar 

36.
Andersson, I. & Backlund, A. Structure and function of Rubisco. Plant Physiol. Biochem. 46, 275–291 (2008).
CAS  PubMed  Google Scholar 

37.
van Lun, M., van der Spoel, D. & Andersson, I. Subunit interface dynamics in hexadecameric Rubisco. J. Mol. Biol. 411, 1083–1098 (2011).
PubMed  Google Scholar 

38.
Schneider, G. et al. Comparison of the crystal structures of L2 and L8S8 Rubisco suggests a functional role for the small subunit. EMBO J. 9, 2045–2050 (1990).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Huynh, K. & Partch, C. L. Analysis of protein stability and ligand interactions by thermal shift assay. Curr. Protoc. Protein Sci. 79, 28.9.1–28.9.14 (2015).
Google Scholar 

40.
Greene, D. N., Whitney, S. M. & Matsumura, I. Artificially evolved Synechococcus PCC6301 Rubisco variants exhibit improvements in folding and catalytic efficiency. Biochem. J. 404, 517–524 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

41.
DePristo, M. A., Weinreich, D. M. & Hartl, D. L. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat. Rev. Genet. 6, 678–687 (2005).
CAS  PubMed  Google Scholar 

42.
Tokuriki, N., Stricher, F., Serrano, L. & Tawfik, D. S. How protein stability and new functions trade off. PLoS Comput. Biol. 4, e1000002 (2008).
PubMed  PubMed Central  Google Scholar 

43.
Tokuriki, N. & Tawfik, D. S. Protein dynamism and evolvability. Science 324, 203–207 (2009).
CAS  PubMed  Google Scholar 

44.
Erb, T. J. & Zarzycki, J. A short history of RubisCO: the rise and fall (?) of Nature’s predominant CO2 fixing enzyme. Curr. Opin. Biotechnol. 49, 100–107 (2018).
CAS  PubMed  Google Scholar 

45.
Badger, M. R., Hanson, D. & Dean Price, G. Evolution and diversity of CO2 concentrating mechanisms in cyanobacteria. Funct. Plant Biol. 29, 161–173 (2002).
CAS  PubMed  Google Scholar 

46.
Studer, R. A., Christin, P.-A., Williams, M. A. & Orengo, C. A. Stability–activity tradeoffs constrain the adaptive evolution of RubisCO. Proc. Natl Acad. Sci. USA 111, 2223–2228 (2014).
CAS  PubMed  Google Scholar 

47.
Zhou, Y. & Whitney, S. Directed evolution of an improved Rubisco; in vitro analyses to decipher fact from fiction. Int. J. Mol. Sci. 20, 5019 (2019).

48.
Wilson, R. H., Alonso, H. & Whitney, S. M. Evolving Methanococcoides burtonii archaeal Rubisco for improved photosynthesis and plant growth. Sci. Rep. 6, 22284 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

49.
Frey, S. & Görlich, D. A new set of highly efficient, tag-cleaving proteases for purifying recombinant proteins. J. Chromatogr. A 1337, 95–105 (2014).
CAS  PubMed  Google Scholar 

50.
Kane, H. J., Wilkin, J. M., Portis, A. R. & John Andrews, T. Potent inhibition of ribulose-bisphosphate carboxylase by an oxidized impurity in ribulose-1,5-bisphosphate. Plant Physiol. 117, 1059–1069 (1998).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Pierce, J., Tolbert, N. E. & Barker, R. Interaction of ribulosebisphosphate carboxylase/oxygenase with transition-state analogues. Biochemistry 19, 934–942 (1980).
CAS  PubMed  Google Scholar 

52.
Pereira, J. H., McAndrew, R. P., Tomaleri, G. P. & Adams, P. D. Berkeley Screen: a set of 96 solutions for general macromolecular crystallization. J. Appl. Crystallogr. 50, 1352–1358 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Winter, G., Lobley, C. M. C. & Prince, S. M. Decision making in xia2. Acta Crystallogr. D. Biol. Crystallogr. 69, 1260–1273 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

54.
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).
CAS  PubMed  Google Scholar 

56.
Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).
CAS  PubMed  Google Scholar 

57.
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
PubMed  Google Scholar 

58.
Davis, I. W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007).
PubMed  PubMed Central  Google Scholar 

59.
Dyer, K. N. et al. High-throughput SAXS for the characterization of biomolecules in solution: a practical approach. Methods Mol. Biol. 1091, 245–258 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Hura, G. L. et al. Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nat. Methods 6, 606–612 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Rambo, R. P. & Tainer, J. A. Accurate assessment of mass, models and resolution by small-angle scattering. Nature 496, 477–481 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
CAS  PubMed  Google Scholar 

63.
Schneidman-Duhovny, D., Hammel, M. & Sali, A. FoXS: a web server for rapid computation and fitting of SAXS profiles. Nucleic Acids Res. 38, W540–W544 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Schneidman-Duhovny, D., Hammel, M., Tainer, J. A. & Sali, A. Accurate SAXS profile computation and its assessment by contrast variation experiments. Biophys. J. 105, 962–974 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

65.
Prins, A. et al. Rubisco catalytic properties of wild and domesticated relatives provide scope for improving wheat photosynthesis. J. Exp. Bot. 67, 1827–1838 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Sharwood, R. E., Ghannoum, O. & Whitney, S. M. Prospects for improving CO2 fixation in C3-crops through understanding C4-Rubisco biogenesis and catalytic diversity. Curr. Opin. Plant Biol. 31, 135–142 (2016).
CAS  PubMed  Google Scholar 

67.
Pei, J., Kim, B.-H. & Grishin, N. V. PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Res. 36, 2295–2300 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2017).
PubMed Central  Google Scholar 

69.
Potterton, E., Briggs, P., Turkenburg, M. & Dodson, E. A graphical user interface to the CCP4 program suite. Acta Crystallogr. D 59, 1131–1137 (2003).
PubMed  Google Scholar 

70.
Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Krissinel, E. Crystal contacts as nature’s docking solutions. J. Comput. Chem. 31, 133–143 (2010).
CAS  PubMed  Google Scholar 

72.
Pettersen, E. F. et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
CAS  Google Scholar 

73.
Diamond, S. et al. Mediterranean grassland soil C-N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nat. Microbiol. 4, 1356–1367 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Lavy, A. et al. Microbial communities across a hillslope–riparian transect shaped by proximity to the stream, groundwater table, and weathered bedrock. Ecol. Evol. 9, 6869–6900 (2019).
PubMed  PubMed Central  Google Scholar 

75.
Knight, S., Andersson, I. & Brändén, C. I. Crystallographic analysis of ribulose 1,5-bisphosphate carboxylase from spinach at 2.4 A resolution. Subunit interactions and active site. J. Mol. Biol. 215, 113–160 (1990).
CAS  PubMed  Google Scholar  More

1.
Gentile, G. & Snell, H. Conolophus marthae sp.nov. (Squamata, Iguanidae), a new species of land iguana from the Galapagos archipelago. Zootaxa 1–10 (2009).
2.
Gentile, G. et al. An overlooked pink species of land iguana in the Galapagos. Proc. Natl. Acad. Sci. 106, 507–511 (2009).
ADS  CAS  Article  Google Scholar 

3.
Gentile, G. Conolophus marthae. The IUCN Red List of Threatened Species 2012: e.T174472A1414375. (2012).

4.
Rivas, L. R. A reinterpretation of the concepts ‘sympatric’ and ‘allopatric’ with proposal of the additional terms ‘syntopic’ and ‘allotopic’. Syst. Biol. 13, 42–43 (1964).
Article  Google Scholar 

5.
MacLeod, A. et al. Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana. Proc. R. Soc. B Biol. Sci. 282, 20150425 (2015).
Article  Google Scholar 

6.
Rassmann, K., Tautz, D., Trillmich, F. & Gliddon, C. The microevolution of the Galápagos marine iguana Amblyrhynchus cristatus assessed by nuclear and mitochondrial genetic analyses. Mol. Ecol. 6, 437–452 (1997).
CAS  Article  Google Scholar 

7.
Di Giambattista, L. et al. Molecular data exclude current hybridization between iguanas Conolophus marthae and C. subcristatus on Wolf Volcano (Galápagos Islands). Conserv. Genet. 19, 1461–1469 (2018).
Article  Google Scholar 

8.
Vuillaume, B., Valette, V., Lepais, O., Grandjean, F. & Breuil, M. Genetic evidence of hybridization between the endangered native species Iguana delicatissima and the invasive Iguana iguana (Reptilia, Iguanidae) in the Lesser Antilles: Management implications. PLoS One 10, (2015).

9.
Jančúchová-Lásková, J., Landová, E. & Frynta, D. Are genetically distinct lizard species able to hybridize? A review. Curr. Zool. 61, 155–180 (2015).
Article  Google Scholar 

10.
Servedio, M. R. Beyond reinforcement: the evolution of premating isolation by direct selection on preferences and postmating, prezygotic incompatibilities. . Evolution (N. Y) 55, 1909–1920 (2001).
CAS  Google Scholar 

11.
Hoskin, C. J., Higgie, M., McDonald, K. R. & Moritz, C. Reinforcement drives rapid allopatric speciation. Nature 437, 1353–1356 (2005).
ADS  CAS  Article  Google Scholar 

12.
Mason, R. T. & Parker, M. R. Social behavior and pheromonal communication in reptiles. J. Comp. Physiol. A Neuroethol. Sensory Neural Behav. Physiol. 196, 729–749 (2010).
CAS  Article  Google Scholar 

13.
Weldon, P. J., Flachsbarth, B. & Schulz, S. Natural products from the integument of nonavian reptiles. Nat. Prod. Rep. 25, 738 (2008).
CAS  Article  Google Scholar 

14.
Barbosa, D., Font, E., Desfilis, E. & Carretero, M. A. Chemically mediated species recognition in closely related Podarcis wall lizards. J. Chem. Ecol. 32, 1587–1598 (2006).
CAS  Article  Google Scholar 

15.
Labra, A., Escobar, C. A. & Niemeyer, H. M. Chemical discrimination in liolaemus lizards: comparison of behavioral and chemical data. In Chemical Signals in Vertebrates 9 439–444 (Springer US, 2001). https://doi.org/10.1007/978-1-4615-0671-3_60

16.
Baeckens, S. et al. Environmental conditions shape the chemical signal design of lizards. Funct. Ecol. 32, 566–580 (2018).
Article  Google Scholar 

17.
Gabirot, M., Castilla, A. M., López, P. & Martín, J. Chemosensory species recognition may reduce the frequency of hybridization between native and introduced lizards. Can. J. Zool. 88, 73–80 (2010).
CAS  Article  Google Scholar 

18.
Gabirot, M., Castilla, A. M., López, P. & Martín, J. Differences in chemical signals may explain species recognition between an island lizard, Podarcis atrata, and related mainland lizards P. hispanica. Biochem. Syst. Ecol. 38, 521–528 (2010).
CAS  Article  Google Scholar 

19.
Ibáñez, A. et al. Diversity of compounds in femoral secretions of Galápagos iguanas (genera: Amblyrhynchus and Conolophus), and their potential role in sexual communication in lek-mating marine iguanas (Amblyrhynchus cristatus ). PeerJ 5, e3689 (2017).
Article  Google Scholar 

20.
Chiu, K. W. & Maderson, P. F. A. The microscopic anatomy of epidermal glands in two species of gekkonine lizards, with some observations on testicular activity. J. Morphol. 147, 23–39 (1975).
CAS  Article  Google Scholar 

21.
Alberts, A. C. Chemical and behavioral studies of femoral gland secretions in iguanid lizards. Brain. Behav. Evol. 41, 255–260 (1993).
CAS  Article  Google Scholar 

22.
John, C. R. MLeval: Machine Learning Model Evaluation (2019).

23.
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. 1, 409 (2018).

24.
Alberts, A. C. Phylogenetic and adaptive variation in lizard femoral gland secretions. Copeia 1991, 69–79 (1991).
Article  Google Scholar 

25.
Gismondi, A. et al. GC–MS detection of plant pigments and metabolites in Roman Julio-Claudian wall paintings. Phytochem. Lett. 25, 47–51 (2018).
CAS  Article  Google Scholar 

26.
Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).
CAS  Article  Google Scholar 

27.
Alberts, A. C., Sharp, T. R., Werner, D. I. & Weldon, P. J. Seasonal variation of lipids in femoral gland secretions of male green iguanas (Iguana iguana). J. Chem. Ecol. 18, 703–712 (1992).
CAS  Article  Google Scholar 

28.
Gabirot, M., Picerno, P., Valencia, J., Lopez, P. & Martin, J. Species recognition by chemical cues in neotropical snakes. Copeia 2012, 472–477 (2012).
Article  Google Scholar 

29.
Gabirot, M., López, P. & Martín, J. Differences in chemical sexual signals may promote reproductive isolation and cryptic speciation between iberian wall lizard populations. Int. J. Evol. Biol. 2012, 1–13 (2012).
Article  Google Scholar 

30.
Alberts, A. C., Phillips, J. A. & Werner, D. I. Sources of intraspecific variability in the protein composition of lizard femoral gland secretions. Copeia 1993, 775 (1993).
Article  Google Scholar 

31.
Shine, R., Phillips, B., Waye, H., LeMaster, M. & Mason, R. T. Chemosensory cues allow courting male garter snakes to assess body length and body condition of potential mates. Behav. Ecol. Sociobiol. 54, 162–166 (2003).
Article  Google Scholar 

32.
Martins, E. P., Ord, T. J., Slaven, J., Wright, J. L. & Housworth, E. A. Individual, sexual, seasonal, and temporal variation in the amount of sagebrush lizard scent marks. J. Chem. Ecol. 32, 881–893 (2006).
CAS  Article  Google Scholar 

33.
Baeckens, S., García-Roa, R., Martín, J. & Van Damme, R. The role of diet in shaping the chemical signal design of lacertid lizards. J. Chem. Ecol. 43, 902–910 (2017).
CAS  Article  Google Scholar 

34.
Martín, J. & Lopez, P. Pheromones and chemical communication in lizards. In Reproductive Biology and Phylogeny of Lizards and Tuatara 43–75 (2014). https://doi.org/10.1016/B978-008045046-9.01825-8

35.
Karnauskas, K. B., Murtugudde, R. & Owens, W. B. Climate and the global reach of the galápagos archipelago. In The Galapagos: A Natural Laboratory for the Earth Sciences 215–231 (2014). https://doi.org/10.1002/9781118852538.ch11

36.
Gentile, G., Marquez, C., Snell, H. L., Tapia, W. & Izurieta, A. Conservation of a new flagship species: the Galápagos Pink Land Iguana (Conolophus marthae Gentile and Snell, 2009). In Problematic Wildlife: A Cross-Disciplinary Approach (ed. Angelici, F. M.) 315–336 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-22246-2

37.
Khannoon, E. R., El-Gendy, A. & Hardege, J. D. Scent marking pheromones in lizards: cholesterol and long chain alcohols elicit avoidance and aggression in male Acanthodactylus boskianus (Squamata: Lacertidae). Chemoecology 21, 143–149 (2011).
CAS  Article  Google Scholar 

38.
Martin, S. J., Shemilt, S., Lima, C. B. D. S. & de Carvalho, C. A. L. are isomeric alkenes used in species recognition among neo-tropical stingless bees (Melipona Spp). J. Chem. Ecol. 43, 1066–1072 (2017).
CAS  Article  Google Scholar 

39.
Greene, M. J. & Gordon, D. M. Structural complexity of chemical recognition cues affects the perception of group membership in the ants Linephithema humile and Aphaenogaster cockerelli. J. Exp. Biol. https://doi.org/10.1242/jeb.02706 (2007).
Article  PubMed  Google Scholar 

40.
Aragón, P., López, P. & Martín, J. Size-dependent chemosensory responses to familiar and unfamiliar conspecific faecal pellets by the iberian rock-lizard Lacerta monticola. Ethology 106, 1115–1128 (2000).
Article  Google Scholar 

41.
Buellesbach, J., Vetter, S. G. & Schmitt, T. Differences in the reliance on cuticular hydrocarbons as sexual signaling and species discrimination cues in parasitoid wasps. Front. Zool. 15, 22 (2018).
Article  Google Scholar 

42.
Moss, J. B. et al. First evidence for crossbreeding between invasive Iguana iguana and the native rock iguana (Genus Cyclura) on Little Cayman Island. Biol. Invasions 20, 817–823 (2018).
Article  Google Scholar 

43.
Lovern, M. B. & Jenssen, T. A. Form emergence and fixation of head bobbing displays in the green anole lizard (Anolis carolinensis): a reptilian model of signal ontogeny. J. Comp. Psychol. 117, 133–141 (2003).
Article  Google Scholar 

44.
Escobar, C. A., Labra, A. & Niemeyer, H. M. Chemical composition of precloacal secretions of Liolaemus lizards. J. Chem. Ecol. 27, 1677–1690 (2001).
CAS  Article  Google Scholar 

45.
Giovannini, D. et al. Lavandula angustifolia Mill. Essential oil exerts antibacterial and anti-inflammatory effect in macrophage mediated immune response to Staphylococcus aureus. Immunol. Invest. 45, 11–28 (2016).
CAS  Article  Google Scholar 

46.
Baeckens, S., Martín, J., García-Roa, R. & Van Damme, R. Sexual selection and the chemical signal design of lacertid lizards. Zool. J. Linn. Soc. 183, 445–457 (2018).
Article  Google Scholar 

47.
Oksanen, J. Multivariate analysis of ecological communities in R: vegan tutorial. (2015).

48.
Oksanen, J. et al. Vegan: Community Ecology Package (2018).

49.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics https://doi.org/10.1111/j.1541-0420.2005.00440.x (2006).
MathSciNet  Article  PubMed  MATH  Google Scholar 

50.
Maindonald, J. & Braun, J. Data Analysis and Graphics Using R. Data Analysis and Graphics Using R (Cambridge University Press, Cambridge , 2006). https://doi.org/10.1017/CBO9780511790935

51.
Gini, C. Variabilità e mutabilità (Variability and Mutability), C. Cuppini, Bologna, 156pp. Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955). (1912).

52.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

53.
Kuhn, M. Caret package. J. Stat. Softw. 28, 1–26 (2008).
Article  Google Scholar  More

1.
Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 
2.
Negredo, A. et al. Discovery of an ebolavirus-like filovirus in Europe. PLoS Pathog. 7, 1–8 (2011).
Google Scholar 

3.
Atherstone, C., Roesel, K. & Grace, D. Ebola Risk Assessment in the Pig Value Chain in Uganda. ILRI Research Report 34. Nairobi, Kenya: International Livestock Research Institute (2014).

4.
CDC. Centers for Disease Control and Prevention (CDC). Ebola Virus Disease Distribution Map: cases of Ebola Virus Disease in Africa Since 1976 (2019). https://www.cdc.gov/vhf/ebola/history/distribution-map.html. Accessed August 3rd 2019.

5.
WHO. World Health Organization (WHO) Ebola virus disease – fact-sheet. (2019). https://www.who.int/health-topics/ebola/#tab=overview. Accessed September 20th 2018.

6.
Swanepoel, R. et al. Experimental inoculation of plants and animals with Ebola virus. Emerg. Infect. Dis. 2, 321–325 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Cantoni, D., Hamlet, A., Michaelis, M., Wass, M. N. & Rossmann, J. S. Risks posed by Reston, the forgotten Ebolavirus. mSphere 1, 1–10 (2016).
Google Scholar 

8.
GIDEON. GIDEON: Stephan Berger. Ebola: Global Status (GIDEON Informatics, Inc., Los Angeles, 2019).
Google Scholar 

9.
Pourrut, X. et al. Spatial and temporal patterns of Zaire ebolavirus antibody prevalence in the possible reservoir bat species. J. Infect. Dis. 196, S176–S183 (2007).
PubMed  Google Scholar 

10.
Gire, S. et al. Genomic surveillance elucidates Ebola virus orgin and transmission during the 2014 outbreak. Science 12, 1–13 (2014).
Google Scholar 

11.
Taniguchi, S. et al. Reston ebolavirus antibodies in bats, the Philippines. Emerg. Infect. Dis. 17, 1559–1560 (2011).
PubMed  PubMed Central  Google Scholar 

12.
Schar, D. & Daszak, P. Ebola economics: the case for an upstream approach to disease emergence. EcoHealth 11, 451–452 (2014).
PubMed  Google Scholar 

13.
Voigt, C. C. Bats in the anthropocene: conservation of bats in a changing world. Springer, Berlin. https://doi.org/10.1007/978-3-319-25220-9 (2015).
Article  Google Scholar 

14.
Leendertz, S. A. J., Gogarten, J. F., Düx, A., Calvignac-Spencer, S. & Leendertz, F. H. Assessing the evidence supporting fruit bats as the primary reservoirs for ebola viruses. EcoHealth 13, 18–25 (2016).
PubMed  Google Scholar 

15.
Pourrut, X. et al. The natural history of Ebola virus in Africa. Microbes Infect. 7, 1005–1014 (2005).
PubMed  Google Scholar 

16.
Pourrut, X. et al. Large serological survey showing cocirculation of Ebola and Marburg viruses in Gabonese bat populations, and a high seroprevalence of both viruses in Rousettus aegyptiacus. BMC Infect. Dis. 9, 159 (2009).
PubMed  PubMed Central  Google Scholar 

17.
Peterson, T. T., Carroll, D. S., Mills, J. N. & Johnson, K. M. Potential mammalian filovirus reservoirs. Emerg. Infect. Dis. 10, 2073–2081 (2004).
PubMed  PubMed Central  Google Scholar 

18.
Allen, T., Murray, K., Olival, K. J. & Daszak, P. The Influcence of global environmental change on infectious disease dynamics: workshop summary. Global change and infectious disease dynamics. Eight critical questions for pandemic prediction (2012).

19.
Olival, K. J., Weekley, C. & Daszak, P. Are bats really ‘special’ as viral reservoirs? What do we know and need to know? In Bats and Viruses: a new frontier of emerging infectious diseases (eds Wang, L.-F. & Cowled, C.) 281–294 (Wiley, Hoboken, 2015).
Google Scholar 

20.
Olival, K. & Hayman, D. Filoviruses in bats: current knowledge and future directions. Viruses 6, 1759–1788 (2014).
PubMed  PubMed Central  Google Scholar 

21.
Leroy, E. M. et al. Fruit bats as reservoirs of Ebola virus. Nature 438, 575–576 (2005).
ADS  CAS  PubMed  Google Scholar 

22.
Hayman, D. T. S. et al. Long-term survival of an urban fruit bat seropositive for ebola and lagos bat viruses. PLoS ONE 5, 2008–2010 (2010).
Google Scholar 

23.
Hayman, D. T. S. et al. Ebola virus antibodies in fruit bats, Ghana, West Africa. Emerg. Infect. Dis. 18, 1207–1209 (2012).
PubMed  PubMed Central  Google Scholar 

24.
De Nys, H. M. et al. Survey of Ebola viruses in frugivorous and insectivorous bats in Guinea, Cameroon, and the Democratic Republic of the Congo, 2015–2017. Emerg. Infect. Dis. 24, 2228–2240 (2018).
PubMed  PubMed Central  Google Scholar 

25.
Sylla, M. et al. Chiropteran and Filoviruses in Africa: unveiling an ancient history. African J. Microbiol. Res. 9, 1446–1472 (2015).
Google Scholar 

26.
Gay, N. et al. Parasite and viral species richness of Southeast Asian bats: fragmentation of area distribution matters. Int. J. Parasitol. Parasites Wildl. 3, 161–170 (2014).
PubMed  PubMed Central  Google Scholar 

27.
CDC. Bushmeat. Centers for Disease Control and Prevention (CDC). (2018). https://www.cdc.gov/importation/bushmeat.html. Accessed January 21st 2020.

28.
Bonwitt, J. et al. Unintended consequences of the ‘bushmeat ban’ in West Africa during the 2013–2016 Ebola virus disease epidemic. Soc. Sci. Med. 200, 166–173 (2018).
PubMed  Google Scholar 

29.
Pigott, D. M. et al. Mapping the zoonotic niche of Ebola virus disease in Africa. Elife 3, e04395 (2014).
PubMed  PubMed Central  Google Scholar 

30.
ACR. African Chiroptera Report 2018. AfricanBats NPC. (2018). https://doi.org/10.13140/RG.2.2.18794.82881

31.
ACR. African Chiroptera Report 2019. AfricanBats NPC. (2019). https://doi.org/10.13140/RG.2.2.27442.76482.1990-6471

32.
Haensler, A., Saeed, F. & Jacob, D. Assessment of projected climate change signals over central Africa based on a multitude of global and regional climate projections. in Climate Change Scenarios for the Congo Basin (eds. Haensler, A., Jacob, D., Kabat, P. & Ludwig, F.) 11–42 (2013).

33.
Voigt, C. C., Schneeberger, K., Voigt-Heucke, S. L., Lewanzik, D. & Supplement, D. Rain increases the energy cost of bat flight Subject collections Email alerting service rain increases the energy cost of bat flight. Society https://doi.org/10.1098/rsbl.2011.0313 (2011).
Article  Google Scholar 

34.
PREDICT. Distribution and seasonality of potential Ebola bat reservoirs. Emerg. Dis. Insights (2016).

35.
Erickson, J. L. & West, S. D. The influence of regional climate and nightly weather conditions on activity patterns of insectivorous bats. Acta Chiropterologica 4, 17–24 (2002).
Google Scholar 

36.
Peterson, A. T. et al. Ecological Niches and Geographic Distributions. Ecological Niches and Geographic Distributions (MPB-49) (2011). https://doi.org/10.23943/princeton/9780691136868.001.0001

37.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 

38.
Peel, A. J. et al. Continent-wide panmixia of an African fruit bat facilitates transmission of potentially zoonotic viruses. Nat. Commun. 4, 1–14 (2013).
MathSciNet  Google Scholar 

39.
Arneberg, P., Skorping, A., Grenfell, B. & Read, A. F. Host densities as determinants of abundance in parasite communities. Proc. R. Soc. B Biol. Sci. 265, 1283–1289 (1998).
Google Scholar 

40.
Altizer, S. et al. Social organization and parasite risk in mammals: integrating theory and empirical studies. Annu. Rev. Ecol. Evol. Syst. 34, 517–547 (2003).
Google Scholar 

41.
Calisher, C. H., Childs, J. E., Field, H. E., Holmes, K. V. & Schountz, T. Bats: Important reservoir hosts of emerging viruses. Clin. Microbiol. Rev. 19, 531–545 (2006).
PubMed  PubMed Central  Google Scholar 

42.
Loehle, C. Social barriers to pathogen transmission in wild animal populations. Ecology 76, 326–335 (1995).
Google Scholar 

43.
Nunn, C. L., Jordán, F., McCabe, C. M., Verdolin, J. L. & Fewell, J. H. Infectious disease and group size: more than just a numbers game. Philos. Trans. R. Soc. B Biol. Sci. 370, (2015).

44.
Alexander, K. A. et al. What factors might have led to the emergence of ebola in West Africa?. PLoS Negl. Trop. Dis. 9, 1–26 (2015).
Google Scholar 

45.
Leroy, E. M. et al. Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007. Vector-Borne Zoonotic Dis. 9, 723–728 (2009).
PubMed  Google Scholar 

46.
Ng, M. et al. Filovirus receptor NPC1 contributes to species-specific patterns of ebolavirus susceptibility in bats. Elife 4, 1–22 (2015).
Google Scholar 

47.
MacNeil, A., Reed, Z. & Rollin, P. E. Serologic cross-reactivity of human IgM and IgG antibodies to five species of Ebola virus. PLoS Negl. Trop. Dis. 5, e1175 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

48.
Schuh, A. J. et al. Comparative analysis of serologic cross-reactivity using convalescent sera from filovirus-experimentally infected fruit bats. Sci. Rep. 9, 1–12 (2019).
ADS  CAS  Google Scholar 

49.
Olival, K. J., Epstein, J. H., Wang, L. F., Field, H. E. & Daszak, P. Are bats unique viral reservoirs? In New Directions in Conservation Medicine Applied Cases of Ecological Health Aguirre (eds Aguirre, A. A. et al.) 195–212 (Oxford University Press, Oxford, 2012).
Google Scholar 

50.
GBIF. Global Biodiversity Information Facility. GBIF Home Page (2018).

51.
Chamberlain, S., Boettiger, C., Ram, K., Brave, V. & McGlinn, D. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 0.9.3. https://github.com/ropensci/rgbif (2016).

52.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2017).

53.
Geluso, K. N. & Geluso, K. Effects of environmental factors on capture rates of insectivorous bats, 1971–2005. J. Mammal. 93, 161–169 (2012).
Google Scholar 

54.
Wolbert, S. J., Zellner, A. S. & Whidden, H. P. Bat activity, insect biomass, and temperature along an elevational gradient. Northeast. Nat. 21, 72–85 (2014).
Google Scholar 

55.
Arino, O. et al. Global land cover map for 2009 (GlobCover 2009). © European Space Agency (ESA) & Université catholique de Louvain (UCL), PANGAEA. https://doi.org/10.1594/PANGAEA.787668 (2012)

56.
Bicheron, P. et al. GLOBCOVER – Products Description and Validation Report (2008).

57.
Phillips, S. J., Dudík, M. & Schapire, R. E. [Internet] Maxent software for modeling species niches and distributions (Version 3.4.1). https://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 2019 (2017).

58.
Elith, J. et al. Novel methods improve prediction of species ’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).
Google Scholar 

59.
Cunze, S. & Tackenberg, O. Decomposition of the maximum entropy niche function: a step beyond modelling species distribution. Environ. Model. Softw. 72, 250–260 (2015).
Google Scholar 

60.
Jiménez-Valverde, A. & Lobo, J. M. Threshold criteria for conversion of probability of the species presence to either-or- presence–absence. Acta Oecologica 31, 361–369 (2007).
ADS  Google Scholar 

61.
Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).
Google Scholar 

62.
Schröder, B. & Richter, O. Are habitat models transferable in space and time?. Zeitschrift für Ökologie und Naturschutz 8, 195–205 (2000).
Google Scholar 

63.
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).
Google Scholar 

64.
IUCN. IUCN (International Union for Conservation of Nature and Natural Resources). The IUCN Red List of Threatened Species. Version 2019-3. https://www.iucnredlist.org (2020). https://www.iucnredlist.org/search.

65.
CDC. Ebola Virus Disease Distribution Map: Cases of Ebola Virus Disease in Africa Since 1976. (2019). https://www.cdc.gov/vhf/ebola/history/distribution-map.html. Accessed July 28th 2020.

66.
Judson, S. D., Fischer, R., Judson, A. & Munster, V. J. Ecological contexts of index cases and spillover events of different Ebolaviruses. PLoS Pathog. 12, 1–17 (2016).
Google Scholar 

67.
ESRI. Environmental Systems Research Institute (ESRI). ArcGIS Release 10.6. Redlands, CA (2018). More

Model
An ideal experimental set-up to study the effect of LCRs on bid prices would include (at least) two identical countries, completely independent of each other, with an auction scheme identical apart from the LCR feature. Any price difference that emerged between the two auction schemes could then be fully attributed to the differences in the LCR feature. Even better would be to include additional identical countries with varying levels of stringency in the level of the LCRs (in weight, value or number of components), to study whether there are discontinuities in bid prices that seem to be attributable to increasing levels of LCR stringency. For instance, one might expect a non-linear increase in bid prices if very high levels of LCRs (say, 95%) were introduced given that the manufacturing capabilities for wafers would be near zero in India35.
The Indian case comes close to an ideal policy experiment given that many tendered capacities were distributed between LCR and non-LCR auctions in similar geographic areas—albeit not always equally in terms of capacity. Importantly, firms were free to bid in both LCR and open auctions, and many firms submitted bids in both auction windows. However, to address the possible remaining issue of selection bias (that is, firms self-selecting into auctions with or without LCRs), we divide firms into two groups: firms that only bid in auctions without LCRs (59 firms, 134 bids) and firms that bid in both auction types, open and closed (26 firms, 143 bids). The data used in the study come from various sources from the Indian government and firm-level data from Mergent Intellect (described in more detail in the Data section).
We considered using a multinomial selection model that would divide firms into three groups: those that only bid in auctions with LCRs, those that only bid in auctions without LCRs and those that bid in both auction types. However, since there are only seven firms in the category of ‘only LCR auctions’, we were unable to run the model. Yet we believe that the main question is whether a firm bid in an LCR auction, because it indicates whether the firm has sufficient local knowledge to either liaise with a local manufacturer or to use its own existing manufacturing facilities. Firms that only bid in the open auction, in contrast, could merely import the required parts. Hence, we expect there to be systematic differences between these two groups.
Thus, we test whether firms that do not bid in the LCR category are different from those that do bid in the LCR category. It could be, for instance, that firms that bid in LCR auctions have more experience in local development than firms that only bid in the open auctions (where there are no restrictions in using imported material). Similarly, firms that only bid in the open auctions might be able to more effectively exploit economies of scale by producing solar PV cells and modules for several markets (for example, Canadian Solar, which has manufacturing capabilities in China).
In addition to using standard ordinary least squares regressions, we therefore make use of a Heckman regression model, which accounts for this possible selection bias. Heckman’s 1979 seminal paper proposes a two-step statistical approach37. In the first step, an economic model is defined in which plausible factors for the probability of falling into (in our case) either Group 1 or 2 are considered. This is modelled as a probit regression,

$${mathrm{Pr}}(G = 1|Z) = Phi (Zb)$$
(1)

where G indicates whether the firm belongs to Group 1 (G = 0 otherwise), Z is a vector of explanatory variables, b is a vector of unknown parameters and Φ is the cumulative distribution function of the standard normal distribution. The explanatory variables we consider are the number of employees of a given firm, whether it is a state-owned enterprise (SOE) and whether the firm is itself a manufacturer or is merely a project developer (an indication of the degree of vertical integration). We also consider whether the firm is primarily focused on energy or merely attempts to diversify from an unrelated field, indicating limited technical experience, and whether the company already bid in the NSM Phase I. The latter factor captures advantages that firms might have in the NSM Phase II due to prior experience with the auction system.
The second stage of the Heckman model then uses the probability that a firm will self-select into Group 1, based on its characteristics, by including that probability as an explanatory variable in the ordinary least squares regression.
For our standard ordinary least squares and Heckman regression model, we also created a number of explanatory variables that we assume influence bid price. We recognize that competition differed substantially between rounds and was on average twice as high in open auctions as in LCR auctions (as measured by our variable defined in equation (2)). Firms are likely to anticipate, or at least have beliefs about, the level of competition in an upcoming auction round, which leads them to adapt their bids accordingly (that is, to make higher bids when less competition is expected; this is well documented in the literature38). In order to exclude the possibility that bids under LCR regulation are higher solely due to this effect, we control for the degree of competition within each round. Therefore, we define the competition for each tender as follows:

$${mathrm{Competition}}_r = frac{{mathop {sum }nolimits_{n = 1}^N B_r}}{{AC_r}}$$
(2)

where B is the capacity in MW of each of the bids submitted for a particular auction round r, AC is the total capacity in MW auctioned in round r and N is the overall number of bids received for each auction round r. For instance, if 20 MW are auctioned off and firms submit 100 MW in bids, the competition would be 5.
We also include the cumulative installed capacity of each developer within the auction windows we cover to account for learning-by-doing of the developers and capacity building (for example, through greater local knowledge and connection to suppliers)39,40. Our time dummy controls for exogenous technological change, such as decrease in the cost of solar PV modules and other equipment over time, that is not directly related to the deployment in India (that is, exogenous technical progress)41. We do not include a state dummy as the variable is correlated too strongly with our time dummy (as certain states only conducted auctions in specific years, leading to high multicollinearity). We do, however, include the mean solar irradiation (annual average kWh m−2 d−1) per state to control for differences in the solar resources across different states (we also use the maximum solar irradiation for each state as a robustness check, which does not affect the results42).
In addition, we include a dummy for the utility that purchases the electricity generated by the awarded projects. It is well documented that the financial solvency of the utility buying the electricity (that is, the offtaker) is an important factor in assessing the risk associated with a project (that is, if an offtaker is less financially stable, the risk of a default increases, making capital more expensive, which in turn increases the cost of power43). Lastly, we consider whether a PV project being in a solar park has an effect on bid price. Solar parks are designated areas where environmental impact assessment, land procurement and interconnection are already taken care of. However, these increased costs may be reflected in the land price for the solar projects. By differentiating between solar parks and normal land, we are able to capture the price differences between the two approaches.
Thus, we use the following specification to study the effect of LCRs on bid price:

$$begin{array}{ll}{mathrm{bid}}_i & = alpha + beta _1{mathrm{LCR}}_r + beta _2{mathrm{Competition}}_r + beta _3{mathrm{Year}} + beta _4{mathrm{Cum}}_{{mathrm{MW}}} \ & + beta _5{mathrm{Offtaker}} + beta _6{mathrm{Solar}},{mathrm{park}} + beta _7{mathrm{Sol}} + varepsilon _iend{array}$$
(3)

where bidi is the individual bid of each firm, r is the auction round, LCRr is the dummy for whether local content was required or not in the auction, Year is the time dummy to control for temporal shocks, CumMW is the cumulative installed capacity prior to the given auction in the NSM Phase II, Offtaker is a dummy for the utility buying the power (1 = SECI, 0 = NTPC), Solar park indicates whether the project is within a solar park, Sol refers to the annualized average solar resources (kWh m−2 d−1) in each state and εi is the error term. We also include an interaction term between LCR and our time dummy, to test whether the effect of LCRs changed over time.
Part of the auctioned capacity was tendered under the viability gap funding (VGF) scheme, where the government fixed a base power purchase agreement (PPA) price and companies could request a top-up on the existing base price to make their project financially viable. Since price-only auctions have been implemented in India, the bidders who quoted the lowest amount of VGF were awarded the contracts until the auctioned capacity was reached (it should be noted that bidders were allowed to quote a lower PPA tariff than proposed and waive the VGF, but this rarely happened). Given that the VGF is dispensed as a capacity-based payment at the beginning of the lifetime of a power plant instead of as a constant subsidy for each unit of electricity generated, we had to levelize the amount to compare the outcomes with the generic auction results, where the payments are made across the entire lifetime of the power plant. Therefore, we applied the following method, which is based on the commonly used levelized cost of electricity (LCOE) calculation44, to calculate levelized VGF:

$${mathrm{VGF}}_{{mathrm{levelized}}} = frac{{{mathrm{VGF}}_{{mathrm{total}}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{E_t}}{{(1 + d)^t}}}} = frac{{C{mathrm{VGF}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{C{mathrm{Flh}}}}{{(1 + d)^t}}}} = frac{{mathop {sum }nolimits_{t = 0}^5 frac{{{mathrm{VGF}}_t}}{{(1 + d)^t}}}}{{{mathrm{Flh}}mathop {sum }nolimits_{t = 1}^{25} frac{1}{{(1 + d)^t}}}}$$
(4)

In equation (4), Et is the electricity generated in year t, C is the project’s capacity in MW and Flh is its full-load hours. We assume constant, region-specific full-load hours, which can be found in ref. 45. For bids that did not indicate the project’s location in India, we assume a capacity factor of 20% and thus full-load hours of Flh = 1,752 h. Moreover, we assume a discount rate of d = 10% and a plant life of t = 25 years. With our approach, we are also able to capture the time value of money induced by the different VGF disbursement methods applied throughout Phase II (Supplementary Table 7; note that there was no VGF disbursement in Batch II). We then add the resulting levelized VGF support to the specific PPA price.
To estimate the possible range of values for the additional cost borne directly by the Indian government due to LCRs, we used the average estimates from our Heckman regression of the additional cost of power of LCR bids when compared to the open bids. We compute this overall cost to the Indian government via an NPV model in which we discount all future payments from the Indian government to solar power plant owners and compare the cost to the clean technology budget in India. We use discount rates of 10%, 12% and 14% and a capacity factor of 20% for the solar PV plants and a 25-year running time based on REN21 (2018) data. These numbers are roughly similar (apart from possibly lower discount rates in this study) for other developing and emerging economies. These discount rates are based on information used by the Indian government for evaluating public projects46. Given the well-known challenges of choosing social discount rates (SDRs)47, we perform a sensitivity analysis by varying the discount rate between 10%, 12% and 14%. Taken together, these values for the SDRs encompass typical values of SDRs used in other developing and emerging economies, something that helps make our results more comparable to other countries46. We use the average real bid price from all open category auctions as our base price to calculate the additional cost of LCRs over the lifetime of an average solar project subject to LCRs.
In order to analyse the possible benefits of the LCR policy, we select a small set of indicators commonly used in the innovation systems and catching-up literature to determine whether a country is ‘narrowing’ the gap between the innovation leader and itself. While there are no perfect sets of metrics, we employ three different metrics commonly used in the innovation and economics literature: (1) domestic and international patent filings in the technology of interest40, (2) domestic production versus international imports5 and (3) exports to other countries from the country of interest4.
This analysis should be understood as correlational rather than causal, in contrast to the first part of our analysis. In addition, given how relatively recent the policy is, this analysis captures only short-term manufacturing and innovation effects. This is a limitation because some of the impacts of the policy, such as ongoing consolidation of the local industry through mergers and acquisitions, may take more time to materialize. Hence, the main contribution of this paper is the empirical assessment of the additional costs of LCRs, while the analysis of the possible benefits provides indicatory evidence of the evolution of important manufacturing and innovation metrics.
Data
In our analysis, we focus on the NSM Phase II auction results from 2014 to 2017. We did not include the bids and results from NSM Phase I since the majority of the projects (61% of total capacity deployed48) in the auction relied on thin film technology (as opposed to silicon panels), which was exempt from LCRs. Furthermore, we focused on the results of the PPA-based scheme and did not consider the EPC programme, which has a different focus: the auctioneer procures and owns the project and does not remunerate the electricity generated over 25 years to the project developer. The different remuneration mechanism, limited availability of data and different auction design elements, as well as different offtakers, hinder the comparability of the data. For the same reason, we neglect auctions conducted by state governments and focus solely on central government tenders conducted by either SECI or NTPC.
Contrary to most other countries conducting auctions, the Indian government shows a high degree of transparency in terms of publishing bids in the NSM Phase II auctions—including information on firms and the bid prices of both successful and unsuccessful applicants. We collected the data about the bid prices and the respective bidders from various government sources, either directly through governmental bodies (for example, SECI) or indirectly through different industry and reputable news sites, such as Mercom India or EQ International Magazine. We include all auction rounds of Phase II that had LCR regulations in place for a total of 28 auction windows across 10 Indian states. As shown in Supplementary Fig. 5, we intentionally excluded from the analysis the state-wise utility-scale PV tenders (around 14 GW by September 2017). In addition, we exclude the central government EPC tenders (1.6 GW) as well as the ‘open category’ rounds in central government auctions in which no counterfactual LCR auction took place (around 4.6 GW), such as the 100 MW auction in Uttar Pradesh in Batch III.
We consider our dataset with 277 bids complete in terms of auction rounds, since LCRs were abolished on 14 December 2017 due to a ruling of the WTO, with NTPC’s 250 MW Indian-wide auction being the last one under LCR regulation (the auction was later cancelled due to the negotiated phase-out of LCR). For further analysis, we consider the available submitted bids, rescale those to 2014 US dollar values to reflect inflation, and use logged bid values in our regression to normalize them. In summary, we consider bids with a total capacity of 21.7 GW, of which 18.7 GW were submitted in the open category and 3 GW under the LCR scheme.
We also collect detailed firm data for all 85 firms within our sample. For each firm, we analyse whether it belongs to a bigger firm. Several firms are so-called special purpose vehicles, which are created merely to bid in a given auction. Given that these firms have access to the human, financial and technical capital of the bigger firm that they belong to, we use the firm characteristics of the parent company. In addition, we collect data on the employment numbers (which could be found for all firms, as opposed to sales numbers, which were unavailable for many privately owned firms), check whether the firm is an SOE and research whether the firms themselves have manufacturing capacities (that is, are vertically integrated). We analyse whether the firm had already bid in the first phase of the NSM, which might give firms a distinct advantage over newcomers due to experience with local regulations. We also check whether the main focus of the company is energy or whether it has just recently diversified its firm activities into energy. Lastly, we analyse whether the firm was founded in India or was registered abroad. We posit that all of these characteristics may influence whether a firm participates in a given auction (for example, we assume that firms that have local manufacturing capabilities are more likely to participate in LCR auctions).
We used solar irradiation maps from the National Renewable Energy Laboratory (NREL) and converted them via QGIS (version 2.8.14) into mean, maximum and minimum values for each state. The NREL dataset provides solar resource in India for surface cells of 0.1 degrees in both latitude and longitude, or nominally 10 km in size. The NREL calculations are based on data from the Meteosat-5 and Meteosat-7 geostationary meteorological satellites42.
Patent data were collected from the Indian Patent Database using web scraping methods (Python package Selenium), as the patent office does not offer an application programming interface (API). We employ a typology from a recent, comprehensive review of international patent classification (IPC) terms and their correspondence to PV system components published in Renewable and Sustainable Energy Reviews49. This typology covers 284 distinct IPC codes in seven groups: cells, panels, electronics, energy storage, monitoring/testing, devices and combined. Studies comparing global trends in patenting to track innovation normally rely on large patent databases such as the European patent database PATSTAT, which aggregates patent statistics across many domestic offices. However, for India the PATSTAT data are woefully incomplete, leading us to resort to web scraping techniques.
The data on domestic production and imports in Fig. 4c are based on the Directorate General of Trade Remedies investigation on the imposition of safeguards on solar PV cells and modules on behalf of five Indian solar producers. The export data in Fig. 4d were exported from the global United Nations trade database Comtrade using the commodity code ‘HS 854140’, which describes ‘photosensitive semi-conductor devices, including photovoltaic cells whether or not assembled in modules or made up into panels’50. More

1.
Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).
ADS  CAS  PubMed  Google Scholar 
2.
Birks, H. J. B. & Birks, H. H. How have studies of ancient DNA from sediments contributed to the reconstruction of Quaternary floras?. New Phytol. 209, 499–506 (2016).
CAS  PubMed  Google Scholar 

3.
Froese, D., Westgate, J., Preece, S. & Storer, J. Age and significance of the late Pleistocene Dawson tephra in eastern Beringia. Quatern. Sci. Rev. 21, 2137–2142 (2002).
ADS  Google Scholar 

4.
Orlando, L. et al. Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse. Nature 499, 74 (2013).
ADS  CAS  PubMed  Google Scholar 

5.
Poinar, H. N. et al. Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA. Science 311, 392–394 (2006).
ADS  CAS  PubMed  Google Scholar 

6.
Waters, M. R. & Stafford, T. W. Redefining the age of Clovis: implications for the peopling of the Americas. Science 315, 1122–1126 (2007).
ADS  CAS  PubMed  Google Scholar 

7.
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165 (2006).
ADS  CAS  PubMed  Google Scholar 

8.
Mackelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

9.
Nikrad, M. P., Kerkhof, L. J. & Häggblom, M. M. The subzero microbiome: microbial activity in frozen and thawing soils. FEMS Microbiol. Ecol. 92, fiw81 (2016).
Google Scholar 

10.
Schuur, E. A. et al. Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. Bioscience 58, 701–714 (2008).
Google Scholar 

11.
Shendure, J. et al. DNA sequencing at 40: past, present and future. Nature 550, 345 (2017).
ADS  CAS  PubMed  Google Scholar 

12.
Weyrich, L. S. et al. Laboratory contamination over time during low-biomass sample analysis. Mol. Ecol. Resour. 19, 982–996 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Skoglund, P. et al. Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proc. Natl. Acad. Sci. 111, 2229–2234 (2014).
ADS  CAS  PubMed  Google Scholar 

14.
Bang-Andreasen, T., Schostag, M., Priemé, A., Elberling, B. & Jacobsen, C. S. Potential microbial contamination during sampling of permafrost soil assessed by tracers. Sci. Rep. 7, 43338 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

15.
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
PubMed  PubMed Central  Google Scholar 

16.
Willerslev, E., Hansen, A. J. & Poinar, H. N. Isolation of nucleic acids and cultures from fossil ice and permafrost. Trends Ecol. Evol. 19, 141–147 (2004).
PubMed  Google Scholar 

17.
Barbato, R. A. et al. Removal of exogenous materials from the outer portion of frozen cores to investigate the ancient biological communities harbored inside. JoVE 3, e54091 (2016).
Google Scholar 

18.
D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457 (2011).
ADS  PubMed  Google Scholar 

19.
Rivkina, E., Petrovskaya, L., Vishnivetskaya, T., Krivushin, K., Shmakova, L., Tutukina, M., Meyers, A., & Kondrashov, F. Metagenomic analyses of the late Pleistocene permafrost—Additional tools for reconstruction of environmental conditions. Biogeosciences 13 (2016).

20.
Kallmeyer, J. Contamination Control for Scientific Drilling Operations Vol. 98, 61–91 (Academic Press, London, 2017).
Google Scholar 

21.
Kallmeyer, J., Mangelsdorf, K., Cragg, B. & Horsfield, B. Techniques for contamination assessment during drilling for terrestrial subsurface sediments. Geomicrobiol. J. 23, 227–239 (2006).
CAS  Google Scholar 

22.
Korlević, P. et al. Reducing microbial and human contamination in DNA extractions from ancient bones and teeth. Biotechniques 59, 87–93 (2015).
PubMed  Google Scholar 

23.
Llamas, B. et al. From the field to the laboratory: controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. STAR: Sci. Technol. Archaeol. Res. 3, 1–14 (2017).
Google Scholar 

24.
Yanagawa, K., Nunoura, T., McAllister, S., Hirai, M., Breuker, A., Brandt, L., House, C., Moyer, C., Birrien, J.-L., Aoike, K., Sunamura, M., Urabe, T., Mottl, M., & Takai, K. The first microbiological contamination assessment by deep-sea drilling and coring by the D/V Chikyu at the Iheya North hydrothermal field in the Mid-Okinawa Trough (IODP Expedition 331). Front. Microbiol. 4 (2013).

25.
Yang, D. Y. & Watt, K. Contamination controls when preparing archaeological remains for ancient DNA analysis. J. Archaeol. Sci. 32, 331–336 (2005).
Google Scholar 

26.
Bollongino, R., Tresset, A. & Vigne, J.-D. Environment and excavation: pre-lab impacts on ancient DNA analyses. C. R. Palevol 7, 91–98 (2008).
Google Scholar 

27.
Smith, D. C. Ajsmrfsahhs. Tracer-based estimates of drilling-induced microbial contamination of Deep Sea Crust. Geomicrobiol. J. 17, 207–219 (2000).
CAS  Google Scholar 

28.
Krivushin, K. et al. Two metagenomes from late pleistocene Northeast Siberian Permafrost. Genome Announc. 3, e01380-e1414 (2015).
PubMed  PubMed Central  Google Scholar 

29.
Vishnivetskaya, T. A. et al. Bacterial community in ancient Siberian permafrost as characterized by culture and culture-independent methods. Astrobiology 6, 400–414 (2006).
ADS  CAS  PubMed  Google Scholar 

30.
Wright, G. D. & Poinar, H. Antibiotic resistance is ancient: implications for drug discovery. Trends Microbiol. 20, 157–159 (2012).
CAS  PubMed  Google Scholar 

31.
Kalmár, T., Bachrati, C. Z., Marcsik, A. & Raskó, I. A simple and efficient method for PCR amplifiable DNA extraction from ancient bones. Nucl. Acids Res. 28, e67–e67 (2000).
PubMed  Google Scholar 

32.
Palmirotta, R. et al. Use of a multiplex polymerase chain reaction assay in the sex typing of DNA extracted from archaeological bone. Int. J. Osteoarchaeol. 7, 605–609 (1997).
Google Scholar 

33.
González-Oliver, A., Márquez-Morfín, L., Jiménez, J. C. & Torre-Blanco, A. Founding Amerindian mitochondrial DNA lineages in ancient Maya from Xcaret, Quintana Roo. Am. J. Phys. Anthropol. 116, 230–235 (2001).
PubMed  Google Scholar 

34.
Kemp, B. M. & Smith, D. G. Use of bleach to eliminate contaminating DNA from the surface of bones and teeth. Forens. Sci. Int. 154, 53–61 (2005).
CAS  Google Scholar 

35.
Rogers, S. O. et al. Comparisons of protocols for decontamination of environmental ice samples for biological and molecular examinations. Appl. Environ. Microbiol. 70, 2540–2544 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Salamon, M., Tuross, N., Arensburg, B. & Weiner, S. Relatively well preserved DNA is present in the crystal aggregates of fossil bones. Proc. Natl. Acad. Sci. USA 102, 13783–13788 (2005).
ADS  CAS  PubMed  Google Scholar 

37.
Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME 11, 2305 (2017).
CAS  Google Scholar 

38.
Vishnivetskaya, T., Kathariou, S., McGrath, J., Gilichinsky, D. & Tiedje, J. M. Low-temperature recovery strategies for the isolation of bacteria from ancient permafrost sediments. Extremophiles 4, 165–173 (2000).
CAS  PubMed  Google Scholar 

39.
Yergeau, E., Hogues, H., Whyte, L. G. & Greer, C. W. The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses. ISME 4, 1206 (2010).
CAS  Google Scholar 

40.
Vishnivetskaya, T. A. et al. Commercial DNA extraction kits impact observed microbial community composition in permafrost samples. FEMS Microbiol. Ecol. 87, 217–230 (2014).
CAS  PubMed  Google Scholar 

41.
Braid, M. D., Daniels, L. M. & Kitts, C. L. Removal of PCR inhibitors from soil DNA by chemical flocculation. J. Microbiol. Methods 52, 389–393 (2003).
CAS  PubMed  Google Scholar 

42.
Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Porter, T. M. et al. Amplicon pyrosequencing late Pleistocene permafrost: the removal of putative contaminant sequences and small-scale reproducibility. Mol. Ecol. Resour. 13, 798–810 (2013).
CAS  PubMed  Google Scholar 

44.
Porter, T. J. et al. Recent summer warming in northwestern Canada exceeds the Holocene thermal maximum. Nat. Commun. 10, 1631 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

45.
Durfee, T. et al. The complete genome sequence of Escherichia coli DH10B: insights into the biology of a laboratory workhorse. J. Bacteriol. 190, 2597–2606 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Guzman, L. M., Belin, D., Carson, M. J. & Beckwith, J. Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J. Bacteriol. 177, 4121–4130 (1995).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Shaner, N. C. et al. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10, 407 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

48.
Cooper, A. & Poinar, H. N. Ancient DNA: do it right or not at all. Science 289, 1139–1139 (2000).
CAS  PubMed  Google Scholar 

49.
Bottos, E. M., Kennedy, D. W., Romero, E. B., Fansler, S. J., Brown, J. M., Bramer, L. M., Chu, R. K., Tfaily, M. M., Jansson, J. K. & Stegen, J. C. Dispersal limitation and thermodynamic constraints govern spatial structure of permafrost microbial communities. FEMS Microbiol. Ecol. 94 (2018).

50.
Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208 (2015).
ADS  CAS  PubMed  Google Scholar 

51.
Smith, D. C., Spivack, A. J., Fisk, M. R., Haveman, S. A. & Staudigel, H. Tracer-based estimates of drilling-induced microbial contamination of deep sea crust. Geomicrobiol J. 17, 207–219 (2000).
CAS  Google Scholar 

52.
Kallmeyer, J., Pockalny, R., Adhikari, R. R., Smith, D. C. & D’Hondt, S. Global distribution of microbial abundance and biomass in subseafloor sediment. Proc. Natl. Acad. Sci. 109, 16213–16216 (2012).
ADS  CAS  PubMed  Google Scholar 

53.
Juck, D. F. et al. Utilization of fluorescent microspheres and a green fluorescent protein-marked strain for assessment of microbiological contamination of permafrost and ground ice core samples from the Canadian High Arctic. Appl. Environ. Microbiol. 71, 1035–1041 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Colwell, F. S., Pryfogle, P. A., Lee, B. D. & Bishop, C. L. Use of a cyanobacterium as a particulate tracer for terrestrial subsurface applications. J. Microbiol. Methods 20, 93–101 (1994).
Google Scholar 

55.
Friese, A. et al. (2017) A simple and inexpensive technique for assessing contamination during drilling operations. Limnol. Oceanogr. Methods 15, 200–211 (2017).
CAS  Google Scholar 

56.
Knapp, M., Clarke, A. C., Horsburgh, K. A. & Matisoo-Smith, E. A. Setting the stage—Building and working in an ancient DNA laboratory. Ann. Anat. Anatomischer Anzeiger 194, 3–6 (2012).
CAS  PubMed  Google Scholar 

57.
Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27, 105–117 (2019).
CAS  PubMed  Google Scholar  More

Experimental design
The trials were conducted in five regions (Boro, Ugunja, Ukwala, Wagai and Yala) of Siaya County in western Kenya. Siaya County is located at 00°08.468′ N, 34°25.378′ E, and at an altitude of 1,336 m above sea level. The experimental sites were in lower midland 1(LM1) and lower midlands 2 (LM2) agro-ecological zones, which experience bimodal rainfall with long rains (LR) starting in March to July and short Rains (SR) starting in Late August to December41 and receive average annual rainfall of 1,500 mm42. The soils are mainly Ferralsols and Acrisols in the higher areas and Vertisols in the low areas.
Trials were conducted in 48 randomly selected villages, using stratification at the sub-county level. Half of them (randomly selected) participated in the trials in the long and short rain of 2014 and the long rain of 2015. The other half started in the short rain of 2014 and continued throughout the long and short rain of 2015. In each village 10 farmers participated in the trial. Half of them were specifically selected for participation in a community meeting. In those meetings, the researchers explained the objectives of the trials and asked the community members to nominate 5 farmers (as well as 5 potential substitutes), including two women, thought to be good farmers and interested in participating in the trials. Such non-random selection of farmers is common practice in research trials (Supplementary Table S1 online). The other half was selected randomly from the list of all the farmers in the village. All selected farmers were visited to obtain consent for the trials and identify the potential trial parcel (chosen by the farmer, conditional on fitting with some criteria for suitability to the research trials). A small number of replacements was done (but always keeping 5 selected by the community and 5 random). In each village 4 random farmers (2 random and 2 selected) were assigned to participate in the maize trial, while 3 random farmers (at least 1 random and 1 selected) were assigned to participate in the soybean trial. The other 3 farmers participated in a maize-soybean intercrop trial. During implementation, the assignment of inputs for the intercrop trial was, however, contaminated. As a result none of the plots in the intercrop trial received a best-bet input package, making the agronomic findings from that trial hard to interpret, and therefore not necessarily of interest for the decomposition proposed in this paper. Nevertheless, for completeness, results for these intercrop trials are shown in Supplementary Table S14 online.
Researcher-designed and farmer-managed trials
The trials would qualify as researcher-designed and farmer-managed (under the supervision of the researchers). The research team had full control over the design of the trials, from the choice of inputs to spacing and other management practices. All inputs were provided by the research team, with the exception of the local maize seed tested in two out of the six plots. A researcher (local expert agronomist) was present and led planting, gapping and thinning, all fertilizer applications, and harvesting. In these activities, labor was typically provided by the farmer. Planting dates were mostly decided by the researchers to best target the onset of rains, also responding to the farmers’ feedback on beginning of rains and availability to schedule the visit for planting. The farmer was in charge of land preparation, weeding and other management, with the researchers providing guidelines on those practices. In each village, a contact person (typically one of the ten farmers) visited the trials weekly to verify that the farmers fulfilled their responsibilities. Farmers were also asked to inform the contact person in case of any pest or disease, in which case the researcher provided the required pesticide or fungicide.
Treatment structure and application
Supplementary Table S2 online presents the full factorial designs of the multi-locational trials for maize and soybean, including details on crop varieties and quantity of inputs. The plot sizes were 4.5 × 5 m and the treatments were completely randomized between the six plots on each parcel. Plot sizes are of a similar order of magnitude as those found in other recently published work. A 1 m inter-plot spacing was planted with sweet potatoes to act as a buffer between plots to prevent inter-plot contamination. The sweet potatoes were planted at 50 cm from each plot, and border rows of the maize and soya plots were excluded for yield estimations to limit any edge effect. Hence the area harvested was 12.9 m2 for maize and 13.5 m2 for soybean. The experiments were repeated for three seasons, and plot layout and treatments were maintained for three seasons.
For the soybean trials, a soybean rhizobia inoculant was tested alone, with Minjingu hyper phosphate (0-30-0 + 38CaO) or Sympal (0:23:15 + 10CaO + 4S + 1MgO + 0.1Zn) in a full factorial design. Phosphorus rate of 30 kg P ha−1 was used to determine the quantity of Sympal and Minjingu hyper phosphate to be applied. On each farm only one replicate was used; hence, 6 plots were installed on each farm. Inoculation was done at planting as a seed coating using the directions for use in the respective product labels. Each plot had 6 soybean lines of 5 m in length each spaced at 5 cm from plant to plant within row and 50 cm from row to row. Inoculation was done on all the rows. Soybean variety TGx1740-2F with medium maturity (95–100 days)43 was used as the test crop. The spatial variability of the soybean response is studied in44. The soybean trials demonstrated that the combination of rhizobia inoculant and P-source led to important yield gains44.
The choice of inputs resulted from prior research conducted as part of the Compro project. Soybean was chosen as test crop mainly because in the prior phase of the project it had shown good response to rhizobia inoculation45 and was agro-ecologically suitable to the region. Kenya is an importer of soybean and multiple efforts are geared towards raising local production. In Compro I, the two rhizobia inoculants were tested and shown to be effective in increasing nodulation, nitrogen fixation and yield when inoculated on the tested soybean variety. Minjingu and Sympal were chosen based on their formulation with respect to the chemical characteristics of the soils in the test sites and results of earlier research46. The soils generally lack phosphorus and are acidic. A mapping study47 specifically identified Western Kenya as a potential K deficient area, and soil acidity has long been identified as a constraining factor in Western Kenya48 hence the importance of CaO. Results from soil sampling of the trial plots confirmed that more than 56.87% of soils were acidic (pH  More

Spatial patterns of stability proxies and background variables
Figure 2 a, b show the spatial distribution of our two proxy variables, the average rank of Rs per position (rankRs) and of the range of the ranks per position (rangeRs) in kriged maps. The middle to southern areas were found to have the largest, whilst the north-eastern areas the smallest rankRs values, whereas a slightly different pattern was characteristic for rangeRs with some additional north-western large values. Similarly, larger average soil organic carbon content (meanSOC) and average soil water content (meanSWC) (Fig. 2 c, d) were detected at the western-middle-southern regions and smaller at the north-eastern part of the study site.
Figure 2

Kriged patterns of stability proxies, rankRs (a) and rangeRs (b), as well as of background factors, meanSOC (%, c) and meanSWC (%, d).

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Correlations between stability proxies and background variables along DEMs: entire dataset
We investigated the potential direct effects of the different terrain attributes (local mean elevation (mALT), standard deviation of elevation (SD), topographic position index (TPI), slope (Sl), Easterness and Northness (East, North)) on the spatial distributions of our proxy variables by using the terrain attributes originating from differently smoothed DEM rasters. DEM1 was the original, 0.2 m resolution model, while DEMs 2–6 were progressively smoothed by a factor of two resulting in different resolution DEM rasters (DEM2: 0.4 m, DEM3: 0.8 m, DEM4: 1.6 m, DEM5: 3.2 m, DEM6: 6.4 m, respectively), and finally DEM7 met the resolution of the field measuring campaigns (10 m). The terrain attributes were filtered out from the rasters for the 78 measuring positions of the sampling grid.
On the basis of the correlation analysis we found an important difference in terrain attribute features between DEM 5 and 6, especially in SD, Sl, North and East. All subsequent results are then based on DEMs 1–5, which were found to be more similar to each other and to the original DEM1. The maps of terrain attributes with the box blur kernel from DEM1-5 can be found in the Supplementary Information (SI) together with the descriptions and calculations. As we couldn’t find any of the blur kernels superior to the other when considering correlations, the results hereafter are only presented for the box blur kernel calculations for simplicity.
When we considered the entire dataset (named hereafter: “A” dataset), we could only find significant correlation between rangeRs and TPI at less smoothed DEMs but the correlation was very weak (black symbols and line in Fig. 3).
Figure 3

Direct correlation between TPI and stability proxy, rangeRs at less smoothed DEMs, DEM1-2 for datasets A (black symbols and line) and S (blue symbols and line, see the information later on). The correlations were significant at p = 0.0076 and p  = 0.0172 levels, although they were weak, r2 = 0.09, r2 = 0.42 for A and S (see the information later on), respectively.

Full size image

Any other correlation between the proxies and the terrain attributes could only be deduced indirectly from the positive correlations between rankRs and meanSOC, meanSWC (cf. Table 1b). These correlations were scale-independent, i.e., we detected them at every DEMs. In general, the larger the soil carbon content and soil moisture at a position (cf. Figure 2c,d, showing quite similar patterns to the proxy patterns in the figure upper row), the larger the Rs activity detected and the opposite was true for lower carbon content and soil moisture positions.
Table 1 (a) Statistically significant (p  More

1.
Duarte, C. M. et al. Front. Ecol. Environ. 11, 91–97 (2012).
Article  Google Scholar 
2.
Firth, L. B. et al. in Oceanography and Marine Biology: An Annual Review Vol. 54 (eds Hughes, R. N. et al.) 193–269 (Taylor & Francis, 2016).

3.
Bugnot, A. B. et al. Nat. Sustain. https://doi.org/10.1038/s41893-020-00595-1 (2020).

4.
Bishop, M. J. et al. J. Exp. Mar. Biol. Ecol. 492, 7–30 (2017).
Article  Google Scholar 

5.
Dong, Y., Huang, X., Wang, W., Li, Y. & Wang, J. et al. Divers. Distrib. 22, 731–744 (2016).
Article  Google Scholar 

6.
Nagelkerken, I., Doney, S. C. & Munday, P. L. Oceanography and Marine Biology: An Annual Review Vol. 57 (eds Hawkins, S. J. et al.) 229–264 (Taylor & Francis, 2019).

7.
Hawkins, S. J. et al. Mar. Pollut. Bull. 156, 111150 (2020).
CAS  Article  Google Scholar 

8.
Bayraktarov, E. et al. Ecol. Appl. 26, 1055–1074 (2016).
Article  Google Scholar 

9.
Jones, P. J. S., Lieberknecht, L. M. & Qiu, W. Mar. Policy 71, 256–264 (2016).
Article  Google Scholar 

10.
Bracewell, S. A., Spencer, M., Marrs, R. H., Iles, M. & Robinson, L. A. PLoS ONE 7, e48863 (2012).
CAS  Article  Google Scholar 

11.
Evans, A. J. et al. Environ. Sci. Policy 91, 60–69 (2019).
Article  Google Scholar 

12.
Firth, L. B. et al. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13683 (2020). More