Ecology

1.
Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & De Meester, L. Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999. https://doi.org/10.1111/mec.12561 (2013).
Article  PubMed  Google Scholar 
2.
Legrand, D. et al. Eco-evolutionary dynamics in fragmented landscapes. Ecography 40, 9–25. https://doi.org/10.1111/ecog.02537 (2017).
Article  Google Scholar 

3.
Slatkin, M. Gene flow and the geographic structure of natural populations. Science 236, 787–792. https://doi.org/10.1126/science.3576198 (1987).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

4.
Dolby, G. A., Dorsey, R. J. & Graham, M. R. A legacy of geo-climatic complexity and genetic divergence along the lower Colorado River: Insights from the geological record and 33 desert-adapted animals. J. Biogeogr. 46, 2479–2505. https://doi.org/10.1111/jbi.13685 (2019).
Article  Google Scholar 

5.
Stevens, V. M. et al. A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals. Ecol. Lett. 17, 1039–1052. https://doi.org/10.1111/ele.12303 (2014).
Article  PubMed  Google Scholar 

6.
Ross, K. G. Molecular ecology of social behaviour: analyses of breeding systems and genetic structure. Mol. Ecol. 10, 265–284. https://doi.org/10.1046/j.1365-294X.2001.01191.x (2001).
CAS  Article  PubMed  Google Scholar 

7.
Beck, N. R., Peakall, R. & Heinsohn, R. Social constraint and an absence of sex-biased dispersal drive fine-scale genetic structure in white-winged choughs. Mol. Ecol. 17, 4346–4358. https://doi.org/10.1111/j.1365-294X.2008.03906.x (2008).
CAS  Article  PubMed  Google Scholar 

8.
Morinha, F. et al. Extreme genetic structure in a social bird species despite high dispersal capacity. Mol. Ecol. 26, 2812–2825. https://doi.org/10.1111/mec.14069 (2017).
Article  PubMed  Google Scholar 

9.
Marzluff, J. M. & Angell, T. Cultural coevolution: how the human bond with crows and ravens extends theory and raises new questions. J. Ecol. Anthropol. 9, 69–75 (2005).
Google Scholar 

10.
Toft, C. A. & Wright, T. F. Parrots of the wild: A natural history of the world’s most captivating birds (Univ. California Press, Oakland, California, USA, 2015).
Google Scholar 

11.
Armansin, N. C. et al. Social barriers in ecological landscapes: The social resistance hypothesis. Trends Ecol. Evol. 35, 137–148. https://doi.org/10.1016/j.tree.2019.10.001 (2020).
Article  PubMed  Google Scholar 

12.
Abdelkrim, J., Hunt, G. R., Gray, R. D. & Gemmell, N. J. Population genetic structure and colonisation history of the tool-using New Caledonian Crow. PLoS ONE 7, e36608. https://doi.org/10.1371/journal.pone.0036608 (2012).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

13.
Rutz, C., Ryder, T. B. & Fleischer, R. C. Restricted gene flow and fine-scale population structuring in tool using New Caledonian crows. Naturwissenschaften 99, 313–320. https://doi.org/10.1007/s00114-012-0904-6 (2012).
CAS  Article  PubMed  ADS  Google Scholar 

14.
Wright, T. F., Rodriguez, A. M. & Fleischer, R. C. Vocal dialects, sex-biased dispersal, and microsatellite population structure in the parrot Amazona auropalliata. Mol. Ecol. 14, 1197–1205. https://doi.org/10.1111/j.1365-294X.2005.02466.x (2005).
CAS  Article  PubMed  Google Scholar 

15.
Hobson, E. A., Avery, M. L. & Wright, T. F. The socioecology of Monk Parakeets: Insights into parrot social complexity. Auk 131, 756–775. https://doi.org/10.1642/AUK-14-14.1 (2014).
Article  Google Scholar 

16.
Wright, T. F. & Dahlin, C. R. Vocal dialects in parrots: patterns and processes of cultural evolution. Emu 118, 50–66. https://doi.org/10.1080/01584197.2017.1379356 (2018).
Article  PubMed  Google Scholar 

17.
Smith-Vidaurre, G., Araya-Salas, M. & Wright, T. F. Individual signatures outweigh social group identity in contact calls of a communally nesting parrot. Behav. Ecol. 31, 448–458. https://doi.org/10.1093/beheco/arz202 (2020).
Article  Google Scholar 

18.
Lowe, W. H., Kovach, R. P. & Allendorf, F. W. Population genetics and demography unite ecology and evolution. Trends Ecol. Evol. 32, 141–152. https://doi.org/10.1016/j.tree.2016.12.002 (2017).
Article  PubMed  Google Scholar 

19.
Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569. https://doi.org/10.1016/j.tree.2011.07.009 (2011).
Article  PubMed  Google Scholar 

20.
Méndez, M., Vögeli, M., Tella, J. L. & Godoy, J. A. Joint effects of population size and isolation on genetic erosion in fragmented populations: finding fragmentation thresholds for management. Evol. Appl. 7, 506–518. https://doi.org/10.1111/eva.12154 (2014).
Article  PubMed  PubMed Central  Google Scholar 

21.
Klauke, N., Schaefer, H. M., Bauer, M. & Segelbacher, G. Limited dispersal and significant fine-scale genetic structure in a tropical montane parrot species. PLoS ONE 11, e0169165. https://doi.org/10.1371/journal.pone.0169165 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Monge, O., Schmidt, K., Vaughan, C. & Gutiérrez-Espeleta, G. Genetic patterns and conservation of the Scarlet Macaw (Ara macao) in Costa Rica. Conserv. Genet. 17, 745–750. https://doi.org/10.1007/s10592-015-0804-3 (2016).
Article  Google Scholar 

23.
Kopps, A. M. et al. Cultural transmission of tool use combined with habitat specializations leads to fine-scale genetic structure in bottlenose dolphins. Proc. R. Soc. Lond., B, Biol. Sci. 281, 20133245. https://doi.org/10.1098/rspb.2013.3245 (2014).

24.
Foote, A. D. et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat. Commun. 7, 1–12. https://doi.org/10.1038/ncomms11693 (2016).
CAS  Article  Google Scholar 

25.
Pilot, M., Dahlheim, M. E. & Hoelzel, A. R. Social cohesion among kin, gene flow without dispersal and the evolution of population genetic structure in the killer whale (Orcinus orca). J. Evol. Biol. 23, 20–31. https://doi.org/10.1111/j.1420-9101.2009.01887.x (2010).
CAS  Article  PubMed  Google Scholar 

26.
Estrada, A. Reintroduction of the scarlet macaw (Ara macao cyanoptera) in the tropical rainforests of Palenque, Mexico: Project design and first year progress. Trop. Conserv. Sci. 7, 342–364. https://doi.org/10.1177/194008291400700301 (2014).
Article  Google Scholar 

27.
Lopes, A. R. et al. The influence of anti-predator training, personality and sex in the behavior, dispersion and survival rates of translocated captive-raised parrots. Glob Ecol. Conserv. 11, 146–157. https://doi.org/10.1016/j.gecco.2017.05.001 (2017).
Article  Google Scholar 

28.
Pitter, E. & Christiansen, M. B. Ecology, status and conservation of the Red-fronted Macaw Ara rubrogenys. Bird Conserv. Int. 5, 61–78. https://doi.org/10.1017/S0959270900002951 (1995).
Article  Google Scholar 

29.
Meyer, C. Spatial ecology and conservation of the endemic and endangered Red-fronted Macaw (Ara rubrogenys) in the Bolivian Andes. Diploma Thesis. Centre for Nature Conservation, Faculty of Biology, Georg-August University Göttingen (2010).

30.
Tella, J. L., Rojas, A., Carrete, M. & Hiraldo, F. Simple assessments of age and spatial population structure can aid conservation of poorly known species. Biol. Conserv. 167, 425–434. https://doi.org/10.1016/j.biocon.2013.08.035 (2013).
Article  Google Scholar 

31.
Leite, K. C. E., Seixas, G. H. F., Berkunsky, I., Collevatti, R. G. & Caparroz, R. Population genetic structure of the blue-fronted Amazon (Amazona aestiva, Psittacidae: Aves) based on nuclear microsatellite loci: Implications for conservation. Genet. Mol. Res. 7, 819–829. https://doi.org/10.4238/vol7-3gmr474 (2008).
CAS  Article  PubMed  Google Scholar 

32.
Masello, J. F. et al. The high Andes, gene flow and a stable hybrid zone shape the genetic structure of a wide-ranging South American parrot. Front. Zool. 8, 16. https://doi.org/10.1186/1742-9994-8-16 (2011).
Article  PubMed  PubMed Central  Google Scholar 

33.
Olah, G., Heinsohn, R. G., Brightsmith, D. J. & Peakall, R. The application of non-invasive genetic tagging reveals new insights into the clay lick use by macaws in the Peruvian Amazon. Conserv. Genet. 18, 1037–1046. https://doi.org/10.1007/s10592-017-0954-6 (2017).
Article  Google Scholar 

34.
Ellegren, H. et al. Microsatellite evolution: A reciprocal study of repeat lengths at homologous loci in cattle and sheep. Mol. Biol. Evol. 14, 854–860. https://doi.org/10.1093/oxfordjournals.molbev.a025826 (1997).
CAS  Article  PubMed  Google Scholar 

35.
Mills, L. S., Citta, J. J., Lair, K. P., Schwartz, M. K. & Tallmon, D. A. Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls. Ecol. Appl. 10, 283–294. https://doi.org/10.1890/1051-0761(2000)010[0283:EAAUND]2.0.CO;2 (2000).
Article  Google Scholar 

36.
Alcaide, M., Serrano, D., Tella, J. L. & Negro, J. J. Strong philopatry derived from capture-recapture methods does not lead to fine-scale genetic differentiation in lesser kestrels. J. Anim. Ecol. 78, 468–475. https://doi.org/10.1111/j.1365-2656.2008.01493.x (2009).
Article  PubMed  Google Scholar 

37.
Barrowclough, G. F. Gene flow, effective population sizes, and genetic variance components in birds. Evolution 34, 789–798. https://doi.org/10.2307/2408033 (1980).
Article  PubMed  Google Scholar 

38.
Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to conservation genetics (Cambridge University Press, Cambridge, 2010).
Google Scholar 

39.
Jones, O. R. & Wang, J. A comparison of four methods for detecting weak genetic structure from marker data. Ecol. Evol. 2, 1048–1055. https://doi.org/10.1002/ece3.237 (2012).
Article  PubMed  PubMed Central  Google Scholar 

40.
van Rees, C. B., Reed, J. M., Wilson, R. E., Underwood, J. G. & Sonsthagen, S. A. Small-scale genetic structure in an endangered wetland specialist: possible effects of landscape change and population recovery. Conserv. Genet. 19, 129–142. https://doi.org/10.1007/s10592-017-1020-0 (2018).
Article  Google Scholar 

41.
Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332. https://doi.org/10.1111/j.1755-0998.2009.02591.x (2009).
Article  PubMed  PubMed Central  Google Scholar 

42.
Graciá, E. et al. Genetic signatures of demographic changes in an avian top predator during the last century: Bottlenecks and expansions of the Eurasian Eagle Owl in the Iberian Peninsula. PLoS ONE 10, e0133954. https://doi.org/10.1371/journal.pone.0133954 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Williamson-Natesan, E. G. Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv. Genet. 6, 551–562. https://doi.org/10.1007/s10592-005-9009-5 (2005).
Article  Google Scholar 

44.
Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418. https://doi.org/10.1111/j.1365-294X.2012.05635.x (2012).
Article  PubMed  Google Scholar 

45.
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318. https://doi.org/10.1046/j.1365-294X.2001.01190.x (2001).
CAS  Article  PubMed  Google Scholar 

46.
BirdLife International. Ara rubrogenys. The IUCN Red List of Threatened Species 2018: e.T22685572A131382876. Downloaded on 30 May 2020 (2018). https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22685572A131382876.en (2018).

47.
El, D. O. reto del espacio andino (Instituto de Estudios Peruanos, Lima, Perú, 1981).
Google Scholar 

48.
Williams, J. J., Gosling, W. D., Coe, A. L., Brooks, S. J. & Gulliver, P. Four thousand years of environmental change and human activity in the Cochabamba Basin Bolivia. Quat. Res. 76, 58–68. https://doi.org/10.1016/j.yqres.2011.03.004 (2011).
Article  Google Scholar 

49.
Flantua, S. G. et al. Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records. Clim. Past 12, 483–523. https://doi.org/10.5194/cp-12-483-2016 (2016).
Article  Google Scholar 

50.
Schlaifer, M., Las especies nativas y la deforestación en los Andes. Una visión histórica, social y cultural en Cochabamba, Bolivia. Bulletin de l’Institut français d’études andines 22, 585–610 (1993).

51.
Sánchez Canedo, W. Inkas,“flecheros” y mitmaqkuna: Cambio social y paisajes culturales en los Valles y en los Yungas de Inkachaca/Paracti y Tablas Monte (Cochabamba-Bolivia, siglos XV-XVI) (Doctoral dissertation, Institutionen för arkeologi och antik historia) Universitetstryckeriet, Uppsala, Sweden (2008).

52.
Cobo, B. Historia del Nuevo Mundo (Obras del P. Bernabé Cobo) II Tomos. Estudio preliminar y edición del P. Francisco Mateos. Biblioteca de Autores Españoles, Madrid. Disponible en: http://www.bibliotecavirtualdeandalucia.es/catalogo/consulta/registro.cmd?id=1014725 (1964) [1652].

53.
Guaman Poma de Ayala, F. El primer Nueva corónica y buen gobierno [1615] (eds J. V. Murra and R. Adorno, Quechua trans. J. L. Urioste), 3 vols. Mexico City: Siglo Veintiuno 1980 [1615].

54.
Tella, J. L. The unknown extent of ancient bird introductions. Ardeola 58, 399–404. https://doi.org/10.13157/arla.58.2.2011.399 (2011).

55.
Wilkinson, D., The influence of Amazonia on state formation in the ancient Andes. Antiquity 92, 1362–1376. https://doi.org/10.15184/aqy.2018.194 (2018).

56.
Gomez Casaverde, Y. Textiles Chimú con aplicaciones de plumas del Sitio Huaca de la Luna (Circa 800 dc-1470 dc): caracterización tecnológica y aproximación a las rutas de intercambio amazónico-andinas (Modelización y Técnicas Analíticas. Universidad Nacional de Trujillo. Trujillo, Perú, Maestría en Arqueología Sudamericana mención Arqueometría, 2020).
Google Scholar 

57.
Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity 98, 172–182. https://doi.org/10.1038/sj.hdy.6800923 (2007).
CAS  Article  PubMed  Google Scholar 

58.
Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: a review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20, 1843–1861. https://doi.org/10.1007/s10531-011-0074-4 (2011).
Article  Google Scholar 

59.
Thévenon, S., Bonnet, A., Claro, F. & Maillard, J. C. Genetic diversity analysis of captive populations: The Vietnamese sika deer (Cervus nippon pseudaxis) in zoological parks. Zool. Biol. 22, 465–475. https://doi.org/10.1002/zoo.10091 (2003).
CAS  Article  Google Scholar 

60.
Kekkonen, J., Wikström, M. & Brommer, J. E. Heterozygosity in an isolated population of a large mammal founded by four individuals is predicted by an individual-based genetic model. PLoS ONE 7, e43482. https://doi.org/10.1371/journal.pone.0043482 (2012).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

61.
Jackson, N. D. & Fahrig, L. Habitat amount, not habitat configuration, best predicts population genetic structure in fragmented landscapes. Landsc. Ecol. 31, 951–968. https://doi.org/10.1007/s10980-015-0313-2 (2016).
Article  Google Scholar 

62.
Gibbs, J. P. Demography versus habitat fragmentation as determinants of genetic variation in wild populations. Biol. Conserv. 100, 15–20. https://doi.org/10.1016/S0006-3207(00)00203-2 (2001).
Article  Google Scholar 

63.
Blanco, G., Hiraldo, F. & Tella, J. L. Ecological functions of parrots: an integrative perspective from plant life cycle to ecosystem functioning. Emu 118, 36–49. https://doi.org/10.1080/01584197.2017.1387031 (2018).
Article  Google Scholar 

64.
Storfer, A. et al. Putting the “landscape” in landscape genetics. Heredity 98, 128–142. https://doi.org/10.1038/sj.hdy.6800917 (2007).
CAS  Article  PubMed  Google Scholar 

65.
Sexton, J. P., Hangartner, S. B. & Hoffmann, A. A. Genetic isolation by environment or distance: which pattern of gene flow is most common?. Evolution 68, 1–15. https://doi.org/10.1111/evo.12258 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

66.
Rojas, A., Yucra, E., Vera, I., Requejo, A. & Tella, J. A new population of the globally endangered red-fronted Macaw Ara rubrogenys unusually breeding in palms. Bird Conserv. Int. 24, 389–392. https://doi.org/10.1017/S095927091200038X (2014).
Article  Google Scholar 

67.
Blanco, G., Hiraldo, F., Rojas, A., Dénes, F. V. & Tella, J. L. Parrots as key multilinkers in ecosystem structure and functioning. Ecol. Evol. 5, 4141–4160. https://doi.org/10.1002/ece3.1663 (2015).
Article  PubMed  PubMed Central  Google Scholar 

68.
Andrews, K. Population genetics in the conservation of cetaceans and primates in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 289–30 (Springer, Japan, 2014).

69.
Manel, S. & Holderegger, R. T. years of landscape genetics. Trends Ecol. Evol. 28, 614–621. https://doi.org/10.1016/j.tree.2013.05.012 (2013).
Article  PubMed  Google Scholar 

70.
Lowe, W. H. & Allendorf, F. W. What can genetics tell us about population connectivity?. Mol. Ecol. 19, 3038–3051. https://doi.org/10.1111/j.1365-294X.2010.04688.x (2010).
Article  PubMed  Google Scholar 

71.
Hatchwell, B. J. Cryptic kin selection: kin structure in vertebrate populations and opportunities for kin-directed cooperation. Ethology 116, 203–216. https://doi.org/10.1111/j.1439-0310.2009.01732.x (2010).
Article  Google Scholar 

72.
Bicknell, A. W. J. et al. Population genetic structure and long-distance dispersal among seabird populations: Implications for colony persistence. Mol. Ecol. 21, 2863–2876. https://doi.org/10.1111/j.1365-294X.2012.05558.x (2012).
CAS  Article  PubMed  Google Scholar 

73.
Welch, A. J. et al. Population divergence and gene flow in an endangered and highly mobile seabird. Heredity 109, 19–28. https://doi.org/10.1038/hdy.2012.7 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

74.
Bonilla, L. M. Monitoreo de la nidificación de la Paraba Frente Roja (Ara rubrogenys) en dos sitios de reproducción en los valles de los Departamentos de Santa Cruz y Cochabamba) en dos sitios de reproducción en los valles de los Departamentos de Santa Cruz y Cochabamba (Universidad Autónoma Gabriel René Moreno, Santa Cruz de La Sierra, Bolivia, 2007).
Google Scholar 

75.
Caparroz, R., Miyaki, C. Y. & Baker, A. J. Contrasting phylogeographic patterns in mitochondrial DNA and microsatellites: evidence of female philopatry and male-biased gene flow among regional populations of the blue-and-yellow macaw (Psittaciformes: Ara ararauna) in Brazil. Auk 126, 359–370. https://doi.org/10.1525/auk.2009.07183 (2009).
Article  Google Scholar 

76.
Alcaide, M. et al. Population fragmentation leads to isolation by distance but not genetic impoverishment in the philopatric Lesser Kestrel: a comparison with the widespread and sympatric Eurasian Kestrel. Heredity 102, 190–198. https://doi.org/10.1038/hdy.2008.107 (2009).
CAS  Article  PubMed  Google Scholar 

77.
Olah, G. et al. Exploring dispersal barriers using landscape genetic resistance modelling in scarlet macaws of the Peruvian Amazon. Landsc. Ecol. 32, 445–456. https://doi.org/10.1007/s10980-016-0457-8 (2017).
Article  Google Scholar 

78.
Pitter, E. & Christiansen, M. B. Behavior of individuals and social interactions of the Red-fronted Macaw Ara rubrogenys in the wild during the mid-day rest. Ornitol. Neotrop. 8, 133–143 (1997).
Google Scholar 

79.
Keighley, M. V., Heinsohn, R., Langmore, N. E., Murphy, S. A. & Peñalba, J. V. Genomic population structure aligns with vocal dialects in Palm Cockatoos (Probosciger aterrimus); evidence for refugial late-Quaternary distribution?. EMU 119, 24–37. https://doi.org/10.1080/01584197.2018.1483731 (2019).
Article  Google Scholar 

80.
Pacífico, E. C. et al. Breeding to non-breeding population ratio and breeding performance of the globally endangered Lear’s Macaw (Anodorhynchus leari): conservation and monitoring implications. Bird Conserv. Int. 24, 466–476. https://doi.org/10.1017/S095927091300049X (2014).
Article  Google Scholar 

81.
Stutchbury, B. J. & Zack, S. Delayed breeding in avian social systems: the role of territory quality and” floater” tactics. Behaviour 123, 194–219. https://doi.org/10.1163/156853992X00020 (1992).
Article  Google Scholar 

82.
Kokko, H. & Sutherland, W. J. Optimal floating and queuing strategies: consequences for density dependence and habitat loss. Am. Nat. 152, 354–366. https://doi.org/10.1086/286174 (1998).
CAS  Article  PubMed  Google Scholar 

83.
Blanco, G., Laiolo, P. & Fargallo, J. A. Linking environmental stress, feeding-shifts and the ‘island syndrome’: a nutritional challenge hypothesis. Popul. Ecol. 56, 203–216. https://doi.org/10.1007/s10144-013-0404-3 (2014).
Article  Google Scholar 

84.
Koenig, W. D. & Dickinson, J. L. Cooperative breeding in vertebrates: studies of ecology, evolution, and behavior. Cambridge University Press (2016).

85.
Gao, H., Bryc, K. & Bustamante, C. D. On identifying the optimal number of population clusters via the deviance information criterion. PLoS ONE 6, e21014. https://doi.org/10.1371/journal.pone.0021014 (2011).
CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

86.
Rodríguez-Ramilo, S. T. & Wang, J. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12, 873–884. https://doi.org/10.1111/j.1755-0998.2012.03156.x (2012).
Article  PubMed  Google Scholar 

87.
Harrisson, K. A. et al. Fine-scale effects of habitat loss and fragmentation despite large-scale gene flow for some regionally declining woodland bird species. Landsc. Ecol. 27, 813–827. https://doi.org/10.1007/s10980-012-9743-2 (2012).
Article  Google Scholar 

88.
Rull, V. Microrefugia. J. Biogeogr. 36, 481–484. https://doi.org/10.1111/j.1365-2699.2008.02023.x (2009).
Article  Google Scholar 

89.
Nadachowska-Brzyska, K., Li, C., Smeds, L., Zhang, G. & Ellegren, H. Temporal dynamics of avian populations during Pleistocene revealed by whole-genome sequences. Curr. Biol. 25, 1375–1380. https://doi.org/10.1016/j.cub.2015.03.047 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

90.
James, J. E., Lanfear, R. & Eyre-Walker, A. Molecular evolutionary consequences of island colonization. Genome Biol. Evol. 8, 1876–1888. https://doi.org/10.1093/gbe/evw120 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

91.
Gregory-Wodzicki, K. M. Uplift history of the central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105. https://doi.org/10.1130/0016-7606(2000)112%3c1091:UHOTCA%3e2.0.CO;2 (2000).
Article  ADS  Google Scholar 

92.
Navarro, G. & Maldonado M. Geografía ecológica de Bolivia: vegetación y ambientes acuáticos. Edit.: Centro de Ecología Simón I. Patiño-Departamento de Difusión. Cochabamba, Bolivia (2002).

93.
López, R. P. Phytogeographical relations of the Andean dry valleys of Bolivia. J. Biogeogr. 30, 1659–1668. https://doi.org/10.1046/j.1365-2699.2003.00919.x (2003).
Article  Google Scholar 

94.
Montesinos-Navarro, A., Hiraldo, F., Tella, J. L. & Blanco, G. Network structure embracing mutualism–antagonism continuums increases community robustness. Nat. Ecol. Evol. 1, 1661–1669. https://doi.org/10.1038/s41559-017-0320-6 (2017).
Article  PubMed  Google Scholar 

95.
Da Silva, A. G., Eberhard, J. R., Wright, T. F., Avery, M. L. & Russello, M. A. Genetic evidence for high propagule pressure and long-distance dispersal in monk parakeet (Myiopsitta monachus) invasive populations. Mol. Ecol. 19, 3336–3350. https://doi.org/10.1111/j.1365-294X.2010.04749.x (2010).
Article  Google Scholar 

96.
Russello, M., Calcagnotto, D., DeSalle, R. & Amato, G. Characterization of microsatellite loci in the endangered St. Vicent parrot, Amazona guildingii. Mol. Ecol. Notes 1, 13–13. https://doi.org/10.1046/j.1471-8278.2001.00061.x (2001).

97.
Bergner, L. M., Jamieson, I. G. & Robertson, B. C. Combining genetic data to identify relatedness among founders in a genetically depauperate parrot, the Kakapo (Strigops habroptilus). Conserv. Genet. 15, 1013–1020. https://doi.org/10.1007/s10592-014-0595-y (2014).
Article  Google Scholar 

98.
Stojanovic, D., Olah, G., Webb, M., Peakall, R. & Heinsohn, R. Genetic evidence confirms severe extinction risk for critically endangered swift parrots: implications for conservation management. Anim. Conserv. 21, 313–323. https://doi.org/10.1111/acv.12394 (2018).
Article  Google Scholar 

99.
Väli, Ü., Einarsson, A., Waits, L. & Ellegren, H. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations?. Mol. Ecol. 17, 3808–3817. https://doi.org/10.1111/j.1365-294X.2008.03876.x (2008).
Article  PubMed  Google Scholar 

100.
Young, A. M., Hobson, E. A., Lackey, L. B. & Wright, T. E. Survival on the ark: Life-history trends in captive parrots. Anim. Conserv. 15, 28–43. https://doi.org/10.1111/j.1469-1795.2011.00477.x (2012).
Article  PubMed  PubMed Central  Google Scholar 

101.
Fraser, D. J. & Bernatchez, L. Adaptive evolutionary conservation: Towards a unified concept for defining conservation units. Mol. Ecol. 10, 2741–2752. https://doi.org/10.1046/j.0962-1083.2001.01411.x (2001).
CAS  Article  PubMed  Google Scholar 

102.
Palsbøll, P. J., Bérubé, M. & Allendorf, F. W. Identification of management units using population genetic data. Trends Ecol. Evol. 22, 11–16. https://doi.org/10.1016/j.tree.2006.09.003 (2007).
Article  PubMed  Google Scholar 

103.
Schiegg, K. Environmental autocorrelation: curse or blessing?. Trends Ecol. Evol. 18, 212–214. https://doi.org/10.1016/S0169-5347(03)00074-0 (2004).
Article  Google Scholar 

104.
Shafer, A. B. A. et al. Genomics and the challenging translation into conservation practice. Trends Ecol. Evol. 30, 78–87. https://doi.org/10.1016/j.tree.2014.11.009 (2015).
Article  PubMed  Google Scholar 

105.
Valière, N. GIMLET: a computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379. https://doi.org/10.1046/j.1471-8286.2002.00228.x-i2 (2002).
Article  Google Scholar 

106.
Jones, O. R. & Wang, J. COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x (2010).
Article  PubMed  Google Scholar 

107.
Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241. https://doi.org/10.1016/S0169-5347(02)02489-8 (2002).
Article  Google Scholar 

108.
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).
CAS  Article  PubMed  Google Scholar 

109.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1111/j.1471-8286.2005.01155.x (2012).

110.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

111.
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

112.
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

113.
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).
Article  Google Scholar 

114.
Tishkoff, S. A., Reed, F. A., Friedlaender, F. R., Ehret, C., Ranciaro, A., Froment, et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044. https://doi.org/10.1126/science.1172257 (2009).

115.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
CAS  Article  PubMed  Google Scholar 

116.
Rousset, F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).
Article  PubMed  Google Scholar 

117.
Botstein, D., White, R. L., Skolnick, M. & Davis, R. W. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32, 314–331 (1980).
CAS  PubMed  PubMed Central  Google Scholar 

118.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106. https://doi.org/10.1111/j.1365-294X.2007.03089.x (2007).
Article  PubMed  Google Scholar 

119.
Ciofi, C., Beaumontf, M. A., Swingland, I. R. & Bruford, M. W. Genetic divergence and units for conservation in the Komodo dragon Varanus komodoensis. Proc. R. Soc. Lond. B Biol. Sci. 266, 2269–2274. https://doi.org/10.1098/rspb.1999.0918 (1999).

120.
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503. https://doi.org/10.1093/jhered/90.4.502 (1999).
Article  Google Scholar 

121.
Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275. https://doi.org/10.1111/j.1558-5646.1989.tb04226.x (1989).
Article  PubMed  Google Scholar 

122.
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).
PubMed  PubMed Central  Google Scholar 

123.
Piry, S. et al. GENECLASS2: a software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539. https://doi.org/10.1093/jhered/esh074 (2004).
CAS  Article  PubMed  Google Scholar 

124.
Waples, R. S. & Do, C. H. I. LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756. https://doi.org/10.1111/mec.12561 (2008).
Article  PubMed  Google Scholar 

125.
Waples, R. S. & Do, C. H. I. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262. https://doi.org/10.1111/j.1752-4571.2009.00104.x (2010).
Article  Google Scholar 

126.
Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

127.
Bohonak, A. J. IBD (isolation by distance): a program for analyses of isolation by distance. J. Hered. 93, 153–154. https://doi.org/10.1093/jhered/93.2.153 (2002).
CAS  Article  Google Scholar  More

1.
Garshelis, D. & Steinmetz, R. Ursus thibetanus. The IUCN Red List of Threatened Species 2016. https://dx.doi.org/Downloaded on 17 January 2020 (2016).
2.
Parter, S. H. The Book of Indian Animal (Bombay Natural History Society and Oxford University Press, India, 1980).
Google Scholar 

3.
Sathyakumar, S. & Choudhury, A. Distribution and status of asiatic black bear in India. J. Bomb. Nat. Hist. Soci. 104, 316–323 (2007).
Google Scholar 

4.
Pocock, R. I. The Fauna of British India, Including Ceylon and Burma, Mammalia Vol. 2 (Taylor and Francis, London, 1941).
Google Scholar 

5.
Sathyakumar, S., Kaul, R., Ashraf, N. V. K., Mookerjee, A. & Menon, V. National Bear Conservation and Welfare Action Plan (Ministry of Environment and Forest, Wildlife Institute of India and Wildlife Trust of India, 2012).
Google Scholar 

6.
Charoo, S.A., Sharma, L.K. & Sathyakumar, S. Asiatic Black Bear—Human Conflicts around Dachigam National Park, Kashmir. Technical Report. Wildlife Institute of India, Dehradun. 29 (2009).

7.
Hou, W. R. et al. A complete mitochondrial genome sequence of Asian black bear Sichuan subspecies (Ursus thibetanus mupinensis). Int. J. Biol. Sci. 3, 85–90 (2007).
CAS  Article  Google Scholar 

8.
Yu, L. et al. Analysis of complete mitochondrial genome sequences increases phylogenetic resolution of bears (Ursidae), a mammalian family that experienced rapid speciation. BMC Evol. Biol. 7, 198 (2007).
Article  Google Scholar 

9.
Hwang, D. S. et al. A comprehensive analysis of three Asiatic black bear mitochondrial genomes (subspecies ussuricus, formosanus and mupinensis), with emphasis on the complete mtDNA sequence of Ursus thibetanus ussuricus (Ursidae). Mitochondrial. DNA 19, 418–429 (2008).
CAS  PubMed  Google Scholar 

10.
Kadariya, R. et al. High genetic diversity and distinct ancient lineage of Asiatic black bears revealed by non-invasive surveys in the Annapurna Conservation Area Nepal. PLoS ONE 13, 0207622 (2018).
Google Scholar 

11.
Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).
CAS  Article  Google Scholar 

12.
Ekblom, R. & Galindo, J. Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity (Edinb) 107, 1–15 (2011).
CAS  Article  Google Scholar 

13.
Edwards, S. V., Shultz, A. J. & Campbell-Staton, S. C. Next-generation sequencing and the expanding domain of phylogeography. Folia Zool. 64, 187–206 (2015).
Article  Google Scholar 

14.
Natesh, M. et al. Conservation priorities for endangered Indian tigers through a genomic lens. Sci. Rep. 7, 9614 (2017).
ADS  Article  Google Scholar 

15.
Song, N., Cai, W. & Li, H. Deep-level phylogeny of Cicadomorpha inferred from mitochondrial genomes sequenced by NGS. Sci. Rep. 7, 10429 (2017).
ADS  Article  Google Scholar 

16.
Delisle, I. & Strobeck, C. Conserved primers for rapid sequencing of the complete mitochondrial genome from carnivores, applied to three species of bears. Mol. Biol. Evol. 19, 357–361 (2002).
CAS  Article  Google Scholar 

17.
Van Dijk, E., Auger, H., Jaszczyszyn, Y. & Thermes, C. T. years of next-generation sequencing technology. Trends Genet. 30(9), 418–426 (2014).
Article  Google Scholar 

18.
Heather, J. & Chain, B. The sequence of sequencers: the history of sequencing DNA. Genomics 107(1), 1–8 (2016).
CAS  Article  Google Scholar 

19.
Wang, S., Wang, B., Wang, F. & Wu, Z. Complete mitochondrial genome of Gallus domesticus (Galliformes: Phasianidae). Mitochondrial. DNA A DNA Mapp. Seq. Anal. 27(2), 978–979 (2016).
Article  Google Scholar 

20.
Zhou, M., Yu, J., Li, J., Ouyang, B. & Yang, J. The complete mitochondrial genome of Budorcas taxicolor tibetana (Artiodactyla: Bovidae) and comparison with other Caprinae species: Insight into the phylogeny of the genus Budorcas Int. J. Biol. Macromol. 121, 223–232 (2019).
CAS  Article  Google Scholar 

21.
Kamalakkannan, R. et al. The complete mitochondrial genome of Indian gaur, Bos gaurus and its phylogenetic implications. Sci. Rep. 10, 11936. https://doi.org/10.1038/s41598-020-68724-6 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads – a baiting and iterative mapping approach. Nucleic Acids Res. 41, 129 (2013).
Article  Google Scholar 

23.
Boore, J. L. Animal mitochondrial genomes. Nucleic Acids Res. 27, 1767–1780 (1999).
CAS  Article  Google Scholar 

24.
Feng, H., Feng, C., Wang, L. & Huang, Y. Complete mitochondrial genome of the golden takin (Budorcas taxicolor bedfordi). Mitochondrial. DNA 1, 186–188 (2016).
Article  Google Scholar 

25.
Kumar, A. et al. Sequencing and characterization of the complete mitochondrial genome of Mishmi takin (Budorcas taxi color taxicolor) and comparison with the other Caprinae species. Int. J. Biol. Macromol. 137, 87–94 (2019).
CAS  Article  Google Scholar 

26.
Sarvani, R. K. et al. Characterization of the complete mitogenome of Indian Mouse Deer, Moschiola indica (Artiodactyla: Tragulidae) and its evolutionary significance. Sci. Rep. 8, 2697 (2018).
ADS  Article  Google Scholar 

27.
Lan, T. et al. Evolutionary history of enigmatic bears in the Tibetan Plateau-Himalaya region and the identity of the yeti. Proc. R. Soc. B 284, 20171804 (2017).
Article  Google Scholar 

28.
Jiagi, W. et al. Phylogeographic and demographic analysis of the Asian Black Bear (Ursus thibetanus) based on Mitochondrial DNA. PLoS ONE 10, e0136398 (2015).
Article  Google Scholar 

29.
Timmermans, M. J. et al. Why barcode? High-throughput multiplex sequencing of mitochondrial genomes for molecular systematics. Nucleic Acids Res. 38, e197 (2010).
CAS  Article  Google Scholar 

30.
Cabrera-Brandt, M. A. & Gaitan-Espitia, J. D. Phylogenetic analysis of the complete mitogenome sequence of the raspberry weevil Aegorhinus superciliosus (Coleoptera: Curculionidae), supports monophyly of the tribe Aterpini. Gene 571, 205–211 (2015).
CAS  Article  Google Scholar 

31.
Grant, J. R. & Stothard, P. The CGView Server: a comparative genomics tool for circular 423 genomes. Nucleic Acids Res. 36, 181–184 (2008).
Article  Google Scholar 

32.
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).
CAS  Article  Google Scholar 

33.
Perna, N. T. & Kocher, T. D. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J. Mol. Evol. 41, 353–358 (1995).
ADS  CAS  Article  Google Scholar 

34.
Beier, S., Thiel, T., Munch, T., Scholz, U. & Mascher, M. MISA-web: a web server for microsatellite prediction. Bioinformatics 33, 2583–2585 (2017).
CAS  Article  Google Scholar 

35.
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 36, 573–580 (1999).
Article  Google Scholar 

36.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. Mega X: molecular evolutionary genetics analysis across computing platform. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  Article  Google Scholar 

37.
Mattei, E., Pietrosanto, M., Ferree, F. & Citterich, M. H. Web-Beagle: a web server for the alignment of RNA secondary structures. Nuc Acids Res. 43, 493–497 (2015).
Article  Google Scholar 

38.
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, 1–5 (2018).
Article  Google Scholar 

39.
Nylander, J.A.A. MrModelTest Version 2. Programme Distributed by the Author. Evolutionary Biology Centre, Uppsala University (2004).

40.
Wayne, R. K., Van, V. B. & O’Brien, S. J. Molecular distance and divergence time in carnivores and primates. Mol. Biol. Evol. 8, 297–319 (1991).
CAS  PubMed  Google Scholar 

41.
Rambaut, A. FigTree, Version1.4.4. Available at: https://tree.bio.ed.ac.uk/software/figtree/. (2018). More

The soil bacterial diversity
The soil bacteriome dataset in this study included 558 soil samples collected from Thailand, the Philippines, Malaysia, and Indonesia (Fig. 1).
Figure 1

The number of soil samples from the selected Southeast Asian countries which were included in this study. The number in each circle represented the number of samples from each country. The Southeast Asia map was redrawn from “Southeast Asia” map (Google Maps retrieved 7 May 2020, from https://www.google.com/maps/@8.2763609,98.123781,4z).

Full size image

Mapping to the global gridded soil information system: SoilGrids21, the soil samples of each selected country encompassed different soil classes (Supplementary Figure S2). The soil from Thailand samples were mostly Acrisols, which comprise clay-rich subsoil with low fertility and high aluminium content. The soil from the Philippines samples were mostly Gleysols, iron-rich wetland soil saturated with groundwater or underwater or in tidal areas. The soil from Malaysia samples were mostly Ferralsols. The soils from Indonesia samples were of mixed soil classes; nearly half (45%) of them belonged to Nitisols, well-drained soil with a moderate-to-high clay content and limited phosphorus availability. Ferralsols took up about 20% of the Indonesia soil samples while another 18% were Histosols (moist soils with thick organic layers). The soil pH levels were significantly different among the soil samples of 4 selected countries (ANOVA, P value  More

1.
Tharme, R. E. A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Res. Appl. 19(5–6), 397–441 (2003).
Article  Google Scholar 
2.
Finn, M. A., Boulton, A. J. & Chessman, B. C. Ecological responses to artificial drought in two Australian rivers with differing water extraction. Fund. Appl. Limnol. 175(3), 231–248 (2009).
Article  Google Scholar 

3.
Dewson, Z. S., James, A. B. & Death, R. G. A review of the consequences of decreased flow for instream habitat and macroinvertebrates. J. N. Am. Benthol. Soc. 26(3), 401–415 (2007).
Article  Google Scholar 

4.
Poff, N. L. & Zimmerman, J. K. Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biol. 55(1), 194–205 (2010).
Article  Google Scholar 

5.
Gillespie, B. R., Desmet, S., Kay, P., Tillotson, M. R. & Brown, L. E. A critical analysis of regulated river ecosystem responses to managed environmental flows from reservoirs. Freshwater Biol. 60(2), 410–425 (2015).
Article  Google Scholar 

6.
GOSL. CEB statistical digest, Ceylon electricity Board, Colombo, Sri Lanka (2012).

7.
Richter, B. D., Baumgartner, J. V., Wigington, R. & Braun, D. P. How much water does a river need?. Freshwater Biol. 37, 231–249 (1997).
Article  Google Scholar 

8.
Dudgeon, D. Effects of water transfer on aquatic insects in a stream in Hong Kong. Regul. River 7, 369–377 (1992).
Article  Google Scholar 

9.
Petts, G. E. & Bickerton, M. A. Influence of water abstraction on the macroinvertebrate community gradient within a glacial stream: La Borgne d’Arolla, Valais Switzerland. Freshwater Biol. 32, 375–386 (1994).
Article  Google Scholar 

10.
Rader, R. B. & Belish, T. A. Influence of mild to severe flow alterations on invertebrates in three mountain streams. Regul. River 15, 353–363 (1999).
Article  Google Scholar 

11.
Dunbar, M. J. et al. River discharge and local-scale physical habitat influence macroinvertebrate LIFE scores. Freshwater Biol. 55(1), 226–242 (2010).
Article  Google Scholar 

12.
Schneider, S. C. & Petrin, Z. Effects of flow regime on benthic algae and macroinvertebrates: a comparison between regulated and unregulated rivers. Sci. Total Environ. 579, 1059–1072 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

13.
Olden, J. D. & Naiman, R. J. Incorporating thermal regimes into environmental flows assessments: modifying dam operations to restore freshwater ecosystem integrity. Freshwater Biol. 55(1), 86–107 (2010).
Article  Google Scholar 

14.
Mueller, M., Pander, J. & Geist, J. The effects of weirs on structural stream habitat and biological communities. J. Appl. Ecol. 48(6), 1450–1461 (2011).
Article  Google Scholar 

15.
Holt, C. R., Pfitzer, D., Scalley, C., Caldwell, B. A. & Batzer, D. P. Macroinvertebrate community responses to annual flow variation from river regulation: an 11-year study. River Res. Appl. 31(7), 798–807 (2015).
Article  Google Scholar 

16.
Krajenbrink, H. J. et al. Macroinvertebrate community responses to river impoundment at multiple spatial scales. Sci. Total Environ. 650, 2648–2656 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Mbaka, J. G. & Wanjiru Mwaniki, M. A global review of the downstream effects of small impoundments on stream habitat conditions and macroinvertebrates. Environ. Rev. 23(3), 257–262 (2015).
Article  Google Scholar 

18.
Anderson, D., Moggridge, H., Warren, P. & Shucksmith, J. The impacts of ‘run-of-river’hydropower on the physical and ecological condition of rivers. Water Environ. J. 29(2), 268–276 (2015).
Article  Google Scholar 

19.
Bilotta, G. S., Burnside, N. G., Turley, M. D., Gray, J. C. & Orr, H. G. The effects of run-of-river hydroelectric power schemes on invertebrate community composition in temperate streams and rivers. Plos One 12(2), e0171634 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Gabbud, C., Robinson, C. T. & Lane, S. N. Summer is in winter: Disturbance-driven shifts in macroinvertebrate communities following hydroelectric power exploitation. Sci. Total Environ. 650, 2164–2180 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Quadroni, S., Crosa, G., Gentili, G. & Espa, P. Response of stream benthic macroinvertebrates to current water management in Alpine catchments massively developed for hydropower. Sci. Total Environ. 609, 484–496 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

22.
Rosero-López, D., Knighton, J., Lloret, P. & Encalada, A. C. Invertebrate response to impacts of water diversion and flow regulation in high-altitude tropical streams. River Res. Appl. 36(2), 223–233 (2019).
Article  Google Scholar 

23.
Ogbeibu, A. E. & Oribhabor, B. J. Ecological impact of river impoundment using benthic macro-invertebrates as indicators. Water Res. 36(10), 2427–2436 (2002).
CAS  PubMed  Article  Google Scholar 

24.
Álvarez-Cabria, M., Barquín, J. & Juanes, J. A. Spatial and seasonal variability of macroinvertebrate metrics: Do macroinvertebrate communities track river health?. Ecol. Indic. 10(2), 370–379 (2010).
Article  CAS  Google Scholar 

25.
Hart, D. D. & Finelli, C. M. Physical-biological coupling in streams: the pervasive effects of flow on benthic organisms. Annu. Rev. Ecol. Syst. 30, 363–395 (1999).
Article  Google Scholar 

26.
Wills, T. C., Baker, E. A., Nuhfer, A. J. & Zorn, T. G. Response of the benthic macroinvertebrate community in a northern Michigan stream to reduced summer stream flows. River Res. Appl. 22(7), 819–836 (2006).
Article  Google Scholar 

27.
James, A. B. W., Dewson, Z. S. & Death, R. G. The influence of flow reduction on macroinvertebrate drift density and distance in three New Zealand streams. J. N. Am. Benthol. Soc. 28, 220–232 (2009).
Article  Google Scholar 

28.
Richter, B. D., Baumgartner, J. V., Braun, D. P. & Powell, J. A spatial assessment of hydrologic alteration within a river network. Regul. River 14(4), 329–340 (1998).
Article  Google Scholar 

29.
Shieh, C. L., Guh, Y. R. & Wang, S. Q. The application of range of variability approach to the assessment of a check dam on riverine habitat alteration. Environ. Geol. 52, 427–435 (2007).
Article  Google Scholar 

30.
Yang, P., Yin, X.-A., Yang, Z.-F. & Tang, J. A revised range of variability approach considering the periodicity of hydrological indicators. Hydrol. Process. 28, 6222–6235 (2014).
ADS  Article  Google Scholar 

31.
Yu, C., Yin, X. & Yang, Z. A revised range of variability approach for the comprehensive assessment of the alteration of flow regime. Ecol. Eng. 96, 200–207 (2016).
Article  Google Scholar 

32.
Ge, J., Peng, W., Huang, W., Qu, X. & Singh, S. K. Quantitative assessment of flow regime alteration using a revised range of variability methods. Water 10, 597 (2018).
Article  Google Scholar 

33.
Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Science Advances 3(11), e1700611 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

34.
Smakhtin, V. U. & Weragala, N. An assessment of hydrology and environmental flows in the Walawe river basin, Sri Lanka. Working Paper 103. International Water Management Institute (IWMI), Colombo, Sri Lanka (2005).

35.
Mood, A. M., Graybill, F. A. & Boes, P. D. C. Introduction to the Theory of Statistics Vol. 3 (McGraw-Hill, New York, 2005).
Google Scholar 

36.
Zhang, Q., Xu, C. Y., Chen, Y. D. & Yang, T. Spatial assessment of hydrologic alteration across the Pearl River Delta, China, and possible underlying causes. Hydrol. Process. 23(11), 1565–1574 (2009).
ADS  Article  Google Scholar 

37.
Lee, A., Cho, S., Kang, D. K. & Kim, S. Analysis of the effect of climate change on the Nakdong river stream flow using indicators of hydrological alteration. J. Hydro Environ. Res. 8(3), 234–247 (2014).
Article  Google Scholar 

38.
Stefanidis, K., Panagopoulos, Y., Psomas, A. & Mimikou, M. Assessment of the natural flow regime in a Mediterranean river impacted from irrigated agriculture. Sci. Total Environ. 573, 1492–1502 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Assahira, C. et al. Tree mortality of a flood-adapted species in response of hydrographic changes caused by an Amazonian river dam. Forest Ecol. Manag. 396, 113–123 (2017).
Article  Google Scholar 

40.
Ali, R., Kuriqi, A., Abubaker, S. & Kisi, O. Hydrologic alteration at the upper and middle part of the yangtze river, China: towards sustainable water resource management under increasing water exploitation. Sustainability 11(19), 5176 (2019).
Article  Google Scholar 

41.
Carlisle, D. M., Falcone, J., Wolock, D. M., Meador, M. R. & Norris, R. H. Predicting the natural flow regime: models for assessing hydrological alteration in streams. River Res. Appl. 26(2), 118–136 (2010).
Google Scholar 

42.
Maynard, C. M. & Lane, S. N. Reservoir compensation releases: Impact on the macroinvertebrate community of the Derwent River, Northumberland, UK—a longitudinal study. River Res. Appl. 28(6), 692–702 (2012).
Article  Google Scholar 

43.
Salmaso, F. et al. Benthic macroinvertebrates response to water management in a lowland river: effects of hydro-power vs irrigation off-stream diversions. Environ. Monit. Assess. 190(1), 33 (2018).
Article  CAS  Google Scholar 

44.
Power, M. E., Sun, A., Parker, G., Dietrich, W. E. & Wootton, J. T. Hydraulic food-chain models. BioScience 45(3), 159–167 (1995).
Article  Google Scholar 

45.
Jayawardana, J. M. C. K., Gunawardana, W. D. T. M., Udayakumara, E. P. N. & Westbrooke, M. Land use impacts on river health of Uma Oya, Sri Lanka: implications of spatial scales. Environ. Monit. Assess. 189(4), 192 (2017).
CAS  PubMed  Article  Google Scholar 

46.
Weliange, W. S., Leichtfried, M., Amarasinghe, U. S. & Füreder, L. Longitudinal variation of benthic macroinvertebrate communities in two contrasting tropical streams in Sri Lanka. Int. Rev. Hydrobiol. 102(3–4), 70–82 (2017).
Article  Google Scholar 

47.
Benzie, J. A. The colonisation mechanisms of stream benthos in a tropical river (Menik Ganga: Sri Lanka). Hydrobiologia 111(3), 171–179 (1984).
Article  Google Scholar 

48.
Amarathunga, A. D. & Fernando, R. W. Suspended sediment concentration and its impact on aquatic invertebrates in the Gin River, Sri Lanka. Journal of Food and Agriculture 9(1–2), 24–38 (2016).
Article  Google Scholar 

49.
Lancaster, J. & Downes, B. J. Aquatic entomology (OUP, Oxford, 2013).
Google Scholar 

50.
Ramos, V., Formigo, N. & Maia, R. Environmental flows under the WFD implementation. Water Resour. Manag. 32(15), 5115–5149 (2018).
Article  Google Scholar 

51.
Rosero-López, D. et al. Streamlined eco-engineering approach helps define environmental flows for tropical Andean headwaters. Freshwater Biol. 64(7), 1315–1325 (2019).
Article  Google Scholar 

52.
Warfe, D. M., Hardie, S. A., Uytendaal, A. R., Bobbi, C. J. & Barmuta, L. A. The ecology of rivers with contrasting flow regimes: identifying indicators for setting environmental flows. Freshwater Biol. 59(10), 2064–2080 (2014).
Article  Google Scholar 

53.
Wu, M., Chen, A., Zhang, X. & McClain, M. E. A comment on Chinese policies to avoid negative impacts on river ecosystems by hydropower projects. Water 12(3), 869 (2020).
Article  Google Scholar 

54.
Chandrapala, L. Long term trends of rainfall and temperature in Sri Lanka. In Climate Variability and Agriculture (eds Abrol, Y. P. et al.) (Narosa Publishing House, New Delhi, 1996).
Google Scholar 

55.
Halwatura, D. & Najim, M. M. M. Application of the HEC-HMS model for runoff simulation in a tropical catchment. Environ. Modell. Softw. 46, 155–162 (2013).
Article  Google Scholar 

56.
USEPA (US ENVIRONMENTAL PROTECTION AGENCY). Field and laboratory methods for macroinvertebrate and habitat assessment of low gradient, non-tidal streams. Mid-Atlantic Coastal Streams (MACS) Workgroup, Environmental Services Division, Region 3, USEPA, Wheeling, West Virginia, USA (1997).

57.
Turner, A. M. & Trexler, J. C. Sampling aquatic invertebrates from marshes: evaluating the options. J. N. Am. Benthol. Soc. 16(3), 694–709 (1997).
Article  Google Scholar 

58.
Mendis, A. S. & Fernando, C. H. A guide to the fresh water fauna of Ceylon (Sri Lanka) (Weerawardhena S. R. and Fernando C. H., eds), Gestetner, Sri Lanka, 42-126 pp. (1962).

59.
Starmühlner, F. Result of the Australian: ceylonese hydrological mission, Part xvii: The freshwater Gastropods of Ceylon. Bull. Fish. Res. St. Sri Lanka (Ceylon) 25(1), 97–181 (1974).
Google Scholar 

60.
APHA. Standard Methods for Examinations of Water and Wastewater, 21st ed. APHA, AWWA and WEF DC, Washington (2005).

61.
Clarke, K. R. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation Vol. 2 (PRIMER-E Ltd, Plymouth, 2001).
Google Scholar 

62.
Clarke, K. R. Non-parametric multivariate analysis of changes in community structure. Australian Journal of Ecology 18, 117–143 (1993).
Article  Google Scholar 

63.
Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods PRIMER-E (Plymouth, UK, 2008).
Google Scholar  More

1.
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Fisher, M. C., Gow, N. A. R. & Gurr, S. J. Tackling emerging fungal threats to animal health, food security and ecosystem resilience. Philos. Trans. R. Soc. B Biol. Sci. 371, 20160332 (2016).
Article  Google Scholar 

3.
Ghosh, P. N., Fisher, M. C. & Bates, K. A. Diagnosing emerging fungal threats: A one health perspective. Front. Genet. 9, 376 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Seyedmousavi, S. et al. Aspergillus and aspergilloses in wild and domestic animals: A global health concern with parallels to human disease. Med. Mycol. 53, 765–797 (2015).
PubMed  Article  Google Scholar 

5.
Stephen, C., Lester, S., Black, W., Fyfe, M. & Raverty, S. Multispecies outbreak of cryptococcosis on southern Vancouver Island, British Columbia. Can. Vet. J. 43, 792–794 (2002).
PubMed  PubMed Central  Google Scholar 

6.
Speare, R., Thomas, A. D., O’Shea, P. & Shipton, W. A. Mucor amphibiorum in the toad, Bufo marinus Australia. J. Wildl. Dis. 30, 399–407 (1994).
CAS  PubMed  Article  Google Scholar 

7.
Connolly, J. H. A review of mucormycosis in the platypus (Ornithorhynchus anatinus). Aust. J. Zool. 57, 235–244 (2009).
Article  Google Scholar 

8.
Gust, N. & Griffiths, J. Platypus mucormycosis and its conservation implications. Austral. Mycol. 28, 1–8 (2009).
Google Scholar 

9.
Thiel, R. P., Mech, L. D., Ruth, G. R., Archer, J. R. & Kaufman, L. Blastomycosis in wild wolves. J. Wildl. Dis. 23, 321–323 (1987).
CAS  PubMed  Article  Google Scholar 

10.
Storms, T. N., Victoria L. Clyde, Linda Munson & Edward C. Ramsay. Blastomycosis in nondomestic felids. J. Zool. Wildl. Med. 34, 231–238 (2003).

11.
Guillot, J., Guérin, C. & Chermette, R. Histoplasmosis in Animals. in Emerging and Epizootic Fungal Infections in Animals (eds. Seyedmousavi, S., de Hoog, G. S., Guillot, J. & Verweij, P. E.) 115–128 (Springer International Publishing, 2018). doi:https://doi.org/10.1007/978-3-319-72093-7_5.

12.
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

13.
Martel, A. et al. Batrachochytrium salamandrivorans sp. nov. causes lethal chytridiomycosis in amphibians. Proc. Natl. Acad. Sci. USA 110, 15325 (2013).

14.
Riley, S. Large-scale spatial-transmission models of infectious disease. Science 316, 1298 (2007).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Johnson, P. T. J., de Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Engering, A., Hogerwerf, L. & Slingenbergh, J. Pathogen–host–environment interplay and disease emergence. Emerg. Microbes Infect. 2, 1–7 (2013).
Article  CAS  Google Scholar 

17.
Shikano, I. & Cory, J. S. Impact of environmental variation on host performance differs with pathogen identity: Implications for host-pathogen interactions in a changing climate. Sci. Rep. 5, 15351 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Kraay, A. N. M. et al. Fomite-mediated transmission as a sufficient pathway: A comparative analysis across three viral pathogens. BMC Infect. Dis. 18, 540 (2018).
PubMed  PubMed Central  Article  Google Scholar 

19.
Stephens, B. et al. Microbial exchange via fomites and implications for human health. Curr. Pollut. Rep. 5, 198–213 (2019).
CAS  Article  Google Scholar 

20.
Langwig, K. E. et al. Host and pathogen ecology drive the seasonal dynamics of a fungal disease, white-nose syndrome. Proc. Biol. Sci. 282, (2015).

21.
Huebschman, J. J. et al. Detection of Pseudogymnoascus destructans during Summer on Wisconsin Bats. J. Wildl. Dis. https://doi.org/10.7589/2018-06-146 (2019).
Article  PubMed  Google Scholar 

22.
Hoyt, J. R. et al. Environmental reservoir dynamics predict global infection patterns and population impacts for the fungal disease white-nose syndrome. Proc. Natl. Acad. Sci. USA 117, 7255 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

23.
Foley, J., Clifford, D., Castle, K., Cryan, P. & Osfeld, R. S. Investigating and managing the rapid emergence of white nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231 (2011).
PubMed  Google Scholar 

24.
Blanco, C. M. & Garrie, J. Species specific effects of prescribed burns on bat occupancy in northwest Arkansas. For. Ecol. Manage. 460, 117890 (2020).
Article  Google Scholar 

25.
Gargas, A., Trest, M., Christensen, M., Volk, T. J. & Blehert, D. Geomyces destructans sp. nov. associated with bat white-nose syndrome. Mycotaxon 108, 147–154 (2009).

26.
Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227 (2009).
CAS  PubMed  Article  Google Scholar 

27.
Cryan, P. M. et al. Electrolyte depletion in white-nose syndrome bats. J. Wildl. Dis. 49, 398–402 (2013).
CAS  PubMed  Article  Google Scholar 

28.
Warnecke, L. et al. Pathophysiology of white-nose syndrome in bats: A mechanistic model linking wing damage to mortality. Biol. Lett. 9, 20130177 (2013).
PubMed  PubMed Central  Article  Google Scholar 

29.
Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

30.
Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329, 679 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

31.
Turner, G. G., Reeder, D. M. & Coleman, J. T. H. A Five-year assessment of mortality and geographic spread of white-nose syndrome in North American Bats, with a Look at the Future. Update of white-nose syndrome in bats. Bat Res. News 52, 13–27 (2011).

32.
Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).
PubMed  Article  Google Scholar 

33.
Langwig, K. E. et al. Invasion dynamics of white-nose syndrome fungus, midwestern United States. Emerg. Infect. Dis. 21, 1023–1026 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
USFW. U.S. Fish and Wildlife Service. 2019. White-nose syndrome: The devastating disease of hibernating bats in North America. Accessed 1 May 2020. https://www.whitenosesyndrome.org/mmedia-education/white-nose-syndrome-fact-sheet-june-2018. (2019).

35.
Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Lorch, J. M. et al. Distribution and environmental persistence of the causative agent of white-nose syndrome, geomyces destructans, in bat hibernacula of the Eastern United States. Appl. Environ. Microbiol. 79, 1293–1301 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Hoyt, J. R. et al. Long-term persistence of Pseudogymnoascus destructans, the Causative Agent of white-nose syndrome, in the absence of bats. EcoHealth 12, 330–333 (2015).
PubMed  Article  Google Scholar 

38.
Campbell, L. J., Walsh, D., Blehert, D. S. & Lorch, J. M. Long-term survival of Pseudogymnoascus destructans at elevated temperatures. J. Wildl. Dis. 56, 278–287 (2020).
PubMed  Article  Google Scholar 

39.
Urbina, J., Chestnut, T., Schwalm, D., Allen, J. & Levi, T. Experimental evaluation of genomic DNA degradation rates for the pathogen Pseudogymnoascus destructans (Pd) in bat guano. PeerJ 8, e8141 (2020).
PubMed  PubMed Central  Article  Google Scholar 

40.
Lorch, J. M. et al. A culture-based survey of fungi in soil from bat hibernacula in the eastern United States and its implications for detection of Geomyces destructans, the causal agent of bat white-nose syndrome. Mycologia 105, 237–252 (2013).
CAS  PubMed  Article  Google Scholar 

41.
Reynolds, H. T., Ingersoll, T. & Barton, H. A. Modeling the environmental growth of Pseudogymnoascus destructans and its impact on the White-nose syndrome epidemic. J. Wildl. Dis. 51, 318–331 (2015).
PubMed  Article  Google Scholar 

42.
Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. USA 109, 6999 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

43.
WNS Multiagency decontamination team. https://www.whitenosesyndrome.org/mmedia-education/united-states-national-white-nose-syndrome-decontamination-protocol-april-2016-2. (2018).

44.
Verant, M., Bohuski, E., Lorch, J. & Blehert, D. Optimized methods for total nucleic acid extraction and quantification of the bat white-nose syndrome fungus, Pseudogymnoascus destructans, from swab and environmental samples. J. VET Diagn. Invest. 28, 110–118 (2016).
CAS  PubMed  Article  Google Scholar 

45.
Rocke, T. E. et al. Virally-vectored vaccine candidates against white-nose syndrome induce anti-fungal immune response in little brown bats (Myotis lucifugus). Sci. Rep. 9, 6788 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Zhelyazkova, V. L. et al. Screening and biosecurity for white-nose Fungus Pseudogymnoascus destructans (Ascomycota: Pseudeurotiaceae) in Hawai‘i. Pac. Sci. 73, 357–365 (2019).
Article  Google Scholar 

47.
Muller, L. K. et al. Bat white-nose syndrome: A real-time TaqMan polymerase chain reaction test targeting the intergenic spacer region of Geomyces destructans. Mycologia 105, 253–259 (2013).
CAS  PubMed  Article  Google Scholar 

48.
Vanderwolf, K. J., Malloch, D. & McAlpine, D. F. Detecting viable Pseudogymnoascus destructans (Ascomycota: Pseudeurotiaceae) from walls of bat hibernacula: Effect of culture media. J. Cave Karst Stud. 78, 158 (2016).
CAS  Article  Google Scholar 

49.
Cheng, T. L. et al. Efficacy of a probiotic bacterium to treat bats affected by the disease white-nose syndrome. J. Appl. Ecol. 54, 701–708 (2017).
Article  Google Scholar 

50.
Micalizzi, E. W., Mack, J. N., White, G. P., Avis, T. J. & Smith, M. L. Microbial inhibitors of the fungus Pseudogymnoascus destructans, the causal agent of white-nose syndrome in bats. PLoS ONE 12, e0179770 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Singh, A., Lasek-Nesselquist, E., Chaturvedi, V. & Chaturvedi, S. Trichoderma polysporum selectively inhibits white-nose syndrome fungal pathogen Pseudogymnoascus destructans amidst soil microbes. Microbiome 6, 139 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
De Mandal, S., Zothansanga, Panda, A. K., Bisht, S. S. & Senthil Kumar, N. First report of bacterial community from a Bat Guano using Illumina next-generation sequencing. Genom. Data 4, 99–101. (2015).

53.
Banskar, S., Bhute, S. S., Suryavanshi, M. V., Punekar, S. & Shouche, Y. S. Microbiome analysis reveals the abundance of bacterial pathogens in Rousettus leschenaultii guano. Sci. Rep. 6, 36948 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Newman, M. M., Kloepper, L. N., Duncan, M., McInroy, J. A. & Kloepper, J. W. Variation in bat guano bacterial community composition with depth. Front. Microbiol. 9, 914 (2018).
PubMed  PubMed Central  Article  Google Scholar 

55.
Cruz, M. R., Graham, C. E., Gagliano, B. C., Lorenz, M. C. & Garsin, D. A. Enterococcus faecalis inhibits hyphal morphogenesis and virulence of Candida albicans. Infect. Immun. 81, 189 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Graham, C. E., Cruz, M. R., Garsin, D. A. & Lorenz, M. C. Enterococcus faecalis bacteriocin EntV inhibits hyphal morphogenesis, biofilm formation, and virulence of Candida albicans. Proc. Natl. Acad. Sci. USA 114, 4507 (2017).
CAS  PubMed  Article  Google Scholar 

57.
Khan, N. et al. Antifungal activity of bacillus species against fusarium and analysis of the potential mechanisms used in biocontrol. Front. Microbiol. 9, 2363 (2018).
PubMed  PubMed Central  Article  Google Scholar 

58.
Kerr, J. R. Bacterial inhibition of fungal growth and pathogenicity. Microb. Ecol. Health Dis. 11, 129–142 (1999).
Google Scholar 

59.
Wheatley, R. E. The consequences of volatile organic compound mediated bacterial and fungal interactions. Antonie Van Leeuwenhoek 81, 357–364 (2002).
CAS  PubMed  Article  Google Scholar 

60.
Cornelison, C. T., Gabriel, K. T., Barlament, C. & Crow, S. A. Inhibition of pseudogymnoascus destructans growth from conidia and mycelial extension by bacterially produced volatile organic compounds. Mycopathologia 177, 1–10 (2014).
CAS  PubMed  Article  Google Scholar 

61.
Sussman, A. & Douthit, H. Dormancy in microbial spores. Annu. Rev. Plant Physiol. 24, 311–352 (1973).
CAS  Article  Google Scholar 

62.
Feofilova, E. P., Ivashechkin, A. A., Alekhin, A. I. & Sergeeva, Ya. E. Fungal spores: Dormancy, germination, chemical composition, and role in biotechnology (review). Appl. Biochem. Microbiol. 48, 1–11 (2012).

63.
Gasch, A. P. Comparative genomics of the environmental stress response in ascomycete fungi. Yeast 24, 961–976 (2007).
CAS  PubMed  Article  Google Scholar 

64.
Marroquin, C. M., Lavine, J. O. & Windstam, S. T. Effect of humidity on development of pseudogymnoascus destructans, the causal agent of bat white-nose syndrome. Northeastern Nat. 24, 54–64 (2017).
Article  Google Scholar 

65.
Raudabaugh, D. B. & Miller, A. N. Nutritional capability of and substrate suitability for pseudogymnoascus destructans, the causal agent of bat white-nose syndrome. PLoS ONE 8, e78300 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Gabriel, K. T., Kartforosh, L., Crow, S. A. & Cornelison, C. T. Antimicrobial activity of essential oils against the fungal pathogens ascosphaera apis and pseudogymnoascus destructans. Mycopathologia 183, 921–934 (2018).
CAS  PubMed  Article  Google Scholar 

67.
Boire, N. et al. Potent inhibition of pseudogymnoascus destructans, the causative agent of white-nose syndrome in bats, by cold-pressed, terpeneless valencia orange oil. PLoS ONE 11, e0148473 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

68.
Turbill, C. & Welbergen, J. A. Anticipating white-nose syndrome in the Southern Hemisphere: Widespread conditions favourable to Pseudogymnoascus destructans pose a serious risk to Australia’s bat fauna. Austral. Ecol. 45, 89–96 (2020).
Article  Google Scholar  More

1.
Müllerová, P. Vietnamese DIASPORA in the Czech Republic. Arch. Orient. 66, 121–126 (1998).
Google Scholar 
2.
Kleisner, K., Chvátalová, V. & Flegr, J. Perceived intelligence is associated with measured intelligence in men but not women. PLoS ONE 9, e81237 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Třebický, V., Havlíček, J., Roberts, S. C., Little, A. C. & Kleisner, K. Perceived aggressiveness predicts fighting performance in mixed-martial-arts fighters. Psychol. Sci. 24, 1664–1672 (2013).
PubMed  Article  PubMed Central  Google Scholar 

4.
Linke, L., Saribay, S. A. & Kleisner, K. Perceived trustworthiness is associated with position in a corporate hierarchy. Pers. Individ. Dif. 99, 22–27 (2016).
Article  Google Scholar 

5.
Little, A. C., Třebický, V., Havlíček, J., Roberts, S. C. & Kleisner, K. Human perception of fighting ability: Facial cues predict winners and losers in mixed martial arts fights. Behav. Ecol. 1, 089 (2015).
Google Scholar 

6.
Todorov, A., Olivola, C. Y., Dotsch, R. & Mende-Siedlecki, P. Social attributions from faces: Determinants, consequences, accuracy, and functional significance. Psychology 66, 519 (2015).
Article  Google Scholar 

7.
Schmälzle, R. et al. Visual cues that predict intuitive risk perception in the case of HIV. PLoS ONE 14, e0211770 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

8.
Asch, S. E. Forming impressions of personality. J. Abnorm. Soc. Psychol. 41, 258–290 (1946).
CAS  Article  Google Scholar 

9.
Bar, M., Neta, M. & Linz, H. Very first impressions. Emotion 6, 269–278 (2006).
PubMed  Article  PubMed Central  Google Scholar 

10.
Willis, J. & Todorov, A. First impressions: Making up your mind after a 100-ms exposure to a face. Psychol. Sci. 17, 592–598 (2006).
PubMed  Article  PubMed Central  Google Scholar 

11.
Bothwell, R. K., Brigham, J. C. & Malpass, R. S. Cross-racial identification. Personal. Soc. Psychol. Bull. 15, 19–25 (1989).
Article  Google Scholar 

12.
Meissner, C. A. & Brigham, J. C. Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychol. Public Policy Law 7, 3 (2001).
Article  Google Scholar 

13.
Sporer, S. L. Recognizing faces of other ethnic groups: An integration of theories. Psychol. Public Policy Law 7, 36–97 (2001).
Article  Google Scholar 

14.
Hugenberg, K., Young, S. G., Bernstein, M. J. & Sacco, D. F. The categorization-individuation model: An integrative account of the other-race recognition deficit. Psychol. Rev. 117, 1168–1187 (2010).
PubMed  Article  PubMed Central  Google Scholar 

15.
Anzures, G. et al. Developmental origins of the other-race effect. Curr. Dir. Psychol. Sci. 22, 173–178 (2013).
PubMed  PubMed Central  Article  Google Scholar 

16.
Suhrke, J. et al. The other-race effect in 3-year-old German and Cameroonian children. Front. Psychol. 5, 198 (2014).
PubMed  PubMed Central  Article  Google Scholar 

17.
Sangrigoli, S. & de Schonen, S. Effect of visual experience on face processing: A developmental study of inversion and non-native effects. Dev. Sci. 7, 74–87 (2004).
PubMed  Article  PubMed Central  Google Scholar 

18.
Scott, L. S. & Monesson, A. The origin of biases in face perception. Psychol. Sci. 20, 676–680 (2009).
PubMed  Article  PubMed Central  Google Scholar 

19.
Ma, F., Xu, F. & Luo, X. Children’s and Adults}’ {Judgments of Facial {Trustworthiness}: The {Relationship} to Facial {Attractiveness}. Percept. Mot. Skills 121, 179–198 (2015).
PubMed  Article  PubMed Central  Google Scholar 

20.
Tanaka, J. W., Kiefer, M. & Bukach, C. M. A holistic account of the own-race effect in face recognition: Evidence from a cross-cultural study. Cognition 93, 1–9 (2004).
Article  Google Scholar 

21.
Webster Michael, A. & MacLeod Donald, I. A. Visual adaptation and face perception. Philos. Trans. R. Soc. B 366, 1702–1725 (2011).
CAS  Article  Google Scholar 

22.
Bukach, C. M., Cottle, J., Ubiwa, J. & Miller, J. Individuation experience predicts other-race effects in holistic processing for both Caucasian and Black participants. Cognition 123, 319–324 (2012).
PubMed  Article  PubMed Central  Google Scholar 

23.
Třebický, V. et al. Cross-{cultural} evidence for apparent {racial} outgroup {advantage}: Congruence between perceived {facial} aggressiveness and fighting {success}. Sci. Rep. 8, 9767 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Hebl, M. R., Williams, M. J., Sundermann, J. M., Kell, H. J. & Davies, P. G. Selectively friending: Racial stereotypicality and social rejection. J. Exp. Soc. Psychol. 48, 1329–1335 (2012).
Article  Google Scholar 

25.
Cassidy, K. D., Quinn, K. A. & Humphreys, G. W. The influence of ingroup/outgroup categorization on same- and other-race face processing: The moderating role of inter- versus intra-racial context. J. Exp. Soc. Psychol. 47, 811–817 (2011).
Article  Google Scholar 

26.
Johnson, K. J. & Fredrickson, B. L. We all look the same to Mepositive emotions eliminate the own-race bias in face recognition. Psychol. Sci. 16, 875–881 (2005).
PubMed  PubMed Central  Article  Google Scholar 

27.
Bernstein, M. J., Young, S. G. & Hugenberg, K. The cross-category effect: Mere social categorization is sufficient to elicit an own-group bias in face recognition. Psychol. Sci. 18, 706–712 (2007).
PubMed  Article  PubMed Central  Google Scholar 

28.
Hugenberg, K., Miller, J. & Claypool, H. M. Categorization and individuation in the cross-race recognition deficit: Toward a solution to an insidious problem. J. Exp. Soc. Psychol. 43, 334–340 (2007).
Article  Google Scholar 

29.
Little, A. C., Jones, B. C. & DeBruine, L. M. Facial attractiveness: Evolutionary based research. Philos. Trans. R. Soc. B. 366, 1638–1659 (2011).
Article  Google Scholar 

30.
Langlois, J. H. et al. Maxims or myths of beauty? A meta-analytic and theoretical review. Psychol. Bull. 126, 390–423 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Penton-Voak, I. S., Jacobson, A. & Trivers, R. Populational differences in attractiveness judgements of male and female faces: Comparing British and Jamaican samples. Evol. Hum. Behav. 25, 355–370 (2004).
Article  Google Scholar 

32.
Saxton, T. K., Little, A. C., DeBruine, L. M., Jones, B. C. & Roberts, S. C. Adolescents’ preferences for sexual dimorphism are influenced by relative exposure to male and female faces. Pers. Individ. Dif. 47, 864–868 (2009).
Article  Google Scholar 

33.
Badaruddoza, A. A paradox of human mate preferences and natural selection. J. Hum. Ecol. 21, 195–197 (2007).
Article  Google Scholar 

34.
Coetzee, V., Greeff, J. M., Stephen, I. D. & Perrett, D. I. Cross-cultural agreement in facial attractiveness preferences: The role of ethnicity and gender. PLoS ONE 9, e99629 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Hulse, F. S. Selection for skin color among the Japanese. Am. J. Phys. Anthropol. 27, 143–155 (1967).
Article  Google Scholar 

36.
Kleisner, K. et al. African and European perception of African female attractiveness. Evol. Hum. Behav. 38, 744–755 (2017).
Article  Google Scholar 

37.
Kleisner, K., Priplatova, L., Frost, P. & Flegr, J. Trustworthy-looking face meets brown eyes. PLoS ONE 8, e53285 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Zebrowitz, L. A., Montepare, J. M. & Lee, H. K. They don’t all look alike: Individual impressions of other racial groups. J. Pers. Soc. Psychol. 65, 85 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Laeng, B., Mathisen, R. & Johnsen, J. A. Why do blue-eyed men prefer women with the same eye color?. Behav. Ecol. Sociobiol. 61, 371–384 (2007).
Article  Google Scholar 

40.
Gründl, M., Knoll, S., Eisenmann-Klein, M. & Prantl, L. The blue-eyes stereotype: Do eye color, pupil diameter, and scleral color affect attractiveness?. Aesthetic Plast. Surg. 36, 234–240 (2012).
PubMed  Article  PubMed Central  Google Scholar 

41.
Langlois, J. H. & Roggman, L. A. Attractive faces are only average. Psychol. Sci. 1, 115–121 (1990).
Article  Google Scholar 

42.
Rhodes, G. & Tremewan, T. Averageness, exaggeration, and facial attractiveness. Psychol. Sci. 7, 105–110 (1996).
Article  Google Scholar 

43.
Rhodes, G. The evolutionary psychology of facial beauty. Annu. Rev. Psychol. 57, 199–226 (2006).
PubMed  Article  PubMed Central  Google Scholar 

44.
Thornhill, R. & Gangestad, S. W. Facial attractiveness. Trends Cogn. Sci. 3, 452–460 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Rhodes, G. et al. Attractiveness of facial averageness and symmetry in non-western cultures: In search of biologically based standards of beauty. Perception 30, 611–625 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Langlois, J. H., Roggman, L. A. & Musselman, L. What is average and what is not average about attractive faces?. Psychol. Sci. 5, 214–220 (1994).
Article  Google Scholar 

47.
Baudouin, J. Y. & Tiberghien, G. Symmetry, averageness, and feature size in the facial attractiveness of women. Acta Psychol. 117, 313–332 (2004).
Article  Google Scholar 

48.
Perrett, D. I., May, K. A. & Yoshikawa, S. Facial shape and judgements of female attractiveness. Nature 368, 239–242 (1994).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Alley, T. R. & Cunningham, M. R. Averaged faces are attractive, but very attractive faces are not average. Psychol. Sci. 2, 123–125 (1991).
Article  Google Scholar 

50.
Pittenger, J. B. On the difficulty of averaging faces: Comments on Langlois and Roggman. Psychol. Sci. 2, 351–353 (1991).
Article  Google Scholar 

51.
Komori, M., Kawamura, S. & Ishihara, S. Averageness or symmetry: Which is more important for facial attractiveness?. Acta Psychol. 131, 136–142 (2009).
Article  Google Scholar 

52.
Rhodes, G., Sumich, A. & Byatt, G. Are average facial configurations attractive only because of their symmetry?. Psychol. Sci. 10, 52–58 (1999).
Article  Google Scholar 

53.
Scott, L. S., Tanaka, J. W., Sheinberg, D. L. & Curran, T. The role of category learning in the acquisition and retention of perceptual expertise: A behavioral and neurophysiological study. Brain Res. 1210, 204–215 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Komori, M., Kawamura, S. & Ishihara, S. Effect of averageness and sexual dimorphism on the judgment of facial attractiveness. Vis. Res. 49, 862–869 (2009).
PubMed  Article  PubMed Central  Google Scholar 

55.
Jones, D. & Hill, K. Criteria of facial attractiveness in five populations. Hum. Nat. 4, 271–296 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Little, A. C., Connely, J., Feinberg, D. R., Jones, B. C. & Roberts, S. C. Human preference for masculinity differs according to context in faces, bodies, voices, and smell. Behav. Ecol. 22, 862–868 (2011).
Article  Google Scholar 

57.
Van den Berghe, P. L. & Frost, P. Skin color preference, sexual dimorphism and sexual selection: A case of gene culture co-evolution?*. Ethn. Racial Stud. 9, 87–113 (1986).
Article  Google Scholar 

58.
Fink, B., Neave, N. & Seydel, H. Male facial appearance signals physical strength to women. Am. J. Hum. Biol. 19, 82–87 (2007).
PubMed  Article  PubMed Central  Google Scholar 

59.
Scheib Joanna, E., Gangestad Steven, W. & Randy, T. Facial attractiveness, symmetry and cues of good genes. Proc. R. Soc. Lond. Ser. B. 266, 1913–1917 (1999).
Article  Google Scholar 

60.
Perrett, D. I. et al. Effects of sexual dimorphism on facial attractiveness. Nature 394, 884–887 (1998).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Penton-Voak, I. S. et al. Menstrual cycle alters face preference [7]. Nature 399, 741–742 (1999).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Rhodes, G., Hickford, C. & Jeffery, L. Sex-typicality and attractiveness: Are supermale and superfemale faces super-attractive?. Br. J. Psychol. 91, 125–140 (2000).
PubMed  Article  PubMed Central  Google Scholar 

63.
Kościński, K. Facial attractiveness: General patterns of facial preferences. Anthropol. Rev. 70, 45–79 (2007).
Article  Google Scholar 

64.
Johnston, V. S., Hagel, R., Franklin, M., Fink, B. & Grammer, K. Male facial attractiveness: Evidence for hormone-mediated adaptive design. Evol. Hum. Behav. 22, 251–267 (2001).
Article  Google Scholar 

65.
Scott, I. M. et al. Human preferences for sexually dimorphic faces may be evolutionarily novel. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1409643111 (2014).
Article  PubMed  PubMed Central  Google Scholar 

66.
Brooks, R. et al. National income inequality predicts women’s preferences for masculinized faces better than health does. Proc. R. Soc. B 278, 810–812 (2011).
PubMed  Article  PubMed Central  Google Scholar 

67.
DeBruine, L. M., Jones, B. C., Little, A. C., Crawford, J. R. & Welling, L. L. M. Further evidence for regional variation in women’s masculinity preferences. Proc. R. Soc. Lond. B. 278, 813–814 (2011).
Google Scholar 

68.
Dunson, D. B., Colombo, B. & Baird, D. D. Changes with age in the level and duration of fertility in the menstrual cycle. Hum. Reprod. 17, 1399–1403 (2002).
PubMed  Article  PubMed Central  Google Scholar 

69.
Hassan, M. A. M. & Killick, S. R. Effect of male age on fertility: Evidence for the decline in male fertility with increasing age. Fertil. Steril. 79, 1520–1527 (2003).
PubMed  Article  PubMed Central  Google Scholar 

70.
Buss, D. M. Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures. Behav. Brain Sci. 12, 1–14 (1989).
Article  Google Scholar 

71.
Maestripieri, D., Klimczuk, A. C. E., Traficonte, D. M. & Wilson, M. C. A greater decline in female facial attractiveness during middle age reflects women’s loss of reproductive value. Front. Psychol. 5, 1–6 (2014).
Article  Google Scholar 

72.
McLellan, B. & McKelvie, S. J. Effects of age and gender on perceived facial attractiveness. Can. J. Behav. Sci. Can. Sci. Comport. 25, 135–142 (1993).
Article  Google Scholar 

73.
Bovet, J., Barkat-Defradas, M., Durand, V., Faurie, C. & Raymond, M. Women’s attractiveness is linked to expected age at menopause. J. Evol. Biol. 31, 229–238 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Coetzee, V., Perrett, D. I. & Stephen, I. D. Facial adiposity: A cue to health?. Perception 38, 1700–1711 (2009).
PubMed  Article  PubMed Central  Google Scholar 

75.
Coetzee, V., Chen, J., Perrett, D. I. & Stephen, I. D. Deciphering faces: Quantifiable visual cues to weight. Perception 39, 51–61 (2010).
PubMed  Article  PubMed Central  Google Scholar 

76.
Schneider, T. M., Hecht, H. & Carbon, C. C. Judging body weight from faces: The height-weight illusion. Perception 41, 121–124 (2012).
PubMed  Article  PubMed Central  Google Scholar 

77.
Grillot, R. L., Simmons, Z. L., Lukaszewski, A. W. & Roney, J. R. Hormonal and morphological predictors of women’s body attractiveness. Evol. Hum. Behav. 35, 176–183 (2014).
Article  Google Scholar 

78.
Hume, D. K. & Montgomerie, R. Facial attractiveness signals different aspects of “quality” in women and men. Evol. Hum. Behav. 22, 93–112 (2001).
PubMed  Article  PubMed Central  Google Scholar 

79.
Tovée, M. J., Swami, V., Furnham, A. & Mangalparsad, R. Changing perceptions of attractiveness as observers are exposed to a different culture. Evol. Hum. Behav. 27, 443–456 (2006).
Article  Google Scholar 

80.
Třebický, V., Fialová, J., Kleisner, K. & Havlíček, J. Focal LENGTH AFFECTS DEPICTED SHAPE AND PERCEPTION OF FACIAL IMAGES. PLoS ONE 11, e0149313 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1 (2017).
Article  Google Scholar 

82.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50, 346–363 (2008).
MathSciNet  MATH  Article  Google Scholar 

83.
Rosseel, Y. Lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 37 (2012).
Article  Google Scholar 

84.
Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).

85.
Mitteroecker, P., Windhager, S., Müller, G. B. & Schaefer, K. The morphometrics of “masculinity” in human faces. PLoS ONE 10, e0118374 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Valenzano, D. R., Mennucci, A., Tartarelli, G. & Cellerino, A. Shape analysis of female facial attractiveness. Vis. Res. 46, 1282–1291 (2006).
PubMed  Article  PubMed Central  Google Scholar 

87.
De Haan, M., Pascalis, O. & Johnson, M. H. Specialization of neural mechanisms underlying face recognition in human infants. J. Cogn. Neurosci. 14, 199–209 (2002).
PubMed  Article  PubMed Central  Google Scholar 

88.
Kelly, D. J. et al. Development of the other-race effect during infancy: Evidence toward universality?. J. Exp. Child Psychol. 104, 105–114 (2009).
PubMed  PubMed Central  Article  Google Scholar 

89.
Krasotkina, A., Götz, A., Höhle, B. & Schwarzer, G. Perceptual narrowing in speech and face recognition: Evidence for intra-individual cross-domain relations. Front. Psychol. 9, 1711 (2018).
PubMed  PubMed Central  Article  Google Scholar 

90.
Kelly, D. J. et al. Cross-race preferences for same-race. Infancy 11, 87–95 (2007).
MathSciNet  PubMed  PubMed Central  Article  Google Scholar 

91.
Kleisner, K. et al. How and why patterns of sexual dimorphism in human faces vary across the world.. Infancy https://doi.org/10.31234/osf.io/7vdmb (2020).
Article  Google Scholar 

92.
Hopper, W. J., Finklea, K. M., Winkielman, P. & Huber, D. E. Measuring sexual dimorphism with a race-gender face space. J. Exp. Psychol. Hum. Percept. Perform. 40, 1779–1788 (2014).
PubMed  Article  PubMed Central  Google Scholar 

93.
Tan, K. W., Tiddeman, B. & Stephen, I. D. Skin texture and colour predict perceived health in Asian faces. Evol. Hum. Behav. 39, 320–335 (2018).
Article  Google Scholar  More

1.
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: a Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).
CAS  PubMed  Article  Google Scholar 
2.
Machado, A. P., Clément, L., Uva, V., Goudet, J. & Roulin, A. The Rocky Mountains as a dispersal barrier between barn owl (Tyto alba) populations in North America. J. Biogeogr. 45, 1288–1300 (2018).
Article  Google Scholar 

3.
Patton, J. L., Da Silva, M. N. F. & Malcolm, J. R. Gene genealogy and differentiation among arboreal spiny rats (Rodentia: Echimyidae) of the Amazon basin: a test of the riverine barrier hypothesis. Evolution 48, 1314–1323 (1994).
PubMed  Article  Google Scholar 

4.
Trinkel, M. et al. Inbreeding and density-dependent population growth in a small, isolated lion population. Anim. Conserv. 13, 374–382 (2010).
Article  Google Scholar 

5.
Vanak, A. T., Thaker, M. & Slotow, R. Do fences create an edge-effect on the movement patterns of a highly mobile mega-herbivore?. Biol. Conserv. 143, 2631–2637 (2010).
Article  Google Scholar 

6.
Parchizadeh, J. et al. Roads threaten Asiatic cheetahs in Iran. Curr. Biol. 28, R1141–R1142 (2018).
CAS  PubMed  Article  Google Scholar 

7.
Williams, S. T., Collinson, W., Patterson-Abrolat, C., Marneweck, D. G. & Swanepoel, L. H. Using road patrol data to identify factors associated with carnivore roadkill counts. PeerJ 7, e6650 (2019).
PubMed  PubMed Central  Article  Google Scholar 

8.
Hayward, M. W. & Kerley, G. I. H. Fencing for conservation: restriction of evolutionary potential or a riposte to threatening processes?. Biol. Conserv. 142, 1–13 (2009).
Article  Google Scholar 

9.
Taylor, A., Lindsey, P., Davies-Mostert, H. & Goodman, P. An assessment of the economic, social and conservation value of the wildlife ranching industry and its potential to support the green economy in South Africa. 1–163 (The Endangered Wildlife Trust, Johannesburg, South Africa, 2015).

10.
Pekor, A. et al. Fencing Africa’s protected areas: costs, benefits, and management issues. Biol. Conserv. 229, 67–75 (2019).
Article  Google Scholar 

11.
Woodroffe, R., Hedges, S. & Durant, S. M. To fence or not to fence. Science 344, 46–48 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

12.
Hayward, M. W. & Somers, M. J. An introduction to fencing for conservation. In Fencing for Conservation: Restriction of Evolutionary Potential or a Riposte to Threatening Processes? (eds Somers, M. J. & Hayward, M.) 1–6 (Springer, Berlin, 2012).
Google Scholar 

13.
Cozzi, G., Broekhuis, F., McNutt, J. W. & Schmid, B. Comparison of the effects of artificial and natural barriers on large African carnivores: implications for interspecific relationships and connectivity. J. Anim. Ecol. 82, 707–715 (2013).
PubMed  Article  Google Scholar 

14.
Kesch, M. K., Bauer, D. T. & Loveridge, A. J. Break on through to the other side: the effectiveness of game fencing to mitigate human—wildlife conflict. Afr. J. Wildl. Res. 45, 76–87 (2015).
Article  Google Scholar 

15.
Pirie, T. J., Thomas, R. L. & Fellowes, M. D. Game fence presence and permeability influences the local movement and distribution of South African mammals. Afr. Zool. 52, 217–227 (2017).
Article  Google Scholar 

16.
Lindsey, P. A., Masterson, C. L., Beck, A. L. & Romañach, S. Ecological, social, and financial issues related to fencing as a conservation tool in Africa. In Fencing for Conservation: Restriction of Evolutionary Potential or a Riposte to Threatening Processes? (eds Somers, M. J. & Hayward, M.) 215–234 (Springer, Berlin, 2012).
Google Scholar 

17.
Connolly, T. A., Day, T. D. & King, C. M. Estimating the potential for reinvasion by mammalian pests through pest-exclusion fencing. Wildl. Res. 36, 410–421 (2009).
Article  Google Scholar 

18.
Kesch, K. M., Bauer, D. T. & Loveridge, A. J. Undermining game fences: who is digging holes in Kalahari sands?. Afr. J. Ecol. 52, 144–150 (2013).
Article  Google Scholar 

19.
Edwards, S., Noack, J., Heyns, L. & Rodenwoldt, D. Evidence of a high-density brown hyena population within an enclosed reserve: the role of fenced systems in conservation. Mammmal Res. 64, 519–527 (2019).
Article  Google Scholar 

20.
Kent, V. T. & Hill, R. A. The importance of farmland for the conservation of brown hyaena, Parahyaena brunnea. Oryx 47, 431–440 (2013).
Article  Google Scholar 

21.
Welch, R. J. & Parker, D. M. Brown hyaena population explosion: rapid population growth in a small, fenced system. Wildl. Res. 43, 178–187 (2016).
Article  Google Scholar 

22.
Rogan, M. S. et al. The influence of movement on the occupancy–density relationship at small spatial scales. Ecosphere 10, e02807 (2019).
Article  Google Scholar 

23.
Efford, M. G. & Fewster, R. M. Estimating population size by spatially explicit capture–recapture. Oikos 122, 918–928 (2013).
Article  Google Scholar 

24.
Noack, J., Heyns, L., Rodenwoldt, D. & Edwards, S. Leopard density estimation within an enclosed reserve, Namibia using spatially explicit capture-recapture models. Animals 9, 724 (2019).
Article  Google Scholar 

25.
Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267 (2019).
Article  Google Scholar 

26.
Noss, A. J. et al. Comparison of density estimation methods for mammal populations with camera traps in the Kaa-Iya del Gran Chaco landscape. Anim. Conserv. 15, 527–535 (2012).
Article  Google Scholar 

27.
Foster, R. J. & Harmsen, B. J. A critique of density estimation from camera-trap data. J. Wildl. Manag. 76, 224–236 (2012).
Article  Google Scholar 

28.
Wiesel, I. Parahyaena brunnea. The IUCN Red List of Threatened Species 2015: e.T10276A82344448., Available from http://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T10276A82344448.en [Accessed 1 March 2020] (2015).

29.
Yarnell, R. et al. A conservation assessment of Parahyaena brunnea. In The Red List of Mammals of South Africa, Swaziland and Lesotho (eds Child, M. F. et al.) (South African National Biodiversity Institute and Endangered Wildlife Trust, Midrand, 2016).
Google Scholar 

30.
QGIS Development Team. QGIS Geographic Information System version 3.10.10. Open Source Geospatial Foundation Project (Available from http://qgis.org) (2020).

31.
Natural Earth.Available from http://www.naturalearthdata.com [Accessed Feb 01 2020] (2020).

32.
Thorn, M., Scott, D. M., Green, M., Bateman, P. W. & Cameron, E. Z. Estimating brown hyaena occupancy using baited camera traps. Afr. J. Wildl. Res. 39, 1–10 (2009).
Article  Google Scholar 

33.
Yarnell, R. W. et al. The influence of large predators on the feeding ecology of two African mesocarnivores: the black-backed jackal and the brown hyaena. Afr. J. Wildl. Res. 43, 155–166 (2013).
Article  Google Scholar 

34.
Falkena, H. B. & van Hoven, W. Bulls, bears and lions: game ranch profitability in southern Africa (The South Africa Financial Sector Forum, Midrand, 2000).
Google Scholar 

35.
Thorn, M., Green, M., Bateman, P. W., Waite, S. & Scott, D. M. Brown hyaenas on roads: estimating carnivore occupancy and abundance using spatially auto-correlated sign survey replicates. Biol. Conserv. 144, 1799–1807 (2011).
Article  Google Scholar 

36.
Wiesel, I. Predatory and foraging behaviour of brown hyenas (Parahyaena brunnea (Thunberg, 1820)) at cape fur seal (Arctocephalus pusillus pusillus Schreber, 1776) colonies PhD thesis, University of Hamburg, (2006).

37.
Brassine, E. & Parker, D. Trapping elusive cats: using intensive camera trapping to estimate the density of a rare African felid. PLoS ONE 10, e0142508 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Ramesh, T., Kalle, R., Rosenlund, H. & Downs, C. T. Low leopard populations in protected areas of Maputaland: a consequence of poaching, habitat condition, abundance of prey, and a top predator. Ecol. Evol. 7, 1964–1973 (2017).
PubMed  PubMed Central  Article  Google Scholar 

39.
Miller, J. R., Pitman, R. T., Mann, G. K., Fuller, A. K. & Balme, G. A. Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition. J. Anim. Ecol. 87, 1709–1726 (2018).
PubMed  Article  Google Scholar 

40.
Trinkel, M. et al. Translocating lions into an inbred lion population in the Hluhluwe-iMfolozi Park, South Africa. Anim. Conserv. 11, 138–143 (2008).
Article  Google Scholar 

41.
Thompson, S., Avent, T. & Doughty, L. S. Range analysis and terrain preference of adult southern white rhinoceros (Ceratotherium simum) in a South African private game reserve: insights into carrying capacity and future management. PLoS ONE 11, e0161724 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Balme, G. A., Slotow, R. & Hunter, L. T. B. Edge effects and the impact of non-protected areas in carnivore conservation: leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
Article  Google Scholar 

43.
Royle, J. A., Chandler, R. B., Sun, C. C. & Fuller, A. K. Integrating resource selection information with spatial capture–recapture. Methods Ecol. Evol. 4, 520–530 (2013).
Article  Google Scholar 

44.
Proffitt, K. M. et al. Integrating resource selection into spatial capture-recapture models for large carnivores. Ecosphere 6, 1–15 (2015).
Article  Google Scholar 

45.
Davies-Mostert, H. T. et al. Long-distance transboundary dispersal of African wild dogs among protected areas in southern Africa. Afr. J. Ecol. 50, 500–506 (2012).
Article  Google Scholar 

46.
Williams, K. S. et al. Utilizing bycatch camera-trap data for broad-scale occupancy and conservation: a case study of the brown hyaena Parahyaena brunnea. Oryx, 1–11, (2020).

47.
Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution – Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).
Article  Google Scholar 

48.
Palmer, M. S., Swanson, A., Kosmala, M., Arnold, T. & Packer, C. Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys. Afr. J. Ecol. 56, 791–803 (2018).
Article  Google Scholar 

49.
Swanepoel, L. H. et al. A conservation assessment of Panthera pardus. In The Red List of South Africa, Swaziland and Lesotho (eds Child, M. F. et al.) (South African National Biodiversity Institute and Endangered Wildlife Trust, Midrand, 2016).
Google Scholar 

50.
Williams, K. S. Human-brown hyaena relationships and the role of mountainous environments as refuges in a postcolonial landscape PhD thesis, Durham University, (2017).

51.
Richmond-Coggan, L. Comparative abundance and ranging behaviour of brown hyaena (Parahyaena brunnea) inside and outside protected areas in South Africa PhD thesis, Nottingham Trent University, (2014).

52.
WorldPop.South Africa 100m population, Available from https://www.worldpop.org/doi/https://doi.org/10.5258/SOTON/WP00246. [Accessed 30 May 2020] (2013).

53.
Welch, R. J. Population estimates and spatial ecology of brown hyaenas in Kwandwe Private Game Reserve MSc thesis, Rhodes University, (2014).

54.
Karanth, K. U., Nichols, J. D. & Samba-Kumar, N. Ch.7: Estimating tiger abundance from camera trap data: field surveys and analytical issues. In Camera traps in animal ecology: methods and analyses (eds O’Connell, A. F. et al.) 97–118 (Springer, Berlin, 2011).
Google Scholar 

55.
Edwards, S. et al. Making the most of by-catch data: assessing the feasibility of utilising non-target camera trap data for occupancy modelling of a large felid. Afr. J. Ecol. 56, 885–894 (2018).
Article  Google Scholar 

56.
Mazzamuto, M. V., Valvo, M. L. & Anile, S. The value of by-catch data: how species-specific surveys can serve non-target species. Eur. J. Wildl. Res. 65, 68 (2019).
Article  Google Scholar 

57.
Sun, C. C., Fuller, A. K. & Royle, J. A. Trap configuration and spacing influences parameter estimates in spatial capture-recapture models. PLoS ONE 10, e0141634 (2014).
Article  CAS  Google Scholar 

58.
Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildlife Monogr. 62, 3–135 (1978).

59.
Kays, R. W. & Slauson, K. M. Ch.5: Remote cameras. In Noninvasive survey methods for carnivores (eds Long, R. A. et al.) 110–140 (Island Press, Washington, 2008).
Google Scholar 

60.
Williams, S. T., Williams, K. S., Lewis, B. P. & Hill, R. A. Population dynamics and threats to an apex predator outside of protected areas: Implications for carnivore management. Roy. Soc. Open. Sci. 4, 1–10 (2017).

61.
Mills, M. G. L. The comparative behavioural ecology of the brown hyaena Hyaena brunnea and the spotted hyaena Crocuta crocuta in the southern Kalahari. Koedoe 27, 237–247 (1984).
Google Scholar 

62.
Kent, V. T. The status and conservation potential of carnivores in semi-arid rangelands, Botswana the Ghanzi farmlands: a case study PhD thesis, Durham University, (2011).

63.
Satter, C. B. et al. Long-term monitoring of ocelot densities in Belize. J. Wildl. Manag. 83, 283–294 (2019).
Article  Google Scholar 

64.
Jordan, M. J., Barrett, R. H. & Purcell, K. L. Camera trapping estimates of density and survival of fishers Martes pennanti. Wildl. Biol. 17, 266–276 (2011).
Article  Google Scholar 

65.
Efford, M. G. secr: Spatially explicit capture-recapture models. R package version 3.2.1. (Available from http://cran.r-project.org/package=secr) (2019).

66.
R Development Core Team. R: A language and environment for statistical computing. Version 3.6.0 (Available from https://www.R-project.org/.) (2019).

67.
Bahaa-ed-din, L. et al. Effects of human land-use on Africa’s only forest-dependent felid: The African golden cat Caracal aurata. Biol. Conserv. 199, 1–9 (2016).
Article  Google Scholar 

68.
Loock, D. J., Williams, S. T., Emslie, K. W., Matthews, W. S. & Swanepoel, L. H. High carnivore population density highlights the conservation value of industrialised sites. Sci. Rep-UK 8, 16575 (2018).
ADS  Article  CAS  Google Scholar 

69.
Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B. & Liu, J. G. Coexistence between wildlife and humans at fine spatial scales. Proc. Natl. Acad. Sci. U.S.A. 109, 15360–15365 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Treves, A., Mwima, P., Plumptre, A. J. & Isoke, S. Camera-trapping forest–woodland wildlife of western Uganda reveals how gregariousness biases estimates of relative abundance and distribution. Biol. Conserv. 143, 521–528 (2010).
Article  Google Scholar 

71.
O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6, 131–139 (2003).
Article  Google Scholar 

72.
Williams, K. S., Williams, S. T., Fitzgerald, L. E., Sheppard, E. C. & Hill, R. A. Brown hyaena and leopard diets on private land in the Soutpansberg Mountains, South Africa. Afr. J. Ecol. 56, 1021–1027 (2018).
Article  Google Scholar 

73.
Maddock, A. H. Analysis of brown hyena (Hyaena brunnea) scats from the central Karoo, South Africa. J. Zool. 231, 679–683 (1993).
Article  Google Scholar 

74.
Maude, G. The comparative ecology of the brown hyaena (Hyaena brunnea) in Makgadikgadi National Park and a neighbouring community cattle area in Botswana MSc thesis, University of Pretoria, (2005).

75.
Harihar, A. & Pandav, B. Influence of connectivity, wild prey and disturbance on occupancy of tigers in the human-dominated western Terai Arc Landscape. PLoS ONE 7, e40105 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach 2nd edn. (Springer, Berlin, 2002).
Google Scholar 

77.
Balme, G. A., Hunter, L. T. B. & Slotow, R. Evaluating methods for counting cryptic carnivores. J. Wildl. Manage. 73, 433–441 (2009).
Article  Google Scholar 

78.
Gopalaswamy, A. M. et al. Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models. Methods Ecol. Evol. 3, 1067–1072 (2012).
Article  Google Scholar 

79.
Williams, S. T. et al. R code and data for estimating brown hyaena density across South Africa. Available from https://figshare.com/s/f958e721d38dff237bab (2020). More

1.
Adeyemi, M. M. H. The potential of secondary metabolites in plant material as deterents against insect pests: a review. Afr. J. Pure Appl. Chem. 4, 243–246 (2010).
CAS  Google Scholar 
2.
Walling, L. L. The myriad plant response to herbivores. J. Plant Growth Regul. 19, 195–216 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Croteau, R., Kutchan, T. M. & Lewis, N. G. Natural products (Secondary metabolites). In Biochemistry & Molecular Biology of Plants (eds Buchanan, B. B. et al.) 1250–1318 (American Society of Plants Biologists, Rockville, 2000).
Google Scholar 

4.
Dewick, P. M. Medicinal Natural Products: A Biosynthetic Approach 2nd edn. (Wiley, Chichester, England, 2002).
Google Scholar 

5.
Pham, A. & Hwang, S. Chemical-based resistance of Brassica oleracea and Rorippa dubia in response to Spodoptera litura attack. J. Appl. Entomol. 144, 201–2011 (2019).
Article  CAS  Google Scholar 

6.
Niemetz, R. & Gross, G. G. Enzymology of gallotannin and ellagitannin biosynthesis. Phytochemistry 66, 2001–2011 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Barbosa, P. et al. Plant allelochemicals and insect parasitoids effects of nicotine on Cotesia congregata (Say) (Hymenoptera:Braconidae) and Hyposoter annulipes (Cresson) (Hymenoptera: Ichneumonidae). J. Chem. Ecol. 12, 1319–1328 (1986).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Reitz, S. R. & Trumble, J. T. Effects of linear furanocoumarins on the herbivore Spodoptera exigua and the parasitoid Archytas marmoratus: host quality and parasitoid success. Entomol. Exp. Appl. 84, 9–16 (1997).
CAS  Article  Google Scholar 

9.
Vinson, S. B. & Barbosa, P. Interrelationships of nutritional ecology of parasitoids. In Nutritional Ecology of Insects, Mites, Spiders, and Related Invertebrates (eds Slansky, F., Jr. & Rodriguez, J. G.) 673–695 (Wiley, New York, 1987).
Google Scholar 

10.
Vinson, S. B. & Iwantsch, G. F. Host Suitability for Insect Parasitoids. Annu. Rev. Entomol. 25, 397–419 (1980).
Article  Google Scholar 

11.
Duffey, S. S., Bloem, K. A. & Campbell, B. C. Consequences of sequestration of plant natural products in plant insect-parasitoid interactions. In Interactions of Plant Resistance and Parasitoids and Predators of Insects (eds Boethel, D. J. & Eikenbary, R. D.) 31–60 (Wiley, New York, 1986).
Google Scholar 

12.
Rowell-Rahier, M., Pasteels, J. M. Phenolglucosides and interactions at three trophic levels: Salicaceae herbivores-predators. In Insect Plant Interactions Volume 2. pp. 75–94. Boca Raton, Florida: CRC. (1990).

13.
Kester, K. M. & Barbosa, P. Behavioral and ecological constraints imposed by plants on insect parasitoids: implications for biological control. Biol. Control 1, 94–106 (1991).
Article  Google Scholar 

14.
Dhir, B. C., Mohapatra, H. K. & Senapati, B. Assessment of crop loss in groundnut due to tobacco caterpillar, Spodoptera litura (F.). Indian J. Plant Prot. 20, 215–217 (1992).
Google Scholar 

15.
Armes, N. J., Wightman, J. A., Jadhav, D. R. & Ranga-Rao, G. V. Status of insecticide resistance in Spodoptera litura in Andhra Pradesh, India. Pesticide Sci. 50, 240–248 (1997).
CAS  Article  Google Scholar 

16.
Kranthi, K. R., Jadhav, D. R., Wanjari, R. R., Ali, S. S. & Russell, D. Carbamate and organophosphate resistance in cotton pests in India, 1995 to 1999. Bull. Entomol. Res. 91, 37–46 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

17.
Brower, J. H., Smith, L., Vail, P. V. & Flinn, P. W. Biological control. In Integrated Management of Insects in Stored Products (eds Subramanyam, B. & Hagstrum, D. W.) 223–286 (Marcel Dekker Inc, New York, 1996).
Google Scholar 

18.
Reinert, J. A. & King, E. W. Action of Bracon hehetor Say as a parasite of Plodia interpunctella at controlled densities. Ann. Entomol. Soc. Am. 64, 1335–1340 (1971).
Article  Google Scholar 

19.
Press, J. W., Flaherty, B. R. & McDonald, I. C. Survival and reproduction of Bracon hebetor on insecticide-treated Ephestia cautella larvae. J. Georgia Entomol. Soc. 16, 231–234 (1981).
CAS  Google Scholar 

20.
Gerling, D. & Rotary, N. Hypersensitivity, resulting from host-unsuitability, as exemplified by two parasite species attacking Spodoptera littoralis (Lepidoptera: Noctuidae). Entomophaga 18, 391–396 (1973).
Article  Google Scholar 

21.
Selin-Rani, S. et al. Toxicity and physiological effect of quercetin on generalist herbivore, Spodoptera litura Fab. and a non-target earthworm Eisenia fetida Savigny. Chemosphere 165, 257–267 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Ghumare, S. S. & Mukherjee, S. N. Performance of Spodoptera litura (Fabricius) on different host plants: influence of nitrogen and total phenolics of plants and mid-gut esterase activity of the insect. Indian J. Exp. Biol. 41, 895–899 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

23
Ananthakrishnan, T. N., Gurusubramanian, G. & Gopichandran, R. Influence of chemical profiles of host plant on the infestation diversity of Retithrips syriacus (Mayet). J. Biosci. 7, 483–489 (1991).
Google Scholar 

24.
Bhattacharya, A. K. & Chenchaiah, K. C. Seed coat phenolic compounds of Cajanus cajan as chemical barrier in formulation of artificial diet of Spodoptera litura (F.). Ann. Plant Prot. Sci. 15, 92–96 (2007).
Google Scholar 

25.
Gautam, S., Samiksha, R., Arora, S. & Sohal, S. K. Chemical profiling of polyphenols in extracts from bark of Acacia nilotica (Linn.) and their efficacy against Spodoptera litura (Fab.). Arch. Phytopathol. Plant Prot. 51, 41–53 (2018).
CAS  Article  Google Scholar 

26.
Bernays, E. A., Driver, G. C. & Bilgener, M. Herbivores and plant tannins. Adv. Ecol. Res. 19, 263–302 (1989).
Article  Google Scholar 

27.
Sharma, R. & Sohal, S. K. Oviposition response of melon fruit fly, Bactrocera cucurbitae (Coquillett) to different phenolic compounds. J. Biopest. 9, 46–51 (2016).
CAS  Google Scholar 

28.
Nathan, S. S. & Kalaivani, K. Combined effects of azadirachtin and nucleopolyhedrovirus (SpltNPV) on Spodoptera litura Fabricius (Lepidoptera: Noctuidae) larvae. Biol. Control 39, 96–104 (2006).
CAS  Article  Google Scholar 

29.
Deota, P. T. & Upadhyay, P. R. Biological studies of azadirachtin and its derivatives against polyphagous pest, Spodoptera litura. Nat. Prod. Res. 19, 529–539 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Shu, B. et al. Azadirachtin affects the growth of Spodoptera litura Fabricius by inducing apoptosis in larval midgut. Frontiers Physiol. 9, 137 (2018).
Article  Google Scholar 

31.
De Moraes, C. M., Lewis, W. J., Pare, P. W., Alborn, H. T. & Tumlinson, J. H. Herbivore-infested plants selectively attract parasitoids. Nature 393, 570–573 (1998).
ADS  Article  Google Scholar 

32.
Camphell, B. C. & Duffey, S. S. Tomatine and parasitic wasps: potential incompatibility of plant antibiosis with biological control. Science 205, 700–702 (1979).
ADS  Article  Google Scholar 

33.
Campbell, B. C. & Duffey, S. S. Alleviation of α-tomatine-induced toxicity to the parasitoid, Hyposoter exiguae, by phytosterols in the diet of the host, Heliothis zea. J. Chem. Ecol. 7, 927–946 (1981).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Bloem, K. A. & Duffey, S. S. Interactive effect of protein and rutin on larval Heliothis zea and the endoparasitoid Hyposoter exiguae. Entomol. Exp. Appl. 54, 149–161 (1990).
CAS  Article  Google Scholar 

35.
El-Heneidy, A. H., Barbosa, P. & Gross, P. Influence of dietary nicotine on fall armyworm, Spodoptera frugiperda and its parasitoid, the ichneumonid wasp Hyposoter annulipes. Entomol. Exp. Appl. 46, 227–232 (1988).
CAS  Article  Google Scholar 

36.
Reitz, S. R. & Trumble, J. T. Tritrophic interactions among linear furanocoumarins, the Herbivore Trichoplusia ni (Lepidoptera: Noctuidae), and the polyembryonic parasitoid Copidosoma floridanum (Hymenoptera: Encyrtidae). Environ. Entomol. 25, 1391–1397 (1996).
Article  Google Scholar 

37.
Mondy, N. et al. Importance of sterols acquired through host feeding in synovigenic parasitoid oogenesis. J. Insect Physiol. 52, 897–904 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Punia, A., Chauhan, N. S., Kaur, S. & Sohal, S. K. Effect of Ellagic acid on the larvae of Spodoptera litura (Lepidoptera: Noctuidae) and its parasitoid Bracon hebetor (Hymenoptera: Braconidae). J. Asia-Pac. Entomol. 23, 660–665 (2020).
Article  Google Scholar 

39.
Barbosa, P. & Saunders, J. A. Plant allelochemicals: Linkages between herbivores and their natural enemies. Rec. Adv. Phytochem. 19, 107–137 (1985).
CAS  Google Scholar 

40.
Ode, P., Berenbaum, J. R., Zangerl, M. R. & Hardy, I. C. W. Host plant, host plant chemistry and the polyembryonic parasitoid Copidosoma sosares: indirect effects in a tritrophic interaction. Oikos 104, 388–400 (2004).
CAS  Article  Google Scholar 

41.
Narendra, G., Khokhar, S. & Ram, P. Effect of insecticides on some biological parameters of Trichogramma chilonis Ishii (Hymenoptera: Trichogrammtidae). J. Biol. Control 21, 130–134 (2013).
Google Scholar 

42.
Abedi, Z., Saber, M., Gharekhani, G., Mehrvar, A. & Kamita, S. G. Lethal and sublethal effects of azadirachtin and cypermethrin on Habrobracon hebetor (Hymenoptera: Braconidae). J. Econ. Entomol. 107, 638–645 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Radcliffe, E. B. Population responses of green peach aphid in Minnesota on potatoes treated with various insecticides. Proc. N Cent. Branch Entomol. Soc. Am. 27, 103–105 (1972).
Google Scholar 

44.
Flanders, S. E. Environmental resistance to the establishment of parasitic hymenoptera. Ann. Entomol. Soc. Am. 33, 245–253 (1940).
Article  Google Scholar 

45.
Kaplan, I., Carrillo, J., Garvey, M. & Ode, P. J. Indirect plant-parasitoid interactions mediated by changes in herbivore physiology. Curr. Opin. Insect Sci. 14, 112–119 (2016).
PubMed  Article  PubMed Central  Google Scholar 

46.
Ayyangar, G. S. G. & Rao, P. J. Changes in haemolymph constituents of Spodoptera litura (Fabr.) under the influence of azadirachtin. Indian J. Entomol. 52, 69–83 (1990).
Google Scholar 

47.
Zibaee, A. & Bandani, A. R. Effects of Artemisia annua L. (Asteracea) on the digestive enzymatic profiles and the cellular immune reactions of the Sunn pest, Eurygaster integriceps (Heteroptera: Scutellaridae), against Beauveria bassiana. Bull. Entomol. Res. 100, 185–196 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

48.
Kalyani, S. S. & Holihosur, R. S. N. Toxic effect of crude aqueous leaf extracts of Clerodendron inerme, on the total haemocyte count of sixth instar larva of Helicoverpa armigera (H). Int. J. Innov. Res. Sci. Technol. 1, 221–224 (2015).
Google Scholar 

49.
Saxena, B. P. & Tikku, K. Effect of plumbagin on haemocytes of Dysdercus koenigii F. Proc. Anim. Sci. 99, 119–124 (1990).
Article  Google Scholar 

50.
Sakihama, Y. Plant phenolic antioxidant and prooxidant activities: phenolics-induced oxidative damage mediated by metals in plants. Toxicology 177, 67–80 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Krishnan, N. & Sehnal, F. Compartmentalization of oxidative stress and antioxidant defense in the larval gut of Spodoptera littoralis. Arch. Insect Biochem. Physiol. 63, 1–10 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Lindroth, R. L. Biochemical detoxication: mechanism of differential tiger swallowtail tolerance to phenolic glycosides. Oecologia 81, 219–224 (1989).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Despres, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).
PubMed  Article  PubMed Central  Google Scholar 

54.
Terriere, L. C. Induction of detoxication enzymes in insects. Annu. Rev. Entomol. 29, 71–88 (1984).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Li, X. C., Schuler, M. A. & Berenbaum, M. R. Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics. Annu. Rev. Entomol. 52, 231–253 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

56.
Koul, O. G., Singh, R. & Singh, J. Bioefficacy and mode-of-action of Aglaroxin B and Aglaroxin C from Aglaia elaeagmoidea (syn. A. Irox burghiana) against Helicoverpa armigera and Spodoptera litura. Biopesticides Int. 1, 54–64 (2005).
Google Scholar 

57.
Waldbauer, G. P. The Consumption and Utilization of Food by Insects. Adv. Insect Physiol. 5, 229–288 (1968).
Article  Google Scholar 

58.
Tauber, O. E. & Yeager, J. F. On total hemolymph (blood) cell counts of insects I. Orthoptera, odonata, hemiptera, and homoptera. Ann. Entomol. Soc. Am. 28, 229–240 (1935).
Article  Google Scholar 

59.
Arnold, J. W. & Hinks, C. F. Insect haemocytes under light microscopy: techniques. In Insect Haemocyte Development, Forms, Functions and Techniques (ed. Gupta, A. P.) 531–538 (Cambridge University Press, Cambridge, 1979).
Google Scholar 

60.
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).
CAS  Article  Google Scholar  More