Philippa Kaur

Time-to-event models for wild animals generally model exposure of individuals to natural conditions that may affect the risk of mortality and disappearance. Most models neglect to consider seasons of high human activity that may affect such risks, or interactions between endpoint hazards (reflected in incidences) that may illuminate ecology. For many large carnivores, which suffer from low natural mortality yet are also subject to high risk of anthropogenic mortality and poaching, seasons of anthropogenic activity may be as important as natural ones in mediating cause-specific mortality and disappearance.Importantly, such anthropogenic seasons of higher mortality need not be specific to the animals being studied, especially if the species is controversial and much mortality illegal: our anthropogenic seasons consist of state hunting and hounding seasons for species other than wolves (i.e., deer or bear hunting, and hounding; not wolf hunting), but that mediate human activity on the landscape during those seasons. Our results support the hypothesis that increases in poaching risk during hunting seasons may be attributable to the surge of individuals with inclination to poach on the landscape14,18,29. Alternatively, it could also suggest enhanced criminal activity of a few poachers during the same periods. We temper this increase in poaching risk by establishing snow cover as a major environmental factor strongly associated with poaching. Moreover, our time-to-event analyses illuminate how to evaluate the effects that such anthropogenic seasons may have on risk of mortality and disappearance of monitored animals throughout their lifetime, and how considering such seasons may elucidate the mechanisms behind anthropogenic mortality and disappearance.Additionally, our analysis period precedes and completely excludes any established public wolf hunting seasons. Hence, our modeled anthropogenic seasons represent the periods of most relevant anthropogenic activity for wolves, as hypothesized by other studies14,29,33 and suggested by social science studies on inclinations to poach self-reported by both deer hunters and bear hunters, as well as acceptance of poaching by hunters and farmers30,31,32.Our analyses show increases in the hazard of disappearances of collared wolves (LTF) relative to the baseline period (which excludes environmental and anthropogenic risks) for all seasons. The highest hazard of LTF occurs during the snow season, whereas increases in hazard are lower (and similar) for the two seasons that included hounding and hunting. LTF may experience changes in hazard due to changes in the hazard of any/all of its components: migration, collar failure, or cryptic poaching.Constant and steep increases in LTF hazard throughout a wolf’s lifetime suggests mechanisms other than migration regulating LTF hazard, given migration for adults is most frequent by yearlings and younger adults, around 1.5 to 2.2 years34,35,36. Moreover, only migration out of state would end monitoring, not routine extraterritorial movements of radio-collared wolves. That our seasonal LTF curves depict the cumulative hazards more than doubling beyond those t generally associated with dispersal (~ t  More

1.Nakamura, A. et al. Forests and their canopies: Achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).PubMed 

Google Scholar 
2.Scheffers, B. R. et al. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).ADS 

Google Scholar 
3.Lefsky, M. A. et al. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett. 32, L22S02 (2005).
Google Scholar 
4.Ellwood, M. D. F. & Foster, W. A. Doubling the estimate of invertebrate biomass in a rainforest canopy. Nature 429, 549–551 (2004).ADS 
CAS 
PubMed 

Google Scholar 
5.Dial, R. et al. Arthropod abundance, canopy structure, and microclimate in a Bornean lowland tropical rain forest. Biotropica 38, 643–652 (2006).
Google Scholar 
6.Valencia, R. et al. High tree alpha-diversity in Amazonian Ecuador. Biodivers. Conserv. 3, 21–28 (1994).
Google Scholar 
7.Stone, M. J. et al. Edge effects and beta diversity in ground and canopy beetle communities of fragmented subtropical forest. PLoS ONE 13, e0193369 (2018).PubMed 
PubMed Central 

Google Scholar 
8.Nadkarni, N. M. Diversity of species and interactions in the upper tree canopy of forest ecosystems. Am. Zool. 34, 70–78 (1994).
Google Scholar 
9.Stanton, D. E. et al. Rapid nitrogen fixation by canopy microbiome in tropical forest determined by both phosphorus and molybdenum. Ecology 100(9), e02795 (2019).PubMed 

Google Scholar 
10.Basset, Y. et al. (eds) Arthropods of Tropical Forests. Spatio-Temporal Dynamics and Resource Use in the Canopy (Cambridge University Press, 2003).
Google Scholar 
11.Schowalter, T. D. et al. Post-hurricane successional dynamics in abundance and diversity of canopy arthropods in a tropical rainforest. Environ. Entomol. 46, 11–20 (2017).CAS 
PubMed 

Google Scholar 
12.Silva, R. R. & Brandão, C. R. F. Morphological patterns and community organization in leaf-litter ant assemblages. Ecol. Monogr. 80, 107–124 (2010).
Google Scholar 
13.McCaig, T., Sam, L., Nakamura, L. & Stork, N. E. Is insect vertical distribution in rainforests better explained by distance from the canopy top or distance from the ground?. Biodivers. Conserv. 29, 1081–1103 (2020).
Google Scholar 
14.Floren, A. & Linsenmair, K. E. The influence of anthropogenic disturbances on the structure of arboreal arthropod communities. Plant Ecol. 153, 153–167 (2001).
Google Scholar 
15.Adis, J. et al. Canopy fogging of an overstory tree—Recommendations for standardization. Ecotropica 4, 93–97 (1998).
Google Scholar 
16.Bar-Ness, Y. D. et al. Sampling forest canopy arthropod biodiversity with three novel minimal-cost trap designs. Aust. J. Entomol. 51, 12–21. https://doi.org/10.1111/j.1440-6055.2011.00836.x (2012).Article 

Google Scholar 
17.Erwin, T. L. Canopy arthropod biodiversity: A chronology of sampling techniques and results. Rev. Peru. Entomol. 2, 71–77 (1990).
Google Scholar 
18.Floren, A. Sampling arthropods from the canopy by insecticidal knockdown. In Manual on Field Recording Techniques and Protocols for All Taxa Biodiversity Inventories, Part 1 Vol. 8 (eds Eymann, J., Degref, J., Häuser, C. et al.) 158–172 (ABC Taxa, 2010).
Google Scholar 
19.Leather, S. R. (ed.) Insect Sampling in Forest Ecosystems (Blackwell Science, 2005).
Google Scholar 
20.Lowman, M., Moffett, M. & Rinker, H. B. A new technique for taxonomic and ecological sampling in rain forest canopies. Selbyana 14, 75–79 (1993).
Google Scholar 
21.Lowman, M. D., Kitching, R. L. & Carruthers, G. Arthropod sampling in Australian subtropical rain forest: How accurate are some of the more common techniques?. Selbyana 17, 36–42 (1996).
Google Scholar 
22.Lowman, M. D., Schowalter, T. D. & Franklin, J. F. Methods in Forest Canopy Research (University of California Press, 2012).
Google Scholar 
23.Majer, J. D. & Recher, H. F. Invertebrate communities on Western Australian eucalypts—A comparison of branch clipping and chemical knockdown procedures. Aust. J. Ecol. 13, 269–278. https://doi.org/10.1111/j.1442-9993.1988.tb00974.x (1988).Article 

Google Scholar 
24.Ozanne, C. M. P. Techniques and methods for sampling canopy insects. In Insect Sampling in forest ecosystems (ed. Leather, S. R.) 146–165 (Blackwell, 2005).
Google Scholar 
25.Paarmann, W. & Stork, N. E. Canopy fogging, a method of collecting living insects for investigation of life history strategies. J. Nat. Hist. 21, 563–566. https://doi.org/10.1080/00222938700770341 (1987).Article 

Google Scholar 
26.Parker, G. G., Smith, A. P. & Hogan, K. P. Access to the upper forest canopy with a large tower crane. Bioscience 42, 664–670. https://doi.org/10.2307/1312172 (1992).Article 

Google Scholar 
27.Skvarla, M. J., Larson, J. L., Fisher, J. R. & Dowling, A. P. G. A review of terrestrial and canopy malaise traps. Ann. Entomol. Soc. Am. 114(1), 27–47. https://doi.org/10.1093/aesa/saaa044 (2021).Article 

Google Scholar 
28.Stork, N. E. Australian tropical forest canopy crane: New tools for new frontiers. Aust. Ecol. 32, 4–9. https://doi.org/10.1111/j.1442-9993.2007.01740.x (2007).Article 

Google Scholar 
29.Basset, Y. et al. IBISCA-Panama, a large-scale study of arthropod beta-diversity and vertical stratification in a lowland rainforest: Rationale, study sites and field protocols. Bull. Inst. R. Sci. Nat. Belg. Entomol. 77, 39–69 (2007).
Google Scholar 
30.Basset, Y., Cizek, L. & Cuénoud, P. Arthropod diversity in a tropical forest. Science 338, 1481–1484. https://doi.org/10.1126/science.1226727 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
31.Kitching, R. L. et al. The biodiversity of arthropods from Australian rainforest canopies: General introduction, methods, sites and ordinal results. Aust. J. Ecol. 18, 181–191. https://doi.org/10.1111/j.1442-9993.1993.tb00442.x (1993).Article 

Google Scholar 
32.Lindo, Z. & Winchester, N. N. Oribatid mite communities and foliar litter decomposition in canopy suspended soils and forest floor habitats of western red cedar forests, Vancouver Island, Canada. Soil Biol. Biochem. 39, 2957–2966. https://doi.org/10.1016/j.soilbio.2007.06.009 (2007).CAS 
Article 

Google Scholar 
33.Schowalter, T. D. Canopy arthropod communities in relation to forest age and alternative harvest practices in western Oregon. For. Ecol. Manage 78, 115–125 (1995).
Google Scholar 
34.Southwood, T. R. E., Moran, V. C. & Kennedy, C. E. J. The assessment of arboreal insect fauna: Comparisons of knockdown sampling and faunal lists. Ecol. Entomol. 7, 331–340. https://doi.org/10.1111/j.1365-2311.1982.tb00674.x (1982).Article 

Google Scholar 
35.Stork, N. E. Guild structure of arthropods from Bornean rain forest trees. Ecol. Entomol. 12, 69–80. https://doi.org/10.1111/j.1365-2311.1987.tb00986.x (1987).Article 

Google Scholar 
36.Stork, N. E. et al. (eds) Canopy Arthropods (Chapman & Hall, 1997).
Google Scholar 
37.DeVries, P. J. Stratification of fruit-feeding nymphalid butterflies in a Costa Rican rain forest. J. Res. Lepid. 26, 98–108 (1988).ADS 

Google Scholar 
38.Hill, C. J., Gillison, A. N. & Jones, R. E. The spatial distribution of rain forest butterflies at three sites in North Queensland, Australia. J. Trop. Ecol. 8, 37–46 (1992).
Google Scholar 
39.Medina, M. C., Robbins, R. K. & Lamas, G. Vertical stratification of flight by Ithomiinae butterflies (Lepidoptera: Nymphalidae) at Pakitza, Manu National Park, Peru. In Manu—The Biodiversity of Southeastern Peru (eds Wilson, D. E. & Sandoval, A.) 211–216 (Smithsonian Institution, 1996).
Google Scholar 
40.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruitfeeding butterfly community in an Ecuadorian rainforest. Biol. J. Linn. Soc. 62, 343–364. https://doi.org/10.1111/j.1095-8312.1997.tb01630.x (1997).Article 

Google Scholar 
41.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruit-feeding butterfly community in an Ecuadorian rain forest. Biol. J. Linn. Soc. 62, 343–364 (1997).
Google Scholar 
42.Beccaloni, G. W. Vertical stratification of ithomiine butterfly (Nymphalidae: Ithomiinae) mimicry complexes: The relationship between adult flight height and larval host-plant height. Biol. J. Linn. Soc. 62, 313–341 (1997).
Google Scholar 
43.Schulze, C. H., Linsenmair, K. E. & Fiedler, K. Understorey versus canopy: Patterns of vertical stratification and diversity among Lepidoptera in a Bornean Rain Forest. Plant Ecol. 153, 133–152. https://doi.org/10.1023/A:1017589711553 (2001).Article 

Google Scholar 
44.Fordyce, J. A. & DeVries, P. J. A tale of two communities: Eotropical butterfly assemblages show higher beta diversity in the canopy compared to the understory. Oecologia 181, 235–243. https://doi.org/10.1007/s00442-016-3562-0 (2016).ADS 
Article 
PubMed 

Google Scholar 
45.Santos, J. P., Iserhard, C. A., Carreira, J. Y. O. & Freitas, A. V. L. Monitoring fruit-feeding butterfly assemblages in two vertical strata in seasonal Atlantic Forest: Temporal species turnover is lower in the canopy. J. Trop. Ecol. 33(5), 345–355 (2017).
Google Scholar 
46.Lourido, G. M., Motta, C. S., Graça, M. B. & Rafael, J. A. Diversity patterns of hawkmoths (Lepidoptera: Sphingidae) in the canopy of an ombrophilous forest in Central Amazon, Brazil. Acta Amazon. 48, 117–125 (2018).
Google Scholar 
47.Araujo, P. F., Freitas, A. V. L., Gonçalves, G. A. S. & Ribeiro, D. B. Vertical stratification on a small scale: The distribution of fruit-feeding butterflies in a semi-deciduous Atlantic forest in Brazil. Stud. Neotrop. Fauna Environ. 56, 10–39 (2021).
Google Scholar 
48.Charles, E. & Basset, Y. Vertical stratification of leaf-beetle assemblages (Coleoptera: Chrysomelidae) in two forest types in Panama. J. Trop. Ecol. 21, 329–336. https://doi.org/10.1017/S0266467405002300 (2005).Article 

Google Scholar 
49.Grimbacher, P. S. & Stork, N. E. Vertical stratification of feeding guilds and body size in beetle assemblages from an Australian tropical rainforest. Aust. Ecol. 32, 77–85. https://doi.org/10.1111/j.1442-9993.2007.01735.x (2007).Article 

Google Scholar 
50.Floren, A. & Schmidl, J. (eds) Canopy Arthropod Research in Europe: Basic and Applied Studies from the High Frontier (Bioform Entomology & Equipment, 2008).
Google Scholar 
51.Stork, N. E. et al. Vertical stratification of beetles in tropical rainforests as sampled by light traps in North Queensland, Australia. Austral Ecol. 41(2), 168–178 (2015).
Google Scholar 
52.Tregidgo, D. J., Qie, L., Barlow, J., Sodhi, N. S. & Lee-Hong, L. S. Vertical stratification responses of an arboreal dung beetle species to tropical forest fragmentation in Malaysia. Biotropica 42, 521–552 (2010).
Google Scholar 
53.Davis, A. J., Sutton, S. L. & Brendell, M. J. D. Vertical distribution of beetles in a tropical rainforest in Sulawesi: The role of the canopy in contributing to Biodiversity. Sepilok Bull. 13 & 14, 59–83 (2011).
Google Scholar 
54.Heatwole, H. Changes in ant assemblages across an arctic treeline. Rev d’Entomol du Quebec 34, 10–22 (1989).
Google Scholar 
55.Roubik, D. W. Tropical pollinators in the canopy and understory: Field data and theory for stratum “preferences”. J. Ins. Behav. 6, 659–673. https://doi.org/10.1007/BF01201668 (1993).Article 

Google Scholar 
56.Longino, J. T. & Colwell, R. K. Biodiversity assessment using structured inventory: Capturing the ant fauna of a tropical rain forest. Ecol. Appl. 7, 1263–1277. https://doi.org/10.1890/1051-0761(1997)007[1263:BAUSIC]2.0.CO;2 (1997).Article 

Google Scholar 
57.Vance, A. C. C., Smith, S. M., Malcolm, J. R., Huber, J. & Bellocq, M. I. Differences between forest type and vertical strata in the diversity and composition of hymenopteran families and mymarid genera in Northeastern Temperate Forests. Environ. Entomol. 36, 1073–1083. https://doi.org/10.1603/0046-225X(2007)36[1073:DBFTAV]2.0.CO;2 (2007).CAS 
Article 
PubMed 

Google Scholar 
58.Hernández-Flores, J. et al. Effect of forest disturbance on ant (Hymenoptera: Formicidae) diversity in a Mexican tropical dry forest canopy. Insect Conserv. Diver. 14(3), 393–402. https://doi.org/10.1111/icad.12466 (2020).Article 

Google Scholar 
59.Roberts, H. R. Arboreal Orthoptera in the rain forest of Costa Rica collected with insecticide: A report on the grasshoppers (Acrididae) including new species. Proc. Acad. Nat. Sci. Phila. 125, 46–66 (1973).
Google Scholar 
60.Rodgers, D. J. & Kitching, R. L. Vertical stratification of rainforest collembolan (Collembola: Insecta) assemblages: Description of ecological patterns and hypotheses concerning their generation. Ecography 21, 392–400. https://doi.org/10.1111/j.1600-0587.1998.tb00404.x (1998).Article 

Google Scholar 
61.Krab, E. J., Oorsprong, H., Berg, M. P. & Cornelissen, J. H. C. Turning northern peatlands upside down: Disentangling microclimate and substrate quality effects on vertical distribution of Collembola. Funct. Ecol. 24, 1362–1369. https://doi.org/10.1111/j.1365-2435.2010.01754.x (2010).Article 

Google Scholar 
62.Coots, C., Lambdin, P., Grant, J., Rhea, R. & Mockford, E. Vertical stratification and co-occurrence patterns of the psocoptera community associated with Eastern Hemlock, Tsuga canadensis (L.) Carrière, in the Southern Appalachians. Forests 3, 127–136. https://doi.org/10.3390/f3010127 (2012).Article 

Google Scholar 
63.Wardhaugh, C. W. et al. Vertical stratification in the spatial distribution of the beech scale insect (Ultracoelostoma assimile) in Nothofagus tree canopies in New Zealand. Ecol. Entomol. 31, 185–195 (2006).
Google Scholar 
64.Brown, B. V. et al. Comprehensive inventory of true flies (Diptera) at a tropical site. Commun. Biol. 1, 1–8 (2018).ADS 

Google Scholar 
65.Borkent, A. et al. Remarkable fly (Diptera) diversity in a patch of Costa Rican cloud forest: Why inventory is a vital science. Zootaxa 4402, 53–90 (2018).PubMed 

Google Scholar 
66.Hebert, P. D. N. et al. Counting animal species with DNA barcodes: Canadian insects. Philos. Trans. R. Soc. Lond. Ser. B. 371, 20150333 (2016).
Google Scholar 
67.Basset, Y. et al. Arthropod distribution in a tropical rainforest: Tackling a four dimensional puzzle. PLoS ONE 10, e0144110 (2015).PubMed 
PubMed Central 

Google Scholar 
68.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).
Google Scholar 
69.Higuchi, N. et al. Governos locais amazônicos e as questões climáticas globais 103 (INPA/edição dos autores, 2009).
Google Scholar 
70.Brown, B. V. Malaise trap catches and the crisis in Neotropical dipterology. Am. Entomol. 51, 180–183 (2005).
Google Scholar 
71.Gressitt, J. L. & Gressitt, M. K. An improved Malaise trap. Pacific Insects 4, 87–90 (1962).
Google Scholar 
72.van Achterberg, K. Can Townes type Malaise traps be improved? Some recent developments. Entomologische Berichten 69, 129–135 (2009).
Google Scholar 
73.R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (Accessed 20 October 2021); https://www.R-project.org/.
74.Konietschke, F. (2011). nparcomp: nparcomp-package. R package version 1.0-1. (Accessed 20 October 2021); http://CRAN.R-project.org/package=nparcomp75.Alboukadel Kassambara (2020). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.3.0. (Accessed 20 October 2021); https://CRAN.R-project.org/package=ggpubr76.Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).PubMed 

Google Scholar 
77.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
CAS 
PubMed 

Google Scholar 
78.Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).
Google Scholar 
79.Gardner, T. A. et al. Predicting the uncertain future of tropical forest species in a data vacuum. Biotropica 39, 25–30 (2007).
Google Scholar  More

1.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
PubMed 

Google Scholar 
2.Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates Inc, 2004).
Google Scholar 
3.Endler, J. A. Gene flow and population differentiation: studies of clines suggest that differentiation along environmental gradients may be independent of gene flow. Science 179, 243–250 (1973).CAS 
PubMed 
ADS 

Google Scholar 
4.Mayr, E. Systematics and the Origin of Species, from the Viewpoint of a Zoologist (Harvard University Press, 1999).
Google Scholar 
5.Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).PubMed 

Google Scholar 
6.Nosil, P. Ernst Mayr and the integration of geographic and ecological factors in speciation. Biol. J. Lin. Soc. 95, 26–46 (2008).
Google Scholar 
7.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 

Google Scholar 
8.Leliaert, F. et al. DNA-based species delimitation in algae. Eur. J. Phycol. 49, 179–196 (2014).
Google Scholar 
9.Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).PubMed 

Google Scholar 
10.Schlick-Steiner, B. C. et al. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu. Rev. Entomol. 55, 421–438 (2010).CAS 
PubMed 

Google Scholar 
11.Capblancq, T., Mavárez, J., Rioux, D. & Després, L. Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae). Ecol. Evol. 9, 6444–6457 (2019).PubMed 
PubMed Central 

Google Scholar 
12.Pedraza-Marrón, C. d. R. et al. Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation. Proc. R. Soc. B 286, 20182924 (2019).PubMed 
PubMed Central 

Google Scholar 
13.Hinojosa, J. C. et al. A mirage of cryptic species: genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol. Ecol. 28, 3857–3868 (2019).PubMed 

Google Scholar 
14.Nygren, A. et al. A mega-cryptic species complex hidden among one of the most common annelids in the North East Atlantic. PLoS ONE 13, e0198356 (2018).PubMed 
PubMed Central 

Google Scholar 
15.Thielsch, A., Knell, A., Mohammadyari, A., Petrusek, A. & Schwenk, K. Divergent clades or cryptic species? Mito-nuclear discordance in a Daphnia species complex. BMC Evol. Biol. 17, 1–9 (2017).
Google Scholar 
16.Eyer, P. A. & Hefetz, A. Cytonuclear incongruences hamper species delimitation in the socially polymorphic desert ants of the Cataglyphis albicans group in Israel. J. Evol. Biol. 31, 1828–1842 (2018).CAS 
PubMed 

Google Scholar 
17.Borkent, A. Biology of Disease Vectors. 2nd edn, i–xxiii + 1–785 (Elsevier Academic Press, 2004).18.Mellor, P., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
PubMed 

Google Scholar 
19.Rushton, J. & Lyons, N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria italiana 51, 401–406 (2015).PubMed 

Google Scholar 
20.Tabachnick, W. J. Culicoides vriipennis and Bluetongue-Virus eidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
Article 
PubMed 

Google Scholar 
21.Wirth, W. W. & Jones, R. H. The North American Subspecies of Culicoides variipennis (Diptera, Heleidae). U. S. Dep. Agric. Tech. Bull 1170, 1–35 (1957).
Google Scholar 
22.Holbrook, F. R. et al. Sympatry in the Culicoides variipennis Complex (Diptera: Ceratopogonidae): a Taxonomic Reassessment. J. Med. Entomol. 37, 65–76. https://doi.org/10.1603/0022-2585-37.1.65 (2000).CAS 
Article 
PubMed 

Google Scholar 
23.Hopken, M. W. Pathogen Vectors at the Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Biology (University of Colorado, 2016).
Google Scholar 
24.Shults, P. A Study of the Taxonomy, Ecology, and Systematics of Culicoides Species (Diptera: Ceratopogonidae) Including those Associated with Deer Breeding Facilities in Southeast Texas (Texas A&M University, 2015).
Google Scholar 
25.Velten, R. K. & Mullens, B. A. Field morphological variation and laboratory hybridization of Culicoides variipennis sonorensis and C. v. occidentalis (Diptera:Ceratopogonidae) in southern California. J. Med. Entomol. 34, 277–284 (1997).CAS 
PubMed 

Google Scholar 
26.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258522 (2015).PubMed 

Google Scholar 
27.Bolnick, D. I. & Otto, S. P. The magnitude of local adaptation under genotype-dependent dispersal. Ecol. Evol. 3, 4722–4735 (2013).PubMed 
PubMed Central 

Google Scholar 
28.Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

Google Scholar 
29.Pante, E. et al. Species are hypotheses: avoid connectivity assessments based on pillars of sand. Mol. Ecol. 24, 525–544 (2015).PubMed 

Google Scholar 
30.Jacquet, S. et al. Colonization of the Mediterranean basin by the vector biting midge species Culicoides imicola: an old story. Mol. Ecol. 24, 5707–5725. https://doi.org/10.1111/mec.13422 (2015).CAS 
Article 
PubMed 

Google Scholar 
31.Onyango, M. G. et al. Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia. BMC Genomics 17, 769. https://doi.org/10.1186/s12864-016-3124-1 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660. https://doi.org/10.1186/s13071-015-1277-4 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Mignotte, A. et al. High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses. Parasit. Vectors 14, 1–14 (2021).
Google Scholar 
34.Sanders, C. J. & Carpenter, S. Assessment of an immunomarking technique for the study of dispersal of Culicoides biting midges. Infect. Genet. Evol. 28, 583–587 (2014).PubMed 

Google Scholar 
35.Kluiters, G., Swales, H. & Baylis, M. Local dispersal of palaearctic Culicoides biting midges estimated by mark-release-recapture. Parasit. Vectors 8, 86 (2015).PubMed 
PubMed Central 

Google Scholar 
36.Ducheyne, E. et al. Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospat. Health 10, 177–189 (2007).
Google Scholar 
37.Purse, B. V. et al. Climate change and the recent emergence of bluetongue in Europe. Nat. Rev. Microbiol. 3, 171–181 (2005).CAS 
PubMed 

Google Scholar 
38.Jacquet, S. et al. Range expansion of the Bluetongue vector, Culicoides imicola, in continental France likely due to rare wind-transport events. Sci. Rep. https://doi.org/10.1038/srep27247 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).
Google Scholar 
40.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 

Google Scholar 
41.Shults, P. A Study of Culicoides Biting Midges in the Subgenus Monoculicoides: Population Genetics, Taxonomy, Systematics, and Control. Ph.D. thesis, Texas A&M University (2021).42.Jewiss-Gaines, A., Barelli, L. & Hunter, F. F. First records of Culicoides sonorensis (Diptera: Ceratopogonidae), a known vector of bluetongue virus, Southern Ontario. J. Med. Entomol. 54, 757–762. https://doi.org/10.1093/jme/tjw215 (2017).CAS 
Article 
PubMed 

Google Scholar 
43.Chan, K. M. & Levin, S. A. Leaky prezygotic isolation and porous genomes: rapid introgression of maternally inherited DNA. Evolution 59, 720–729 (2005).CAS 
PubMed 

Google Scholar 
44.Harrison, R. G. Hybrid zones: windows on evolutionary process. Oxf. Surv. Evol. Biol. 7, 69–128 (1990).
Google Scholar 
45.Harrison, R. G. Animal mitochondrial DNA as a genetic marker in population and evolutionary biology. Trends Ecol. Evol. 4, 6–11 (1989).CAS 
PubMed 

Google Scholar 
46.Després, L. One, Two or More Species? Mitonuclear Discordance and Species Delimitation. Molecular ecology 28(17), 3845–3847 (2019).PubMed 

Google Scholar 
47.Janes, J. K. et al. The K= 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 

Google Scholar 
48.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).PubMed 

Google Scholar 
49.Ballard, J. W. O., Chernoff, B. & James, A. C. Divergence of mitochondrial DNA is not corroborated by nuclear DNA, morphology, or behavior in Drosophila simulans. Evolution 56, 527–545 (2002).PubMed 

Google Scholar 
50.Behura, S., Sahu, S., Mohan, M. & Nair, S. Wolbachia in the Asian rice gall midge, Orseolia oryzae (Wood-Mason): Correlation between host mitotypes and infection status. Insect Mol. Biol. 10, 163–171 (2001).CAS 
PubMed 

Google Scholar 
51.Covey, H. et al. Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) detection and prevalence in Culicoides (Diptera: Ceratopogonidae) midge populations in the United States. J. Med. Entomol. 57, 1262–1269. https://doi.org/10.1093/jme/tjaa003 (2020).Article 
PubMed 

Google Scholar 
52.Pagès, N., Muñoz-Muñoz, F., Verdún, M., Pujol, N. & Talavera, S. First detection of Wolbachia-infected Culicoides (Diptera: Ceratopogonidae) in Europe: Wolbachia and Cardinium infection across Culicoides communities revealed in Spain. Parasit. Vectors 10, 582. https://doi.org/10.1186/s13071-017-2486-9 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Pilgrim, J. et al. Cardinium symbiosis as a potential confounder of mtDNA based phylogeographic inference in Culicoides imicola (Diptera: Ceratopogonidae), a vector of veterinary viruses. Parasit. Vectors 14, 100. https://doi.org/10.1186/s13071-020-04568-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Hare, M. P. Prospects for nuclear gene phylogeography. Trends Ecol. Evol. 16, 700–706 (2001).
Google Scholar 
55.Onyango, M. G. et al. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites. Vet. Res. 46, 108. https://doi.org/10.1186/s13567-015-0250-8 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Fonseca, D. M., Smith, J. L., Kim, H.-C. & Mogi, M. Population genetics of the mosquito Culex pipiens pallens reveals sex-linked asymmetric introgression by Culex quinquefasciatus. Infect. Genet. Evol. 9, 1197–1203 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Goubert, C., Minard, G., Vieira, C. & Boulesteix, M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity 117, 125–134 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).CAS 
PubMed 

Google Scholar 
59.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2006).CAS 
Article 
PubMed 

Google Scholar 
60.Manni, M. et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit. Vectors 8, 1–11 (2015).
Google Scholar 
61.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between Crested and Marbled Newts (Triturus cristatus and T. marmoratus). Evolution 63, 1191–1202. https://doi.org/10.1111/j.1558-5646.2009.00611.x (2009).Article 
PubMed 

Google Scholar 
62.Gibeaux, R. et al. Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus. Nature 553, 337. https://doi.org/10.1038/nature25188 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
63.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741 (2008).CAS 
PubMed 

Google Scholar 
64.Servedio, M. R. & Kirkpatrick, M. The effects of gene flow on reinforcement. Evolution 51, 1764–1772. https://doi.org/10.1111/j.1558-5646.1997.tb05100.x (1997).Article 
PubMed 

Google Scholar 
65.Howard, D. J. Reinforcement: origin, dynamics, and fate of an evolutionary hypothesis. Hybrid zones and the evolutionary process, 46–69 (1993).66.Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446. https://doi.org/10.1111/j.1558-5646.2011.01534.x (2012).Article 
PubMed 

Google Scholar 
67.Downes, J. A. The Culicoides variipennis complex: a necessary re-alignment of nomenclature (Diptera: Ceratopogonidae). Can. Entomol. 110, 63–69 (1978).
Google Scholar 
68.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
PubMed 

Google Scholar 
69.Smith, H. & Mullens, B. A. Seasonal activity, size, and parity of Culicoides occidentalis (Diptera: Ceratopogonidae) in a coastal southern California salt marsh. J. Med. Entomol. 40, 352–355. https://doi.org/10.1603/0022-2585-40.3.352 (2003).Article 
PubMed 

Google Scholar 
70.Linley, J. The effect of salinity on oviposition and egg hatching in Culicoides variipennis sonorensis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 2, 79–82 (1986).CAS 
PubMed 

Google Scholar 
71.Gerry, A. C. & Mullens, B. A. Response of Male Culicoides variipennis sonorensis (Diptera: Ceratopogonidae) to carbon dioxide and observations of mating behavior on and near cattle. J. Med. Entomol. 35, 239–244. https://doi.org/10.1093/jmedent/35.3.239 (1998).CAS 
Article 
PubMed 

Google Scholar 
72.Nolan, D. V. et al. Rapid diagnostic PCR assays for members of the Culicoides obsoletus and Culicoides pulicaris species complexes, implicated vectors of bluetongue virus in Europe. Vet. Microbiol. 124, 82–94 (2007).CAS 
PubMed 

Google Scholar 
73.Sebastiani, F. et al. Molecular differentiation of the Old World Culicoides imicola species complex (Diptera, Ceratopogonidae), inferred using random amplified polymorphic DNA markers. Mol. Ecol. 10, 1773–1786 (2001).CAS 
PubMed 

Google Scholar 
74.Carlson, D. Identification of mosquitoes of Anopheles gambiae species complex A and B by analysis of cuticular components. Science 207, 1089–1091 (1980).CAS 
PubMed 
ADS 

Google Scholar 
75.Palacios, G. et al. Characterization of the Sandfly fever Naples species complex and description of a new Karimabad species complex (genus Phlebovirus, family Bunyaviridae). J. Gen. Virol. 95, 292 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Rivas, G., Souza, N. & Peixoto, A. A. Analysis of the activity patterns of two sympatric sandfly siblings of the Lutzomyia longipalpis species complex from Brazil. Med. Vet. Entomol. 22, 288–290 (2008).CAS 
PubMed 

Google Scholar 
77.Wilson, W. C. et al. Current status of bluetongue virus in the Americas. Bluetongue 10, 197–220 (2009).
Google Scholar 
78.Allen, S. E. et al. Epizootic Hemorrhagic Disease in White-Tailed Deer, Canada. Emerg. Infect. Dis. 25, 832–834. https://doi.org/10.3201/eid2504.180743 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
79.McGregor, B. L. et al. Field data implicating Culicoides stellifer and Culicoides venustus (Diptera: Ceratopogonidae) as vectors of epizootic hemorrhagic disease virus. Parasit. Vectors 12, 258. https://doi.org/10.1186/s13071-019-3514-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
80.Shults, P., Ho, A., Martin, E. M., McGregor, B. L. & Vargo, E. L. Genetic diversity of Culicoides stellifer (Diptera: Ceratopogonidae) in the Southeastern United States compared with sequences from Ontario, Canada. J. Med. Entomol. 57, 1324–1327. https://doi.org/10.1093/jme/tjaa025 (2020).CAS 
Article 
PubMed 

Google Scholar 
81.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

Google Scholar 
82.Ciota, A. T., Chin, P. A. & Kramer, L. D. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasit. Vectors 6, 1–4 (2013).
Google Scholar 
83.Meiswinkel, R., Gomulski, L., Delécolle, J., Goffredo, M. & Gasperi, G. The taxonomy of Culicoides vector complexes-unfinished business. Vet. Ital. 40, 151–159 (2004).CAS 
PubMed 

Google Scholar 
84.Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (Oxford, England) 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
Article 

Google Scholar 
85.Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).86.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 
PubMed 

Google Scholar 
87.Morales-Hojas, R. et al. The genome of the biting midge Culicoides sonorensis and gene expression analyses of vector competence for bluetongue virus. BMC Genomics 19, 624. https://doi.org/10.1186/s12864-018-5014-1 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
88.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England) 25, 1754–1760 (2009).CAS 

Google Scholar 
89.Pante, E. et al. Use of RAD sequencing for delimiting species. Heredity 114, 450–459 (2015).CAS 
PubMed 

Google Scholar 
90.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 

Google Scholar 
91.Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics (Oxford, England) 28, 298–299 (2012).CAS 

Google Scholar 
92.Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: An improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).CAS 
PubMed 

Google Scholar 
93.Raj, A., Stephens, M. & Pritchard, J. K. Variational Inference of Population Structure in Large SNP Datasets. bioRxiv 10, 001073 (2013).
Google Scholar 
94.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.http://www.R-project.org/ (2013).95.Jombart, Thibaut, and Caitlin Collins. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling (2015).96.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30, 1312–1313 (2014).CAS 

Google Scholar 
97.Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A.N.-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: New acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).PubMed 
PubMed Central 

Google Scholar 
98.Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).MathSciNet 
CAS 
PubMed 

Google Scholar 
99.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 

Google Scholar 
102.Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
PubMed 

Google Scholar 
103.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 30. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
Article 
PubMed 

Google Scholar 
104.Rousset, F. genepop’007: a complete re‐implementation of the genepop software for Windows and Linux. Molecular ecology resources 8(1), 103–106 (2008).
Google Scholar 
105.Rousset, F. Genetic differentiation between individuals. J Evol Biol 13, 58–62 (2000).
Google Scholar 
106.Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
Google Scholar 
107.Hardy, O. & Vekemans, X. SPAGeDi 1.5. A program for Spatial Pattern Analysis of Genetic Diversity. User’s manual http://ebe.ulb.ac.be/ebe/SPAGeDi_files/SPAGeDi_1.5_Manual.pdf. Université Libre de Bruxelles, Brussells, Belgium.[Google Scholar] (2015).108.Jay, F., Sjödin, P., Jakobsson, M. & Blum, M. G. Anisotropic isolation by distance: the main orientations of human genetic differentiation. Mol. Biol. Evol. 30, 513–525 (2013).CAS 
PubMed 

Google Scholar 
109.Piry, S. et al. Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods Ecol. Evol. 7, 1463–1475 (2016).
Google Scholar 
110.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics (Oxford, England) 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

Google Scholar 
111.Hopken, M. W. Pathogen Vectors at The Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Ph.D. thesis, Colorado State University (2016).112.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
113.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).CAS 
Article 
PubMed 

Google Scholar  More

The presents study indicates that Bracon predatorius generally oviposits during early stages of gall development (Fig. 1d) on galls induced by Aceria doctersi mostly on tender leaves (Fig 1a–c) and rarely on petioles and stems13. The number of B. predatorius larvae in parasitized galls ranged from 1–27 (n=93). Eighty-five percent of the examined galls (n=109) were parasitized by B. predatorius. Different development stages of larvae (Fig. 1f,g) and pupae (Fig. 1i) of B. predatorius were found together in some large galls (n=31) (Fig. 1i), which suggests multiple oviposition at different stages of gall development. Dissection of leaf galls two hours after oviposition by B. predatorius always revealed only a single egg (n=8). No live A. doctersi individuals were found close to the parasitoid wasp pupae (Fig. 1h). Aceria doctersi galls parasitised by B. predatorius have also been found in Kodakara (Thrissur district, Kerala) about 100 km away from the type locality in Kozhikode.The larval stages of B. predatorius feed on both juvenile and adults of A. doctersi (Fig 2d–f, Supplementary Video 1) which usually remain close to the erineal hairs on which they feed16; no egg predation occurs. Young larvae of B. predatorius wriggle through in between erineal hairs (Supplementary Video 1). They use their sickle-shaped mandibles (Fig 3b–e) to hunt mites (Supplementary Video 1). Continuous outward and inward movement of mandibles of B. predatorius larvae occurs along with the wriggling movement (Supplementary Video 1). The final instar larvae of B. predatorius are the most active and they feed voraciously at the rate of 5–7 A. doctersi individuals/min (n=8) (Supplementary Video 1).Figure 2Predatory behaviour of Bracon predatorius Ranjith & Quicke sp. nov. (a–c) Relationships between presence/absence and number of B. predatorius, gall size and numbers of mites (median, upper and lower quartiles, 1.5 × interquartile range and outliers): (a) galls without Bracon predatorius (n = 16) are significantly smaller than those with one or more Bracon predatorius (n = 93) (t = 3.7592, DF = 97.265, p-value = 0.000291), (b) galls without Bracon predatorius contain significantly more mites than those with (t = 6.308, DF = 15.877, p-value = 0.0001), (c) mite number as a function of number of Bracon predatorius larvae (only in parasitised galls) with gall volume as co-variate (n = 93, adjusted R2 = 0.4657,F = 21.13 on 3 and 89DF, p-value = 0.0001), gall volume and interaction were non-significant. (d–f) Sequential images of predatory behaviour of Bracon predatorius.Full size imageFigure 3Final instar larval cephalic structure of Bracon predatorius Ranjith & Quicke sp. nov. (a–d) Slide microphotographs of larval head capsule and mandible (a) macerated head capsule in anterior view, (b) head capsule, in dorsal view, (c) head capsule (in part), ventral view, (d) right mandible, in dorsal view, (e) anterior view of living final instar larva of B. predatorius consuming mite.Full size imageUnattacked galls were significantly smaller than those containing B. predatorius (means 217 and 595 respectively; p More

Surveys at the two sites in Kanazawa City showed that the 1st instar larvae had hatched by June 2020 (Fig. 1). After 1 June, the population age structure changed every two weeks until the emergence of 4th instar larvae, which were numerous on 15 and 28 July. Adults were mainly detected on 12 August. This suggests that L. delicatula has a univoltine life cycle in this region, as reported in South Korea22 and Pennsylvania, USA3. The results also indicate that the 1st to 3rd instar larvae molt approximately every two weeks, and the period of development from the 4th instar to the adult phase is approximately one month in this region.The patterns in which adults were captured differed significantly between females and males (Fig. 2). In August, a larger number of females were captured than males. After mid-September, when breeding began, the numbers of females and males captured were approximately equal. Our survey revealed that all individuals had reached adulthood by late August (Fig. 1). Hence, it is unlikely that males emerged much later than females, at least in the survey area up to three meters above the ground. Domingue et al.23 reported a similar female bias just after adult emergence based on a survey of large A. altissima trees up to four meters above the ground. They reported all-female aggregations on the trunks and exposed roots of larger A. altissima trees in the same period as that observed in this study (Fig. S2c,d). Female aggregation is suggested to be a behavior that causes them to crowd into a limited area to feed on optimal resources for producing viable egg masses23. It has also been reported that a high proportion of males are distributed on smaller trees of A. altissima, Vitis sp., and other plant species; however, the number of L. delicatula males on such plants are remarkably lower than those on the larger A. altissima23. Therefore, it is not fully understood why there were fewer males during the early adult emergence period in the survey areas. It is possible that males are distributed in higher positions of the host trees in the early stage of adult development. During the breeding season, courtship behaviour by males (Movie S1) and mating (Movie S2) were frequently observed in the survey area, as previously reported24. Males might change their distribution to nearer ground level during these periods. To clarify this, it will be necessary to expand the survey area to the upper parts of trees in the future.Lycorma delicatula is known to be polyphagous but feeds mainly on A. altissima1,3,4,8,25. In the present study, most L. delicatula were observed on A. altissima (Fig. S2a–d), although some individuals were also observed on wild grapevine A. glandulosa var. heterophylla (Fig. S2e). Wild grapevine is also a favourite host plant of L. delicatula, as previously reported3,8,26. In addition to the host plants, many egg masses were laid on non–plant materials such as building walls (Fig. S2f), as reported previously3,4,8,27.This study showed that most of the eggs of L. delicatula were covered with waxy deposits (99/100 egg masses), as reported previously3,8. The role of wax in L. delicatula is thought to protect eggs from environmental and biotic factors such as natural enemies14,28. In this study, we obtained data supporting the possibility that wax functions against some environmental factors. We observed a significant decrease in the number of eggs per egg mass in exposed environments compared to that in sheltered environments due to peeling off, likely a result of wind and rainfall action. When the wax was removed, the egg numbers per egg mass decreased further (Fig. 3). Moreover, this study showed that the hatching rate of overwintered eggs was significantly reduced when the wax was removed from the egg mass that formed in exposed places (Fig. 4). These results suggest that egg survival is greatly affected by environmental factors, such as wind and rainfall, and that wax may play a role in protecting eggs from these factors. To clarify this, a more detailed analysis should be conducted in an environment where the amount and intensity of wind and rainfall are strictly controlled.To determine the genetic structure of L. delicatula populations in Japan, we conducted a phylogenetic analysis using ND2 and ND6 gene sequences for the samples collected from nine sites in the Hokuriku region and one site in the Okayama Prefecture (Fig. S1a,b, and Table S1). The occurrence of L. delicatula was recently confirmed from Okayama18; in this population, in addition to individuals with white hindwings, many individuals with blue-green coloured hindwings28 have also been reported18. In our analysis, we included both colour types collected from Okayama, and the gene sequence data obtained in previous studies11,21. The results showed that all the samples were classified into one of nine different lineages (i.e. haplotypes), whose geographic distributions were almost consistent with the results of the previous study by Du et al.21. All samples collected from the Hokuriku region (Fig. S1b) in Japan, except for that from Hakusan (JPN_IKHS), had identical sequences and belonged to the same clade as samples from the northwestern area of China (Fig. 5 and Fig. S1a). However, both hindwing colour variations (white and blue-green) from Okayama had identical sequences, and belonged to the same haplotype as the samples from the central area of China, South Korea, and the USA (Fig. 5 and Fig. S1). These results indicate that the genetic structure of L. delicatula in Japan is divided into at least two groups and supports that each group has a history of invasion and colonisation from different regions. Interestingly, this study revealed that the sample collected from Hakusan in Japan in 2010 (site no. 2 in Fig. S1b and Table S1) belonged to the same haplotype as the samples from the central areas of China, South Korea, and the USA, but not to those collected from the same Hokuriku region in Japan in 2020 (Fig. 5 and Fig. S1b). This may indicate that in the last decade, the central China haplotype previously existing in the Hokuriku area has been replaced by the northwestern China haplotype. To clarify this, a more detailed analysis using high-resolution markers7,21,29 and a larger sample size, including old, preserved specimens that were captured during the first invasion into the Hokuriku area, is required.Lycorma delicatula has rapidly expanded its distribution in several countries. In South Korea, the first specimen-confirmed report of L. delicatula was published in 2004. Thereafter, its distribution expanded throughout South Korea, and population densities increased by 20114,8. In the USA, it was first detected in Pennsylvania in 20149, and by 2021, had expanded its distribution into 12 other surrounding states4,10 (Fig. S1c). In contrast, in Japan, the distribution of L. delicatula has been limited to the Hokuriku region (Fig. S1b) since it was first reported in the Ishikawa Prefecture in 200914 until it was detected in Osaka Prefecture in 201717, even though the preferred host plant, A. altissima, is distributed throughout Japan19,20. Various biotic and/or abiotic factors seem to be involved in this relatively slow expansion of distribution in Hokuriku, Japan. The most likely factor is the influence of climate, as shown previously22,30,31. Hokuriku has a large amount of precipitation, including snowfall in winter. For example, mean annual precipitation in Kanazawa is 2401.5 mm32, much higher than that of Philadelphia (1060.0 mm), and Seoul (1460.0 mm)33. Precipitation appears to cause a decrease in egg viability (Figs. 3 and 4). This might explain the suppressed distributional range expansion of L. delicatula from Hokuriku, although it would be necessary to confirm that egg mortality in the Hokuriku region is higher than in other regions in future studies. In addition, indigenous predators and parasitoids in the region may play an important role in suppressing the population of L. delicatula, which should also be explored in future research.In Japan, L. delicatula has recently been found in Osaka17 and Okayama18, which are warm regions with relatively low-precipitation (mean annual precipitation in these areas are 1338.3 mm and 1143.1 mm, respectively32). The Okayama population has the same haplotype as the one that has rapidly increased in South Korea and the USA (Fig. 5 and Fig. S1). This may mean that the southwestern region of Japan is at high risk of L. delicatula invasion. Hence, detailed monitoring of L. delicatula is needed in these regions. Simultaneous preventative action to control the spread of L. delicatula is also required. Control using pesticides may adversely affect the indigenous species, therefore alternative methods should be used. Further verification on the vulnerability of dewaxed eggs of L. delicatula to precipitation (Figs. 3 and 4) is needed, but this study has provided valuable insights into how this pest insect could be managed in an environmentally friendly way. A deeper understanding of the specific ecology of invasive alien species is necessary for sustainable environmental conservation. More

cSAR modelWe used the numerical cSAR model16 to calculate native species loss of four taxonomic groups (mammals, amphibians, reptiles, birds) caused by 45 LU types that were mapped onto a reference 5 × 5 arcmin grid (we also call individual grid cells landscapes in the following) of the global land area excluding Greenland and Antarctica. Calculations were based on (a) gridded LU-intensity and LU-type information (see below), (b) effects of LU-intensity on species richness derived from recently published meta-analyses5,21, and (c) information on species distributions and habitat affiliations from IUCN and Birdlife International databases41,42. For presentation of results, we aggregated the calculated effects of the 45 LU-types into those of six broad LU-types (cropland (30 annual crop types); pastures (non-grassland converted to grassland); grazing land (natural/ near-natural areas with livestock grazing); builtup (sealed areas); plantations (11 permanent crop types plus timber plantations), and forests (natural/ near-natural forest under forestry); see Supplementary Data 2 for details).In the below formulae, we use the following indices: g = taxonomic group, n = grid cell, b = broad LU-type. We calculated the total number of native species losses for each taxonomic group g and grid cell n as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}times left(1-{left(frac{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}+{sum }_{{{{{{rm{b}}}}}}=1}^{{{{{{{rm{b}}}}}}}_{{{{{{rm{n}}}}}}}}{{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}times {{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}}{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}right)}^{{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}right)$$
(1)
Here, ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the potential species richness in pristine ecosystems, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}) is the pristine ecosystem area where no LU occurs (in m2), ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the grid cell’s terrestrial area (in m2), ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the affinity parameter, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the area of the LU-type, and ({{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}) is the grid cell’s SAR exponent taken from ref. 43. The model’s components are described below.Potential species richness ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})
We defined potential species richness of a landscape as the number of species for which the area-of-habitat (AOH) under pre-human or pristine conditions overlap the landscape (here referred to as native species and native AOH). Following ref. 44, we used range maps of all mammal, reptile and amphibia species provided by the IUCN45 and bird species by Birdlife International46 databases to calculate gridded species richness via, first, overlapping each species’ range polygons with a 5 × 5 arcmin reference raster, second, constraining the resulting list of species per raster cell to those adapted to the pristine ecosystem(s) of these raster cell as defined in ref. 25, and, third, constraining the resulting list of species by each species’ elevational range, also provided by the IUCN42. Here, we are interested in the total historical range of extant species46 and hence included all parts of the range where the species were indicated as (i) Extant, Probably Extant, Possibly Extinct, Extinct and Presence Uncertain, (ii) Native and Reintroduced, and (iii) Resident or present during the Breeding Season or the Non-breeding Season, in the cited data sources.We first rasterized each species’ range polygons using the raster and fasterize packages in R47. Second, for each terrestrial grid cell in our reference raster, we created a species list by extracting each species’ gridded range using the velox package in R. Third, we ascertained that each cell’s species list contained only terrestrial species by excluding species which exclusively have aquatic habitat affiliations. The species’ habitat affiliations were directly taken from the IUCN and Birdlife databases42,46. Fourth, we removed species from this cell’s species list which, according to the IUCN, are not affiliated with that cell’s pristine ecosystem. We therefore manually assigned the habitats distinguished in the ICUN habitat affiliation scheme to one or several of the 14 broad ecosystem types distinguished and mapped in ref. 25 (Supplementary Data 4). The maps in the referenced study “approximate the original extent of natural communities prior to major land-use change”48 and, hence, represent pristine ecosystems or potential vegetation types. Fifth, we excluded species whose elevational range did not overlap the elevational range of the grid cell using the GMTED2010 dataset (www.usgs.gov). These refinement steps were taken because species’ range maps usually deliver coarse-scale extent of occurrence rather than AOH information44. Finally, we counted the species identities in each grid cell as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). The species lists created in this step were also used for later steps, referred to in the appropriate sections.Areas of pristine ecosystems (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}),({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) and LU-types (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The potential pristine ecosystem area ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is defined as the cell’s entire terrestrial area (excluding water bodies as defined by the land mask of the HYDE 3.2.1 database49). As the area of pristine ecosystems currently found in each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}})), we used the proportion of ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) marked as wilderness and non-productive/ snow areas as described below. The area of each of the 45 LU-types within each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) was extracted from respective land cover and LU maps applying the approach outlined in ref. 50 – with 2010 as year of reference wherever possible (Supplementary Data 2).Following ref. 50, builtup land, total cropland (including annual and permanent crops/ plantations), permanent pastures (areas used as pastures for more than five years) and rangeland (available in the two sub-categories natural and converted) extents were taken from the LU database HYDE 3.2.149 which was adapted to include rural infrastructure areas by assigning 5% of each grid cell’s cropland area to builtup land. We then split the total cropland cover into areas used for 41 different annual and permanent crops by integrating data from the Spatial Production Allocation Model (SPAM) for 201051,52 and adjusting them to cropland extent in the data from ref. 50. To comply with the IUCN habitats classification scheme42, some of these crops were grouped into the plantation category (permanent crops), while the remainder was grouped into the cropland category (annual crops; see Supplementary Data 2 for details).Wilderness areas were derived from the combination of human footprint data, i.e., a spatially explicit inventory of human artefact density available for 1993 and 200953,54 and intact forest landscape data for 2000 and 201355. Core wilderness areas without human use were defined as having a value of zero human footprint and, in forests, being part of an intact forest landscape55. Within forests, the additional category of peripheral wilderness was introduced for areas where either only zero human footprint is recorded, or only an intact forest landscape exists.The area remaining in each grid cell after allocating the above land cover types represents area covered by used forests and other land with mixed land uses56. Hence, in addition to the approach in ref. 50, forests were split into deciduous and coniferous forests based on the description of the ESA CCI land cover categories57. This distinction was necessary for the differentiated allocation of wood harvest (see below). A further refinement was applied by identifying plantation forests, defined as areas in non-forest biomes converted to forests for forestry and areas in forest biomes converted to non-native forest types58, which were linked to the IUCN habitat class plantations (Supplementary Data 2).As in ref. 50, the remaining area not allocated to any of the land cover or LU types above is denoted as “other land, maybe grazed”56. These lands, typically treeless or bearing scattered tress, were allocated to converted grasslands on areas that potentially carry forests or to natural grassland on areas where the potential vegetation would not consist of forests25.To arrive at the six broad LU-type aggregates compatible with the IUCN and Birdlife habitat affiliation schemes42,46 and PREDICTS categories21 (needed for quantifying LU-intensity effects, see below in section “Affinity parameter” for details), we rearranged and aggregated the described LU layers as needed (see Supplementary Data 2 for an overview). (a) Builtup remained as described above. (b) Cropland was defined as annual crops, covering the respective 29 SPAM categories plus fodder. (c) Pastures were defined as areas where pristine ecosystems were converted to grasslands and includes permanent pastures and converted rangelands from HYDE 3.2.149, plus those parts of “other land maybe grazed” located in forest25. (d) Grazing land was defined as natural or near-natural areas where grazing occurs and includes natural rangelands from HYDE 3.2.149, plus 50% of each grid cell’s open forest area and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally grazed and hence given low grazing intensity (see below), plus those parts of “other land maybe grazed” located in non-forest25. (e) Forests were defined as forests where forestry occurs and includes 100% of each grid cell’s closed forest area, 50% of each grid cell’s open forest area, and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally used for forestry and given low intensity (see below). (f) Plantations were defined as areas where pristine ecosystems were converted into plantation-like LU and include the 11 SPAM categories representing permanent / plantation crops, plus used forests identified by ref. 58 as plantations (see above). As stated above, these aggregated broad LU-types were needed to align the different LU categorizations used in the different data sources with each other. The effects on biodiversity were then calculated on each of the 45 LU-types and afterwards aggregated to the six broad LU-types to give a better overview.Continuous LU-intensity indices (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))We constructed continuous LU-intensity indices ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) for each of the 45 LU-types based on gridded management descriptors15. For this purpose, we used two different sets of intensity indicators (called Set 1 and Set 2) to compare and combine their impact on predicted species loss. We used two indicator sets to account for the multidimensional nature of LU-intensity9,12 and to include a wide range of available data products. For an overview of which data products and assumptions went into the individual sets, please refer to Supplementary Data 2.Set 1Set 1 is taken from the human appropriation of net primary production (HANPP) framework, a socioecological indicator basically describing the LU mediated extraction of biotic resources in the context of global biogeochemical cycles23. We used the ratio of HANPPharv to NPPpot as a systemic metric to assess LU-intensity12,22, with HANPPharv being harvested or extracted biomass and NPPpot being NPP of potential natural vegetation, i.e. the vegetation existing under current climate conditions in the hypothetical absence of LU23. The ratio HANPPharv/NPPpot relates harvest to the productivity potential of the land where the harvest takes place and is, thus, robust against geographic differences in natural productivity. As it is related to energy availability in ecosystem food chains, it may be linked to the species-energy relationship, the strongest correlate of spatial biodiversity patterns at larger scales59.For calculating NPPpot, LPJ-GUESS60 version 4.0.1 was used in its standard configuration but with nitrogen limitation disabled and forced by the CRU-NCEP climate data61,62 aggregated from 6-hourly to monthly fields.HANPPharv of all LU-types except builtup was calculated based on the FAOSTAT database by principally accounting total biomass flows via conversion and expansion factors as outlined in ref. 63. As a special case, HANPPharv of built-up was assumed to be half of the actual NPP, which was defined as 1/3 of the potential vegetation in ref. 64. This results in a constant intensity on built-up land of ~17% of NPPpot.HANPPharv of permanent and non-permanent crops was spatially downscaled following 40 permanent and non-permanent crop-specific production patterns from the Spatially-Disaggregated Crop Production Statistics Database (SPAM52), merging minor SPAM categories such as “robusta coffee” and “arabica coffee” to ensure consistency with FAOSTAT reporting. Additionally, we added the LU-type fodder, which was downscaled following NPPpot patterns.Harvest of natural and plantation forest is reported by FAOSTAT in the four categories industrial roundwood, wood fuel, and coniferous and deciduous. We allocated industrial roundwood harvest to closed forests, while we split wood fuel harvest in proportion to productivity between closed and open forests, independently for deciduous and coniferous forests, respectively. For Set 1, we assumed forestry harvest to follow the patterns of forest NPPpot65. These intensity definitions were used for both natural and plantation forest.Reported harvest on grazing land and pastures was allocated following patterns of aboveground NPP accessible for grazing as reported in ref. 63. Following the assumption that systems with low natural productivity allow for a lower maximum harvest than systems with high productivity, we assigned a maximum harvest intensity of 40% at a level of accessible NPP of 20 gC/m² and increased this linearly to a maximum grazing intensity of 80% at 250 gC/m². Such, harvest was concentrated on grazing land and pastures with high productivity. In cases where the calculated national grazing land and pasture harvest demand surpassed NPP availability on grassland, we used information on fertilization rates on grassland66 to either adjust NPP or harvest data: NPP was boosted in countries where more than 5% of overall fertilizer consumption was applied to grasslands, while countries where no relevant fertilization of grasslands occurred, the reported harvest demand was reduced accordingly, assuming it will be met from other sources. This intensity definition was applied to both (natural) gazing land and (converted) pastures.Set 2For the LU-intensity indicator Set 2, we used published data from different sources. For cropland we used the input metric nitrogen application rates (in kg N/ha of cropland)12,22, available for 17 major non-permanent crops67,68. For crops from the SPAM categories (see above) not covered by these data, we used the within-grid-cell area-weighted average of other crops in the same cell. For areas designated as cropland in our data (see above) but not in the available N application data, we assumed national average values of the respective crop.For pastures and grazing land, we used gridded livestock information69. We used information on the typical weight per animal to calculate livestock units70 and aggregated the data for all ruminant species (buffalo, horses, cattle, sheep, goats). This data on livestock numbers per grid cell was then divided by land area per grid cell to arrive at livestock densities, which were applied to the extent of grazing land and pastures. Please note that this dataset contains information on the number of livestock (per species group) per area in a grid cell and thereby differs from the grazing intensity metric applied in Set 171, as grazing animals may be fed from other sources than grassland72.For builtup, we aggregated a 1 km built-up area density map for 201473 to the target resolution of 5 arc min and used it as is as intensity indicator.For natural and plantation forest, we used the same data as described above for Set 1, but we assumed forestry harvest to follow another pattern. We calculated the difference between potential and actual biomass stocks74 and allocated forestry harvest within each country according to these patterns, i.e., the share of national forest harvest allocated to a forest cell corresponds to its share in the national difference between potential and actual biomass stocks.Scaling of LU-intensity indicesFor the purpose of applying linear functions on species richness loss caused by LU-intensity (see below, affinity parameter), we scaled each LU-intensity indicator to values between 0 and 1, with 0 being no intensity (hypothetical) and 1 being the intensity threshold above which an increase of intensity causes no further increase of species loss. This threshold is not necessarily the highest recorded value of an intensity indicator, as effects may be regionally variable. We therefore winsorized some LUI indicators to that intensity threshold before scaling them (dividing by this threshold). These thresholds were defined as follows.In Set 1, maximum intensity was assumed to be reached at harvesting 100% of NPPpot on cropland.In forests (natural and plantation), maximum intensity was derived from ref. 75, which limits sustainably harvestable aboveground biomass in forests to 30% of NPPpot. In concordance with the HANPP framework, we included the belowground biomass destroyed by forestry using biome-specific factors76.On grazing land and pastures, maximum intensity was defined as removal of all NPP accessible for grazing. This considers only the aboveground and non-woody parts of NPPpot. The maximum removable aboveground share was estimated as 50% of NPPpot, and the proportion of non-woody vegetation was estimated as 30% (in closed-canopy land cover types) or 100% (on open land cover types)71. HANPPharv/NPPpot was assumed to be at its maximum intensity level when the maximum level of grazing intensity, as described above, was reached. The resulting thresholds are in line with literature77,78, and assume that maximum intensities will be reached faster in systems with low natural productivity.In Set 2, for all crop types (permanent and non-permanent) except legumes, N application rates were capped at 150 kg N/ha, i.e., we assumed that 150 kg N/ha was the maximum LU-intensity on cropland, beyond which no further species richness loss occurs, i.e., after which an increase of N application rates causes no further increase in species loss based on ref. 79. For legumes, under the assumption that they need less N fertilizer due to their N-fixing capabilities, we assumed the following cap values, based on information provided in ref. 80: beans and lentils at 110 kg N/ha, chickpeas at 100 kg N/ha, soybean at 70 kg N/ha and cowpeas, pigeon peas and other pulses at kg N/ha 90.For pastures and grazing land, maximum intensity was defined as the per biome 80th percentile of livestock-density.The intensity of builtup area was not winsorized.Affinity parameter (({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) can be regarded as a LU-intensity dependent weighting factor for the area of each of the 45 LU-types used here. For low affinity, i.e., a small fraction of native species is left due to LU, the area of this LU-type (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) in formula 1) is down-weighted, resulting in higher species loss ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) (and vice versa). The affinity parameter consists of two terms, (a) ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of species affiliated with a given LU-type, and (b) ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) that remains when LU-intensity (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) rises to a particular level.The fraction of species affiliated with a certain LU-type under minimal ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) (({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) is based on the habitat affiliation information taken from the IUCN Red List API45 and BirdLife data46 cross-tabulated with our mapped LU-types (Supplementary Data 2). We calculated ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) by dividing the number of species affiliated with a certain LU-type (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}})) by the number of native species expected in this cell under pristine ecosystem conditions (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) as$${{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}=frac{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}}{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}$$
(2)
Please note that for the two unconverted broad LU-types grazing land and forests, respectively (see above), we assumed no land conversion prior to its use, leading to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). We further assumed that the whole fraction of LU-type affiliated species ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) are present in a given LU-type as long as ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is minimal (here 0.83, we argue that extrapolation outside the measured intensity range is uncertain, and that an increase in LUI above 0.83 (i.e., Intense) might not necessarily result in even stronger effects on SR. See Supplementary Fig. 5, which illustrates the results of these considerations and shows the continuous effect of ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) on SR used in this study.The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) was then calculated as follows and inserted into formula 1 (cSAR model).$${{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={left({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}times {left({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}$$
(5)
Species loss caused by LU-intensity (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}))In order to calculate the relative impact of LU-intensity on species richness, we re-ran the model with ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 in all grid cells and LU types, thereby effectively setting ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 and ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}). The results of this model can be considered as delivering the land conversion effect without any possible enhancement by intensification. In addition, we designed a hypothetical, back-of-the-envelope intensification scenario where ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 in all grid cells and LU-types.The contribution of intensity to the species richness loss was then calculated as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}=left({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}right)/{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}$$
(6)
With ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}) being the results of the ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 model and ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) from Eq. (1).Native area-of-habitat loss of individual speciesThe cSAR model calculates by how many species the native species pool is reduced in response to LU in each 5 arcmin grid cell. However, it does not identify the individual species lost. To estimate each species’ native AOH loss, we randomly drew the predicted number of species lost from the native species pool of each cell.First, we rounded the number of species lost as calculated by the cSAR model to the next integer for losses from both conversion (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}})) and intensification (here taken as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}), see section above: “Species loss caused by LU-intensity”). To avoid rounding all values below 0.5 to 0, and, hence, to underestimate low levels of species loss, particularly in species-poor regions, we used a two-step rounding routine. First, prior to actual rounding, we randomly decided whether a number is rounded to the next higher or lower integer, with the likelihood of either decision depending on the decimal number’s (positive or negative) distance to 0.5 (i.e., the decimal number gave the likelihood of rounding up). Second, we took the species list used to generate ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) (see above under potential species richness) and modified it to either contain only species affiliated or unaffiliated with each LU-type, yielding two species lists for each grid cell and LU-type, respectively. The list of species affiliated with a particular LU-type was then used to select species predicted to get lost due to intensification, while the list of species not affiliated with it was used to select species lost due to conversion.From each grid cell, we then randomly drew as many species from these lists as determined by the rounding routine above, considering each LU-type and whether the number of lost species was caused by intensification or conversion. However, in each cell, each species could only be drawn once, independently of whether it was affiliated with several LU-types. As a consequence, the order in which LU-types are considered when drawing species is relevant for the outcome of the calculation. For instance, species simultaneously unaffiliated with cropland and affiliated with natural forest may never be drawn in response to intensification of natural forest if losses due to conversion into cropland are always handled first. Therefore, we randomly iterated the sequence by which LU-types were considered, i.e., the order of LU-types, in the random draw routine in each of 100 repeated runs.We repeated the random-draws 100 times to yield a representative sample and processed the resulting 100 lists of species-per-cell losses in the following way. For each of the 100 runs, we summed the areas of all cells each species was drawn from, i.e., predicted to be lost, across all LU-types and within individual LU-types, yielding 100 area sums per species (one per run). From these 100 areas, we calculated the mean and the 0.025th and 0.975th quantiles as 95% confidence intervals (CIs). The means and CIs were then divided by the species’ global AOH (sums of cell areas in native range), thereby yielding the proportional global AOH loss attributable to current LU in general, and to different LU-types or land conversion vs. LU-intensity in particular.Description/ presentation of resultsAll cSAR model calculations were based on global land use maps that distinguish 45 LU-types as described above. For the sake of simplicity, we present results aggregated to the six broad LU-types cropland, pastures, natural grazing land, built-up, plantations, and forests (natural/ near-natural forest under forestry; see Supplementary Data 2). All calculated SR decreases are expressed in percentage losses relative to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}).Summary statistics mentioned in the text and Supplementary Data 1, 5 and 6 were calculated as follows. Global, biome-wide and nation-wide average species losses due to conversion, LU-intensity or both were calculated as cell-area weighted means across all cells with native terrestrial vertebrate species either excluding or including wilderness areas (which, for this purpose, are defined as cells where the sum of all LU area equals 0). The percentual land area exceeding a certain threshold of calculated SR decline were calculated by dividing the area sum of all cells exceeding that threshold by the area sum of all cells with native species excluding wilderness.Differences among average AOH losses (across all taxonomic groups) mentioned in the text and Supplementary Data 3 were modelled using generalized linear models assuming a binomial distribution (proportional AOH loss between 0 and 1), each species’ mean AOH loss (mean of 100 random draw runs) as response, and either (a) IUCN categories, (b) land use types, or (c) taxonomic group as predictor variables. Differences between predictor variable levels were then alculated by multiple comparisons via p-values adjusted with the Tukey method. A p-value of  More

1.Assadi, A., Pirnalouti, A. G., Malekpoor, F., Teimori, N. & Assadi, L. Impact of air pollution on physiological and morphological characteristics of Eucalyptus camaldulensis Den. J. Food Agric. Environ. 9, 676–679 (2011).
Google Scholar 
2.Rai, R., Rajput, M., Agrawal, M. & Agrawal, S. B. Gaseous air pollutants: A review on current and future trends of emissions and impact on agriculture. J. Sci. Res. 55, 77–102 (2011).
Google Scholar 
3.Brooker, R. W. Plant-plant interactions and environmental change. New Phytol. 171, 271–284. https://doi.org/10.1111/j.1469-8137.2006.01752.x (2006).Article 
PubMed 

Google Scholar 
4.Jefferies, R. L. & Maron, J. L. The embarrassment of riches: Atmospheric deposition of nitrogen and community and ecosystem processes. Trends Ecol. Evol. 12, 74–78 (1997).CAS 
Article 

Google Scholar 
5.Egerton-Warburton, L. M. & Allen, E. B. Shifts in arbuscular mycorrhizal communities along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10, 484–496 (2000).Article 

Google Scholar 
6.Stiling, P. & Cornelissen, T. How does elevated carbon dioxide (CO2) affect plant herbivore interactions? A field experiment and meta-analysis of CO2-mediated changes on plant chemistry and herbivore performance. Glob. Change Biol. 13, 1823–1842. https://doi.org/10.1111/j.1365-2486.2007.01392.x (2007).ADS 
Article 

Google Scholar 
7.Zvereva, E. L. & Kozlov, M. V. Consequences of simultaneous elevation of carbon dioxide and temperature for plant-herbivore interactions: A metaanalysis. Glob. Change Biol. 12, 27–41. https://doi.org/10.1111/j.1365-2486.2005.01086.x (2006).ADS 
Article 

Google Scholar 
8.Kopper, B. J. & Lindroth, R. L. Effects of elevated carbon dioxide and ozone on the phytochemistry of aspen and performance of an herbivore. Oecologia 134, 95–103. https://doi.org/10.1007/s00442-002-1090-6 (2003).ADS 
Article 
PubMed 

Google Scholar 
9.Maron, J. L. & Crone, E. Herbivory: Effects on plant abundance, distribution and population growth. Proc. Biol. Sci. 273, 2575–2584. https://doi.org/10.1098/rspb.2006.3587 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Karban, R. & Baldwin, I. T. Induced Responses to Herbivory (University of Chicago Press, 2007).
Google Scholar 
11.Lambers, H. Rising CO2, secondary plant metabolism, plant-herbivore interactions and litter decomposition. Vegetation 104(105), 263–271 (1993).Article 

Google Scholar 
12.Poorter, H. et al. The effect of elevated CO2 on the chemical composition and construction costs of leaves of 27 C3 species. Plant Cell Environ. 20, 472–482 (1997).CAS 
Article 

Google Scholar 
13.Thaler, J. S., Stout, M. J., Karban, R. & Duffey, S. S. Exogenous jasmonates simulate insect wounding in tomato plants (Lycopersicon esculentum) in the laboratory and field. J. Chem. Ecol. 22, 1767–1781 (1996).CAS 
Article 

Google Scholar 
14.De Moraes, C. M. et al. Herbivore-infested plants selectively attract parasitoids. Nature 393(6685), 570–573 (1998).ADS 
Article 

Google Scholar 
15.Thaler, J. S. Jasmonate-inducible plant defenses cause increased parasitism of herbivores. Nature 399, 686–688 (1999).ADS 
CAS 
Article 

Google Scholar 
16.Kessler, A. & Baldwin, I. T. Defensive function of herbivore-induced plant volatile emissions in nature. Science 291(5511), 2141–2144 (2001).ADS 
CAS 
Article 

Google Scholar 
17.Orrock, J., Connolly, B. & Kitchen, A. Induced defences in plants reduce herbivory by increasing cannibalism. Nat. Ecol. Evol. 1, 1205–1207. https://doi.org/10.1038/s41559-017-0231-6 (2017).Article 
PubMed 

Google Scholar 
18.Blande, J. D., Holopainen, J. K. & Niinemets, Ü. Plant volatiles in polluted atmospheres: Stress responses and signal degradation. Plant Cell Environ. 37, 1892–1904. https://doi.org/10.1111/pce.12352 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Bidart-Bouzat, M. G. & Imeh-Nathaniel, A. Global change effects on plant chemical defenses against insect herbivores. J. Integr. Plant Biol. 50, 1339–1354. https://doi.org/10.1111/j.1744-7909.2008.00751.x (2008).CAS 
Article 
PubMed 

Google Scholar 
20.Tao, L., Berns, A. R., Hunter, M. D. & Johnson, M. Why does a good thing become too much? Interactions between foliar nutrients and toxins determine performance of an insect herbivore. Funct. Ecol. 28, 190–196. https://doi.org/10.1111/1365-2435.12163 (2014).Article 

Google Scholar 
21.Forieri, I., Hildebrandt, U. & Rostás, M. Salinity stress effects on direct and indirect defence in maize. Environ. Exp. Bot. 122, 68–77. https://doi.org/10.1016/j.envexpbot.2015.09.007 (2016).CAS 
Article 

Google Scholar 
22.Maathuis, F. J. Sodium in plants: Perception, signaling, and regulation of sodium fluxes. J. Exp. Bot. 65, 849–858. https://doi.org/10.1093/jxb/ert326 (2014).CAS 
Article 
PubMed 

Google Scholar 
23.Rengasamy, P. World salinization with emphasis on Australia. J. Exp. Bot. 57(5), 1017–1023 (2006).CAS 
Article 

Google Scholar 
24.Harmon, J. P. & Daigh, A. L. M. Attempting to predict the plant-mediated trophic effects of soil salinity: A mechanistic approach to supplementing insufficient information. Food Webs 13, 67–77. https://doi.org/10.1016/j.fooweb.2017.02.002 (2017).Article 

Google Scholar 
25.Zribi, L. et al. Application of chlorophyll fluorescence for the diagnosis of salt stress in tomato “Solanum lycopersicum (variety Rio Grande)”. Sci. Hortic. 120, 367–372. https://doi.org/10.1016/j.scienta.2008.11.025 (2009).CAS 
Article 

Google Scholar 
26.Farooq, M. et al. Effects, tolerance mechanisms and management of salt stress in grain legumes. Plant Physiol. Biochem. PPB 118, 199–217. https://doi.org/10.1016/j.plaphy.2017.06.020 (2017).CAS 
Article 
PubMed 

Google Scholar 
27.Zhang, C. et al. Uptake and translocation of organic pollutants in plants: A review. J. Integr. Agric. 16, 1659–1668. https://doi.org/10.1016/s2095-3119(16)61590-3 (2017).CAS 
Article 

Google Scholar 
28.Dumbroff, E. B. & Cooper, A. W. Effects of salt stress applied in balanced nutrient solution at several stages during growth of tomato. Bot. Gazette 135, 219–224 (1974).Article 

Google Scholar 
29.Aucejo-Romero, S., Gómez-Cadenas, A. & Jacas-Miret, J.-A. Effects of NaCl-stressed citrus plants on life-history parameters of Tetranychus urticae (Acari: Tetranychidae). Exp. Appl. Acarol. 33, 55–67 (2004).Article 

Google Scholar 
30.Polack, L. A., Pereyra, P. C. & Sarandón, S. J. Effects of plant stress and habitat manipulation on aphid control in greenhouse sweet peppers. J. Sustain. Agric. 35, 699–725. https://doi.org/10.1080/10440046.2011.606489 (2011).Article 

Google Scholar 
31.Dombrowski, J. E. Salt stress activation of wound-related genes in tomato plants. Plant Physiol. 132, 2098–2107. https://doi.org/10.1104/pp.102.019927 (2003).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Younginger, B., Barnouti, J. & Moon, D. Interactive effects of mycorrhizal fungi, salt stress, and competition on the herbivores of Baccharis halimifolia. Ecol. Entomol. 34(5), 580–587 (2009).Article 

Google Scholar 
33.Orrock, J. L. et al. Plants eavesdrop on cues produced by snails and induce costly defenses that affect insect herbivores. Oecologia 186, 703–710. https://doi.org/10.1007/s00442-018-4070-1 (2018).ADS 
Article 
PubMed 

Google Scholar 
34.Rodriguez-Saona, C., Chalmers, J. A., Raj, S. & Thaler, J. S. Induced plant responses to multiple damagers: Differential effects on an herbivore and its parasitoid. Oecologia 143, 566–577. https://doi.org/10.1007/s00442-005-0006-7 (2005).ADS 
Article 
PubMed 

Google Scholar 
35.Connolly, B. M., Guiden, P. W. & Orrock, J. L. Past freeze–thaw events on Pinus seeds increase seedling herbivory. Ecosphere 8, e01748. https://doi.org/10.1002/ecs2.1748 (2017).Article 

Google Scholar 
36.Ainsworth, E. A. & Gillespie, K. M. Estimation of total phenolic content and other oxidation substrates in plant tissues using Folin–Ciocalteu reagent. Nat. Protoc. 2, 875–877. https://doi.org/10.1038/nprot.2007.102 (2007).CAS 
Article 
PubMed 

Google Scholar 
37.Connolly, B., Ripka, M., & Ebersole, W. Microclimate measurements (15-minute intervals) at Fish Lake Environmental Education Center (Eastern Michigan University; Lapeer County, Michigan, USA), Dryad, Dataset. https://doi.org/10.5061/dryad.3n5tb2rh4 (2021).38.Edwards, P. J. & Wratten, S. D. Wound induced defenses in plants and their consequences for patterns of insect grazing. Oecologia 59, 88–93 (1983).ADS 
CAS 
Article 

Google Scholar 
39.R Core Team. R Foundation for Statistical Computing. R: A language and environment for statistical computing. Vienna, Austria. https://www.R-project.org/ (2019)40.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
41.Therneau, T. A package for survival analysis in R. R package version 3.2-11. https://CRAN.R-project.org/package=survival (2021)42.Kassambara, A., Kosinski, M., & Biecek, P. survminer: Drawing survival curves using ‘ggplot2’. R package version 0.4.9. https://CRAN.R-project.org/package=survminer (2021)43.Wickham, H., François, R., Henry, L., & Müller, K. dplyr: A grammar of data manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr (2021)44.Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.6.2-1. https://CRAN.R-project.org/package=emmeans (2021)45.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar 
46.Katerji, N., van Hoorn, J. W., Hamdy, A. & Mastrorilli, M. Salinity effect on crop development and yield, analysis of salt tolerance according to several classification methods. Agric. Water Manag. 62, 37–66. https://doi.org/10.1016/s0378-3774(03)00005-2 (2003).Article 

Google Scholar 
47.Snell-Rood, E. C., Espeset, A., Boser, C. J., White, W. A. & Smykalski, R. Anthropogenic changes in sodium affect neural and muscle development in butterflies. Proc. Natl. Acad. Sci. U.S.A. 111, 10221–10226. https://doi.org/10.1073/pnas.1323607111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Negrão, S., Schmöckel, S. M. & Tester, M. Evaluating physiological responses of plants to salinity stress. Ann. Bot. 119, 1–11. https://doi.org/10.1093/aob/mcw191 (2017).Article 
PubMed 

Google Scholar 
49.Mogren, C. L. & Trumble, J. T. The impacts of metals and metalloids on insect behavior. Entomol. Exp. Appl. 135, 1–17. https://doi.org/10.1111/j.1570-7458.2010.00967.x (2010).CAS 
Article 

Google Scholar 
50.Schultz, J. C. Habitat selection and foraging tactics of caterpillars in heterogeneous trees. In Variable Plants and Herbivores in Natural and Managed Systems (eds Denno, R. F. & McClure, M. S.) 61–90 (Academic Press Inc, 1983).Chapter 

Google Scholar 
51.Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval lepidoptera. Annu. Rev. Entomol. 47, 361–393 (2002).CAS 
Article 

Google Scholar 
52.Elvira, S., Williams, T. & Caballero, P. Juvenile hormone analog technology: Effects on larval cannibalism and the production of Spodoptera exigua (Lepidoptera: Noctuidae) nucleopolyhedrovirus. J. Econ. Entomol. 103, 577–582. https://doi.org/10.1603/ec09325 (2010).Article 
PubMed 

Google Scholar 
53.Elderd, B. D. Bottom-up trait-mediated indirect effects decrease pathogen transmission in a tritrophic system. Ecology 100, e02551 (2019).Article 

Google Scholar 
54.Mitchell, T. S., Shephard, A. M., Kalinowski, C. R., Kobiela, M. E. & Snell-Rood, E. C. Butterflies do not alter oviposition or larval foraging in response to anthropogenic increases in sodium. Anim. Behav. 154, 121–129. https://doi.org/10.1016/j.anbehav.2019.06.015 (2019).Article 

Google Scholar 
55.Beaton, L. L. & Dudley, S. A. Tolerance to salinity and manganese in three common roadside species. Int. J. Plant Sci. 165, 37–51 (2004).CAS 
Article 

Google Scholar 
56.Kim, H. et al. Effect of methyl jasmonate on phenolics, isothiocyanate, and metabolic enzymes in radish sprout (Raphanus sativus L.). J. Agric. Food Chem. 54, 7263–7269 (2006).CAS 
Article 

Google Scholar 
57.Inbar, M., Doostdar, H. & Mayer, R. T. Suitability of stressed and vigorous plants to various insect herbivores. Oikos 94, 228–235 (2001).Article 

Google Scholar 
58.English-Loeb, G., Stout, M. J. & Duffey, S. S. Drought stress in tomatoes: Changes in plant chemistry and potential nonlinear consequences for insect herbivores. Oikos 79, 456–468 (1997).Article 

Google Scholar 
59.Welti, E. A. R. & Kaspari, M. Sodium addition increases leaf herbivory and fungal damage across four grasslands. Funct. Ecol. https://doi.org/10.1111/1365-2435.13796 (2021).Article 

Google Scholar 
60.Caparrotta, S. et al. Induction of priming by salt stress in neighboring plants. Environ. Exp. Bot. 147, 261–270. https://doi.org/10.1016/j.envexpbot.2017.12.017 (2018).CAS 
Article 

Google Scholar 
61.Nedjimi, B. & Daoud, Y. Cadmium accumulation in Atriplex halimus subsp. Schweinfurthii and its influence on growth, proline, root hydraulic conductivity and nutrient uptake. Flora Morphol. Distrib. Funct. Ecol. Plants 204, 316–324. https://doi.org/10.1016/j.flora.2008.03.004 (2009).Article 

Google Scholar 
62.Methenni, K. et al. Salicylic acid and calcium pretreatments alleviate the toxic effect of salinity in the Oueslati olive variety. Sci. Hortic. 233, 349–358. https://doi.org/10.1016/j.scienta.2018.01.060 (2018).CAS 
Article 

Google Scholar 
63.Song, Y. Y. et al. Hijacking common mycorrhizal networks for herbivore-induced defence signal transfer between tomato plants. Sci. Rep. 4, 3915. https://doi.org/10.1038/srep03915 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Evelin, H., Kapoor, R. & Giri, B. Arbuscular mycorrhizal fungi in alleviation of salt stress: A review. Ann. Bot. 104, 1263–1280. https://doi.org/10.1093/aob/mcp251 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Armbrust EV. The life of diatoms in the world’s oceans. Nature. 2009;459:185–92.CAS 
PubMed 

Google Scholar 
2.Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeo- dactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008;456:239–44.CAS 
PubMed 

Google Scholar 
3.Amin SA, Parker MS, Armbrust EF. Interactions between Diatoms and Bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Cirri E, Pohnert G. Algae- bacteria interactions that balance the planktonic microbiome. New Phytologist. 2019;223:100–6.
Google Scholar 
5.Mühlenbruch M, Grossart H, Eigemann F, Voss M. Mini‐review: Phytoplankton‐ derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ Microbiol. 2018;20:2671–85.PubMed 

Google Scholar 
6.Koedooder C, Stock W, Willems A, Mangelinckx S, de Troch M, Vyverman W, et al. Diatom-bacteria interactions modulate the composition and productivity of benthic diatom biofilms. Front Microbiol. 2019;10:1255.PubMed 
PubMed Central 

Google Scholar 
7.Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.PubMed 
PubMed Central 

Google Scholar 
8.von Scheibner M, Sommer U, Jürgens K. Tight coupling of glaciecola spp. and diatoms during cold-water phytoplankton spring blooms. Front Microbiol. 2017;8:27.
Google Scholar 
9.Zhang H, Hou F, Xie W, Wang K, Zhou X, Zhang D, et al. Interaction and assembly processes of abundant and rare microbial communities during a diatom bloom process. Environ Microbiol. 2020;22:1707–19.CAS 
PubMed 

Google Scholar 
10.Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 

Google Scholar 
11.Bigalke A, Pohnert G. Algicidal bacteria trigger contrasting responses in model diatom communities of different composition. MicrobiologyOpen. 2019;8:e00818.PubMed 
PubMed Central 

Google Scholar 
12.Meyer N, Pohnert G. Isolate-specific resistance to the algicidal bacterium Kordia algicida in the diatom Chaetoceros genus. Botanica Marina. 2019;62:527–35.CAS 

Google Scholar 
13.Sison-Mangus MP, Jiang S, Tran KN, Kudela RM. Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota. ISME J. 2014;8:63–76. https://doi.org/10.1038/ismej.2013.138CAS 
Article 
PubMed 

Google Scholar 
14.Stock W, Blommaert L, de Troch M, Mangelinckx S, Willems A, Vyverman W, et al. Host specificity in diatom–bacteria interactions alleviates antagonistic effects. FEMS Microbiol Ecol. 2019;95:fiz171.CAS 
PubMed 

Google Scholar 
15.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
PubMed Central 

Google Scholar 
16.Ahern OM, Whittaker KA, Williams TC, Hunt DE, Rynearson TA. Host genotype structures the microbiome of a globally dispersed marine phytoplankton. Proc Natl Acad Sci. 2021;118:e2105207118.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Vega NM, Gore J. Stochastic assembly produces heterogeneous communities in the Caenorhabditis elegans intestine. PLoS Biol. 2017;15:e2000633.PubMed 
PubMed Central 

Google Scholar 
18.Lazzaro BP, Fox GM. Host–microbe interactions: winning the colonization lottery. Curr Biol. 2017;27:R642–R644.CAS 
PubMed 

Google Scholar 
19.Burke C, Steinberg P, Rusch D, Kjelleberg S, Thomas T. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci. 2011;108:14288–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Foster KR, Schluter J, Coyte KZ, Rakoff-nahoum S. The evolution of the host microbiome as an ecosystem on a leash. Nature. 2017;548:43–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.PubMed 
PubMed Central 

Google Scholar 
23.Behringer G, Ochsenkühn MA, Fei C, Fanning J, Koester JA, Amin SA. Bacterial communities of diatoms display strong conservation across strains and time. Front Microbiol. 2018;9:1–15.
Google Scholar 
24.Crenn K, Duffieux D, Jeanthon C. Bacterial Epibiotic Communities of Ubiquitous and Abundant Marine Diatoms Are Distinct in Short- and Long-Term Associa- tions. Front Microbiol. 2018;9:1–12.
Google Scholar 
25.Grossart HP, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 

Google Scholar 
26.Guannel ML, Horner-Devine MC, Rocap G. Bacterial community composition differs with species and toxigenicity of the diatom Pseudo-nitzschia. Aquatic Microbial Ecol. 2011;64:117–33.
Google Scholar 
27.Ajani PA, Kahlke T, Siboni N, Carney R, Murray SA, Seymour JR. The microbiome of the cosmopolitan diatom Leptocylindrus reveals significant spatial and temporal variability. Front Microbiol. 2018;9:1–12.
Google Scholar 
28.Kaczmarska I, Ehrman JM, Bates SS, Green DH, Léger C, Harris J. Diversity and distribution of epibiotic bacteria on Pseudo-nitzschia multiseries (Bacillar- iophyceae) in culture, and comparison with those on diatoms in native seawater. Harmful Algae. 2005;4:725–41.
Google Scholar 
29.Sapp M, Wichels A, Gerdts G. Impacts of cultivation of marine diatoms on the associated bacterial community. Appl Environ Microbiol. 2007;73:3117–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Mönnich J, Tebben J, Bergemann J, Case R, Wohlrab S, Harder T. Niche-based assembly of bacterial consortia on the diatom Thalassiosira rotula is stable and reproducible. ISME J. 2020;14:1614–25.PubMed 
PubMed Central 

Google Scholar 
31.Baker LJ, Kemp PF. Exploring bacteria-diatom associations using single-cell whole genome amplification. Aquatic Microbial Ecol. 2014;72:73–88.
Google Scholar 
32.Candela M, Biagi E, Maccaferri S, Turroni S, Brigidi P. Intestinal microbiota is a plastic factor responding to environmental changes. Trends Microbiol. 2012;20:385–91.CAS 
PubMed 

Google Scholar 
33.Goh C, Vallejos DFV, Nicotra AB, Mathesius U. The Impact of Beneficial Plant-Associated Microbes on Plant Phenotypic Plasticity. J Chem Ecol. 2013;826–39.34.Vanormelingen P, Vanelslander B, Sato S, Gillard J, Trobajo R, Sabbe K, et al. Heterothallic sexual reproduction in the model diatom Cylindrotheca. Eur J Phycol. 2013;48:93–105.
Google Scholar 
35.Li H, Yang G, Sun Y, Wu S, Zhang X. Cylindrotheca closterium is a species complex as was evidenced by the variations of rbcL gene and SSU rDNA. J Ocean Univer China. 2007;6:167–74.
Google Scholar 
36.Stock W, Vanelslander B, Rüdiger F, Sabbe K, Vyverman W, Karsten U. Thermal niche differentiation in the benthic diatom Cylindrotheca closterium (Bacillar- iophyceae) complex. Front Microbiol. 2019;10:1395.PubMed 
PubMed Central 

Google Scholar 
37.de Brouwer JFC, Wolfstein K, Ruddy GK, Jones TER, Stal LJ. Biogenic stabilization of intertidal sediments: the importance of extracellular polymeric substances produced by benthic diatoms. Micro Ecol. 2005;49:501–12.CAS 

Google Scholar 
38.Najdek M, Blažina M, Djakovac T, Kraus R. The role of the diatom Cylindrotheca closterium in a mucilage event in the northern Adriatic Sea: Coupling with high salinity water intrusions. J Plankton Res. 2005;27:851–62.
Google Scholar 
39.Eaton Jw, Moss B. The estimation of numbers and pigment content in epipelic algal populations. Limnol Oceanogr. 1966;11:584–95.
Google Scholar 
40.Anderson RA. (editor). Algal Culturing Techniques. Elsevier Academic Press; 2005.41.Muyzer G, de Waal EC, Uitterlinden AG. Profiling of complex microbial popula- tions by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Tytgat B, Verleyen E, Sweetlove M, D’hondt S, Clercx P, van Ranst E, et al. Bacterial community composition in relation to bedrock type and macrobiota in soils from the Sør Rondane Mountains, East Antarctica. FEMS Microbiol Ecol. 2016;92.43.D’Hondt AS, Stock W, Blommaert L, Moens T, Sabbe K. Nematodes stimulate biomass accumulation in a multispecies diatom biofilm. Marine Environ Res. 2018;140:78–89.
Google Scholar 
44.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
PubMed 

Google Scholar 
45.Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 

Google Scholar 
46.Murali A, Bhargava A, Wright ES. IDTAXA: a novel approach for accurate taxo- nomic classification of microbiome sequences. Microbiome. 2018;6:1–14.
Google Scholar 
47.Wright ES. Using DECIPHER v2. 0 to analyze big biological sequence data in R. R Journal. 2016;8.48.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D596.PubMed 
PubMed Central 

Google Scholar 
49.Nawrocki EP. Structural RNA Homology Search and Alignment using Covariance Models. Washington University in St. Louis; 2009.50.Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
51.Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
PubMed 

Google Scholar 
52.Vanelslander B, Créach V, Vanormelingen P, Ernst A, Chepurnov VA, Sahan E, et al. Ecological differentiation between sympatric pseudocryptic species in the estuarine benthic diatom Navicula phyllepta (Bacillariophyceae). J Phycol. 2009;45:1278–89.CAS 
PubMed 

Google Scholar 
53.Borcard D, Legendre P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol Modelling. 2002;153:51–68.
Google Scholar 
54.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 

Google Scholar 
55.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Green- genes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolu- tionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
PubMed 

Google Scholar 
58.Stone L, Roberts A. The checkerboard score and species distributions. Oecologia. 1990;85:74–9.PubMed 

Google Scholar 
59.Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol. 2006;8:732–40.PubMed 

Google Scholar 
60.Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Con- tribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.CAS 
PubMed 

Google Scholar 
61.Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32.
Google Scholar 
62.Chiarello M, Auguet JC, Bettarel Y, Bouvier C, Claverie T, Graham NAJ, et al. Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome. 2018;6:1–14.
Google Scholar 
63.Easson CG, Thacker RW. Phylogenetic signal in the community structure of host- specific microbiomes of tropical marine sponges. Front Microbiol. 2014;5:1–11.
Google Scholar 
64.Swierts T, Cleary DFR, de Voogd NJ. Prokaryotic communities of Indo-Pacific giant barrel sponges are more strongly influenced by geography than host phylogeny. FEMS Microbiol Ecol. 2018;94:1–12.
Google Scholar 
65.Mazel F, Davis KM, Loudon A, Kwong WK. Is Host Filtering the Main Driver of Phylosymbiosis across the Tree of Life. Msystems. 2018;3:1–15.
Google Scholar 
66.Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci. 2020;117.7:3656–62.
Google Scholar 
67.Taylor JD, Cunliffe M. Coastal bacterioplankton community response to diatom- derived polysaccharide microgels. Environ Microbiol Rep. 2017;9:151–7.CAS 
PubMed 

Google Scholar 
68.Becker JW, Berube PM, Follett CL, Waterbury JB, Chisholm SW, Delong EF, et al. Closely related phytoplankton species produce similar suites of dissolved organic matter. Front Microbiol. 2014;5:1–14.CAS 

Google Scholar 
69.Jackrel SL, Yang JW, Schmidt KC, Denef VJ. Host specificity of microbiome assembly and its fitness effects in phytoplankton. ISME J. 2021;15:774–88.PubMed 

Google Scholar 
70.Eigemann F, Hilt S, Salka I, Grossart HP. Bacterial community composition asso- ciated with freshwater algae: Species specificity vs. dependency on environ- mental conditions and source community. FEMS Microbiol Ecol. 2013;83:650–63.CAS 
PubMed 

Google Scholar 
71.Barreto Filho MM, Walker M, Ashworth MP, Morris JJ. Structure and Long-Term Stability of the Microbiome in Diverse Diatom Cultures. Microbiol Spectr. 2021;9:e00269–21.PubMed Central 

Google Scholar 
72.Horner-Devine MC, Bohannan BJM. Phylogenetic clustering and overdispersion in bacterial communities. Ecology. 2006;87:S100–8.PubMed 

Google Scholar 
73.Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci. 2015;112:6449–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Li Y, Shipley B, Price JN, Dantas V, de L, Tamme R, et al. Habitat filtering deter- mines the functional niche occupancy of plant communities worldwide. J Ecol. 2018;106:1001–9.
Google Scholar 
76.Louca S, Jacques SMS, Pires APF, Leal JS, Srivastava DS, Parfrey LW, et al. High taxonomic variability despite stable functional structure across microbial com- munities. Nat Ecol Evol. 2016;1:0015.
Google Scholar 
77.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:1–12.
Google Scholar 
78.Geng H, Belas R. Molecular mechanisms underlying Roseobacter–phytoplankton symbioses. Curr Opinion Biotechnol. 2010;21:332–8.CAS 

Google Scholar 
79.Christie-Oleza JA, Sousoni D, Lloyd M, Armengaud J, Scanlan DJ. Nutrient recy- cling facilitates long-term stability of marine microbial phototroph-heterotroph interactions. Nat Microbiol. 2017;2:1–10.
Google Scholar 
80.Edmundson SJ, Huesemann MH. The dark side of algae cultivation: characterizing night biomass loss in three photosynthetic algae, Chlorella sorokiniana, Nanno- chloropsis salina and Picochlorum sp. Algal Res. 2015;12:470–6.
Google Scholar 
81.Grossart H-P. Interactions between marine bacteria and axenic various conditions in the lab. Aquatic Microbial Ecol. 1999;19:1–11.
Google Scholar  More