Ecology

1.Angel, S. et al. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75, 53–107 (2011).
Google Scholar 
2.Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).ADS 
CAS 
PubMed 

Google Scholar 
3.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Google Scholar 
4.Groffman, P. M. et al. Ecological homogenization of urban USA. Front. Ecol. Environ. 12, 74–81 (2014).
Google Scholar 
5.Bolnick, D. I. et al. (Non)Parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).
Google Scholar 
6.Donihue, C. M. & Lambert, M. R. Adaptive evolution in urban ecosystems. Ambio 44, 194–203 (2015).PubMed 

Google Scholar 
7.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
Google Scholar 
8.Rivkin, L. R. et al. A roadmap for urban evolutionary ecology. Evol. Appl. 12, 384–398 (2019).PubMed 

Google Scholar 
9.Santangelo, J. S. et al. Urban environments as a framework to study parallel evolution. In Urban Evolutionary Biology (eds Szulkin, M. et al.) (Oxford University Press, 2020).
Google Scholar 
10.Cosentino, B. J., Moore, J.-D., Karraker, N. E., Ouellet, M. & Gibbs, J. P. Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecol. Evol. 7, 5426–5434 (2017).PubMed 
PubMed Central 

Google Scholar 
11.Koprowski, J. L., Munroe, K. E. & Edelman, A. J. Gray not grey: Ecology of Sciurus carolinensis in their native range in North America. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
12.McRobie, H., Thomas, A. & Kelly, J. The genetic basis of melanism in the gray squirrel (Sciurus carolinensis). J. Hered. 100, 709–714 (2009).CAS 
PubMed 

Google Scholar 
13.Gibbs, J. P., Buff, M. F. & Cosentino, B. J. The biological system: Urban wildlife, adaptation and evolution: Urbanization as a driver of contemporary evolution in gray squirrels (Sciurus carolinensis). In Understanding Urban Ecology (eds Hall, M. A. & Balogh, S.) (Springer, 2019).
Google Scholar 
14.Lehtinen, R. M. et al. Dispatches form the neighborhood watch: Using citizen science and field survey data to document color morph frequency in space and time. Ecol. Evol. 10, 1526–1538 (2020).PubMed 
PubMed Central 

Google Scholar 
15.Perlut, N. G. Long-distance dispersal by eastern gray squirrels in suburban habitats. Northeast. Nat. 27, 195–200 (2020).
Google Scholar 
16.Goheen, J. R., Swihart, R. K., Gehring, T. M. & Miller, M. S. Forces structuring tree squirrel communities in landscapes fragmented by agriculture: Species differences in perceptions of forest connectivity and carrying capacity. Oikos 102, 95–103 (2003).
Google Scholar 
17.Ducharme, M. B., Larochelle, J. & Richard, D. Thermogenic capacity in gray and black morphs of the gray squirrel, Sciurus carolinensis. Physiol. Zool. 62, 1273–1292 (1989).
Google Scholar 
18.Linnen, C. R. & Hoekstra, H. E. Measuring natural selection on genotypes and phenotypes in the wild. Cold Spring Harb. Symp. Quant. Biol. 74, 155–168 (2010).PubMed Central 

Google Scholar 
19.Campbell-Staton, S. C. et al. Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands. Nat. Ecol. Evol. 4, 652–658 (2020).PubMed 

Google Scholar 
20.Reid, N. M. et al. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305–1308 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Bowers, M. A. & Breland, B. Foraging of gray squirrels on an urban-rural gradient: Use of the GUD to assess anthropogenic impact. Ecol. Appl. 6, 1135–1142 (1996).
Google Scholar 
22.McCleery, R. A., Lopez, R. R., Silvy, N. J. & Gallant, D. L. Fox squirrel survival in urban and rural environments. J. Wildl. Manage. 72, 133–137 (2008).
Google Scholar 
23.Benson, E. The urbanization of the eastern gray squirrel in the United States. J. Am. Hist. 100, 691–710 (2013).
Google Scholar 
24.Leveau, L. United colours of the city: A review about urbanization impact on animal colours. Austral Ecol. 46, 670–679 (2021).
Google Scholar 
25.Ducrest, A.-L., Keller, L. & Roulin, A. Pleiotropy in the melanocortin system, coloration, and behavioural syndromes. Trends Ecol. Evol. 23, 502–510 (2008).PubMed 

Google Scholar 
26.Stothart, M. R. & Newman, A. E. M. Shades of grey: Host phenotype dependent effect of urbanization on the bacterial microbiome of a wild mammal. Anim. Microbiome. 3, 46 (2021).PubMed 
PubMed Central 

Google Scholar 
27.Vasemägi, A. The adaptive hypothesis of clinal variation revisited: Single-locus clines as a result of spatially restricted gene flow. Genetics 173, 2411–2414 (2006).PubMed 
PubMed Central 

Google Scholar 
28.Merrick, M. J., Evans, K. L. & Bertolino, S. Urban grey squirrel ecology, associated impacts, and management challenges. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
29.Chipman, R., Slate, D., Rupprecht, C. & Mendoza, M. Downside risk of wildlife translocation. In Towards the Elimination of Rabies in Eurasia (eds Dodet, B. et al.) (Dev. Biol Basel, Karger, 2008).
Google Scholar 
30.Allen, D. L. Michigan Fox Squirrel Management (Michigan Department of Conservation, 1943).
Google Scholar 
31.Schorger, A. W. Squirrels in early Wisconsin. Trans. Wis. Acad. Sci. Arts Lett. 39, 195–247 (1949).
Google Scholar 
32.Robertson, G. I. Distribution of Color Morphs of Sciurus carolinensis in Eastern North America (University of Western Ontario, 1973).
Google Scholar 
33.MacCleery, D. W. American Forests: A History of Resiliency and Recovery (Forest History Society, 2011).
Google Scholar 
34.Foster, D. R. et al. Wildlands and Woodlands: A Vision for the New England Landscape (Harvard University Press, 2010).
Google Scholar 
35.Thompson, R. T., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8(9), e72540 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Lambert, M. R. et al. Adaptive evolution in cities: Progress and misconceptions. Trends Ecol. Evol. 36, 239–257 (2021).PubMed 

Google Scholar 
37.Farquhar, D. N. Some Aspects of Thermoregulation as Related to the Geographic Distribution of the Northern Melanic Phase of the Grey Squirrel (York University, 1974).
Google Scholar 
38.Innes, S. & Lavigne, D. M. Comparative energetics of coat colour polymorphs in the eastern gray squirrel Sciurus carolinensis. Can. J. Zool. 57, 585–592 (1979).
Google Scholar 
39.Santangelo, J. S. et al. Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient. Evol. Lett. 4, 212–225 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Fidino, M. et al. Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol. Appl. 31, e02253 (2021).PubMed 

Google Scholar 
41.Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
42.Alberti, M. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. U.S.A. 114, 8951–8956 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.United States Census Bureau. 2019 TIGER/Line Shapefiles (machine-readable data files) https://www2.census.gov/geo/tiger/TIGER2019/UAC/ (2019).44.XX. Statistics Canada. Population Centre Boundary File, Census year 2016 https://www150.statcan.gc.ca/n1/en/catalogue/92-166-X (2017).45.Aiello-Lammens, M. E. et al. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Google Scholar 
46.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).47.Brown de Colstoun, E. C. et al. Documentation for the Global Man-made Impervious Surface (GMIS) Dataset from Landsat (NASA Socioeconomic Data and Applications Center, 2017).
Google Scholar 
48.Steele, M. A. & Koprowski, J. L. North American Tree Squirrels (Smithsonian Books, 2001).
Google Scholar 
49.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
PubMed 

Google Scholar 
50.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
51.Hijmans, R. L. raster: Geographic data analysis and modeling. R package version 3.3–13. https://CRAN.R-project.org/package=raster (2020).52.Baston, D. exactextractr: Fast extraction from raster datasets using polygons. R package version 0.5.1. https://CRAN.R-project.org/package=exactextractr (2020).53.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 

Google Scholar 
54.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
55.Gelman, A. & Su, Y. arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.11–2. https://CRAN.R-project.org/package=arm (2020).56.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar 
57.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).
Google Scholar 
58.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 

Google Scholar 
59.Bardos, D. C., Guillera-Arroita, G. & Wintle, B. A. Valid auto-models for spatially autocorrelated occupancy and abundance data. Methods Ecol. Evol. 6, 1137–1149 (2015).
Google Scholar  More

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.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

Improving our understanding of “viral tagging” flow cytometric signalsVT is a deceptively simple idea whereby a mixture of natural viruses are labeled with a DNA-binding fluorescent dye and ‘bait’ hosts infected by these stained viruses can be detected with flow cytometry via the fluorescent shift of “viral-tagged cells” [38, 39] (Fig. 1A, B). These viral-tagged cells can then be sorted, and the viral DNA separated using isotopic fractionation (the DNA of the cultured host is pre-labeled with “heavy” DNA) to access the metagenomes of the viruses that were experimentally determined to have infected these cell types. However, in practice, VT has been only minimally adopted by the community [43], presumably because it requires costly equipment (a high-performance flow sorter) and diverse technical expertise (flow cytometry, phage biology, and bioinformatics), while lacking sufficient benchmarking. To the latter, we sought to use a cultured phage-host model system (Pseudoalteromonas strain H71, hereafter H71, and its specific myophage PSA-HM1, hereafter HM1) to systematically assess the impact of various multiplicities of infection (MOIs; the ratio of the number of virus particles to the number of target cells, [48]) on the resultant VT signals. Further, we sought to augment VT to add an “and grow” capability whereby scalable single-virus cultivation, characterization, and sequencing could be enabled (Fig. 1C).Fig. 1: Overview of viral tagging, and the variant developed here—viral tag and grow.A Viruses are labeled with a green fluorescent dye and then mixed with potential host bacteria. B Fluorescence detection of individual cells with fluorescently-labeled viruses (FLVs) by flow cytometer. The flow cytometry plot (side scatter or forward scatter versus green fluorescence) shows the expected locations of FLV-tagged (VTs) and nontagged cells (NTs), which are flow-cytometrically green positive and negative, respectively. C Single-cell sorting of VTs is followed by subsequent amplification of infectious viruses. Single VTs are sorted into a 96-well plate that contains host culture. Culture growth is monitored by measuring optical density (OD) over time. A decrease in the OD curve from VT-containing wells (relative to the phage-negative control) indicates cell lysis by progeny viruses produced from a single isolated VT cell.Full size imageTo gain a better understanding of the biology behind VT signatures, we examined how H71 interacts with HM1, a phage specific for this host, and HS8, a phage that does not adsorb to this host – both assayed via flow cytometry and microscopy (for details, see Methods and online protocol, https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-captbwutpewn?form=MY01SV&OCID=MY01SV). Briefly, phages were stained with SybrGold (fluoresces green upon blue-light excitation) and for microscopy, H71 cells were stained with DAPI (fluoresces blue upon blue-light excitation, 4′,6-diamidino-2-phenylindole), as previously described [39, 49]. Replicate cultures of stained cells were then mixed with fluorescently-labeled phages (either HM1 or HS8 in each treatment) at infective MOIs = 1, 2, and 4, then these infections were incubated for 10 min, and processed (centrifuged and resuspended; see Methods for details) three times to remove free phages (see Methods for details). For microscopy, the relative fraction of virus-tagged (VTs) and nontagged cells (NTs) was measured from the available cells up to ~500 cells for each sample. For flow cytometry, cell detection was optimized to minimize background noise [50], and negative controls consisted of stained and washed sheath buffer and filtered Q water samples, as previously described [39].Overall, the resulting VT experiments were robust and informative. First, our cell-only optimizations resulted in controls that were impeccably clean (see representative cytograms and gating counts in Fig. 2A–C and  Supplementary Fig. S1). Second, in “virus addition” treatments, the resultant VT signal was distinct for specific (HM1) versus nonspecific (HS8) phages. Specifically, adding HM1 at MOIs = 1, 2, and 4 corresponded to VT population shifts of an average of 25%, 50%, and 80%, respectively, while NT populations proportionally decreased (Fig. 2D, E, linear regression r2 = 0.98). In contrast, for all tested MOIs of the nonspecific HS8 phage, the shifted populations were negligible (range: ~1.0–1.9%) and uncorrelated (Supplementary Fig. S2A, B; r2 = 0.14).Fig. 2: Flow cytometric and microscopic analyses of Pseudoalteromonas-phage associations.A Hierarchical gating for detection of Pseudoalteromonas strain H71 (hereafter, H71) and its subpopulations of viral tagged (VTs) and nontagged cells (NTs). A parent gate was drawn on H71 cells using FSC vs. SSC (Fig. S1) and represented in two types of contour and dot plots (left and right in the top of the gray box, respectively). From this gate, green-positive (VT) and -negative (NT) populations were sub-gated in the green fluorescence vs. SSC (right, dot plot) and quantified as percentage fractions of a parent population (bar charts in the gray box). B, C Flow cytometric plots of sheath buffer only (B) and stained/washed sheath buffer without phages (C) (see Methods and Fig. S1). D Flow cytometric detections for H71 cells (~106/ml) that were incubated with fluorescently-labeled specific phage HM1 at MOIs of 1, 2, and 4, respectively (from left to right). E Linear regression relationships between the MOIs (x-axis) and the percentages (Y-axis) of flow cytometric VT (green) and NT (black) populations for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square values are represented. F DAPI (4′,6-diamidino-2-phenylindole, blue)-stained H71 cells were mixed with fluorescent phages HM1 (SybrGold, green) at MOIs of 1, 2, and 4, respectively (Methods for details). Above, the merged images of phage-host mixtures (Additional images are shown in Figs. S4–7). Below, an enlarged view of four regions selected from the above images. Interpretations of virus-tagged cells, nontagged cells, and “free” viruses are represented in the results and discussion and methods, respectively. Arrows point to phages found on the margin of bacterial cells. Scale bar, 2 µm. Microscopic observations for nonspecific phage HS8-H71 are shown in Fig. S8. G Correlation between the MOI (x-axis) and the microscopic fractions (y-axis) of VTs (green) and NTs (black) for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square value is shown. H Impact of cell physiology on viral tagging signals. H71 cells (~106/ml) in the early log, late log, and stationary phase were infected by phage HM1 at MOIs of 1 (Left) and 4 (Right), respectively. Percentages of tagged populations were measured at the time point after fluorescently-labeled HM1 were inoculated for 20 min at various MOIs followed by centrifugation and resuspension to remove free viruses (see Methods for details). Each test was done in duplicate (error bars show standard deviations).Full size imageDespite observing a strong linear correlation between MOI and %VT for HM1, it was surprising that even at high MOIs = 1, 2, and 4, the resultant population shifts were 1.2- to 2.5-fold less than expected from theory alone based on Poisson distribution (see Supplementary Fig. S3). To investigate this, we used microscopy to inspect for virus clumping, positioning relative to cell surfaces, and background noise. These results revealed spot-like green signals of various sizes outside of host cells, which we interpreted as free viruses, and this was true even (a) at these higher MOIs, and (b) despite centrifugation to remove free viruses following incubation (see Methods; Fig. 2F and  Supplementary Figs. S4–7). We suspect these unincorporated SYBR-stained particles are viral aggregates, possibly due to host cell parts and/or debris in the lysate [51,52,53] or tangling of phage tails [54]. Prior work has shown that these and other mechanisms that decrease the accessibility of viral particles to host receptors could reduce observed infectious particles [48].Our third key observation in these experiments rested with an improved understanding of the ‘signal shift’ between VT and NT populations in the flow cytogram across varied MOIs. Again, comfortably, increasing the MOI pushed VT signals toward higher fluorescence, with NTs decreasing proportionally (Fig. 2F). We posited that such increased “VT” signal could result from multiple phages adsorbing per cell. Indeed, microscopy visualization of ~500 single cells per treatment revealed that the number of detectable phages per infected cell increased proportionally to the MOI (Fig. 2F, G and  Supplementary Figs. S4–6). For example, of the tagged cells, few (~14%) cells exhibited multiple phages adsorbed at an MOI = 1, whereas those numbers increased drastically at MOIs = 2 and 4, where most (~55% and 67%) tagged cells exhibited multiple adsorbed phages per cell. As a negative control, we examined VT signals for a nonspecific phage, and this revealed that virtually all of the 545 single cells that were examined were nontagged (99.3%) even at an MOI = 10 (Supplementary Fig. S7). Presumably, the remaining ~0.7% of cells that appeared to have a phage adsorbed represent promiscuous, reversible binding to nonhost cells as is known to occur in other phage model systems [39]. Mechanistically, multiple phages can bind to a single host cell. For example, under very high-titer infection conditions (e.g., MOI = 100) phages can distribute over an entire cell surface [55], presumably accessing broadly distributed receptors [56]. Prior VT work has demonstrated strong VT signals under very high MOI (e.g., MOI = 1000) conditions [43], though no optimization experiments were presented to understand these patterns and the false positives that would result from free phages coincidently sorted (see further discussion later).Finally, we re-evaluated the impact of cell physiology (e.g., early, middle, and late log phase host growth) and adsorption time (e.g., 20 min intervals from 0 to 120 min) on Pseudoalteromonas VT signals—and did so at two MOIs = 1 and 4, respectively (Fig. 2H). At both MOIs tested, growth phase was seen to impact the VT signals, with late log phase cells showing the highest fluorescent shift for VT cells in contrast to signals that were reduced in early log phase cells and nearly absent from stationary phase cells (Fig. 2H). This finding is consistent with our prior optimizations with Pseudoalteromonas phage-host model systems [39]. However, we observed that VT signals were optimal at 20 min after adsorption (see Methods) and, rather than stay high as we had previously observed, these experiments revealed that the VT signals were reduced by nearly half at subsequent time points. Though conflicting with our prior work [39], these current experiments employ hierarchical gating (Supplementary Fig. S1; see Methods), which we feel more appropriately quantify these patterns. This is because we interpret the signal reduction to be due to the lysis of first-adsorbed tagged cells and/or the injection of fluorescent DNA of the adsorbed virus(es) into cells as the latent period of phage HM1 for H71 cells under these conditions dictates [24]. Indeed, it has been reported that for phage lambda—E.coli system, the injection of fluorescent phage DNA followed by signal diffusion inside the cells decreased ~40% of the overall signal intensities of individual virus–host pairs [57].Together, though an extensive set of experiments, these findings are largely confirmatory with our prior work characterizing Pseudoalteromonas phages [39]. However, and critically, our prior work failed to rigorously investigate these phenomena with respect to their (i) flow cytogram population signatures, (ii) single-cell microscopy imaging, and (iii) hierarchically gated tagged-cell timing estimates. We hope that these additional clarifications here provide a better mechanistic understanding of VT signals, and encourage wider adoption of this promising high-throughput method to identify viruses that infect a particular host.Introducing VT and grow: VT coupled to plate-based cultivation assaysGiven this improved understanding of the VT signal, we next sought to expand VT to include an “and grow” capability to scalably capture and characterize viruses linked to hosts (conceptually presented in Fig. 1C). Pragmatically, this should also help resolve long-standing questions of (i) what fraction of VT cells lead to productive infections (i.e., does adsorption equal infection?, [45]), and (ii) whether sample processing (e.g., laser detection, sheath fluid growth inhibition [37, 58]) or cell density effects resulting from single-cell sorts [59, 60] would prohibit downstream growth assays.To this end, we used the Pseudoalteromonas-virus HM1 model system to optimize sorting and growth conditions. Specifically, we wondered how many cells from sorted populations would be required to observe lysis (both dynamically, and terminally) under various MOI conditions. To test this, viral-tagged cells (the “VT” treatment) or nontagged cells (the “NT” treatment) were sorted into individual wells of a 96-well plate containing growth medium; fresh host cells were added, and growth-lysis curves were established by measuring optical density (OD) over time (see Methods). Treatment variables included the number of cells sorted (n = 1, 3, or 9) and infection conditions (MOI = 1 or 4), while controls included (i) NT cells to control for false-positive culture lyses by free viruses coincidently sorted with target cells, and (ii) sorting process controls against host cell lysis and growth in plates consisting of wells containing cultures with and without phage HM1, respectively. For all experiments, cells were infected during late-exponential phase for 10 min, followed by dilution to halt further infection, and centrifugation to remove free viruses (see Methods, [41]).We first analyzed the reduced-titer MOI = 1 infection. When only single cells were sorted, the growth curves from those wells as compared to those of phage-free controls, showed that more than half (56%; 20/36) of the VT wells with detectably reduced OD, whereas only a single NT well (8%; 1/12) showed such a decrease (Fig. 3A). This low rate of false-positive culture lysis in NT wells suggests that in most of the VT wells, progeny phages produced from an isolated parent VT—not free viruses―infect and lyse the host culture (For more details, see the burst size distribution of sorted single VTs below). Presumably, the 16 VT wells that did not lyse were due to one of the following: (i) reduced viability of isolated VTs through multiple steps of sample preparation or sorting with high sheath pressure [37, 58], (ii) possible reversible virus adsorption from the VT cell prior to well capture, and/or (iii) mis-diagnoses due to the weak fluorescent shift of singly-VT cells as is a known challenge in fluorescence-based cell sorting [58, 61].Fig. 3: Evaluation of viral growth assay under various infection conditions.Two liquid cultures of Pseudoalteromonas strain H71 (105/ml) in the late-logarithmic growth phase were infected by specific phage HM1 at MOIs of 1 and 4, respectively. From each infected culture, varying numbers of tagged (VT) and nontagged (NT) cells were sorted into individual wells of a 96-well plate containing growth medium followed by the addition of fresh host cells (104 cells per well). Positive and negative controls (host culture with HM1 at an MOI of 0.1 and without HM1, respectively) were included in each plate (see Methods for details). From top to bottom, left to right in panels (A) MOI = 1 and (B) MOI = 4, respectively, pie charts depict the percentages of lysed (yellow) and nonlysed (gray) wells from the total wells containing the given numbers (n = 1, 3, and 9) of isolated VTs and NTs. Culture lysis for VT- and NT-containing wells was determined by comparing their growth curves (next to each pie chart, black lines) to those of negative (red) and positive controls (blue). The X-axis indicates the OD590nm and the Y-axis, the time in hours.Full size imageTo assess the MOI = 1 infections further, we evaluated the data for wells containing more than 1 cell sorted to each well. This revealed that sorting 3 or 9 cells improved the fraction of wells lysed in the VT treatments to 88 and 100%, respectively, but this came at the cost of increased false positives in the NT treatment (pie charts in Fig. 3A). The latter is likely due to the same challenges described above of differentiating the NT from VT populations when signal intensity was relatively low. Given the 96-well plate format, these experiments demonstrate the ability to follow growth kinetics for each well (time course OD figures in Fig. 3A). This revealed that single VT cell sorts had delayed lysis relative to the multiple-cell sorts and hints at the power such kinetics data could provide for scalably characterizing new en masse captured phage isolates from field samples. Stepping back, however, it is promising that the number of sorted cells per well, for both VT and NT wells, was linearly proportional to the percentages of lysed wells (r2 = 0.73 and 0.99), respectively (Supplementary Fig. S8). This suggests a robustness and repeatability for these experiments.Beyond the fraction of the VT and NT wells displaying clear lysis, the kinetics of lysis—particularly for single-cell sorts—can be a valuable first read-out for variability in virus infection dynamics. To assess this in our dataset, we examined the kinetics of OD readings through 20 h (growth-lysis curves in Fig. 3A). Focusing on the 36 wells containing a single VT cell, 20 lysed (reported above), but their lysis kinetics drastically differed—some wells showed stepwise decreases after early increases in OD and the others a very low or no increase followed by the curve recovery. Similar lysis patterns have been observed in other phage-host systems, where host culture growth depended on phage concentration, with suppression of host cells increasing with higher phage titers and vice versa [62, 63]. Our observation of the well-to-well variation in culture lysis is likely due to different progeny production from isolated VT per well, relating to the stochasticity of viral infection [37, 64,65,66,67]. However, the stochastic infection alone cannot explain such diverse lysis patterns, given the random nature of diffusion and contact of progeny particles from infected cells to neighboring susceptible cells in the fluid (i.e., the host culture) [68, 69]. Either biological or physical infection process, or both, could impact varied lysis pattern. Further experiments are required to test this hypothesis (e.g., single-cell burst size assay, [37]; see below).Finally, given that flow cytometric population separation was critical for optimizing lysis success and that simply sorting more cells comes at the cost of increased false-positive lysis, we next explored the impact of increasing the per-cell fluorescent VT signal with MOI = 4 infections. Indeed, sorting from these better-resolved populations improved our per-well lysis results as all of the VT wells lysed, and this was the case whether sorting 1, 3, or 9 cells per well (pie charts in Fig. 3B). For the NT wells, false positives were less problematic, but they did remain a minor problem as some wells (4–8%) lysed, and this increased in the multiple-cell sorted wells. Though VT and NT populations are likely better resolved, thereby reducing false-positive lysis in the NT wells from the MOI = 1 infections, presumably the higher MOI infections lead to free viruses being coincidently co-sorted in the sort droplets. Notably, the kinetic read-outs (growth-lysis curves in Fig. 3B) were relatively invariable, possibly suggesting that the much higher number of viruses-per-cell in these infections obscured virus-to-virus variability in life history traits [66, 67, 70].Together, these experiments provide strong baseline data for assessing the impact of VT signal quality, MOIs, and growth data and hint that the approach may also open up new windows into variation in trait space across virus isolates.New biology enabled by viral tag and grow: a window into “viral individuality”?A major challenge in viral ecology is scaling from the handful of viruses that might be well characterized to the millions of virus types in an average seawater or field sample. While diversity surveys have come a long way (e.g., hundreds of thousands of viruses in a single study [23]), the pragmatic challenges of taking physiological measurements across many viral isolates leaves modeling efforts with very little empirical data on virus life history traits, severely bottlenecking the viruses brought into predictive models [71]. Further, microbiologists have revealed that even among “clonal” isolates, there can be remarkable phenotypic heterogeneity, or “microbial individuality” [72,73,74]; does the same exist for viruses? Hints that there is such “virus individuality” among DNA viruses, including phages, are emerging with data demonstrating variability in single-cell burst size (progeny per infected cell), with up to ~100-fold differences and these differences attributed to stochastic events such as variation in starting points in cell size, growth stage, and resources [37, 64,65,66].Of particular interest in understanding ‘virus individuality’ are recent single-cell analyses developed for a Synechococcus phage-host model system that revealed a wide range of burst sizes (from 2 to 200 infective viruses/cell) within a laboratory clonal isolate [37]. Methodologically, this approach sorts cells—infected or not—into wells (e.g., of a 96-well plate) and follows their infection dynamics. This has the benefit of assessing a single cell’s growth-lysis curve in each well. However, a drawback is that experiments are more conveniently done at high MOI conditions (e.g., an MOI = 3 was used) to get larger numbers of wells lysing among the randomly sorted cells (see Methods). Increasing MOI will lead to more virus-containing and, therefore, lysing wells, subsequently greatly increasing the number of cells with multiple viruses attached such that it will confound measurements of lysis dynamics since they will be a function of both virus-to-virus ‘individuality’ and an unknown, but variable per-cell MOI [70, 75].Inspired by this latter work, we sought to improve such single-cell growth-lysis assays in ways that might leverage the scalability of VT + Grow. For these experiments, we wanted to reduce the MOI (to MOI = 0.5) since theory predicts that most (77%) of the infected cells would be singly infected (Poisson distribution), but keep it high enough to have a reasonably separated VT cell population (see Methods). After cells and viruses were mixed, individual VT cells were sorted into different wells containing growth medium, plates were incubated to allow lysis of the single sorted VT cell, and the number of plaques per well were determined by pour plate plaque assays (Fig. 4A; see Methods for details). This operationally single-cell burst size assay showed a wide range of infective viruses per cell (2 to 397, X-axis) from a total of 72 individual cells assessed (Y-axis) (on average = 100; Fig. 4B), with similar average population burst sizes of 110 ± 15 [24]. Though a clonal virus isolate, these findings suggest, just as seen for cyanophages [37], that stochastic events must dictate the specific burst size for any given interaction. However, unlike the prior work, it is unlikely that cells with multiple viruses adsorbed any of this signal since such events should be much rarer at an MOI = 0.5 instead of MOI = 3. This suggests that these stochastic events are of a biological nature, which we posit might mechanistically result from the timing of initial virus–host interactions and/or cell-to-cell or virus-to-virus variation in nonheritable traits such as per-cell nutrient stores. If we interpret such infected cell variability as ecologically relevant variation in “virocells” (sensu [13, 76, 77]), then these findings open a window into “virus individuality” via a more scalable and controllable characterization approach than previously available.Fig. 4: Distribution of virus burst sizes per single viral-tagged cell.A Schematic overview of single-cell assay for viral burst size determination by viral tagging and grow. In the latent period of infection, single viral-tagged cells (VTs) were sorted by flow cytometer from Pseudoalteromonas sp. H71 cells infected by phage HM1 at an MOI of 0.5 (see Methods for details). Following sorting single VTs into different wells of the 96-well plate containing growth medium (MSM), the plate was incubated to allow for viral progenies to release from infected cells. The number of viruses produced per VT was then determined by the number of plaques per poured plate using the traditional plaque assay. B Distribution of viral burst size from individual tagged cells. The number of progeny viruses (X-axis) per cell (Y-axis) are represented in bins of 20, with the exception of the first bin excluding single plaques. The number (n) of individual tagged cells assessed is represented at the top right corner.Full size imageLimitations and future development opportunities for VT and GrowThough these efforts provide a more robust foundation for broadening the use of VT related methods, there remain challenges. First, researchers must be aware that VT is not a simple method, and its success depends on instrument calibration and ultraclean sample processing to establish maximally separated VT and NT populations (see the link below for details on flow cytometric setup and optimization). Second, sorting purity, particularly in field applications, will be challenged by suboptimal VT flow cytometric signatures, e.g., mis-identification of NT cells. Though this can be overcome with very high MOI infections (e.g., 1000 viruses per cell, [43]), two issues remain: (i) the effective MOIs cannot be measured in field samples (and thus, unknown), and (ii) at such high MOIs, the experiments will suffer from coincident sorting of free viruses that will increase false positives. Another factor that could affect sorting purity is nonviral DNA in the environmental sample, whether it is associated with bacterial cells or not, which could be coincidently sorted. It is thus necessary to ensure that prior to any VT work, environmental samples are properly processed or treated for the removal of nonviral genes and other materials (e.g., filtration and/or centrifugation). Fortunately, the “and grow” approach added to VT provides an additional screening step whereby false-negatives and false positives can be discerned via growth-lysis monitoring. Further, the “and grow” component, a plate-based assay, enables faster and more scalable lysis screening (e.g., 96-well format) than the time- and labor-intensive traditional plaque assay [62, 63]. Third, viral aggregates that alter the effective MOI infection conditions could lead to confounding results when comparing results across laboratories. Here, we invite efforts to find and optimize approaches to reduce viral aggregates (e.g., detergents, sonication, syringe pumping), and until viral aggregates are eliminated, to microscopically examine the state of free viruses in new sample types, particularly for outlier results. Fourth, the methods remain dependent upon a cultivable host, and though VT has been applied to multiple heterotroph and cyanobacterial phage-host pairs [39], two big unknowns remain: (i) how will the “and grow” processing impact growth of these strains, and (ii) will non-marine model systems be amenable to these approaches. The in-depth optimizations presented here for a Pseudoalteromonas phage-host model system serve a foundation for understanding other target virus–host pairs. To this end, we suggest deep investigation for any new model systems being studied, and as information becomes more broadly available, invite a community-standards and benchmarking approach to determine ideal setups for infectious conditions (e.g., growth curve, MOIs) and instrumental parameters. To facilitate this, we have established a VT forum on the Viral Ecology VERVE Net living protocols at protocols.io (below) as a way to empower and broadly engage researchers interested in these new methods and the many variants that could blossom from this base. Specifically, the details for viral and bacterial sample processing can be found at https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-capt-bwutpewn?form=MY01SV&OCID=MY01SV and for flow cytometric optimization at https://www.protocols.io/view/bd-influx-cell-sorter-start-up-and-shut-427down-for-v-bv8cn9sw. Both protocols provide additional notes for critical steps to improve methodological reproducibility and/or sensitivity, and particularly for the latter, it will be updated regularly to better optimize, calibrate, and standardize a flow cytometer. More

1.Mendes R, Raaijmakers JM. Cross-kingdom similarities in microbiome functions. ISME J. 2015;9:1905–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Berg G, Rybakova D, Fischer D, Cernava T, Vergès M-CC, Charles T, et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020;8:103.PubMed 
PubMed Central 

Google Scholar 
3.Hall AB, Tolonen AC, Xavier RJ. Human genetic variation and the gut microbiome in disease. Nat Rev Genet. 2017;18:690–9.CAS 
PubMed 

Google Scholar 
4.Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant-microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 

Google Scholar 
5.Ramírez-Puebla ST, Servín-Garcidueñas LE, Jiménez-Marín B, Bolaños LM, Rosenblueth M, Martínez J, et al. Gut and root microbiota commonalities. Appl Environ Microbiol. 2013;79:2–9.PubMed 
PubMed Central 

Google Scholar 
6.Lu T, Ke M, Lavoie M, Jin Y, Fan X, Zhang Z, et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome. 2018;6:231.PubMed 
PubMed Central 

Google Scholar 
7.Zhang J, Liu Y-X, Zhang N, Hu B, Jin T, Xu H, et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat Biotechnol. 2019;37:676–84.CAS 
PubMed 

Google Scholar 
8.Kwak MJ, Kong HG, Choi K, Kwon SK, Song JY, Lee J, et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat Biotechnol. 2018;36:1100–9.CAS 

Google Scholar 
9.Li H, La S, Zhang X, Gao L, Tian Y. Salt-induced recruitment of specific root-associated bacterial consortium capable of enhancing plant adaptability to salt stress. ISME J. 2021;15:2865–82.CAS 
PubMed 

Google Scholar 
10.Xu L, Dong Z, Chiniquy D, Pierroz G, Deng S, Gao C, et al. Genome-resolved metagenomics reveals role of iron metabolism in drought-induced rhizosphere microbiome dynamics. Nat Commun. 2021;12:3209.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E. Dysbiosis and the immune system. Nat Rev Immunol. 2017;17:219–32.CAS 
PubMed 

Google Scholar 
12.Lee SM, Kong HG, Song GC, Ryu CM. Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. ISME J. 2021;15:330–47.CAS 
PubMed 

Google Scholar 
13.Stacy A, Andrade-Oliveira V, McCulloch JA, Hild B, Oh JH, Perez-Chaparro PJ, et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell. 2021;184:615–.e17.CAS 
PubMed 

Google Scholar 
14.Sanders ME, Merenstein DJ, Reid G, Gibson GR, Rastall RA. Probiotics and prebiotics in intestinal health and disease: from biology to the clinic. Nat Rev Gastroenterol Hepatol. 2019;16:605–16.PubMed 

Google Scholar 
15.Bhattacharyya PN, Jha DK. Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture. World J Microbiol Biotechnol. 2012;28:1327–50.CAS 
PubMed 

Google Scholar 
16.Bashan Y, de-Bashan LE, Prabhu SR, Hernandez J-P. Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998–2013). Plant Soil. 2014;378:1–33.CAS 

Google Scholar 
17.Zmora N, Zilberman-Schapira G, Suez J, Mor U, Dori-Bachash M, Bashiardes S, et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell. 2018;174:1388–.e21.CAS 

Google Scholar 
18.Kaakoush NO. Fecal transplants as a microbiome-based therapeutic. Curr Opin Microbiol. 2020;56:16–23.CAS 
PubMed 

Google Scholar 
19.Kassam Z, Lee CH, Yuan Y, Hunt RH. Fecal microbiota transplantation for Clostridium difficile infection: systematic review and meta-analysis. Am J Gastroenterol. 2013;108:500–8.PubMed 

Google Scholar 
20.Baruch EN, Youngster I, Ben-Betzalel G, Ortenberg R, Lahat A, Katz L, et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science. 2021;371:602–9.CAS 

Google Scholar 
21.Weller DM, Raaijmakers JM, Gardener BBM, Thomashow LS. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu Rev Phytopathol. 2002;40:309–48.CAS 
PubMed 

Google Scholar 
22.Gopal M, Gupta A, Thomas GV. Bespoke microbiome therapy to manage plant diseases. Front Microbiol. 2013;4:355.PubMed 
PubMed Central 

Google Scholar 
23.Raaijmakers JM, Bonsall RF, Weller DM. Effect of population density of Pseudomonas fluorescens on production of 2,4-diacetylphloroglucinol in the rhizosphere of wheat. Phytopathology. 1999;89:470–5.CAS 
PubMed 

Google Scholar 
24.Mazurier S, Corberand T, Lemanceau P, Raaijmakers JM. Phenazine antibiotics produced by fluorescent pseudomonads contribute to natural soil suppressiveness to Fusarium wilt. ISME J. 2009;3:977–91.CAS 
PubMed 

Google Scholar 
25.Siddiqui ZA, Shakeel U. Screening of Bacillus isolates for potential biocontrol of the wilt disease complex of pigeon pea (Cajanus cajan) under greenhouse and small-scale field conditions. J Plant Pathol. 2007;89:179–83.
Google Scholar 
26.Yadav K, Damodaran T, Dutt K, Singh A, Muthukumar M, Rajan S, et al. Effective biocontrol of banana fusarium wilt tropical race 4 by a bacillus rhizobacteria strain with antagonistic secondary metabolites. Rhizosphere. 2021;18:100341.
Google Scholar 
27.Meng Q, Yin J, Rosenzweig N, Douches D, Hao JJ. Culture-based assessment of microbial communities in soil suppressive to potato common scab. Plant Dis. 2012;96:712–7.PubMed 

Google Scholar 
28.Carrión VJ, Cordovez V, Tyc O, Etalo DW, de Bruijn I, de Jager VCL, et al. Involvement of Burkholderiaceae and sulfurous volatiles in disease-suppressive soils. ISME J. 2018;12:2307–21.PubMed 
PubMed Central 

Google Scholar 
29.Gómez Expósito R, de Bruijn I, Postma J, Raaijmakers JM. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front Microbiol. 2017;8:2529.PubMed 
PubMed Central 

Google Scholar 
30.Raaijmakers JM, Mazzola M. Soil immune responses. Science. 2016;352:1392–3.CAS 

Google Scholar 
31.Bakker PAHM, Pieterse CMJ, de Jonge R, Berendsen RL. The soil-borne legacy. Cell. 2018;172:1178–80.CAS 
PubMed 

Google Scholar 
32.Schlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology. 2017;107:1284–97.PubMed 

Google Scholar 
33.Kyselková M, Kopecký J, Frapolli M, Défago G, Ságová-Marecková M, Grundmann GL, et al. Comparison of rhizobacterial community composition in soil suppressive or conducive to tobacco black root rot disease. ISME J. 2009;3:1127–38.PubMed 

Google Scholar 
34.Rosenzweig N, Tiedje JM, Quensen JF, Meng Q, Hao JJ. Microbial communities associated with potato common scab-suppressive soil determined by pyrosequencing analyses. Plant Dis. 2012;96:718–25.PubMed 

Google Scholar 
35.Cha JY, Han S, Hong H-J, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29.CAS 
PubMed 

Google Scholar 
36.Liu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, et al. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016;6:36773.CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Mendes R, Kruijt M, de Bruijn I, Dekkers E, van der Voort M, Schneider JHM, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332:1097–100.CAS 

Google Scholar 
38.Wei Z, Gu Y, Friman VP, Kowalchuk GA, Xu Y, Shen Q, et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci Adv. 2019;5:eaaw0759.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Rillig MC, Antonovics J, Caruso T, Lehmann A, Powell JR, Veresoglou SD, et al. Interchange of entire communities: microbial community coalescence. Trends Ecol Evol. 2015;30:470–6.PubMed 

Google Scholar 
40.Deng S, Caddell DF, Xu G, Dahlen L, Washington L, Yang J, et al. Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome. ISME J. 2021;15:3181–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Mendes LW, Mendes R, Raaijmakers JM, Tsai SM. Breeding for soil-borne pathogen resistance impacts active rhizosphere microbiome of common bean. ISME J. 2018;12:3038–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Hu J, Wei Z, Kowalchuk GA, Xu Y, Shen Q, Jousset A. Rhizosphere microbiome functional diversity and pathogen invasion resistance build up during plant development. Environ Microbiol. 2020;22:5005–18.PubMed 

Google Scholar 
43.Schreiter S, Ding G-C, Heuer H, Neumann G, Sandmann M, Grosch R, et al. Effect of the soil type on the microbiome in the rhizosphere of field-grown lettuce. Front Microbiol. 2014;5:144.PubMed 
PubMed Central 

Google Scholar 
44.Wei Z, Hu J, Gu Y, Yin S, Xu Y, Jousset A, et al. Ralstonia solanacearum pathogen disrupts bacterial rhizosphere microbiome during an invasion. Soil Biol Biochem. 2018;118:8–17.CAS 

Google Scholar 
45.Jiang G, Wei Z, Xu J, Chen H, Zhang Y, She X, et al. Bacterial wilt in China: History, current status, and future perspectives. Front Plant Sci. 2017;8:1549.PubMed 
PubMed Central 

Google Scholar 
46.Manda RR, Addanki VA, Srivastava S. Bacterial wilt of solanaceous crops. Int J Chem Stud. 2020;8:1048–57.CAS 

Google Scholar 
47.Barik S, Reddy AC, Ponnam N, Kumari M, C AG, Reddy DCL, et al. Breeding for bacterial wilt resistance in eggplant (Solanum melongena L.): progress and prospects. Crop Prot. 2020;137:105270.CAS 

Google Scholar 
48.Wei Z, Yang X, Yin S, Shen Q, Ran W, Xu Y. Efficacy of Bacillus-fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl Soil Ecol. 2011;48:152–9.
Google Scholar 
49.Park E-J, Lee S-D, Chung E-J, Lee M-H, Um H-Y, Murugaiyan S, et al. MicroTom – A model plant system to study bacterial wilt by Ralstonia solanacearum. Plant Pathol J. 2007;23:239–44.
Google Scholar 
50.Schandry N. A practical guide to visualization and statistical analysis of R. solanacearum infection data using R. Front Plant Sci. 2017;8:623.PubMed 
PubMed Central 

Google Scholar 
51.Gu S, Wei Z, Shao Z, Friman VP, Cao K, Yang T, et al. Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nat Microbiol. 2020;5:1002–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

Google Scholar 
53.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–642.CAS 

Google Scholar 
54.Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
56.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 

Google Scholar 
58.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Google Scholar 
59.Shenhav L, Thompson M, Joseph TA, Briscoe L, Furman O, Bogumil D, et al. FEAST: fast expectation-maximization for microbial source tracking. Nat Methods. 2019;16:627–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Mendiburu F de. agricolae: Statistical procedures for agricultural research. R package version 1.3–5. 2021.61.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
62.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 

Google Scholar 
63.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.PubMed 
PubMed Central 

Google Scholar 
64.Deng Y, Jiang Y-H, Yang Y, He Z, Luo F, Zhou J. Molecular ecological network analyses. BMC Bioinformatics. 2012;13:113.PubMed 
PubMed Central 

Google Scholar 
65.Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. mBio. 2010;1:e00169–10.PubMed 
PubMed Central 

Google Scholar 
66.Ma B, Wang Y, Ye S, Liu S, Stirling E, Gilbert JA, et al. Earth microbial co-occurrence network reveals interconnection pattern across microbiomes. Microbiome. 2020;8:82.PubMed 
PubMed Central 

Google Scholar 
67.Kuntal BK, Chandrakar P, Sadhu S, Mande SS. ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J. 2019;13:442–54.PubMed 

Google Scholar 
68.Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Choi K, Choi J, Lee PA, Roy N, Khan R, Lee HJ, et al. Alteration of bacterial wilt resistance in tomato plant by microbiota transplant. Front Plant Sci. 2020;11:1186.PubMed 
PubMed Central 

Google Scholar 
70.Davar D, Dzutsev AK, McCulloch JA, Rodrigues RR, Chauvin J-M, Morrison RM, et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science. 2021;371:595–602.CAS 

Google Scholar 
71.D’Haens GR, Jobin C. Fecal microbial transplantation for diseases beyond recurrent Clostridium difficile infection. Gastroenterology. 2019;157:624–36.PubMed 

Google Scholar 
72.Gough E, Shaikh H, Manges AR. Systematic review of intestinal microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium difficile infection. Clin Infect Dis. 2011;53:994–1002.PubMed 

Google Scholar 
73.Durack J, Lynch SV. The gut microbiome: relationships with disease and opportunities for therapy. J Exp Med. 2019;216:20–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Danne C, Rolhion N, Sokol H. Recipient factors in faecal microbiota transplantation: one stool does not fit all. Nat Rev Gastroenterol Hepatol. 2021;18:503–13.PubMed 

Google Scholar 
75.Jiang G, Wang N, Zhang Y, Zhang Y, Yu J, Zhang Y, et al. The relative importance of soil moisture in predicting bacterial wilt disease occurrence. Soil Ecol Lett. 2021;3:356–66.76.Wei Z, Friman VP, Pommier T, Geisen S, Jousset A, Shen Q. Rhizosphere immunity: targeting the underground for sustainable plant health management. Front Agric Sci Eng. 2020;7:317–28.
Google Scholar 
77.Hu J, Wei Z, Friman VP, Gu SH, Wang X-F, Eisenhauer N, et al. Probiotic diversity enhances rhizosphere microbiome function and plant disease suppression. mBio. 2016;7:e01790–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Bakker PAHM, Doornbos RF, Zamioudis C, Berendsen RL, Pieterse CMJ. Induced systemic resistance and the rhizosphere microbiome. Plant Pathol J. 2013;29:136–43.PubMed 
PubMed Central 

Google Scholar 
79.Wei Z, Yang T, Friman VP, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Commun. 2015;6:8413.CAS 
PubMed 

Google Scholar 
80.Li M, Wei Z, Wang J, Jousset A, Friman V-P, Xu Y, et al. Facilitation promotes invasions in plant-associated microbial communities. Ecol Lett. 2019;22:149–58.PubMed 

Google Scholar 
81.Mendes LW, Raaijmakers JM, de Hollander M, Mendes R, Tsai SM. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function. ISME J. 2018;12:212–24.PubMed 

Google Scholar 
82.Rosales PF, Bordin GS, Gower AE, Moura S. Indole alkaloids: 2012 until now, highlighting the new chemical structures and biological activities. Fitoterapia. 2020;143:104558.CAS 
PubMed 

Google Scholar 
83.Sarbu LG, Bahrin LG, Babii C, Stefan M, Birsa ML. Synthetic flavonoids with antimicrobial activity: a review. J Appl Microbiol. 2019;127:1282–90.CAS 
PubMed 

Google Scholar 
84.Madadi E, Mazloum-Ravasan S, Yu JS, Ha JW, Hamishehkar H, Kim KH. Therapeutic application of betalains: a review. Plants. 2020;9:E1219.PubMed 

Google Scholar 
85.Ryan RP, Monchy S, Cardinale M, Taghavi S, Crossman L, Avison MB, et al. The versatility and adaptation of bacteria from the genus Stenotrophomonas. Nat Rev Microbiol. 2009;7:514–25.CAS 
PubMed 

Google Scholar 
86.Kolton M, Erlacher A, Berg G, Cytryn E. The Flavobacterium genus in the plant holobiont: ecological, physiological, and applicative insights. In: Castro-Sowinski S, editor. Microbial models: from environmental to industrial sustainability. Singapore: Springer; 2016. p. 189–207.87.Haas D, Défago G. Biological control of soil-borne pathogens by fluorescent pseudomonads. Nat Rev Microbiol. 2005;3:307–19.CAS 
PubMed 

Google Scholar 
88.Fira D, Dimkić I, Berić T, Lozo J, Stanković S. Biological control of plant pathogens by Bacillus species. J Biotechnol. 2018;285:44–55.CAS 
PubMed 

Google Scholar  More

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

Google Scholar 
2.Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).ADS 
CAS 
PubMed 

Google Scholar 
3.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143 (2016).ADS 
CAS 
PubMed 

Google Scholar 
4.Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).CAS 
PubMed 

Google Scholar 
5.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
6.Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Google Scholar 
7.Fletcher, R. J. et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).
Google Scholar 
8.Fahrig, L. Habitat fragmentation: a long and tangled tale. Glob. Ecol. Biogeogr. 28, 33–41 (2019).
Google Scholar 
9.Fahrig, L. et al. Is habitat fragmentation bad for biodiversity? Biol. Conserv. 230, 179–186 (2019).
Google Scholar 
10.Miller-Rushing, A. J. et al. How does habitat fragmentation affect biodiversity? A controversial question at the core of conservation biology. Biol. Conserv. 232, 271–273 (2019).
Google Scholar 
11.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
12.Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
Google Scholar 
13.Hanski, I. Habitat fragmentation and species richness. J. Biogeogr. 42, 989–993 (2015).
Google Scholar 
14.Pfeifer, M. et al. Creation of forest edges has a global impact on forest vertebrates. Nature 551, 187–191 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239 (2019).ADS 
CAS 
PubMed 

Google Scholar 
16.Pardini, R. et al. Beyond the fragmentation threshold hypothesis: regime shifts in biodiversity across fragmented landscapes. PLoS ONE 5, e13666 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
17.Villard, M.-A. & Metzger, J. P. Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J. Appl. Ecol. 51, 309–318 (2014).
Google Scholar 
18.Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl Acad. Sci. USA 105, 20770–20775 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Franklin, J. F. & Lindenmayer, D. B. Importance of matrix habitats in maintaining biological diversity. Proc. Natl Acad. Sci. USA 106, 349–350 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
20.MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 1967).21.Haila, Y. A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecol. Appl. 12, 321–334 (2002).
Google Scholar 
22.Watson, D. M. A conceptual framework for studying species composition in fragments, islands and other patchy ecosystems. J. Biogeogr. 29, 823–834 (2002).
Google Scholar 
23.Watson, J. E. M., Whittaker, R. J. & Freudenberger, D. Bird community responses to habitat fragmentation: how consistent are they across landscapes? J. Biogeogr. 32, 1353–1370 (2005).
Google Scholar 
24.Mendenhall, C. D., Karp, D. S., Meyer, C. F. J., Hadly, E. A. & Daily, G. C. Predicting biodiversity change and averting collapse in agricultural landscapes. Nature 509, 213–217 (2014).ADS 
CAS 
PubMed 

Google Scholar 
25.Daily, G. C., Ceballos, G., Pacheco, J., Suzán, G. & Sánchez‐Azofeifa, A. Countryside biogeography of Neotropical mammals: conservation opportunities in agricultural landscapes of Costa Rica. Conserv. Biol. 17, 1814–1826 (2003).
Google Scholar 
26.Green, R. E., Cornell, S. J., Scharlemann, J. P. W. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005).ADS 
CAS 
PubMed 

Google Scholar 
27.Perfecto, I. & Vandermeer, J. Biodiversity conservation in tropical agroecosystems. Ann. N. Y. Acad. Sci. 1134, 173–200 (2008).ADS 
PubMed 

Google Scholar 
28.Law, E. A. & Wilson, K. A. Providing context for the land-sharing and land-sparing debate. Conserv. Lett. 8, 404–413 (2015).
Google Scholar 
29.Phalan, B. T. What have we learned from the land sparing-sharing model? Sustainability 10, 1760 (2018).
Google Scholar 
30.Balmford, B., Green, R. E., Onial, M., Phalan, B. & Balmford, A. How imperfect can land sparing be before land sharing is more favourable for wild species? J. Appl. Ecol. 56, 73–84 (2019).
Google Scholar 
31.Prevedello, J. A. & Vieira, M. V. Does the type of matrix matter? A quantitative review of the evidence. Biodivers. Conserv. 19, 1205–1223 (2010).
Google Scholar 
32.Ferreira, A. S., Peres, C. A., Bogoni, J. A. & Cassano, C. R. Use of agroecosystem matrix habitats by mammalian carnivores (Carnivora): a global-scale analysis. Mammal. Rev. 48, 312–327 (2018).
Google Scholar 
33.Battin, J. When good animals love bad habitats: ecological traps and the conservation of animal populations. Conserv. Biol. 18, 1482–1491 (2004).
Google Scholar 
34.Martin, L. J., Blossey, B. & Ellis, E. Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations. Front. Ecol. Environ. 10, 195–201 (2012).
Google Scholar 
35.Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).ADS 
PubMed 

Google Scholar 
36.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).PubMed 
PubMed Central 

Google Scholar 
37.Arroyo‐Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).PubMed 

Google Scholar 
38.Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. Lond. B Biol. Sci. 267, 1947–1952 (2000).CAS 

Google Scholar 
39.Fisher, D. O., Blomberg, S. P. & Owens, I. P. F. Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials. Proc. R. Soc. Lond. B Biol. Sci. 270, 1801–1808 (2003).
Google Scholar 
40.Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).ADS 
CAS 
PubMed 

Google Scholar 
41.Davidson, A. D., Hamilton, M. J., Boyer, A. G., Brown, J. H. & Ceballos, G. Multiple ecological pathways to extinction in mammals. Proc. Natl Acad. Sci. USA 106, 10702–10705 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Di Marco, M., Collen, B., Rondinini, C. & Mace, G. M. Historical drivers of extinction risk: using past evidence to direct future monitoring. Proc. R. Soc. B Biol. Sci. 282, 20150928 (2015).
Google Scholar 
43.Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
44.Rondinini, C., Marco, M. D., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN Red List. Conserv. Lett. 7, 126–130 (2014).
Google Scholar 
45.Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).
Google Scholar 
46.Crooks, K. R. et al. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl Acad. Sci. USA 114, 7635–7640 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Lucas, P. M., González‐Suárez, M. & Revilla, E. Range area matters, and so does spatial configuration: predicting conservation status in vertebrates. Ecography 42, 1103–1114 (2019).
Google Scholar 
48.Arregoitia, L. D. V. Biases, gaps, and opportunities in mammalian extinction risk research. Mammal. Rev. 46, 17–29 (2016).
Google Scholar 
49.Rondinini, C. et al. Global habitat suitability models of terrestrial mammals. Philos. Trans. R. Soc. B Biol. Sci. 366, 2633–2641 (2011).
Google Scholar 
50.Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).
Google Scholar 
52.Tucker, M. A. et al. Moving in the Anthropocene: global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).ADS 
CAS 
PubMed 

Google Scholar 
53.Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).ADS 
CAS 
PubMed 

Google Scholar 
54.Breiman, L. Random forests. Mach. Learn 45, 5–32 (2001).MATH 

Google Scholar 
55.Laurance, W. F. Ecological correlates of extinction proneness in Australian tropical rain forest mammals. Conserv. Biol. 5, 79–89 (1991).
Google Scholar 
56.Viveiros de Castro, E. B. & Fernandez, F. A. S. Determinants of differential extinction vulnerabilities of small mammals in Atlantic forest fragments in Brazil. Biol. Conserv. 119, 73–80 (2004).
Google Scholar 
57.Reider, I. J., Donnelly, M. A. & Watling, J. I. The influence of matrix quality on species richness in remnant forest. Landsc. Ecol. 33, 1147–1157 (2018).
Google Scholar 
58.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).PubMed 

Google Scholar 
59.Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 

Google Scholar 
60.Tracewski, Ł. et al. Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates. Conserv. Biol. 30, 1070–1079 (2016).PubMed 

Google Scholar 
61.Cardillo, M. et al. The predictability of extinction: biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 
62.Murray, K. A., Arregoitia, L. D. V., Davidson, A., Marco, M. D. & Fonzo, M. M. I. D. Threat to the point: improving the value of comparative extinction risk analysis for conservation action. Glob. Change Biol. 20, 483–494 (2014).ADS 

Google Scholar 
63.Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H. & Possingham, H. P. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol. Lett. 9, 1136–1145 (2006).PubMed 

Google Scholar 
64.Galán-Acedo, C. et al. The conservation value of human-modified landscapes for the world’s primates. Nat. Commun. 10, 152 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
65.Watling, J. I., Nowakowski, A. J., Donnelly, M. A. & Orrock, J. L. Meta-analysis reveals the importance of matrix composition for animals in fragmented habitat. Glob. Ecol. Biogeogr. 20, 209–217 (2011).
Google Scholar 
66.Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: an empirical review and synthesis. Ecol. Soc. 14, 21 (2009).67.Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proc. Natl Acad. Sci. USA 112, 4531–4540 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
68.May, S. A. & Norton, T. W. Influence of fragmentation and disturbance on the potential impact of feral predators on native fauna in Australian forest ecosystems. Wildl. Res 23, 387–400 (1996).
Google Scholar 
69.Peres, C. A. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian Forest vertebrates. Conserv. Biol. 15, 1490–1505 (2001).
Google Scholar 
70.Laurance, W. F. & Useche, D. C. Environmental synergisms and extinctions of tropical species. Conserv. Biol. 23, 1427–1437 (2009).PubMed 

Google Scholar 
71.Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B Biol. Sci. 283, 20152592 (2016).
Google Scholar 
72.Didham, R. K., Kapos, V. & Ewers, R. M. Rethinking the conceptual foundations of habitat fragmentation research. Oikos 121, 161–170 (2012).
Google Scholar 
73.Ruffell, J., Banks‐Leite, C. & Didham, R. K. Accounting for the causal basis of collinearity when measuring the effects of habitat loss versus habitat fragmentation. Oikos 125, 117–125 (2016).
Google Scholar 
74.Morante‐Filho, J. C. et al. Direct and cascading effects of landscape structure on tropical forest and non-forest frugivorous birds. Ecol. Appl. 28, 2024–2032 (2018).PubMed 

Google Scholar 
75.Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. L. Southeast Asian biodiversity: an impending disaster. Trends Ecol. Evol. 19, 654–660 (2004).PubMed 

Google Scholar 
76.Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 

Google Scholar 
77.Segan, D. B., Murray, K. A. & Watson, J. E. M. A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Glob. Ecol. Conserv. 5, 12–21 (2016).
Google Scholar 
78.Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).PubMed 

Google Scholar 
79.IUCN. IUCN Red List of Threatened Species. Version 2021-1. (2021).80.IUCN. A global standard for the identification of Key Biodiversity Areas. Version 1.0. (IUCN, Gland, 2016).81.Crooks, K. R., Burdett, C. L., Theobald, D. M., Rondinini, C. & Boitani, L. Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. B Biol. Sci. 366, 2642–2651 (2011).
Google Scholar 
82.Ripple, W. J., Bradshaw, G. A. & Spies, T. A. Measuring forest landscape patterns in the cascade range of Oregon, USA. Biol. Conserv. 57, 73–88 (1991).
Google Scholar 
83.Li, B.-L. & Archer, S. Weighted mean patch size: a robust index for quantifying landscape structure. Ecol. Model. 102, 353–361 (1997).
Google Scholar 
84.Di Marco, M., Rondinini, C., Boitani, L. & Murray, K. A. Comparing multiple species distribution proxies and different quantifications of the human footprint map, implications for conservation. Biol. Conserv. 165, 203–211 (2013).
Google Scholar 
85.IUCN. IUCN Red List of Threatened Species. Version 2012-1. (2012).86.Cutler, D. R. et al. Random forests for Classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 

Google Scholar 
87.Jetz, W., Carbone, C., Fulford, J. & Brown, J. H. The scaling of animal space use. Science 306, 266–268 (2004).ADS 
CAS 
PubMed 

Google Scholar 
88.McNab, B. K. The influence of food habits on the energetics of eutherian mammals. Ecol. Monogr. 56, 1–19 (1986).
Google Scholar 
89.Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).
Google Scholar 
90.Murphy, M. A., Evans, J. S. & Storfer, A. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252–261 (2010).PubMed 

Google Scholar 
91.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 
92.Cohen, J. Statistical Power Analysis for the Behavioral Sciences. (Academic Press, 1988).93.ESRI. ArcGIS Pro version 2.8.2, https://www.esri.com/en-us/home (2021).94.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2017).95.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
96.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Google Scholar 
97.Molnar, C. & Schratz, P. iml: Interpretable Machine Learning. R package version 0.10.1, https://CRAN.R-project.org/package=iml (2020).98.Torchiano, M. effsize: Efficient Effect Size Computation. R package version 0.8.1, https://CRAN.R-project.org/package=effsize (2020).99.Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0, https://CRAN.R-project.org/package=rredlist (2020).100.Smith, F. A. et al. Body mass of late Quaternary mammals. Ecology 84, 3403–3403 (2003).
Google Scholar 
101.Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).
Google Scholar 
102.Tacutu, R. et al. Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res. 41, D1027–D1033 (2013).CAS 
PubMed 

Google Scholar 
103.Verde Arregoitia, L. D., Blomberg, S. P. & Fisher, D. O. Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk. Proc. R. Soc. B Biol. Sci. 280, 20131092 (2013).
Google Scholar 
104.Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626–2626 (2018).PubMed 

Google Scholar 
105.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).
Google Scholar 
106.Kissling, W. D. et al. Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide. Ecol. Evol. 4, 2913–2930 (2014).PubMed 
PubMed Central 

Google Scholar  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