Philippa Kaur

1.Gese E. M. Monitoring of terrestrial carnivore populations. Carnivore Conservation. (2001).2.Oconnell, A. F. et al. (eds) Camera Traps in Animal Ecology: Methods and Analyses (Springer Science & Business Media, 2010).
Google Scholar 
3.Tobler, M. W., Carrillo-Percastegui, S. E., Pitman, R. L., Mares, R. & Powell, G. An evaluation of camera traps for inventorying large-and medium-sized terrestrial rainforest mammals. Anim. Conserv. 11(3), 169–178 (2008).
Google Scholar 
4.MacKenzie D. I., Nichols J. D., Royle J. A., Pollock K. H., Bailey L. A., Hines J. E. Occupancy Modeling and Estimation (2017).5.Carbone, C. et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Anim. Conserv. 4(1), 75–79 (2001).
Google Scholar 
6.Rowcliffe, J. M., Field, J., Turvey, S. T. & Carbone, C. Estimating animal density using camera traps without the need for individual recognition. J. Appl. Ecol. 1, 1228–1236 (2008).
Google Scholar 
7.Karanth, K. U. Estimating tiger Panthera tigris populations from camera-trap data using capture-recapture models. Biol. Conserv. 71(3), 333–338 (1995).
Google Scholar 
8.Silver, S. C. et al. The use of camera traps for estimating jaguar Panthera onca abundance and density using capture/recapture analysis. Oryx 38(2), 148–154 (2004).
Google Scholar 
9.Jhala, Y., Qureshi, Q. & Gopal, R. Can the abundance of tigers be assessed from their signs?. J. Appl. Ecol. 48(1), 14–24 (2011).
Google Scholar 
10.Sollmann, R. et al. Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture-recapture models for jaguars in central Brazil. Biol. Conserv. 144(3), 1017–1024 (2011).
Google Scholar 
11.Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5(11), 1170–1179 (2014).
Google Scholar 
12.Roy, M. et al. Demystifying the Sundarban tiger: Novel application of conventional population estimation methods in a unique ecosystem. Popul. Ecol. 58(1), 81–89 (2016).
Google Scholar 
13.Howe, E. J., Buckland, S. T., Després-Einspenner, M. L. & Kühl, H. S. Distance sampling with camera traps. Methods Ecol. Evol. 8(11), 1558–1565 (2017).
Google Scholar 
14.Bridges, A. S., Vaughan, M. R. & Klenzendorf, S. Seasonal variation in American black bear Ursus americanus activity patterns: Quantification via remote photography. Wildl. Biol. 10(1), 277–284 (2004).
Google Scholar 
15.Beck, H. & Terborgh, J. Groves versus isolates: How spatial aggregation of Astrocaryum murumuru palms affects seed removal. J. Trop. Ecol. 1, 275–288 (2002).
Google Scholar 
16.Kinnaird, M. F., Sanderson, E. W., O’Brien, T. G., Wibisono, H. T. & Woolmer, G. Deforestation trends in a tropical landscape and implications for endangered large mammals. Conserv. Biol. 17(1), 245–257 (2003).
Google Scholar 
17.MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8), 2248–2255 (2002).
Google Scholar 
18.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52(3), 675–685 (2015).
Google Scholar 
19.O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6(2), 131–139 (2003).
Google Scholar 
20.Datta, A., Anand, M. O. & Naniwadekar, R. Empty forests: Large carnivore and prey abundance in Namdapha National Park, north-east India. Biol. Cons. 141(5), 1429–1435 (2008).
Google Scholar 
21.Weckel, M., Giuliano, W. & Silver, S. Jaguar (Panthera onca) feeding ecology: Distribution of predator and prey through time and space. J. Zool. 270(1), 25–30 (2006).
Google Scholar 
22.Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Spatio-temporal partitioning among large carnivores in relation to major prey species in Western Ghats. J. Zool. 287(4), 269–275 (2012).
Google Scholar 
23.Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Role of body size in activity budgets of mammals in the Western Ghats of India. J. Trop. Ecol. 32, 315–323 (2015).
Google Scholar 
24.Edwards, S. et al. Making the most of by-catch data: Assessing the feasibility of utilising non-target camera trap data for occupancy modelling of a large felid. Afr. J. Ecol. 56(4), 885–894 (2018).
Google Scholar 
25.Harmsen, B. J., Foster, R. J., Silver, S., Ostro, L. & Doncaster, C. P. Differential use of trails by forest mammals and the implications for camera-trap studies: A case study from Belize. Biotropica 42(1), 126–133 (2010).
Google Scholar 
26.Di Bitetti M. S., Paviolo A. J. & de Angelo C. D. Camera Trap Photographic Rates on Roads vs. Off Roads: Location Does Matter, Vol. 21, 37–46 (2014).27.Blake, J. G. & Mosquera, D. Camera trapping on and off trails in lowland forest of eastern Ecuador: Does location matter?. Mastozool. Neotrop. 21(1), 17–26 (2014).
Google Scholar 
28.Cusack, J. J. et al. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS ONE 10(5), e0126373 (2015).PubMed 
PubMed Central 

Google Scholar 
29.Kolowski, J. M. & Forrester, T. D. Camera trap placement and the potential for bias due to trails and other features. PLoS ONE 12(10), e0186679 (2017).PubMed 
PubMed Central 

Google Scholar 
30.Srbek-Araujo, A. C. & Chiarello, A. G. Influence of camera-trap sampling design on mammal species capture rates and community structures in southeastern Brazil. Biota. Neotrop. 13(2), 51–62 (2013).
Google Scholar 
31.Wearn, O. R., Rowcliffe, J. M., Carbone, C., Bernard, H. & Ewers, R. M. Assessing the status of wild felids in a highly-disturbed commercial forest reserve in Borneo and the implications for camera trap survey design. PLoS ONE 8(11), e77598 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Sadhu, A. et al. Demography of a small, isolated tiger population in a semi-arid region of western India. BMC Zool. 2(1), 1–13 (2017).
Google Scholar 
33.Sunquist, M. What is a tiger? Ecology and behavior. In Tigers of the World 19–33 (William Andrew Publishing, 2010).
Google Scholar 
34.Gotelli, N. J. & Colwell, R. K. Estimating species richness. Biol. Divers. Front. Meas. Assess. 12, 39–54 (2011).
Google Scholar 
35.Colwell, R. K., Mao, C. X. & Chang, J. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85(10), 2717–2727 (2004).
Google Scholar 
36.Rovero, F. & Marshall, A. R. Camera trapping photographic rate as an index of density in forest ungulates. J. Appl. Ecol. 46(5), 1011–1017 (2009).
Google Scholar 
37.Jhala, Y. V., Qureshi, Q., Nayak, A. K. Status of tigers, copredators and prey in India, 2018. ISBN No. 81-85496-50-1 https://wii.gov.in/tiger_reports (National Tiger Conservation Authority, Government of India and Wildlife Institute of India, 2020).38.Nichols, J. D. et al. Multi-scale occupancy estimation and modelling using multiple detection methods. J. Appl. Ecol. 45(5), 1321–1329 (2008).
Google Scholar 
39.Hines J. E. PRESENCE 3.1 Software to estimate patch occupancy and related parameters. http://www.mbr-pwrc.usgs.gov/software/presence.html. (2006).40.Meredith, M., & Ridout, M. Overview of the overlap package. R. Project. 1–9 (2014).41.Rowcliffe M, Rowcliffe M. M. Package ‘activity’. Animal activity statistics R Package Version. 1 (2016).42.Soberón, M. J. & Llorente, B. J. The use of species accumulation functions for the prediction of species richness. Conserv. Biol. 7(3), 480–488 (1993).
Google Scholar 
43.Broadley, K., Burton, A. C., Avgar, T. & Boutin, S. Density-dependent space use affects interpretation of camera trap detection rates. Ecol. Evol. 9(24), 14031–14041 (2019).PubMed 
PubMed Central 

Google Scholar 
44.Bunnell, F. L. & Gillingham, M. P. Foraging behavior: Dynamics of dining out. Bioenerget. Wild herbiv. 1, 53–79 (1985).
Google Scholar 
45.Mishra H. R. The ecology and behaviour of chital (Axis axis) in the Royal Chitwan National Park, Nepal: with comparative studies of hog deer (Axis porcinus), sambar (Cervus unicolor) and barking deer (Muntiacus muntjak) (Doctoral dissertation, University of Edinburgh). 1982.46.Raman, T. S. Factors influencing seasonal and monthly changes in the group size of chital or axis deer in southern India. J. Biosci. 22(2), 203–218 (1997).
Google Scholar 
47.Karanth, K. U. & Sunquist, M. E. Behavioral correlates of predation by tiger, leopard and dhole in Nagarhole National Park. India. J Zool. 250(2), 255–265 (2000).
Google Scholar 
48.Harmsen, B. J., Foster, R. J., Silver, S. C., Ostro, L. E. & Doncaster, C. P. Spatial and temporal interactions of sympatric jaguars (Panthera onca) and pumas (Puma concolor) in a neotropical forest. J. Mammal. 90(3), 612–620 (2009).
Google Scholar 
49.Nichols, J. D., Karanth, K. U. & O’Connell, A. F. Science, conservation, and camera traps. In Camera Traps in Animal Ecology 45–56 (Springer, 2011).
Google Scholar  More

1.Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 

Google Scholar 
2.Jankowski, T., Collins, A. G. & Campbell, R. Global diversity of inland water cnidarians. In Freshwater Animal Diversity Assessment 35–40 (Springer, 2008).
Google Scholar 
3.Pelosse, J. Étude biologique sur la méduse d’eau douce, Limnocodium Sowerbyi Ray Lankester, du Parc de la Tête-d’Or de Lyon. Publ. Société Linn. Lyon 65, 53–62 (1919).
Google Scholar 
4.Lüskow, F., López-González, P. J. & Pakhomov, E. A. Freshwater jellyfish in northern temperate lakes: Craspedacusta sowerbii in British Columbia, Canada. Aquat. Biol. 30, 69–84 (2021).
Google Scholar 
5.McClary, A. The effect of temperature on growth and reproduction in Craspedacusta sowerbii. Ecology 40, 158–162 (1959).
Google Scholar 
6.McClary, A. Experimental studies of bud development in Craspedacusta sowerbii. Trans. Am. Microsc. Soc. 80, 343–353 (1961).
Google Scholar 
7.McClary, A. Histological changes during regeneration of Craspedacusta sowerbii. Trans. Am. Microsc. Soc. 83, 349–357 (1964).
Google Scholar 
8.Acker, T. S. & Muscat, A. M. The ecology of Craspedacusta sowerbii Lankester, a freshwater hydrozoan. Am. Midl. Nat. 95, 323–336 (1976).
Google Scholar 
9.Boothroyd, I. K., Etheredge, M. K. & Green, J. D. Spatial distribution, size structure, and prey of Craspedacusta sowerbyi Lankester in a shallow New Zealand lake. Hydrobiologia 468, 23–32 (2002).
Google Scholar 
10.Turquin, M. J. Progrès dans la connaissance de la métagenèse chez Craspedacusta sowerbii (= sowerbyi) (Limnoméduse, Olindiidae). Bourgogne-Nat. 9, 162–174 (2010).
Google Scholar 
11.Marchessaux, G. & Bejean, M. From frustules to medusae: A new culture system for the study of the invasive hydrozoan Craspedacusta sowerbii in the laboratory. Invertebr. Biol. 139, e12308 (2020).
Google Scholar 
12.Bouillon, J. & Boero, F. The hydrozoa: A new classification in the ligth of old knowledge. Thalass. Salentina 24, 3–45 (2000).
Google Scholar 
13.Dumont, H. J. The distribution and ecology of the fresh-and brackish-water medusae of the world. In Studies on the Ecology of Tropical Zooplankton 1–12 (Springer, 1994).
Google Scholar 
14.Duggan, I. C. The freshwater aquarium trade as a vector for incidental invertebrate fauna. Biol. Invasions 12, 3757–3770 (2010).
Google Scholar 
15.Marchessaux, G., Gadreaud, J. & Belloni, B. The freshwater jellyfish Craspedacusta sowerbii lankester, 1880: An overview of its distribution in France. Vie Milieu 69, 201–213 (2019).
Google Scholar 
16.Pennak, R. W. The fresh-water jellyfish Craspedacusta in Colorado with some remarks on its ecology and morphological degeneration. Trans. Am. Microsc. Soc. 75, 324–331 (1956).
Google Scholar 
17.Matthews, D. C. A Comparative study of Craspedacusta sowerbyi and Calpasoma dactyloptera life cycles (1966).18.Lundberg, S. & Svensson, J. E. Medusae invasions in Swedish lakes. Fauna Flora 98, 18–28 (2003).
Google Scholar 
19.Jakovčev-Todorović, D., Đikanović, V., Skorić, S. & Cakić, P. Freshwater jellyfish Craspedacusta sowerbyi Lankester, 1880 (Hydrozoa, Olindiidae): 50 years’ observations in Serbia. Arch. Biol. Sci. 62, 123–127 (2010).
Google Scholar 
20.Bosso, L., De Conno, C. & Russo, D. Modelling the risk posed by the zebra mussel Dreissena polymorpha: Italy as a case study. Environ. Manag. 60, 304–313 (2017).ADS 

Google Scholar 
21.Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).PubMed 
PubMed Central 
ADS 

Google Scholar 
22.Hosmer, D. W., Jovanovic, B. & Lemeshow, S. Best subsets logistic regression. Biometrics 45, 1265–1270 (1989).MATH 

Google Scholar 
23.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 
24.Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250 (2005).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
25.Walther, G. Inference and modeling with log-concave distributions. Stat. Sci. 24, 319–327 (2009).MathSciNet 
MATH 

Google Scholar 
26.Mangano, M. C. et al. Moving toward a strategy for addressing climate displacement of marine resources: A proof-of-concept. Front. Mar. Sci. 7, 408 (2020).ADS 

Google Scholar 
27.Perkins-Taylor, I. & Frey, J. Predicting the distribution of a rare chipmunk (Neotamias quadrivittatus oscuraensis): Comparing MaxEnt and occupancy models. J. Mammal. 101, 1035–1048 (2020).PubMed 
PubMed Central 

Google Scholar 
28.Di Pasquale, G. et al. Coastal pine-oak glacial refugia in the Mediterranean basin: A biogeographic approach based on charcoal analysis and spatial modelling. Forests 11, 673 (2020).
Google Scholar 
29.Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).PubMed 
PubMed Central 

Google Scholar 
30.Fernández, M. & Hamilton, H. Ecological niche transferability using invasive species as a case study. PLoS ONE 10, e0119891 (2015).PubMed 
PubMed Central 

Google Scholar 
31.Sarà, G., Palmeri, V., Rinaldi, A., Montalto, V. & Helmuth, B. Predicting biological invasions in marine habitats through eco-physiological mechanistic models: A case study with the bivalve B rachidontes pharaonis. Divers. Distrib. 19, 1235–1247 (2013).
Google Scholar 
32.Sarà, G., Porporato, E. M., Mangano, M. C. & Mieszkowska, N. Multiple stressors facilitate the spread of a non-indigenous bivalve in the Mediterranean Sea. J. Biogeogr. 45, 1090–1103 (2018).
Google Scholar 
33.Markovic, D., Freyhof, J. & Wolter, C. Where are all the fish: Potential of biogeographical maps to project current and future distribution patterns of freshwater species. PLoS ONE 7, e40530 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Hamner, W. M., Gilmer, R. W. & Hamner, P. P. The physical, chemical, and biological characteristics of a stratified, saline, sulfide lake in Palau 1. Limnol. Oceanogr. 27, 896–909 (1982).CAS 
ADS 

Google Scholar 
35.Hamner, W. M. & Hauri, I. R. Long-distance horizontal migrations of zooplankton (Scyphomedusae: Mastigias) 1. Limnol. Oceanogr. 26, 414–423 (1981).ADS 

Google Scholar 
36.Duggan, I. C. & Eastwood, K. R. Detection and distribution of Craspedacusta sowerbii: Observations of medusae are not enough. (2012).37.Galarce, L. C., Riquelme, K. V., Osman, D. Y. & Fuentes, R. A. A new record of the non indigenous freshwater jellyfish Craspedacusta sowerbii Lankester, 1880 (Cnidaria) in Northern Patagonia (40 S, Chile). Bioinvasions Rec. 2, 263–270 (2013).
Google Scholar 
38.Stanković, I. & Ternjej, I. New ecological insight on two invasive species: Craspedacusta sowerbii (Coelenterata: Limnomedusae) and Dreissenia polymorpha (Bivalvia: Dreissenidae). J. Nat. Hist. 44, 2707–2713 (2010).
Google Scholar 
39.Stefani, F., Leoni, B., Marieni, A. & Garibaldi, L. A new record of Craspedacusta sowerbii, Lankester 1880 (Cnidaria, Limnomedusae) in northern Italy. J. Limnol. 69, 189 (2010).
Google Scholar 
40.Jankowski, T., Strauss, T. & Ratte, H. T. Trophic interactions of the freshwater jellyfish Craspedacusta sowerbii. J. Plankton Res. 27, 811–823 (2005).CAS 

Google Scholar 
41.Adams, I. B. The effect of light and prey availability on the activity of the freshwater jellyfish, Craspedacusta sowerbii (Hydrozoan) (Mém. B Sc Univ James Madison À Harrisonburg Virginie, 2009).
Google Scholar 
42.Marchessaux, G. & Bejean, M. Growth and ingestion rates of the freshwater jellyfish Craspedacusta sowerbii. J. Plankton Res. 42, 783–786 (2020).CAS 

Google Scholar 
43.Himchik, V., Marenkov, O. & Shmyhol, N. Biology of reproduction of aquatic organisms: The course of oogenesis of freshwater jellyfish Craspedacusta sowerbii Lancester, 1880 in the Dnieper reservoir. World Sci. News 160, 1–15 (2021).
Google Scholar 
44.Caputo, L., Huovinen, P., Sommaruga, R. & Gómez, I. Water transparency affects the survival of the medusa stage of the invasive freshwater jellyfish Craspedacusta sowerbii. Hydrobiologia 817, 179–191 (2018).CAS 

Google Scholar 
45.Bozman, A., Titelman, J., Kaartvedt, S., Eiane, K. & Aksnes, D. L. Jellyfish distribute vertically according to irradiance. J. Plankton Res. 39, 280–289 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Salonen, K. et al. Limnocnida tanganyicae medusae (Cnidaria: Hydrozoa): A semiautonomous microcosm in the food web of Lake Tanganyika. In Jellyfish Blooms IV 97–112 (Springer, 2012).
Google Scholar 
47.Dodson, S. I. & Cooper, S. D. Trophic relationships of the freshwater jellyfish Craspedacusta sowerbyi Lankester 1880. Limnol. Oceanogr. 28, 345–351 (1983).ADS 

Google Scholar 
48.Smith, A. S. & Alexander, J. E. Jr. Potential effects of the freshwater jellyfish Craspedacusta sowerbii on zooplankton community abundance. J. Plankton Res. 30, 1323–1327 (2008).
Google Scholar 
49.Spadinger, R. & Maier, G. Prey selection and diel feeding of the freshwater jellyfish, Craspedacusta sowerbyi. Freshw. Biol. 41, 567–573 (1999).
Google Scholar 
50.Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).PubMed 

Google Scholar 
51.Uchida, T. A new sporozoan-like reproduction in the hydromedusa. Gonionemus vertens. Proc. Jpn. Acad. 52, 387–388 (1976).
Google Scholar 
52.Williams, A. B. Shrimps, Lobsters, and Crabs of the Atlantic Coast of the Eastern United States, Maine to Florida (1984).53.Parent, G. H. La découverte lorraine de Craspedacusta sowerbyi Lank. dans son contexte chorologique et écologique européen. Bull. Soc. D’Histoire Nat. Moselle 43, 317–337 (1982).
Google Scholar 
54.Amemiya, I. Freshwater medusa found in the tank of my laboratory. Jpn. J. Zool. Trans. Abstr. 3, Abstract (1930).55.Joshi, M. V. & Tonapi, G. T. A new record of freshwater medusa from India. Curr. Sci. 34, 665–666 (1965).
Google Scholar 
56.El Moussaoui, N. & Beisner, B. L. La méduse d’eau douce Craspedacusta sowerbii: espèce exotique répandue dans les lacs du Québec. Nat. Can. 141, 40–46 (2017).
Google Scholar 
57.Fish, G. R. Craspedacusta sowerbyi Lankester (Coelenterata: Limnomedusae) in New Zealand lakes. N. Z. J. Mar. Freshw. Res. 5, 66–69 (1971).
Google Scholar 
58.Rayner, N. A. First record of Craspedacusta sowerbyi Lankester (Cnidaria: Limnomedusae) from Africa. Hydrobiologia 162, 73–77 (1988).
Google Scholar 
59.Somveille, M., Manica, A., Butchart, S. H. & Rodrigues, A. S. Mapping global diversity patterns for migratory birds. PLoS ONE 8, e70907 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
60.Newton, I. & Dale, L. C. Bird migration at different latitudes in eastern North America. Auk 113, 626–635 (1996).
Google Scholar 
61.Zhang, J. et al. Determination of original infection source of H7N9 avian influenza by dynamical model. Sci. Rep. 4, 1–16 (2014).
Google Scholar 
62.Fuentes, R., Cárdenas, L., Abarzua, A. & Caputo, L. Southward invasion of Craspedacusta sowerbii across mesotrophic lakes in Chile: Geographical distribution and genetic diversity of the medusa phase. Freshw. Sci. 38, 193–202 (2019).
Google Scholar 
63.Harrell, F. E. Hmisc: Harrell Miscellaneous (Version 4.5-0) (2021).64.Marchessaux, G., Lüskow, F., Sarà, G. & Pakhomov, E. Mapping the global distribution of the freshwater hydrozoan Craspedacusta sowerbii. Pangaea https://doi.org/10.1594/PANGAEA.936074 (2021).65.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
66.McGarvey, D. J. et al. On the use of climate covariates in aquatic species distribution models: Are we at risk of throwing out the baby with the bath water?. Ecography 41, 695–712 (2018).
Google Scholar 
67.Zeng, Y. & Yeo, D. C. Assessing the aggregated risk of invasive crayfish and climate change to freshwater crabs: A Southeast Asian case study. Biol. Conserv. 223, 58–67 (2018).
Google Scholar 
68.Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).
Google Scholar 
69.R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).70.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 

Google Scholar 
71.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
72.Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
73.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).
Google Scholar 
74.Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
Google Scholar 
75.Zhang, K., Yao, L., Meng, J. & Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 634, 1326–1334 (2018).CAS 
PubMed 
ADS 

Google Scholar 
76.Silva, C., Leiva, F. & Lastra, J. Predicting the current and future suitable habitat distributions of the anchovy (Engraulis ringens) using the Maxent model in the coastal areas off central-northern Chile. Fish. Oceanogr. 28, 171–182 (2019).
Google Scholar 
77.Nenzén, H. K. & Araújo, M. B. Choice of threshold alters projections of species range shifts under climate change. Ecol. Model. 222, 3346–3354 (2011).
Google Scholar 
78.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Google Scholar 
79.DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 837–845 (1988). More

1.Agetsuma, N. Foraging strategies of Yakushima Macaques (Macaca-fuscata Yakui). Int. J. Primatol. 16, 595–609. https://doi.org/10.1007/bf02735283 (1995).Article 

Google Scholar 
2.Hill, D. A. Seasonal variation in the feeding behavior and diet of Japanese macaques (Macaca fuscata yakui) in lowland forest of Yakushima. Am. J. Primatol. 43, 305–322 (1997).CAS 
Article 

Google Scholar 
3.Otani, Y. et al. Factors influencing riverine utilization patterns in two sympatric macaques. Sci. Rep. https://doi.org/10.1038/s41598-020-79259-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Maruhashi, T. Feeding behavior and diet of the Japanese monkey Macaca-fuscata-yakui on Yakushima Island, Japan. Primates 21, 141–160. https://doi.org/10.1007/bf02374030 (1980).Article 

Google Scholar 
5.Rothman, J. M., Raubenheimer, D., Bryer, M. A. H., Takahashi, M. & Gilbert, C. C. Nutritional contributions of insects to primate diets: Implications for primate evolution. J. Hum. Evol. 71, 59–69. https://doi.org/10.1016/j.jhevol.2014.02.016 (2014).Article 
PubMed 

Google Scholar 
6.Hanya, G. et al. Not only annual food abundance but also fallback food quality determines the Japanese macaque density: evidence from seasonal variations in home range size. Primates 47, 275–278. https://doi.org/10.1007/s10329-005-1076-2 (2006).ADS 
Article 
PubMed 

Google Scholar 
7.Nakagawa, N. Determinants of the dramatic seasonal changes in the intake of energy and protein by Japanese monkeys in a cool temperate forest. Am. J. Primatol. 41, 267–288. https://doi.org/10.1002/(sici)1098-2345(1997)41:4%3c267::aid-ajp1%3e3.0.co;2-v (1997).CAS 
Article 
PubMed 

Google Scholar 
8.Tsuji, Y., Ito, T. Y., Wada, K. & Watanabe, K. Spatial patterns in the diet of the Japanese macaque Macaca fuscata and their environmental determinants. Mammal Rev. 45, 227–238. https://doi.org/10.1111/mam.12045 (2015).Article 

Google Scholar 
9.Wada, K. & Tokida, E. Habitat utlization by wintering Japanese monkeys Macaca fuscata-fuscata in Shiga Heights Japan. Primates 22, 330–348. https://doi.org/10.1007/bf02381574 (1981).Article 

Google Scholar 
10.Suzuki, A. An ecological study of wild Japanese monkeys in snowy area focused on their food habits. Primates 6, 31–71 (1965).Article 

Google Scholar 
11.Izawa, K. & Nishida, T. Monkeys living in the northern limits of their distribution. Primates 4, 67–88 (1963).Article 

Google Scholar 
12.Enari, H. & Sakamaki-Enari, H. Influence of heavy snow on the feeding behavior of Japanese Macaques (Macaca Fuscata) in Northern Japan. Am. J. Primatol. 75, 534–544. https://doi.org/10.1002/ajp.22128 (2013).Article 
PubMed 

Google Scholar 
13.Agetsuma, N. Dietary selection by Yakushima macaques (Macaca-fustcata Yakui): the influence of food availability and temperature. Int. J. Primatol. 16, 611–627. https://doi.org/10.1007/bf02735284 (1995).Article 

Google Scholar 
14.Agetsuma, N. & Nakagawa, N. Effects of habitat differences on feeding behaviors of Japanese monkeys: comparison between Yakushima and Kinkazan. Primates 39, 275–289. https://doi.org/10.1007/bf02573077 (1998).Article 

Google Scholar 
15.Izumiyama, S. In: High Altitude Primates, Developments in Primatology: Progress and Prospects Vol. 44 (ed N.B. Grow et al.) 153–181 (Springer, New York, 2014).16.Go, M. Seasonal changes in food resource distribution and feeding sites selected by Japanese macaques on Koshima Islet, Japan. Primates 51, 149–158. https://doi.org/10.1007/s10329-009-0179-5 (2010).Article 
PubMed 

Google Scholar 
17.Hanya, G. Diet of a Japanese macaque troop in the coniferous forest of Yakushima. Int. J. Primatol. 25, 55–71. https://doi.org/10.1023/b:ijop.0000014645.78610.32 (2004).Article 

Google Scholar 
18.Sakamaki, H., Enari, H., Aoi, T. & Kunisaki, T. Winter food abundance for Japanese monkeys in differently aged Japanese cedar plantations in snowy regions. Mammal Study 36, 1–10. https://doi.org/10.3106/041.036.0101 (2011).Article 

Google Scholar 
19.Enari, H. In: High Altitude Primates. Developments in Primatology, Progress and Prospects (eds N Grow, S Gursky-Doyen, & Krzton A) (Springer, New York, 2014).20.Tsuji, Y. & Nakagawa, N. Monkeys of Japan: A Mammalogical Studies of Japanese Macaques (University of Tokyo Press, 2017).
Google Scholar 
21.Suzuki, S., Hill, D. A., Maruhashi, T. & Tsukuhara, T. Frog and Lizard-eating behaviour of wild Japanese Macaques in Yakushima, Japan. Primates 31, 421–426 (1990).Article 

Google Scholar 
22.Watanabe, K. Fish: a new addition to the diet of Japanese macaques on Koshima Island. Folia Primatol. 52, 124–131. https://doi.org/10.1159/000156391 (1989).CAS 
Article 

Google Scholar 
23.Leca, J. B., Gunst, N., Watanabe, K. & Huffman, M. A. A new case of fish-eating in Japanese macaques: Implications for social constraints on the diffusion of feeding innovation. Am. J. Primatol. 69, 821–828. https://doi.org/10.1002/ajp.20401 (2007).Article 
PubMed 

Google Scholar 
24.Stewart, A. M. E., Gordon, C. H., Wich, S. A., Schroor, P. & Meijaard, E. Fishing in Macaca fascicularis: a rarely observed innovative behavior. Int. J. Primatol. 29, 543–548. https://doi.org/10.1007/s10764-007-9176-y (2008).Article 

Google Scholar 
25.Hamilton, W. J. & Tilson, R. L. Fishing Baboons at desert waterholes. Am. J. Primatol. 8, 255–257. https://doi.org/10.1002/ajp.1350080308 (1985).Article 
PubMed 

Google Scholar 
26.Tamura, M. Extractive foraging on hard-shelled walnuts and variation of feeding techniques in wild Japanese macaques (Macada fuscata). Am. J. Primatol. 82, e23130 (2020).Article 

Google Scholar 
27.Iwamoto, T. A bioeconomic study on a provisioned troop at a Japanese monkeys Macada fuscata-fuscata at Koshima Islet Miyazaki. Primates 15, 241–262. https://doi.org/10.1007/bf01742286 (1974).Article 

Google Scholar 
28.Tsuji, Y. & Takatsuki, S. Effects of a typhoon on foraging behavior and foraging success of Macaca fuscata on Kinkazan Island, Northern Japan. Int. J. Primatol. 29, 1203–1217. https://doi.org/10.1007/s10764-008-9293-2 (2008).Article 

Google Scholar 
29.Gumert, M. D. & Malaivijitnond, S. Marine prey processed with stone tools by burmese long-tailed macaques (Macaca fascicularis aurea) in intertidal habitats. Am. J. Phys. Anthropol. 149, 447–457. https://doi.org/10.1002/ajpa.22143 (2012).Article 
PubMed 

Google Scholar 
30.Tan, A., Tan, S. H., Vyas, D., Malaivijitnond, S. & Gumert, M. D. There is more than one way to crack an oyster: identifying variation in burmese long-tailed Macaque (Macaca fascicularis aurea) stone-tool use. PLoS ONE https://doi.org/10.1371/journal.pone.0124733 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Urabe, M. The present distribution and issues regarding the control of the exotic snail Potamopyrgus antipodarum in Japan. Jpn. J. Limnol. 68, 491–496 (2007).Article 

Google Scholar 
32.Hamada, K. T. Y. & Urabe, M. Survey of mitochondrial DNA haplotypes of Potamopyrgus antipodarum (Caenogastropoda: Hydrobiidae) introduced into Japan. Limnology 14, 223–228 (2013).Article 

Google Scholar 
33.Izumiyama, S., Mochizuki, T. & Shiraishi, T. Troop size, home range area and seasonal range use of the Japanese macaque in the Northern Japan Alps. Ecol. Res. 18, 465–474. https://doi.org/10.1046/j.1440-1703.2003.00570.x (2003).Article 

Google Scholar 
34.Milner, A. M., Docherty, C., Windsor, F. M. & Tojo, K. Macroinvertebrate communities in streams with contrasting water sources in the Japanese Alps. Ecol. Evol. 10, 7812–7825. https://doi.org/10.1002/ece3.6507 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples: a case study on prey DNA in Antarctic krill stomachs. Front. Zool. https://doi.org/10.1186/1742-9994-5-12 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. https://doi.org/10.1186/1742-9994-10-34 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, 3. doi:https://doi.org/10.14806/ej.17.1.200 (2011).38.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Ratnasingham, S. & Hebert, P. D. N. BOLD: the barcode of life data system (www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364. doi:https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).40.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/aem.00062-07 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Wickham, H. ggplot2: elegant graphics for data analysis 2nd edn. (Springer, 2016).Book 

Google Scholar  More

As a starting point, we compare the vulnerability of four districts in Lyon, Paris (France), Firenze (Italy) and New York (US). These cities were chosen as emblematic of different topologies, resulting from different historical urban layering. The historic center of Firenze (panel b in Fig. 1) is mainly characterized by a dense urban fabric with a medieval signature of narrow and winding streets24. In Paris (panel c), Haussman’s renovation plan at the end of the 19th century supplemented the North–South and East–West ancient crossroad by a second network of concentric large avenues25. The rectilinear grid of Manhattan, New York, originates from 181126,27 and extends along the spine of Manhattan island (panel d). Despite the significant difference in size, a similar regular pattern is found in the modern urban area of Lyon (panel a), developed in the second half of the 19th century. In the insets of Fig. 1, we report for each city a polar histogram of the orientation of the streets. Although greater variability is observable for the orientation of the streets in the urban areas of Firenze (panel b) and Paris (panel c), two main orthogonal axes are found in the spatial structure of each city.The urban networks analysed in this work were delimited in order to be large enough to include the distinctive patterns of these four cities. The edges of the areas were traced along physical boundaries (e.g., rivers, parks, railways, large avenues) which act as elements of discontinuity in the dispersion process. Where not possible, the break was forced along wide streets.We promptly computed vulnerability maps for the selected urban areas by means of the centrality metric we derived in20 and recall in the “Methods” section. The nodes with the highest centrality values (V) are the most vulnerable as they correspond to the best spreading locations in the urban fabric. The spreading potential of a node is evaluated based on the extent of the area that is contaminated when the release takes place in this same node.We report in Fig. 1 the vulnerability maps of the four urban areas for the indicative scenario of a wind blowing at an angle (phi =45^circ). In the insets of Fig. 1, the wind direction is indicated with a red arrow. Given the different orientation and structure of the street networks, (phi) is defined as a clockwise angle with respect to the main axis of the city, which is identified as the longest bar in the polar histogram of street orientation.To extend the analysis to multiple meteorological scenarios, we estimated the vulnerability of each node (seen as a spreading source) for eight different wind directions ((phi =0^circ), (45^circ), (90^circ), (135^circ), (180^circ), (225^circ), (270^circ), (315^circ)). In this way, for each city, we obtained an extended dataset of vulnerability values that we represent in a compact way by means of a cumulative distribution function, as shown in Fig. 2a. The intercept of the cdf represents the nodes with null vulnerability. These are mostly located along the physical edges of the domain where the pollutant gas is blown away by the wind without affecting other streets. Where the delimitation of the network is forced (for example on the sides of central park as regards Manhattan), the interruption of the propagation, in the vulnerability model, is also constrained. This does not result in any artificial effect when the boundary is located upwind with respect to the network (propagation carries on from the boundary towards the considered urban area). On the other hand, when the boundary is downwind, vulnerability can be there underestimated. Considering the multiple wind directions simulated and the small number of nodes belonging to these edges (1% of the total number of network nodes), this effect has been calculated negligible to the purposes of this work.According to the mean values (vertical dashed lines) of the distributions reported in Fig. 2, New York is the most vulnerable city on average, while Firenze is the most protected. The vulnerability of New York and Lyon are the most sensitive to changes in wind direction, as shown by Fig. 2.b, where a polar histogram reports the mean vulnerability for each city for the eight directions of the approaching wind. In general, the spreading potential is more effective when the wind is oblique ((phi =45^circ ,) (135^circ), (225^circ), and (315^circ)) to the main orthogonal axes of the street network, as evidenced by the higher vulnerability observed for the dark gray sectors of Fig. 2b. We also notice that vulnerability for parallel ((phi =0^circ ,) (phi =180^circ)) and perpendicular ((phi =90^circ ,) (phi =270^circ)) wind directions is quite similar. This seems counterintuitive as previous studies (e.g.,28,29) have reported that a perpendicular wind is much more unfavorable for the dispersion of pollutants in a street. In this regard, we underline that (phi) is here defined with respect to the main axis of the city, so for (phi =90^circ) not all streets will be perpendicular to the wind direction. For example, in the regular network of Manhattan we expect the number of perpendicular streets to be similar to that of parallel streets, when (phi =90^circ).Figure 1Vulnerability maps for (a) Lyon, (b) Firenze, (c) Paris, and (d) New York for a wind direction of (45^circ) with respect to the main axis of the urban fabric. The polar histograms in the insets report the distribution of street orientation, while the red arrows represent the wind direction with respect to the street network. Panels a1–d1 show the urban pattern in a rectangular area of 0.5 km(^2) (reported in panels a–d) for the cities of Lyon, Firenze, Paris, and New York, respectively. Background images made with QGIS 2.18 (https://qgis.org).Full size imageFigure 2Vulnerability distribution for different cities and wind directions. (a) Cdf of node vulnerability for the different cities under eight different wind directions. The mean vulnerability is shown as a dashed line and reported numerically together with the standard deviation (in parentheses). (b) Mean vulnerability of city networks for each wind direction. Colors blue, yellow, green and magenta correspond to the urban networks of Lyon, Firenze, Paris and New York, respectively.Full size imageThe reasons for the different resilience of cities (and their patterning) to gas propagation are embedded in the centrality metric adopted to compute urban vulnerability. The key factors for node vulnerability can then be analytically recognized in the metric definition (Eqs. 4–5 in “Methods”): the highest vulnerabilities are achieved when the set of reachable nodes ((mathcal {V})) from the source node is large, and the paths connecting the source and the reachable nodes ((d_{sr})) are short, i.e. the propagation cost ((omega)) along the paths is minimal. In other words, the spots in a city (i.e. nodes in a network) with the highest spreading potential are those from which a toxic plume can reach many other locations with significant concentration. Going beyond the vulnerability results, we aim here to decompose the aforementioned elaborate and meaningful quantities (the set of reachable nodes, the shortest paths, the propagation cost) in elementary properties of the urban area in order to link the vulnerability of a city to its tangible characteristics.We start by disassembling the propagation cost associated to each street. Given a source node, a pollutant plume will propagate along the streets downwind the node. The propagation cost of each street (Eq. 4) describes the decay of concentration that the plume undergoes when it propagates along the street. Neglecting physico-chemical transformations, this cost depends on the transport processes within the streets and is a function of two dimensionless quantities: a geometrical ratio between the length (l) and height (h) of the street canyon, and a dynamic ratio between the exchange rate of pollutants towards the atmosphere above roof level (v) and the advective velocity along the longitudinal (u) axis of the street. According to30 and31, these two velocities can be parametrized as a function of the external wind intensity, the cosine ((theta)) of the angle between the wind direction and the orientation of the street, the geometry of the street canyon (its length l, height h and width w) and the aerodynamic roughness of building walls. As detailed in the Methods, the dependence of the propagation cost on the external wind intensity disappears as both velocities u and v scale linearly with it. Assuming constant aerodynamic resistance of the surfaces, the parameters l, h, w, (theta), remain the relevant building blocks for the propagation cost along a street.We underline that the parametrizations adopted here for the transport mechanisms in a street are based on the up-to-date literature and are currently employed in operational models (see the Methods section for mode details). Any refinements to this transport model may be included in the future. In this case, the cost associated to each street may depend on additional parameters that, however, we expect to be of second-order importance to those listed above.While pollutant transport in a single street canyon (i.e. the propagation cost) has been easily broken down into its basic elements, the information enclosed in the shortest paths ((d_{sr})) and in the set of reachable nodes from the source ((mathcal {V})) is much more challenging to trace back to evident properties of the city. These quantities depend on the sequence of streets that must be traveled to connect a source node to the surrounding nodes, i.e. on the way the streets are interconnected. The information is thus primarily topological. However, we point out that the interconnectivity of the network is not frozen, but dynamic, as it is given by the reaction of the urban structure to the direction of the external wind. In fact, the links of the street network are directed according to the orientation of the approaching wind. Moreover, the connectivity between the nodes is limited by the decay of the concentration along the streets. Although a target node may be reached from the source node by means of a path across the network, the two nodes may not actually be connected by a propagation path as the pollutant concentration may vanish along the path. For these reasons, traditional descriptors of network topology cannot be applied directly to describe the topological component of the vulnerability. Instead, we have to look for tailored and simple indicators that can express the wind-driven interconnectivity of the street network and the reachability potential between the nodes.Focusing on a node as spreading source, we infer that the number of links in its downwind area gives a first estimate of the potential for a release in the node to affect many other locations in the network. To delimit this downwind area, we adopt the concept of n-hop neighborhood32,33. Two nodes are n hops apart if it is possible to reach the target node from the source node by traveling n links. We identify the downwind area of the source node as the subnetwork composed by the nodes that are reachable from the source via at most n hops along the directed links. We propose the number of links in this neighborhood (k) as a suitable measure of reachability from the node. This reachability depends upon three features: (i) the local structure of the street network, (ii) the direction of the wind, and (iii) the topological distance n. This latter parameter is intuitively correlated to the intensity of the release. More precisely, it depends on the ratio between the magnitude of the toxic release at the source and the threshold value for pollutant concentration to be significant. In this work, n is taken as constant and its value is obtained from an optimization analysis detailed in the “Methods” section (Fig. 6) .Once the (n-hop) neighborhood of a node is delimited, the number of links k is not exhaustive in giving information about the properties of the paths connecting the source to the other nodes of the neighborhood. For the same k, different structures of the neighborhood can take place (see Fig. 6b), with consequent different outcomes for the propagation process that we are breaking down to basic components. The higher the number of links outgoing each node of the neighborhood, the higher the potential concentration for the k links, as they are topologically closer to the source. This feature can be accounted for by means of a simple branching index (b) for the node neighborhood, defined in Eq. 8 as the average outdegree for the nodes belonging to the neighborhood34.The disassembling analysis presented above suggests that the spreading potential of a node, and thus its vulnerability, mainly depends on the topological parameters k and b and on the geometrical characteristics of its neighborhood, i.e. L, H, W, (Theta), where the capital letters are used to indicate the local average (over the n-hop neighborhood) for the length (l), height (h), width (w), and orientation ((theta)) of the street canyons.In adopting averaged geometrical properties, we are assuming that these characteristics are rather homogeneous in the surroundings of a node. While the height, width and length of the street canyons are actually quite uniform on a local scale, especially in European city centers, the same does not apply to the orientation of the streets. The streets of a neighborhood intersect each other at different angles (e.g., at (90^circ) in grid plans), and the intensity of the wind in the streets changes strongly with their orientation. Low wind streets act as bottlenecks in the propagation paths, thus strongly influencing the spreading dynamics. For this reason, the standard deviation of street orientation in the neighborhood ((sigma)) is expected to be an additional topological index of node vulnerability.To assess whether the identified parameters are valuable basic elements of node vulnerability, we perform a regression analysis adopting a simple (but versatile) non linear model of the form:$$begin{aligned} V_{pred}=alpha L^beta H^gamma W^delta Theta ^epsilon k^zeta b^eta (1-sigma )^lambda . end{aligned}$$
(1)
We estimate the coefficients (alpha) to (lambda) by means of a nonlinear least square technique (namely the fitnlm function in Matlab) that minimizes the sum of the squares of the residuals between the predicted vulnerability (V_{pred}) and the vulnerability V obtained from the centrality metric (Eq. 5 in “Methods”). The regression is performed considering all the scenarios presented in this study: four different urban networks and eight different wind directions. The p-values for the coefficients (alpha) to (lambda) tend to zero, indicating that the relationships between the independent variables and the observations (V) are statistically significant. Note that in Eq. (1) we adopt (1-sigma) as predictor, instead of (sigma), to avoid null entries, as (sigma) takes value in [0 1). To explain the reason for this range for (sigma), we point out that the angle between the wind direction and the street axis is defined in [(-90^circ) (90^circ)]. As a consequence, the cosine ((theta)) of the angle varies in [0 1] and the standard deviation of (theta) (i.e. (sigma)) varies in [0 1).The scatter plot in Fig. 3 compares (V_{pred}) against V. Points correspond to the nodes of the four urban networks in the eight wind scenarios. The figure suggests that 80% of the spreading capacity (V) of a spot in a city can be grasped from the basic geometrical and topological characteristics of its neighborhood. To identify the most influential parameters in the regression, we evaluate the gain in the coefficient of determination (R^2) as they are progressively included in the model (red circles in the inset). The quantities are entered in order to optimize (R^2) at each addition. Alternatively, the role of each parameter can be evaluated adopting the concept of unique contribution (triangles in the inset), i.e. the loss in the coefficient of determination induced by the exclusion of the parameter from the model35. Both analyses reveal k and (sigma) as the main indicators for the vulnerability of a node. Actually, more than 60% of the total variance (inset in Fig. 3) is explained by these two parameters, unveiling the crucial role of topology in governing the dynamics of pollutants in urban areas. The effect of the geometrical properties (L, H, W, (Theta)) of the street canyons is secondary. Among these, the contribution of the building height (H) is the most remarkable as its contribution, combined with that of the two topological parameters k and (sigma), brings the correlation to almost its maximum value.Given these results, it is enlightening to show some tangible examples of how the three simple indicators k, (sigma) and H dominate urban vulnerability. We wonder which of these properties determine the distinct vulnerability of neighboring areas belonging to the same district, and which ones differentiate the resilience of cities with a different urban history.Figure 3Correlation of node vulnerability with basic geometrical and topological parameters of the street network. Color (blue to red) is associated to point density. Left y-axis of inset: trend of the coefficient of determination (R^2) as the urban indicators are progressively included in the model. Right y-axis of inset: unique contribution of the indicators.Full size imageFigure 4a shows the spatial distribution of the key parameters k, (sigma) and H and of node vulnerability, for Manhattan and a wind direction (phi =45^circ). In panel b, high street reachability (k) is observed in the central part of Midtown, in the heart of Downtown, and near Wall Street. An homogeneous distribution in the orientation of the streets with respect to the incident wind (low values for (sigma) and thus high (1-sigma)) is especially found in Midtown (panel c). Finally, in panel d, high-buildings (H) distinguish the Financial District and East Midtown. A perfect match between the four layers is not expected as vulnerability is given by the synergistic contribution of the different parameters. However, in line with the results of the regression model shown in Fig. 3, a positive correlation is observable between the most vulnerable areas (circled areas comprising the nodes with highest V in panel a of Fig. 4) and those with the highest values for the three indicators. In these areas, high buildings inhibit the vertical exchange of pollutants between the streets and the atmosphere above, as largely discussed in literature (see e.g.36,37). This inhibition limits the concentration decay along the propagation paths and facilitates large-scale contamination. Moreover, the great number (k) of streets topologically close to the node increase the impact of the release. The effect of (sigma) is significant especially for the vulnerability of Midtown. Here, since (phi =45^circ) and the street network is regular, (theta) (the cosine of the wind-street angle) is almost the same for all the streets. Therefore, the standard deviation of (theta) ((sigma)) is low and the predictor (1-sigma) is high. Physically, this means that the external wind approaches all the streets with almost the same angle. As a consequence, the intensity of the longitudinal wind in the streets is similar (the street aspect ratio is also similar) and the propagation takes place equally along both the dominant and lateral segments of the street network38, thus favoring the spread over large areas. Although high values of H and (1-sigma) can be detected in the North-East corner of Midtown too, here the vulnerability is mitigated by a higher discontinuity in the urban pattern (low k). This feature, together with the great overlapping of red areas in panel a with those in panel b, evidences the key role of street reachability (k) in the heterogeneity of vulnerability between areas of the same urban district.Figure 4Street network of Midtown and Downtown Manhattan. Node color is associated to node vulnerability (V), and to its key indicators: street reachability (k), inhomogeneity in street orientation ((sigma)), and average height of buildings in the node neighborhood (H).Full size imageFrom these observations, we move to a broader view and investigate the structural fragility of a city as a whole. In Fig. 5a–c, we report the probability density function (pdf) of the key parameters k, (1-sigma) and H. For each city, the pdf is calculated over all the network nodes and for the different wind directions. So, each pdf is representative of eight different networks for the same urban area. In panel a, the distributions for the four cities are quite similar but the tails of the pdfs highlight that the highest values for street reachability (k) occur in the street networks of Lyon and New York. The homogeneity in street orientation with respect to the wind (panel b), expressed by (1-sigma), exhibits a bimodal distribution and a slightly higher mean for the regular street network of New York. The two peaks are associated to distinctive wind scenarios, as will be discussed below. Also in this case, the observation of the tails of the pdfs reveals that high values of the vulnerability indicator are more probable in Lyon and New York. Finally, the distribution of building height (panel c) presents the most marked difference between the considered architectures, with high-rise buildings contributing to the heavy pdf tail of Manhattan. Comparing these results with those in Fig. 2a, Manhattan’s greatest vulnerability appears to be due to the greater depth of the urban canyons (high H) and the greater homogeneity, on average, in wind-street orientation (high (1-sigma)). Conversely, the medieval structure of Firenze, with higher heterogeneity in street orientation (low (1-sigma)) and low buildings (low H), enhances street ventilation and hinders propagation over long distances. Moreover, the tails of the pdfs for k and (1-sigma) reveal the role of topology in the higher variability of vulnerability values (given by the standard deviation of the pdfs in Fig. 2) for the street networks of Lyon and Manhattan.After discussing the behavior of the single parameters, we assess the synergistic contribution of the three quantities. To this aim, we define a simple correlation index (rho =widehat{k} cdot (1-widehat{sigma }) cdot widehat{H}), where the hat denotes a min-max normalization of the parameters, i.e. the range of values of each parameter is rescaled in [0, 1]. For the urban areas of Manhattan, Lyon, Paris, and Firenze, (rho) gives 0.039, 0.017, 0.012, and 0.011, respectively. This ranking complies with the ranking inferable in Fig. 2 for the average vulnerability of the cities. This result confirms that vulnerability occurs when the three parameters are correlated, as already evidenced in Fig. 4.To make the picture even more fascinating, it is worth noting that the role of topology, shown above as key, is dynamic as it varies according to the direction of the wind impacting the urban fabric. In panels d to f of Fig. 5, the pdfs of k, (1-sigma) and H are distinguished for four wind directions ((phi =0^circ), (45^circ), (90^circ) and (135^circ)). For each angle, the statistics are calculated over the examined cities, together. Although wind orientation alters the direction of the network links, and thus the delimitation of the n-hop neighboring area of each node, street reachability (panel d) and building height (panel f) remain statistically invariant for the different wind directions, suggesting a rather isotropic structure of the urban fabric. On the other hand, the variability in street orientation with respect to the wind (panel e) presents two distinctive trends for wind directions aligned with or oblique to the main axes of the street network. To explain this behavior, we refer to the simple case of a grid-like urban plan, like Manhattan’s plan. When (phi =0^circ) or (90^circ), (theta) (the cosine of the angle between the street and the wind direction) mostly switches between 0 (for the streets aligned with the wind) and 1 (for the orthogonal streets), resulting in a high standard deviation over the neighborhood (low (1-sigma)). When (phi =45^circ) or (135^circ), instead, the incident angle (theta) mainly takes intermediate values, leading to higher values for (1-sigma). This distinctive behavior is clearly detectable in the two peaks that we have observed in panel b for the regular grid of Lyon and New York. The left peak of the bimodal distribution corresponds to the scenarios with aligned wind directions, while the right peak occurs for oblique wind directions over the city. A more irregular street pattern in Firenze and Paris adds random contributions to the way the wind approaches the street, thus altering this bimodal shape. Going back to panel e, the greater homogeneity in wind-street orientation (higher (1-sigma)) for (phi =45^circ) or (135^circ) gives insights into the higher vulnerability found for the scenarios with these wind directions in almost all cities (dark gray sectors in Fig. 2b). This result is confirmed by the correlation ((rho)) between the three rescaled parameters ((widehat{k}), (1-widehat{sigma }), (widehat{H})). The correlation (rho) is estimated separately for the different wind directions, but considering the nodes from the four urban areas together. For oblique wind directions, (rho) is about twice ((rho =0.035)) the value found for the aligned wind directions ((rho =0.018)).Figure 5Probability density function of the key parameters k, (1-sigma), H. In the first row, each curve refers to a city and includes vulnerability data from eight different wind directions. In the second row, each curve corresponds to a specific wind direction and includes vulnerability data from the four cities, together.Full size image More

1.Minter, N. J., Franks, N. R. & Brown, K. A. R. Morphogenesis of an extended phenotype: Four-dimensional ant nest architecture. J. R. Soc. Interface 9, 586–595 (2012).PubMed 

Google Scholar 
2.Dawkins, R. The Extended Phenotype: The Long Reach of the Gene (Oxford University Press, 2016).
Google Scholar 
3.Tschinkel, W. R. The architecture of subterranean ant nests: Beauty and mystery underfoot. J. Bioecon. 17, 271–291 (2015).
Google Scholar 
4.Brian, M. V. & Brian, M. V. Production Ecology of Ants and Termites (Cambridge University Press, 1978).
Google Scholar 
5.De Bruyn, L. A. L. & Conacher, A. J. The role of termites and ants in soil modification: A review. Soil Res. 28, 55–93 (1990).
Google Scholar 
6.Sankovitz, M. A. & Breed, M. D. Effects of Formica podzolica ant colonies on soil moisture, nitrogen, and plant communities near nests. Ecol. Entomol. 44, 71–80 (2019).
Google Scholar 
7.Tschinkel, W. R. Subterranean ant nests: Trace fossils past and future?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 192, 321–333 (2003).
Google Scholar 
8.Pinter-Wollman, N. Nest architecture shapes the collective behaviour of harvester ants. Biol. Lett. 11, 20150695 (2015).PubMed 
PubMed Central 

Google Scholar 
9.Rosengren, R., Fortelius, W., Lindström, K. & Luther, A. Phenology and causation of nest heating and thermoregulation in red wood ants of the Formica rufa group studied in coniferous forest habitats in southern Finland. Ann. Zool. Fennici 24, 147–155 (1987).
Google Scholar 
10.Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, 1990).
Google Scholar 
11.Savolainen, R. & Vepsäläinen, K. A competition hierarchy among boreal ants: Impact on resource partitioning and community structure. Oikos 51, 135–155 (1988).
Google Scholar 
12.Frouz, J., Jílková, V. & Sorvari, J. Contribution of wood ants to nutrient cycling and ecosystem function. Wood Ant Ecol. Conserv. https://doi.org/10.1017/CBO9781107261402.010 (2016).Article 

Google Scholar 
13.Seeley, T. & Heinrich, B. Regulation of Temperature in the Nests of Social Insects (FAO, 1981).
Google Scholar 
14.Hillel, D. Environmental Soil Physics: Fundamentals, Applications, and Environmental Considerations (Elsevier, 1998).
Google Scholar 
15.Blomqvist, M. M., Olff, H., Blaauw, M. B., Bongers, T. & Van Der Putten, W. H. Interactions between above- and belowground biota: Importance for small-scale vegetation mosaics in a grassland ecosystem. Oikos 90, 582–598 (2000).
Google Scholar 
16.MacMahon, J. A., Mull, J. F. & Crist, T. O. Harvester ants (Pogonomyrmex spp.): Their community and ecosystem influences. Annu. Rev. Ecol. Syst. 31, 265–291 (2000).
Google Scholar 
17.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management 130–147 (Springer, 1994).
Google Scholar 
18.Jouquet, P., Dauber, J., Lagerlöf, J., Lavelle, P. & Lepage, M. Soil invertebrates as ecosystem engineers: Intended and accidental effects on soil and feedback loops. Appl. Soil Ecol. 32, 153–164 (2006).
Google Scholar 
19.Khuong, A. et al. Stigmergic construction and topochemical information shape ant nest architecture. Proc. Natl. Acad. Sci. U. S. A. 113, 1303–1308 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Bishop, T. R. et al. Thermoregulatory traits combine with range shifts to alter the future of montane ant assemblages. Glob. Change Biol. 25, 2162–2173 (2019).ADS 

Google Scholar 
21.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 

Google Scholar 
22.Braschler, B. et al. Realised rather than fundamental thermal niches predict site occupancy: Implications for climate change forecasting. J. Anim. Ecol. 89, 2863–2875 (2020).PubMed 

Google Scholar 
23.Roeder, K. A., Bujan, J., Beurs, K. M., Weiser, M. D. & Kaspari, M. Thermal traits predict the winners and losers under climate change: An example from North American ant communities. Ecosphere 12, e03645 (2021).
Google Scholar 
24.Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. U. S. A. 105, 6668–6672 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Diamond, S. E., Sorger, D. M., Hulcr, J. & Pelini, S. L. Who likes it hot? A global analysis of the climatic, ecological, and evolutionary determinants of warming tolerance in ants. Glob. Change Biol. 18, 448–456 (2012).ADS 

Google Scholar 
26.Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: How constrained are they?. Funct. Ecol. 27, 934–949 (2013).
Google Scholar 
27.Wilson, E. O. The effects of complex social life on evolution and biodiversity. Oikos 63, 13–18 (1992).CAS 

Google Scholar 
28.Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. Am. Zool. 19, 357–366 (1979).
Google Scholar 
29.Deslippe, R. J. & Savolainen, R. Colony foundation and polygyny in the ant Formica podzolica. Behav. Ecol. Sociobiol. 37, 1–6 (1995).
Google Scholar 
30.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Chambers, J. M., Freeny, A. & Heiberger, R. M. Analysis of variance; designed experiments. Stat. Models S 5, 145–193 (1992).
Google Scholar 
32.Fox, J. Applied Regression Analysis and Generalized Linear Models (SAGE Publications, 2015).
Google Scholar 
33.Mikheyev, A. S. & Tschinkel, W. R. Nest architecture of the ant Formica pallidefulva: Structure, costs and rules of excavation. Insectes Soc. 51, 30–36 (2004).
Google Scholar 
34.Coppernoll-Houston, D. & Potter, C. Field measurements and satellite remote sensing of daily soil surface temperature variations in the lower Colorado desert of California. Climate 6, 94 (2018).
Google Scholar 
35.Jílková, V., Cajthaml, T. & Frouz, J. Respiration in wood ant (Formica aquilonia) nests as affected by altitudinal and seasonal changes in temperature. Soil Biol. Biochem. 86, 50–57 (2015).
Google Scholar 
36.Kadochová, Š, Frouz, J. & Roces, F. Sun basking in red wood ants Formica polyctena (Hymenoptera, Formicidae): Individual behaviour and temperature-dependent respiration rates. PLoS ONE 12, e0170570 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Bollazzi, M., Kronenbitter, J. & Roces, F. Soil temperature, digging behaviour, and the adaptive value of nest depth in South American species of Acromyrmex leaf-cutting ants. Oecologia 158, 165–175 (2008).ADS 
PubMed 

Google Scholar 
38.Stockan, J. A. & Robinson, E. J. H. Wood Ant Ecology and Conservation (Cambridge University Press, 2016).
Google Scholar 
39.Porter, S. D. Impact of temperature on colony growth and developmental rates of the ant, Solenopsis invicta. J. Insect Physiol. 34, 1127–1133 (1988).
Google Scholar 
40.Lapointe, S. L., Serrano, M. S. & Jones, P. G. Microgeographic and vertical distribution of Acromynnex landolti (Hymenoptera: Formicidae) nests in a Neotropical Savanna. Environ. Entomol. 27, 636–641 (1998).
Google Scholar 
41.Fowler, H. G. Leaf-cuttings ants of the genera Atta and Acromyrmex of Paraguay (Hymenoptera: Formicidae). Mmitt. Mus. Naturkunde Berl. Dtsch. Entomol. Z. 32, 19–34 (2008).
Google Scholar 
42.Hansell, M. & Hansell, M. H. Animal Architecture (OUP, 2005).
Google Scholar 
43.Shik, J. Z., Arnan, X., Oms, C. S., Cerdá, X. & Boulay, R. Evidence for locally adaptive metabolic rates among ant populations along an elevational gradient. J. Anim. Ecol. 88, 1240–1249 (2019).PubMed 

Google Scholar 
44.Cerda, X., Retana, J. & Cros, S. Critical thermal limits in Mediterranean ant species: Trade-off between mortality risk and foraging performance. Funct. Ecol. 12, 45–55 (1998).
Google Scholar 
45.Kaspari, M., Clay, N. A., Lucas, J., Yanoviak, S. P. & Kay, A. Thermal adaptation generates a diversity of thermal limits in a rainforest ant community. Glob. Change Biol. 21, 1092–1102 (2015).ADS 

Google Scholar 
46.Talbot, M. Distribution of ant species in the Chicago region with reference to ecological factors and physiological toleration. Ecology 15, 416–439 (1934).
Google Scholar 
47.Arnan, X. & Blüthgen, N. Using ecophysiological traits to predict climatic and activity niches: Lethal temperature and water loss in Mediterranean ants: Using physiology to predict niches. Glob. Ecol. Biogeogr. 24, 1454–1464 (2015).
Google Scholar 
48.Arnan, X., Blüthgen, N., Molowny-Horas, R. & Retana, J. Thermal characterization of European ant communities along thermal gradients and its implications for community resilience to temperature variability. Front. Ecol. Evol. 3, 138 (2015).
Google Scholar 
49.Baudier, K. M. & O’Donnell, S. Structure and thermal biology of subterranean army ant bivouacs in tropical montane forests. Insectes Soc. 63, 467–476 (2016).
Google Scholar 
50.Penick, C. A. & Tschinkel, W. R. Thermoregulatory brood transport in the fire ant, Solenopsis invicta. Insectes Soc. 55, 176–182 (2008).
Google Scholar 
51.Penick, C. A., Diamond, S. E., Sanders, N. J. & Dunn, R. R. Beyond thermal limits: Comprehensive metrics of performance identify key axes of thermal adaptation in ants. Funct. Ecol. 31, 1091–1100 (2017).
Google Scholar 
52.Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘cold-blooded’ animals against climate warming. Proc. Natl. Acad. Sci. U. S. A. 106, 3835–3840 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).PubMed 

Google Scholar  More

1.Limpricht KG. Die laubmoose. In: Rabenhorst L (ed). Kryptogamen-Flora von Deutschland, Oesterreich und der Schweiz, Zweite Auflage. 1890. Kummer, Leipzig.2.Basilier K. Fixation and uptake of nitrogen in Sphagnum blue-green algal associations. Oikos. 1980;34:239.CAS 

Google Scholar 
3.Granhall U, Selander H. Nitrogen fixation in a subarctic mire. Oikos. 1973;24:8.
Google Scholar 
4.Basilier K, Granhall U, Stenström T-A. Nitrogen fixation in wet minerotrophic moss communities of a subarctic mire. Oikos. 1978;31:236.CAS 

Google Scholar 
5.Basilier K. Moss-associated nitrogen fixation in some mire and coniferous forest environments around Uppsala, Sweden. Lindbergia. 1979;5:84–88.CAS 

Google Scholar 
6.Meeks JC. Physiological adaptations in nitrogen-fixing Nostoc–plant symbiotic associations. In: Pawlowski K (ed). Prokaryotic symbionts in plants. 2007. Springer, Berlin, Heidelberg, pp 181–205.7.Adams DG. Cyanobacteria in symbiosis with hornworts and liverworts. Cyanobacteria in symbiosis. 2002. Springer, Dordrecht, pp 117-35.8.Meeks JC, Elhai J. Regulation of cellular differentiation in filamentous cyanobacteria in free-living and plant-associated symbiotic growth states. Microbiol Mol Biol Rev. 2002;66:94–121.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Kostka JE, Weston DJ, Glass JB, Lilleskov EA, Shaw AJ, Turetsky MR. The Sphagnum microbiome: new insights from an ancient plant lineage. N Phytol. 2016;211:57–64.CAS 

Google Scholar 
10.Granhall U, Hofsten AV. Nitrogenase activity in relation to intracellular organisms in Sphagnum mosses. Physiol Plant. 1976;36:88–94.
Google Scholar 
11.van den Elzen E, Kox MAR, Harpenslager SF, Hensgens G, Fritz C, Jetten MSM, et al. Symbiosis revisited: phosphorus and acid buffering stimulate N2 fixation but not Sphagnum growth. Biogeosciences. 2017;14:1111–22.
Google Scholar 
12.Yu Z, Loisel J, Brosseau DP, Beilman DW, Hunt SJ. Global peatland dynamics since the last glacial maximum. Geophys Res Lett. 2010;37:1–5.
Google Scholar 
13.Lindo Z, Nilsson MC, Gundale MJ. Bryophyte-cyanobacteria associations as regulators of the northern latitude carbon balance in response to global change. Glob Chang Biol. 2013;19:2022–35.PubMed 

Google Scholar 
14.Carrell AA, Kolton M, Glass JB, Pelletier DA, Warren MJ, Kostka JE, et al. Experimental warming alters the community composition, diversity, and N2 fixation activity of peat moss (Sphagnum fallax) microbiomes. Glob Chang Biol. 2019;25:2993–3004.PubMed 
PubMed Central 

Google Scholar 
15.Rai AN, Söderbäck E, Bergman B. Tansley Review No. 116. N Phytol. 2000;147:449–81.CAS 

Google Scholar 
16.Adams DG, Duggan PS. Cyanobacteria-bryophyte symbioses. J Exp Bot. 2008;59:1047–58.CAS 
PubMed 

Google Scholar 
17.Bay G, Nahar N, Oubre M, Whitehouse MJ, Wardle DA, Zackrisson O, et al. Boreal feather mosses secrete chemical signals to gain nitrogen. N Phytol. 2013;200:54–60.CAS 

Google Scholar 
18.Warshan D, Espinoza JL, Stuart RK, Richter RA, Kim S-Y, Shapiro N, et al. Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant–cyanobacteria symbiosis. ISME J. 2017;12:1–13.
Google Scholar 
19.Stuart RK, Pederson ERA, Weyman PD, Weber PK, Rassmussen U, Dupont CL. Bidirectional C and N transfer and a potential role for sulfur in an epiphytic diazotrophic mutualism. ISME J. 2020;14:3068–78.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Veličković D, Chu RK, Carrell AA, Thomas M, Paša-Tolić L, Weston DJ, et al. Multimodal MSI in conjunction with broad coverage spatially resolved MS2 increases confidence in both molecular identification and localization. Anal Chem. 2018;90:702–7.PubMed 

Google Scholar 
21.Nagy G, Veličković D, Chu RK, Carrell AA, Weston DJ, Ibrahim YM, et al. Towards resolving the spatial metabolome with unambiguous molecular annotations in complex biological systems by coupling mass spectrometry imaging with structures for lossless ion manipulations. Chem Commun. 2019;55:306–9.CAS 

Google Scholar 
22.Warshan D, Liaimer A, Pederson E, Kim S-Y, Shapiro N, Woyke T, et al. Genomic changes associated with the evolutionary transitions of Nostoc to a plant symbiont. Mol Biol Evol. 2018;35:1160–75.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Rippka R, Deruelles J, Waterbury JB, Herdman M, Stanier RY. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. J Gen Microbiol. 1979;111:1–61.
Google Scholar 
24.Hanson PJ, Riggs JS, Robert Nettles W, Phillips JR, Krassovski MB, Hook LA, et al. Attaining whole-ecosystem warming using air and deep-soil heating methods with an elevated CO2 atmosphere. Biogeosciences. 2017;14:861–83.CAS 

Google Scholar 
25.Frank W, Decker EL, Reski R. Molecular tools to study Physcomitrella patens. Plant Biol. 2005;7:220–7.CAS 
PubMed 

Google Scholar 
26.Yao Y, Sun T, Wang T, Ruebel O, Northen T, Bowen BP. Analysis of metabolomics datasets with high-performance computing and metabolite atlases. Metabolites. 2015;5:431–2.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3:211–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.CAS 

Google Scholar 
29.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Seemann T. Genome analysis Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 

Google Scholar 
32.Finn RD, Clements J, Eddy SR. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, et al. MapMan4: a refined protein classification and annotation framework applicable to multi-omics data analysis. Mol Plant. 2019;12:879–92.CAS 
PubMed 

Google Scholar 
34.Black K, Osborne B. An assessment of photosynthetic downregulation in cyanobacteria from the Gunnera-Nostoc symbiosis. N Phytol. 2004;162:125–32.CAS 

Google Scholar 
35.Santi C, Bogusz D, Franche C. Biological nitrogen fixation in non-legume plants. Ann Bot. 2013;111:743–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Clymo RS. The growth of Sphagnum: some effects of environment. Br Ecol Soc. 1973;61:849–69.
Google Scholar 
37.Lamers LPM, Farhoush C, Van Groenendael JM, Roelofs JGM. Calcareous groundwater raises bogs; the concept of ombrotrophy revisited. J Ecol. 1999;87:639–48.
Google Scholar 
38.Nayak S, Prasanna R. Soil pH and its role in cyanobacterial abundance and diversity in rice field soils. Appl Ecol Environ Res. 2007;5:103–13.
Google Scholar 
39.Jassey VEJ, Meyer C, Dupuy C, Bernard N, Mitchell EAD, Toussaint ML, et al. To what extent do food preferences explain the trophic position of heterotrophic and mixotrophic microbial consumers in a Sphagnum peatland? Micro Ecol. 2013;66:571–80.
Google Scholar 
40.Meeks JC. Symbiosis between nitrogen- fixing cyanobacteria and plants. Symbiosis. 1998;48:266–76.
Google Scholar 
41.Pate S, Lindblad P, Atkins A. Planta in coralloid roots of cycad-Nostoc symbioses. Planta. 1988;176:461–71.CAS 
PubMed 

Google Scholar 
42.Xie B, Chen DS, Zhou K, Xie YQ, Li YG, Hu GY, et al. Symbiotic abilities of Sinorhizobium fredii with modified expression of purL. Appl Microbiol Biotechnol. 2006;71:505–14.CAS 
PubMed 

Google Scholar 
43.Xie B, Chen D, Cheng G, Ying Z, Xie F, Li Y, et al. Effects of the purl gene expression level on the competitive nodulation ability of Sinorhizobium fredii. Curr Microbiol. 2009;59:193–8.CAS 
PubMed 

Google Scholar 
44.Giraud E, Moulin L, Vallenet D, Barbe V, Cytryn E, Avarre JC, et al. Legumes symbioses: absence of Nod genes in photosynthetic bradyrhizobia. Science. 2007;316:1307–12.PubMed 

Google Scholar 
45.Kim JK, Jang HA, Won YJ, Kikuchi Y, Heum Han S, Kim CH, et al. Purine biosynthesis-deficient Burkholderia mutants are incapable of symbiotic accommodation in the stinkbug. ISME J. 2014;8:552–63.CAS 
PubMed 

Google Scholar 
46.An R, Grewal PS. Molecular mechanisms of persistence of mutualistic bacteria Photorhabdus in the entomopathogenic nematode host. PLoS ONE. 2010;5:e13154.PubMed 
PubMed Central 

Google Scholar 
47.Zrenner R, Stitt M, Sonnewald U, Boldt R. Pyrimidine and purine biosynthesis and degradation in plants. Annu Rev Plant Biol. 2006;57:805–36.CAS 
PubMed 

Google Scholar 
48.Atkins CA, Smith PMC. Translocation in legumes: assimilates, nutrients, and signaling molecules. Plant Physiol. 2007;144:550–61.49.Ueda S, Ikeda M, Yamakawa T. Provision of carbon skeletons for amide synthesis in non-nodulated soybean and pea roots in response to the source of nitrogen supply. Soil Sci Plant Nutr. 2008;54:732–7.CAS 

Google Scholar 
50.Kaur H, Chowrasia S, Gaur VS, Mondal TK. Allantoin: emerging role in plant abiotic stress tolerance. Plant Mol Biol Rep. 2021;39:648–61.
Google Scholar 
51.Paul MJ, Primavesi LF, Jhurreea D, Zhang Y. Trehalose metabolism and signaling. Annu Rev Plant Biol. 2008;59:417–41.CAS 
PubMed 

Google Scholar 
52.Iturriaga G, Suárez R, Nova-Franco B. Trehalose metabolism: from osmoprotection to signaling. Int J Mol Sci. 2009;10:3793–810.CAS 
PubMed 
PubMed Central 

Google Scholar 
53.John R, Raja V, Ahmad M, Jan N, Majeed U, Ahmad S, et al. Trehalose: metabolism and role in stress signaling in plants. Stress signaling in plants: genomics and proteomics perspective, Volume 2. 2016. Springer International Publishing, pp 261–75.54.Sharma K, Palatinszky M, Nikolov G, Berry D, Shank EA. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. Elife. 2020;9:1–28.
Google Scholar 
55.Streeter JG. Effect of trehalose on survival of Bradyrhizobium japonicum during desiccation. J Appl Microbiol. 2003;95:484–91.CAS 
PubMed 

Google Scholar 
56.Sugawara M, Cytryn EJ, Sadowsky MJ. Functional role of Bradyrhizobium japonicum trehalose biosynthesis and metabolism genes during physiological stress and nodulation. Appl Environ Microbiol. 2010;76:1071–81.CAS 
PubMed 

Google Scholar 
57.Suárez R, Wong A, Ramírez M, Barraza A, Orozco MDC, Cevallos MA, et al. Improvement of drought tolerance and grain yield in common bean by overexpressing trehalose-6-phosphate synthase in rhizobia. Mol Plant-Microbe Interact. 2008;21:958–66.PubMed 

Google Scholar 
58.Mackay MA, Norton RS, Borowitzka LJ. Organic osmoregulatory solutes in cyanobacteria. J Gen Microbiol. 1984;130:2177–91.CAS 

Google Scholar 
59.Reed RH, Richardson DL, Warr SRC, Stewart WDP. Carbohydrate accumulation and osmotic stress in cyanobacteria. J Gen Microbiol. 1984;130:1–4.CAS 

Google Scholar 
60.Csonka LN. Physiological and genetic responses of bacteria to osmotic stress. Microbiol Rev. 1989,53:121–47.61.Moore EK, Hopmans EC, Rijpstra WIC, Villanueva L, Dedysh SN, Kulichevskaya IS, et al. Novel mono-, di-, and trimethylornithine membrane lipids in northern wetland planctomycetes. Appl Environ Microbiol. 2013;79:6874–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Clifford EL, Varela MM, De Corte D, Bode A, Ortiz V, Herndl GJ, et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic ocean off the Iberian Peninsula. Micro Ecol. 2019;78:299–312.CAS 

Google Scholar 
63.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 
64.Dixon RA, Paiva NL. Stress-induced phenylpropanoid metabolism. Plant Cell. 1995,7:1085–97.65.Payyavula RS, Navarre DA, Kuhl JC, Pantoja A, Pillai SS. Differential effects of environment on potato phenylpropanoid and carotenoid expression. BMC Plant Biol. 2012;12:39.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Huang J, Gu M, Lai Z, Fan B, Shi K, Zhou YH, et al. Functional analysis of the Arabidopsis PAL gene family in plant growth, development, and response to environmental stress. Plant Physiol. 2010;153:1526–38.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Kouchi H. Large-scale analysis of gene expression profiles during early stages of root nodule formation in a model legume, Lotus japonicus. DNA Res. 2004;11:263–74.CAS 
PubMed 

Google Scholar 
68.Chen Y, Li F, Tian L, Huang M, Deng R, Li X, et al. The phenylalanine ammonia lyase gene LjPAL1 is involved in plant defense responses to pathogens and plays diverse roles in Lotus japonicus -rhizobium symbioses. Mol Plant-Microbe Interact. 2017;30:739–53.CAS 
PubMed 

Google Scholar 
69.Bragina A, Berg C, Cardinale M, Shcherbakov A, Chebotar V, Berg G. Sphagnum mosses harbour highly specific bacterial diversity during their whole lifecycle. ISME J. 2012;6:802–13.CAS 
PubMed 

Google Scholar 
70.Bragina A, Berg C, Müller H, Moser D, Berg G. Insights into functional bacterial diversity and its effects on Alpine bog ecosystem functioning. Sci Rep. 2013;3:1955.PubMed 
PubMed Central 

Google Scholar  More

1.World Health Organization. Vector-borne diseases. Available at: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue (2020).2.Wilke, A. B. B., Beier, J. C. & Benelli, G. Complexity of the relationship between global warming and urbanization—an obscure future for predicting increases in vector-borne infectious diseases. Curr. Opin. Insect Sci. 35, 1–9 (2019).PubMed 

Google Scholar 
3.Wilke, A. B. B. et al. Proliferation of Aedes aegypti in urban environments mediated by the availability of key aquatic habitats. Sci. Rep. 10, 12925 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Wilke, A. B. B., Wilk-da-Silva, R. & Marrelli, M. T. Microgeographic population structuring of Aedes aegypti (Diptera: Culicidae). PLoS ONE 12, e0185150 (2017).PubMed 
PubMed Central 

Google Scholar 
5.Gubler, D. J. Dengue, urbanization and globalization: The unholy trinity of the 21st Century. Trop. Med. Health 39, S3–S11 (2011).
Google Scholar 
6.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, 8327 (2017).
Google Scholar 
7.Zohdy, S., Schwartz, T. S. & Oaks, J. R. The coevolution effect as a driver of spillover. Trends Parasitol. 35, 399–408 (2019).PubMed 

Google Scholar 
8.Rochlin, I., Faraji, A., Ninivaggi, D. V., Barker, C. M. & Kilpatrick, A. M. Anthropogenic impacts on mosquito populations in North America over the past century. Nat. Commun. 7, 13604 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
9.Wilke, A. B. B. et al. Community composition and year-round abundance of vector species of mosquitoes make Miami-Dade County, Florida a receptive gateway for arbovirus entry to the United States. Sci. Rep. 9, 8732 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
10.Burkett-Cadena, N. D. & Vittor, A. Y. Deforestation and vector-borne disease: Forest conversion favors important mosquito vectors of human pathogens. Basic Appl. Ecol. 26, 101–110 (2018).PubMed 

Google Scholar 
11.Rochlin, I., Harding, K., Ginsberg, H. S. & Campbell, S. R. Comparative analysis of distribution and abundance of West Nile and eastern equine encephalomyelitis virus vectors in Suffolk County, New York, using human population density and land use/cover data. J. Med. Entomol. 45, 563–571 (2008).CAS 
PubMed 

Google Scholar 
12.Monaghan, A. J. et al. Consensus and uncertainty in the geographic range of Aedes aegypti and Aedes albopictus in the contiguous United States: Multi-model assessment and synthesis. PLoS Comput. Biol. 15, 1–19 (2019).
Google Scholar 
13.Wilke, A. B. B., Benelli, G. & Beier, J. C. Beyond frontiers: On invasive alien mosquito species in America and Europe. PLoS Negl. Trop. Dis. 14, e0007864 (2020).PubMed 
PubMed Central 

Google Scholar 
14.Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035 (2015).PubMed 
PubMed Central 

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

Google Scholar 
16.Lewis, S. L. & Maslin, M. A. Defining the anthropocene. Nature 519, 171–180 (2015).ADS 
CAS 
PubMed 

Google Scholar 
17.Law, K. L. & Thompson, R. C. Microplastics in the seas. Science 345, 144–145 (2014).ADS 
CAS 
PubMed 

Google Scholar 
18.Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).ADS 
CAS 
PubMed 

Google Scholar 
19.Turner, W. R., Oppenheimer, M. & Wilcove, D. S. A force to fight global warming. Nature 462, 278–279 (2009).ADS 
CAS 
PubMed 

Google Scholar 
20.United Nations. World population prospects 2019. Department of Economic and Social Affairs. World Population Prospects 2019. (2019).21.Multini, L. C., de Souza, A. L. & da S., Marrelli, M. T. & Wilke, A. B. B.,. The influence of anthropogenic habitat fragmentation on the genetic structure and diversity of the malaria vector Anopheles cruzii (Diptera: Culicidae). Sci. Rep. 10, 18018 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Wilke, A. B. B. et al. Urbanization creates diverse aquatic habitats for immature mosquitoes in urban areas. Sci. Rep. 9, 15335 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
23.Pernat, N., Kampen, H., Jeschke, J. M. & Werner, D. Buzzing homes: Using citizen science data to explore the effects of urbanization on indoor mosquito communities. Insects 12, 1–13 (2021).
Google Scholar 
24.Blosser, E. M. & Burkett-cadena, N. D. Acta Tropica Culex (Melanoconion) panocossa from peninsular Florida, USA. Acta Trop. 167, 59–63 (2017).PubMed 

Google Scholar 
25.Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Sun, K. et al. Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015–2016 ZIKV epidemic. BMC Med. 16, 195 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Rose, N. H. et al. Climate and urbanization drive mosquito preference for humans. Curr. Biol. 30, 3570-3579.e6 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Wilke, A. B. B. et al. Mosquito adaptation to the extreme habitats of urban construction sites. Trends Parasitol. 35, 607–614 (2019).PubMed 

Google Scholar 
29.Ajelli, M. et al. Host outdoor exposure variability affects the transmission and spread of Zika virus: Insights for epidemic control. PLoS Negl. Trop. Dis. 11, e0005851 (2017).PubMed 
PubMed Central 

Google Scholar 
30.Mutebi, J.-P. et al. Zika virus MB16-23 in mosquitoes, Miami-Dade County, Florida, USA, 2016. Emerg. Infect. Dis. 24, 808–810 (2018).PubMed Central 

Google Scholar 
31.Little, E. et al. Socio-ecological mechanisms supporting high densities of Aedes albopictus (Diptera: Culicidae) in Baltimore, MD. J. Med. Entomol. 54, 1183–1192 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Burkett-Cadena, N. D., McClure, C. J. W., Estep, L. K. & Eubanks, M. D. What drives the spatial distribution of mosquitoes?. Ecosphere 4, 1–16 (2013).
Google Scholar 
33.LaDeau, S. L., Leisnham, P. T., Biehler, D. & Bodner, D. Higher mosquito production in low-income neighborhoods of Baltimore and Washington, DC: Understanding ecological drivers and mosquito-borne disease risk in temperate cities. Int. J. Environ. Res. Public Health 10, 1505–1526 (2013).PubMed 
PubMed Central 

Google Scholar 
34.Dowling, Z. et al. Linking mosquito infestation to resident socioeconomic status, knowledge, and source reduction practices in Suburban Washington, DC. EcoHealth 10, 36–47 (2013).PubMed 

Google Scholar 
35.Scavo, N. A., Barrera, R., Reyes-Torres, L. J. & Yee, D. A. Lower socioeconomic status neighborhoods in Puerto Rico have more diverse mosquito communities and higher Aedes aegypti abundance. J. Urban Ecol. 7, 1–11 (2021).
Google Scholar 
36.Trewin, B. J. et al. The elimination of the dengue vector, Aedes aegypti, from Brisbane, Australia: The role of surveillance, larval habitat removal and policy. PLoS Negl. Trop. Dis. 11, e0005848 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Multini, L. C., de Souza, A. L. & da S., Marrelli, M. T. & Wilke, A. B. B.,. Population structuring of the invasive mosquito Aedes albopictus (Diptera: Culicidae) on a microgeographic scale. PLoS ONE 14, e0220773 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Leta, S. et al. Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. Int. J. Infect. Dis. 67, 25–35 (2018).PubMed 

Google Scholar 
39.Benelli, G., Wilke, A. B. B. & Beier, J. C. Aedes albopictus (Asian Tiger Mosquito). Trends Parasitol. 36, 942–943 (2020).PubMed 

Google Scholar 
40.Benelli, G. & Mehlhorn, H. Declining malaria, rising of dengue and Zika virus: Insights for mosquito vector control. Parasitol. Res. 115, 1747–1754 (2016).PubMed 

Google Scholar 
41.Danauskas, J. X., Ehrenkranz, N. J., Davies, J. E. & Pond, W. L. Arboviruses and human disease in South Florida. Am. J. Trop. Med. Hyg. 15, 205–210 (1966).PubMed 

Google Scholar 
42.Gill, J., Stark, L. M. & Clark, G. G. Dengue surveillance in Florida, 1997–98. Emerg. Infect. Dis. 6, 30–35 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Rey, J. Dengue in Florida (USA). Insects 5, 991–1000 (2014).PubMed 
PubMed Central 

Google Scholar 
44.Vitek, C. J., Richards, S. L., Mores, C. N., Day, J. F. & Lord, C. C. Arbovirus transmission by Culex nigripalpus in Florida, 2005. J. Med. Entomol. 45, 483–493 (2008).CAS 
PubMed 

Google Scholar 
45.Messenger, A. M. et al. Serological evidence of ongoing transmission of dengue virus in permanent residents of Key West, Florida. Vector Borne Zoonotic Dis. 14, 783–787 (2014).PubMed 

Google Scholar 
46.Patterson, K. D. Yellow fever epidemics and mortality in the United States, 1693–1905. Soc. Sci. Med. 34, 855–865 (1992).CAS 
PubMed 

Google Scholar 
47.Grubaugh, N. D. et al. Genomic epidemiology reveals multiple introductions of Zika virus into the United States. Nature 546, 401–405 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Likos, A. et al. Local mosquito-borne transmission of zika virus—Miami-Dade and Broward Counties, Florida, June–August 2016. Morb. Mortal. Wkly. Rep. 65, 1032–1038 (2016).
Google Scholar 
49.Florida Department of Health. Available at: http://www.floridahealth.gov/diseases-and-conditions/mosquito-borne-diseases/_documents/week52arbovirusreport-12-31-16.pdf (2016).50.Florida Department of Health. Available at: http://www.floridahealth.gov/diseases-and-conditions/mosquito-borne-diseases/_documents/alert-dade-wnv-human-10-19-20.pdf (2020)51.Wilke, A. B. B. et al. Local conditions favor dengue transmission in the contiguous United States. Entomol. Gen. 41, 523–529 (2021).
Google Scholar 
52.Alto, B. W., Connelly, C. R., O’Meara, G. F., Hickman, D. & Karr, N. Reproductive biology and susceptibility of Florida Culex coronator to infection with West Nile virus. Vector-Borne Zoonotic Dis. 14, 606–614 (2014).PubMed 
PubMed Central 

Google Scholar 
53.Honório, N. A., Wiggins, K., Câmara, D. C. P., Eastmond, B. & Alto, B. W. Chikungunya virus vector competency of Brazilian and Florida mosquito vectors. PLoS Negl. Trop. Dis. 12, 1–16 (2018).
Google Scholar 
54.Richards, S. L., Anderson, S. L. & Lord, C. C. Vector competence of Culex pipiens quinquefasciatus (Diptera: Culicidae) for West Nile virus isolates from Florida. Trop. Med. Int. Heal. 19, 610–617 (2014).
Google Scholar 
55.Hribar, L. J., Smith, J. M., Vlach, J. J. & Verna, T. N. Survey of container-breeding mosquitoes from the Florida Keys, Monroe County, Florida. J. Am. Mosq. Control Assoc. 17, 245–248 (2001).CAS 
PubMed 

Google Scholar 
56.United States Environmental Protection Agency. Growing for a sustainable future: Miami-Dade County urban development boundary assessment. Available at: http://www.epa.gov/smartgrowth/pdf/Miami-Dade_Final_Report_12-12-12.pdf (2012).57.Miami-Dade County Building Permits. Available at, http://www.miamidade.gov/permits/.58.Wilke, A. B. B., Carvajal, A., Vasquez, C., Petrie, W. D. & Beier, J. C. Urban farms in Miami-Dade County, Florida have favorable environments for vector mosquitoes. PLoS ONE 15, e0230825 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Reba, M., Reitsma, F. & Seto, K. C. Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000. Sci. Data 3, 1–16 (2016).
Google Scholar 
60.Ceretti-Júnior, W. et al. Mosquito faunal survey in a central park of the city of São Paulo, Brazil. J. Am. Mosq. Control Assoc. 31, 172–176 (2015).PubMed 

Google Scholar 
61.Ferraguti, M. et al. Effects of landscape anthropization on mosquito community composition and abundance. Sci. Rep. 6, 29002 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Zahouli, J. B. Z. et al. Effect of land-use changes on the abundance, distribution, and host-seeking behavior of Aedes arbovirus vectors in oil palm-dominated landscapes, southeastern Côte d’Ivoire. PLoS ONE 12, e0189082 (2017).PubMed 
PubMed Central 

Google Scholar 
63.Westby, K. M., Adalsteinsson, S. A., Biro, E. G., Beckermann, A. J. & Medley, K. A. Aedes albopictus populations and larval habitat characteristics across the landscape: Significant differences exist between urban and rural land use types. Insects 12, 196 (2021).PubMed 
PubMed Central 

Google Scholar 
64.Estallo, E. L. et al. Modelling the distribution of the vector Aedes aegypti in a central Argentine city. Med. Vet. Entomol. 32, 451–461 (2018).CAS 
PubMed 

Google Scholar 
65.Messina, J. P. et al. A global compendium of human dengue virus occurrence. Sci. Data 1, 140004 (2014).PubMed 
PubMed Central 

Google Scholar 
66.Cunha, M. S. et al. Epizootics due to yellow fever virus in São Paulo State, Brazil: viral dissemination to new areas (2016–2017). Sci. Rep. 9, 5474 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
67.Ronca, S. E., Murray, K. O. & Nolan, M. S. Cumulative incidence of West Nile virus infection, continental United States, 1999–2016. Emerg. Infect. Dis. 25, 325–327 (2019).PubMed 
PubMed Central 

Google Scholar 
68.Poletti, P. et al. Transmission potential of chikungunya virus and control measures: The case of Italy. PLoS ONE 6, e18860 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Wilk-da-Silva, R. & de Souza Leal Diniz, M. M. C., Marrelli, M. T. & Wilke, A. B. B.,. Wing morphometric variability in Aedes aegypti (Diptera: Culicidae) from different urban built environments. Parasit. Vectors 11, 561 (2018).PubMed 
PubMed Central 

Google Scholar 
70.Wilke, A. B. B. et al. Cemeteries in Miami-Dade County, Florida are important areas to be targeted in mosquito management and control efforts. PLoS ONE 15, e0230748 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Weaver, S. C. Urbanization and geographic expansion of zoonotic arboviral diseases: Mechanisms and potential strategies for prevention. Trends Microbiol. 21, 360–363 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Wilke, A. B. B., Vasquez, C., Petrie, W. & Beier, J. C. Tire shops in Miami-Dade County, Florida are important producers of vector mosquitoes. PLoS ONE 14, 2 (2019).
Google Scholar 
73.Kothera, L., Godsey, M., Mutebi, J. P. & Savage, H. M. A comparison of aboveground and belowground populations of Culex pipiens (Diptera: Culicidae) mosquitoes in Chicago, Illinois, and New York City, New York, using microsatellites. J. Med. Entomol. 47, 805–813 (2010).PubMed 

Google Scholar 
74.World Health Organization. Handbook for Integrated Vector Management (World Health Organization, 2012).
Google Scholar 
75.Lizzi, K. M., Qualls, W. A., Brown, S. C. & Beier, J. C. Expanding Integrated Vector Management to promote healthy environments. Trends Parasitol. 30, 394–400 (2014).PubMed 
PubMed Central 

Google Scholar 
76.Souza, R. L. et al. Effect of an intervention in storm drains to prevent Aedes aegypti reproduction in Salvador, Brazil. Parasit. Vectors 10, 1–6 (2017).
Google Scholar 
77.Wilke, A. B. B., Beier, J. C. & Benelli, G. Transgenic mosquitoes—Fact or fiction?. Trends Parasitol. 34, 456–465 (2018).PubMed 

Google Scholar 
78.Beier, J. C., Wilke, A. B. B. & Benelli, G. Newer approaches for malaria vector control and challenges of outdoor transmission. Towards Malaria Elimination – A Leap Forward https://doi.org/10.5772/intechopen.75513 (2018).Article 

Google Scholar 
79.World Health Organization. Tenth Meeting of the WHO Vector Control Advisory Group. (2019).80.Wilke, A. B. B. et al. Effectiveness of adulticide and larvicide in controlling high densities of Aedes aegypti in urban environments. PLoS ONE 16, e0246046 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 110, 52–57 (2013).ADS 
CAS 
PubMed 

Google Scholar 
82.Rifat, S. A. & Al & Liu, W.,. Quantifying spatiotemporal patterns and major explanatory factors of urban expansion in Miami metropolitan area during 1992–2016. Remote Sens. 11, 2493 (2019).ADS 

Google Scholar 
83.Fuller, D. O. & Wang, Y. Recent trends in satellite vegetation index observations indicate decreasing vegetation biomass in the southeastern saline Everglades wetlands. Wetlands 34, 67–77 (2014).
Google Scholar 
84.Wilke, A. B. B. et al. Assessment of the effectiveness of BG-Sentinel traps baited with CO2 and BG-Lure for the surveillance of vector mosquitoes in Miami-Dade County. Florida. PLoS One 14, e0212688 (2019).CAS 
PubMed 

Google Scholar 
85.Darsie, R. F. Jr. & Morris, C. D. Keys to the adult females and fourth-instar larvae of the mosquitoes of Florida (Diptera, Culicidae). 1st ed. Vol. 1. Tech Bull Florida Mosq Cont Assoc (2000).86.Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. 1–15 (2017) DOI:https://doi.org/10.1002/9781118445112.stat07841.87.Alencar, J. et al. Culicidae community composition and temporal dynamics in Guapiaçu ecological reserve, Cachoeiras de Macacu, Rio de Janeiro, Brazil. PLoS ONE 10, 1–16 (2015).
Google Scholar 
88.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
Google Scholar 
89.Hammer, Ø., Harper, D. A. T. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
90.Ryan, P. A., Lyons, S. A., Alsemgeest, D., Thomas, P. & Kay, B. H. Spatial statistical analysis of adult mosquito (Diptera: Culicidae) counts: An example using light trap data, in Redland Shire, southeastern Queensland, Australia. J. Med. Entomol. 41, 1143–1156 (2004).PubMed 

Google Scholar 
91.O’Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
Google Scholar 
92.Wilke, A. B. B., Medeiros-Sousa, A. R., Ceretti-Junior, W. & Marrelli, M. T. Mosquito populations dynamics associated with climate variations. Acta Trop. 166, 343–350 (2016).PubMed 

Google Scholar 
93.Cohen, J. Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educ. Psychol. Meas. 33, 107–112 (1973).
Google Scholar  More

ParticipantsThe Ministry of Health, Labour, and Welfare of Japan has been conducting The Longitudinal Survey of Newborns in the 21st Century since 2001 to establish strategies to counter the declining birthrate in Japan. The survey targeted all babies born in Japan between January 10 and 17 or between July 10 and 17 of 2001. Baseline questionnaires were sent to a total of 53,575 families when eligible babies reached the age of 6 months and 47,015 families initially completed the baseline questionnaire (88% response rate). These respondents were mailed follow-up questionnaires to investigate medical conditions and behaviors when children reached the ages of 1.5, 2.5, 3.5, 4.5, 5.5, 7, 8, 9, 10, 11, 12, 13, 14, and 15 years20,21,22,23. Birth record data from Vital Statistics of Japan are also linked for each child participating in the study. The current study included data for children/families who responded both to the baseline questionnaire and the fifteenth questionnaire at age 15 years.The baseline survey at age 6 months included questions regarding children’s perinatal status as well as household and socioeconomic factors such as parental academic attainment, parental smoking status, and daycare attendance. The subsequent annual surveys starting at age 1.5 years included questions regarding each child’s height, weight and health status. We excluded 2382 children born before 37 weeks of pregnancy and one child with responses only for the baseline survey and the survey at age 15 years. A total of 26,778 children (315,581 data points) were included in the final analysis. A total of 11,141 children (41.61%) had responses to all 15 questionnaires between the ages of 6 months and 15 years, and responses to more than 12 questionnaires were available for the majority (91.94%) of children (Fig. 1, Table S1).Figure 1Flowchart of study participants.Full size imageMeasuresWe calculated BMI based on each participant’s reported annual height and weight. Each participant’s annual BMI was converted to a BMI Z-score using smoothed L, M, and S values for BMI standards from a representative population of Japanese children24. Briefly, the LMS (lambda–mu–sigma) method is a method proposed by Cole et al. to monitor changes in the skewness of the distribution during childhood as a way of constructing normalized growth standards25. Participants were then classified into four BMI categories based on the World Health Organization (WHO) criteria26: underweight (BMI standard deviation [SD] score of − 5 or more but less than − 2), normal weight (BMI SD score of − 2 or more but less than 1), overweight (BMI SD score of 1 or more but less than 2), and obese (BMI SD score of 2 or more but less than 5). The definitions of overweight and obesity were different for children under 5 years of age: a BMI Z-score of 2 SD or more was categorized as overweight and a BMI Z-score of 3 SD or more was categorized as obese. BMI category at age 15 years was the main outcome of interest in the current study.We also calculated annual height growth for each participant by subtracting the height reported at the previous survey from that reported in the current survey. For annual height growth between 5.5 and 7 years of age, this value was multiplied by 2/3 because of the 1.5-year interval between surveys.Statistical analysesWe first compared baseline characteristics among the four BMI categories (underweight, normal weight, overweight and obese) at age 15 years. To evaluate potential selection bias resulting from losses to follow-up, we also compared the baseline characteristics of children included in the analysis and those of children lost to follow-up through to the fifteenth survey (at age 15 years).We retrospectively examined annual aggregate categorical changes in individuals of the four BMI categories (groups) at age 15 years. For each group, the proportion of each BMI category at each survey between the ages of 1.5 and 14 years was calculated. In addition, we prospectively calculated the proportion of children in each BMI category at each survey between the ages of 1.5 and 14 years who eventually became underweight, normal weight, overweight, or obese at age 15 years. Note that these analyses were based on aggregate data and do not describe individual BMI changes and were performed using only the data obtained without imputation of missing values.Under the assumption that missing data were missing at random, mixed effect models with natural cubic regression splines were applied to calculate the trajectories of BMI Z-scores and annual BMI Z-score changes through age 15 years for participants of each BMI category at age 15 years. Knots at seven locations were placed in percentiles of age to yield a sufficient number of measurements between each consecutive knot (age 1.5, 3.5, 5.5, 8.5, 11, 13 and 15 years), as recommended by Harrell27. The mixed effect model is useful for describing population average growth trajectories and individual growth trajectories even when data are not available for all children at all ages28,29,30,31. Briefly, the population average growth trajectory was modeled with fixed effects, while the individual variability is represented as random effects.After fitting individual BMI trajectories using a mixed-effects model with natural cubic spline function, we estimated individual adiposity rebound timing as the age where the first derivative of the trajectory reached its minimum and the second derivative was positive32. Children were then classified into five categories (1.5–2.5 years, 3.5–4.5 years, 5.5–7 years, 8–10 years, and 11 years or older) for analysis of adiposity rebound timing33,34. The distribution of adiposity rebound timing was calculated for individuals of each BMI status at age 15 years overall and by gender.Finally, we modelled annual height change and its associations with BMI status at age 15 years separately for each gender using mixed-effects models with natural cubic regression splines.All statistical analyses were performed using Stata version 16 (StataCorp LLC, College Station, TX, USA). This study was approved by the Institutional Review Board at Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences (No.1506-073) and was conducted in accordance with the 1964 Helsinki Declaration and Ethical Guidelines for Medical and Health Research Involving Human Subjects. Informed consent was obtained by the opt-out method on the university’s website. More