Ecology

1.Bradshaw, A. D. Evolutionary significance of phenotypic plasticity in plants. Adv. Genet. 13, 115–155 (1965).Article 

Google Scholar 
2.Weiss, L. C. & Tollrian, R. Predator induced defenses in Crustacea. in The Natural History of Crustacea: Life Histories, Volume 5 (eds. Welborn, G. & Thiel, M.) 303–321 (Oxford University Press, 2018).
Google Scholar 
3.Tollrian, R. Predator-induced helmet formation in Daphnia cucullata (Sars). Arch. für Hydrobiol. 119, 191–196 (1990).
Google Scholar 
4.Krueger, D. A. & Dodson, S. I. Embryological induction and predation ecology in Daphnia pulex. Limnol. Oceanogr. https://doi.org/10.4319/lo.1981.26.2.0219 (1981).Article 

Google Scholar 
5.Grant, J. W. G. & Bayly, I. A. E. Predator induction of crests in morphs of the Daphnia carinata King complex. Limnol. Oceanogr. https://doi.org/10.4319/lo.1981.26.2.0201 (1981).Article 

Google Scholar 
6.Macháček, J. Indirect effect of planktivorous fish on the growth and reproduction of Daphnia galeata. Hydrobiologia https://doi.org/10.1007/BF00028397 (1991).Article 

Google Scholar 
7.Stibor, H. & Luning, J. Predator-induced phenotypic variation in the pattern of growth and reproduction in Daphnia hyalina (Crustacea: Cladocera). Funct. Ecol. https://doi.org/10.2307/2390117 (1994).Article 

Google Scholar 
8.Dodson, S. I., Tollrian, R. & Lampert, W. Daphnia swimming behaviour during vertical migration. J. Plankton Res. 19, 969–978 (1997).Article 

Google Scholar 
9.Tollrian, R., Duggen, S., Weiss, L. C., Laforsch, C. & Kopp, M. Density-dependent adjustment of inducible defenses. Sci. Rep. 5, 12736 (2015).ADS 
CAS 
Article 

Google Scholar 
10.Miyakawa, H. et al. Gene up-regulation in response to predator kairomones in the water flea Daphnia pulex. BMC Dev. Biol. https://doi.org/10.1186/1471-213X-10-45 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Oda, S., Kato, Y., Watanabe, H., Tatarazako, N. & Iguchi, T. Morphological changes in Daphnia galeata induced by a crustacean terpenoid hormone and its analog. Environ. Toxicol. Chem. https://doi.org/10.1002/etc.378 (2011).Article 
PubMed 

Google Scholar 
12.Miyakawa, H., Sato, M., Colbourne, J. K. & Iguchi, T. Ionotropic glutamate receptors mediate inducible defense in the water flea Daphnia pulex. PLoS ONE 10, 1–12 (2015).Article 

Google Scholar 
13.Weiss, L. C., Kruppert, S., Laforsch, C. & Tollrian, R. Chaoborus and Gasterosteus anti-predator responses in Daphnia pulex are mediated by independent cholinergic and gabaergic neuronal signals. PLoS ONE 7, e36879 (2012).ADS 
CAS 
Article 

Google Scholar 
14.Weiss, L. C., Leese, F., Laforsch, C. & Tollrian, R. Dopamine is a key regulator in the signalling pathway underlying predatorinduced defences in Daphnia. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2015.1440 (2015).Article 

Google Scholar 
15.Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).Article 

Google Scholar 
16.Bullock, T. & Horridge, G. A. Structure and function in the nervous systems of invertebrates. (San Francisco, 1965).17.Fritsch, M., Kaji, T., Olesen, J. & Richter, S. The development of the nervous system in Laevicaudata (Crustacea, Branchiopoda): insights into the evolution and homologies of branchiopod limbs and ‘frontal organs’. Zoomorphology 132, 163–181 (2013).Article 

Google Scholar 
18.Kolb, B. & Whishaw, I. Q. Brain plasticity and behavior. Annu. Rev. Psychol. https://doi.org/10.1146/annurev.psych.49.1.43 (1998).Article 
PubMed 

Google Scholar 
19.Turner, A. M. & Greenough, W. T. Differential rearing effects on rat visual cortex synapses. I. Synaptic and neuronal density and synapses per neuron. Brain Res. https://doi.org/10.1016/0006-8993(85)90525-6 (1985).Article 
PubMed 

Google Scholar 
20.Woodley, S. K., Mattes, B. M., Yates, E. K. & Relyea, R. A. Exposure to sublethal concentrations of a pesticide or predator cues induces changes in brain architecture in larval amphibians. Oecologia 179, 655–665 (2015).ADS 
Article 

Google Scholar 
21.Gronenberg, W., Heeren, S. & Hölldobler, B. Age-dependent and task-related morphological changes in the brain and the mushroom bodies of the ant Camponotus floridanus. J. Exp. Biol. 199, 2011–2019 (1996).CAS 
Article 

Google Scholar 
22.Barth, M. & Heisenberg, M. Vision affects mushroom bodies and central complex in Drosophila melanogaster. Learn. Mem. https://doi.org/10.1101/lm.4.2.219 (1997).Article 
PubMed 

Google Scholar 
23.Barth, M., Hirsch, H. V. B., Meinertzhagen, I. A. & Heisenberg, M. Experience-dependent developmental plasticity in the optic lobe of Drosophila melanogaster. J. Neurosci. https://doi.org/10.1523/jneurosci.17-04-01493.1997 (1997).Article 
PubMed 
PubMed Central 

Google Scholar 
24.van Dijk, L. J. A., Janz, N., Schäpers, A., Gamberale-Stille, G. & Carlsson, M. A. Experience-dependent mushroom body plasticity in butterflies: Consequences of search complexity and host range. Proc. R. Soc. B Biol. Sci. 284, 0–7 (2017).
Google Scholar 
25.Withers, G. S., Fahrbach, S. E. & Robinson, G. E. Selective neuroanatomical plasticity and division of labour in the honeybee. Nature https://doi.org/10.1038/364238a0 (1993).Article 
PubMed 

Google Scholar 
26.Heisenberg, M., Heusipp, M. & Wanke, C. Structural plasticity in the Drosophila brain. J. Neurosci. https://doi.org/10.1523/jneurosci.15-03-01951.1995 (1995).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Niven, J. E. & Laughlin, S. B. Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. https://doi.org/10.1242/jeb.017574 (2008).Article 
PubMed 

Google Scholar 
28.Berlucchi, G. & Buchtel, H. A. Neuronal plasticity: historical roots and evolution of meaning. Exp. Brain Res. https://doi.org/10.1007/s00221-008-1611-6 (2009).Article 
PubMed 

Google Scholar 
29.Zhai, R. G. & Bellen, H. J. The architecture of the active zone in the presynaptic nerve terminal. Physiology https://doi.org/10.1152/physiol.00014.2004 (2004).Article 
PubMed 

Google Scholar 
30.Horn, G., Bradley, P. & McCabe, B. J. Changes in the structure of synapses associated with learning. J. Neurosci. https://doi.org/10.1523/jneurosci.05-12-03161.1985 (1985).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Beaulieu, C. & Colonnier, M. Richness of environment affects the number of contacts formed by boutons containing flat vesicles but does not alter the number of these boutons per neuron. J. Comp. Neurol. https://doi.org/10.1002/cne.902740305 (1988).Article 
PubMed 

Google Scholar 
32.Anderson, B. J. Plasticity of gray matter volume: The cellular and synaptic plasticity that underlies volumetric change. Dev. Psychobiol. https://doi.org/10.1002/dev.20563 (2011).Article 
PubMed 

Google Scholar 
33.Tyagarajan, S. K. & Fritschy, J.-M. Gephyrin: a master regulator of neuronal function?. Nat. Rev. Neurosci. 15, 141–156 (2014).CAS 
Article 

Google Scholar 
34.Dutertre, S., Becker, C. M. & Betz, H. Inhibitory glycine receptors: an update. J. Biol. Chem. https://doi.org/10.1074/jbc.R112.408229 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Fritschy, J. M., Harvey, R. J. & Schwarz, G. Gephyrin: where do we stand, where do we go?. Trends Neurosci. https://doi.org/10.1016/j.tins.2008.02.006 (2008).Article 
PubMed 

Google Scholar 
36.Choii, G. & Ko, J. Gephyrin: a central GABAergic synapse organizer. Exp. Mol. Med. https://doi.org/10.1038/emm.2015.5 (2015).Article 
PubMed 

Google Scholar 
37.Phillips-Portillo, J. & Strausfeld, N. J. Representation of the brain’s superior protocerebrum of the flesh fly, Neobellieria bullata, in the central body. J. Comp. Neurol. https://doi.org/10.1002/cne.23094 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods https://doi.org/10.1038/nmeth.2019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
39.de Reuille, P. B. et al. MorphoGraphX: a platform for quantifying morphogenesis in 4D. Elife https://doi.org/10.7554/eLife.05864 (2015).Article 

Google Scholar 
40.Cignoni, P. et al. MeshLab: An open-source 3D mesh processing tool. In 6th Eurographics Italian Chapter Conference 2008 – Proceedings (2008).
Google Scholar 
41.Horstmann, M. et al. Scan, extract, wrap, compute—a 3D method to analyse morphological shape differences. PeerJ 2018, 1–20 (2018).
Google Scholar 
42.R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://doi.org/10.1007/978-3-540-74686-7 (2011).Article 

Google Scholar 
43.Wickham, H. et al. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag. New York (2016).44.Ohyama, T. et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015).ADS 
CAS 
Article 

Google Scholar 
45.Simpson, J. H. Chapter 3 Mapping and Manipulating Neural Circuits in the Fly Brain. Advances in Genetics. https://doi.org/10.1016/S0065-2660(09)65003-3 (2009).Article 

Google Scholar 
46.Boyan, G., Williams, L. & Liu, Y. Conserved patterns of axogenesis in the panarthropod brain. Arthropod Struct. Dev. https://doi.org/10.1016/j.asd.2014.11.003 (2015).Article 
PubMed 

Google Scholar 
47.Cayre, M., Strambi, C. & Strambi, A. Neurogenesis in an adult insect brain and its hormonal control. Nature https://doi.org/10.1038/368057a0 (1994).Article 

Google Scholar 
48.Harzsch, S. & Dawirs, R. R. Neurogenesis in the developing crab brain: Postembryonic generation of neurons persists beyond metamorphosis. J. Neurobiol. https://doi.org/10.1002/(SICI)1097-4695(199603)29:3%3c384::AID-NEU9%3e3.0.CO;2-5 (1996).Article 
PubMed 

Google Scholar 
49.Sandeman, R., Clarke, D., Sandeman, D. & Manly, M. Growth-related and antennular amputation-induced changes in the olfactory centers of crayfish brain. J. Neurosci. https://doi.org/10.1523/jneurosci.18-16-06195.1998 (1998).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Harzsch, S., Miller, J., Benton, J. & Beltz, B. From embryo to adult: Persistent neurogenesis and apoptotic cell death shape the lobster deutocerebrum. J. Neurosci. https://doi.org/10.1523/jneurosci.19-09-03472.1999 (1999).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Letourneau, J. G. Addition of sensory structures and associated neurons to the crayfish telson during development. J. Comp. Physiol. A https://doi.org/10.1007/BF00656778 (1976).Article 

Google Scholar 
52.Sandeman, D. C. Organization of the central nervous system. in The Biology of Crustacea. Vol. 3. Neurobiology: Structure and Function 1–61 (Academic Press, 1982).
Google Scholar 
53.Laverack, M. S. The numbers of neurones in decapod Crustacea. J. Crustac. Biol. 8, 1–11 (1988).Article 

Google Scholar 
54.Moss, S. J. & Smart, T. G. Constructing inhibitory synapses. Nat. Rev. Neurosci. 2, 240–250 (2001).CAS 
Article 

Google Scholar 
55.Bliss, T. V. P. & Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature https://doi.org/10.1038/361031a0 (1993).Article 
PubMed 

Google Scholar 
56.Collingridge, G. L., Isaac, J. T. R. & Yu, T. W. Receptor trafficking and synaptic plasticity. Nat. Rev. Neurosci. https://doi.org/10.1038/nrn1556 (2004).Article 
PubMed 

Google Scholar 
57.Atwood, H. L. & Wojtowicz, J. M. Short-term and long-term plasticity and physiological differentiation of crustacean motor synapses. Int. Rev. Neurobiol. 28, 275–362 (1986).CAS 
Article 

Google Scholar 
58.Liu, G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat. Neurosci. https://doi.org/10.1038/nn1206 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Hao, J., Wang, X. D., Dan, Y., Poo, M. M. & Zhang, X. H. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons. Proc. Natl. Acad. Sci. USA. https://doi.org/10.1073/pnas.0912022106 (2009).Article 
PubMed 

Google Scholar 
60.Fu, A. K. & Ip, N. Y. Regulation of postsynaptic signaling in structural synaptic plasticity. Curr. Opin. Neurobiol. https://doi.org/10.1016/j.conb.2017.05.016 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Chater, T. E. & Goda, Y. The role of AMPA receptors in postsynaptic mechanisms of synaptic plasticity. Front. Cell. Neurosci. https://doi.org/10.3389/fncel.2014.00401 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Velazquez, J. L., Thompson, C. L., Barnes, E. M. & Angelides, K. J. Distribution and lateral mobility of GABA/benzodiazepine receptors on nerve cells. J. Neurosci. 9, 2163–2169 (1989).CAS 
Article 

Google Scholar 
63.Gaiarsa, J. L., Caillard, O. & Ben-Ari, Y. Long-term plasticity at GABAergic and glycinergic synapses: mechanisms and functional significance. Trends Neurosci. https://doi.org/10.1016/S0166-2236(02)02269-5 (2002).Article 
PubMed 

Google Scholar 
64.Nusser, Z., Hájos, N., Somogyi, P. & Mody, I. Increased number of synaptic GABA(A) receptors underlies potentiation at hippocampal inhibitory synapses. Nature https://doi.org/10.1038/25999 (1998).Article 
PubMed 

Google Scholar  More

1.Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147, 813–846 (1996).Article 

Google Scholar 
2.Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
3.Coleman, D. C., Andrews, R., Ellis, J. E. & Singh, J. S. Energy flow and partitioning in selected man-managed and natural ecosystems. Agro-Ecosyst. 3, 45–54 (1976).Article 

Google Scholar 
4.Steffan, S. A. et al. Unpacking brown food-webs: Animal trophic identity reflects rampant microbivory. Ecol. Evol. 7, 3532–3541 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Coleman, D. C. Energetics of detritivory and microbivory in soil in theory and practice. In Food Webs (eds. Polis G. A. & Winemiller K. O.) 39–50 (Springer, 1996).Chapter 

Google Scholar 
6.Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 
Article 

Google Scholar 
7.Hagen, E. M. et al. A meta-analysis of the effects of detritus on primary producers and consumers in marine, freshwater, and terrestrial ecosystems. Oikos 121, 1507–1515 (2012).Article 

Google Scholar 
8.Danovaro, R., Snelgrove, P. V. R. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29, 465–475 (2014).PubMed 
Article 

Google Scholar 
9.Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K. & Arbizu, P. M. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
Article 

Google Scholar 
10.Gage, J. D. & Tyler, P. A. Deep-Sea Biology: A Natural History of Organisms at the Deep-Sea Floor. (Cambridge University Press, 1991).Book 

Google Scholar 
11.De La Rocha, C. L. & Passow, U. Factors influencing the sinking of POC and the efficiency of the biological carbon pump. Deep Res. Part II Top. Stud. Oceanogr. 54, 639–658 (2007).Article 

Google Scholar 
12.Smith, K. L., Ruhl, H. A., Huffard, C. L., Messié, M. & Kahru, M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc. Natl. Acad. Sci. USA. 115, 12235–12240 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Ramirez-Llodra, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
Article 

Google Scholar 
14.Ruhl, H. A. Abundance and size distribution dynamics of abyssal epibenthic megafauna in the northeast Pacific. Ecology 88, 1250–1262 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Billett, D. S. M. Deep-sea holothurians. Oceanogr. Mar. Biol. An Annu. Rev. 29, 259–317 (1991).
Google Scholar 
16.Bett, B. J., Malzone, M. G., Narayanaswamy, B. E. & Wigham, B. D. Temporal variability in phytodetritus and megabenthic activity at the seabed in the deep northeast Atlantic. Prog. Oceanogr. 50, 349–368 (2001).ADS 
Article 

Google Scholar 
17.Durden, J. M. et al. Response of deep-sea deposit-feeders to detrital inputs: A comparison of two abyssal time-series sites. Deep. Res. Part II Top. Stud. Oceanogr. 173, 104677 (2020).18.Khripounoff, A. & Sibuet, M. L. nutrition d’echinodermes abyssaux I. Alimentation des holothuries. Mar. Biol. 60, 17–26 (1980).Article 

Google Scholar 
19.Roberts, D., Gebruka, A., Levin, V. & Manship, B. A. D. Feeding and digestive strategies in deposit-feeding holothurians. Oceanogr. Mar. Biol. Annu. Rev. 38, 257–310 (2000).
Google Scholar 
20.FitzGeorge-Balfour, T., Billett, D. S. M., Wolff, G. A., Thompson, A. & Tyler, P. A. Phytopigments as biomarkers of selectivity in abyssal holothurians; interspecific differences in response to a changing food supply. Deep. Res. Part II Top. Stud. Oceanogr. 57, 1418–1428 (2010).21.Miller, R. J., Smith, C. R., Demaster, D. J. & Fornes, W. L. Feeding selectivity and rapid particle processing by deep-sea megafaunal deposit feeders : A 234 Th tracer approach. J. Mar. Res. 58, 653–673 (2000).Article 

Google Scholar 
22.Witte, U. et al. In situ experimental evidence of the fate of a phytodetritus pulse at the abyssal sea floor. Lett. Nat. 424, 763–766 (2003).CAS 
Article 

Google Scholar 
23.Moore, H., Manship, B. & Roberts, D. Gut structure and digestive strategies in three species of abyssal holothurians. in Echinoderm Research 111–119 (Balkema, 1995).24.Deming, J. W. & Colwell, R. R. Barophilic bacteria associated with digestive tracts of abyssal holothurians. Appl. Environ. Microbiol. 44, 1222–1230 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Amaro, T., Luna, G. M., Danovaro, R., Billett, D. S. M. & Cunha, M. R. High prokaryotic biodiversity associated with gut contents of the holothurian Molpadia musculus from the Nazaré Canyon (NE Atlantic). Deep. Res. Part I Oceanogr. Res. Pap. 63, 82–90 (2012).26.Roberts, D. et al. Sediment distribution, hydrolytic enzyme profiles and bacterial activities in the guts of Oneirophanta mutabilis, Psychropotes longicauda and Pseudostichopus villosus: What do they tell us about digestive strategies of abyssal holothurians?. Prog. Oceanogr. 50, 443–458 (2001).ADS 
Article 

Google Scholar 
27.Sibuet, M., Khripounoff, A., Deming, J., Colwell, R. & Dinet, A. Modification of the gut contents in the digestive tract of abyssal holothurians. In Proceedings of the International Echinoderm Conference. Tampa Bay. (ed Lawrence, J. M.) 421–428 (Balkema, 1982).
Google Scholar 
28.Bradley, C. J. et al. Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13, 476–493 (2015).Article 

Google Scholar 
29.Ohkouchi, N. et al. Advances in the application of amino acid nitrogen isotopic analysis in ecological and biogeochemical studies. Org. Geochem. 113, 150–174 (2017).CAS 
Article 

Google Scholar 
30.Popp, B. N. et al. Stable isotopes as indicators of ecological change. Terr. Ecol. 1, 173–190 (2007).
Google Scholar 
31.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
Article 

Google Scholar 
32.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

Google Scholar 
33.Steffan, S. A. et al. Microbes are trophic analogs of animals. Proc. Natl. Acad. Sci. U. S. A. 112, 15119–15124 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Yamaguchi, Y. T. et al. Fractionation of nitrogen isotopes during amino acid metabolism in heterotrophic and chemolithoautotrophic microbes across Eukarya, Bacteria, and Archaea: Effects of nitrogen sources and metabolic pathways. Org. Geochem. 111, 101–112 (2017).CAS 
Article 

Google Scholar 
35.Iken, K., Brey, T., Wand, U., Voigt, J. & Junghans, P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): A stable isotope analysis. Prog. Oceanogr. 50, 383–405 (2001).ADS 
Article 

Google Scholar 
36.Romero-Romero, S. et al. Seasonal pathways of organic matter within the Avilés submarine canyon: Food web implications. Deep. Res. Part I. Oceanogr. Res. Pap. 117, 1–10 (2016).ADS 
CAS 
Article 

Google Scholar 
37.Reid, W., Wigham, B., McGill, R. & Polunin, N. Elucidating trophic pathways in benthic deep-sea assemblages of the Mid-Atlantic Ridge north and south of the Charlie-Gibbs fracture zone. Mar. Ecol. Prog. Ser. 463, 89–103 (2012).ADS 
Article 

Google Scholar 
38.Drazen, J. et al. Bypassing the abyssal benthic food web: Macrourid diet in the eastern North Pacific inferred from stomach content and stable isotopes analyses. Limnol. Oceanogr. 53, 2644–2654 (2008).ADS 
Article 

Google Scholar 
39.Post, D. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
40.Witbaard, R., Duineveld, G. C. A., Kok, A., Van Der Weele, J. & Berghuis, E. M. The response of Oneirophanta mutabilis (Holothuroidea) to the seasonal deposition of phytopigments at the Porcupine Abyssal Plain in the Northeast Atlantic. Prog. Oceanogr. 50, 423–441 (2001).ADS 
Article 

Google Scholar 
41.Wigham, B. D., Hudson, I. R., Billett, D. S. M. & Wolff, G. A. Is long-term change in the abyssal Northeast Atlantic driven by qualitative changes in export flux? Evidence from selective feeding in deep-sea holothurians. Prog. Oceanogr. 59, 409–441 (2003).ADS 
Article 

Google Scholar 
42.Hudson, I. R., Wigham, B. D., Billett, D. S. M. & Tyler, P. A. Seasonality and selectivity in the feeding ecology and reproductive biology of deep-sea bathyal holothurians. Prog. Oceanogr. 59, 381–407 (2003).ADS 
Article 

Google Scholar 
43.Lauerman, L. M. L., Smoak, J. M., Shaw, T. J., Moore, W. S. & Smith, K. L. 234Th and 210Pb evidence for rapid ingestion of settling particles by mobile epibenthic megafauna in the abyssal NE Pacific. Limnol. Oceanogr. 42, 589–595 (1997).ADS 
CAS 
Article 

Google Scholar 
44.Roberts, D., Billett, D. S. M., McCartney, G. & Hayes, G. E. Procaryotes on the tentacles of deep-sea holothurians: A novel form of dietary supplementation. Limnol. Oceanogr. 36, 1447–1451 (1991).ADS 
Article 

Google Scholar 
45.Larsen, T., Lee Taylor, D., Leigh, M. B. & O’Brien, D. M. Stable isotope fingerprinting: A novel method for identifying plant, fungal, or bacterial origins of amino acids. Ecology 90, 3526–3535 (2009).46.Plante, C. J., Jumars, P. A. & Baross, J. A. Digestive associations between marine detritivores and bacteria. Annu. Rev. Ecol. Syst. 21, 93–127 (1990).Article 

Google Scholar 
47.Drazen, J. C., Phleger, C. F., Guest, M. A. & Nichols, P. D. Lipid, sterols and fatty acid composition of abyssal holothurians and ophiuroids from the North-East Pacific Ocean: Food web implications. Comp. Biochem. Physiol. – B Biochem. Mol. Biol. 151, 79–87 (2008).48.Amaro, T. et al. Possible links between holothurian lipid compositions and differences in organic matter (OM) supply at the western Pacific abyssal plains. Deep. Res. Part I Oceanogr. Res. Pap. 152, (2019).49.Kharlamenko, V. I., Maiorova, A. S. & Ermolenko, E. V. Fatty acid composition as an indicator of the trophic position of abyssal megabenthic deposit feeders in the Kuril Basin of the Sea of Okhotsk. Deep. Res. Part II Top. Stud. Oceanogr. 154, 374–382 (2018).50.Ginger, M. L. et al. Organic matter assimilation and selective feeding by holothurians in the deep sea: Some observations and comments. Prog. Oceanogr. 50, 407–421 (2001).ADS 
Article 

Google Scholar 
51.McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kaufmann, R. S. & Smith, K. L. Activity patterns of mobile epibenthic megafauna at an abyssal site in the eastern North Pacific: Results from a 17-month time-lapse photographic study. Deep. Res. Part I Oceanogr. Res. Pap. 44, 559–579 (1997).53.Kuhnz, L. A., Ruhl, H. A., Huffard, C. L. & Smith, K. L. Rapid changes and long-term cycles in the benthic megafaunal community observed over 24 years in the abyssal northeast Pacific. Prog. Oceanogr. 124, 1–11 (2014).ADS 
Article 

Google Scholar 
54.Vardaro, M. F., Ruhl, H. A. & Smith, K. L. Climate variation, carbon flux, and bioturbation in the abyssal north pacific. Limnol. Oceanogr. 54, 2081–2088 (2009).ADS 
Article 

Google Scholar 
55.Ziegler, A., Mooi, R., Rolet, G. & De Ridder, C. Origin and evolutionary plasticity of the gastric caecum in sea urchins (Echinodermata: Echinoidea). BMC Evol. Biol. 10, 313 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
56.McCarthy, M. D., Benner, R., Lee, C. & Fogel, M. L. Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744 (2007).ADS 
CAS 
Article 

Google Scholar 
57.Calleja, M. L., Batista, F., Peacock, M., Kudela, R. & McCarthy, M. D. Changes in compound specific δ15N amino acid signatures and d/l ratios in marine dissolved organic matter induced by heterotrophic bacterial reworking. Mar. Chem. 149, 32–44 (2013).CAS 
Article 

Google Scholar 
58.Smith, K. L., Ruhl, H. A., Kaufmann, R. S. & Kahru, M. Tracing abyssal food supply back to upper-ocean processes over a 17-year time series in the northeast Pacific. Limnol. Oceanogr. 53, 2655–2667 (2008).ADS 
Article 

Google Scholar 
59.Huffard, C. L., Kuhnz, L. A., Lemon, L., Sherman, A. D. & Smith, K. L. Demographic indicators of change in a deposit-feeding abyssal holothurian community (Station M, 4000 m). Deep. Res. Part I Oceanogr. Res. Pap. 109, 27–39 (2016).60.Hannides, C. C. S., Popp, B. N., Anela Choy, C. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1936 (2013).61.Neto, R. R., Wolff, G. A., Billett, D. S. M., Mackenzie, K. L. & Thompson, A. The influence of changing food supply on the lipid biochemistry of deep-sea holothurians. Deep. Res. Part I Oceanogr. Res. Pap. 53, 516–527 (2006).62.Amaro, T., Witte, H., Herndl, G. J., Cunha, M. R. & Billett, D. S. M. Deep-sea bacterial communities in sediments and guts of deposit-feeding holothurians in Portuguese canyons (NE Atlantic). Deep Res. Part I Oceanogr. Res. Pap. 56, 1834–1843 (2009).ADS 
Article 

Google Scholar 
63.Silfer, J. A., Engel, M. H., Macko, S. A. & Jumeau, E. J. Stable carbon isotope analysis of amino acid enantiomers by conventional isotope ratio mass spectrometry and combined gas chromatography/isotope ratio mass spectrometry. Anal. Chem. 63, 370–374 (1991).CAS 
Article 

Google Scholar 
64.Jarman, C. L. et al. Diet of the prehistoric population of Rapa Nui (Easter Island, Chile) shows environmental adaptation and resilience. Am. J. Phys. Anthropol. 164, 343–361 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Dauwe, B., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R. Linking diagenetic alteration of amino acids and bulk organic matter reactivity. Limnology 44, 1809–1814 (1999).CAS 

Google Scholar 
66.McCarthy, M. D., Benner, R., Lee, C., Hedges, J. I. & Fogel, M. L. Amino acid carbon isotopic fractionation patterns in oceanic dissolved organic matter: An unaltered photoautotrophic source for dissolved organic nitrogen in the ocean?. Mar. Chem. 92, 123–134 (2004).CAS 
Article 

Google Scholar 
67.R: A Language and Environment for Statistical Computing. https://www.R-project.org (R Core Team. R Foundation for Statistical Computing, 2020). More

1.Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Stem, C., Margoluis, R., Salafsky, N. & Brown, M. Monitoring and evaluation in conservation: a review of trends and approaches. Conserv. Biol. 19, 295–309 (2005).Article 

Google Scholar 
4.Atwood, T. B. et al. Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Sci. Adv. 6, eabb8458 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol 2, e197 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. R. Soc. Open Sci. 3, 160252 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Kissling, W. D. et al. Building essential biodiversity variables (EBV s) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Nesshöver, C., Livoreil, B., Schindler, S. & Vandewalle, M. Challenges and solutions for networking knowledge holders and better informing decision-making on biodiversity and ecosystem services. Biodivers. Conserv. 25, 1207–1214 (2016).Article 

Google Scholar 
10.Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Field, S. A., Tyre, A. J. & Possingham, H. P. Optimizing allocation of monitoring effort under economic and observational constraints. J. Wildl. Manag. 69, 473–482 (2005).Article 

Google Scholar 
12.Braunisch, V. & Suchant, R. Predicting species distributions based on incomplete survey data: The trade-off between precision and scale. Ecography 33, 826–840 (2010).Article 

Google Scholar 
13.Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Deiner, K. et al. Long-range PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods Ecol. Evol. 8, 1888–1898 (2017).Article 

Google Scholar 
15.Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Conserv. 183, 53–63 (2015).Article 

Google Scholar 
16.Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis—A review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).Article 

Google Scholar 
17.Sales, N. G. et al. Fishing for mammals: Landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from riverine systems. J. Appl. Ecol. 57, 707–716 (2020).CAS 
Article 

Google Scholar 
18.Sales, N. G. et al. Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic Forest, Brazil. Mammal Rev. 50, 221–225 (2020).Article 

Google Scholar 
19.Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 
Article 

Google Scholar 
20.Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Leempoel, K., Hebert, T. & Hadly, E. A. A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity. Proc. R. Soc. B Biol. Sci. 287, 20192353 (2020).CAS 
Article 

Google Scholar 
22.Harper, L. R. et al. Environmental DNA (eDNA) metabarcoding of pond water as a tool to survey conservation and management priority mammals. Biol. Conserv. 238, 108225 (2019).Article 

Google Scholar 
23.Rodgers, T. W. & Mock, K. E. Drinking water as a source of environmental DNA for the detection of terrestrial wildlife species. Conserv. Genet. Resour. 7, 693–696 (2015).Article 

Google Scholar 
24.Ushio, M. et al. Environmental DNA enables detection of terrestrial mammals from forest pond water. Mol. Ecol. Resour. 17, e63–e75 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Williams, K. E., Huyvaert, K. P., Vercauteren, K. C., Davis, A. J. & Piaggio, A. J. Detection and persistence of environmental DNA from an invasive, terrestrial mammal. Ecol. Evol. 8, 688–695 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Merkes, C. M., McCalla, S. G., Jensen, N. R., Gaikowski, M. P. & Amberg, J. J. Persistence of DNA in carcasses, slime and avian feces may affect interpretation of environmental DNA data. PLoS ONE 9, e113346 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 1–13 (2018).ADS 
CAS 
Article 

Google Scholar 
28.Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).Article 

Google Scholar 
29.Withers, P. C., Cooper, C. E., Maloney, S. K., Bozinovic, F. & Cruz-Neto, A. P. Ecological and Environmental Physiology of Mammals Vol. 5 (Oxford University Press, 2016).Book 

Google Scholar 
30.Bicudo, J. E. P., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds Vol. 2 (Oxford University Press, 2010).Book 

Google Scholar 
31.Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
32.Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9, 1–11 (2019).CAS 
Article 

Google Scholar 
33.Tarboton, D. G., Bras, R. L. & Rodriguez-Iturbe, I. The fractal nature of river networks. Water Resour. Res. 24, 1317–1322 (1988).ADS 
Article 

Google Scholar 
34.Ishige, T. et al. Tropical-forest mammals as detected by environmental DNA at natural saltlicks in Borneo. Biol. Conserv. 210, 281–285 (2017).Article 

Google Scholar 
35.Joseph, L. N., Field, S. A., Wilcox, C. & Possingham, H. P. Presence–absence versus abundance data for monitoring threatened species. Conserv. Biol. 20, 1679–1687 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lacy, R. C. Lessons from 30 years of population viability analysis of wildlife populations. Zoo Biol. 38, 67–77 (2019).PubMed 
Article 

Google Scholar 
37.Gärdenfors, U., Hilton-Taylor, C., Mace, G. M. & Rodríguez, J. P. The application of IUCN Red List criteria at regional levels. Conserv. Biol. 15, 1206–1212 (2001).Article 

Google Scholar 
38.Munro, R., Nielsen, S. E., Price, M., Stenhouse, G. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
39.Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Hunter, M. E. et al. Detection limits of quantitative and digital PCR assays and their influence in presence–absence surveys of environmental DNA. Mol. Ecol. Resour. 17, 221–229 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Roussel, J.-M., Paillisson, J.-M., Treguier, A. & Petit, E. The downside of eDNA as a survey tool in water bodies. J. Appl. Ecol. 52, 823–826 (2015).CAS 
Article 

Google Scholar 
42.Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations (Academic Press, 2002).
Google Scholar 
43.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
44.Morris, W. F. et al. Quantitative Conservation Biology (Sinauer Sunderland, 2002).
Google Scholar 
45.Parks, B. C. South Chilcotin Mountains Park and Big Creek Park Management Plan (2019).46.McLellan, M. L. et al. Divergent population trends following the cessation of legal grizzly bear hunting in southwestern British Columbia, Canada. Biol. Conserv. 233, 247–254 (2019).Article 

Google Scholar 
47.Kays, R. et al. Camera traps as sensor networks for monitoring animal communities. In 2009 IEEE 34th Conference on Local Computer Networks 811–818. https://doi.org/10.1109/LCN.2009.5355046 (IEEE, 2009).48.Hendry, H. & Mann, C. Camelot–intuitive software for camera trap data management. BioRxiv 203216 (2017).49.Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For biodiversity research and monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
51.Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Wearn, O. & Glover-Kapfer, P. Camera-trapping for conservation: A guide to best-practices. WWF Conserv. Technol. Ser. 1, 2019–2104 (2017).
Google Scholar 
55.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
56.Plummer, M., et al.. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing vol. 124, 1–10 (Vienna, Austria, 2003).57.Tuomisto, H. A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33, 2–22 (2010).Article 

Google Scholar 
58.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article 

Google Scholar 
59.Samejima, H., Ong, R., Lagan, P. & Kitayama, K. Camera-trapping rates of mammals and birds in a Bornean tropical rainforest under sustainable forest management. For. Ecol. Manag. 270, 248–256 (2012).Article 

Google Scholar 
60.Parsons, A. W. et al. Do occupancy or detection rates from camera traps reflect deer density?. J. Mammal. 98, 1547–1557 (2017).Article 

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

Google Scholar 
62.Villette, P., Krebs, C. J., Jung, T. S. & Boonstra, R. Can camera trapping provide accurate estimates of small mammal (Myodes rutilus and Peromyscus maniculatus) density in the boreal forest?. J. Mammal. 97, 32–40 (2016).Article 

Google Scholar 
63.Feldhamer, G. A., Thompson, B. C. & Chapman, J. A. Wild Mammals of North America: Biology, Management, and Conservation (The Johns Hopkins University Press, 2003).
Google Scholar 
64.Burnham, K. P. & Anderson, D. R. A Practical Information-Theoretic Approach. Model Selection Multimodel Inference 2nd edn, Vol. 2 (Springer, Berlin, 2002).MATH 

Google Scholar  More

Fungal culture and insect rearingThe fungus F. verticillioides was isolated from sugarcane plants and cultivated in potato dextrose (PD) medium (Difco, Sparks, NV, USA) at 25 °C with a 12 h photoperiod in climatic chambers. A. nidulans (A4 strain) was used as a control because it is not involved in red rot disease. It was cultivated in minimal medium (MM) [24] and maintained in climatic chambers at 37 °C in the dark.The D. saccharalis was provided by Prof. Dr. José R. P. Parra from the University of São Paulo, Piracicaba. The caterpillars were fed an artificial diet [25] and maintained in a room under controlled conditions (temperature 25 ± 4 °C, relative humidity 60 ± 10% and 14 h of light). Adults were kept in cages covered with white paper sheets, where the eggs were deposited, collected and sanitized with 1% copper sulfate solution daily. Newly hatched caterpillars were transferred to the artificial diet [25].Olfactory preference assayFive days before the experiment, a total of 105 fungal conidia of F. verticillioides or A. nidulans were inoculated in a Falcon tube (15 mL) containing 7 mL of MM. The negative control was sterile MM. Tubes containing fungus-colonized medium and control medium were placed at opposite ends of the Petri dish (15 cm diameter) bottom, lined with moistened filter paper. A group of ten third-instar D. saccharalis caterpillars was released in the central region of the arena. The choice was quantified in the end of the experiment when the caterpillar remained in the Falcon tube to feed. The medium in the tubes represents a food source, once the caterpillars find it, they remain in the chosen tube. The Petri dishes were closed, sealed and kept in a dark room for 5 h at 25 °C; then, the number of caterpillars inside each tube was recorded. The assay was also performed using third-instar Spodoptera frugiperda, to detect specific attractiveness, and with fifth-instar D. saccharalis, to find changes in insect behavior during different immature stages.To confirm insect attraction to fungal volatiles, VOCs collected from F. verticillioides were used to attract D. saccharalis. This assay was performed as described; however, only the control medium was added to the tubes. The hexane solvent was removed from the samples using nitrogen gas and the fungal VOCs were eluted in mineral oil. In addition to the control medium, each tube contained a piece of cotton loaded with either 50 µL of an aerated sample of F. verticillioides VOCs or solvent control (mineral oil). The dishes were placed in the dark for 7 h at 25 °C. All assays were repeated 10 times. Statistical analyses were performed using t-test (p  More

1.Craik, D. J., Daly, N. L., Bond, T. & Waine, C. Plant cyclotides: A unique family of cyclic and knotted proteins that defines the cyclic cystine knot structural motif. J. Mol. Biol. 294, 1327–1336 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Gran, L. On the effect of a polypeptide isolated from “Kalata-Kalata” (Oldenlandia affinis DC) on the oestrogen dominated uterus. Acta Pharmacol. Toxicol. (Copenh) 33, 400–408 (1973).CAS 
Article 

Google Scholar 
3.Schoepke, T., Hasan Agha, M. I., Kraft, R., Otto, A. & Hiller, K. Haemolytisch aktive Komponenten aus Viola tricolor L. und Viola arvensis murray. Sci. Pharm. 61, 145–153 (1993).CAS 

Google Scholar 
4.Claeson, P., Göransson, U., Johansson, S., Luijendijk, T. & Bohlin, L. Fractionation protocol for the isolation of polypeptides from plant biomass. J. Nat. Prod. 61, 77–81 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Göransson, U., Luijendijk, T., Johansson, S., Bohlin, L. & Claeson, P. Seven novel macrocyclic polypeptides from Viola arvensis. J. Nat. Prod. 62, 283–286 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Poth, A. G. et al. Discovery of cyclotides in the Fabaceae plant family provides new insights into the cyclization, evolution, and distribution of circular proteins. ACS Chem. Biol. 6, 345–355 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Poth, A. G. et al. Cyclotides associate with leaf vasculature and are the products of a novel precursor in Petunia (Solanaceae). J. Biol. Chem. 287, 27033–27046 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Burman, R. et al. Distribution of circular proteins in plants: Large-scale mapping of cyclotides in the Violaceae. Front. Plant Sci. 6, 20 (2015).ADS 
Article 

Google Scholar 
9.Hernandez, J. F. et al. Squash trypsin inhibitors from Momordica cochinchinensis exhibit an atypical macrocyclic structure. Biochemistry 39, 5722–5730 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Nguyen, G. K. T. et al. Discovery of linear cyclotides in monocot plant Panicum laxum of Poaceae family provides new insights into evolution and distribution of cyclotides in plants. J. Biol. Chem. 288, 3370–3380 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Saether, O. et al. Elucidation of the primary and three-dimensional structure of the uterotonic polypeptide kalata B1. Biochemistry 34, 4147–4158 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Ravipati, A. S. et al. Understanding the diversity and distribution of cyclotides from plants of varied genetic origin. J. Nat. Prod. 80, 1522–1530 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Gruber, C. W. et al. Distribution and evolution of circular miniproteins in flowering plants. Plant Cell 20, 2471–2483 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Simonsen, S. M. et al. A continent of plant defense peptide diversity: Cyclotides in Australian Hybanthus (Violaceae). Plant Cell 17, 3176–3189 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Slazak, B., Jacobsson, E., Kuta, E. & Göransson, U. Exogenous plant hormones and cyclotide expression in Viola uliginosa (Violaceae). Phytochemistry 117, 527–536 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Lindholm, P. et al. Cyclotides: A novel type of cytotoxic agents. Mol. Cancer Ther. 1, 365–369 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Ovesen, R. G. et al. Biomedicine in the environment: Cyclotides constitute potent natural toxins in plants and soil bacteria. Environ. Toxicol. Chem. 30, 1190–1196 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Pränting, M., Lööv, C., Burman, R., Göransson, U. & Andersson, D. I. The cyclotide cycloviolacin O2 from Viola odorata has potent bactericidal activity against Gram-negative bacteria. J. Antimicrob. Chemother. 65, 1964–1971 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
19.Tam, J. P., Lu, Y. A., Yang, J. L. & Chiu, K. W. An unusual structural motif of antimicrobial peptides containing end-to-end macrocycle and cystine-knot disulfides. Proc. Natl. Acad. Sci. USA 96, 8913–8918 (1999).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Slazak, B. et al. How Does the sweet violet (Viola odorata L.) fight pathogens and pests—cyclotides as a comprehensive plant host defense system. Front. Plant Sci. 9, 20 (2018).Article 

Google Scholar 
21.Colgrave, M. L. et al. Anthelmintic activity of cyclotides: In vitro studies with canine and human hookworms. Acta Trop. 109, 163–166 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Jennings, C., West, J., Waine, C., Craik, D. & Anderson, M. A. Biosynthesis and insecticidal properties of plant cyclotides: The cyclic knotted proteins from Oldenlandia affinis. Proc. Natl. Acad. Sci. USA. 98, 10614–10619 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Gilding, E. K. et al. Gene coevolution and regulation lock cyclic plant defence peptides to their targets. New Phytol. 210, 717–730 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Mylne, J. S., Wang, C. K., van der Weerden, N. L. & Craik, D. J. Cyclotides are a component of the innate defense of Oldenlandia affinis. Biopolymers 94, 635–646 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Dörnenburg, H. Cyclotide synthesis and supply: From plant to bioprocess. Biopolymers 94, 602–610 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
26.Trabi, M. et al. Variations in cyclotide expression in Viola species. J. Nat. Prod. 67, 806–810 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Lista de especies silvestres de Canarias (hongos, plantas y animales terrestres). (Consejería de Política Territorial y Medio Ambiente. Gobierno de Canarias., 2001).28.Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Gómez, M. V. M., Esquivel, J. L. M., Díaz, J. R. D. & Izquierdo, M. S. Viola guaxarensis (Violaceae): A new Viola from Tenerife, Canary Islands, Spain. Willdenowia 50, 13–21 (2020).Article 

Google Scholar 
30.Rodríguez-Rodríguez, P., De Castro, A. G. F., Seguí, J., Traveset, A. & Sosa, P. A. Alpine species in dynamic insular ecosystems through time: Conservation genetics and niche shift estimates of the endemic and vulnerable Viola cheiranthifolia. Ann. Bot. 123, 505–519 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Ireland, D. C., Colgrave, M. L. & Craik, D. J. A novel suite of cyclotides from Viola odorata: Sequence variation and the implications for structure, function and stability. Biochem. J. 400, 1–12 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Burman, R., Gunasekera, S., Strömstedt, A. A. & Göransson, U. Chemistry and biology of cyclotides: Circular plant peptides outside the box. J. Nat. Prod. 77, 724–736 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Trabi, M. & Craik, D. J. Tissue-specific expression of head-to-tail cyclized miniproteins in Violaceae and structure determination of the root cyclotide Viola hederacea root cyclotide1. Plant Cell 16, 2204–2216 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Ballard, H. E., Sytsma, K. J. & Kowal, R. R. Shrinking the violets: Phylogenetic relationships of infrageneric groups in Viola (Violaceae) based on internal transcribed spacer DNA sequences. Syst. Bot. 23, 439 (1998).Article 

Google Scholar 
35.Batista, F. & Sosa, P. A. Allozyme diversity in natural populations of Viola palmensis. Webb & Berth (Violaceae) from La Palma (Canary Islands): Implications for conservation genetics. Ann. Bot. 90, 725–733 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Marcussen, T., Heier, L., Brysting, A. K., Oxelman, B. & Jakobsen, K. S. From gene trees to a dated allopolyploid network: Insights from the angiosperm genus Viola (Violaceae). Syst. Biol. 64, 84–101 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Marcussen, T., Oxelman, B., Skog, A. & Jakobsen, K. S. Evolution of plant RNA polymerase IV/V genes: Evidence of subneofunctionalization of duplicated NRPD2/NRPE2-like paralogs in Viola (Violaceae). BMC Evol. Biol. 10, 45 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Gilli, A. Viola anagae Gilli sp. Nov.. Feddes Repert. 89, 595–596 (1979).Article 

Google Scholar 
39.Moreno-Saiz, J. Lista Roja 2008 de la Flora Vascular Española (Dirección General de Medio Natural y Política Forestal, Ministerio de Medio Ambiente, y Medio Rural y Marino, y Sociedad Española de Biología de la Conservación de Plantas, 2008).
Google Scholar 
40.Broussalis, A. M. et al. First cyclotide from Hybanthus (Violaceae). Phytochemistry 58, 47–51 (2001).41.Mulvenna, J. P., Wang, C. & Craik, D. J. CyBase: A database of cyclic protein sequence and structure. Nucleic Acids Res. 34, D192–D194 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Hellinger, R. et al. Peptidomics of circular cysteine-rich plant peptides—analysis of the diversity of cyclotides from Viola tricolor by transcriptome- and proteome-mining. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.5b00681 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Slazak, B., Haugmo, T., Badyra, B. & Göransson, U. The life cycle of cyclotides: Biosynthesis and turnover in plant cells. Plant Cell Rep. 39, 1359–1367 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Colgrave, M. L., Jones, A. & Craik, D. J. Peptide quantification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry: Investigations of the cyclotide kalata B1 in biological fluids. J. Chromatogr. A 1091, 187–193 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Marcussen, T. Allozymic variation in the widespread and cultivated Viola odorata (Violaceae) in western Eurasia. Bot. J. Linn. Soc. 151, 563–571 (2006).Article 

Google Scholar 
46.Källback, P., Nilsson, A., Shariatgorji, M. & Andrén, P. E. msIQuant—quantitation software for mass spectrometry imaging enabling fast access, visualization, and analysis of large data sets. Anal. Chem. 88, 4346–4353 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Pohlert, T. PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended.48.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Media (Springer, 2009). https://doi.org/10.1007/978-0-387-98141-3.Book 
MATH 

Google Scholar 
49.R Development Core Team, R. R A Language and Environment for Statistical Computing, Vol 1 409 (R Foundation for Statistical Computing, 2011).
Google Scholar 
50.Package, T. Package ‘ PMCMRplus ’ R topics documented (2019).51.Kolde, R. pheatmap: Pretty Heatmaps. R package version 1.0.12. (2019). https://cran.r-project.org/package=pheatmap.52.Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Sigrist, C. J. A. et al. PROSITE: A documented database using patterns and profiles as motif descriptors. Brief. Bioinform. 3, 265–274 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Rice, P., Longden, I. & Bleasby, A. EMBOSS: The European molecular biology open software suite. Trends Genet. 16, 276–277 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Burman, R. et al. Cyclotide proteins and precursors from the genus Gloeospermum: Filling a blank spot in the cyclotide map of Violaceae. Phytochemistry 71, 13–20 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Levenfors, J. J., Hedman, R., Thaning, C., Gerhardson, B. & Welch, C. J. Broad-spectrum antifungal metabolites produced by the soil bacterium Serratia plymuthica A 153. Soil Biol. Biochem. 36, 677–685 (2004).CAS 
Article 

Google Scholar 
58.Broekaert, W. F., Terras, R. F. G., Cammue, B. P. A. & Vandedeyden, J. An automated quantitative assay for fungal growth inhibition. Most 69, 20 (1990).
Google Scholar 
59.CLSI. M38–A2 reference method for broth dilution antifungal susceptibility testing of filamentous fungi; approved standard—second edition. Clin. Lab. Stand. Inst. 20, 20 (2008).
Google Scholar  More

Data collectionWe extracted hourly air temperature and SH from the North America Land Data Assimilation System project46, a near real-time dataset with a 0.125° × 0.125° grid resolution. We spatially and temporally averaged these data into daily county-level records. SH is the mass of water vapor in a unit mass of moist air (g kg−1). Daily downward UV radiation at the surface, with a wavelength of 0.20–0.44 µm, was extracted from the European Centre for Medium-Range Weather Forecasts ERA5 climate reanalysis47.Other characteristics of each county, including geographic location, population density, demographic structure of the population, socioeconomic factors, proportion of healthcare workers, intensive care unit (ICU) bed capacity, health risk factors, long-term and short-term air pollution, and climate zone were collected from multiple sources. Geographic coordinates, population density, median household income, percent of people older than 60 years, percent Black residents, percent Hispanic residents, percent owner-occupied housing, percent residents aged 25 years and over without a high school diploma, and percent healthcare practitioners or support staff were collected from the U.S. Census Bureau48. Total ICU beds in each county were derived from Kaiser Health News49. The prevalence of smoking and obesity among adults in each county was obtained from the Robert Wood Johnson Foundation’s 2020 County Health Rankings50. We extracted annual PM2.5 concentrations in the U.S. from 2014 to 2018 from the 0.01° × 0.01° grid resolution PM2.5 estimation provided by the Atmospheric Composition Analysis Group51, and calculated average PM2.5 levels during this 5-year period for each county to represent long-term PM2.5 exposure (Supplementary Fig. 5). Short-term air quality data during the study period, including daily mean PM2.5 and daily maximum 8-h O3, were obtained from the United States Environmental Protection Agency52. We categorized study counties into one of five climate zones based on the guide released by U.S. Department of Energy53 (Supplementary Fig. 6).The county-level COVID-19 case and death data were downloaded from the John Hopkins University Coronavirus Resource Center1. The U.S. county-to-county commuting data were available from the U.S. Census Bureau48. Daily numbers of inter-county visitors to points of interest (POI) were provided by SafeGraph54.Data ethicsSafeGraph utilizes data from mobile applications of which users optionally consent to provide their anonymous location data.Estimation of reproduction numberWe estimated the daily reproduction number (Rt) in all 3142 U.S. counties using a dynamic metapopulation model informed by human mobility data31,55. Rt is the mean number of new infections caused by a single infected person, given the public health measures in place, in a population in which everyone is assumed to be susceptible. In the metapopulation model, two types of movement were considered: daily work commuting and random movement. During the daytime, some commuters travel to a county other than their county of residence, where they work and mix with the populations of that county; after work, they return home and mix with individuals in their home, residential county. Apart from regular commuting, a fraction of the population in each county, assumed to be proportional to the number of inter-county commuters, travels for purposes other than work. As the population present in each county is different during daytime and night-time, we modelled the transmission dynamics of COVID-19 separately for these two time periods, each depicted by a set of ordinary differential equations (Supplementary Notes).To account for case underreporting, we explicitly simulated reported and unreported infections, for which separate transmission rates were defined. Recent studies from several countries indicate that asymptomatic cases of COVID-19, which are typically unreported, are less contagious than symptomatic cases56,57,58,59. Studies on the early transmission of SARS-CoV-2 in China18 and the U.S.60 also showed that undocumented infections are less transmissible than documented infections.In order to reflect the spatiotemporal variation of disease transmission rate and reporting, we allowed transmission rates and ascertainment rates to vary across counties and to change over time. The transmission model simulated daily confirmed cases and deaths for each county. To map infections to deaths, we used an age-stratified infection fatality rate (IFR)61 and computed the weekly IFR for each county as a weighted average using state-level age structure of confirmed cases reported by the U.S. Centers for Disease Control and Prevention. We further adjusted for reporting lags using an observational delay model informed by a U.S. line-list COVID-19 data record62.For the period prior to March 15, 2020, we used commuting data from the U.S. census survey to prescribe the inter-county movement in the transmission model48. Starting March 15, the census survey data are no longer representative due to changes in mobility behavior following the implementation of non-pharmaceutical interventions. We, therefore, used estimates of the reduction of inter-county visitors to POI (e.g., restaurants, stores, etc.) from SafeGraph54 to account for the change in inter-county movement on a county-by-county basis. Because there is no direct relationship between population-level mobility patterns and COVID-19 transmission rates63, we did not model local transmission rate as a function of inter-county mobility. Instead, the SafeGraph data were only used to inform the change of population mixing across counties.To infer key epidemiological parameters, we fitted the transmission model to county-level daily cases and deaths reported from March 15, 2020 to December 31, 2020. The estimated reproduction number was computed as follows:$${R}_{t}=beta Dleft[alpha +left(1-alpha right)mu right],$$
(1)
where β is the county-specific transmission rate, μ is the relative transmissibility of unreported infections, α is the county-specific ascertainment rate, and D is the average duration of infectiousness. Note (beta) and (alpha) were defined for each county separately and were allowed to vary over time. Unlike previous studies using effective reproduction number$${R}_{e}=beta Dleft[alpha +left(1-alpha right)mu right]s,$$
(2)
where s is the estimated local population susceptibility, we used reproduction number Rt to exclude the influence of population susceptibility on disease transmission rate.D, (mu), (Z) (the average latency period from infection to contagiousness), and a multiplicative factor adjusting random movement ((theta)) were randomly drawn from the posterior distributions inferred from case data through March 13, 202060: (D=3.56) (3.21–3.83), (mu =0.64) (0.56–0.70), (Z=3.59) (95% CI: 3.28–3.99), and (theta =0.15) (0.12–0.17). (Z) and (theta) are used in ordinary differential equations used to model transmission dynamics (Supplementary Notes).The daily transmission rate (beta) and ascertainment rate (alpha) were estimated sequentially for each county using the ensemble adjustment Kalman filter (EAKF)64. Specifically, parameters ({beta }_{i}) and ({alpha }_{i}) for county (i) were updated each day using incidence and death data. We used the estimates on day (t-1) as the prior parameters on day (t), and then updated the priors to posteriors using the EAKF and observations. The posteriors are the estimated parameter values on day (t). To ensure a smooth parameter estimation, we imposed a (pm 30 %) limit on the daily change of parameters ({beta }_{i}) and ({alpha }_{i}). Other smoothing constraints were tested and the results were similar. To avoid possible inaccurate estimation for counties with few cases, we inferred Rt in the 2669 U.S. counties with at least 400 cumulative confirmed cases as of December 31, 2020 (Supplementary Fig. 7).Statistical analysisAll statistical analyses were conducted with R software (version 3.6.1) using the mgcv and dlnm packages.Association between meteorological factors and R
t
Given the potential non-linear and temporally delayed effects of meteorological factors, a distributed lag non-linear model65 combined with generalized additive mixed models66 was applied to estimate the associations of daily mean temperature, daily mean SH, and daily mean UV radiation with SARS-CoV-2 Rt. To quantify the total contribution, independent effects, and relative importance of meteorological factors (i.e., temperature, SH, and UV radiation), we included all three variables in the same model. To reduce collinearity, we used cross-basis terms rather than the raw variables (Supplementary Tables 5–6). The full model can be expressed as:$$log (E({{{R}}}_{i,j,t}))= alpha +te(s({{rm{latitude}}}_{i}{,{rm{longitude}}}_{i},{rm{k}}=200),s({{rm{time}}}_{t},{rm{k}}=30))+{rm{cb}}.{rm{temperature}}+{rm{cb}}.{rm{SH}}+ {rm{cb}}.{rm{UV}}\ +{beta }_{1}({rm{population}},{rm{density}}_{i})+{beta }_{2}({rm{percent}},{rm{Black}},{rm{residents}}_{i})+{beta }_{3}({rm{percent}},{rm{Hispanic}},{rm{residents}}_{i})\ +{beta }_{4}({rm{percent}},{rm{people}},{rm{older}},{rm{than}},60,{rm{years}}_{i})+{beta }_{5}({rm{median}},{rm{household}},{rm{income}}_{i})\ +{beta }_{6}({rm{percent}},{rm{owner}}-{rm{occupied}},{rm{housing}}_{i})\ +{beta }_{7}({rm{percent}},{rm{residents}},{rm{older}},{rm{than}},25,{rm{years}},{rm{without}},{rm{a}},{rm{high}},{rm{school}},{rm{diploma}}_{i})\ +{beta }_{8}({rm{number}},{rm{of}},{rm{ICU}},{rm{beds}},{rm{per}},10,000,{rm{people}}_{i})+{beta }_{9}({rm{percent}},{rm{healthcare}},{rm{workers}}_{i})\ quad , {beta }_{10}({rm{day}},{rm{when}},100,{rm{cumulative}},{rm{cases}},{rm{per}},100,000,{rm{people}},{rm{was}},{rm{reached}}_{i})+{re}({rm{county}}_{i})+{re}({rm{state}}_{j})$$
(3)
where E(Ri,j,t) refers to the expected Rt in county i, state j, on day t, and α is the intercept. Given the distribution of Rt in our data close to a lognormal distribution (Supplementary Fig. 8), we used log-transformed Rt as the outcome variable, and the Gaussian family in the model. A thin plate spline with a maximum of 200 knots was used to control the coordinates of the centroid of each county; the time trend was controlled by a flexible natural cubic spline over the range of study dates with a maximum of 30 knots; due to the unique pattern of the non-linear time trend of Rt in each county (Supplementary Fig. 4), we constructed tensor product smooths (te) of the splines of geographical coordinates and time, to better control for the temporal and spatial variations (Supplementary Fig. 3).Cb.temperature, cb.SH, and cb.UV are cross-basis terms for the mean air temperature, mean SH and mean UV radiation, respectively. We modeled exposure-response associations (meteorological factors vs. percent change in Rt) using a natural cubic spline with 3 degrees of freedom (df) and modeled the lag-response association using a natural cubic spline with an intercept and 3 df with a maximum lag of 13 days. We adjusted for county-level characteristics, including population density, percent Black residents, percent Hispanic residents, percent people older than 60 years, median household income, percent owner-occupied housing, percent residents older than 25 years without a high school diploma, number of ICU beds per 10,000 people, and percent healthcare workers, given their potential relationship with SARS-CoV-2 transmission67,68,69,70. Day when 100 cumulative cases per 100,000 people was reached in each county was used to approximate local epidemic stage45 (Supplementary Fig. 9). The random effects of state and county were modeled by parametric terms penalized by a ridge penalty (re), to further control for unmeasured state- and county-level confounding. Residual plots were used to diagnose the model (Supplementary Fig. 10). In additional analyses, we included air temperature, SH, and UV radiation in separate models (Supplementary Fig. 2).Based on the estimated exposure-response curves, between the 1st and the 99th percentiles of the distribution of air temperature, SH, and UV radiation, we determined the value of exposure associated with the lowest relative risk of Rt to be the optimum temperature, the optimum SH, or the optimum UV radiation, respectively. The natural cubic spline functions of the exposure-response relationship were then re-centered with the optimum values of meteorological factors as reference values. We report the cumulative relative risk of Rt associated with daily temperature, SH, or UV radiation exposure in the previous two weeks (0– 13 lag days) as the percent changes in Rt when comparing the daily exposure with the optimum reference values (i.e., the cumulative relative risk of Rt equals one and the percent change in Rt equals zero when the temperature, SH, or UV radiation exposure is at its optimum value).Attribution of R
t to meteorological factorsWe used the optimum value of temperature, SH, or UV radiation as the reference value for calculating the fraction of Rt attributable to each meteorological factor; i.e., the attributable fraction (AF). For these calculations, we assumed that the associations of meteorological factors with Rt were consistent across the counties. For each day in each county, based on the cumulative lagged effect (cumulative relative risk) corresponding to the temperature, SH, or UV radiation of that day, we calculated the attributable Rt in the current and next 13 days, using a previously established method71. Specifically, in a given county, the Rt attributable to a meteorological factor (xt) for a given day t was defined as the attributable absolute excess of Rt (AEx,t, the excess reproduction number on day t attributable to the deviation of temperature or SH from the optimum value) and the attributable fraction of Rt (AFx,, the fraction of Rt attributable to the deviation of the meteorological factor from its optimum value), each accumulated over the current and next 13 days. The formulas can be expressed as:$${{AF}}_{x,t}=1-{rm{exp }}left(-mathop{sum }limits_{l=0}^{13}{beta }_{{x}_{t},l}right)$$
(4)
$${{AE}}_{x,t}={{AF}}_{x,t}times mathop{sum }limits_{l=0}^{13}frac{{n}_{t+1}}{13+1},$$
(5)
where nt is the Rt on day t, and ({sum }_{l=0}^{13}{beta }_{{x}_{t},l}) is the overall cumulative log-relative risk for exposure xt on day t obtained by the exposure-response curves re-centered on the optimum values. Then, the total absolute excess of Rt attributable to temperature, SH, or UV radiation in each county was calculated by summing the absolute excesses of all days during the study period, and the attributable fraction was calculated by dividing the total absolute excess of Rt for the county by the sum of the Rt of all days during the study period for the county. The attributable fraction for the 2669 counties combined was calculated in a similar manner at the national level. We derived the 95% eCI for the attributable absolute excess and attributable fraction by 1000 Monte Carlo simulations71. The total fraction of Rt attributable to meteorological factors was the sum of the attributable fraction for temperature, SH, and UV radiation. We also calculated the attributable fractions by month in the study period.Sensitivity analysesWe conducted several sensitivity analyses to test the robustness of our results: (a) the lag dimension was redefined using a natural cubic spline and three equally placed internal knots in the log scale; (b) an alternative four df was used in the cross-basis term for meteorological factors in the exposure-response function; (c) the maximum number of knots was reduced to 25 in the flexible natural cubic spline to control time trend in the tensor product smooths; (d) all demographic and socioeconomic variables were excluded from the model; (e) adjustment for the prevalence of smoking and obesity among adults was included in the model; (f) adjustment for climate zone was included in the model; (g) additional adjustment was made for the average PM2.5 concentration in each county during 2014–201845; (h) additional adjustment was made for daily mean PM2.5, and daily maximum 8-h O3. For daily covariates with available data in only some of the counties or study period, the results of sensitivity analyses were compared to the main model re-run on the same partial dataset.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Crick FHC, Barnett FRSL, Brenner S, Watts-Tobin RJ. General Nature of the Genetic Code for Proteins. Nature. 1961;192:1227–32.CAS 
PubMed 
Article 

Google Scholar 
2.Hershey AD, Chase M. Independent functions of viral protein and nucleic acid in growth of bacteriophage. J Gen Physiol. 1952;36:39–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Luria S, Delbrück M. Mutations of Bacteria from Virus Sensitivity to Virus Resistance. Genetics. 1943;28:491–511.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Kortright KE, Chan BK, Koff JL, Turner PE. Phage Therapy: a Renewed Approach to Combat Antibiotic-Resistant Bacteria. Cell Host Microbe. 2019;25:219–32.CAS 
PubMed 
Article 

Google Scholar 
5.Mushegian AR. Are there 10^31 virus particles on Earth, or more, or less? J Bacteriol. 2020;202:e00052–20.PubMed 
PubMed Central 
Article 

Google Scholar 
6.Dennehy JJ. What Can Phages Tell Us about Host-Pathogen Coevolution? Int J Evol Biol. 2012;2012:1–12.Article 

Google Scholar 
7.Jessup CM, Kassen R, Forde SE, Kerr B, Buckling A, Rainey PB, et al. Big questions, small worlds: microbial model systems in ecology. Trends Ecol Evol. 2004;19:189–97.PubMed 
Article 

Google Scholar 
8.Tecon R, Mitri S, Ciccarese D, Or D, Meer JR, van der, Johnson DR. Bridging the Holistic-Reductionist Divide in Microbial Ecology. MSystems. 2019;4:e00265–18.PubMed 
PubMed Central 
Article 

Google Scholar 
9.Bohannan BJM, Lenski RE. Linking genetic change to community evolution: insights from studies of bacteria and bacteriophage. Ecol Lett. 2000;3:362–77.Article 

Google Scholar 
10.Buckling A, Brockhurst MA. Bacteria-Virus Coevolution. In: Orkun S Soyer, editor. Evolutionary Systems Biology. 2012. New York, NY: Springer; 2012. p. 347–70.11.Koskella B, Brockhurst MA. Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol Rev. 2014;38:1–16.Article 
CAS 

Google Scholar 
12.De Sordi L, Lourenço M, Debarbieux L. The Battle Within: interactions of Bacteriophages and Bacteria in the Gastrointestinal Tract. Cell Host Microbe. 2019;25:210–8.PubMed 
Article 
CAS 

Google Scholar 
13.Scanlan PD. Bacteria–Bacteriophage Coevolution in the Human Gut: implications for Microbial Diversity and Functionality. Trends Microbiol. 2017;25:614–23.CAS 
PubMed 
Article 

Google Scholar 
14.Breitbart M. Marine viruses: truth or dare. Annu Rev Mar Sci. 2012;4:425–48.Article 

Google Scholar 
15.Pratama AA, van Elsas JD. The ‘neglected’ soil virome–potential role and impact. Trends Microbiol. 2018;26:649–62.CAS 
PubMed 
Article 

Google Scholar 
16.Lourenço M, De Sordi L, Debarbieux L. The diversity of bacterial lifestyles hampers bacteriophage tenacity. Viruses. 2018;10:1–11.Article 
CAS 

Google Scholar 
17.Martiny JBH, Riemann L, Marston MF, Middelboe M. Antagonistic Coevolution of Marine Planktonic Viruses and Their Hosts. Annu Rev Mar Sci. 2014;6:393–414.Article 

Google Scholar 
18.Díaz-Muñoz SL, Koskella B. Bacteria–Phage Interactions in Natural Environments. In: Sariaslani S, Gadd GM, editors. Advances in Applied Microbiology. Cambridge, MA:Academic Press; 2014. p.135–83.19.Avrani S, Schwartz DA, Lindell D. Virus-host swinging party in the oceans. Mob Genet Elem. 2012;2:88–95.Article 

Google Scholar 
20.Winter C, Bouvier T, Weinbauer MG, Thingstad TF. Trade-Offs between Competition and Defense Specialists among Unicellular Planktonic Organisms: the “Killing the Winner” Hypothesis Revisited. Microbiol Mol Biol Rev. 2010;74:42–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Hansen MF, Svenningsen SL, Røder HL, Middelboe M, Burmølle M. Big Impact of the Tiny: bacteriophage–bacteria Interactions in Biofilms. Trends Microbiol. 2019;27:739–52.CAS 
PubMed 
Article 

Google Scholar 
22.O’Brien S, Hodgson DJ, Buckling A. The interplay between microevolution and community structure in microbial populations. Curr Opin Biotechnol. 2013;24:821–5.PubMed 
Article 
CAS 

Google Scholar 
23.Brockhurst MA, Koskella B. Experimental coevolution of species interactions. Trends Ecol Evol. 2013;28:367–75.PubMed 
Article 

Google Scholar 
24.Geredew Kifelew L, Mitchell JG, Speck P. Mini-review: efficacy of lytic bacteriophages on multispecies biofilms. Biofouling. 2019;35:472–81.CAS 
PubMed 
Article 

Google Scholar 
25.Miki T, Jacquet S. Complex interactions in the microbial world: Underexplored key links between viruses, bacteria and protozoan grazers in aquatic environments. Aquat Micro Ecol. 2008;51:195–208.Article 

Google Scholar 
26.Johnke J, Cohen Y, de Leeuw M, Kushmaro A, Jurkevitch E, Chatzinotas A. Multiple micro-predators controlling bacterial communities in the environment. Curr Opin Biotechnol. 2014;27:185–90.CAS 
PubMed 
Article 

Google Scholar 
27.Hall AR, Ashby B, Bascompte J, King KC. Measuring Coevolutionary Dynamics in Species-Rich Communities. Trends Ecol Evol. 2020;35:539–50.PubMed 
Article 

Google Scholar 
28.Strauss SY. Ecological and evolutionary responses in complex communities: implications for invasions and eco-evolutionary feedbacks. Oikos. 2014;123:257–66.Article 

Google Scholar 
29.Strauss SY, Irwin RE. Ecological and evolutionary consequences of multispecies plant-animal interactions. Annu Rev Ecol Evol Syst. 2004;35:435–66.Article 

Google Scholar 
30.Inouye B, Stinchcombe JR. Relationships between ecological interaction modifications and diffuse coevolution: similarities, differences, and causal links. Oikos. 2011;95:353–60.Article 

Google Scholar 
31.Barraclough TG. How Do Species Interactions Affect Evolutionary Dynamics Across Whole Communities? Annu Rev Ecol Evol Syst. 2015;46:25–48.Article 

Google Scholar 
32.Bottery MJ, Pitchford JW, Friman V-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 2021;15:939–48.PubMed 
Article 

Google Scholar 
33.Gómez P, Bennie J, Gaston KJ, Buckling A. The Impact of Resource Availability on Bacterial Resistance to Phages in Soil. PLoS ONE. 2015;10:e0123752.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Gorter FA, Scanlan PD, Buckling A. Adaptation to abiotic conditions drives local adaptation in bacteria and viruses coevolving in heterogeneous environments. Biol Lett. 2016;12:20150879.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Scanlan JG, Hall AR, Scanlan PD. Impact of bile salts on coevolutionary dynamics between the gut bacterium Escherichia coli and its lytic phage PP01. Infect Genet Evol. 2019;73:425–32.CAS 
PubMed 
Article 

Google Scholar 
36.Gómez P, Buckling A. Bacteria-phage antagonistic coevolution in soil. Science. 2011;332:106–9.PubMed 
Article 
CAS 

Google Scholar 
37.Weinbauer MG, Rassoulzadegan F. Are viruses driving microbial diversification and diversity? Environ Microbiol. 2004;6:1–11.PubMed 
Article 

Google Scholar 
38.Johnke J, Baron M, de Leeuw M, Kushmaro A, Jurkevitch E, Harms H, et al. A generalist protist predator enables coexistence in multitrophic predator-prey systems containing a phage and the bacterial predator Bdellovibrio. Front Ecol Evol. 2017;5:1–12.Article 

Google Scholar 
39.R Core Team. R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.40.Mumford R, Friman VP. Bacterial competition and quorum-sensing signalling shape the eco-evolutionary outcomes of model in vitro phage therapy. Evol Appl. 2017;10:161–9.CAS 
PubMed 
Article 

Google Scholar 
41.Connell JH. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology. 1961;42:710–23.Article 

Google Scholar 
42.Vellend M. Conceptual Synthesis in Community Ecology. Q Rev Biol. 2010;85:183–206.PubMed 
Article 

Google Scholar 
43.Alseth EO, Pursey E, Lujan AM, McLeod I, Rollie C, Westra ER. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance in Pseudomonas aeruginosa. Nature. 2019;574:549–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Goldhill DH, Turner PE. The evolution of life history trade-offs in viruses. Curr Opin Virol. 2014;8:79–84.PubMed 
Article 

Google Scholar 
45.Keen EC. Tradeoffs in bacteriophage life histories. Bacteriophage. 2014;4:e28365.PubMed 
PubMed Central 
Article 

Google Scholar 
46.Gómez P, Buckling A. Real-time microbial adaptive diversification in soil. Ecol Lett. 2013;16:650–5.PubMed 
Article 

Google Scholar 
47.Houte S, van, Buckling A, Westra ER. Evolutionary Ecology of Prokaryotic Immune Mechanisms. Microbiol Mol Biol Rev. 2016;80:745–63.PubMed 
PubMed Central 
Article 

Google Scholar 
48.Middelboe M, Hagström A, Blackburn N, Sinn B, Fischer U, Borch NH, et al. Effects of bacteriophages on the population dynamics of four strains of pelagic marine bacteria. Micro Ecol. 2001;42:395–406.CAS 
Article 

Google Scholar 
49.Gómez P, Buckling A. Coevolution with phages does not influence the evolution of bacterial mutation rates in soil. ISME J. 2013;7:2242–4.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.De Sordi L, Khanna V, Debarbieux L. The Gut Microbiota Facilitates Drifts in the Genetic Diversity and Infectivity of Bacterial Viruses. Cell Host Microbe. 2017;22:801–8.e3.CAS 
PubMed 
Article 

Google Scholar 
51.De Sordi L, Lourenço M, Debarbieux L. “I will survive”: A tale of bacteriophage-bacteria coevolution in the gut. Gut Microbes. 2019;10:92–9.CAS 
PubMed 
Article 

Google Scholar 
52.Landsberger M, Gandon S, Meaden S, Chabas H, Buckling A, Westra ER, et al. Anti-CRISPR phages cooperate to overcome CRISPR-Cas immunity. Cell. 2018;174:908–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Westra ER, van Houte S, Oyesiku-Blakemore S, Makin B, Broniewski JM, Best A, et al. Parasite exposure drives selective evolution of constitutive versus inducible defense. Curr Biol. 2015;25:1043–9.CAS 
PubMed 
Article 

Google Scholar 
54.Dy RL, Richter C, Salmond GP, Fineran PC. Remarkable mechanisms in microbes to resist phage infections. Annu Rev Virol. 2014;1:307–31.PubMed 
Article 
CAS 

Google Scholar 
55.Rostøl JT, Marraffini L. (Ph)ighting phages: how bacteria resist their parasites. Cell Host Microbe. 2019;25:184–94.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Burmeister AR, Turner PE. Trading-off and trading-up in the world of bacteria–phage evolution. Curr Biol. 2020;30:R1120–R1124.CAS 
PubMed 
Article 

Google Scholar 
57.Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Vienna, Austria: Proc. 3rd Int. Workshop Distrib. Stat. Comput; 2003. p. 1–10.58.Wickham H. ggplot2: elegant Graphics for Data Analysis. Verlag New York: Springer; 2016.59.Wickham H. tidyr: Tidy Messy Data. 2020.60.Plummer M. rjags: Bayesian Graphical Models using MCMC. 2019.61.Wickham H, François R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation. 2020.62.Gandon S, Buckling A, Decaestecker E, Day T. Host-parasite coevolution and patterns of adaptation across time and space. J Evol Biol. 2008;21:1861–6.CAS 
PubMed 
Article 

Google Scholar  More

Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyVeronika EyringInstitute of Environmental Physics (IUP), University of Bremen, Bremen, GermanyVeronika EyringCivil Engineering and Earth Sciences, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, IndiaVimal MishraNorwegian Polar Institute, FRAM – High North Research Centre on Climate and the Environment, Tromsø, NorwayGary P. GriffithLevin Lab, Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USAGary P. GriffithKey Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, ChinaLei ChenDepartment of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USATrevor KeenanEcology and Evolutionary Biology Department, University of Colorado, Boulder, CO, USAMerritt R. TuretskyDepartment of Life and Environmental Sciences, Bournemouth University, Poole, UKSally BrownAustralian National University, Crawford School of Public Policy, Canberra, Australian Capital Territory, AustraliaFrank JotzoEnvironmental Science and Policy, University of California, Davis, Davis, CA, USAFrances C. MooreDepartment of Psychology, School of Biological Sciences, University of Cambridge, Cambridge, UKSander van der Linden More