Ecology

Case dataMedically attended influenza B cases in New Zealand were identified from samples taken from patients with influenza-like illness (ILI) attended by a network of general practitioners recruited for surveillance (2430 cases with an identified influenza B lineage) and from non-surveillance hospital samples (1606 cases with an identified lineage) analyzed by regional diagnostic laboratories and by the World Health Organization (WHO) National Influenza Centre at the Institute for Environmental Science and Research (ESR). Briefly, general practice surveillance operates from May to September, with participating practices collecting nasopharyngeal or throat swabs from the first ILI patient examined on each Monday, Tuesday, and Wednesday. ILI is defined as an “acute respiratory tract infection characterized by an abrupt onset of at least two of the following: fever, chills, headache, and myalgia”38. A subset of the New Zealand data (cases from 2002 to 2013) was previously compiled by Vijaykrishna et al.28 along with cases from Australia reported to the WHO Collaborating Centre for Reference and Research on Influenza in Melbourne.Statistical model of influenza B susceptibility based on infection historyFor lineage V (B/Victoria), we modeled the number of cases in people born in birth year b observed in season y as a multinomial draw with probabilities given by:$${theta }_{V}(b,y)=D(b,y)beta (b,y){Z}_{V}(b,y)rho (b,y)$$
(1)
with an analogous equation defining the multinomial distribution θY(b, y) for lineage Y (B/Yamagata). D(b, y) is the fraction of the population that was born in year b as of observation season y. Z(b, y) is the susceptibility to lineage V during season y of a person born in year b relative to that of an unexposed person. β(b, y) is a baseline probability of infection with influenza B that captures differences in transmission associated with age (thus depending on b and y) and is equal to β1 if people born in year b are in preschool during season y (0–5 years old), β2 if they are school-age children or teenagers (6–17 years old), or β3 if they are 18 or older. ρ(b,y) is an age-specific factor equal to a parameter ρ if people are  0. Letting α1 and α2 be the instantaneous attack rates for preschoolers and school-age children:$${P}_{mathrm{N}}(A)=left{begin{array}{ll}{e}^{-{alpha }_{1}(A-m)}frac{(1-{e}^{-{alpha }_{1}})}{{alpha }_{1}},hfill&,{text{if}},A; le; {A}_{mathrm{s}}\ {e}^{-{alpha }_{1}({A}_{mathrm{s}}-m)}{e}^{-{alpha }_{2}(A-{A}_{mathrm{s}})}frac{(1-{e}^{-{alpha }_{2}})}{{alpha }_{2}},&,{text{if}},A; > ; {A}_{mathrm{s}}end{array}right.$$
(26)
where As is the age at which children start going to school (4 years old in the Netherlands69). It is noteworthy that for school-age children (the equation for A  > As on the bottom), the correction term for uncertainty in sampling is not necessary for the time spent in preschool (assumed to be exactly As years), only for the time after preschool (A − As).Handling cases with missing lineage informationWe assumed cases with missing lineage information in 2002 (n = 61), 2011 (n = 312), and 2019 (n = 206) belonged to B/Victoria, as 99% or more of identified cases in those seasons were B/Victoria (86/87 cases in 2002, 276/280 cases in 2011, and 552/552 cases in 2019) as were 94%, 92%, and 92%, respectively, of isolates from sequence databases (for Australia and New Zealand combined). We assumed cases with missing lineage information belonged to B/Yamagata in 2013 (n = 37), 2014 (n = 77), and 2017 (n = 87), when the majority of identified cases were B/Yamagata (268/272, 131/138, and 473/489, respectively), as were 99%, 94%, and 84%, respectively, of isolates in sequence databases. Unidentified cases in other seasons were disregarded, because both lineages were present at higher frequencies among identified cases. Removing unidentified cases altogether in all seasons led to similar parameter estimates.Sequence divergence analysisTo estimate the amount of evolution within and between lineages, we analyzed all complete HA and NA sequences from human influenza B isolates available on GISAID in July 2019. The set of isolates used in this analysis differs from the set used to estimate lineage frequencies, because we required isolates to have complete sequences (although not all sequences listed as complete on GISAID were in fact complete). Two isolates collected in 2000 (B/Hong Kong/548/2000 and B/Victoria/504/2000) were deposited as B/Victoria but our BLAST assignment indicated they were in fact B/Yamagata (their low divergence from B/Yamagata strains was a clear outlier). NA sequences from isolates B/Kanagawa/73 and B/Ann Arbor/1994 were only small fragments (99 and 100 amino acids long) poorly aligned with other sequences and were thus excluded. We also excluded NA sequences from B/Yamagata isolates B/Catalonia/NSVH100773835/2018 and B/Catalonia/NSVH100750997/2018, because they were extremely diverged (60% and 38%) from the reference strain B/Yamagata/16/88 and aligned poorly with other sequences.To compare sequence diversity within and between lineages over time, we aligned sequences using MAFFT v. 7.31070 and calculated percent amino acid differences in pairs of sequences from the same lineage and in pairs with one sequence from each. For each year, we sampled 100 sequences from each lineage (or used all sequences if 100 or fewer were available) to limit the number of pairwise calculations. To estimate how much B/Yamagata and B/Victoria evolved since the late 1980s, we calculated percent amino acid differences between each B/Yamagata and B/Victoria sequence, and the corresponding HA and NA sequences of reference strains B/Yamagata/16/88 and B/Victoria/2/87. Unlike in the analysis of pairwise divergence within each time point, we used all sequences from each lineage in each year. We excluded sites in which one or both sequences had gaps or ambiguous amino acids.To compare HA and NA divergence between influenza B lineages with divergence between influenza A subtypes, we downloaded complete HA and NA sequences from H3N2 and H1N1 isolated since 1977 and available on GISAID in August 2019. Homologous sites in the HA of H3N2 and H1N1 are difficult to identify by conventional sequence alignment, and instead we used the algorithm by Burke and Smith71 implemented on the Influenza Research Database website72. Both H3N2 and H1N1 sequences were aligned with the reference H3N2 sequence A/Aichi/2/68. We verified that this method matched sites on the stalk and head of the H1N1 HA with sites on the stalk and head of H3N2 HA by comparing the resulting alignment with the alignment in Supplementary Fig. S2 of Kirkpatrick et al.73. To limit the total number of influenza A sequences analyzed, we randomly selected 100 H3N2 and 100 H1N1 sequences for years in which more than 100 sequences were available, and used all available sequences for the remaining years. Isolates A/Canterbury/58/2000, A/Canterbury/87/2000, and A/Canterbury/55/2000 were excluded, because both H1N1-like and H3N2-like sequences were available under the same isolate name on GISAID.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science—Policy Platform on Biodiversity and Ecosystem Services (eds Brondizio, E. S. et al.) (IPBES Secretariat, 2019).2.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Biodiversity Synthesis ed Ma (World Resources Institute, 2005). http://www.loc.gov/catdir/toc/ecip0512/2005013229.html. Accessed June 2019.3.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486(7401), 105 (2012).ADS 
CAS 
Article 

Google Scholar 
4.Vellend, M. et al. Homogenization of forest plant communities and weakening of species–environment relationships via agricultural land use. J. Ecol. 95(3), 565–573. https://doi.org/10.1111/j.1365-2745.2007.01233.x (2007).Article 

Google Scholar 
5.Karp, D. S. et al. Intensive agriculture erodes β-diversity at large scales. Ecol. Lett. 15(9), 963–970. https://doi.org/10.1111/j.1461-0248.2012.01815.x (2012).Article 
PubMed 

Google Scholar 
6.Anderson, M. J. et al. Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecol. Lett. 14(1), 19–28. https://doi.org/10.1111/j.1461-0248.2010.01552.x (2011).ADS 
Article 
PubMed 

Google Scholar 
7.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31(1), 67–80. https://doi.org/10.1016/j.tree.2015.11.005 (2016).Article 
PubMed 

Google Scholar 
8.Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33(7), 549–564 (2018).Article 

Google Scholar 
9.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85(2), 183–206 (2010).Article 

Google Scholar 
10.Wang, S., Lamy, T., Hallett, L. M. & Loreau, M. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: Linking theory to data. Ecography (Cop) 42(6), 1200–1211. https://doi.org/10.1111/ecog.04290 (2019).Article 

Google Scholar 
11.Olden, J. D. Biotic homogenization: A new research agenda for conservation biogeography. J. Biogeogr. 33(12), 2027–2039. https://doi.org/10.1111/j.1365-2699.2006.01572.x (2006).Article 

Google Scholar 
12.Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. 100(22), 12765–12770 (2003).ADS 
CAS 
Article 

Google Scholar 
13.Harrison, S. Species Diversity, Spatial Scale, and Global Change (Sinauer Sunderland, 1993).
Google Scholar 
14.Sax, D. F. & Gaines, S. D. Species diversity: From global decreases to local increases. Trends Ecol. Evol. 18(11), 561–566 (2003).Article 

Google Scholar 
15.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: Consolidation and progress in functional biodiversity research. Ecol. Lett. 12(12), 1405–1419 (2009).Article 

Google Scholar 
16.Magurran, A. E. & McGill, B. J. Biological Diversity: Frontiers in Measurement and Assessment (Oxford University Press, 2010).
Google Scholar 
17.Usseglio, P. Quantifying reef fishes: Bias in observational approaches. In Ecology of Fishes on Coral Reefs (ed Mora, C.) 270–273 (Cambridge University Press, 2015). https://www.cambridge.org/core/books/ecology-of-fishes-on-coral-reefs/quantifying-reef-fishes-bias-in-observational-approaches/660760F9E62CC61DEB48C8124AD44CDC. Accessed June 2019.18.Caldwell, Z. R., Zgliczynski, B. J., Williams, G. J. & Sandin, S. A. Reef Fish survey techniques: Assessing the potential for standardizing methodologies. PLoS One 11(4), e0153066. https://doi.org/10.1371/journal.pone.0153066 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314(5800), 787–790 (2006).ADS 
CAS 
Article 

Google Scholar 
20.Barbier, E. B. Marine ecosystem services. Curr. Biol. 27(11), R507–R510 (2017).CAS 
Article 

Google Scholar 
21.Goodwin, K. D. et al. DNA sequencing as a tool to monitor marine ecological status. Front. Mar. Sci. 4, 107. https://doi.org/10.3389/fmars.2017.00107 (2017).Article 

Google Scholar 
22.Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895. https://doi.org/10.1111/mec.14350 (2017).Article 
PubMed 

Google Scholar 
23.Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21(8), 2045–2050. https://doi.org/10.1111/j.1365-294X.2012.05470.x (2012).CAS 
Article 
PubMed 

Google Scholar 
24.Creer, S. et al. The ecologist’s field guide to sequence-based identification of biodiversity. Methods Ecol. Evol. 7(9), 1008–1018. https://doi.org/10.1111/2041-210X.12574 (2016).Article 

Google Scholar 
25.Stat, M. et al. Ecosystem biomonitoring with eDNA: Metabarcoding across the tree of life in a tropical marine environment. Sci. Rep. 7(1), 12240. https://doi.org/10.1038/s41598-017-12501-5 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Bakker, J. et al. Environmental DNA reveals tropical shark diversity in contrasting levels of anthropogenic impact. Sci. Rep. 7(1), 16886. https://doi.org/10.1038/s41598-017-17150-2 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25(2), 527–541. https://doi.org/10.1111/mec.13481 (2016).CAS 
Article 
PubMed 

Google Scholar 
28.Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS One 12(4), e0176343. https://doi.org/10.1371/journal.pone.0176343 (2017).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368. https://doi.org/10.1038/srep40368 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.O’Donnell, J. L. et al. Spatial distribution of environmental DNA in a nearshore marine habitat. PeerJ 5, e3044. https://doi.org/10.7717/peerj.3044 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Jeunen, G.-J. et al. Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement. Mol. Ecol. Resour. 19(2), 426–438. https://doi.org/10.1111/1755-0998.12982 (2019).CAS 
Article 
PubMed 

Google Scholar 
32.Stat, M. et al. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv. Biol. 33(1), 196–205 (2019).Article 

Google Scholar 
33.West, K. M. et al. eDNA metabarcoding survey reveals fine-scale coral reef community variation across a remote, tropical island ecosystem. Mol. Ecol. 29(6), 1069–1086. https://doi.org/10.1111/mec.15382 (2020).CAS 
Article 
PubMed 

Google Scholar 
34.Graham, H. M. Effects of local deforestation on the diversity and structure of Southern California giant kelp forest food webs. Ecosystems 7(4), 341–357. https://doi.org/10.1007/s10021-003-0245-6 (2004).Article 

Google Scholar 
35.Miller, R. J. et al. Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering. Proc R Soc B Biol Sci 285(1874), 20172571 (2018).Article 

Google Scholar 
36.Lamy, T. et al. Scale-specific drivers of kelp forest communities. Oecologia 186(1), 217–233 (2018).ADS 
Article 

Google Scholar 
37.Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl. Acad. Sci. 113(48), 13791–13796 (2016).Article 

Google Scholar 
38.Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29(04), 436–459 (2003).Article 

Google Scholar 
39.Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26(4), 867–878. https://doi.org/10.1046/j.1365-2699.1999.00305.x (1999).Article 

Google Scholar 
40.Claisse, J. T. et al. Biogeographic patterns of communities across diverse marine ecosystems in southern California. Mar. Ecol. 39(S1), e12453. https://doi.org/10.1111/maec.12453 (2018).MathSciNet 
Article 

Google Scholar 
41.Jerde, C. L., Wilson, E. A. & Dressler, T. L. Measuring global fish species richness with eDNA metabarcoding. Mol. Ecol. Resour. 19(1), 19–22. https://doi.org/10.1111/1755-0998.12929 (2019).Article 
PubMed 

Google Scholar 
42.Sigsgaard, E. E. et al. Seawater environmental DNA reflects seasonality of a coastal fish community. Mar. Biol. 164(6), 128. https://doi.org/10.1007/s00227-017-3147-4 (2017).CAS 
Article 

Google Scholar 
43.Nickols, K. J., Wilson White, J., Largier, J. L. & Gaylord, B. Marine population connectivity: Reconciling large-scale dispersal and high self-retention. Am. Nat. 185(2), 196–211. https://doi.org/10.1086/679503 (2015).Article 
PubMed 

Google Scholar 
44.Nickols, K. J., Gaylord, B. & Largier, J. L. The coastal boundary layer: Predictable current structure decreases alongshore transport and alters scales of dispersal. Mar. Ecol. Prog. Ser. 464, 17–35 (2012).ADS 
Article 

Google Scholar 
45.Sassoubre, L. M., Yamahara, K. M., Gardner, L. D., Block, B. A. & Boehm, A. B. Quantification of environmental DNA (eDNA) shedding and decay rates for three marine fish. Environ. Sci. Technol. 50(19), 10456–10464. https://doi.org/10.1021/acs.est.6b03114 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
46.Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. 1(1), 185. https://doi.org/10.1038/s42003-018-0192-6 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Andruszkiewicz Allan, E., Zhang, W. G., Lavery, C. A. & Govindarajan, F. A. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3(2), 492–514. https://doi.org/10.1002/edn3.141 (2021).Article 

Google Scholar 
48.Hansen, B. K., Bekkevold, D., Clausen, L. W. & Nielsen, E. E. The sceptical optimist: Challenges and perspectives for the application of environmental DNA in marine fisheries. Fish Fish. 19(5), 751–768. https://doi.org/10.1111/faf.12286 (2018).Article 

Google Scholar 
49.Weltz, K. et al. Application of environmental DNA to detect an endangered marine skate species in the wild. PLoS One 12(6), e0178124. https://doi.org/10.1371/journal.pone.0178124 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Fram, J. P. et al. Physical pathways and utilization of nitrate supply to the giant kelp, Macrocystis pyrifera. Limnol. Oceanogr. 53(4), 1589–1603. https://doi.org/10.4319/lo.2008.53.4.1589 (2008).ADS 
CAS 
Article 

Google Scholar 
51.Jackson, G. A. & Winant, C. D. Effect of a kelp forest on coastal currents. Cont. Shelf. Res. 2(1), 75–80 (1983).ADS 
Article 

Google Scholar 
52.Grant, W. D. & Madsen, O. S. The continental-shelf bottom boundary layer. Annu. Rev. Fluid Mech. 18(1), 265–305. https://doi.org/10.1146/annurev.fl.18.010186.001405 (1986).ADS 
MathSciNet 
Article 
MATH 

Google Scholar 
53.Leary, P. R. et al. “Internal tide pools” prolong kelp forest hypoxic events. Limnol. Oceanogr. 62(6), 2864–2878. https://doi.org/10.1002/lno.10716 (2017).ADS 
CAS 
Article 

Google Scholar 
54.Gaylord, B. et al. Spatial patterns of flow and their modification within and around a giant kelp forest. Limnol. Oceanogr. 52(5), 1838–1852 (2007).ADS 
Article 

Google Scholar 
55.Lafferty, K. D., Benesh, K. C., Mahon, A. R., Jerde, C. L. & Lowe, C. G. Detecting Southern California’s white sharks with environmental DNA. Front. Mar. Sci. 5, 355. https://doi.org/10.3389/fmars.2018.00355 (2018).Article 

Google Scholar 
56.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2(7), 150088 (2015).ADS 
CAS 
Article 

Google Scholar 
57.Hyde, J. R. & Vetter, R. D. The origin, evolution, and diversification of rockfishes of the genus Sebastes (Cuvier). Mol. Phylogenet. Evol. 44(2), 790–811 (2007).CAS 
Article 

Google Scholar 
58.Min, M. A., Barber, P. H. & Gold, Z. MiSebastes: An eDNA metabarcoding primer set for rockfishes (genus Sebastes). bioRxiv. (2020). http://biorxiv.org/content/early/2020/10/30/2020.10.29.360859.abstract. Accessed January 2021.59.Gold, Z., Sprague, J., Kushner, D. J., Zerecero Marin, E. & Barber, P. H. eDNA metabarcoding as a biomonitoring tool for marine protected areas. PLoS One 16(2), e0238557. https://doi.org/10.1371/journal.pone.0238557 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS One 11(6), e0157366 (2016).Article 

Google Scholar 
61.Berry, T. E. et al. Marine environmental DNA biomonitoring reveals seasonal patterns in biodiversity and identifies ecosystem responses to anomalous climatic events. PLoS Genet. 15(2), e1007943. https://doi.org/10.1371/journal.pgen.1007943 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
62.Ausubel, J. H., Stoeckle, M. Y. & Gaffney, P. Final Report of the 1st US National Conference on Marine Environmental DNA (eDNA). (2019).
63.Reed, D. C. SBC LTER: Reef: Annual time series of biomass for kelp forest species, ongoing since 2000. Environ. Data Initiat. https://doi.org/10.6073/pasta/23965abf42954f345cfd6642fe3c4810 (2018).64.O’Donnell, J. L., Kelly, R. P., Lowell, N. C. & Port, J. A. Indexed PCR primers induce template-specific bias in large-scale DNA sequencing studies. PLoS One 11(3), e0148698 (2016).Article 

Google Scholar 
65.Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30(5), 614–620 (2014).CAS 
Article 

Google Scholar 
66.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).Article 

Google Scholar 
67.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17(1), 10–12 (2011).Article 

Google Scholar 
68.Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: Robust and fast clustering method for amplicon-based studies. PeerJ 2, e593 (2014).Article 

Google Scholar 
69.Huson, D. H. et al. MEGAN community edition—interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12(6), 1–12 (2016).Article 

Google Scholar 
70.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Crist, T. O., Veech, J. A., Gering, J. C. & Summerville, K. S. Partitioning species diversity across landscapes and regions: A hierarchical analysis of alpha, beta, and gamma diversity. Am. Nat. 162(6), 734–743 (2003).Article 

Google Scholar 
72.Muggeo, V. M. R. Estimating regression models with unknown break-points. Stat. Med. 22(19), 3055–3071 (2003).Article 

Google Scholar 
73.Legendre, P., Borcard, D. & Roberts, D. W. Variation partitioning involving orthogonal spatial eigenfunction submodels. Ecology 93(5), 1234–1240. https://doi.org/10.1890/11-2028.1 (2012).Article 
PubMed 

Google Scholar 
74.Silva, A. R., Dias, C. T. S., Cecon, P. R. & Rêgo, E. R. An alternative procedure for performing a power analysis of Mantel’s test. J. Appl. Stat. 42(9), 1984–1992. https://doi.org/10.1080/02664763.2015.1014894 (2015).MathSciNet 
Article 
MATH 

Google Scholar 
75.Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymetrical approach. Ecol. Monogr. 67(3), 345–366 (1997).
Google Scholar 
76.Team, R. C. R: A language and environment for statistical computing. (2018). https://www.r-project.org/. Accessed June 2018.77.Oksanen, J. et al. Package ‘vegan.’ Community Ecol Packag version:2. (2015). More

Study systemWe studied a D. magna population (OHZ) from a small, shallow man-made pond in Oud-Heverlee, Belgium (50°50´ N– 4°39′ E). This pond was constructed for pisciculture in 1970 and has a detailed record of fish-stocking densities for 16 years (Fig. 1a). Dormant stages of D. magna were sampled from three depths of a sediment core, corresponding to three time periods that varied in the level of fish-predation pressure: (1) the pre-fish period (1970–1972), during which no fish were stocked in the pond; (2) the high-fish period (1976–1979), a period with high fish-predation pressure due to intensive fish stocking; (3) the reduced-fish period (1988–1990), with relaxed fish predation pressure due to a reduction in fish stocking (Fig. 1a)9,10,37. This archive was previously sampled using a standard Plexiglas corer with inner diameter of 5.2 cm10. Dating of the sedimentary archive could not be completed with traditional radioisotope analysis, but was based on dry weight and organic matter content under the assumption of a constant sedimentation rate since the establishment of the pond10. The cores contained the full sediment archive, including the transition to the mineral sediment. Sediment cores were aligned using the patterns of Daphnia dormant egg abundance and changes in size of the dormant egg cases as described in Cousyn et al.10. The dormant stages were hatched in the laboratory and taking advantage of the parthenogenetic reproduction mode of D. magna as long as conditions are favorable, we started up clonal lines. The resulting clonal lines are each genetically unique, as dormant stages in D. magna are the result of sexual reproduction. Our approach was thus to sequence the full genome of a random sample of 12 individuals from each of three depth layers of a sediment core representing populations that occurred in three periods with distinct fish-predation pressure.In addition to the reconstruction of temporal genome dynamics, we used twelve regional populations of D. magna distributed along strong environmental gradients of fish-predation pressure in the region. Six populations (DANA, U2, TER1, MO, KNO15, and TER2) were sampled from fishless ponds, while six populations (ZW4, LRV, ZW3, OHN, OM2, and OM3) were sampled from ponds that harbored fish (Supplementary Table 1). These genotypes were hatched from dormant eggs isolated from the upper 2–3 cm of sediment of the study ponds.Whole-genome sequencingTo reconstruct the genomic history, we resequenced the 36 D. magna lines resurrected from the OHZ pond and validated it with additional whole-genome resequencing of 144 D. magna genotypes spread across twelve spatial populations along a fish gradient in the region (Supplementary Table 1). Twelve individuals from each temporal subpopulation of the sediment core and 8–17 individuals per population in the spatial survey were used for genomic DNA extraction using the Nucleo Spin Tissue extraction kit (Macherey-Nagel, Germany), with overnight incubation at 56 °C and following the manufacturer’s instructions. We quantified DNA using PicoGreen reagent (Life Technologies) on a DTX 880 spectrofluorometer (Beckman Coulter). For each sample, 1 µg of gDNA was normalized in a final volume of 50 µl of Tris Buffer, pH 8.5, and sheared using an E220 Focused Ultrasonicator in conjunction with a microTube plate (Covaris) in accordance with the manufacturer’s recommendations. Sheared genomic DNA was assayed on a 2200 TapeStation (Agilent) with High Sensitivity DNA Screentapes to determine the distribution of sheared fragments. The sheared genomic DNA was then prepared into Illumina compatible DNA Sequencing (DNASeq) 100-bp paired-end libraries utilizing NextFlex chemistry (Bio Scientific). Following library construction, libraries were assayed on a 2200 TapeStation (Agilent) with High Sensitivity DNA Screentapes to determine the final library size. Libraries were quantified using the Illumina Library Quantification Kit (Kapa Biosystems) and normalized to an average concentration of 2 nM prior to pooling. Genomic DNA quantification and normalization, shearing setup, library construction, library quantification, library normalization, and library pooling were performed utilizing a Biomek FXP dual-hybrid automated liquid handler (Beckman Coulter). C-Bot (TruSeq PE Cluster Kit v3, Illumina) was used for cluster generation and the Illumina HiSeq2500 platform (TruSeq SBS Kit v3 reagent kit) for paired-end sequencing with 100-bp read length following the manufacturer’s instructions.Short-read mapping and variant callingThe paired end reads (100 × 2) of each individual were first analyzed using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) for quality checks. Subsequently, low-quality base trimming and adapter cleaning was performed using the Trimmomatic software38. Here, parameter values to remove adapter sequences were chosen for seedMismatches (2), palindromeClipThreshold (30), and simpleClipThreshold (10). The minimum phred quality required to keep a base was set to 28, and the minimum read length to 50 bp. Furthermore, the cleaned reads were mapped to the D. magna genome version 2.439 using Bowtie240 software with very-sensitive parameter settings (-D 20 -R 3 -N 0 -L 20 -i S,1,0.50) and insert size between 200 and 700 bp. The mapped reads were then marked for duplicates using the MarkDuplicates feature of Picard tools (http://broadinstitute.github.io/picard/) to avoid PCR duplicates. The resulting sorted BAM files were then used for variant calling using FreeBayes41. FreeBayes41 is a haplotype-based variant caller that calls SNPs, indel, and complex variants. Minimum base quality was set to 30 with minimum coverage of four reads. We obtained more than 3 × 106 raw variants (3 441 615) for the OHZ temporal subpopulations and 6 × 106 raw variants for the spatial populations.Only biallelic SNPs supported by at least four reads and sequenced in at least 90% of individuals were retained after filtering. The draft genome of Daphnia magna consists of thousands of scaffolds and contigs. To remove repetitive and paralogous regions in the genome, we used the 293 scaffolds greater than 5 kb that altogether represent 84% of the sequenced genome. Further SNP filtering was performed based on D. magna gene models, such that each polymorphic SNP contained within genic regions could be unambiguously assigned to only one gene locus, thereby removing uncertainties attributed to sequence reads mapping to paralogs and to overlapping genes coding on alternative strands of DNA. Finally, SNPs at frequencies below 5% (pooled subpopulations) were removed retaining a total of 724,321 SNPs (mean coverage 20 reads per SNP/individual; 99.6% SNPs with missing values less than 5%) for the temporal analysis of the OHZ population and 748,511 SNPs for the spatial populations. These SNPs were used for downstream analyses.Population differentiationWe calculated both genome-wide and locus-specific levels of genetic differentiation (FST; Weir & Cockerham 198442) using the diffCalc function of the diveRsity43 package in R44. These calculations were performed for each pair of temporal subpopulations (i.e., pairwise FST) in the temporal setting (OHZ) and for six random pairs of nonfish and fish populations in the spatial survey.To calculate allele frequencies in the temporal analysis, we used vcfglxgt function of vcflib (https://github.com/vcflib/vcflib) to set genotypes that are most likely to be true based on maximum genotype likelihood. We then identified the significant differences in allele frequencies between temporal subpopulations over time (P value < 0.01) using a modified chi-square test developed by RS Waples (Waples 1989)11 and implemented in the TAFT software45 (hereafter referred to as Waples test) that accounts for effective population size (Ne), yielding 30,669 significant SNPs (4.23% of total OHZ SNPs) by comparing the prefish and high-fish temporal subpopulation and yielding 11,257 SNPs (1.55% of total OHZ SNPs) for the high-fish and reduced-fish temporal subpopulation comparison; 1771 SNPs showed significant allele-frequency changes in both transitions, most of them also showing a significant reversal in the second transition. To determine whether the observed number of SNPs that showed a significant reversal in allele frequencies is higher than expected by chance, we estimated the null distribution by randomly permuting the temporal subpopulation labels (i.e., prefish, high fish, and reduced fish) and alleles per locus (724,321 SNPs), and recalculating the number of reversals based on Buffalo and Coop 202012.Estimation of effective population size (N e)Effective population sizes (Ne) were calculated from θ = 4Neµ, across the whole genome and with a mutation rate per generation of 4 × 10−946 and a generation time of one year (Daphnia undergoes 10–15 asexual generations and one sexual generation per year), where Ɵ is Watterson’s diversity index and µ is mutation rate. Watterson Ɵ was calculated using the folded SFS option in ANGSD software47 and found to be stable, i.e., near 0.03 across the three subpopulations (prefish, high fish, and reduced fish). The calculated Ne was found to be ~1.66 million in the prefish temporal subpopulation and ~1.72 million for the high-fish and reduced-fish temporal subpopulations. Similarly, for spatial populations, the value ranges from ~1.06 to 1.45 million (Supplementary Table 2).Detecting genomic islands of differentiationFor each scaffold, and for each pairwise comparison among temporal subpopulations and six independent fish and no-fish replicate pairs in the spatial survey, a hidden Markov model (HMM) was used to distinguish genomic regions of high, moderate, or low differentiation among (sub)populations. We used a similar approach as used earlier by Soria-Carrasco et al. (2014)48. In brief, for each of these three levels of genetic differentiation (i.e., the hidden states), a Gaussian distribution of log10(FST + 1) was assumed with the mean and variance initialized as those of the log10(FST + 1) values within each respective level. We then used the Baum–Welch algorithm49 to refine the Gaussian model for each state and the transition matrix among the states. Direct transition from the low to the high state was not allowed. Hidden states were then estimated from the data and we estimated the parameters by the Viterbi algorithm using the R package HiddenMarkov50. A high differentiating island between genomes is defined to contain at least three consecutive SNPs categorized as high-state SNPs by HMM, yielding 6111 and 2879 islands of genomic differentiation between the prefish vs high-fish (mean length: 2428 bp) and the high-fish vs reduced-fish comparison (mean length: 1713 bp), respectively. Similarly, for six independent spatial fish vs no-fish comparisons, the number of islands of differentiation ranged between 4136 and 7493 (with range of mean length 1879–3290 bp), depending upon the comparison (Supplementary Table 3).Functional annotation and enrichment analysisWe investigated the function of the outlier SNPs (P values smaller than 0.01) in the comparison among temporal subpopulations (prefish vs high fish and high fish vs reduced fish) and in HMM-based high-differentiation islands in spatial comparisons. Transcriptome-based functional annotation was performed using the Daphnia magna genome version 2.439. The pathway enrichment analysis was performed using the orthologous genes of D. magna in the D. pulex genome51 based on OrthoDB gene families52 and the KEGG pathway database53. Out of ~29,000 annotated genes of D. magna, 17,400 genes have 17,832 orthologs in D. pulex. However, due to the fragmented status of the D. magna genome assembly, manual curation for high-quality gene models resulted in a total of 12,264 D. magna genes used in our study, of which 2402 genes are annotated to KEGG pathways. Ortholog mapping is not unique. A given gene from the source species, here D. magna, can map to a single, multiple, or no ortholog in the target species, here D. pulex. This can bias statistical tests when referencing to D. pulex genomics resources. We used the number of nonunique mappings for each D. magna gene on the KEGG pathways of D. pulex to weight-adjust the confusion matrix for Fisher’s exact test to obtain the correct P values. Significant pathways are defined as those with FDR corrected (Benjamini–Hochberg method) P values smaller than 0.05. The data analysis was performed using Python packages (NumPy v1.17.454, SciPy v1.4.155, statsmodels v0.11.1, and plotly v4.8.1).Rarefaction analysisRarefaction analyses were used to determine the rate at which outlier SNPs accumulate in the temporal subpopulation or in the set of regional populations as a function of sample size, i.e., the number of individuals sampled from a given population or group of populations. These analyses were performed separately for the prefish as well as for the reduced-fish temporal subpopulation, in both cases to assess the number of individuals needed to accumulate a given percentage of the SNPs that were suggested to be important for the evolution in response to fish through an outlier analysis of the prefish to high-fish transition. With the rarefaction analysis on the prefish population, we estimate the minimum number of individuals that are needed to reach sufficient genetic variation to enable the observed level of adaptation to fish in this population. The rarefaction analysis on the reduced-fish temporal subpopulation was to assess whether the level of genetic polymorphism declined or increased following the period of strong selection by fish. We thus aimed to evaluate how much evolutionary potential a certain number of individuals from the oldest (i.e., before the introduction of fish) as well as the youngest temporal subpopulation (i.e., after a wave of selection) represent. In the first set of analyses, we used the 1109 SNPs belonging to the divergent SNPs that showed significant changes in allele frequencies in the prefish to high-fish transition and also a significant reversal during the high-fish to reduced-fish and were potentially under positive selection (i.e. excluding hitchhiked SNPs) in the OHZ population. This group of SNPs represent polymorphisms that are presumably adaptive or at least contribute to adaptive allelic variants and hence contribute to the adaptive potential of the temporal subpopulations. The analyses were performed by rarefying the genotype matrix of all 12 individuals from either the prefish or the reduced-fish population to all possible (i.e., 4095) subsets of samples of 1–12 genotypes. For each of these subsets we then calculated the average proportion of polymorphic SNPs. These values were plotted against sample size to generate rarefaction curves. Similarly, rarefaction analyses were performed for the 1003 SNPs belonging to the outlier SNPs that were potentially under positive selection (i.e., excluding hitchhiked SNPs) in the OHZ population and that were also present as SNPs in the full spatial dataset (90.4% of the total number of 1109 SNPs). In this case, rarefaction curves were plotted by randomly resampling (1000 times) 1–30 individuals from the total of the 111 individuals of the Leuven regional populations in the spatial data set (i.e., the cluster of populations that represents a sample of nearby populations (within a radius of 10 km) to the focal OHZ population).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Niinemets, Ü. Within-canopy variations in functional leaf traits: Structural, chemical and ecological controls and diversity of responses. In Canopy Photosynthesis: From Basics to Applications (eds Hikosaka, K. et al.) 101–142 (Springer, Dordrecht, 2016).Chapter 

Google Scholar 
2.Field, C. Allocating leaf nitrogen for the maximization of carbon gain: Leaf age as a control on the allocation program. Oecologia 56, 341–347 (1983).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Hikosaka, K. et al. A meta-analysis of leaf nitrogen distribution within plant canopies. Ann. Bot. 118(2), 239–247 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Kitao, M. et al. Canopy nitrogen distribution is optimized to prevent photoinhibition throughout the canopy during sun flecks. Sci. Rep. 8, 503 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Lambers, H., Chapin, F. S. III & Pons, T. L. Ecological biochemistry: Allelopathy and defense against herbivores. In Plant Physiological Ecology 2nd edn (eds Lambers, H. et al.) 445–477 (Springer, New York, 2008).Chapter 

Google Scholar 
6.Bachofen, C., D’Odorico, P. & Buchmenn, N. Light and VPD gradients drive foliar nitrogen partitioning and photosynthesis in the canopy of European beech and silver fir. Oecologia 192, 323–339 (2020).ADS 
PubMed 
Article 

Google Scholar 
7.Mole, S., Ross, J. A. M. & Waterman, P. G. Light-induced variation in phenolic levels in foliage of rain-forest plants, I. Chemical changes. J. Chem. Ecol. 14(1), 1–21 (1988).CAS 
PubMed 
Article 

Google Scholar 
8.Yamasaki, M. & Kikuzawa, K. Temporal and spatial variations in leaf herbivory within a canopy of Fagus crenata. Oecologia 137(2), 226–232 (2003).ADS 
PubMed 
Article 

Google Scholar 
9.Niinemets, Ü., Ellsworth, D. S., Lukjanova, A. & Tobias, M. Site fertility and the morphological and photosynthetic acclimation of Pinus sylvestris needles to light. Tree Physiol. 21, 1231–1244 (2001).CAS 
PubMed 
Article 

Google Scholar 
10.Niinemets, Ü., Cescatti, A., Lukjanova, A., Tobias, M. & Truus, L. Modification of light-acclimation of Pinus sylvestris shoot architecture by site fertility. Agric. For. Meteorol. 111, 121–140 (2002).ADS 
Article 

Google Scholar 
11.Ishii, H., Kitaoka, S., Fujisawa, T., Maruyama, Y. & Koike, T. Plasticity of shoot and needle morphology and photosynthesis of two Picea species with different site preferences in northern Japan. Tree Physiol. 27, 1595–1605 (2007).CAS 
PubMed 
Article 

Google Scholar 
12.Bryant, J. P., Chapin, F. S. III. & Klein, D. R. Carbon/nutrient balance of boreal plants to vertebrate herbivory. Oikos 40, 357–368 (1983).CAS 
Article 

Google Scholar 
13.Coley, P. D., Bryant, J. P. & Chapin, F. S. III. Resource availability and plant antiherbivore defense. Science 230(4728), 895–899 (1985).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Herms, D. A. & Mattson, W. J. The dilemma of plants: To grow or defend. Q. Rev. Biol. 63(3), 283–335 (1992).Article 

Google Scholar 
15.Sun, Y. et al. Negative effects of the simulated nitrogen deposition on plant phenolic metabolism: A meta-analysis. Sci. Total Environ. 719, 137442 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Kolstad, A. L., Asplund, J., Nilsson, M.-C., Ohlson, M. & Nybakken, L. Soil fertility and charcoal as determinants of growth and allocation of secondary plant metabolites in seedlings of European beech and Norway spruce. Environ. Exp. Bot. 131, 39–46 (2016).CAS 
Article 

Google Scholar 
17.Caldwell, E., Read, J. & Sanson, G. D. Which leaf mechanical traits correlate with insect herbivory among feeding guilds. Ann. Bot. 117, 349–361 (2016).PubMed 

Google Scholar 
18.Warren, C. R. & Adams, M. A. Distribution of N, rubisco and photosynthesis in Pinus pinaster and acclimation to light. Plant Cell Environ. 24(6), 597–609 (2001).CAS 
Article 

Google Scholar 
19.Koike, T., Kitao, M., Maruyama, Y., Mori, S. & Lei, T. T. Leaf morphology and photosynthetic adjustments among deciduous broad-leaved trees within the vertical canopy profile. Tree Physiol. 21, 951–958 (2001).CAS 
PubMed 
Article 

Google Scholar 
20.Iio, A., Fukasawa, H., Nose, Y., Kato, S. & Kakubari, Y. Vertical, horizontal and azimuthal variations in leaf photosynthetic characteristics within a Fagus crenata crown in relation to light acclimation. Tree Physiol. 25, 533–544 (2005).PubMed 
Article 

Google Scholar 
21.Scartazza, A., Baccio, D. D., Bertolotto, P., Gavrichkova, O. & Matteucci, G. Investigating the European beech (Fagus sylvatica L.) leaf characteristics along the vertical canopy profile: Leaf structure, photosynthetic capacity, light energy dissipation and photoprotection mechanisms. Tree Physiol. 36, 1060–1076 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.McClure, J. W. Physiology of flavonoids in plants. In Plant Flavonoids in Biology and Medicine (eds Cody, V. et al.) 77–85 (Alan R. Liss Inc, New York, 1985).
Google Scholar 
23.Løvdal, T., Plsen, K. M., Slimestad, R., Verheul, M. & Lillo, C. Synergetic effects of nitrogen depletion, temperature, and light on the content of phenolic compounds and gene expression in leaves of tomato. Phytochemistry 71, 605–613 (2010).PubMed 
Article 
CAS 

Google Scholar 
24.Christopoulos, M. V. & Tsantili, E. Participation of phenylalanine ammonia-lyase (PAL) in increased phenolic compounds in fresh cold stressed walnut (Juglans regia L.) kernels. Postharvest Biol. Technol. 104, 17–25 (2015).CAS 
Article 

Google Scholar 
25.Tanaka, K. et al. Changes in photosynthesis and leaf characteristics with tree height in five dipterocarp species in a tropical rain forest. Tree Physiol. 26, 865–873 (2006).Article 

Google Scholar 
26.Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytol. 182, 565–588 (2009).PubMed 
Article 

Google Scholar 
27.Rowe, W. J. & Potter, D. A. Vertical stratification of feeding by Japanese beetle within linden tree canopies: Selective foraging or height per se?. Oecologia 108, 459–466 (1996).ADS 
PubMed 
Article 

Google Scholar 
28.Le Corff, J. & Marquis, R. J. Differences between understorey and canopy in herbivore community composition and leaf quality for two oak species in Missouri. Ecol. Entomol. 24, 46–58 (1999).Article 

Google Scholar 
29.Jamieson, M. A., Schwartzberg, E. G., Raffa, K. F., Reich, P. B. & Lindroth, R. L. Experimental climate warming alters aspen and birch phytochemistry and performance traits for an outbreak insect herbivore. Glob. Chang. Biol. 21, 2698–2710 (2015).ADS 
PubMed 
Article 

Google Scholar 
30.Tripler, C. E., Canham, C. D., Inouye, R. S. & Schnurr, J. L. Soil nitrogen availability, plant luxury consumption, and herbivory by white-tailed deer. Oecologia 133, 517–524 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
31.Galloway, J. N. et al. Nitrogen cycles: Past, present and future. Biogeochemistry 70, 153–226 (2004).CAS 
Article 

Google Scholar 
32.Kimura, S. D., Saito, M., Hara, H., Xu, Y. H. & Okazaki, M. Comparison of nitrogen dry deposition on cedar and oak leaves in the Tama hills using foliar rinsing method. Water Air Soil Pollut. 202, 369–377 (2009).ADS 
CAS 
Article 

Google Scholar 
33.Imamura, N., Tanaka, N., Ohte, N. & Yamamoto, H. Natural transfer with rainfall in the canopies of a broad-leaved deciduous forest in okuchichibu. J. Jpn. For. Soc. 94, 74–83 (2012) ((In Japanese)).CAS 
Article 

Google Scholar 
34.Ogasawara, R., Yamamoto, T. & Arita, T. Biomass and production of the Konara (Quercus serrata) secondary stand. Hardwood Res. 4, 257–262 (1987) ((In Japanese)).
Google Scholar 
35.Kitao, M. et al. Increased phytotoxic O3 dose accelerates autumn senescence in an O3-sensitive beech forest even under the present-level O3. Sci. Rep. 6, 32549 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Kume, A., Nasahara, K. N., Nagai, S. & Muraoka, H. The ratio transmitted near-infrared radiation to photosynthetically active radiation (PAR) increases in proportion to the adsorbed PAR in the canopy. J. Plant Res. 124(1), 99–106 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Ivančič, I. & Degobbis, D. An optimal manual procedure for ammonia analysis in natural waters by the indophenol blue method. Water Res. 18(9), 1143–1147 (1984).Article 

Google Scholar 
38.Bray, R. H. & Kurtz, L. T. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59(1), 39–46 (1945).ADS 
CAS 
Article 

Google Scholar 
39.Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chem. Acta 27, 31–36 (1962).CAS 
Article 

Google Scholar 
40.Watanabe, M., Ryu, K., Kita, K., Takagi, K. & Koike, T. Effects of nitrogen load on the growth and photosynthesis of hybrid larch F1 (Larix gmelinii var. japonica × L. kaempferi) grown on serpentine soil. Environ. Exp. Bot 83, 73–81 (2012).CAS 
Article 

Google Scholar 
41.Watanabe, M. et al. Photosynthetic traits of Siebold’s beech and oak saplings grown under free air ozone exposure in northern Japan. Environ. Pollut. 174, 50–56 (2013).CAS 
PubMed 
Article 

Google Scholar 
42.Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).ADS 
CAS 
Article 

Google Scholar 
43.Barnes, J. D., Balaguer, L., Manrique, E., Elvira, S. & Davison, A. W. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ. Exp. Bot. 32(2), 85–100 (1992).CAS 
Article 

Google Scholar 
44.Julkunen-Tiitto, R. Phenolic constituents in the leaves of northern willows: Methods for the analysis of certain phenolics. J. Agric. Food Chem. 33, 213–217 (1985).CAS 
Article 

Google Scholar 
45.Bate-Smith, E. C. Astringent tannins of Acer species. Phytochemistry 16, 1421–1426 (1977).CAS 
Article 

Google Scholar 
46.Clegg, M. S., Keen, C. L., Lönnerdal, B. & Hurley, L. S. Influence of ashing techniques on the analysis of trace elements in animal tissue I. Wet Ashing. Biol. Trace Elem. Res. 3, 107–115 (1981).CAS 
PubMed 
Article 

Google Scholar 
47.Takashima, T., Hikosaka, K. & Hirose, T. Photosynthesis or persistence: Nitrogen allocation in leaves of evergreen and deciduous Quercus species. Plant Cell Environ. 27, 1047–1054 (2004).CAS 
Article 

Google Scholar 
48.Vogan, P. J. & Sage, R. F. Effects of low atmospheric CO2 and elevated temperature during growth on the gas exchange responses of C3, C3–C4 intermediate, and C4 species from three evolutionary lineages of C4 photosynthesis. Oecologia 169, 341–352 (2012).ADS 
PubMed 
Article 

Google Scholar 
49.Evans, J. R. & Seemann, J. R. The allocation of protein nitrogen in the photosynthetic apparatus: Costs, consequences and control. In Photosynthesis (ed. Briggs, W. R.) 183–205 (Alan R Liss Inc, New York, 1989).
Google Scholar 
50.Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).Article 

Google Scholar 
51.Niinemets, Ü., Keenan, T. F. & Hallik, L. A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types. New Phytol. 205, 973–993 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Migita, C., Chiba, Y. & Tange, T. Seasonal and spatial variations in leaf nitrogen content and resorption in a Quercus serrata canopy. Tree Physiol. 27, 63–70 (2007).CAS 
PubMed 
Article 

Google Scholar 
53.Kitao, M. et al. Effects of chronic elevated ozone exposure on gas exchange responses of adult beech trees (Fagus sylvatica) as related to the within-canopy light gradient. Environ. Pollut. 157, 537–544 (2009).CAS 
PubMed 
Article 

Google Scholar 
54.R Development Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria. (2018).55.Imaizumi, T. An introductory guide to statistical analysis-generalized linear models for proportion data using R. J. Weed Sci. Tech. 55(4), 275–286 (2010) ((In Japanese)).Article 

Google Scholar 
56.Underwood, A. J. Techniques of analysis of variance in experimental marine biology and ecology. Oceanogr. Mar. Biol. Ann. Rev. 19, 513–605 (1981).
Google Scholar  More

1.Bhattacharya, A., Bhaumik, A., Pathipati, U., Mandel, S. & Epidi, T. T. Nano-particles: A recent approach to insect pest control. Afr. J. Biotechnol. 9, 3489–3493 (2010).
Google Scholar 
2.Barik, T. K., Sahu, B. & Swain, V. Nanosilica- from medicine to pest control. Parasitol. Res. 103, 253–258 (2008).CAS 
PubMed 
Article 

Google Scholar 
3.Gajbhiye, M., Kesharwani, J., Ingle, A., Gade, A. & Rai, M. Fungus mediated synthesis of silver nanoparticles and its activity against pathogenic fungi in combination of fluconazole. Nanomedicine 5, 382–386 (2009).CAS 
PubMed 
Article 

Google Scholar 
4.Goswami, A., Roy, I., Sengupta, S. & Debnath, N. Novel applications of solid and liquid formulations of nanoparticles against insect pests and pathogens. Thin Solid Films 519, 1252–1257 (2010).ADS 
CAS 
Article 

Google Scholar 
5.Abbasi, A., Sufyan, M., Arif, M. J. & Sahi, S. T. Effect of silicon on tritrophic interaction of cotton, Gossypium hirsutum (Linnaeus), Bemisia tabaci (Gennadius) (Homoptera: Aleyrodidae) and the predator, Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae). Arthropod. Plant Interect. 14, 717–725 (2020).Article 

Google Scholar 
6.Croissant, J. G. et al. Synthetic amorphous silica nanoparticles: Toxicity, biomedical and environmental implications. Nat. Rev. Mater. 5, 886–909 (2020).ADS 
Article 
CAS 

Google Scholar 
7.Zhang, H. et al. Formation and enhanced biocidal activity of water-dispersable organic nanoparticles. Nat. Nanotechnol. 3, 506–511 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Ayoub, H. A., Khairy, M., Rashwan, F. A. & Abdel-Hafez, H. F. Synthesis and characterization of silica nanostructures for cotton leaf worm control. J. Nanostruct. Chem. 7, 91–100 (2017).CAS 
Article 

Google Scholar 
9.Shoaib, A. et al. Entomotoxic effect of silicon dioxide nanoparticles on Plutella xylostella (L.) (Lepidoptera: Plutellidae) under laboratory conditions. Toxicol. Environ. Chem. 100, 80–91 (2018).CAS 
Article 

Google Scholar 
10.Rastogi, A. et al. Application of silicon nanoparticles in agriculture. 3 Biotech 9, 90 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Galal, O. A. & El Samahy, M. F. M. Genetical effects of using silica nanoparticles as biopesticide on Drosophila melanogaster. Egypt. J. Genet. Cytol 41, 87–106 (2012).Article 

Google Scholar 
12.Smith, B. C. Effects of silica on the survival of Coleomegilla maculata lengi (Coleoptera: Coccinellidae) and Leptinotarsa decemlineata (Coleoptera: Chrysomelidae). Can. Entomol. 101, 460–462 (1969).Article 

Google Scholar 
13.Mousa, K. M., Elsharkawy, M. M., Khodeir, I. A., El-Dakhakhni, T. N. & Youssef, A. E. Growth perturbation, abnormalities and mortality of oriental armyworm Mythimna separata (Walker) (Lepidoptera: Noctuidae) caused by silica nanoparticles and Bacillus thuringiensis toxin. Egypt. J. Biol. Pest Control 24, 283–287 (2014).
Google Scholar 
14.El-Samahy, M. F. M., Khafagy, I. F. & El-Ghobary, A. M. A. Efficiency of silica nanoparticles, two bioinsecticides, peppermint extract and insecticide in controlling cotton leafworm, Spodoptera littoralis Boisd. and their effects on some associated natural enemies in sugar beet fields. J. Plant Prot. Pathol. Mansoura Univ. 6, 1221–1230 (2015).
Google Scholar 
15.El-Samahy, M. F. M. & Galal, O. A. Evaluation of silica nanoparticles as a new approach to control faba bean (Vicia faba L.) insects and its genotoxic effect on M2 plants. Egypt. J. Agric. Res. 90, 869–888 (2012).
Google Scholar 
16.Hodson, M. J., White, P. J., Mead, A. & Broadley, M. R. Phylogenetic variation in the silicon composition of plants. Ann. Bot. 96, 1027–1046 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cooke, J. & Leishman, M. R. Consistent alleviation of abiotic stress with silicon addition: A meta-analysis. Funct. Ecol. 30, 1340–1357 (2016).Article 

Google Scholar 
18.Sangster, A. G. & Hodson, M. J. Silica in higher plants, in Evered, D. & O’Connor, M. (eds.) 90–111, Silicon Biochemistry, Ciba Found. Symp. 121 (Wiley, Chichester, U. K., 1986).19.Johnson, S. N., Hartley, S. E., Ryalls, J. M. W., Frew, A. & Hall, C. R. Targeted plant defense: Silicon conserves hormonal defense signaling impacting chewing but not fluid-feeding herbivores. Ecology https://doi.org/10.1002/ecy.3250 (2021).Article 
PubMed 

Google Scholar 
20.Painter, R. H. Insect resistance in crop plants 520 (MacMillan, 1951).
Google Scholar 
21.Sasamoto, K. Studies on the relation between insect pests and silica content in rice plant (III). On the relation between some physical properties of silicified rice plant and injuries by rice stem borer, rice plant skipper and rice stem maggot. Oyo Kontyu 11, 66–69 (1955).
Google Scholar 
22.Takahashi, E. Uptake mode and physiological functions of silica. Science of the Rice Plant: Physiology, 420–433 (Food and Agriculture Policy Resource Center, Tokyo, 1995).23.Keeping, M. G. & Meyer, J. H. Calcium silicate enhances resistance of sugarcane to the African stalk borer Eldana saccharina Walker (Lepidoptera: Pyralidae). Agric. For. Entomol. 4, 265–274 (2002).Article 

Google Scholar 
24.Reynolds, O. L., Keeping, M. G. & Meyer, J. H. Silicon-augmented resistance of plants to herbivorous insects: A review. Ann. Appl. Biol. 155, 171–186 (2009).CAS 
Article 

Google Scholar 
25.Massey, F. P. & Hartley, S. E. Physical defences wear you down: Progressive and irreversible impacts of silica on insect herbivores. J. Anim. Ecol. 78, 281–291 (2009).PubMed 
Article 

Google Scholar 
26.Agarie, S. et al. Effects of silicon on tolerance to water deficit and heat stress in rice plants (Oryza sativa L.), monitored by electrolyte leakage. Plant Prod. Sci. 1, 96–103 (1998).Article 

Google Scholar 
27.Ye, M. et al. Priming of jasmonate mediated antiherbivore defence responses in rice by silicon. Proc. Natl. Acad. Sci. USA 110, E3631–E3639 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Mumm, R. & Dicke, M. Variation in natural plant products and the attraction of bodyguards involved in indirect plant defense. Can. J. Zool. 88, 628–667 (2010).CAS 
Article 

Google Scholar 
29.Dudareva, N., Negre, F., Nagegowda, D. A. & Orlova, I. Plant volatiles: Recent advances and future perspectives. Crit. Rev. Plant Sci. 25, 417–440 (2006).CAS 
Article 

Google Scholar 
30.Leroy, N., de Tombeur, F., Walgraffe, Y., Cornélis, J.-T. & Verheggen, F. J. Silicon and plant natural defenses against insect pests: Impact on plant volatile organic compounds and cascade effects on multitrophic interactions. Plants 8, 444 (2019).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
31.Gurr, G. M. & Kvedaras, O. L. Synergizing biological control: Scope for sterile insect technique, induced plant defences and cultural techniques to enhance natural enemy impact. Biol. Control 52, 198–207 (2010).Article 

Google Scholar 
32.Reynolds, O. L., Padula, M. P., Zeng, R. & Gurr, G. M. Silicon: Potential to promote direct and indirect effects on plant defense against arthropod pests in agriculture. Front. Plant Sci. 7, 744 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Hall, C. R., Waterman, J. M., Vandegeer, R. K., Hartley, S. E. & Johnson, S. N. The role of silicon in antiherbivore phytohormonal signalling. Front. Plant Sci. 10, 1132 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Liu, J. et al. Silicon supplementation alters the composition of herbivore-induced plant volatiles and enhances attraction of parasitoids to infested rice plants. Front. Plant Sci. 8, 1–8 (2017).
Google Scholar 
35.Johnson, S. N., Rowe, R. C. & Hall, C. R. Silicon is an inducible and effective herbivore defence against Helicoverpa punctigera (Lepidoptera: Noctuidae) in soybean. Bull. Entomol. Res. 110, 417–422 (2019).PubMed 
Article 
CAS 

Google Scholar 
36.Kvedaras, O. L., An, M., Choi, Y. S. & Gurr, G. M. Silicon enhances natural enemy attraction and biological control through induced plant defences. Bull. Entomol. Res. 100, 367–371 (2010).CAS 
PubMed 
Article 

Google Scholar 
37.Connick, V. J. The impact of silicon fertilisation on the chemical ecology of grapevine, Vitis vinifera constitutive and induced chemical defences against arthropod pests and their natural enemies. Ph.D. Thesis, Charles Sturt University, Albury-Wodonga, NSW, Australia (2011).38.Moraes, J. C. et al. Silicon influence on the tritrophic interaction: Wheat plants, the greenbug Schizaphis graminum (Rondani) (Hemiptera: Aphididae), and its natural enemies, Chrysoperla externa (Hagen) (Neuroptera: Chrysopidae) and Aphidius colemani Viereck (Hymenoptera: Aphidiidae). Neotrop. Entomol. 33, 619–624 (2004).Article 

Google Scholar 
39.Bao-shan, L., Chun-hui, L., Li-jun, F., Shu-chun, Q. & Min, Y. Effect of TMS (nanostructured silicon dioxide) on growth of Changbai larch seedlings. J. For. Res. (Harbin) 15, 138–140 (2004).Article 

Google Scholar 
40.Azimi, R., Borzelabad, M. J., Feizi, H. & Azimi, A. Interaction of SiO2 nanoparticles with seed prechilling on germination and early seedling growth of tall wheatgrass (Agropyron elongatum L.). Pol. J. Chem. Technol. 16, 25–29 (2014).CAS 
Article 

Google Scholar 
41.Suriyaprabha, R., Karunakaran, G., Yuvakkumar, R., Rajendran, V. & Kannan, N. Foliar application of silica nanoparticles on the phytochemical responses of maize (Zea mays L.) and its toxicological behavior. Synth. React. Inorgan. Met. Org. Nano-Met. Chem. 44, 1128–1131 (2014).CAS 
Article 

Google Scholar 
42.Alsaeedi, A. H., Elgarawany, M. M., El-Ramady, H., Alshaal, T. & AL-Otaibi A. O. A. Application of silica nanoparticles induces seed germination and growth of cucumber (Cucumis sativus). Met. Environ. Arid. Land Agric. Sci. 28, 57–68 (2019).43.Roohizadeh, G., Majd, A. & Arbabian, S. The effect of sodium silicate and silica nanoparticles on seed germination and some of growth indices in the Vicia faba L. Trop. Plant Res. 2, 85–89 (2015).
Google Scholar 
44.Thabet, A. F., Galal, O. A., El-Samahy, M. F. M. & Tuda, M. Higher toxicity of nano-scale TiO2 and dose-dependent genotoxicity of nano-scale SiO2 on the cytology and seedling development of broad bean Vicia faba. Appl. Sci. 1, 956 (2019).CAS 

Google Scholar 
45.Yang, Z. et al. Assessment of the phytotoxicity of metal oxide nanoparticles on two crop plants, maize (Zea mays L.) and rice (Oryza sativa L.). Int. J. Environ. Res. Public Health 12, 15100–15109 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Sharifi-Rad, J., Sharifi-Rad, M. & Teixeira da Silva, J. A. Morphological, physiological and biochemical responses of crops (Zea mays L., Phaseolus vulgaris L.), medicinal plants (Hyssopus officinalis L., Nigella sativa L.), and weeds (Amaranthus retroflexus L., Taraxacum officinale F. H. Wigg) exposed to SiO2 nanoparticles. J. Agric. Sci. Technol. 18, 1027–1040 (2016).
Google Scholar 
47.Silva, G. H. & Monteiro, R. T. Toxicity assessment of silica nanoparticles on Allium cepa. Ecotox. Environ. Contam. 12, 25–31 (2017).
Google Scholar 
48.Khan, Z. & Ansari, M. Y. K. Impact of engineered Si nanoparticles on seed germination, vigour index and genotoxicity assessment via DNA damage of root tip cells in Lens culinaris. J. Plant. Biochem. Physiol. 6, 5243–5246 (2018).Article 

Google Scholar 
49.Galal, O. A., Thabet, A. F., Tuda, M. & El-Samahy, M. F. M. RAPD analysis of genotoxic effects of nano-scale SiO2 and TiO2 on broad bean (Vicia faba L.). J. Fac. Agric. Kyushu Univ. 65, 57–63 (2020).CAS 
Article 

Google Scholar 
50.Elsadany, M. F. I., Aboulila, A. A., Abo-Sein, T. M. & Magouz, R. I. E. Effect of silica nano-particles in control of mite Tetranychus cucurbitacearum (Sayed) and agronomic traits of soybean plants and qualitative assessment of its genotoxicity using total protein and RAPD analysis. J. Agric. Chem. Biotechnol. Mansoura Univ. 6, 529–544 (2015).
Google Scholar 
51.Salama, H. S., Dimetry, N. Z. & Salem, S. A. On the host preference and biology of the cotton leaf worm Spodoptera littoralis Bois. Zeitung Angew Entomol. 67, 261–266 (1971).Article 

Google Scholar 
52.Anonymous,. Data sheets on quarantine organisms. EPPO list A2 (European and Mediterranean Plant Protection Organization, 1981).
Google Scholar 
53.Hassan, A. S., Moussa, M. A. & Nasr, E. A. Behaviour of larvae and adults of the cotton leaf worm, Prodenia litura. Bull. Soc. Ent. Egypt 44, 337–343 (1960).
Google Scholar 
54.Talati, G. M. & Butani, P. G. Reproduction and population dynamics of groundnut aphid. Guj. Agric. Univ. Res. J. 5, 54–56 (1980).
Google Scholar 
55.Dixon, A. F. G. Structure of aphid populations. Annu. Rev. Entomol. 30, 155–174 (1985).Article 

Google Scholar 
56.Jackai, L. E. N. & Daoust, R. A. Insect pests of cowpeas. Annu. Rev. Entomol. 31, 95–119 (1986).Article 

Google Scholar 
57.Singh, S. R. Insects damaging cowpeas in Asia. In Cowpea research, production and utilization (eds Singhand, S. R. & Rachie, K. O.) 247–264 (Wiley, 1985).
Google Scholar 
58.Atiri, G. I. & Thottappilly, G. Aphis craccivora settling behaviour and acquisition of cowpea aphid borne mosaic virus in aphid-resistant cowpea lines. Entomol. Exp. Appl. 39, 241–245 (1985).Article 

Google Scholar 
59.Aamer, N. A. & Hegazi, E. M. Parasitoids of the leaf miners Liriomyza spp. (Diptera: Agromyzidae) attacking faba bean in Alexandria, Egypt. Egypt. J. Biol. Pest Control 24, 301–305 (2014).
Google Scholar 
60.Bassiony, R. A., Abou-Attia, F. A., Samy, M. A., Youssef, A. E. & Ueno, T. Infestation caused by the agromyzid leafminer Liriomyza trifolii of bean crops in Kafr EL-Shiekh, Egypt. J. Fac. Agric. Kyushu Univ. 62, 435–438 (2017).Article 

Google Scholar 
61.Borges, I., Soares, A. O., Magro, A. & Hemptinne, J. L. Prey availability in time and space is a driving force in life history evolution of predatory insects. Evol. Ecol. 25, 1307–1319 (2011).Article 

Google Scholar 
62.Hendawy, M. A., Saleh, A. A. A., Jabbar, A. S. & El-Hadary, A. S. N. Efficacy of some insecticides against the cowpea aphid, Aphis craccivora Koch infesting cowpea plants and their associated predators under laboratory and field conditions. Zagazig J. Agric. Res. 45, 2367–2375 (2018).Article 

Google Scholar 
63.Jabbar, A. S., Zawrah, M. F. M., Amer, S. A. M. & Saleh, A. A. A. Ecological and biological studies of certain predatory insects of aphid Aphis craccivora (koch.) on cowpea. Res J Parasitol 15, 20–30 (2020).Article 

Google Scholar 
64.Khodeir, I. A. et al. Population densities of pest aphids and their associated natural enemies on faba bean in Kafr EL–Sheikh, Egypt. J. Fac. Agric. Kyushu Univ. 65, 97–102 (2020).Article 

Google Scholar 
65.Lattin, J. D. Bionomics of the anthocoridae. Annu. Rev. Entomol. 44, 207–231 (1999).CAS 
PubMed 
Article 

Google Scholar 
66.Tuda, M. & Shima, K. Relative importance of weather and density dependence on the dispersal and on-plant activity of the predator Orius minutus. Popul. Ecol. 44, 251–257 (2002).Article 

Google Scholar 
67.Henderson, C. F. & Tilton, E. W. Tests with acaricides against the brow wheat mite. J. Econ. Entomol. 48, 157–161 (1955).CAS 
Article 

Google Scholar 
68.Kergoat, G. J. et al. A novel reference dated phylogeny for the genus Spodoptera Guenée (Lepidoptera: Noctuidae: Noctuinae): new insights into the evolution of a pest-rich genus. Mol. Phylogenet. Evol. 161, 107161 (2021).PubMed 
Article 

Google Scholar 
69.Emrani, S. N., Arzani, A. & Saeidi, G. Seed viability, germination and seedling growth of canola (Brassica napus L.) as influenced by chemical mutagens. Afr. J. Biotechnol. 10, 12602–12613 (2011).CAS 
Article 

Google Scholar 
70.Edmond, J. B. & Drapala, W. J. The effects of temperature, sand and soil, and acetone on germination of okra seed. Proc. Am. Soc. Hort. Sci. 71, 428–434 (1958).
Google Scholar 
71.Ranal, M. A. & de Santana, D. G. How and why to measure the germination process?. Braz. J. Bot. 29, 1–11 (2006).Article 

Google Scholar 
72.Dahindwal, A. S., Lather, B. P. S. & Singh, J. Efficacy of seed treatment on germination, seedling emergence and vigor of cotton (Gossypium hirsutum) genotypes. Seed Res. 19, 59–61 (1991).
Google Scholar 
73.Derbalah, A. S., Morsey, S. Z. & El-Samahy, M. Some recent approaches to control Tuta absoluta in tomato under greenhouse conditions. Afr. Entomol. 20, 27–34 (2012).Article 

Google Scholar 
74.Borei, H. A., El-Samahy, M. F. M., Galal, O. A. & Thabet, A. F. The efficiency of silica nanoparticles in control cotton leafworm, Spodoptera littoralis Boisd. (Lepidoptera: Noctuidae) in soybean under laboratory conditions. Glob. J. Agric. Food Saf. Sci. 1, 161–168 (2014).
Google Scholar 
75.Debnath, N., Mitra, S., Das, S. & Goswami, A. Synthesis of surface functionalized silica nanoparticles and their use as entomotoxic nanocides. Powder Technol. 221, 252–256 (2012).CAS 
Article 

Google Scholar 
76.El-Bendary, H. M. & El-Helaly, A. A. First record nanotechnology in agricultural: Silica nanoparticles a potential new insecticide for pest control. Appl. Sci. Rep. 4, 241–246 (2013).
Google Scholar 
77.Rowen, E. & Kaplan, I. Eco-evolutionary factors drive induced plant volatiles: A meta-analysis. New Phytol. 210, 284–294 (2016).CAS 
PubMed 
Article 

Google Scholar 
78.Fawe, A., Abou-Zaid, M., Menzies, J. & Bélanger, R. Silicon-mediated accumulation of flavonoid phytoalexins in cucumber. Phytopathology 88, 396–401 (1998).CAS 
PubMed 
Article 

Google Scholar 
79.Coscun, D. et al. The controversies of silicon’s role in plant biology. New Phytol. 221, 67–85 (2019).Article 

Google Scholar 
80.Murakami, S. et al. Insect-induced Daidzein, Formononetin and their conjugates in soybean leaves. Metabolites 4, 532–546 (2014).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
82.Horiuchi, J.-I. et al. A comparison of the responses of Tetranychus urticae (Acari: Tetranychidae) and Phytoseiulus persimilis (Acari: Phytoseiidae) to volatiles emitted from lima bean leaves with different levels of damage made by T. urticae or Spodoptera exigua (Lepidoptera: Noctuidae). Appl. Entomol. Zool. 38, 109–116 (2003).Article 

Google Scholar 
83.Yoneya, K., Kugimiya, S. & Takabayashi, J. Can herbivore-induced plant volatiles inform predatory insect about the most suitable stage of its prey?. Physiol. Entomol. 34, 379–386 (2009).CAS 
Article 

Google Scholar 
84.Acevedo, F. E. et al. Quantitative proteomic analysis of the fall armyworm saliva. Insect Biochem. Mol. Biol. 86, 81–92 (2017).CAS 
PubMed 
Article 

Google Scholar 
85.Vet, L. E. & Dicke, M. Ecology of infochemical use by natural enemies in a tritrophic context. Annu. Rev. Entomol. 37, 141–172 (1992).Article 

Google Scholar 
86.Yan, Z. G. & Wang, C. Z. Similar attractiveness of maize volatiles induced by Helicoverpa armigera and Pseudaletia separata to the generalist parasitoid Campoletis chlorideae. Entomol. Exp. Appl. 118, 87–96 (2006).CAS 
Article 

Google Scholar 
87.McCormick, A. C., Unsicker, S. B. & Gershenzon, J. The specificity of herbivore-induced plant volatiles in attracting herbivore enemies. Trends Plant Sci. 17, 303–310 (2012).Article 
CAS 

Google Scholar 
88.Lee, C. W. et al. Developmental phytotoxicity of metal oxide nanoparticles to Arabidopsis thaliana. Environ. Toxicol. Chem. 29, 669–675 (2010).CAS 
PubMed 
Article 

Google Scholar 
89.Sabaghnia, N. & Janmohammadi, M. Effect of nanosilicon particles application on salinity tolerance in early growth of some lentil genotypes. Ann. UMCS Biol. 69, 39–55 (2014).
Google Scholar 
90.Slomberg, D. L. & Schoenfisch, M. H. Silica nanoparticle phytotoxicity to Arabidopsis thaliana. Environ. Sci. Technol. 46, 10247–10254 (2012).CAS 
PubMed 

Google Scholar 
91.Le, V. N. et al. Uptake, transport, distribution and bio-effects of SiO2 nanoparticles in Bt-transgenic cotton. J. Nanobiotechnol. 12, 50 (2014).Article 
CAS 

Google Scholar  More

1.Müller, A. et al. The Role of Biomass in the Sustainable Development Goals: A Reality Check and Governance Implications. (Institue for Advances Sustainability Studies, 2015).2.Keesstra, S. et al. Soil-related sustainable development goals: Four concepts to make land degradation neutrality and restoration work. Land 7, 133 (2018).Article 

Google Scholar 
3.Li, X., Rubæk, G. H. & Sørensen, P. High plant availability of phosphorus and low availability of cadmium in four biomass combustion ashes. Sci. Total Environ. 557–558, 851–860 (2016).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
4.Hannam, K. D., Deschamps, C., Kwiaton, M., Venier, L. & Hazlett, P. W. Regulations and Guidelines for the Use of Wood Ash as a Soil Amendment in Canadian Forests (Natural Resources Canada Canadian Forest Service, 2016).5.Nieminen, M., Laiho, R., Sarkkola, S. & Penttilä, T. Whole-tree, stem-only, and stump harvesting impacts on site nutrient capital of a Norway spruce-dominated peatland forest. Eur. J. For. Res. 135, 531–538 (2016).CAS 
Article 

Google Scholar 
6.Adams, M., Burger, J., Jenkins, A. & Zelazny, L. Impact of harvesting and atmospheric pollution on nutrient depletion of eastern US hardwood forests. For. Ecol. Manag. 138, 301–319 (2000).Article 

Google Scholar 
7.Ågren, A., Buffam, I., Bishop, K. & Laudon, H. Sensitivity of pH in a boreal stream network to a potential decrease in base cations caused by forest harvest. Can. J. Fish. Aquat. Sci. 67, 1116–1125 (2010).Article 
CAS 

Google Scholar 
8.Jerabkova, L., Prescott, C. E., Titus, B. D., Hope, G. D. & Walters, M. B. A meta-analysis of the effects of clearcut and variable-retention harvesting on soil nitrogen fluxes in boreal and temperate forests. Can. J. For. Res. 41, 1852–1870 (2011).CAS 
Article 

Google Scholar 
9.Cronan, C. S. & Grigal, D. F. Use of calcium/aluminum ratios as indicators of stress in forest ecosystems. J. Environ. Qual. 24, 209–226 (1995).CAS 
Article 

Google Scholar 
10.Augusto, L., Bakker, M. R. & Meredieu, C. Wood ash applications to temperate forest ecosystems—Potential benefits and drawbacks. Plant Soil 306, 181–198 (2008).CAS 
Article 

Google Scholar 
11.Pitman, R. M. Wood ash use in forestry—A review of the environmental impacts. Forestry 79, 563–588 (2006).Article 

Google Scholar 
12.Pugliese, S. et al. Wood ash as a forest soil amendment: The role of boiler and soil type on soil property response. Can. J. Soil Sci. 94, 621–634 (2014).CAS 
Article 

Google Scholar 
13.Saarsalmi, A., Mälkönen, E. & Kukkola, M. Effect of wood ash fertilization on soil chemical properties and stand nutrient status and growth of some coniferous stands in Finland. Scand. J. For. Res. 19, 217–233 (2004).Article 

Google Scholar 
14.Zimmermann, S. & Frey, B. Soil respiration and microbial properties in an acid forest soil: Effects of wood ash. Soil Biol. Biochem. 34, 1727–1737 (2002).CAS 
Article 

Google Scholar 
15.Perkiömäki, J. & Fritze, H. Short and long-term effects of wood ash on the boreal forest humus microbial community. Soil Biol. Biochem. 34, 1343–1353 (2002).Article 

Google Scholar 
16.Etiégni, L. & Campbell, A. G. Physical and chemical characteristics of wood ash. Bioresour. Technol. 37, 173–178 (1991).Article 

Google Scholar 
17.Saarsalmi, A., Smolander, A., Kukkola, M. & Arola, M. Effect of wood ash and nitrogen fertilization on soil chemical properties, soil microbial processes, and stand growth in two coniferous stands in Finland. Plant Soil 331, 329–340 (2010).CAS 
Article 

Google Scholar 
18.Saarsalmi, A. & Levula, T. Wood ash application and liming: Effects on soil chemical properties and growth of Scots pine transplants. Balt. For. 13, 149–157 (2007).
Google Scholar 
19.Demeyer, A., Voundi Nkana, J. & Verloo, M. Characteristics of wood ash and influence on soil properties and nutrient uptake: An overview. Bioresour. Technol. 77, 287–295 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Zhou, H. Reducing, reusing and recycling solid wastes from wood fibre processing. In Towards Sustainable Management of the Boreal Forest (eds Burton, P. J. et al.) 759–798 (NRC Research Press, 2003) https://doi.org/10.1139/9780660187624.Chapter 

Google Scholar 
21.Rosenberg, O., Persson, T., Högbom, L. & Jacobson, S. Effects of wood-ash application on potential carbon and nitrogen mineralisation at two forest sites with different tree species, climate and N status. For. Ecol. Manag. 260, 511–518 (2010).Article 

Google Scholar 
22.Saarsalmi, A., Smolander, A., Kukkola, M., Moilanen, M. & Saramäki, J. 30-Year effects of wood ash and nitrogen fertilization on soil chemical properties, soil microbial processes and stand growth in a Scots pine stand. For. Ecol. Manag. 278, 63–70 (2012).Article 

Google Scholar 
23.McDonald, M. A., Hawkins, B. J., Prescott, C. E. & Kimmins, J. P. Growth and foliar nutrition of western red cedar fertilized with sewage sludge, pulp sludge, fish silage, and wood ash on northern Vancouver Island. Can. J. For. Res. 24, 297–301 (1994).Article 

Google Scholar 
24.Park, B. B., Yanai, R. D., Sahm, J. M., Lee, D. K. & Abrahamson, L. P. Wood ash effects on plant and soil in a willow bioenergy plantation. Biomass Bioenergy. 28, 355–365 (2005).CAS 
Article 

Google Scholar 
25.Batjes, N. H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 47, 151–163 (1996).CAS 
Article 

Google Scholar 
26.Nohrstedt, H. -Ö. Response of coniferous forest ecosystems on mineral soils to nutrient additions: A review of Swedish experiences. Scand. J. For. Res. 16, 555–573 (2001).Article 

Google Scholar 
27.Rennenberg, H. & Dannenmann, M. Nitrogen nutrition of trees in temperate forests—The significance of nitrogen availability in the pedosphere and atmosphere. Forests 6, 2820–2835 (2015).Article 

Google Scholar 
28.Solla-Gullón, F., Santalla, M., Pérez-Cruzado, C., Merino, A. & Rodríguez-Soalleiro, R. Response of Pinus radiata seedlings to application of mixed wood-bark ash at planting in a temperate region: Nutrition and growth. For. Ecol. Manag. 255, 3873–3884 (2008).Article 

Google Scholar 
29.Saarsalmi, A., Kukkola, M., Moilanen, M. & Arola, M. Long-term effects of ash and N fertilization on stand growth, tree nutrient status and soil chemistry in a Scots pine stand. For. Ecol. Manag. 235, 116–128 (2006).Article 

Google Scholar 
30.Ohno, T. Neutralization of soil acidity and release of phosphorus and potassium by wood ash. J. Environ. Qual. 21, 433–438 (1992).Article 

Google Scholar 
31.Bang-Andreasen, T. et al. Wood ash induced pH changes strongly affect soil bacterial numbers and community composition. Front. Microbiol. 8, 1400 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Clapham, W. M. & Zibilske, L. M. Wood ash as a liming amendment. Commun. Soil Sci. Plant Anal. 23, 1209–1227 (1992).CAS 
Article 

Google Scholar 
33.Ulery, A. L., Graham, R. C. & Amrhein, C. Wood-ash composition and soil pH following intense burning. Soil Sci. 156, 358–364 (1993).ADS 
CAS 
Article 

Google Scholar 
34.Muse, J. K. & Mitchell, C. C. Paper mill boiler ash and lime by-products as soil liming materials. Agron. J. 87, 432–438 (1995).Article 

Google Scholar 
35.Bramryd, T. & Fransman, B. Silvicultural use of wood ashes – Effects on the nutrient and heavy metal balance in a pine (Pinus sylvestris, L) forest soil. Water Air Soil Pollut. 85, 1039–1044 (1995).ADS 
CAS 
Article 

Google Scholar 
36.Jones, D. L. & Quilliam, R. S. Metal contaminated biochar and wood ash negatively affect plant growth and soil quality after land application. J. Hazard. Mater. 276, 362–370 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Kim, K.-D. Species alterations caused by nitrogen and carbon addition in nutrient-deficient municipal waste landfills. J. Ecol. Environ. 30, 161–170 (2007).Article 

Google Scholar 
38.Jacobson, S. Addition of stabilized wood ashes to Swedish coniferous stands on mineral soils—Effects on stem growth and needle nutrient concentrations. Silva Fenn. 37, 437–450 (2003).Article 

Google Scholar 
39.Hånell, B. & Magnusson, T. An evaluation of land suitability for forest fertilization with biofuel ash on organic soils in Sweden. For. Ecol. Manag. 209, 43–55 (2005).Article 

Google Scholar 
40.Hannam, K. D. et al. Wood ash as a soil amendment in Canadian forests: What are the barriers to utilization?. Can. J. For. Res. 48, 442–450 (2018).Article 

Google Scholar 
41.Woods, S. W. & Balfour, V. N. The effect of ash on runoff and erosion after a severe forest wildfire, Montana, USA. Int. J. Wildl. Fire 17, 535–548 (2008).Article 

Google Scholar 
42.Someshwar, A. Wood and combination wood-fired boiler ash characterization. J. Environ. Qual. 25, 962–972 (1996).CAS 
Article 

Google Scholar 
43.National Institute of Agricultural Science and Technology. Methods of Soil and Plant Analysis (ed. Im, J.-N.) 202 (National Institute of Agricultural Science and Technology, RDA, 2000).44.Sparks, D. L., Page, A. L., Helmke, P. A. & Leoppert, R. H. Methods of Soil Analysis Part3—Chemical Methods, SSSA Book Ser. 5.3. (Soil Science Society of America, American Society of Agronomy, 1996) https://doi.org/10.2136/sssabookser5.3.45.Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 
Article 

Google Scholar 
46.Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis Part3—Chemical Methods, SSSA Book Ser. 5.3 (eds Sparks, D. L. et al.) 961–1010 (Soil Science Society of America, American Society of Agronomy, 1996) https://doi.org/10.2136/sssabookser5.3.c34.Chapter 

Google Scholar 
47.Bremner, J. M. Nitrogen-total. In Methods of Soil Analysis Part 3—Chemical Methods, SSSA Book Ser. 5.3 (eds Sparks, D. L. et al.) 1085–1121 (Soil Science Society of America, American Society of Agronomy, 1996) https://doi.org/10.2136/sssabookser5.3.c37.Chapter 

Google Scholar 
48.Kuo, S. Phosphorus. In Methods of Soil Analysis Part 3—Chemical Methods, SSSA Book Ser. 5.3 (eds Sparks, D. L. et al.) 869–919 (Soil Science Society of America, American Society of Agronomy, 1996) https://doi.org/10.2136/sssabookser5.3.c32.Chapter 

Google Scholar 
49.Cox, M. S. The Lancaster soil test method as an alternative to the Mehlich 3 soil test method1. Soil Sci. 166, 484–489 (2001).ADS 
CAS 
Article 

Google Scholar 
50.Sumner, M. E. & Miller, W. P. Cation exchange capacity and exchange coefficients. In Methods of Soil Analysis Part 3—Chemical Methods, SSSA Book Ser. 5.3 (eds Sparks, D. L. et al.) 1201–1229 (Soil Science Society of America, American Society of Agronomy, 1996).
Google Scholar  More

Study site and set-upThe studied hemiboreal riparian forest is a 40-year old Filipendula type gray alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural land. It is situated in the Agali Village (58°17′N; 27°17′E) in eastern Estonia within the Lake Peipsi Lowland50 (Supplementary Figs. 12 and 13).The area is characterized by a flat relief with an average elevation of 32 m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15–20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3−–N), ammonia NH4+–N, Ca, and Mg per dry matter in 10 cm topsoil was 3.8 and 0.33%, and 2.42, 2.89, 1487 and 283 mg kg−1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20 cm deep zone (Supplementary Table 3).The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and –6.7 °C in January. The duration of the growing season is typically 175–180 days from mid-April to October51.The mean height of the forest stand is 17.5 m, stand density 1520 trees per ha, the mean stem diameter at breast height 15.6 cm, basal area 30.5 m2 ha−1, the growing stock 245 m3 ha−1, and the current annual increment of stems 12.0 m3 ha−1 year−1 (based on Uri et al.52 and Becker et al.53). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench, shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.), and young trees (A. incana, Prunus padus (L.)) dominate. In the moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst are overwhelming.Environmental characteristics of hot momentsBased on high emissions of N2O, dynamics of SWC, and near-ground air temperature, we identified four hot moments and related them to soil and environmental variables (see numbers in Fig. 1): wet (1), dry (2) with drought onset (2a), freeze-thaw (3), and dry-minor (4). The main criterion for the hot moments was a rapid increase in N2O emissions of any source.Anomalies from the mean of each hot moment period illustrate the pattern of fluxes during the hot moments (Supplementary Fig. 2). At the end of the freeze-thaw period, the rising SWC is driven by snowmelt became a leading determinant (Supplementary Fig. 2). During the wet period, the rise in soil emissions was accompanied by a remarkable increase in the EC-based ecosystem fluxes. However, all the other hot moments were isolated to soil surfaces.Soil flux measurementsSoil fluxes were measured using 12 automatic dynamic chambers located at 1–2 m distance from each studied tree and installed in June 2017 (Supplementary Fig. 11, see also54). The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min−1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 min each. The flushing time of the whole system with ambient air between measurement periods was 1 min. Thus, there were ~12 measurements per chamber per day, making a total of 144 flux measurements per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of ~1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer allowing a sequent practically continuous measurement.To account for initial stabilization after chamber closing and flushing time, we used 5 min out of the total 9 min closing time (~350 concentration measurements) to estimate slope change of N2O concentration, which was the basis for soil flux calculations.After the quality check, 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period.Stem flux measurementsThe tree stem fluxes were measured manually with frequency 1–2 times per week from September 2017 until December 2018. Twelve representative mature gray alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements20. Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in the following order: at the bottom part of the tree stem (~10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For the chamber, preparation see Schindler et al.54. Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analyzed in the laboratory at the University of Tartu within a week using gas chromatography (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al.55.Soil and stem flux calculationFluxes were quantified on a linear approach according to the change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston and Hutchison56.Stem fluxes were quantified on a linear approach according to the change of N2O concentrations in the chamber headspace over time. A data quality control (QC) was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values.To compare the contribution of soil and stems, the stem fluxes were upscaled to hectares of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of the tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al.54.EC instrumentationEC system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain three wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed an airflow rate of 15 L min−1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10 Hz and the gas-analyzer reported concentrations per dry air (dry mixing ratios).Eddy-covariance flux calculation and data QCThe fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-min periods. Despiking of the raw data was performed following Mauder et al.57. Anemometer tilt was corrected with the double-axis rotation. Linear detrending was chosen over block averaging to minimize the influence of possible fluctuations of a gas analyzer. Time lags were detected using covariance maximization in a given time window (5 ± 2 s was chosen based on the tube length and flow rate). While Webb-Pearman-Leuning (WPL) correction58 is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported dry mixing ratios. Both low and high-frequency spectral corrections were applied using fully analytic corrections59,60.Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300–350 ppb), QC flags higher than 761.The footprint area was estimated using Kljun et al.62 implemented in TOVI software (Li-Cor Inc.). A footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis.Storage fluxes were estimated using concentration measurements from the eddy system (Eq. (1)), assuming the uniform change within the air column under the tower during every 30 min period63,64:$${mathrm{S}} = {Delta}{mathrm{C}}/{Delta}{mathrm{t}} ast {mathrm{z}}_{mathrm{m}},$$
(1)
where S is storage, ΔC is change in the dry mixing ratio of N2O, Δt is time period (30 min), zm is measurement height (21 m).In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al.36). Overall, the QC procedures resulted in 61% data coverage.While friction velocity (u*) threshold is used to filter eddy fluxes of CO265, visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that the 1–9 QC flag system already marks the times when the turbulence is not sufficient.To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al.66).MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis.Ancillary measurementsAir temperature, relative and absolute humidity were measured within the canopy at 10 m height using the HC2A-S3—Standard Meteo Probe/RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). The potential amount of dissolved N2O in the atmospheric water was calculated based on the absolute humidity and the maximum solubility of N2O in water67. DPD was calculated from air temperature and estimated dew point temperature to characterize the chance of fog formation within the canopy. The solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site68 using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated as the ratio of diffuse solar radiation to total solar radiation.Near-ground air temperature, soil temperature (Campbell Scientific Inc.), and SWC sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0–10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017, composite topsoil samples were taken with a soil corer from a depth of 0–10 cm for physical and chemical analysis using standard methods69.Statistical analysisR version 4.0.2 (R Development Core Team, 2020) was used to examine, analyze and visualize the data. The significance level (alpha) considered for all the tests was 0.05. The “akima” package version 0.6–2.1 was used to create interpolated contour plots representing a three-dimensional surface70 by plotting soil temperature and SWC against soil N2O emissions as the independent variable. Linear regression models were fitted and Spearman’s rank correlation coefficients were shown for change of SWC and soil N2O flux in period drought onset and air temperature and soil N2O flux in period freeze-thaw. Spearman’s rank correlation coefficients were also shown characterizing the relationship between the monthly average number of days with a high chance of sunshine and fog formation and the difference between the N2O flux from soil and ecosystem. Regarding all measurements of soil temperature, SWC, and soil N2O flux, relationships were better represented by nonlinear than linear models. In addition, the Bragg equation with four parameters71 was used for describing the relationship between SWC and soil N2O flux in the dry period. A workflow for the nonlinear regression analysis was used72 and regression models were fitted in R using functions lm, nls, or loess. More

1.Valles, S. M. et al. A picorna-like virus from the red imported fire ant, Solenopsis invicta: Initial discovery, genome sequence, and characterization. Virology 328(1), 151–157. https://doi.org/10.1016/j.virol.2004.07.016 (2004).CAS 
Article 
PubMed 

Google Scholar 
2.Russell, J. A. The ants (Hymenoptera: Formicidae) are unique and enigmatic hosts of prevalent Wolbachia (Alphaproteobacteria) symbionts. Myrmecol. News 16, 7–23 (2012).
Google Scholar 
3.Mongkolsamrit, S. et al. Life cycle, host range and temporal variation of Ophiocordyceps unilateralis/Hirsutella formicarum on Formicine ants. J. Invertebr. Pathol. 111(3), 217–224. https://doi.org/10.1016/j.jip.2012.08.007 (2012).CAS 
Article 
PubMed 

Google Scholar 
4.Espadaler, X. & Santamaria, S. Ecto-and endoparasitic fungi on ants from the Holarctic region. Psyche https://doi.org/10.1155/2012/168478 (2012).Article 

Google Scholar 
5.Boomsma, J. J., Schmid-Hempel, P. & Hughes, W. O. H. Life histories and parasite pressure across the major groups of social insects. in Insect Evolutionary Ecology: Proceedings of the Royal Entomological Society’s 22nd Symposium, Reading, UK, 2003. (CABI Publishing, 2005).6.Lachaud, J. P., Lenoir, A. & Hughes, D. P. Ants and their parasites. Psyche https://doi.org/10.1155/2013/264279 (2013).Article 

Google Scholar 
7.de Bekker, C., Will, I., Das, B. & Adams, R. M. The ants (Hymenoptera: Formicidae) and their parasites: Effects of parasitic manipulations and host responses on ant behavioral ecology. Myrmecol. News 28, 1–24. https://doi.org/10.25849/myrmecol.news_028:001 (2018).Article 

Google Scholar 
8.Witek, M., Barbero, F. & Markó, B. Myrmica ants host highly diverse parasitic communities: From social parasites to microbes. Insect. Soc. 61(4), 307–323. https://doi.org/10.1007/s00040-014-0362-6 (2014).Article 

Google Scholar 
9.Tartally, A., Szücs, B. & Ebsen, J. R. The first records of Rickia wasmannii Cavara, 1899, a myrmecophilous fungus, and its Myrmica Latreille, 1804 host ants in Hungary and Romania (Ascomycetes: Laboulbeniales, Hymenoptera: Formicidae). Myrmecol. News 10, 123 (2007).
Google Scholar 
10.García, F., Espadaler, X., Echave, P. & Vila, R. Hormigas (Hymenoptera, Formicidae) de los acantilados de l’Avenc de Tavertet (Barcelona, Península Ibérica). Boletín de la Sociedad entomológica Aragonesa 47, 363–367 (2010).
Google Scholar 
11.Bezdĕčková, K. & Bezdĕčka, P. First records of the myrmecophilous fungus Rickia wasmannii (Ascomycetes: Laboulbeniales) in the Czech Republic. Acta Musei Moraviae Scientiae Biologicae 96(1), 193–197 (2011).
Google Scholar 
12.Cavara, F. Di una nuova Laboulbeniacea: Rickia wasmannii, nov. gen. e nov.spec. Malpighia 13(1-2), 173–188. (1899).13.Hulden, L. Floristic notes on palaearctic Laboulbeniales (Ascomycetes). Karstenia 25, 1–16 (1985).Article 

Google Scholar 
14.Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: Changes in host mortality and behavior. Insect. Soc. 61(3), 247–252. https://doi.org/10.1007/s00040-014-0349-3 (2014).Article 

Google Scholar 
15.Báthori, F., Csata, E. & Tartally, A. Rickia wasmannii increases the need for water in Myrmica scabrinodis (Ascomycota: Laboulbeniales; Hymenoptera: Formicidae). J. Invertebr. Pathol. 126, 78–82. https://doi.org/10.1016/j.jip.2015.01.005 (2015).Article 
PubMed 

Google Scholar 
16.Báthori, F., Rádai, Z. & Tartally, A. The effect of Rickia wasmannii (Ascomycota, Laboulbeniales) on the aggression and boldness of Myrmica scabrinodis (Hymenoptera, Formicidae). J. Hymenopt. Res. 58, 41. https://doi.org/10.3897/jhr.58.13253 (2017).Article 

Google Scholar 
17.Csata, E. et al. Lock-picks: Fungal infection facilitates the intrusion of strangers into ant colonies. Sci. Rep. UK 7(1), 1–14. https://doi.org/10.1038/srep46323 (2017).CAS 
Article 

Google Scholar 
18.Csata, E., Billen, J., Bernadou, A., Heinze, J. & Markó, B. Infection-related variation in cuticle thickness in the ant Myrmica scabrinodis (Hymenoptera: Formicidae). Insect. Soc. 65(3), 503–506. https://doi.org/10.1007/s00040-018-0628-5 (2018).Article 

Google Scholar 
19.Tartally, A. et al. Patterns of host use by brood parasitic Maculinea butterflies across Europe. Philos. T. Roy. Soc. B. 374(1769), 20180202. https://doi.org/10.1098/rstb.2018.0202 (2019).Article 

Google Scholar 
20.Fauser-Misslin, A., Sadd, B. M., Neumann, P. & Sandrock, C. Influence of combined pesticide and parasite exposure on bumblebee colony traits in the laboratory. J. Appl. Ecol. 51(2), 450–459. https://doi.org/10.1111/1365-2664.12188 (2014).Article 

Google Scholar 
21.Müller, C. B. & Schmid-Hempel, P. Correlates of reproductive success among field colonies of Bombus lucorum: The importance of growth and parasites. Ecol. Entomol. 17, 343–353. https://doi.org/10.1111/j.1365-2311.1992.tb01068.x (1992).Article 

Google Scholar 
22.Porter, S. D. Impact of temperature on colony growth and developmental rates of the ant, Solenopsis invicta. J. Insect Physiol. 34(12), 1127–1133. https://doi.org/10.1016/0022-1910(88)90215-6 (1988).Article 

Google Scholar 
23.Nooten, S. S. & Rehan, S. M. Historical changes in bumble bee body size and range shift of declining species. Biodivers. Conserv. 29, 451–467. https://doi.org/10.1007/s10531-019-01893-7 (2020).Article 

Google Scholar 
24.Schmid-Hempel, P. On the evolutionary ecology of host–parasite interactions: Addressing the question with regard to bumblebees and their parasites. Naturwissenschaften 88, 147–158. https://doi.org/10.1007/s001140100222 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
25.Molet, M., Péronnet, R., Couette, S., Canovas, C. & Doums, C. Effect of temperature and social environment on worker size in the ant Temnothorax nylanderi. J. Therm. Biol. 67, 22–29. https://doi.org/10.1016/j.jtherbio.2017.04.013 (2017).Article 
PubMed 

Google Scholar 
26.Haelewaters, D., Boer, P., Gort, G. & Noordijk, J. Studies of Laboulbeniales (Fungi, Ascomycota) on Myrmica ants (II): Variation of infection by Rickia wasmannii over habitats and time. Anim. Biol. 65(3–4), 219–231. https://doi.org/10.1163/15707563-00002472 (2015).Article 

Google Scholar 
27.Báthori, F., Pfliegler, W. P., Rádai, Z. & Tartally, A. Host age determines parasite load of Laboulbeniales fungi infecting ants: Implications for host-parasite relationship and fungal life history. Mycoscience 30, 1–6. https://doi.org/10.1016/j.myc.2017.09.004 (2017).Article 

Google Scholar 
28.Bezdĕčka, P. & Bezdĕčková, K. First record of the myrmecophilous fungus Rickia wasmannii (Ascomycetes: Laboulbeniales) in Slovakia. Folia Faunistica Slovaca 16(2), 71–72 (2011).
Google Scholar 
29.Csősz, S. & Majoros, G. Ontogenetic origin of mermithogenic Myrmica phenotypes (Hymenoptera, Formicidae). Insect. Soc. 56, 70–76. https://doi.org/10.1007/s00040-008-1040-3 (2009).Article 

Google Scholar 
30.Di Salvo, M. et al. The microbiome of the Maculinea-Myrmica host-parasite interaction. Sci. Rep. UK 9(1), 1–10. https://doi.org/10.1038/s41598-019-44514-7 (2019).ADS 
CAS 
Article 

Google Scholar 
31.Tragust, S., Tartally, A., Espadaler, X. & Billen, J. Histopathology of Laboulbeniales (Ascomycota: Laboulbeniales): Ectoparasitic fungi on ants (Hymenoptera: Formicidae). Myrmecol. News 23, 81–89. https://doi.org/10.25849/myrmecol.news_023:081 (2016).Article 

Google Scholar 
32.Konrad, M., Grasse, A. V., Tragust, S. & Cremer, S. Anti-pathogen protection versus survival costs mediated by an ectosymbiont in an ant host. P. Roy. Soc. B-Biol. Sci. 282(1799), 20141976. https://doi.org/10.1098/rspb.2014.1976 (2015).CAS 
Article 

Google Scholar 
33.Peeters, C., Molet, M., Lin, C. C. & Billen, J. Evolution of cheaper workers in ants: A comparative study of exoskeleton thickness. Biol. J. Linn. Soc. 121, 556–563. https://doi.org/10.1093/biolinnean/blx011 (2017).Article 

Google Scholar 
34.Cammaerts-Tricot, M. C. Production and perception of attractive pheromones by differently aged workers of Myrmica rubra (Hymenoptera Formicidae). Insect. Soc. 21(3), 235–247. https://doi.org/10.1007/BF02226916 (1974).Article 

Google Scholar 
35.Csősz, S. et al. Insect morphometry is reproducible under average investigation standards. Ecol. Evol. 11(1), 547–559. https://doi.org/10.1002/ece3.7075 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Wolak, M. E., Fairbairn, D. J. & Paulsen, Y. R. Guidelines for estimating repeatability. Methods Ecol. Evol. 3(1), 129–137. https://doi.org/10.1111/j.2041-210X.2011.00125.x (2012).Article 

Google Scholar 
37.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. R version 4.0.2 (2020-06-22) (Vienna, Austria, 2020).38.Wright, K. nipals: Principal Components Analysis using NIPALS or Weighted EMPCA, with Gram-Schmidt Orthogonalization. R package version 0.7., published 2020-01-24. https://CRAN.R-project.org/package=nipals/index.html (2020).39.Bates, D., Machler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).40.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: Tests in linear mixed effects models. J. Stat. Soft. 82(13), 1–26. https://doi.org/10.18637/JSS.V082.I13 (2017).Article 

Google Scholar 
41.Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Google Scholar 
42.Jennrich, R. I. An asymptotic χ2 test for the equality of two correlation matrices. J. Am. Stat. Assoc. 65(330), 904–912. https://doi.org/10.1080/01621459.1970.10481133 (1970).MathSciNet 
Article 
MATH 

Google Scholar  More