Ecology

1.Grinnell, J. The niche-relationships of the California thrasher. Auk 34, 427–433 (1917).Article 

Google Scholar 
2.Elton, C. S. Animal Ecology (Univ. Chicago Press, 2001).3.Hutchinson, G. E. Concluding remarks Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).4.Hutchinson, G. E. An Introduction to Population Ecology (Yale Univ. Press, 1978).5.Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: the once and future niche. Proc. Natl Acad. Sci. USA 106, 19651–19658 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Polechová, J. & Storch, D. in Encyclopedia of Ecology 2nd edn, Vol. 3 (ed Fath, B.) 72–80 (Elsevier, 2018).7.Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Hutchinson, G. E. Population studies: animal ecology and demography. Bull. Math. Biol. 53, 193–213 (1991).Article 

Google Scholar 
9.Odum, E. P. Fundamentals of Ecology (Saunders, 1959).10.Begon, M., Townsend, C. R. & JL., H. Ecology: From Individuals to Ecosystems (Wiley, 2006).11.Levin, S. & Carpenter, S. The Princeton Guide to Ecology (Princeton Univ. Press, 2009).12.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
13.Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).Article 

Google Scholar 
14.Austin, M. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Modell. 157, 101–118 (2002).Article 

Google Scholar 
15.Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. 2, 1–10 (2005).Article 

Google Scholar 
16.Pires, M. M. & Guimarães, P. R. Interaction intimacy organizes networks of antagonistic interactions in different ways. J. R. Soc. Interface 10, 20120649 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Ashby, B., Watkins, E., Lourenço, J., Gupta, S. & Foster, K. R. Competing species leave many potential niches unfilled. Nat. Ecol. Evol. 1, 1495–1501 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Pérez-Gutiérrez, R. A. et al. Antagonism influences assembly of a Bacillus guild in a local community and is depicted as a food-chain network. ISME J. 7, 487–497 (2013).PubMed 
Article 
CAS 

Google Scholar 
19.Russel, J., Røder, H. L., Madsen, J. S., Burmølle, M. & Sørensen, S. J. Antagonism correlates with metabolic similarity in diverse bacteria. Proc. Natl Acad. Sci. USA 114, 10684–10688 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Ricklefs, R. E. Evolutionary diversification, coevolution between populations and their antagonists, and the filling of niche space. Proc. Natl Acad. Sci. USA 107, 1265–1272 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Stadler, B. & AFG, D. Ecology and evolution of aphid–ant interactions. Annu. Rev. Ecol. Evol. Syst. 107, 345–372 (2005).Article 

Google Scholar 
22.Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 
Article 
CAS 

Google Scholar 
23.Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).PubMed 
Article 
CAS 

Google Scholar 
24.Hom, E. & Murray, A. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–95 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274, 303–313 (2007).PubMed 
Article 

Google Scholar 
26.Yurtsev, E. A., Conwill, A. & Gore, J. Oscillatory dynamics in a bacterial cross-protection mutualism. Proc. Natl Acad. Sci. USA 113, 6236–6241 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Pereira, F. C. & Berry, D. Microbial nutrient niches in the gut. Environ. Microbiol. 19, 1366–1378 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).PubMed 
Article 

Google Scholar 
29.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Schink, B. Energetics of syntrophic cooperation in methanogenic degradation. Microbiol. Mol. Biol. Rev. 61, 262–280 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Matthews, B., Aebischer, T., Sullam, K. E., Lundsgaard-Hansen, B. & Seehausen, O. Experimental evidence of an eco-evolutionary feedback during adaptive divergence. Curr. Biol. 26, 483–489 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Hendry, A. Eco-evolutionary Dynamics (Princeton Univ. Press, 2017).34.Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Giri, S. et al. Metabolic dissimilarity determines the establishment of cross- feeding interactions in bacteria. Preprint at bioRxiv https://doi.org/10.1101/2020.10.09.333336 (2020).36.Preussger, D., Giri, S., Muhsal, L. K., Oña, L. & Kost, C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr. Biol. 30, 3580–3590.e7 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Stearns, S. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).Article 

Google Scholar 
38.Agrawal, A. A., Conner, J. K. & Rasmann, S. in Evolution Since Darwin (eds Bell, M. A. et al.) Ch. 10 (Sinauer Associates, 2010).39.González-Cabaleiro, R., Ofiţeru, I. D., Lema, J. M. & Rodríguez, J. Microbial catabolic activities are naturally selected by metabolic energy harvest rate. ISME J. 9, 2630–2641 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).Article 

Google Scholar 
41.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).Article 

Google Scholar 
42.May, R. & Arthur, R. Niche overlap as a function of environmental variability. Proc. Natl Acad. Sci. USA 69, 1109–1113 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Bono, L. M., Draghi, J. A. & Turner, P. E. Evolvability costs of niche expansion. Trends Genet. 36, 14–23 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Treves, D. S., Manning, S. & Adams, J. Repeated evolution of an acetate-cross-feeding polymorphism in long-term populations of Escherichia coli. Mol. Biol. Evol. 15, 789–797 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Rozen, D. E., Schneider, D. & Lenski, R. E. Long-term experimental evolution in Escherichia coli. XIII. Phylogenetic history of a balanced polymorphism. J. Mol. Evol. 61, 171–180 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535.e6 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Gentile, C. L. & Weir, T. L. The gut microbiota at the intersection of diet and human health. Science 362, 776–780 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Ruff, W. E., Greiling, T. M. & Kriegel, M. A. Host–microbiota interactions in immune-mediated diseases. Nat. Rev. Microbiol. 18, 521–538 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Morris, B. E. L., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013).CAS 
PubMed 
Article 

Google Scholar 
52.D’Souza, G. et al. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat. Prod. Rep. 35, 455–488 (2018).PubMed 
Article 

Google Scholar 
53.Johnson, W. M. et al. Auxotrophic interactions: a stabilizing attribute of aquatic microbial communities? FEMS Microbiol. Ecol. 96, 1–14 (2020).Article 
CAS 

Google Scholar 
54.Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Bernhardsson, S., Gerlee, P. & Lizana, L. Structural correlations in bacterial metabolic networks. BMC Evol. Biol. 11, 20 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Hester, E. R., Jetten, M. S. M., Welte, C. U. & Lücker, S. Metabolic overlap in environmentally diverse microbial communities. Front. Genet. https://doi.org/10.3389/fgene.2019.00989 (2019).58.Mitri, S. & Richard Foster, K. The genotypic view of social interactions in microbial communities. Annu. Rev. Genet. 47, 247–273 (2013).CAS 
PubMed 
Article 

Google Scholar 
59.Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).CAS 
PubMed 
Article 

Google Scholar 
60.Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 
Article 

Google Scholar 
61.Vanstockem, M., Michiels, K., Vanderleyden, J. & van Gool, A. P. Transposon mutagenesis of Azospirillum brasilense and Azospirillum lipoferum: physical analysis of Tn5 and Tn5-Mob insertion mutants. Appl. Environ. Microbiol. 53, 410–415 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Thomason, L. C., Costantino, N. & Court, D. L. E. coli genome manipulation by P1 transduction. Curr. Protoc. Mol. Biol. 79, 1.17.1–1.17.8 (2007).63.Pande, S. et al. Metabolic cross-feeding via intercellular nanotubes among bacteria. Nat. Commun. 6, 6238 (2015).CAS 
PubMed 
Article 

Google Scholar 
64.Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Konkol, M. A., Blair, K. M. & Kearns, D. B. Plasmid-encoded comi inhibits competence in the ancestral 3610 strain of Bacillus subtilis. J. Bacteriol. 195, 4085–4093 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Koo, B. M. et al. Construction and analysis of two genome-scale deletion libraries for Bacillus subtilis. Cell Syst. 4, 291–305.e7 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Thompson, I., Lilley, A., Ellis, R., Bramwell, P. & Bailey, M. Survival, colonization and dispersal of genetically modified Pseudomonas fluorescens SBW25 in the phytosphere of field grown sugar beet. Nat. Biotechnol. 13, 1493–1497 (1995).CAS 
Article 

Google Scholar 
68.Rainey, P. B. Adaptation of Pseudomonas fluorescens to the plant rhizosphere. Environ. Microbiol. 1, 243–257 (1999).CAS 
PubMed 
Article 

Google Scholar 
69.Horton, R., Hunt, H., Ho, S., Pullen, J. & Pease, L. Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. Gene 77, 61–68 (1989).CAS 
PubMed 
Article 

Google Scholar 
70.Ditta, G., Stanfield, S., Corbin, D. & Helinski, D. R. Broad host range DNA cloning system for Gram-negative bacteria: construction of a gene bank of Rhizobium meliloti. Proc. Natl Acad. Sci. USA 77, 7347–7351 (1980).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Zhang, X. X. & Rainey, P. B. Genetic analysis of the histidine utilization (hut) genes in Pseudomonas fluorescens SBW25. Genetics 176, 2165–2176 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Lassak, J., Henche, A. L., Binnenkade, L. & Thormann, K. M. ArcS, the cognate sensor kinase in an atypical arc system of Shewanella oneidensis MR-1. Appl. Environ. Microbiol. 76, 3263–3274 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).CAS 
Article 
PubMed 

Google Scholar 
74.Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Bochner, B. R. Global phenotypic characterization of bacteria. FEMS Microbiol. Rev. 33, 191–205 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
Google Scholar 
4.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
6.Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).Article 

Google Scholar 
8.Falaschi, M., Manenti, R., Thuiller, W. & Ficetola, G. F. Continental‐scale determinants of population trends in European amphibians and reptiles. Glob. Change Biol. 25, 3504–3515 (2019).Article 

Google Scholar 
9.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 
Article 
CAS 

Google Scholar 
11.Jarzyna, M. A. & Jetz, W. Detecting the multiple facets of biodiversity. Trends Ecol. Evol. 31, 527–538 (2016).PubMed 
Article 

Google Scholar 
12.Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).CAS 
PubMed 
Article 

Google Scholar 
13.Bell, J. R. et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century. Glob. Change Biol. 25, 1982–1994 (2019).Article 

Google Scholar 
14.Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).PubMed 
Article 

Google Scholar 
15.van Strien, A. J., van Swaay, C. A., van Strien-van Liempt, W. T., Poot, M. J. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234, 116–122 (2019).Article 

Google Scholar 
16.Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—a macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).Article 

Google Scholar 
18.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
PubMed 
Article 

Google Scholar 
19.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
20.Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

Google Scholar 
21.Kondratyeva, A., Grandcolas, P. & Pavoine, S. Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution. Biol. Rev. 94, 1317–1337 (2019).PubMed 
Article 

Google Scholar 
22.Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non‐native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).Article 

Google Scholar 
23.WallisDeVries, M. F. & van Swaay, C. A. A nitrogen index to track changes in butterfly species assemblages under nitrogen deposition. Biol. Conserv. 212, 448–453 (2017).Article 

Google Scholar 
24.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 

Google Scholar 
25.Sgardeli, V., Zografou, K. & Halley, J. M. Climate change versus ecological drift: assessing 13 years of turnover in a butterfly community. Basic Appl. Ecol. 17, 283–290 (2016).Article 

Google Scholar 
26.van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2019).Article 
CAS 

Google Scholar 
27.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).CAS 
PubMed 
Article 

Google Scholar 
30.Marta, S. et al. ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna. Sci. Data 6, 195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Koleff, P., Gaston, K. J. & Lennon, J. T. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
32.Legendre, P. A temporal beta‐diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecol. Evol. 9, 3500–3514 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Suggit, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 

Google Scholar 
34.Baselga, A., Bonthoux, S. & Balent, G. Temporal beta diversity of bird assemblages in agricultural landscapes: land cover change vs. stochastic processes. PLoS ONE 10, e0127913 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).
Google Scholar 
36.Mason, N. W., de Bello, F., Mouillot, D., Pavoine, S. & Dray, S. A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients. J. Veg. Sci. 24, 794–806 (2013).Article 

Google Scholar 
37.Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).38.Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).Article 

Google Scholar 
39.Brunetti, M., Maugeri, M., Monti, F. & Nanni, T. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345–381 (2006).Article 

Google Scholar 
40.Terzago, S., von Hardenberg, J., Palazzi, E. & Provenzale, A. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models. Cryosphere 11, 1625–1645 (2017).Article 

Google Scholar 
41.Beniston, M. et al. The European mountain cryosphere: a review of its current state, trends and future challenges. Cryosphere 12, 759–794 (2018).Article 

Google Scholar 
42.Wang, J. et al. Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. 11, 528 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with nonstationary climate–fire models. Nat. Commun. 9, 3821 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Jacobson, A. R., Provenzale, A., von Hardenberg, A., Bassano, B. & Festa-Bianchet, M. Climate forcing and density dependence in a mountain ungulate population. Ecology 85, 1598–1610 (2004).Article 

Google Scholar 
45.Imperio, S., Bionda, R., Viterbi, R. & Provenzale, A. Climate change and human disturbance can lead to local extinction of Alpine rock ptarmigan: new insight from the Western Italian Alps. PLoS ONE 8, e81598 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Hoffmann, S., Beierkuhnlein, C., Field, R., Provenzale, A. & Chiarucci, A. Uniqueness of protected areas for conservation strategies in the European Union. Sci. Rep. 8, 6445 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).Article 

Google Scholar 
48.Queiroz, C., Beilin, R., Folke, C. & Lindborg, R. Farmland abandonment: threat or opportunity for biodiversity conservation? A global review. Front. Ecol. Environ. 12, 288–296 (2014).Article 

Google Scholar 
49.Falcucci, A., Maiorano, L. & Boitani, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).Article 

Google Scholar 
50.Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Ranganathan, S., Swain, R. B. & Sumpter, D. J. T. The demographic transition and economic growth: implications for development policy. Palgrave Commun. 1, 15033 (2015).Article 

Google Scholar 
52.Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience 53, 941–952 (2003).Article 

Google Scholar 
53.Lacasella, F. et al. From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability. Ecol. Appl. 27, 575–588 (2017).PubMed 
Article 

Google Scholar 
54.Ficetola, G. F. & Maiorano, L. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia 181, 683–693 (2016).PubMed 
Article 

Google Scholar 
55.Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).CAS 
PubMed 
Article 

Google Scholar 
56.Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).PubMed 
Article 

Google Scholar 
59.Poff, N. L. et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change 6, 25–34 (2017).Article 

Google Scholar 
60.Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).PubMed 
Article 

Google Scholar 
61.Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
62.Galland, T. et al. Colonization resistance and establishment success along gradients of functional and phylogenetic diversity in experimental plant communities. J. Ecol. 107, 2090–2104 (2019).Article 

Google Scholar 
63.Lister, A. M. et al. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).PubMed 
Article 

Google Scholar 
64.Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. B 345, 101–118 (1994).CAS 
Article 

Google Scholar 
65.Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).Article 

Google Scholar 
66.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019).67.Chazdon, R. L., Colwell, R. K., Denslow, J. S. & Guariguata, M.R. in Forest Biodiversity Research, Monitoring and Modeling: Conceptual Background and Old World Case Studies (eds. Dallmeir, F. & Cominsky, J. A.) 285–309 (Parthenon, 1998).68.Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).Article 

Google Scholar 
69.van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
70.Osborn, T. J. & Jones, P. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst. Sci. Data 6, 61–68 (2014).Article 

Google Scholar 
71.New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000).Article 

Google Scholar 
72.Brunetti, M. et al. Projecting north eastern Italy temperature and precipitation secular records onto a high resolution grid. Phys. Chem. Earth. 40, 9–22 (2012).Article 

Google Scholar 
73.Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. High-resolution temperature climatology for Italy: interpolation method intercomparison. Int. J. Climatol. 34, 1278–1296 (2014).Article 

Google Scholar 
74.Crespi, A., Brunetti, M., Lentini, G. & Maugeri, M. 1961–1990 high-resolution monthly precipitation climatologies for Italy. Int. J. Climatol. 38, 878–895 (2018).Article 

Google Scholar 
75.Peterson, T. C. et al. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517 (1998).Article 

Google Scholar 
76.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
77.Burnham, K. & Anderson, D. Model Selection and Multi-model Inference (Springer, 2002).78.Blonder, B & Harris, D. J. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12 (2019).79.Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n‐dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).Article 

Google Scholar 
80.Barros, C., Thuiller, W., Georges, D., Boulangeat, I. & Münkemüller, T. N‐dimensional hypervolumes to study stability of complex ecosystems. Ecol. Lett. 19, 729–742 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 

Google Scholar 
82.Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Community Ecol. 19, 77–83 (2018).Article 

Google Scholar 
83.Signorell, A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.40 (2021).84.Maclean, I. M. D., Suggitt, A. J., Wilson, R. J., Duffy, J. P. & Bennie, J. J. Fine-scale climate change: modelling fine-scale spatial variation in biologically meaningful rates of warming. Glob. Change Biol. 23, 256–268 (2017).Article 

Google Scholar 
85.Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).Article 

Google Scholar 
86.Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991).Article 

Google Scholar 
87.Bivand, R. S. & Wong, D. W. Comparing implementations of global and local indicators of spatial association. Test 27, 716–748 (2018).Article 

Google Scholar 
88.Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).Article 

Google Scholar 
89.Bivand, R. S., Gómez-Rubio, V. & Rue, H. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31 (2015).
Google Scholar 
90.Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).PubMed 
Article 

Google Scholar 
91.R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).92.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar  More

Assumptions for the space-for-time substitutionThe main methodological concept in this study is the notion of a space-for-time substitution. Such approach has previously been used in various studies to estimate the effect of land cover change on temperature20,22 or on the surface energy balance22,72. The overarching assumption behind the method is that the difference in properties of neighbouring patches of land can serve as a surrogate for changes in time. While this main assumption largely holds for land surface properties, such as skin temperature, it requires a more detailed articulation into several underlying assumptions in order to apply the approach to atmospheric properties such as cloud cover. This is because atmospheric properties are prone to lateral movements, partially decoupling them from the land cover directly below them, and thus adding considerable complexity to the analysis.The first underlying assumption is that the method will be mostly sensitive to low-level convective clouds generated in the boundary layer (i.e. cumulus clouds). These are typically formed under stable conditions of high pressure and low wind, and are thus expected to have a higher spatial correlation with the underlying landscape elements. Other types of low-level clouds, such as stratus clouds, are typically much more uniformly spread across the landscape, which would result in no difference in CFrC when comparing two distinct and neighbouring vegetation classes. For medium- or high-level clouds, their position will be determined mostly by the state of the atmosphere rather than by the land surface, resulting in a very low correlation with vegetation spatial patterns. The space-for-time substitution approach would thus similarly result in white noise.The second assumption is that the boundary layer cumulus clouds will see very limited lateral advection between the moment of their formation and the satellite observation. Cumulus clouds over land show a very stable climatology where the peak formation is largely confined to the early afternoon (around 14:00), timing which remains very stable across space and season43. Therefore this assumption should largely hold if the observations are made at this time.The third assumption is that if we consider topographically flat terrain that is away from a coastline, general weather conditions are essentially the same at a local scale (i.e. a region of radius circa 25 km around a given point). Within such an area, we then assume that variations in low cloud cover are mostly determined by local differences in surface properties, themselves determined by the type and condition of the present land cover.Preparation of input datasetsThis study requires gridded geospatial datasets for two variables: cloud fractional cover and land fractional cover. Both datasets used here have been prepared in the frame of the European Space Agency’s (ESA) Climate Change Initiative (CCI)73.The Cloud CCI55 provides a series of cloud properties derived from distinct satellite Earth observation platforms in a harmonized way. Here we use their cloud fractional cover variable (henceforth CFrC), which describes the fraction of a 0.05° × 0.05° pixel covered by clouds based on observations made at a finer spatial resolution at the given time of the satellite overpass. We chose to use Cloud CCI dataset based on the MODIS instrument on-board of the Aqua platform for two reasons. First, the timing of overpass of the Aqua platform (circa 13:30 local time at the Equator) coincides very well with the timing of peak of cumulus cloud formation43, thus greatly limiting the extent of possible cloud advection between the moment of cloud formation and observation. Second, native spatial resolution of the MODIS instrument is superior to the alternative (AVHRR), and should result in a better sensitivity to the presence of small cumulus clouds. More specifically, out of the 5 spectral bands of the MODIS instrument used by the Cloud CCI to characterize cloud properties (bands 1, 2, 20, 31 and 32), two of them (bands 1 and 2) have a native spatial resolution of 250 m. While these are aggregated to 1 km (the spatial resolution of the other MODIS bands) prior to their ingestion in the cloud retrieval algorithm, their finer native granularity and quality should prove to be an asset for small cumulus cloud detection. The CCI MODIS-AQUA CFrC data is available for the period 2004–2014. The values are first averaged from daily to monthly scale, and then a single monthly value is calculated for every pixel over the period 2004–2014. The results are 12 layers each representing the multi-annual average CFrC for a given month.The second type of data needed for the analysis is the fraction of the 0.05° × 0.05° pixels that are covered by distinct vegetation types (essentially trees and grasses) and by other land cover classes (urban areas, bare soil, etc.). These are derived from the Land Cover CCI54, a set of consistent annual maps describing, with a spatial resolution of 300 m, how the terrestrial surface is covered based on The United Nations Land Cover Classification Scheme74. This information is aggregated both spatially and thematically using a specifically designed framework75 to produce maps of general land fractional cover with a spatial resolution of 0.05° to match that of the cloud fractional cover data. The procedure is very similar to that done in a previous study22. For the context of this study, which has a focus on afforestation, the interest lies on transitions among three main vegetated classes, namely: deciduous forest, evergreen forests and herbaceous vegetation. Herbaceous vegetation is composed of both grasses and crops, irrespective of management practice such as irrigation. While irrigation has a clear biophysical effect of its own60, we deemed the land cover product was not consistent enough for this specific class. For reasons that are explained in the respective methodological section below, the full compositional description of the landscape is necessary (i.e. beyond the classes of interest), and therefore land cover fractions of the following classes are also generated: shrublands, savannas, wetlands, water, bare or sparsely vegetated, snow or ice, and urban.Retrieving potential cloud fractional cover changeUnder the above-mentioned assumptions, we apply a space-for-time substitution algorithm developed in a previous study22 to the cloud fractional cover and land fractional cover datasets. We summarize the main aspects of the methodology, along with the few necessary adaptations, but the reader requiring more detail is redirected to the original papers22,76. The approach consists in applying an un-mixing operation over a spatially moving window containing n pixels. Over each window we apply a linear regression based on a matrix X containing the explanatory variables, in which each column of X represents the fractional cover of a given land cover type for each of the n pixels. The response variable is a vector y containing the n values of CFrC for the n pixels, while the vector β represents the regression coefficients:$${bf{y}}={bf{X}}beta$$
(1)
This is equivalent to solving the following system of equations:$$left{begin{array}{ll}{y}_{1}=&{beta }_{1}{x}_{11}+{beta }_{2}{x}_{12}+…+{beta }_{m}{x}_{1m}\ {y}_{2}=&{beta }_{1}{x}_{21}+{beta }_{2}{x}_{22}+…+{beta }_{m}{x}_{2m}\ vdots &\ {y}_{n}=&{beta }_{1}{x}_{n1}+{beta }_{2}{x}_{n2}+…+{beta }_{m}{x}_{nm}end{array}right.$$
(2)
in which the digits of the subscript of x, e.g. xij, represent the land cover fraction j in pixel i, for the n pixels in the moving window and the m classes that are considered. Once identified, we can use the β coefficients to predict the local y value corresponding to a given composition x, including that composed of a single land cover j by setting xj = 1 and all other x values to zero. However, applying a regression directly on X carries a risk due to the compositional nature of the data (i.e. the sum of each row adds up to one), as the analysis of any given subset of compositional components can lead to very different patterns, results and conclusions77. To avoid this, we reduce the dimensionality of X through singular value decomposition (SVD) after removing the mean of each column:$$({bf{X}}-{bf{M}})={bf{U}}{bf{D}}{{bf{V}}}^{t}$$
(3)
where M is the appropriate matrix of column means, U and V are the matrices containing, respectively, the left-hand and right-hand singular vectors, and D is a diagonal matrix containing the singular values representing the standard deviations of the ensuing dimensions. The squared values of D represent the variance explained by each dimension, and can thus serve to define z, a reduced subset of dimensions that conserves 100% of the original matrix’s variation. The corresponding z right-hand singular vectors, Vz, can then be used to find the appropriately transformed predictor matrix of reduced dimension Z as follows:$${bf{Z}}=({bf{X}}-{bf{M}}){{bf{V}}}_{z}$$
(4)
which can now be regressed onto the CFrC y:$$y={bf{Z}}{beta }_{z}+varepsilon$$
(5)
where Z has been augmented with a leading column of 1s to accommodate an intercept term in the regression. We then use the standard method to obtain an estimate of βz:$${beta }_{z}={left({{bf{Z}}}^{t}{bf{Z}}right)}^{-1}{{bf{Z}}}^{t}y$$
(6)
However, because of the matrix transformation from X to Z, the regression coefficients βz do not provide direct information on the relationship between land fractional cover and cloud fractional cover (as in a normal regression). To identify the z values associated with a particular vegetation or land cover type (within the local analysis defined by the moving window), we define a ‘dummy pixel’ whose composition contains only a single class, with all other classes in its composition set to zero. This pixel’s composition is then transformed, and its y value predicted. This is the y associated with that vegetation type. To generalize this for all compositional components of interest, we define a matrix P with as many rows as these compositional components that we wish to predict. P is centred on the same column means as above (M, specific to each local analysis), and then multiplied by the correct number of transposed right-hand singular vectors (Vz, again, specific to each local analysis).$${{bf{Z}}}_{{rm{p}}}=({bf{P}}-{bf{M}}){{bf{V}}}_{z}$$
(7)
Predicted yp values for each vegetation or land cover type (identified by predicting the appropriately transformed ‘dummy pixels’) are then calculated as:$${y}_{{rm{p}}}={{bf{Z}}}_{{rm{p}}}{beta }_{z}$$
(8)
The expected change in variable y associated with a transition from vegetation type A (e.g. herbaceous vegetation) to vegetation type B (e.g. deciduous forest) at the centre of the local window is then the difference between the yp predicted for each ‘pure’ vegetation type:$${{Delta }}{y}_{{rm{A}}to {rm{B}}}={y}_{rm{B}}-{y}_{rm{A}}$$
(9)
The uncertainty in the estimation of ΔyA→B can be expressed as a standard deviation using the following expression:$${sigma }_{{rm{A}}to {rm{B}}}=sqrt{{sigma }_{rm{A}}^{2}+{sigma }_{rm{B}}^{2}-2{sigma }_{rm{AB}}}$$
(10)
where ({sigma }_{rm{A}}^{2}) and ({sigma }_{rm{B}}^{2}) are the variances in the estimates of yA and yB, and σAB is their covariance. These variances and covariances are in turn obtained from the covariance matrix, defined from the regression as:$${mathbf{Sigma }}={{bf{Z}}}_{{rm{p}}}{rm{Var}}[beta ]{{bf{Z}}}_{{rm{p}}}^{t}$$
(11)
The diagonal terms in Σ are the variances of individual predictions of (individual) classes. The off-diagonal parts of Σ hold the covariances between these predictions. As a reminder, the uncertainty σA→B calculated in this way is related to the methodological uncertainty and does not include the uncertainty in the input variables of land cover or cloud fractional cover.In the default set-up for this study, we concentrate on two transitions: herbaceous vegetation to deciduous forest and herbaceous vegetation to evergreen forest. These are calculated using a spatial window of 7 × 7 pixels, each pixel being of 0.05°, resulting in a squared spatial window of circa 35 km in size. To ensure there are enough values to do the un-mixing over each window, we established that there must be a minimum of 60% of valid values in each window, and that at least 40% must have distinct compositions. The operation is applied to all 12 monthly layers of CFrC, resulting in 12 maps of Δy with a 0.05° spatial resolution for each of the two vegetation cover transitions.Post-processingA series of post-processing steps are required to ensure the results of the Δy maps can be used to evaluate the effect of land on cloud cover. The first step is to mask all pixels in which there is insufficient co-occurrence of the two vegetation classes involved in the transition. This co-occurrence is quantified by an index of vegetation co-occurrence76, Ic, calculated from the land fractional cover layers using the same spatial moving window of 7 × 7 pixels as used before. This index is calculated pairwise, i.e. for 2 vegetation classes of interest A and B, using two vectors pA and pB, describing the presence of these two vegetation classes in each of the i pixels in the moving window. It also requires the definition of another i point evenly distributed along a hypothetical line B = 1 − A in the two-dimensional space describing the presences of vegetation class A and vegetation class B. These points, whose position in the 2-D space are labelled qA and qB, represent an ideal situation of maximum co-occurrence that serves as a reference to establish the index. The formal definition of the index is thus:$${I}_{{rm{c}}}=1-frac{{sum }_{i}min {sqrt{{left({q}_{A}-{p}_{A}right)}^{2}+{left({q}_{B}-{p}_{B}right)}^{2}}}}{{sum }_{i}sqrt{{q}_{A}^{2}+{q}_{B}^{2}}}$$
(12)
The minimum operator in the numerator selects the smallest distance that a given point p can have to any of the q points. The sum relates to the sum of this distance for all i points in the spatial moving window. The denominator characterizes the maximum distance that the point p can encounter. Ic will range from 0 to 1 corresponding to a gradient of ‘no presence of either class’ to ‘full and evenly balanced presence of both classes’. As in76, we retain only pixels with Ic ≥ 0.5 where we consider that there is sufficient information at local scale concerning both vegetation types to derive meaningful information about the target land cover transition.The second step is to remove the potential orographical effects, which can be especially problematic given that forests are more likely to be located over mountainous areas due to human action56. Here we mask the areas where considerable topographical variation occurs within the 7 × 7 pixel moving window of interest using the same implementation described in76. This involves using 3 different indicators, v1, v2 and v3, calculated over the moving window based on μh and σh, which are, respectively, the mean and the standard deviation of elevation over each grid cell of the input cloud dataset. These are defined as follows:$${v}_{1}=frac{1}{n}mathop{sum }limits_{i=1}^{n}{sigma }_{h,i}$$
(13)
$${v}_{2}=| {mu }_{h}-frac{1}{n}mathop{sum }limits_{i=1}^{n}{mu }_{h,i}|$$
(14)
$${v}_{3}=| {sigma }_{h}-{v}_{1}|$$
(15)
For an interpretation of these metrics, readers are invited to read76. These three indicators are combined together in a single layer depicting all pixels satisfying all of the following conditions: v1  More

1.Schultze, M., Pokrandt, K.-H. & Hille, W. Pit lakes of the Central German lignite mining district: Creation, morphometry and water quality aspects. Limnologica 40, 148–155 (2010).Article 
CAS 

Google Scholar 
2.Kodir, A., Hartono, D. M., Haeruman, H. & Mansur, I. Integrated post mining landscape for sustainable land use: A case study in South Sumatera, Indonesia. Sustain. Environ. Res. 27(4), 203–213 (2017).CAS 
Article 

Google Scholar 
3.Blanchette, M. L. & Lund, M. A. Pit lakes are a global legacy of mining: An integrated approach to achieving sustainable ecosystems and value for communities. Curr. Opin. Sustain. 23, 28–34 (2016).Article 

Google Scholar 
4.Manjón, G., Galván, J., Mantero, J., Díaz, I. & García-Tenorio, R. Norm levels in mine pit lakes in south-western Spain. NORM VII, Beijing, China, IAEA Proceedings Series: 277–288 (2015).5.Mantero, J. et al. Pit lakes from Southern Sweden: Natural radioactivity and elementary characterization. Sci. Rep. 10, 13712 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Dolný, A. & Harabiš, F. Underground mining can contribute to freshwater biodiversity conservation: Allogenic succession forms suitable habitats for dragonflies. Biol. Conserv. 145, 109–117 (2012).Article 

Google Scholar 
7.European Commission. Directive of the European Parliament and of the Council 2000/60/EC Establishing a Framework for Community Action in the Field of Water Policy. Official Journal 2000 L 327/1 (European Commission, 2000).
Google Scholar 
8.European Commission. Common Implementation Strategy for the Water Framework Directive (2000/60/EC): Guidance Document on Eutrophication Assessment in the Context of European Water Policies (Office for Official Publications of the European Communities, 2009).
Google Scholar 
9.Blindow, I., Hargeby, A. & Hilt, S. Facilitation of clear-water conditions in shallow lakes by macrophytes: Differences between charophyte and angiosperm dominance. Hydrobiologia 737, 99–110 (2014).CAS 
Article 

Google Scholar 
10.Conde-Álvarez, R. M., Bañares-España, E., Nieto-Caldera, J. M., Flores-Moya, A. & Figueroa, F. L. Submerged macrophyte biomass distribution in the shallow saline lake Fuente de Piedra (Spain) as function of environmental variables. Anal. Jardín Bot. Madrid 69(1), 119–127 (2012).Article 

Google Scholar 
11.Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecol. 419, 33 (2018).Article 

Google Scholar 
12.Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9(1), 16113 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Skrzypczak, A. R. & Napiórkowska-Krzebietke, A. Identification of hydrochemical and hydrobiological properties of mine waters for use in aquaculture. Aquac. Rep. 18, 100460 (2020).Article 

Google Scholar 
14.Environment Agency. RBC2 Method Statement: Rivers at risk from diffuse source pressures (mines and minewaters). http://www.environmentagency.gov.uk/subjects/waterquality/955573/1001324/1654756/1903912/?version=1&lang=_e (2008).15.Gogacz, M. The analysis of the quality of waters from mining plant open pit brown coal “Belchatow” joint—stock company off the surface waterways. In Mining Workshops from the cycle “Natural hazards in mining”: Symposium Materials: Occasional Session: Problems of Natural Hazards in Brown Coal mining. Bełchatów, June 2–4, 2004. IGSMiE PAN, Cracow, Series: Symposia and Conferences, Vol 62 (ed. Pilecka, E.) 139–151 (Springer, 2004).16.Pękala, A. The mineral character and geomechanical properties of the transitional rocks from the Mesozoic-Neogene Contact Zone in the Bełchatów lignite deposit. J. Sustain. Min. 13(1), 10–14. https://doi.org/10.7424/jsm140103 (2014).Article 

Google Scholar 
17.Davison, W. Iron and manganese in lakes. Earth Sci. Rev. 34(2), 119–163 (1993).ADS 
CAS 
Article 

Google Scholar 
18.Wittkop, C. et al. Controls on iron- and manganese-mineral solubility in ferruginous lakes. Geol. Soc. Am. Abstr. Progr. 49, 6 (2017).
Google Scholar 
19.Martyniak, R. & Sołtyk, W. Changes in groundwater chemistry resulting from dewatering lignite deposits Belchatow. Min. Geoeng. 33(2), 307–316 (2009).
Google Scholar 
20.Regulation of the Minister of Maritime Economy and Inland Navigation of 11 October 2019 on the classification of ecological status, ecological potential, chemical status and the method of classifying the status of surface water bodies as well as environmental quality standards for priority substances. Official Journal of the Laws of 2019, item 2149.21.Zdechlik, R. & Kania, J. Hydrogeochemical background and distribution of indicator ion concentrations in the region of the Bełchatów lignite deposit. Contemp. Probl. Hydrog. 11(2), 327–334 (2003) (in Polish, with English summary).
Google Scholar 
22.Marszelewski, W., Dembowska, E., Napiórkowski, P. & Solarczyk, A. Understanding abiotic and biotic conditions in post-mining pit lakes for efficient management: A case study (Poland). Mine Water Environ. 36, 418–428 (2017).CAS 
Article 

Google Scholar 
23.Sobolev, D., Moore, K. & Morris, A. L. Nutrients and light limitation of phytoplankton biomass in a Turbid Southeastern reservoir. Implic. Water Qual. Southeast Nat. 8(2), 255–266 (2009).Article 

Google Scholar 
24.Laurenceau-Cornec, E. C. et al. The relative importance of phytoplankton aggregates and zooplankton fecal pellets to carbon export: Insights from free drifting sediment trap deployments in naturally iron-fertilized waters near the Kerguelen Plateau. Biogeosciences 12, 1007–1027. https://doi.org/10.5194/bg-12-1007-2015 (2015).ADS 
Article 

Google Scholar 
25.Stottmeister, U. et al. Strategies for remediation of former open cast mining areas in eastern Germany. In Environmental Impacts of Mining Activities: Emphasis on Mitigation and Remediation (ed. Azcue, J. M.) 263–296 (Springer, 1999).Chapter 

Google Scholar 
26.Søndergaard, M., Larsen, S. E., Johansson, L. S., Lauridsen, T. L. & Jeppesen, E. Ecological classification of lakes: Uncertainty and the influence of year-to-year variability. Ecol. Indic. 61, 248–257 (2016).Article 

Google Scholar 
27.Napiórkowska-Krzebietke, A. Phytoplankton of artificial ecosystems—an attempt to assess water quality. Arch. Pol. Fish. 22, 81–96 (2014).Article 

Google Scholar 
28.Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).Article 

Google Scholar 
29.Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).Article 

Google Scholar 
30.Bucka, H. & Wilk-Woźniak, E. Pro-and Eukaryotic Algae of Phytoplankton Community in Water Bodies of Southern Poland (IOP PAN, 2007) ((in Polish)).
Google Scholar 
31.Siemińska, J. et al. Red list of the algae in Poland. In Red List of Plants and Fungi in Poland (eds Mirek, Z. et al.) 37–52 (W. Szafer Institute of Botany, Polish Academy of Science, 2006).
Google Scholar 
32.IUCN Red List of Threatened Species (ver. 2011.1). http://www.iucnredlist.org (2011).33.Krajewski, Ł et al. New data on the distribution and habitat conditions of stoneworts (Characeae) in Poland (2010–2012) including protected areas and lands involved in agri-environmental programmes. Water Environ. Rural Areas T15, Z 2(50), 65–85 (2015) ((in Polish with English summary)).
Google Scholar 
34.Urbaniak, J. & Gąbka, M. Polish Charophytes—An Illustrated Guide to Identification (Wydawnictwo Uniwersytetu Wrocławskiego, 2014).
Google Scholar 
35.Siong, K. & Asaeda, T. Does calcite encrustation in Chara provide a phosphorus nutrient sink?. J. Environ. Qual. 35, 490–494. https://doi.org/10.2134/jeq2005.0276 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Asaeda, T., Senavirathna, M. D. H. J., Kaneko, Y. & Rashid, M. H. Effect of calcium and magnesium on the growth and calcite encrustation of Chara fibrosa. Aquat. Bot. 113, 100–106. https://doi.org/10.1016/j.aquabot.2013.11.002 (2014).CAS 
Article 

Google Scholar 
37.Cassanova, M. T. & Brock, M. A. Charophyte occurance, seed banks and establishment in farm dams in New South Wales. Aust. J. Bot. 47, 437–444 (1999).Article 

Google Scholar 
38.Bueno, N. C. & Bicudo, C. E. M. Biomass and chemical composition of Nitella furcata subsp. mucronata var. mucronata f. oligospira (A. Braun) R. D. Wood (Chlorophyta, Characeae) in the littoral region of Ninféias Pond, São Paulo, Southeast Brazil. Rev. Bras. Bot. 3(3), 499–505 (2008).
Google Scholar 
39.Fontanini, D. et al. The phytochelatin synthase from Nitella mucronata (Charophyta) plays a role in the homeostatic control of iron (II)/(III). Plant Physiol. Biochem. 127, 88–96 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Mulderij, G., van Nes, E. & van Donk, E. Macrophyte–phytoplankton interactions: The relative importance of allelopathy versus other factors. Ecol. Model. 204, 85–92 (2007).Article 

Google Scholar 
41.Blindow, I., Hargeby, A. & Andersson, G. Alternative stable state in shallow lakes: What causes a shift? In The Structuring Role of Submerged Macrophytes in Lakes (eds Jeppesen, E. et al.) 353–360 (Springer, 1998).Chapter 

Google Scholar 
42.Höhne, L. et al. Environmental determinants of perch (Perca fluviatilis) growth in gravel pit lakes and the relative performance of simple versus complex ecological predictors. Ecol. Freshw. Fish 29, 557–573 (2020).Article 

Google Scholar 
43.Sabel, M., Eckmann, R., Jeppesen, E., Rösch, R. & Straile, D. Long-term changes in littoral fish community structure and resilience of total catch to reoligotrophication in a large, peri-alpine European lake. Freshw. Biol. 65, 1325–1336 (2020).Article 

Google Scholar 
44.APHA Standard Methods for the Examination of Water and Wastewater. American Public Health Association, Washington DC (1999).45.Utermöhl, H. Guidance on the quantitative analysis of phytoplankton—methods. Mitteilungen Internationale Vereinigung für Theoretische und Angewandte Limnologie 9, 1–38 (1958) (in German).
Google Scholar 
46.Napiórkowska-Krzebietke, A. & Kobos, J. Assessment of the cell biovolume of phytoplankton widespread in coastal and inland water bodies. Water Res. 104, 532–546 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Guiry, M. D. & Guiry, G. M. AlgaeBase; World-wide electronic publication. National University of Ireland: Galway, Ireland, 2008. http://www.algaebase.org. Accessed 4 Mar 2021.48.Pełechaty, M. & Pukacz, A. The Key to Determining Characeae Species in Rivers and Lakes. Environmental Protection Inspection (Monitoring Library of the Environment, 2008).
Google Scholar 
49.Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 
Article 

Google Scholar 
50.Pielou, E. C. An Introduction to Mathematical Ecology (Wiley, 1969).MATH 

Google Scholar 
51.Phillips, G. et al. Water Framework Directive Intercalibration Technical Report: Central Baltic Lake Phytoplankton Ecological Assessment Methods (Publications Office of the European Union, 2014).
Google Scholar 
52.Napiórkowska-Krzebietke, A., Chybowski, Ł, Prus, P. & Adamczyk, M. Assessment criteria and ecological classification of Polish lakes and rivers: Limitations and current state. In Polish River Basins and Lakes—Part II Biological Status and Water Management. The Handbook of Environmental Chemistry, Vol. 87 (eds Korzeniewska, E. & Harnisz, M.) 295–325 (Springer, 2020).
Google Scholar 
53.Burns, N., McIntosh, J. & Scholes, P. Strategies for Managing the Lakes of the Rotorua District, New Zealand. Lake Reserv. Manag. 21(1), 61–72 (2005).CAS 
Article 

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

Our study addressed the interaction between the SASL and the operation of the Chinook salmon fishery in the Toltén River, supported by a non-native and invasive species found populating many rivers and adjacent sea of Chile6. Our results demonstrate that the interaction between SASLs and the small-scale fishing communities of Caleta La Barra vary depending on different factors, both operational, such as the number of boats, and environmental, such as moon luminosity during the fishing operations. We were able to determine common patterns among fishers that allow us to establish different social profiles that shape their relationship with the SASL and also their relationship with the natural environment in which they live.Despite the frequent presence of SASL during fishing operations ( > 90%), we assessed that only 35% of them constituted interactions. This frequency of interaction is lower than recorded by other studies in this species18,20,21, but higher than in others17. These differences indicate that there is a high level of variability in the frequency of interactions between the SASL and the artisanal fisheries, both at spatial20,21 and temporal17,18 scales. According to de María et al.40, these differences can be explained by different factors such as the season in which the study was carried out, the productivity of each area, the fishing gear used and the captured species, among others. As the Chinook salmon is only fished during the austral summer months (January and February), it was not possible to analyze potential temporal variations in the frequency of interactions in this study.A positive relationship was observed between the number of SASL and the number of boats operating. A similar relationship was reported by Goetz et al.19, who observed groups of SASLs following boats during fishing operations. This can be explained by a number of factors. First, Szteren and Paéz15 reported that SASL in Uruguay are able to recognize the sound emitted by the boats during a fishing operation, and that is an indicator of prey availability (“dinner bell” effect). Second, it is possible that some individuals have learned to feed during fishing operations, specializing in a type of prey or in a particular feeding strategy associated with fishing operations41. Likewise, due to the general decrease in resource availability due to overexploitation in Chile42, SASLs could be attracted to fishing activities, associating a higher number of boats with greater availability and easy-to-capture prey19,43.We also found a positive relationship between the number of SASLs and the moon’s luminosity. Nights with high lunar luminosity (full moon) are associated with the largest tidal ranges of the month. These tides are related to a high productivity due to the formation of tidal fronts characterized by the abrupt difference in temperature, oxygen and fluorescence, commonly observed during spring tides, increasing the concentration of zooplankton and therefore attracting more predators44. This could result in greater resource availability. Largest tidal ranges may be also linked to influx of returning Chinook salmon in higher numbers than the rest of the month (authors’ unpublished results). However, it cannot be ruled out that the better visibility provided by the increased luminosity would have allowed the observer to count a greater number of SASLs around the boat16,19.Although non-significant, we found that the interactions between SASLs and the fishing operation occurred more often when fishing nets were deeper and the total catch was higher. A relationship between the occurrence of interactions and depth could be related to the time the net is underwater. At greater depths the time of hauling increases, which in turn increase the opportunity for the SASL to interact with the boat. This trend is opposite to what was found by other authors in other species45 who observed more interaction close to the surface as a learned strategy to reduce energetic demands. However, in our study the maximum depth of a fishing net was 10 m which is much lower than the depth SASLs usually dive during their foraging trips46. With respect to the relationship between the occurrence of interactions and total catch, different studies have demonstrated that fishing operations and seals and sea lions coincide in areas where the resource is more abundant15,47, and thus the number of SASL raiding the nets increases when there are more fish caught in the net and easily accessible.We found no relationship between CPUE and the interaction of SASL during fishing operations, i.e. no variations in the standardized catch of Chinook salmon per haul were recorded regardless of interactions with SASL. Similar results have been identified by other authors in studies of gillnet and purse-seine fisheries in other areas, both in Chile and elsewhere for this same species18,19,26,40. This lack of relationship could be explained by a number of factors. Firstly, the number of fish consumed by SASLs in the fishing gear is not high enough to generate differences in the CPUE at the fishery scale15,18,20. Secondly, and as mentioned before, SASLs and fishing operations coincide in areas where the resource is more abundant. Therefore, and even if an interaction with the SASL was reported, the CPUE was maintained or was higher in areas where SASLs are present, in comparison to areas where this predator is absent15. Finally, the volume of fish catches is affected by additional factors besides the presence of SASLs, such as environmental conditions, the presence of other predators, and the abundance of resources in each area, and therefore do not exclusively depend on interactions with the SASL40.South American sea lions are frequently blamed for causing significant impacts to the economy of local fishers26. However, at the scale of the whole fishery, damage to catch from SASLs interactions was recorded in only four of 22 fishing events and in 2,5% of the total catch, suggesting that damage is smaller than perceived by fishers. It is important to note, however, that the assessment of damage by SASLs in this study is conservative, since we do not consider fish that could be wholly removed from the net, which can increase the total biomass lost by SASLs. Competition for fishing resources between SASLs and artisanal fishers has been largely documented in different areas and situations and will likely exacerbate negative perception about this mammal and an obstacle to fishing operations17,24. In a recent study, Oliveira et al.26 demonstrated that the actual economic losses caused by sea lions to the local fishery in Brazil are much smaller (0.8–3% of the productivity of the monitored boats for the analyzed year) compared to the large damage perceived by the fishers. This perception extends to other cases of human-big marine carnivore interactions and competition during fishing activities48,49. Therefore, it is possible that the observed negative perception about SASLs is independent of a particular event, but it rather relates to the continuous presence of SASLs in the area, and also to the general perception that the SASLs can eat hundreds of fish over the course of one interaction26.However, it is important to note that economic losses could be relevant on a boat-to-boat basis, and this clearly contributes to exacerbate a negative perception towards SASLs. In the case of the Chinook salmon fishery from Toltén River, we quickly appraised that SASL interactions may result in significant losses on a boat-to-boat basis. Ex-vessel price of Chinook salmon has been on average CLP$3500 per kg (USD$5; currency rate at 27 April 2021), suggesting that a 10–15 kg salmon may be worth CLP$35,000–53,000 (USD$50–76). Boats with a damaged catch by SASL lost up to 11% of their revenue during the duration of the survey. Considering fishers’ sole reliance on a short fishing season8 and the overexploitation of alternative, native fishery resources42, fishers’ perceptions are likely to be negative towards SASL given these economic losses.The negative perception of SASLs by fishers needs to be understood within the context in which nature has been historically and socially altered. Since Chinook salmon was successfully introduced around 25 years ago50, both human fishing practices and the behavior of SASLs have changed in important ways. Fishing has moved from a year-round communitarian activity performed in the open sea, focused on abundant small native marine species, to a mostly familiar activity, performed in the estuary during a certain period of the year and focused on a big and profitable catch. The SASLs have also learned to feed on this new species and changed their predatory practices, moving from the sea to the river, and predominantly predating on salmon in the short period in which they arrive in the estuary. There is a mutual coproduction31,32,33 process in which these three parties—salmon, fisher, and SASLs—have each modified their behaviors and condition of reproduction and existence. The salmon colonized a new habitat, fishers learned to fish this new attractive species, and the SASL also learned to prey on this new species, colonizing a new space in the estuary, following fishing boats. This coproduction has presented new opportunities for the human population, as they have been able to exploit a new economic resource that has revitalized the economy of the town, but it has also refueled a long-standing conflict between humans and SASLs. Furthermore, these changes in fishing activity may have increased the perceived interactions with SASLs and the damage they cause. Moving fishing operations from the sea to the smaller and calmer estuarine waters makes the sea lion more visible. Also, a fishing based on a big (~ 15–20 kg each) and profitable Chinook salmon species, makes any sea lion attack more damaging than former fishing practices in which SASLs took one or two small fish from big net hauls.In order to address the negative perception of SASLs amongst artisanal fishers, we framed the discourse about SASLs within a broader discourse with nature using the concept of valuation language from Martinez-Alier51. Using Likert scales we observed that, contrary to what was expected and seen in other contexts, fishers do not demonstrate a view in line with the “environmentalism of the poor”. However, they did possess a view that values sustainability, though not in a conservationist context that would be expected from a community that rely on natural resources for their livelihood, but it is not purely instrumental either. This leads us to rethink the Martinez-Alier51 categories in order to describe the combination that was found among fishers in this study: a mix between elements of utilitarian rationality, viewing fish solely as a resource, and elements of a deep caring for the river and sea life, linking local economics with environmental wellbeing. However, this view did not include some of the more substantive and political aspects of an “environmentalism of the poor” perception, such as the right for all natural beings to exist. This discourse was better described as part of the “popular sustainable development” view, as the sustainable development paradigm is based more on an economic, rather than ecological, rationality that transforms ‘nature’ into ‘environment’ and resources52. However, in this case, caring for the river and sea, and economic wellbeing were seen as mutually necessary in a discourse that is accompanied by the respect of natural forces as being important to the life and wellbeing of the community.We also found that most fishers, from all socio-demographic characteristics, hold this “popular sustainable development” view. This was followed by instrumental rationality (4.0), held mostly by older fishers with lower levels of formal education. These results are consistent with those found in other studies24,53,54, in which older fishers have the most negative attitude and perception of SASLs because of the several negative encounters with these species24. This commonality of the popular sustainable development view, accompanied by a more conservationist view held by younger fishers with more years of formal education, opens up the possibility for a better relationship among Chinook salmon, fishers, and SASLs, and a better coexistence. We hypothesize that it is possible to make local effective governance changes to improve this coexistence55, for example, by changing the fishing practices to modify the SASL behavior. Currently, fishers process the fish and dispose of the waste in the river where they fish. In their own words this practice “domesticate” SASLs, and the community is making agreements to move this practice away from the shore. It also seems that SASLs follow the boats as they have learned that boats mean availability of prey. This requires improved governance systems to redistribute fishing places within the river, as fishers already partially and informally do it. Also, it is recommendable the implementation of a long-term education program to the fishing community that include, among other issues, the critical role of this marine top predator on the trophic webs and consequently the negative impacts of their removal18,24. Actions like these could help to move from a paradigm of “defending the fishing from the SASLs” to a better local understanding of the relationship between Chinook salmon, SASLs, and human behavior. In recent years, due to the important economic opportunity that Chinook salmon has meant for the community, young fishers with more years of formal education, and a more sustainable and conservationist view of fishing, have returned to La Barra to occupy leadership roles in the community. We cannot be sure how this leadership will evolve as they grow older and more experienced, but the formal education and experience backgrounds they hold separate them from their parents and may anticipate a different trajectory. We speculate that this is a step in the right direction at resolving SASL-fisheries conflicts. More