Ecology

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

1.Reddin, C. J., Kocsis, Á. T., Aberhan, M. & Kiessling, W. Victims of ancient hyperthermal events herald the fates of marine clades and traits under global warming. Glob. Chang. Biol. 27, 868–878 (2021).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Reddin, C. J., Kocsis, Á. T. & Kiessling, W. Marine invertebrate migrations trace climate change over 450 million years. Glob. Ecol. Biogeogr. 27, 704–713 (2018).Article 

Google Scholar 
3.Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Bio. Ecol. 400, 218–226 (2011).Article 

Google Scholar 
4.Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 1–21 (2016).Article 

Google Scholar 
5.Hanken, J. & Wake, D. B. Miniaturization of body size: Organismal consequences and evolutionary significance. Annu. Rev. Ecol. Syst. 24, 501–519 (1993).Article 

Google Scholar 
6.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Chang. 1, 401–406 (2011).ADS 
Article 

Google Scholar 
7.Forster, J., Hirst, A. G. & Atkinson, D. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proc. Natl. Acad. Sci. U. S. A. 109, 19310–19314 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Ohlberger, J. Climate warming and ectotherm body size – from individual physiology to community ecology. Funct. Ecol. 27, 991–1001 (2013).Article 

Google Scholar 
9.Garilli, V. et al. Physiological advantages of dwarfing in surviving extinctions in high-CO 2 oceans. Nat. Clim. Chang. 5, 678–682 (2015).ADS 
CAS 
Article 

Google Scholar 
10.Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.0902080106 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Verberk, W. C. E. P. et al. Shrinking body sizes in response to warming: explanations for the temperature–size rule with special emphasis on the role of oxygen. Biol. Rev. 96, 247–268 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Angilletta, M. J., Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: Fitting pieces of a life-history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Hoving, H. J. T. et al. Extreme plasticity in life-history strategy allows a migratory predator (jumbo squid) to cope with a changing climate. Glob. Chang. Biol. 19, 2089–2103 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Angilletta, M. J. & Dunham, A. E. The temperature-size rule in ectotherms: simple evolutionary explanations may not be general. Am. Nat. 162, 332–342 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Vinarski, M. V. On the applicability of Bergmann’s rule to ectotherms: the state of the art. Biol. Bull. Rev. 4, 232–242 (2014).Article 

Google Scholar 
16.Atkinson, D. Temperature and organism size: a biological law for organisms?. Adv. Ecol. Res. 25, 1–58 (1994).Article 

Google Scholar 
17.Atkinson, D. Effects of temperature on the size of aquatic ectotherms: Exceptions to the general rule. J. Therm. Biol. 20, 61–74 (1995).Article 

Google Scholar 
18.Forster, J. & Hirst, A. G. The temperature-size rule emerges from ontogenetic differences between growth and development rates. Funct. Ecol. 26, 483–492 (2012).Article 

Google Scholar 
19.Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Schulte, P. M. The effects of temperature on aerobic metabolism: Towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Gillooly, J. F., Charnov, E. L., West, G. B., Savage, V. M. & Brown, J. H. Effects of size and temperature on developmental time. Nature 417, 70–73 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Riemer, K., Anderson-Teixeira, K. J., Smith, F. A., Harris, D. J. & Ernest, S. K. M. Body size shifts influence effects of increasing temperatures on ectotherm metabolism. Glob. Ecol. Biogeogr. 27, 958–967 (2018).Article 

Google Scholar 
23.Rosa, R. et al. Ocean warming enhances malformations, premature hatching, metabolic suppression and oxidative stress in the early life stages of a keystone squid. PLoS ONE 7, e38282 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Pecl, G. T. & Jackson, G. D. The potential impacts of climate change on inshore squid: Biology, ecology and fisheries. Rev. Fish Biol. Fish. 18, 373–385 (2008).Article 

Google Scholar 
25.Twitchett, R. J. The Lilliput effect in the aftermath of the end-Permian extinction event. Palaeogeogr. Palaeoclimatol. Palaeoecol. 252, 132–144 (2007).Article 

Google Scholar 
26.Harries, P. J. & Knorr, P. O. What does the ‘Lilliput Effect’ mean?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 284, 4–10 (2009).Article 

Google Scholar 
27.Metcalfe, B., Twitchett, R. J. & Price-Lloyd, N. Changes in size and growth rate of ‘Lilliput’ animals in the earliest Triassic. Palaeogeogr. Palaeoclimatol. Palaeoecol. 308, 171–180 (2011).Article 

Google Scholar 
28.Chu, D. et al. Lilliput effect in freshwater ostracods during the Permian-Triassic extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 435, 38–52 (2015).Article 

Google Scholar 
29.Urbanek, A. Biotic crises in the history of upper silurian graptoloids: a palaeobiological model. Hist. Biol. https://doi.org/10.1080/10292389309380442 (1993).Article 

Google Scholar 
30.Urlichs, M. Stunting in invertebrates from the type area of the Cassian Formation (Early Carnian) of the dolomites (Italy). GeoAlp 8, 164–169 (2011).
Google Scholar 
31.Morten, S. D. & Twitchett, R. J. Fluctuations in the body size of marine invertebrates through the Pliensbachian-Toarcian extinction event. Palaeogeogr. Palaeoclimatol. Palaeoecol. https://doi.org/10.1016/j.palaeo.2009.08.023 (2009).Article 

Google Scholar 
32.Piazza, V., Ullmann, C. V. & Aberhan, M. Temperature-related body size change of marine benthic macroinvertebrates across the Early Toarcian Anoxic Event. Sci. Rep. 10, 1–13 (2020).Article 
CAS 

Google Scholar 
33.Calosi, P., Putnam, H. M., Twitchett, R. J. & Vermandele, F. Marine metazoan modern mass extinction: improving predictions by integrating fossil, modern, and physiological data. Ann. Rev. Mar. Sci. 11, 369–390 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Gerber, S. Comparing the differential filling of morphospace and allometric space through time: the morphological and developmental dynamics of Early Jurassic ammonoids. Paleobiology 37, 369–382 (2011).Article 

Google Scholar 
35.Pálfy, J. & Smith, P. L. Synchrony between Early Jurassic extinction, oceanic anoxic event, and the Karoo-Ferrar flood basalt volcanism. Geology 28, 747–750 (2000).ADS 
Article 

Google Scholar 
36.Caruthers, A. H., Smith, P. L. & Gröcke, D. R. The Pliensbachian-Toarcian (Early Jurassic) extinction, a global multi-phased event. Palaeogeogr. Palaeoclimatol. Palaeoecol. 386, 104–118 (2013).Article 

Google Scholar 
37.Wignall, P. B. Large igneous provinces and mass extinctions. Earth Sci. Rev. 53, 1–33 (2001).ADS 
CAS 
Article 

Google Scholar 
38.Percival, L. M. E. et al. Globally enhanced mercury deposition during the end-Pliensbachian extinction and Toarcian OAE: A link to the Karoo-Ferrar Large Igneous Province. Earth Planet. Sci. Lett. 428, 267–280 (2015).ADS 
CAS 
Article 

Google Scholar 
39.Foster, G. L., Hull, P., Lunt, D. J. & Zachos, J. C. Placing our current ‘hyperthermal’ in the context of rapid climate change in our geological past. Phil. Trans. R. Soc. A 376, 20170086 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Suan, G. et al. Secular environmental precursors to Early Toarcian (Jurassic) extreme climate changes. Earth Planet. Sci. Lett. 290, 448–458 (2010).ADS 
CAS 
Article 

Google Scholar 
41.Fantasia, A. et al. Global versus local processes during the Pliensbachian-Toarcian transition at the Peniche GSSP, Portugal: A multi-proxy record. Earth Sci. Rev. 198, 102932 (2019).CAS 
Article 

Google Scholar 
42.Müller, T. et al. Ocean acidification during the early Toarcian extinction event: Evidence from Boron isotopes in brachiopods. Geology 48, 1184–1188 (2020).ADS 
Article 

Google Scholar 
43.Suan, G., Mattioli, E., Pittet, B., Mailliot, S. & Lécuyer, C. Evidence for major environmental perturbation prior to and during the Toarcian (Early Jurassic) oceanic anoxic event from the Lusitanian Basin Portugal. Paleoceanography 23, A1202 (2008).ADS 
Article 

Google Scholar 
44.Dera, G. et al. High-resolution dynamics of early Jurassic marine extinctions: The case of Pliensbachian-Toarcian ammonites (Cephalopoda). J. Geol. Soc. London 167, 21–33 (2010).CAS 
Article 

Google Scholar 
45.Dera, G. et al. Climatic ups and downs in a disturbed Jurassic world. Geology 39, 215–218 (2011).ADS 
CAS 
Article 

Google Scholar 
46.Miguez-Salas, O., Rodríguez-Tovar, F. J. & Duarte, L. V. Selective incidence of the Toarcian oceanic anoxic event on macroinvertebrate marine communities: a case from the Lusitanian basin Portugal. Lethaia 50, 548–560 (2017).Article 

Google Scholar 
47.Correia, V. F., Riding, J. B., Duarte, L. V., Fernandes, P. & Pereira, Z. The palynological response to the Toarcian Oceanic Anoxic Event (Early Jurassic) at Peniche, Lusitanian Basin, western Portugal. Mar. Micropaleontol. 137, 46–63. https://doi.org/10.1016/j.marmicro.2017.10.004 (2017).ADS 
Article 

Google Scholar 
48.Rita, P., Nätscher, P., Duarte, L. V., Weis, R. & De Baets, K. Mechanisms and drivers of belemnite body-size dynamics across the Pliensbachian-Toarcian crisis. R. Soc. Open Sci. 6, 190494 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Caswell, B. A. & Coe, A. L. The impact of anoxia on pelagic macrofauna during the Toarcian Oceanic Anoxic Event (Early Jurassic). Proc. Geol. Assoc. 125(4), 383–391. https://doi.org/10.1016/j.pgeola.2014.06.001 (2014).Article 

Google Scholar 
50.Ullmann, C. V., Thibault, N., Ruhl, M., Hesselbo, S. P. & Korte, C. Effect of a Jurassic oceanic anoxic event on belemnite ecology and evolution. Proc. Natl. Acad. Sci. U. S. A. 111, 10073–10076 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Dera, G., Toumoulin, A. & de Baets, K. Diversity and morphological evolution of Jurassic belemnites from South Germany. Palaeogeogr. Palaeoclimatol. Palaeoecol. 457, 80–97 (2016).Article 

Google Scholar 
52.Neige, P., Weis, R. & Fara, E. Ups and downs of belemnite diversity in the Early Jurassic of Western Tethys. Palaeontology 64, 263–283 (2021).Article 

Google Scholar 
53.Rita, P., De Baets, K. & Schlott, M. Rostrum size differences between Toarcian belemnite battlefields. Foss. Rec. 21, 171–182 (2018).Article 

Google Scholar 
54.Rita, P. et al. Biogeographic patterns of belemnite body size responses to episodes of environmental crisis. PeerJ Prepr. (2019).55.Hoffmann, R. & Stevens, K. The palaeobiology of belemnites – foundation for the interpretation of rostrum geochemistry. Biol. Rev. 95, 94–123 (2020).Article 

Google Scholar 
56.Adams, D. C. & Otárola-Castillo, E. Geomorph: An r package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).Article 

Google Scholar 
57.Schlegelmilch, R. Die Belemniten des süddeutschen Jura. Die Belemniten des süddeutschen Jura https://doi.org/10.1007/978-3-8274-3083-0 (1998).Article 

Google Scholar 
58.McArthur, J. M. et al. Sr-isotope stratigraphy (87Sr/86Sr) of the lowermost Toarcian of Peniche, Portugal, and its relation to ammonite zonations. Newsletters Stratigr. 53, 297–312 (2020).Article 

Google Scholar 
59.Hesselbo, S. P., Jenkyns, H. C., Duarte, L. V. & Oliveira, L. C. V. Carbon-isotope record of the Early Jurassic (Toarcian) Oceanic Anoxic Event from fossil wood and marine carbonate (Lusitanian Basin, Portugal). Earth Planet. Sci. Lett. 253, 455–470 (2007).ADS 
CAS 
Article 

Google Scholar 
60.Klug, C., Schweigert, G., Fuchs, D., Kruta, I. & Tischlinger, H. Adaptations to squid-style high-speed swimming in Jurassic belemnitids. Biol. Lett. 12, 20150877 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Seibel, B. A., Thuesen, E. V., Childress, J. J. & Gorodezky, L. A. Decline in pelagic cephalopod metabolism with habitat depth reflects differences in locomotory efficiency. Biol. Bull. 192, 262–278 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Mattioli, E., Pittet, B., Petitpierre, L. & Mailliot, S. Dramatic decrease of pelagic carbonate production by nannoplankton across the Early Toarcian anoxic event (T-OAE). Glob. Planet. Change 65, 134–145 (2009).ADS 
Article 

Google Scholar 
63.Chamberlain, J. A. Locomotion in ancient seas: Constraint and opportunity in Cephalopod adaptive design. Geobios 15, 49–61 (1993).Article 

Google Scholar 
64.Rexfort, A. & Mutterlose, J. The role of biogeography and ecology on the isotope signature of cuttlefishes (Cephalopoda, Sepiidae) and the impact on belemnite studies. Palaeogeogr. Palaeoclimatol. Palaeoecol. 284, 153–163 (2009).Article 

Google Scholar 
65.Holland, S. M. The quality of the fossil record: a sequence stratigraphic perspective. Paleobiology 26, 148–168 (2000).Article 

Google Scholar 
66.Holland, S. M. The non-uniformity of fossil preservation. Philos. Trans. R. Soc. B Biol. Sci. 371, 2 (2016).
Google Scholar 
67.Korn, D. Impact of enviornmental perturbations o heterochronic develpments in Palaeozoic ammonoids. Evol. Chang. Heterochrony 245–260 (1995).68.Yacobucci, M. M. Plasticity of developmental timing as the underlying cause of high speciation rates in ammonoids. in Advancing research on living and fossil cephalopods 59–76 (Springer, Boston, MA, 1999).69.Landman, N. H. & Gyssant, J. R. Heterochrony and ecology in Jurassic and Cretaceous ammonites. Geobios 26, 247–255 (1993).Article 

Google Scholar 
70.McNamara, K. J. Heterochrony: the evolution of development. Evol. Educ. Outreach 5, 203–218 (2012).Article 

Google Scholar 
71.Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H. O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Pörtner, H. O. & Farrell, A. P. Ecology: Physiology and climate change. Science https://doi.org/10.1126/science.1163156 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
73.Pimentel, M. S. et al. Impact of ocean warming on the early ontogeny of cephalopods: A metabolic approach. Mar. Biol. 159, 2051–2059 (2012).Article 

Google Scholar 
74.Komoroske, L. M. et al. Ontogeny influences sensitivity to climate change stressors in an endangered fish. Conserv. Physiol. 2, 1–13 (2014).Article 
CAS 

Google Scholar 
75.Pörtner, H. O., Bock, C. & Mark, F. C. Oxygen- & capacity-limited thermal tolerance: Bridging ecology & physiology. J. Exp. Biol. 220, 2685–2696 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Harnik, P. G., Simpson, C. & Payne, J. L. Long-term differences in extinction risk among the seven forms of rarity. Proc. R. Soc. B Biol. Sci. 279, 4969–4976 (2012).Article 

Google Scholar 
77.Reddin, C. J., Kocsis, Á. T. & Kiessling, W. Climate change and the latitudinal selectivity of ancient marine extinctions. Paleobiology 45, 70–84 (2019).Article 

Google Scholar 
78.Dorey, N. et al. Ocean acidification and temperature rise: Effects on calcification during early development of the cuttlefish Sepia officinalis. Mar. Biol. 160, 2007–2022 (2013).CAS 
Article 

Google Scholar 
79.Sigwart, J. D. et al. Elevated pCO2 drives lower growth and yet increased calcification in the early life history of the cuttlefish Sepia officinalis (Mollusca: Cephalopoda) Julia. ICES J. Mar. Sci. 73, 970–980 (2016).Article 

Google Scholar 
80.Gutowska, M. A., Melzner, F., Pörtner, H. O. & Meier, S. Cuttlebone calcification increases during exposure to elevated seawater pCO2 in the cephalopod Sepia officinalis. Mar. Biol. 157, 1653–1663 (2010).CAS 
Article 

Google Scholar 
81.Kaplan, M. B., Mooney, T. A., McCorkle, D. C. & Cohen, A. L. Adverse effects of ocean acidification on early development of squid (Doryteuthis pealeii). PLoS ONE 8, e63714 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Otjacques, E. et al. Cuttlefish buoyancy in response to food availability and ocean acidification. Biology (Basel). https://doi.org/10.3390/biology9070147 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
83.Neige, P. & Boletzky, S. Morphometrics of the shell of three Sepia species (Mollusca: Cephalopoda): Intra- and interspecific variation. Zool. Beitraege. 38, 137–156 (1997).
Google Scholar 
84.Rita, P., Weis, R., Duarte, L. V. & De Baets, K. Taxonomical diversity and palaeobiogeographical affinity of belemnites from the Pliensbachian-Toarcian GSSP (Lusitanian Basin, Portugal). Pap. Palaeontol. https://doi.org/10.1002/spp2.1343 (2020).Article 

Google Scholar 
85.MacArthur, R. H. Geographical ecology: patterns in the distribution of species. (Princeton University Press, 1972).86.Gaston, K. J. The structure and dynamics of geographic ranges. (Oxford University Press on Demand, 2003).87.Duarte, L. Sequence stratigraphy and depositional setting of the Pliensbachian and Toarcian marly limestones in the Lusitanian Basin Portugal. Ciências da Terra 16, 17–23 (2007).
Google Scholar 
88.Reddin, C. J., Nätscher, P. S., Kocsis, Á. T., Pörtner, H.-O. & Kiessling, W. Marine clade sensitivities to climate change conform across timescales. Nat. Clim. Chang. 10, 249–253 (2020).ADS 
Article 

Google Scholar 
89.Doyle, P. & Kelly, R. A. The Jurassic and Cretaceous belemnites of Kong Karls Land, Svalbard. (Norsk Polarinstitutt Oslo, 1988).90.Doyle, P. New records of dimitobelid belemnites from the cretaceous of james ross island Antarctica. Alcheringa 14, 159–175 (1990).Article 

Google Scholar 
91.Fedorov, A. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging https://doi.org/10.1016/j.mri.2012.05.001 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
92.Rohatgi, A., Rehberg, S. & Stanojevic, Z. Webplotdigitizer: Version 4.1 of Webplotdigitizer. (2018).https://doi.org/10.5281/zenodo.1137880.93.Plate, T. & Heiberger, R. Package ‘ abind ’. (2016).94.Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).Article 

Google Scholar 
95.Sherratt, E. Quick Guide to Geomorph v. 2.0. public.iastate.edu (2014).96.Torchiano, M. Package ‘ effsize ’. (2020).97.Gotelli, N. J., Dorazio, R. M., Ellison, A. M. & Grossman, G. D. Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models. Philos. Trans. 365, 3621–3631 (2010).Article 

Google Scholar 
98.Hervé, M. Package ‘ RVAideMemoire ’. (2021).99.Mangiafico, S. Package ‘ rcompanion ’. (2021).100.Oksanen, J. et al. Package ‘vegan’ title community ecology package. Commun. Ecol. Packag. 2, 1–297 (2019).MathSciNet 

Google Scholar 
101.Pinheiro, J. et al. Package ‘nlme’. (2021).102.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).Article 

Google Scholar 
103.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria (2019).104.Garnier, S., Ross, N., Rudis, B. & Sciaini, M. Package ‘viridis’. (2021). More

These authors contributed equally: Claire R. Peart, Sergio Tusso, Saurabh D. Pophaly.Science of Life Laboratories and Department of Evolutionary Biology, Uppsala University, Uppsala, SwedenClaire R. Peart, Sergio Tusso, Chi-Chih Wu, Aaron B. A. Shafer & Jochen B. W. WolfDivision of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, GermanyClaire R. Peart, Sergio Tusso, Saurabh D. Pophaly, Fidel Botero-Castro & Jochen B. W. WolfMax Planck Institute for Plant Breeding Research, Cologne, GermanySaurabh D. PophalyLaboratorio de Ecología de Pinnípedos ‘Burney J. Le Boeuf’, Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional, Baja California Sur, MéxicoDavid Aurioles-GamboaDepartment of Natural Sciences, University of Houston-Downtown, Houston, TX, USAAmy B. BairdDepartment of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USAJohn W. BickhamBritish Antarctic Survey, Natural Environment Research Council, Cambridge, UKJaume Forcada & Joseph I. HoffmanElephant Seal Research Group, Sea Lion Island, Falkland IslandsFilippo Galimberti & Simona SanvitoDepartment of Anatomy, University of Otago, Dunedin, New ZealandNeil J. GemmellDepartment of Animal Behaviour, Bielefeld University, Bielefeld, GermanyJoseph I. HoffmanNorwegian Polar Institute, Fram Centre, Tromsø, NorwayKit M. Kovacs & Christian LydersenDepartment of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, FinlandMervi Kunnasranta & Tommi NymanNatural Resources Institute Finland (Luke), Joensuu, FinlandMervi KunnasrantaDepartment of Ecosystems in the Barents Region, Norwegian Institute of Bioeconomy Research, Svanhovd Research Station, Svanvik, NorwayTommi NymanLaboratory of Mammal Ecology, Universidade do Vale do Rio dos Sinos, São Leopoldo, BrazilLarissa Rosa de OliveiraNational Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, Marine Mammal Laboratory, Seattle, WA, USAAnthony J. OrrThe Saimaa Ringed Seal Genome Project, Institute of Biotechnology, University of Helsinki, Helsinki, FinlandMia ValtonenForensic Science & Environmental Life Sciences, Trent University, Peterborough, Ontario, CanadaAaron B. A. Shafer More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

The authors acknowledge valuable discussion with other members of the International Synthesis Consortium, including M. de Araujo Mamede, M. Palmer, J. Kramer, R. Beilinson, J. Arnott and P. Kille. J.B. thanks USGS internal reviewers K. Bagstad and W. Sanford. We also acknowledge all synthesis group members who have been incredibly creative in continuing their research dynamic despite going 100% virtual. M.W. acknowledges funding by the DFG (via iDiv: FZT 118, 02548816). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. More