Ecology

1.Inskip, C. & Zimmermann, A. Human-felid conflict: a review of patterns and priorities worldwide. Oryx 43(1), 18–34 (2009).
Google Scholar 
2.Weber, W. & Rabinowitz, A. A global perspective on large carnivore conservation. Conserv. Biol. 10(4), 1046–1054 (1996).
Google Scholar 
3.Treves, A. & Karanth, U. K. Human-carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17(6), 1491–1499 (2003).
Google Scholar 
4.Romero-Muñoz, A., Morato, R., Tortato, F. & Kuemmerle, T. Beyond fangs: beef and soybean trade drive jaguar extinction. Front. Ecol. Environ. 18(2), 67–68 (2020).
Google Scholar 
5.Packer, C. et al. Conserving large carnivores: dollars and fence. Ecol. Lett. 16(5), 635–641 (2013).CAS 
PubMed 

Google Scholar 
6.Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253 (2000).CAS 
PubMed 

Google Scholar 
7.Quigley, H., Foster, R., Petracca, L., Payan, E., Salom, R. & Harmsen, B. Panthera onca. The IUCN Red List of Threatened Species 2017, e.T15953A123791436 (2017).8.Menezes, J. F. S., Tortato, F. R., Oliveira-Santos, L. G., Roque, F. O. & Morato, R. G. Deforestation, fires, and lack of governance are displacing thousands of jaguars in Brazilian Amazon. Conserv. Sci. Pract. 3(8), e477 (2021).
Google Scholar 
9.Morato, R. G. et al. Resource selection in an apex predator and variation in response to local landscape characteristics. Biol. Conserv. 228, 233–240 (2018).
Google Scholar 
10.Sanderson, E. W. et al. Planning to save a species: the jaguar as a model. Conserv. Biol. 16(1), 1–15 (2002).
Google Scholar 
11.De Paula, R. C., Desbiez, A. & Cavalcanti, S. M. C. Plano de Ação Nacional para Conservação da Onça-pintada. Série Espécies Ameaçadas (Instituto Chico Mendes de Conservação da Biodiversidade, Atibaia, 2013).
Google Scholar 
12.Seidl, A. F., Silva, J. S. V. & Moraes, A. S. Cattle ranching and deforestation in the Brazilian Pantanal. Ecol. Econ. 36(3), 413–425 (2001).
Google Scholar 
13.Tomas, W. M. et al. Sustainability agenda for the Pantanal wetland: perspectives on a collaborative interface for science, policy, and decision-making. Trop. Conserv. Sci. 12, 1–30 (2019).ADS 

Google Scholar 
14.Tortato, F. R. & Izzo, T. J. Advances and barriers to the development of jaguar-tourism in the Brazilian Pantanal. Perspect. Ecol. Conserv. 15(1), 61–63 (2017).
Google Scholar 
15.Tortato, F. R., Hoogesteijn, R. & Elbroch, M. Have natural disasters created opportunities to initiate Big Cat Tourism in South America?. Biotropica 52(3), 400–403 (2020).
Google Scholar 
16.Quigley, H. & Crawshaw, P. G. Jr. A conservation plan for the jaguar (Panthera onca) in the Pantanal region of Brazil. Biol. Conserv. 61(3), 149–157 (1992).
Google Scholar 
17.Tortato, F. R., Izzo, T. J., Hoogesteijn, R. & Peres, C. A. The numbers of the beast: valuation of jaguar (Panthera onca) tourism and cattle depredation in the Brazilian Pantanal. Glob. Ecol. Conserv. 11, 106–114 (2017).
Google Scholar 
18.Junk, W. J. et al. Biodiversity and its conservation in the Pantanal of Mato Grosso, Brazil. Aqua Sci. 69, 278–309 (2006).
Google Scholar 
19.Guerra, A. et al. Drivers and projections of vegetation loss in the Pantanal and surrounding ecosystems. Land Use Policy 91, 104388 (2020).
Google Scholar 
20.Marengo, J. A. et al. Extreme drought in the Brazilian Pantanal in 2019–2020: characterization, causes, and impacts. Front. Water 3, 1–20 (2021).
Google Scholar 
21.Berlinck, C. N. et al. The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world. Brazilian Journal of Biology 82, e244200 (2021).CAS 

Google Scholar 
22.Garcia, L. C. et al. Record-breaking wildfires in the world’s largest continuous tropical wetland: integrative fire management is urgently needed for both biodiversity and humans. J. Environ. Manag. 293, 112870 (2021).CAS 

Google Scholar 
23.Libonati, R., Sander, L. A., Peres, L. F., DaCamara, C. C. & Garcia, L. C. Rescue Brazil’s burning Pantanal wetlands. Nature 588, 217–220 (2020).ADS 
CAS 
PubMed 

Google Scholar 
24.Hoogesteijn, A. & Hoogesteijn, R. Cattle ranching and biodiversity conservation as allies in South America’s flooded savannas. Great Plains Res. 20, 37–50 (2010).
Google Scholar 
25.Ferraz, K. M. P. M. B., Ferraz, S. F. B., De Paula, R. C., Beisiegel, B. & Breitenmoser, C. Species distribution modeling for conservation purposes. Natureza Conservação 10(2), 214–220 (2012).
Google Scholar 
26.Zimmermann, A., Walpole, M. J. & Leader-Williams, N. Cattle ranchers’ attitudes to conflicts with jaguar Panthera onca in the Pantanal of Brazil. Oryx 39(4), 406–412 (2005).
Google Scholar 
27.Marchini, S. & Macdonald, D. W. Predicting rancher’s intention to kill jaguars: case studies in Amazonia and Pantanal. Biol. Conserv. 147(1), 213–221 (2012).
Google Scholar 
28.Abreu, U. G. P., McManus, C. & Santos, A. S. Cattle ranching, conservation and transhumance in Brazilian Pantanal. Pastoralism 1(1), 99–114 (2010).
Google Scholar 
29.Alho, C. J. R. & Sabino, J. A conservation agenda for the Pantanal’s biodiversity. Braz. J. Biol. 71(1), 327–335 (2011).CAS 
PubMed 

Google Scholar 
30.Hoogesteijn, R. et al. Conservación de Jaguares fuera de Áreas Protegidas: Turismo de Observación de Jaguares en Propiedades Privadas en El Pantanal. In Conservación de grandes vertebrados en áreas no protegidas de Colombia, Venezuela y Brasil (eds Payan-Garrido, E. et al.) 259–274 (Panthera. Fundación Herencia Ambiental Caribe e Instituto de Investigaciones de Recursos Biológicos Alexander von Humboldt, Cartagena, 2015).
Google Scholar 
31.Tyagi, A. et al. Physiological stress responses of tigers due to anthropogenic disturbance especially tourism in two central Indian tiger reserves. Conservation Physiology 7(1), coz045 (2020).
Google Scholar 
32.Hayward, M. W. & Hayward, G. J. The impact of tourists on lion Panthera leo behaviour, stress and energetics. Acta Theriol. 54(3), 219–224 (2009).
Google Scholar 
33.Romanach, S., Lindsey, P. A. & Woodroffe, R. Determinants of attitudes towards predators in central Kenya and suggestions for increasing tolerance in livestock dominated landscapes. Oryx 41(2), 185–195 (2007).
Google Scholar 
34.Hemson, G. S., Maclennan, S., Mills, G., Johnson, P. & Macdonald, D. Community, lions, livestock and money: a spatial and social analysis of attitudes to wildlife and the conservation value of tourism in a human–carnivore conflict in Botswana. Biol. Conserv. 142(11), 2718–2725 (2009).
Google Scholar 
35.Mossaz, A., Buckley, R. C. & Castley, J. G. Ecotourism contributions to conservation of African big cats. J. Nat. Conserv. 28, 112–118 (2015).
Google Scholar 
36.Macdonald, C. et al. Conservation potential of apex predator tourism. Biol. Conserv. 215, 132–141 (2017).
Google Scholar 
37.Campos, Z., Mourão, G. & Magnusson, W. Drought drastically reduces suitable habitat for Yacare caiman. Crocodile Specialist Group Newsl. 39(4), 14–16 (2020).
Google Scholar 
38.Marengo, J. A., Oliveira, G. S. & Alves, L. M. Climate change scenarios in the Pantanal. In Dynamics of the Pantanal Wetland in South America (eds Bergier, I. & Assine, M. L.) 227–238 (Springer International Publishing, Heidelberg, 2016).
Google Scholar 
39.Thielen, D. et al. Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLOS ONE 15(1), e0227437 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Bergier, I. et al. Amazon rainforest modulation of water security in the Pantanal wetland. Sci. Total Environ. 619, 1116–1125 (2018).ADS 
PubMed 

Google Scholar 
41.Araujo, A. et al. Relationships between variability in precipitation, river levels, and beef cattle production in the Brazilian Pantanal. Wetl. Ecol. Manage. 26(5), 829–848 (2018).
Google Scholar 
42.Filho, W. L., Azeiteira, U. M., Salvia, A. L., Fritzen, B. & Libonati, R. Fire in Paradise: why the Pantanal is burning. Environ. Sci. Policy 123, 31–34 (2021).
Google Scholar 
43.Brown, J. L. SDM toolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (2014).
Google Scholar 
44.Morato, R. G. et al. Space use and movement of a Neotropical top predator: the endangered jaguar. PLOS ONE 11(12), e0168176 (2016).PubMed 
PubMed Central 

Google Scholar 
45.Fick, S. E. & Hijmans, R. J. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
Google Scholar 
46.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).
Google Scholar 
47.Phillips, S. J. & Dudik, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161–175 (2008).
Google Scholar 
48.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40(7), 887–893 (2017).
Google Scholar 
49.Pinto, M. M., Libonati, R., Trigo, R. M., Trigo, I. F. & DaCamara, C. C. A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images. ISPRS J. Photogramm. Remote. Sens. 160, 260–274 (2020).ADS 

Google Scholar 
50.LASA – Laboratório de Aplicações de Satélites Ambientais. ALARMES – LASA. https://lasa.ufrj.br/alarmes/ (2021).51.Wickham, H. tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse (2017).52.Bray, A. et al. infer: Tidy Statistical Inference. R package version 0.5.4. https://cran.r-project.org/web/packages/infer/index.html (2021).53.Vallejos, R., Osorio, F. & Bevilacqua, M. Spatial Relationships Between Two Georeferenced Variables: with Applications in R (Springer, Berlin, 2020).MATH 

Google Scholar  More

1.Klupsaite, D. & Gražina, J. Legume: Composition, protein extraction and functional properties. A review. Chem. Technol. 66 (2015).2.Tassoni, A., et al. State-of-the-art production chains for peas, beans and chickpeas-valorization of agro-industrial residues and applications of derived extracts. Molecules (Basel, Switzerland) 25 (2020).3.Vilariño, M. V., Franco, C. & Quarrington, C. Food loss and waste reduction as an integral part of a circular economy. Front. Environ. Sci. 5 (2017).4.Malenica, D. & Bhat, R. Review article: Current research trends in fruit and vegetables wastes and by-products management-scope and opportunities in the estonian context. Agron. Res. 18, 1760–1795 (2020).
Google Scholar 
5.Tharanathan, R. N. & Mahadevamma, S. Grain legumes—a boon to human nutrition. Trends Food Sci. Technol. 14, 507–518 (2003).CAS 

Google Scholar 
6.Nguyen, T. M., Phoukham, K. & Ngo, T. V. Formulation and quality evaluation of pearl oyster mushroom soup powder supplement with some kinds of legumes and vegetables. Acta Sci. Polonorum Technol. Aliment. 19, 435–443 (2020).CAS 

Google Scholar 
7.Apprich, S. et al. Wheat bran-based biorefinery 2: Valorization of products. LWT Food Sci. Technol. 56, 222–231 (2014).CAS 

Google Scholar 
8.Xia, N. et al. Characterization and in vitro digestibility of rice protein prepared by enzyme-assisted microfluidization: Comparison to alkaline extraction. J. Cereal Sci. 56, 482–489 (2012).CAS 

Google Scholar 
9.Zhu, K.-X., Zhou, H.-M. & Qian, H.-F. Proteins extracted from defatted wheat germ: Nutritional and structural properties. Cereal Chem. 83, 69–75 (2006).CAS 

Google Scholar 
10.Tanongkankit, Y., Chiewchan, N. & Devahastin, S. Evolution of antioxidants in dietary fiber powder produced from white cabbage outer leaves: Effects of blanching and drying methods. J. Food Sci. Technol. 52, 2280–2287 (2015).CAS 
PubMed 

Google Scholar 
11.Stojceska, V., Ainsworth, P., Plunkett, A., İbanoğlu, E. & İbanoğlu, Ş. Cauliflower by-products as a new source of dietary fibre, antioxidants and proteins in cereal based ready-to-eat expanded snacks. J. Food Eng. 87, 554–563 (2008).CAS 

Google Scholar 
12.Babbar, N., Oberoi, H. S., Uppal, D. S. & Patil, R. T. Total phenolic content and antioxidant capacity of extracts obtained from six important fruit residues. Food Res. Int. 44, 391–396 (2011).CAS 

Google Scholar 
13.Elbadrawy, E. & Sello, A. Evaluation of nutritional value and antioxidant activity of tomato peel extracts. Arab. J. Chem. 9, S1010–S1018 (2016).CAS 

Google Scholar 
14.Wadhwa, M., Kaushal, S. & Bakshi, M. P. S. Nutritive evaluation of vegetable wastes as complete feed for goat bucks. Small Rumin. Res. 64, 279–284 (2006).
Google Scholar 
15.Wadhwa, M. & Bakshi, M. Vegetable wastes-a potential source of nutrients for ruminants. Indian J. Anim. Nutr. 22, 70–76 (2005).
Google Scholar 
16.Garg, M. Nutritional evaluation and utilization of pea pod powder for preparation of jaggery biscuits. J. Food Process. Technol. 6, 522–528 (2015).
Google Scholar 
17.Belghith Fendri, L. et al. Wheat bread enrichment by pea and broad bean pods fibers: Effect on dough rheology and bread quality. LWT 73, 584–591 (2016).CAS 

Google Scholar 
18.Hanan, E., Rudra, S. G., Sharma, V., Sagar, V. R. & Sehgal, S. Pea pod powder to enhance the storage quality of buckwheat bread. Vegetos (2021).19.Hanan, E., Rudra, G. S, Sagar, V. R. & Sharma, V. Utilization of pea pod powder for formulation of instant pea soup powder. J. Food Process. Preserv. (2020).20.Upasana, V. D. Nutritional evaluation of pea peel and pea peel extracted byproducts. Int. J. Food Sci. Nutr. 3, 65–67 (2018).
Google Scholar 
21.Abd-Allah, I., Rabie, M., Mostfa, D. M., Sulieman, A. & El-Badawi, A. Nutritional evaluation, chemical composition and antioxidant activity of some food processing wastes. Zag. J. Agric. Res. 43, 2115–2132 (2016).
Google Scholar 
22.El-Gohery, S. S. Quality aspects for high nutritional value pretzel. Curr. Sci. Int. 9, 583–593 (2020).
Google Scholar 
23.Hassanien, M. Impact of adding chickpea (Cicer arietinum L.) flour to wheat flour on the rheological properties of toast bread. Int. Food Res. J. 19, 521–525 (2012).
Google Scholar 
24.Sharoba, P. A., El-Desouky, A., Mahmoud, M. & Youssef, M. K. Quality attributes of some breads made from wheat flour substituted by different levels of whole amaranth meal. J. Agric. Sci. Mansoura Univ. 34, 6601–6617 (2009).
Google Scholar 
25.El-Sharnouby, G. Nutritional quality of biscuit supplemented with wheat bran and date palm fruits (Phoenix dactylifera L.). Food Nutr. Sci. 03, 322–328 (2012).CAS 

Google Scholar 
26.Abou El-Ez, A., Rania, W. Y., Shalaby, H. S., Abu El-Maaty, S. M. & Guirguis, A. H. Utlization of fruit and vegetable waste powders for fortification of some food products. Zag. J. Agric. Res. 6, 2189–2201 (2017).
Google Scholar 
27.Abd El-Salam, A. M., Morsy, O. M. & Abd El Mawla, E. M. Production and evaluation crackers and instant noodles supplement with spirulina algae. Curr. Sci. Int. 6, 908–919 (2017).
Google Scholar 
28.DRI. Dietary Reference Intakes, Dietary Reference Intakes for Energy, Carbohydrate, fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients) (The National Academies Press, Washington, 2005).
Google Scholar 
29.FAO/WHO. World health organization, food and agriculture organization of the united nations, United Nations University, 2007. Protein and amino acid requirements in human nutrition: Report of a joint who/fao/unu expert consultation. In: Joint expert consultation on protein and amino acid requirements in human nutrition; who technical report series. WHO, Geneva, Switzerland (2007).30.Shah, A. M., Wang, Z. & Ma, J. Glutamine metabolism and its role in immunity, a comprehensive review. Anim. Open Access J. MDPI 10, 326–332 (2020).
Google Scholar 
31.Rowayshed, G., Salama, A., Abul-Fadl, M., Akila-Hamza, S. & Emad, A. M. Nutritional and chemical evaluation for pomegranate (Punica granatum L.) fruit peel and seeds powders by products. Middle East J. Appl. Sci. 3, 169–179 (2013).
Google Scholar 
32.Hussein, A. M. S., Amal, S. A., Amany, M. H., Abeer, A. A. & Gamal, H. R. Physiochemical sensory and nutritional properties of corn-fenugreek flour composite biscuits. Aust. J. Basic Appl. Sci. 5, 84–95 (2011).CAS 

Google Scholar 
33.Mihiranie, S., Jayasundera, M. & Perera, N. Development of snack crackers incorporated with defatted coconut flour. J. Microbiol. Biotechnol. Food Sci. 7, 153–159 (2019).
Google Scholar 
34.Abdel-Haleem, A. M. & Omran, A. A. Preparation of dried vegetarian soup supplemented with some legumes. J. Food Nutr. Sci. 5, 2274–2282 (2014).
Google Scholar 
35.Holbrook, J. T. et al. Sodium and potassium intake and balance in adults consuming self-selected diets. Am. J. Clin. Nutr. 40, 786–793 (1984).CAS 
PubMed 

Google Scholar 
36.Schwalfenberg, G. K. & Genuis, S. J. The importance of magnesium in clinical healthcare. Scientifica 2017, 4179326 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Hanif, R., Iqbal, Z., Iqbal, M., Hanif, S. & Rasheed, M. Use of vegetables as nutritional food role in human health. J. Agric. Biol. Sci. 1, 18–22 (2006).
Google Scholar 
38.Ibidapo, O. et al. Some functional properties of flours from commonly consumed selected nigerian food crops. Int. Res. J. Agric. Food Sci. 1, 92–98 (2016).
Google Scholar 
39.Fellows, P. Processing Technology Principles and Practice 2nd edn. (Woodhead Publishing Limited and Crc Press LlC, Washington, 2000).
Google Scholar 
40.Monteiro, M. et al. Flours and instant soup from tilapia wastes as healthy alternatives to the food industry. Food Sci. Technol. Res. 20, 571–581 (2014).CAS 

Google Scholar 
41.Hanan, E., Rudra, S., Sagar, V. R. & Sharma, V. Utilization of pea pod powder for formulation of instant pea soup powder short running title: Formulation of instant pea soup powder. J. Food Process. Preserv. 44, e14888 (2020).CAS 

Google Scholar 
42.Belghith-Fendri, L. et al. Pea and broad bean pods as a natural source of dietary fiber: The impact on texture and sensory properties of cake. J. Food Sci. 81, C2360-c2366 (2016).CAS 
PubMed 

Google Scholar 
43.Ravindran, G. & Matia-Merino, L. Starch–fenugreek (Trigonella foenum-graecum L.) polysaccharide interactions in pure and soup systems. Food Hydrocoll. 23, 1047–1053 (2009).CAS 

Google Scholar 
44.Verma, A. Process for the preparation of value added instant tomato-mushroom soup mix incorporated with psyllium husk and its quality evaluation. Int. J. Pure Appl. Biosci. 5, 1502–1507 (2017).
Google Scholar 
45.Bose, D. & Shams-Ud-Din, M. The effect of chickpea (cicer arietinim) husk on the properties of cracker biscuits. J. Bangladesh Agric. Univ. 8, 147–152 (2010).
Google Scholar 
46.Yadav, A. R., Guha, M., Tharanathan, R. N. & Ramteke, R. S. Influence of drying conditions on functional properties of potato flour. Eur. Food Res. Technol. 223, 553–560 (2006).CAS 

Google Scholar 
47.Knezevic, D., Djukic, N., Paunovic, A. & Madic, M. Amino acid contents in grains of different winter wheat (Triticum aestivum L.) varieties. Cereal Res. Commun. 37, 647–650 (2009).CAS 

Google Scholar 
48.Gaines C. Associations among quality attributes of red and white soft wheat cultivars across locations and crop years. Cereal Chem. 68 (1991).49.Chitomarat S. Effects of drying on characteristic of powdered corn milk yoghurt (in thai). B.Sc. Thesis, Chiang Mai University, Thailand. (2002).50.Krokida, M. K. & Marinos-Kouris, D. Rehydration kinetics of dehydrated products. J. Food Eng. 57 (2003).51.Malomo, O., Ogunmoyela, O. O. A., Jimoh, M. & Oluwajoba, S. O. S. Rheological and functional properties of soy-poundo yam flour. Int. J. Food Sci. Nutr. Eng. 2, 101–107 (2013).
Google Scholar 
52.Piga, A. et al. Texture evolution of “amaretti” cookies during storage. Eur. Food Res. Technol. 221, 387–391 (2005).CAS 

Google Scholar 
53.Salem E. Nutritional quality of purslane and its crackers (2016).54.Wang, R., Zhang, M., Mujumdar, A. S. & Sun, J.-C. Microwave freeze–drying characteristics and sensory quality of instant vegetable soup. Drying Technol. 27, 962–968 (2009).
Google Scholar  More

AffiliationsDepartment of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Umeå, SwedenHyungwoo Lim, Torgny Näsholm, Tomas Lundmark & Harald GripNicholas School of the Environment, Duke University, Durham, NC, USARam OrenDepartment of Forest Sciences, University of Helsinki, Helsinki, FinlandRam OrenDepartment of Soil and Environment, SLU, Uppsala, SwedenMonika StrömgrenSouthern Swedish Forest Research Centre, SLU, Alnarp, SwedenSune LinderAuthorsHyungwoo LimRam OrenTorgny NäsholmMonika StrömgrenTomas LundmarkHarald GripSune LinderCorresponding authorsCorrespondence to
Hyungwoo Lim or Ram Oren. More

1.Landman, N. H. et al. (eds) Ammonoid Paleobiology (Plenum, 1996). https://doi.org/10.1007/978-1-4757-9153-2_16.Book 

Google Scholar 
2.Klug, C. et al. (eds) Ammonoid Paleobiology: From Anatomy to Ecology (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_18.Book 

Google Scholar 
3.Klug, C. et al. (eds) Ammonoid Paleobiology: From Macroevolution to Paleogeography (Springer, 2015). https://doi.org/10.1007/978-94-017-9633-0.Book 

Google Scholar 
4.Ritterbush, K. A., Hoffmann, R., Lukeneder, A. & De Baets, K. Pelagic palaeoecology: The importance of recent constraints on ammonoid palaeobiology and life history. J. Zool. 292(4), 229–241. https://doi.org/10.1111/jzo.12118 (2014).Article 

Google Scholar 
5.Westermann, G. E. G. Ammonoid life and habitat. In Ammonoid Paleobiology (eds Landman, N. H. et al.) 607–707 (Plenum, 1996). https://doi.org/10.1007/978-1-4757-9153-2_16.Chapter 

Google Scholar 
6.Lukeneder, A. Ammonoid habitats and life history. In Ammonoid Paleobiology: From Anatomy to Ecology (eds Klug, C. et al.) 689–791 (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_18.Chapter 

Google Scholar 
7.Hoffmann, R. et al. A novel multiproxy approach to reconstruct the paleoecology of extinct cephalopods. Gondwana Res. 67, 64–81. https://doi.org/10.1016/j.gr.2018.10.011 (2018).ADS 
CAS 
Article 

Google Scholar 
8.Hoffmann, R. et al. Recent advances in heteromorph ammonoid palaeobiology. Biol. Rev. Cambr. Philos. Soc. 96, 576–610. https://doi.org/10.1111/brv.12669 (2021).Article 

Google Scholar 
9.Moriya, K., Nishi, H., Kawahata, H., Tanabe, K. & Takayanagi, Y. Demersal habitat of Late Cretaceous ammonoids: Evidence from oxygen isotopes for the Campanian (Late Cretaceous) northwestern Pacific thermal structure. Geology 31, 167–170 (2003).ADS 
CAS 
Article 

Google Scholar 
10.Moriya, K. Isotope signature of ammonoid shells. In Ammonoid Paleobiology: From Anatomy to Ecology (eds Klug, C. et al.) 793–836 (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_19.Chapter 

Google Scholar 
11.Sessa, J. A. et al. Ammonite habitat revealed via isotopic composition and comparisons with co-occurring benthic and planktonic organisms. PNAS 112, 15562–15567. https://doi.org/10.1073/pnas.1507554112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Stevens, K., Mutterlose, J. & Wiedenroth, K. Stable isotope data (δ18O, δ13C) of the ammonite genus Simbirskites—Implications for habitat reconstructions of extinct cephalopods. Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 164–175. https://doi.org/10.1016/j.palaeo.2014.10.031 (2015).Article 

Google Scholar 
13.Surlyk, F., Dons, T., Clausen, C. K. & Higham, J. Upper Cretaceous. In The Millennium Atlas: Petroleum Geology of the Central and Northern North Sea (eds Copestake, P. et al.) 213–233 (Geological Society of London, 2003).
Google Scholar 
14.Thibault, N., Harlou, R., Schovsbo, N. H., Stemmerik, L. & Surlyk, F. Late Cretaceous (late Campanian–Maastrichtian) sea surface temperature record of the Boreal Chalk Sea. Clim. Past 12, 429–438. https://doi.org/10.5194/cp-12-429-2016 (2016).Article 

Google Scholar 
15.Wilmsen, M. & Niebuhr, B. High-resolution Campanian-Maastrichtian carbon and oxygen stable isotopes of bulk-rock and skeletal component: Palaeoceanographic and palaeoenvironmental implications for the Boreal shelf sea. Acta Geol. Pol. 67, 47–74. https://doi.org/10.1515/agp-2017-0004 (2017).ADS 
CAS 
Article 

Google Scholar 
16.Birkelund, T. Ammonites from the Maastrichtian White Chalk of Denmark. Bull. Geol. Soc. Denmark 40, 33–81 (1993).Article 

Google Scholar 
17.Niebuhr, B. Late Campanian and Early Maastrichtian ammonites from the white chalk of Kronsmoor (northern Germany)—Taxonomy and stratigraphy. Acta Geol. Pol. 53, 257–281 (2003).
Google Scholar 
18.Kruta, I. & Landman, N. H. Injuries on Nautilus jaws: Implications for the function of ammonite aptychi. Veliger 50, 241–247 (2008).
Google Scholar 
19.Tanabe, K., Kruta, I. & Landman, N. H. Ammonoid buccal mass and jaw apparatus. In Ammonoid Paleobiology: From Macroevolution to Paleogeography (eds Klug, C. et al.) 439–494 (Springer, 2015).
Google Scholar 
20.Kruta, I., Landman, N. H. & Cochran, J. K. A new approach for the determination of ammonite and nautilid habitats. PLoS ONE 9, e87479. https://doi.org/10.1371/journal.pone.0087479 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Machalski, M. Late Maastrichtian and earliest Danian scaphitid ammonites from central Europe: Taxonomy, evolution, and extinction. Acta Palaeontol. Pol. 50(4), 653–696 (2005).
Google Scholar 
22.Machalski, M. Correlation of shell and aptychus growth provides insights into the palaeobiology of a scaphitid ammonite. Palaeontology 64, 225–247. https://doi.org/10.1111/pala.12519 (2021).Article 

Google Scholar 
23.Dubicka, Z. & Peryt, D. Integrated biostratigraphy of Upper Maastrichtian chalk at Chełm (SE Poland). Ann. Soc. Geol. Pol. 81, 185–197 (2011).
Google Scholar 
24.Dubicka, Z. & Peryt, D. Latest Campanian and Maastrichtian palaeoenvironmental changes: Implications from an epicontinental sea (SE Poland and western Ukraine). Cret. Res. 37, 272–284. https://doi.org/10.1016/j.cretres.2012.04.009 (2012).Article 

Google Scholar 
25.Machalski, M. & Malchyk, O. Durophagous predation on late Maastrichtian (Cretaceous) scaphitid ammonites from Poland. In 10th International Symposium “Cephalopods—Present and Past”, Program and Abstracts. Münstersche Forschungen zur Geologie und Paläontologie 110, 77–78 (2018).
Google Scholar 
26.Keupp, H. Sublethal punctures in body chambers of Mesozoic ammonites (forma Aegra fenestra n. f.), a tool to interpret synecological relationships, particularly predator–prey interactions. Paläontol. Z. 80, 112–123. https://doi.org/10.1007/BF02988971 (2006).Article 

Google Scholar 
27.Mironenko, A. Sublethal injuries on the shells of Jurassic ammonites from Central Russia. In Jurassic Deposits of the Southern Part of the Moscow Syneclise and Their Fauna (eds Rogov, M. A. & Zakharov, V. A.) 183–208 (Transactions of the Geological Institute, GEOS, 2017) (in Russian).
Google Scholar 
28.Moriya, K. Evolution of habitat depth in the Jurassic-Cretaceous ammonoids. PNAS 112, 15540–15541. https://doi.org/10.1073/pnas.1520961112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Leszczyński, K. The internal geometry and lithofacies pattern of the Upper Cretaceous-Danian sequence in the Polish Lowlands. Geol. Q. 56, 363–386. https://doi.org/10.7306/gq.1028 (2012).Article 

Google Scholar 
30.Jurkowska, A. & Świerczewska-Gładysz, E. New model of Si balance in the Late Cretaceous epicontinental European Basin. Global Planet. Change 186, 103108. https://doi.org/10.1016/j.gloplacha.2019.103108 (2020).Article 

Google Scholar 
31.Müller, R. D. et al. GPlates: Building a virtual Earth through deep time. Geochem. Geophys. Geosyst. 19, 2243–2261. https://doi.org/10.1029/2018GC007584 (2018).ADS 
Article 

Google Scholar 
32.Walaszczyk, I., Dubicka, Z., Olszewska-Nejbert, D. & Remin, Z. Integrated biostratigraphy of the Santonian through Maastrichtian (Upper Cretaceous) of extra-Carpathian Poland. Acta Geol. Pol. 66, 321–358. https://doi.org/10.1515/agp-2016-0016 (2016).ADS 
CAS 
Article 

Google Scholar 
33.Surlyk, F. et al. Upper Campanian-Maastrichtian holostratigraphy of the eastern Danish Basin. Cret. Res. 46, 232–256. https://doi.org/10.1016/j.cretres.2013.08.006 (2013).Article 

Google Scholar 
34.Tagliavento, M., Lauridsen, B. W. & Stemmerik, L. Episodic dysoxia during Late Cretaceous cyclic chalk-marl deposition—Evidence from framboidal pyrite distribution in the upper Maastrichtian Rørdal Mb., Danish Basin. Cret. Res. 106, 104223. https://doi.org/10.1016/j.cretres.2019.104223 (2020).Article 

Google Scholar 
35.Dubicka, Z., Wierzbowski, H. & Wierny, W. Oxygen and carbon isotope records of Upper Cretaceous foraminifera from Poland: Vital and microhabitat effects. Palaeogeogr. Palaeoclimatol. Palaeoecol. 500, 33–51. https://doi.org/10.1016/j.palaeo.2018.03.029 (2018).Article 

Google Scholar 
36.Klompmaker, A. A., Waljaard, N. A. & Fraaije, R. H. B. Ventral bite marks in Mesozoic ammonoids. Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 245–257. https://doi.org/10.1016/j.palaeo.2009.06.013 (2009).Article 

Google Scholar 
37.Fraaye, R. H. B. Late Cretaceous swimming crabs: Radiation, migration, competition, and extinction. Acta Geol. Pol. 46, 269–278 (1996).
Google Scholar 
38.Caldwell, R. L. & Dingle, H. Stomatopods. Sci. Am. 234, 80–89 (1976).ADS 
Article 

Google Scholar 
39.Dunstan, A. J., Ward, P. D. & Marshall, N. J. Vertical distribution and migration patterns of Nautilus pompilius. PLoS ONE 6, e16311. https://doi.org/10.1371/journal.pone.0016311 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Ward, P., Dooley, F. & Barord, G. J. Nautilus: Biology, systematics, and paleobiology as viewed from 2015. Swiss J. Palaeontol. 135, 169–185. https://doi.org/10.1007/s13358-016-0112-7 (2016).Article 

Google Scholar 
41.Landman, N. H., Cobban, W. A. & Larson, N. L. Mode of life and habitat of scaphitid ammonites. Geobios 45, 87–98. https://doi.org/10.1016/j.geobios.2011.11.006 (2012).Article 

Google Scholar 
42.Peterman, D. J. et al. Syn vivo hydrostatic and hydrodynamic properties of scaphitid ammonoids from the U.S. Western Interior. Geobios 60, 79–98. https://doi.org/10.1016/j.geobios.2020.04.004 (2021).Article 

Google Scholar 
43.Tsujita, C. J. & Westermann, G. Ammonoid habitats and habits in the Western Interior Seaway: A case study from the Upper Cretaceous Bearpaw Formation of southern Alberta, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 135–160. https://doi.org/10.1016/S0031-0182(98)00090-X (1998).Article 

Google Scholar 
44.Fraaije, R. H. B., Van Bakel, B. W. M., Jagt, J. W. M. & Viegas, P. A. The rise of a novel, plankton-based marine ecosystem during the Mesozoic: A bottom-up model to explain new higher-tier invertebrate morphotypes. Boletín de la Sociedad Geol. Mexicana 70, 187–200. https://doi.org/10.18268/bsgm2018v70n1a11 (2018).Article 

Google Scholar 
45.Alldredge, A. L. & King, J. M. The distance demersal zooplankton migrate above the benthos: Implications for predation. Marine Biol. 84, 253–260. https://doi.org/10.1007/BF00392494 (1985).Article 

Google Scholar 
46.Hammer, O., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Pal. Electron. 4, 1–9 (2001).
Google Scholar 
47.Anderson, T. F. & Arthur, M. A. Stable isotopes of oxygen and carbon and their application to sedimentologic and paleonvironmental problems. In Stable Isotopes in Sedimentary Geology, The Society of Economic Paleontologists and Mineralogists Short Course Vol. 10 (eds Arthur, M. A. et al.) 1–151 (SEPM, 1983). https://doi.org/10.2110/scn.83.01.0000.Chapter 

Google Scholar 
48.Coplen, T. B., Kendall, C. & Hopple, J. Comparison of stable isotope reference samples. Nature 302, 236–238. https://doi.org/10.1038/302236a0 (1983).ADS 
CAS 
Article 

Google Scholar 
49.McLennan, S. M. Rare earth elements in sedimentary rocks: Influence of provenance and sedimentary process. Rev. Mineral. 21, 169–200 (1989).CAS 

Google Scholar 
50.Webb, G. E. & Kamber, B. S. Rare earth elements in Holocene reefal microbialites: a new shallow seawater proxy. Geochim. Cosmochim. Acta 64, 1557–1565. https://doi.org/10.1016/S0016-7037(99)00400-7 (2000).ADS 
CAS 
Article 

Google Scholar  More

Calculating landscape, attractor, and restrictorIn this work, we considered communities with commensal, mutualistic, and exploitative interactions. Below, we describe the differential equations for each type of interaction, and how we calculate the corresponding community function landscape, species-composition attractor, and Newborn restrictor.Commensal H–M community: The model community for most simulations is the same commensal H–M community used in our previous work15. The community function landscape plots P(T) as a function of ϕM(0) and ({overline{f}}_{P}(0)). Assume that a Newborn community has 100 biomass units, that all cells have the same genotype (all M cells have the same ({f}_{P}={overline{f}}_{P}(0))), that death and birth processes are deterministic, and that there is no mutation. P(T) can then be numerically integrated from the following set of scaled differential equations for any given pair of ϕM(0) and ({overline{f}}_{P}(0))15:$$frac{dR}{dt}=-{c}_{{RM}}{g}_{M}M-{c}_{{RH}}{g}_{H}H$$
(1)
$$frac{dB}{dt}={g}_{H}H-{c}_{{BM}}{g}_{M}M$$
(2)
$$frac{dP}{dt}={f}_{P}{g}_{M}M$$
(3)
$$frac{dH}{dt}={g}_{H}H-{delta }_{H}H$$
(4)
$$frac{dM}{dt}={g}_{M}left(1-{f}_{P}right)M-{delta }_{M}M$$
(5)
where$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}$$
(6)
$${g}_{M}(R, B)={g}_{{Mmax}}frac{{R}_{M}{B}_{M}}{{R}_{M}+{B}_{M}}left(frac{1}{{R}_{M}+1}+frac{1}{{B}_{M}+1}right)$$
(7)
and RM = R/KMR and BM = B/KMB. Unless otherwise specified, landscapes in this paper are obtained by integrating Equations (1–5) from t = 0 to t = 17.Equation (1) states that Resource R is depleted by biomass growth of M and H, where cRM and cRH represent the amount of R consumed per unit of M and H biomass, respectively. Equation (2) states that Byproduct B is released as H grows, and is decreased by biomass growth of M due to consumption (cBM amount of B per unit of M biomass). Equation (3) states that Product P is produced as fP fraction of potential M growth. Equation (4) states that H biomass increases at a rate dependent on Resource R in a Monod fashion (Equation (6)) and decreases at the death rate δH. Note that Agricultural waste is not a state variable here as it is present in excess. Equation (5) states that M biomass increases at a rate dependent on Resource R and Byproduct B according to the Mankad and Bungay model (Equation (7)51) discounted by (1 − fP) due to the fitness cost of making Product, and decreases at the death rate δM. In the Monod growth model (Equation (6)), gHmax is the maximal growth rate of H and KHR is the R at which gHmax/2 is achieved. In the Mankad and Bungay model (Equation (7)), KMR is the R at which gMmax/2 is achieved when B is in excess; KMB is the B at which gMmax/2 is achieved when R is in excess.Mutualistic H–M community: If Byproduct is harmful for H, then the community is mutualistic: H and M promote the growth of each other. Such a mutualistic community can still be described by Equations (1–5) and (7), but Equation (6) is replaced with$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}exp left(-frac{B}{{B}_{0}}right)$$
(8)
where larger B0 indicates lower sensitivity, or higher resistance of H to its Byproduct B.Exploitative H–M community: If M releases an antagonistic byproduct A that inhibits the growth of H, then the interaction is exploitative: H promotes the growth of M, but M inhibits the growth of H. Besides Eqs (1–5) and (7), we then need to add an equation that describes the dynamics of A$$frac{dwidetilde{A}}{dt}={r}_{A}{g}_{M}left(1-{f}_{P}right)M$$where rA is the amount of A released when M’s biomass grows by 1 unit. We can then normalize (widetilde{A}) with rA$$A=widetilde{A}/{r}_{A}$$so that$$frac{dA}{dt}={g}_{M}left(1-{f}_{P}right)M.$$
(9)
We also need to modify the growth rates for H:$${g}_{H}={g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}frac{{A}_{0}}{A+{A}_{0}}$$
(10)
where larger A0 indicates lower sensitivity, or higher resistance of H to M’s Antagonistic by product A.To calculate the community function landscape, species attractor, and Newborn restrictor, all phenotype parameters, except ({overline{f}}_{P}(0)) take the value from the Bounds column in Table 1. To construct the landscape such as in Fig. 2c, we calculated P(T) for every grid point on a 2D quadrilateral mesh of 10−2 ≤ ϕM(0) ≤ 0.99 and (1{0}^{-2} le {overline{f}}_{P}(0) le 0.99) with a mesh size of ΔϕM(0) = 10−2 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-2}). To construct the landscapes in Fig. 5b(ii) and b(iii), P(T) was similarly calculated on a 2D grid with a finer mesh of ΔϕM(0) = 5 × 10−3 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-4}).To calculate the species composition attractor, we integrated Equations (1–5) to obtain ϕM(T) − ϕM(0) for each grid point on the 2D mesh of ϕM(0) and ({overline{f}}_{P}(0)). The contour of ϕM(T) − ϕM(0) = 0 is then the species attractor (blue dashed curve in Fig. 2b).The attractor-induced Newborn restrictor at a given ({overline{f}}_{P}(0)) is calculated from its definition: if ϕM(0) of a parent Newborn is on the restrictor, then so is the average ϕM(0) among its offspring Newborns. Under no spiking, since the average ϕM(0) among offspring Newborn is the same as ϕM(T) of their parent Adult, the Newborn restrictor coincides with the species attractor (Fig. 3b and Fig. 5b ii). Under x% H spiking, x% of the biomass in Newborns is replaced with H cells. Thus if the parent Adult’s fraction of M biomass is ϕM(T), the average ϕM(0) among its offspring Newborns is (1 − x%)ϕM(T) under x% H spiking. The Newborn restrictor therefore is the contour of (1 − x%)ϕM(T) − ϕM(0) = 0 (teal curve in Fig. 5a ii and b iii, Fig. 2d ii). Compared with the orange restrictor under no spiking, the teal restrictor is shifted down.Parameter choicesDetails justifying our parameter choices are given in the Methods section of our previous work15. Briefly, our parameter choices are based on experimental measurements of microorganisms (e.g., S. cerevisiae and E. coli). To ensure the coexistence of H and M, M must grow faster than H for part of the maturation cycle since M has to wait for H’s Byproduct at the beginning of a cycle. Because we have assumed M and H to have similar affinities for Resource (Table 1), the maximal growth rate of M (gMmax) must exceed the maximal growth rate of H (gHmax), and M’s affinity for Byproduct (1/KMB) must be sufficiently large. Moreover, metabolite release and consumption need to be balanced to avoid extreme species ratios. We assume that H and M consume the same amount of Resource per new cell (cRH = cRM) since the biomass of various microbes shares similar elemental (e.g., carbon or nitrogen) compositions. We set consumption value so that the input Resource can support a maximum of 104 total biomass. The evolutionary bounds are set, such that evolved H and M could coexist for fp  0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}). Because integer number of cells is assigned to each Newborn, the total biomass might not be exactly BMtarget but within a small deviation of ~2 biomass units.To mimic reproducing through pipetting, each M and H cell in an Adult community is assigned a random integer between 1 and dilution factor nD (Equation (12)). All cells assigned with the same random integer are then dealt to the same Newborn, generating nD Newborn communities. If φS  > 0, the number of H cells supplemented into each Newborn is a random number drawn from a Poisson distribution of a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}).To mimic reproducing through cell sorting, each Newborn receives a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult. Suppose that the fraction of M biomass in the parent Adult is ϕM(T), then M cells from the parent Adult are randomly assigned to the Newborn, until the total biomass of M comes closest to (B{M}_{{{{{{{{rm{target}}}}}}}}}{phi }_{M}(T)left(1-{varphi }_{S}right)) without exceeding it. H cells with a total biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{phi }_{M}(T)right)left(1-{varphi }_{S}right)) are assigned similarly. If φS  > 0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}) where LH is the biomass of individual H cell in the parent Adult. Because each of M and H cells had a length between 1 and 2, the actual biomass of M and H assigned to a Newborn could vary from the target by up to 2 biomass units. Consequently, deviations of BM(0) from BMtarget and of ϕM(0) from parent Adult’s ϕM(T) are only a few percent.Simulating species spiking when both H and M cells evolveIn the more complex scenario, both H and M evolve. We thus need to spike with evolved H and M clones. Additionally, Newborns are spiked with H or M clones from their own lineage as demonstrated in Supplementary Fig. 11a. Below, we describe the simulation code for the experimental procedure (Supplementary Fig. 11a) we simulated.In all simulations where 6 or 7 phenotypes are modified by mutations, chosen Adults are reproduced through pipetting in a similar fashion as described above. After Newborns are reproduced from a chosen Adult in Cycle C − 1, a preset number of H or M cells are randomly picked from the remaining of this Adult to form H or M-spiking mix for Cycle C. At the end of Cycle C, we choose 10 Adults with the highest functions. Assuming that each chosen Adult is reproduced through pipetting with φS-H-spiking strategy, a Newborn receives on average a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult community and on average a biomass of BMtargetφS from H spiking mix generated at the end of Cycle C − 1. Since each chosen Adult usually gives rise to 10 Newborns, the number of cells distributed from the chosen Adult to each Newborn is drawn from a multinomial distribution. Specifically, denote the integer random numbers of cells that would be assigned to 10 Newborns to be {x1, x2,…, x10}. If the chosen Adult has a total biomass of BM(T) composed of IM M cells and IH H cells (both IM and IH are integers), the probability that {x1, x2,…, x10} cells are assigned to 10 Newborns, respectively, and x11 cells remain, is$$Pr left({{x}_{1},{x}_{2},…,{x}_{10},{x}_{11}}right)=frac{({I}_{H}+{I}_{M})!}{{x}_{1}!cdots {x}_{10}!{x}_{11}!},{p}_{0}{{,}^{{x}_{1}+cdots +{x}_{10}}},{p}_{11}^{{x}_{11}}.$$Here, ({p}_{0}=B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)/BM(T)) is the probability that a cell is assigned to one of 10 Newborns, p11 = 1 − 10p0 is the probability that a cell is not assigned to Newborns. Thus, ({x}_{11}={I}_{H}+{I}_{M}-mathop{sum }nolimits_{i = 1}^{10}{x}_{i}) is the number of cells remaining after reproduction, from which H and M cells are randomly picked to generate the spiking mix for Cycle C + 1.Suppose that the current spiking strategy is φS-H, then these 10 Newborns are spiked with H-spiking mix generated in Cycle C − 1. An average of BMtargetφS of H biomass is spiked into each Newborn so that the total biomass of Newborns is on average BMtarget. Suppose that five H cells from the parent Adult’s lineage are randomly picked at the end of Cycle C − 1, and that they have biomass {LH1, LH2, LH3, LH4, LH5}, respectively. The total number of H cells assigned to each Newborn, xH, is then randomly drawn from a Poisson distribution with a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}/{overline{L}}_{H}), where ({overline{L}}_{H}=frac{1}{5}mathop{sum }nolimits_{j = 1}^{5}{L}_{Hj}) is the average biomass of the five H cells. Each spiked H cell has an equal chance of being one of the five cells.Updating spiking percentage based on heritability checksWhen the community function landscape is unknown, we can estimate heritability of community function under different spiking percentages through parent–offspring regression. In most simulations (e.g., Fig. 7), heritability evaluation is carried out about every 100 cycles (“periodic heritability check”). In the simulations demonstrated in Supplementary Fig. 17, the average improvement rate in community function is estimated from the chosen Adults over the last 50 cycles. Heritability evaluation is carried out when this average improvement rate becomes negative (“adaptive heritability check”). For both periodic and adaptive checks, heritability evaluation can be postponed until within-community selection improves cell growth sufficiently to provide sufficient biomass for heritability check.During one round of heritability evaluation, heritability of community function is estimated through parent–offspring community function regression under all candidate spiking strategies (Supplementary Fig. 11b). The current spiking strategy is updated if an alternative spiking strategy confers significantly higher community function heritability.To evaluate heritability under one spiking strategy, up to 100 Newborn communities are generated under this spiking strategy. After these mature into Adults, their functions are the parent functions. Each Adult parent then gives rise to six Newborn offspring under the same spiking strategy. When the six Newborn offspring mature into Adults, the median of their functions is the average offspring function. When offspring functions are plotted against their parent functions, the slope of the least-squares linear regression (green dashed line in Supplementary Fig. 11b) quantifies the heritability of community function. Heritability of a community function is thus similar to heritability of an individual trait, except that we use median instead of mean of offspring functions, because median is less sensitive to outliers. The 95% confidence interval of heritability is then estimated by nonparametric bootstrap58,59. More specifically, first, 100 pairs of parent–offspring community functions are resampled with replacement. Second, heritability is calculated with the resampled data. Third, 1000 heritabilities are calculated from 1000 independent resamplings, from which the 95% confidence interval is estimated from the 5th and 95th percentile.An alternative spiking strategy is considered significantly more advantageous than the current spiking strategy if heritability of the alternative spiking strategy is higher than the right endpoint of the 95% confidence interval of the heritability of the current spiking strategy. If more than one alternative spiking strategies are more advantageous, the one with the highest heritability is implemented to replace the current strategy. Similarly, an alternative spiking strategy is considered more disadvantageous if heritability of the alternative spiking strategy is lower than the left endpoint of the 95% confidence interval of the heritability of the current spiking strategy. When implementing random spiking strategy, the current spiking strategy is updated with a strategy randomly picked from candidate spiking strategies.Simulating community selection with large population sizeWhen the population size of each community is scaled up by 10 or 100 times (Supplementary Figs. 2 and 18b), the simulation codes described above become inefficient. Instead of tracking the biomass and phenotype of each cell in a large population, we divide the cells into categories and track the number of cells from different categories, where a category is defined by a unique combination of cell biomass and phenotype ranges. In our simulations, the biomass of each cell ranges between 1 and 2, fP of each M cell ranges between 0 and 1. Since H cells do not mutate, H cells are divided into 100 categories. H cells that belong to category i have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] where ΔL = 10−2. Since only fP of M cells are modified by mutations, M cells are divided into 100 × 105 categories. M cells that belong to category (i, j) have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] and fP between [(j − 1) × ΔfP, j × ΔfP] where ΔfP = 10−5. Every time fP of a M cell is modified by mutations, this cell jumps from the current category to a new category determined by its new fP value.Similar to simulations with small population sizes, each selection cycle starts with ntot = 100 Newborn communities. Maturation time T is divided into time steps of length Δτ = 0.05. Over each time step, the growth in cell biomass and the changes in metabolites are simulated in a similar fashion as described above. At the end of each time step, the number of cells to die or to mutate in each category is drawn from a bionomial distribution. If fP of a M cell is modified by mutation, the mutation effect is drawn from the same distribution as described above: (frac{1}{2}) of mutations reduce fP to 0 and the other (frac{1}{2}) is randomly drawn from the distribution in Equation (11).At the end of a maturation cycle, top 10 Adults with the highest functions are chosen. Each then reproduces 10 Newborns via pipetting for the next cycle. The fold of dilution is similarly adjusted, so that the average of Newborn total biomass is BMtarget over all selection cycles. From each category of a chosen Adult, the number of cells assigned to a Newborn community is randomly drawn from a multinomial distribution.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Catchpole, C. K. Variation in the song of the great reed warbler Acrocephalus arundinaceus in relation to mate attraction and territorial defence. Anim. Behav. 31, 1217–1225 (1983).
Google Scholar 
2.Andersson, M. Sexual selection (University Press, 1994).
Google Scholar 
3.Searcy, W. A. & Yasukawa, K. Song and female choice. In Ecology and evolution of acoustic communication in birds (eds Kroodsma, D. E. & Miller, E. H.) 454–473 (Cornell University Press, 1996).
Google Scholar 
4.O’Loghlen, A. L. & Beecher, M. D. Mate, neighbour and stranger song: A female song sparrow perspective. Anim. Behav. 58, 13–20 (1999).
Google Scholar 
5.Gentner, T. Q. & Hulse, S. H. Female European starling preference and choice for variation in conspecific male song. Anim. Behav. 59, 443–458 (2000).CAS 
PubMed 

Google Scholar 
6.Molles, L. E. & Vehrencamp, S. L. Neighbour recognition by resident males in the banded wren Thryothorus pleurostictus a tropical songbird with high song type sharing. Anim. Behav. 61, 119–127 (2001).PubMed 

Google Scholar 
7.Ballentine, B., Hyman, J. & Nowicki, S. Vocal performance influences female response to male bird song: An experimental test. Behav. Ecol. 15, 163–168 (2004).
Google Scholar 
8.Forstmeier, W., Kempenaers, B., Meyer, A. & Leisler, B. A novel song parameter correlates with extra-pair paternity and reflects male longevity. Proc. R. Soc. Lond. B 269, 1479–1485 (2002).
Google Scholar 
9.de Kort, S. R., Eldermire, E. R. B., Valderrama, S., Botero, C. A. & Vehrencamp, S. L. Trill consistency is an age-related assessment signal in banded wrens. Proc. R. Soc. Lond. B 276, 2315–2321 (2009).
Google Scholar 
10.Węgrzyn, E., Leniowski, K. & Osiejuk, T. Whistle duration and consistency reflect philopatry and harem size in great reed warblers. Anim. Behav. 79, 1363–1372 (2010).
Google Scholar 
11.Węgrzyn, E., Leniowski, K. & Osiejuk, T. Introduce yourself at the beginning – Possibile identification function of the initial part of the song in the great reed warbler Acrocephalus arundinaceus. Ornis Fennica 86, 61–70 (2009).
Google Scholar 
12.Węgrzyn, E. & Leniowski, K. Middle Spotted Woodpecker territory owners distinguish between stranger and familiar foaters based on their vocal characteristics. Eur. Zool. J. 87, 58–72 (2020).
Google Scholar 
13.Podos, J. Motor constraints on vocal development in a songbird. Anim. Behav. 51, 1061–1070 (1996).
Google Scholar 
14.Podos, J., Southall, J. A. & Rossi-Santos, M. R. Vocal mechanics in Darwin’s finches: Correlation of beak gape and song frequency. J. Exp. Biol. 207, 607–619 (2004).PubMed 

Google Scholar 
15.Nelson, B. S., Deckers, G. J. L. & Suthers, R. A. Vocal tract filtering and sound radiation in a songbird. J. Exp. Biol. 208, 297–308 (2005).PubMed 

Google Scholar 
16.Falls, J. B. Individual recognition by sounds in birds. In Acoustic communication in birds Vol. 2 (eds Kroodsma, D. E. & Miller, E. H.) 237–278 (Academic Press, 1982).
Google Scholar 
17.Wiley, R. H., Hatchwell, B. J. & Davies, N. B. Recognition of individual males songs by female dunnocks: A mechanism increasing the number of copulatory partners and reproductive success. Ethology 88, 145–153 (1991).
Google Scholar 
18.Lind, H., Dabelsteen, T. & McGregor, P. K. Female great tits can identify mates by song. Anim. Behav. 52, 667–671 (1996).
Google Scholar 
19.Aubin, T., Jouventin, P. & Hildebrand, C. Penguins use the two-voice system to recognize each other. Proc. R. Soc. Lond. B 267, 1081–1087 (2000).CAS 

Google Scholar 
20.Charrier, I., Jouventin, P., Mathevon, N. & Aubin, T. Individual identity coding depends on call type in the South Polar Skua Catharacta maccormicki. Polar Biol. 24, 378–382 (2001).
Google Scholar 
21.Stoddard, P. K., Beecher, M. D., Horning, C. L. & Willis, M. S. Strong neighbor– stranger discrimination in song sparrows. Condor 92, 1051–1056 (1990).
Google Scholar 
22.Stoddard, P. K., Beecher, M. D., Horning, C. L. & Campbell, S. E. Recognition of individual neighbors by song in the song sparrow, a species with song repertoires. Behav. Ecol. Sociobiol. 29, 211–215 (1991).
Google Scholar 
23.Godard, R. Long–term memory of individual neighbours in a migratory songbird. Nature 350, 228–229 (1991).ADS 

Google Scholar 
24.Stoddard, P. K. Vocal recognition of neighbors by territorial passerines. In Ecology and evolution of acoustic communication in birds (eds Kroodsma, D. E. & Miller, E. H.) 56–374 (Cornell University Press, 1996).
Google Scholar 
25.Hyman, J. Seasonal variation in response to neighbors and strangers by a territorial songbird. Ethology 111, 951–961 (2010).
Google Scholar 
26.Mackin, W. A. Neighbor–stranger discrimination in Audubon’s Shearwater Puffinus l. lherminieri explained by a “real enemy” effect. Behav. Ecol. Sociobiol. 59(2), 326–332 (2005).
Google Scholar 
27.Charrier, I., Mathevon, N., Jouventin, P. & Aubin, T. Acoustic communication in a Black-headed Gull colony: How do chicks identify their parents?. Ethology 107, 961–974 (2001).
Google Scholar 
28.Lengagne, T., Lauga, J. & Aubin, T. Intra–syllabic acoustic signatures used by the King Penguin in parent–chick recognition: An experimental approach. J. Exp. Biol. 204, 663–672 (2001).CAS 
PubMed 

Google Scholar 
29.Jouventin, P. & Aubin, T. Acoustic systems are adapted to breeding ecologies: Individual recognition in nesting penguins. Anim. Behav. 64, 747–757 (2002).
Google Scholar 
30.Cucco, M. & Malacarne, G. Is the song of black restart males an honest signal of status?. Condor 101, 689–694 (1999).
Google Scholar 
31.Christie, P. J., Mennill, D. J. & Ratcliffe, L. M. Chickadee song structure is individually distinctive over long broadcast distances. Behaviour 141, 101–124 (2004).
Google Scholar 
32.Sherman, P. W., Reeve, H. K. & Pfennig D. W. Recognition systems. In: Krebs JR,DaviesNB, editors. Behavioural ecology: An evolutionary approach. Oxford: Blackwell Scientific. pp. 69–96 (1997).33.Kilham, L. Behavior and methods of communication of Pileated woodpeckers. Condor 61, 377–387 (1959).
Google Scholar 
34.Lawrence, L. & de Kort, S. R. A comparative life–history study of four species of woodpeckers. Ornithol. Monogr. 5, 1–155 (1967).
Google Scholar 
35.Winkler, H. & Short, L. A comparative analysis of acoustical signals in Pied woodpeckers (Aves, Picoides). Bull. Am. Mus. Nat. Hist. 160, 1–110 (1978).
Google Scholar 
36.Crusoe, D. A. Acoustic behavior and its role in the social relations of the red-headed wood-pecker: Picidae, Melanerpes erythrocephalus. Doctoral dissertation, University of Illinois at Chicago Circle (1980).37.Pardo, M. A. et al. Wild acorn woodpeckers recognize associations between individuals in other groups. Proc. R. Soc. B 285, 20181017 (2018).PubMed 
PubMed Central 

Google Scholar 
38.Leniowski, K. & Węgrzyn, E. The carotenoid-based red cap of the middle spotted woodpecker Dendrocopos medius reflects individual quality and territory size. Ibis 155(4), 804–813 (2013).
Google Scholar 
39.Podos, J., Lahti, D. C. & Moseley, D. L. Vocal performance and sensorimotor learning in songbirds. Adv. Study Behav. 40, 159–195 (2009).
Google Scholar 
40.Tremain, S. B., Swiston, K. A. & Mennill, D. J. Seasonal variation in acoustic signals of Pileated Woodpeckers. Wilson J. Ornithol. 120(3), 499–504 (2008).
Google Scholar 
41.Kilham, L. Reproductive behavior of red–bellied woodpeckers. Wilson Bull. 73, 237–254 (1961).
Google Scholar 
42.Catchpole, C. K. & Slater, P. J. B. Bird Song. Biological themes and variation 2nd edn. (Cambridge University Press, Cambridge, 2008).
Google Scholar 
43.Falls, J. B. & McNicholl, M. K. Neighbor–stranger discrimination by song in male blue grouse. Can. J. Zool. 57(2), 457–462 (1979).
Google Scholar 
44.Galeotti, P. & Pavan, G. Individual recognition of male tawny owls Strix aluco using spectrograms of their territorial calls. Ethol. Ecol. Evol. 3(2), 113–126 (1991).
Google Scholar 
45.Prum, R. O. Sexual selection and the evolution of mechanical sound production in manakins Aves: Pipridae. Anim. Behav. 55(4), 977–994 (1998).CAS 
PubMed 

Google Scholar 
46.Rebbeck, M., Corrick, R., Eaglestone, B. & Stainton, C. Recognition of individual European Nightjars Caprimulgus europaeus from their song. Ibis 143, 468–475 (2001).
Google Scholar 
47.Dodenhoff, D. J. An analysis of acoustic communication within the social system of downy woodpeckers Picoides pubescens. Doctoral dissertation, The Ohio State University. (2002).48.Budka, M., Deoniziak, K., Tumiel, T. & Białas, J. T. Vocal individuality in drumming in great spotted woodpecker—A biological perspective and implications for conservation. PLoS ONE 13(2), e0191716 (2018).PubMed 
PubMed Central 

Google Scholar 
49.Ydenberg, R. C., Giraldeau, L. A. & Falls, J. B. Neighbours, strangers, and the asymmetric war of attrition. Anim. Behav. 36(2), 343–347 (1988).
Google Scholar 
50.Delport, W., Kemp, A. C. & Ferguson, W. H. Vocal identification of individual African Wood Owls Strix woodfordii: a technique to monitor long-term adult turnover and residency. Ibis 144, 30–39 (2002).
Google Scholar 
51.Peake, T. M. et al. Individuality in Corncrake Crex crex vocalisations. Ibis 140, 120–217 (1998).
Google Scholar 
52.Hoodless, A. N., Inglis, J. G., Doucet, J.-P. & Aebischer, N. J. Vocal individuality in the roding calls of Woodcock Scolopax rusticola and their use to validate a survey method. Ibis 150, 80–89 (2008).
Google Scholar 
53.Grava, T., Mathevon, N., Place, E. & Balluet, P. Individual acoustic monitoring of the European Eagle Owl Bubo bubo. Ibis 150, 279–287 (2008).
Google Scholar 
54.Odom, K. J., Slaght, J. C. & Gutierrez, R. J. Distinctiveness in the territorial calls of Great horned owls within and among years. J. Raptor Res. 47, 21–30 (2013).
Google Scholar 
55.Aubin, T., Mathevon, N., Staszewski, V. & Boulinier, T. Acoustic communication in the Kittiwake Rissa tridactyla: potential cues for sexual and individual signatures in ling calls. Polar Biol. 30, 1027–1033 (2007).
Google Scholar 
56.Bretagnolle, V. & Laquette, B. Structural variation in the call of the Cory’s Shearwater (Colonectris diodemea, Aves, Procellaridae). Ethology 85, 313–323 (1990).
Google Scholar 
57.de Broke, M. L. Sexual differences in the voice and individual vocal recognition in the Manx Shearwater (Puffinus Puffinus). Anim. Behav. 26, 622–629 (1978).
Google Scholar 
58.Dreiss, A. N., Ruppli, C. A. & Roulin, A. Individual vocal signatures in barn owl nestling: does individual recognition have an adaptive role in sibling vocal competition?. J. Evol. Biol. 27, 63–75 (2014).CAS 
PubMed 

Google Scholar 
59.Volodin, I. A., Volodina, E. V., Klenova, A. V. & Filatova, O. A. Individual and sexual differences in the calls of the monomorphic White-faced Whistling Duck Dendrocygna viduata. Acta Ornithol. 40, 43–52 (2005).
Google Scholar 
60.Bragina, E. & Beme, J. sexual and individual features in the long range and short range calls of the White-naped crane. Condor 115, 501–507 (2013).
Google Scholar 
61.Terry, A. M. R., Peake, T. M. & McGregor, P. K. The role of vocal individuality in conservation. Front. Zool. 2, 10 (2005).PubMed 
PubMed Central 

Google Scholar 
62.Pasinelli, G. Oaks (Quercus sp.) and only oaks? Relations between habitat structure and home range size of the middle spotted woodpecker (Dendrocopos medius). Biol. Conserv. 93(2), 227–235 (2000).
Google Scholar 
63.Pasinelli, G. Dendrocopos medius middle spotted woodpecker. BWP Update 5(1), 49–99 (2003).
Google Scholar 
64.Michalek, K. G. & Winkler, H. Parental care and parentage in monogamous great spotted woodpeckers Picoides major and middle spotted woodpeckers Picoides medius. Behaviour 138(10), 1259–1285 (2001).
Google Scholar 
65.Pasinelli, G., Hegelbach, J. & Reyer, H.-U. Spacing behavior of the Middle Spotted Woodpecker in central Europe. J. Wildl. Manag. 65, 432–441 (2001).
Google Scholar 
66.Pasinelli, G. Breeding performance of the middle spotted woodpecker Dendrocopos medius in relation to weather and territory quality. Ardea 89, 353–361 (2001).
Google Scholar 
67.Kosiński, Z. & Winiecki, A. Ocena liczebności dzięcioła średniego Dendrocopos medius – Porównanie metody kartograficznej z użyciem stymulacji magnetofonowej z metodą wyszukiwania gniazd. Notatki Ornitologiczne 44, 43–55 (2003).
Google Scholar 
68.Specht, R. Avisoft-SASLab Pro: sound analysis and synthesis laboratory (Avisoft Bioacoustics, 2002).
Google Scholar 
69.Mundry, R. & Sommer, C. Discriminant function analysis with nonindependent data: consequences and an alternative. Anim. Behav. 74, 965–976 (2007).
Google Scholar 
70.Tabachnick, B. G. & Fidell, L. S. Using multivariate statistics 4th edn. (Allyn and Bacon, 2001).
Google Scholar 
71.Leniowski, K. Signaling quality in the Middle Spotted Woodpecker Dendrocopos medius: home ranges, colour ornaments and calls. (PhD thesis) Adam Mickiewicz University, Poznań, Poland (2011). More

1.Howell, K. L. et al. A decade to study deep-sea life. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01352-5 (2020).Article 

Google Scholar 
2.Howell, K. L. et al. A blueprint for an inclusive, global deep-sea ocean decade field program. Front. Mar. Sci. 7, 1–25. https://doi.org/10.3389/fmars.2020.584861 (2020).ADS 
Article 

Google Scholar 
3.Ramirez-Llodra, E. et al. Man and the last great wilderness: Human impact on the deep sea. PLoS ONE 6, 22588. https://doi.org/10.1371/journal.pone.0022588 (2011).ADS 
CAS 
Article 

Google Scholar 
4.Bernardino, A. F., Levin, L. A., Thurber, A. R. & Smith, C. R. Comparative composition, diversity and trophic ecology of sediment macrofauna at vents, seeps and organic falls. PLoS ONE 7, e33515. https://doi.org/10.1371/journal.pone.0033515 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Thiel, M. & Gutow, L. The ecology of rafting in the marine environment. II. The rafting organisms and community. Ocean. Mar. Biol. 43, 279–418. https://doi.org/10.1201/9781420037449.ch7 (2005).Article 

Google Scholar 
6.McClain, C. & Barry, J. Beta-diversity on deep-sea wood falls reflects gradients in energy availability. Biol. Lett. 10, 20140129. https://doi.org/10.1098/rsbl.2014.0129 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Knudsen, J. The Bathyal and Abyssal Xylophaga (Pholadidae, Bivalvia) (Danish Science Press Ltd., 1961).
Google Scholar 
8.Turner, R. Wood-boring bivalves, opportunistic species in the deep sea. Science 180, 1377–1379. https://doi.org/10.1126/science.180.4093.1377 (1973).ADS 
CAS 
Article 
PubMed 

Google Scholar 
9.Voight, J. R. Deep-sea wood-boring bivalves of Xylophaga (Myoida: Pholadidae) on the continental shelf: A new species described. J. Mar. Biol. Assoc. UK 88, 1459–1464. https://doi.org/10.1017/S0025315408002117 (2008).Article 

Google Scholar 
10.Turner, R. D. A survey and Illustrated Catalogue of the Teredinidae (Mollusca: Bivalvia) (Harvard University, 1966).Book 

Google Scholar 
11.Hoppe, K. N. Teredo Navalis—the Cryptogenic Shipworm. in Invasive Aquatic Species of Europe. Distribution, Impacts and Management. (ed. Leppäkoski E., Gollasch S., O. S.) 116–119, https://doi.org/10.1007/978-94-015-9956-6_12 (2002).12.Distel, D. L. & Roberts, S. J. Bacterial endosymbionts in the gills of the deep-sea wood-boring bivalves Xylophaga atlantica and X. washingtona. Biol. Bull. 192, 253–261. https://doi.org/10.2307/1542719 (1997).CAS 
Article 
PubMed 

Google Scholar 
13.Distel, D. L., Morrill, W., MacLaren-Toussaint, N., Franks, D. & Waterbury, J. Teredinibacter turnerae gen. nov., sp. Nov., a dinitrogen-fixing, cellulolytic, endosymbiotic gamma-proteobacterium isolated from the gills of wood-boring molluscs (Bivalvia: Teredinidae). Int. J. Syst. Evol. Microbiol. 52, 2261–2269 (2002).CAS 
PubMed 

Google Scholar 
14.O’Connor, R. M. et al. Gill bacteria enable a novel digestive strategy in a wood-feeding mollusk. Proc. Natl. Acad. Sci. U. S. A. 111, 5096–5104. https://doi.org/10.1073/pnas.1413110111 (2014).CAS 
Article 

Google Scholar 
15.Sabbadin, F. et al. Uncovering the molecular mechanisms of lignocellulose digestion in shipworms. Biotechnol. Biofuels 11, 1–14. https://doi.org/10.1186/s13068-018-1058-3 (2018).CAS 
Article 

Google Scholar 
16.Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge University Press, 2010).
Google Scholar 
17.Sarà, G., Palmeri, V., Montalto, V., Rinaldi, A. & Widdows, J. Parameterisation of bivalve functional traits for mechanistic eco-physiological dynamic energy budget (DEB) models. Mar. Ecol. Prog. Ser. 480, 99–117. https://doi.org/10.3354/meps10195 (2013).ADS 
Article 

Google Scholar 
18.Sarà, G., Rinaldi, A. & Montalto, V. Thinking beyond organism energy use: A trait-based bioenergetic mechanistic approach for predictions of life-history traits in marine organisms. Mar. Ecol. 35, 506–515. https://doi.org/10.1111/maec.12106 (2014).ADS 
Article 

Google Scholar 
19.Mangano, M. C. et al. Moving toward a strategy for addressing climate displacement of marine resources: A proof-of-concept. Front. Mar. Sci. 7, 1–16. https://doi.org/10.3389/fmars.2020.00408 (2020).ADS 
Article 

Google Scholar 
20.Romano, C. et al. Wooden stepping stones: Diversity and biogeography of deep-sea wood-boring Xylophagaidae (Mollusca: Bivalvia) in the North-East Atlantic Ocean, with the description of a new genus. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.579959 (2020).Article 

Google Scholar 
21.Culliney, J. L. & Turner, R. D. Larval development of the deep-water wood boring bivalve, Xylophaga atlantica Richards (Mollusca, bivalvia, pholadidae). Ophelia 15, 149–161. https://doi.org/10.1080/00785326.1976.10425455 (1976).Article 

Google Scholar 
22.Romey, W., Bullock, R. & Dealteris, J. Rapid growth of a deep-sea wood-boring bivalve. Cont. Shelf Res. 14, 1349–1359. https://doi.org/10.1016/0278-4343(94)90052-3 (1994).ADS 
Article 

Google Scholar 
23.Gaudron, S. M. et al. Colonization of organic substrates deployed in deep-sea reducing habitats by symbiotic species and associated fauna. Mar. Environ. Res. 70, 1–12. https://doi.org/10.1016/j.marenvres.2010.02.002 (2010).CAS 
Article 
PubMed 

Google Scholar 
24.Gaudron, S. M., Haga, T., Wang, H., Laming, S. R. & Duperron, S. Plasticity in reproduction and nutrition in wood-boring bivalves (Xylophaga atlantica) from the Mid-Atlantic Ridge. Mar. Biol. 163, 1–12. https://doi.org/10.1007/s00227-016-2988-6 (2016).CAS 
Article 

Google Scholar 
25.Childress, J. J., Cowles, D. L., Favuzzi, J. A. & Mickel, T. J. Metabolic rates of benthic deep-sea decapod crustaceans decline with increasing depth primarily due to the decline in temperature. Deep Sea Res. Part A Oceanogr. Res. Pap. 37, 929–949. https://doi.org/10.1016/0198-0149(90)90104-4 (1990).ADS 
CAS 
Article 

Google Scholar 
26.Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals?. Trends Ecol. Evol. 10, 30–36. https://doi.org/10.1016/S0169-5347(00)88957-0 (1995).CAS 
Article 
PubMed 

Google Scholar 
27.Tittensor, D. P., Rex, M. A., Stuart, C. T., Mcclain, C. R. & Smith, C. R. Species—energy relationships in deep-sea molluscs subject collections species—energy relationships in deep-sea molluscs. Biol. Lett. 7, 718–722 (2011).Article 

Google Scholar 
28.McClain, C. R., Allen, A. P., Tittensor, D. P. & Rex, M. A. Energetics of life on the deep seafloor. Proc. Natl. Acad. Sci. U. S. A. 109, 15366–15371. https://doi.org/10.1073/pnas.1208976109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Mickel, T. J. & Childress, J. J. Effects of pressure and temperature on the EKG and heart rate of the hydrothermal vent crab Bythograea Thermydron (Brachyura). Biol. Bull. 162, 70–82. https://doi.org/10.2307/1540971 (1982).Article 

Google Scholar 
30.Voight, J. R., Cooper, J. C. & Lee, R. W. Stable isotopic evidence of mixotrophy in Xylophagaids, deep-sea wood-boring bivalves. Front. Mar. Sci. 7, 50. https://doi.org/10.3389/fmars.2020.00050 (2020).Article 

Google Scholar 
31.Lika, K. et al. The ‘covariation method’ for estimating the parameters of the standard dynamic energy budget model I: Philosophy and approach. J. Sea Res. 66, 270–277. https://doi.org/10.1016/j.seares.2011.07.010 (2011).ADS 
Article 

Google Scholar 
32.Marques, G. M. et al. The AmP project: Comparing species on the basis of dynamic energy budget parameters. PLoS Comput. Biol. 14, 1–23. https://doi.org/10.1371/journal.pcbi.1006100 (2018).CAS 
Article 

Google Scholar 
33.Mariño, J., Augustine, S., Dufour, S. C. & Hurford, A. Dynamic Energy Budget theory predicts smaller energy reserves in thyasirid bivalves that harbour symbionts. J. Sea Res. 143, 119–127. https://doi.org/10.1016/j.seares.2018.07.015 (2019).ADS 
Article 

Google Scholar 
34.Brown, A. et al. Metabolic costs imposed by hydrostatic pressure constrain bathymetric range in the lithodid crab Lithodes maja. J. Exp. Biol. 220, 3916–3926. https://doi.org/10.1242/jeb.158543 (2017).Article 
PubMed 

Google Scholar 
35.Eisenmenger, M. J. & Reyes-De-Corcuera, J. I. High pressure enhancement of enzymes: A review. Enzyme Microb. Technol. 45, 331–347. https://doi.org/10.1016/j.enzmictec.2009.08.001 (2009).CAS 
Article 

Google Scholar 
36.Kalenitchenko, D. et al. Bacteria alone establish the chemical basis of the wood-fall chemosynthetic ecosystem in the deep-sea. ISME J. 12, 367–379. https://doi.org/10.1038/ismej.2017.163 (2018).CAS 
Article 
PubMed 

Google Scholar 
37.Levesque, C., Limén, H. & Juniper, S. K. Origin, composition and nutritional quality of particulate matter at deep-sea hydrothermal vents on Axial Volcano NE pacific. Mar. Ecol. Prog. Ser. 289, 43–52. https://doi.org/10.3354/meps289043 (2005).ADS 
Article 

Google Scholar 
38.Limén, H., Levesque, C. & Kim Juniper, S. POM in macro-/meiofaunal food webs associated with three flow regimes at deep-sea hydrothermal vents on Axial Volcano, Juan de Fuca Ridge. Mar. Biol. 153, 129–139. https://doi.org/10.1007/s00227-007-0790-1 (2007).Article 

Google Scholar 
39.Culliney, J. L. Comparative larval development of the shipworms Bankia gouldi and Teredo navalis. Mar. Biol. 29, 245–251. https://doi.org/10.1007/BF00391850 (1975).Article 

Google Scholar 
40.Ramirez Llodra, E. Fecundity and life-history strategies in marine invertebrates. Adv. Mar. Biol. 43, 87–170. https://doi.org/10.1016/S0065-2881(02)43004-0 (2002).Article 
PubMed 

Google Scholar 
41.Fernandez-Arcaya, U. et al. Bathymetric gradients of fecundity and egg size in fishes: A Mediterranean case study. Deep Sea Res. Part A Oceanogr. Res. Pap. 116, 106–117. https://doi.org/10.1016/j.enzmictec.2009.08.001 (2016).ADS 
CAS 
Article 

Google Scholar 
42.Young, C. M., Emson, R. H., Rice, M. E. & Tyler, P. A. A paradoxical mismatch of fecundity and recruitment in deep-sea opportunists: cocculinid and pseudococculinid limpets colonizing vascular plant remains on the Bahamian Slope. Deep Sea Res. 92, 36–45. https://doi.org/10.1016/j.dsr2.2013.01.027 (2013).ADS 
Article 

Google Scholar 
43.Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25, 1–45. https://doi.org/10.1111/j.1469-185X.1950.tb00585.x (1950).CAS 
Article 
PubMed 

Google Scholar 
44.Hitt, N. T. et al. Growth and longevity of New Zealand black corals. Deep. Res. Part I Oceanogr. Res. Pap. 162, e103298. https://doi.org/10.1016/j.dsr.2020.103298 (2020).Article 

Google Scholar 
45.McNichol, J. et al. Primary productivity below the seafloor at deep-sea hot springs. Proc. Natl. Acad. Sci. U. S. A. 115, 6756–6761. https://doi.org/10.1073/pnas.1804351115 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Levin, L. A. et al. Hydrothermal vents and methane seeps: Rethinking the sphere of influence. Front. Mar. Sci. 3, 1–23. https://doi.org/10.3389/fmars.2016.00072 (2016).ADS 
Article 

Google Scholar 
47.Nedoncelle, K., Lartaud, F., de Rafelis, M., Boulila, S. & Le Bris, N. A new method for high-resolution bivalve growth rate studies in hydrothermal environments. Mar. Biol. 160, 1427–1439. https://doi.org/10.1007/s00227-013-2195-7 (2013).CAS 
Article 

Google Scholar 
48.Turekian, K. K., Cochran, J. K. & Bennett, J. T. Growth rate of a vesicomyid clam from the 21° N East Pacific Rise hydrothermal area. Nature 303, 55–56. https://doi.org/10.1038/303055a0 (1983).ADS 
Article 

Google Scholar 
49.Lutz, R. A. et al. Rapid growth at deep-sea vents. Nature 371, 663–664. https://doi.org/10.1038/371663a0 (1994).ADS 
Article 

Google Scholar 
50.Reed, A. J., Morris, J. P., Linse, K. & Thatje, S. Plasticity in shell morphology and growth among deep-sea protobranch bivalves of the genus Yoldiella (Yoldiidae) from contrasting Southern ocean regions. Deep. Res. Part I Oceanogr. Res. Pap. 81, 14–24. https://doi.org/10.1016/j.dsr.2013.07.006 (2013).ADS 
Article 

Google Scholar 
51.Oliver, G., Allen, J. A. & Yonge, M. The functional and adaptive morphology of the deep-sea species of the Arcacea (Mollusca: Bivalvia) from the Atlantic. Philos. Trans. R. Soc. London. B Biol. Sci. 291, 45–76. https://doi.org/10.1098/rstb.1980.0127 (1980).ADS 
Article 

Google Scholar 
52.Romano, C., Voight, J. R., Pérez-Portela, R. & Martin, D. Morphological and genetic diversity of the wood-boring Xylophaga (Mollusca, Bivalvia): New species and records from deep-sea Iberian canyons. PLoS ONE 9, 102887. https://doi.org/10.1371/journal.pone.0102887 (2014).ADS 
CAS 
Article 

Google Scholar 
53.Saulsbury, J. et al. Evaluating the influences of temperature, primary production, and evolutionary history on bivalve growth rates. Paleobiology 45, 405–420. https://doi.org/10.1017/pab.2019.20 (2019).Article 

Google Scholar 
54.Moss, D. K. et al. Lifespan, growth rate, and body size across latitude in marine bivalvia, with implications for phanerozoic evolution. Proc. R. Soc. B Biol. Sci. 283, 20161364. https://doi.org/10.1098/rspb.2016.1364 (2016).Article 

Google Scholar 
55.Tyler, P. A., Young, C. M. & Dove, F. Settlement, growth and reproduction in the deep-sea wood-boring bivalve mollusc Xylophaga depalmai. Mar. Ecol. Prog. Ser. 343, 151–159. https://doi.org/10.3354/meps06832 (2007).ADS 
Article 

Google Scholar 
56.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789. https://doi.org/10.1890/03-9000 (2004).Article 

Google Scholar 
57.Maino, J. L., Kearney, M. R., Nisbet, R. M. & Kooijman, S. A. L. M. Reconciling theories for metabolic scaling. J. Anim. Ecol. 83, 20–29. https://doi.org/10.1111/1365-2656.12085 (2014).Article 
PubMed 

Google Scholar 
58.Gaudron, S. M., Demoyencourt, E. & Duperron, S. Reproductive traits of the cold-seep symbiotic mussel Idas modiolaeformis: gametogenesis and larval biology. Biol. Bull. 222, 6–16. https://doi.org/10.1086/bblv222n1p6 (2012).Article 

Google Scholar 
59.Hilário, A. et al. Estimating dispersal distance in the deep sea: Challenges and applications to marine reserves. Front. Mar. Sci. 2, 6. https://doi.org/10.3389/fmars.2015.00006 (2015).ADS 
Article 

Google Scholar 
60.Marsh, A. G., Mullineaux, L. S., Young, C. M. & Manahan, D. T. Larval dispersal potential of the tubeworm Riftia pachyptila at deep-sea hydrothermal vents. Nature 411, 77–80. https://doi.org/10.1038/35075063 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
61.Young, C. M. et al. Dispersal of deep-sea larvae from the intra-American seas: Simulations of trajectories using ocean models. Integr. Comp. Biol. 52, 483–496. https://doi.org/10.1093/icb/ics090 (2012).Article 
PubMed 

Google Scholar 
62.Yearsley, J. M., Salmanidou, D. M., Carlsson, J., Burns, D. & Van Dover, C. L. Biophysical models of persistent connectivity and barriers on the northern Mid-Atlantic Ridge. Deep. Res. Part II Top. Stud. Oceanogr. 180, 104819. https://doi.org/10.1016/j.dsr2.2020.104819 (2020).Article 

Google Scholar 
63.Levin, L. A. et al. Global observing needs in the deep ocean. Front. Mar. Sci. 6, 1–32. https://doi.org/10.3389/fmars.2019.00241 (2019).ADS 
Article 

Google Scholar 
64.McClain, C. R., Boyer, A. G. & Rosenberg, G. The island rule and the evolution of body size in the deep sea. J. Biogeogr. 33, 1578–1584. https://doi.org/10.1111/j.1365-2699.2006.01545.x (2006).Article 

Google Scholar 
65.Zonneveld, C. & Kooijman, S. A. L. M. Application of a dynamic energy budget model to Lymnaea stagnalis (L.). Funct. Ecol. 3, 269–278. https://doi.org/10.2307/2389365 (1989).Article 

Google Scholar 
66.Mueller, C. A., Augustine, S., Kooijman, S. A. L. M., Kearney, M. R. & Seymour, R. S. The trade-off between maturation and growth during accelerated development in frogs. Comp. Biochem. Physiol. A 163, 95–102. https://doi.org/10.1016/j.cbpa.2012.05.190 (2012).CAS 
Article 

Google Scholar 
67.MacArthur, R.H. & Wilson, E. The Theory of Island Biogeography (1967).68.Kooijman, S. A. L. M. Metabolic acceleration in animal ontogeny: An evolutionary perspective. J. Sea Res. 94, 128–137. https://doi.org/10.1016/j.seares.2014.06.005 (2014).ADS 
Article 

Google Scholar  More

The crystalline nature and phase purity of the synthesized NiO, PdO and PdO–NiO nanocomposite were confirmed by XRD analysis and shown in Fig. 1a. All three XRD patterns show the highly crystalline nature, which confirms the purity of the samples. In PdO–NiO mixed metal oxide, The intense diffraction peaks at 2θ = 37.16, 43.24, 62.81, 75.32 and 79.34° indexed to (101), (012), (110), (113) and (202) planes of the cubic phase of NiO, which is highly consistent with standard JCPDS NO: 01-071-475120. On the other hand, the sharp peaks at 2θ = 34.54 and 55.79° indexed to (101) and (112) planes corresponding to the tetragonal crystalline phase of PdO with an average lattice parameter 3.043 Å, which is highly associated with standard JCPDS NO: 043-102421. Interestingly, metallic diffraction peaks and other impurity phases were not observed in the hybrid PdO–NiO nanocomposite. This confirming that metal source (Pd) completely oxidized to the metal oxide (PdO) and formed hybrid PdO–NiO nanocomposite. In addition, the single metal oxide (PdO, NiO) diffraction patterns were compared in Fig. 1a.Figure 1(a) X-ray diffraction pattern and (b) FTIR spectra of NiO, PdO and PdO–NiO (c) TGA curve of PdO–NiO nanocomposite.Full size imageThe sharp peaks of each metal oxide confirm the high crystallinity of the tetragonal phase of the PdO and cubic phase of NiO. Both diffraction pattern of the single metal oxides is well associated with standard JCPDS file: 01-071-4751 and 043-1024 respectively. The average crystallite size, micro-strains of the NiO, PdO and PdO doped NiO were calculated by using Scherrer analysis and W–H analysis and plotted in Fig. 1b. A small variation was observed in the crystallite size of the catalysts (PdO, NiO and PdO–NiO) in Fig. 1b, which is due to the difference in the distribution of the crystal in the catalysts. The average crystallite size of the PdO, NiO and PdO–NiO was found to be 10.8, 7.8 and 7.36 nm, respectively. The PdO doping reduces the crystallite size in the PdO–NiO composite, consistent with previous reports. The calculated crystallite sizes, d-spacing, micro-strains, and binding energies of the PdO, NiO, and PdO–NiO are shown in the Table 1. In addition, the experimentally calculated d-spacing value of the pure PdO and NiO was well correlated with theoretical values and shown in Table SI. 1. The synthesized catalyst showed the negative and positive slopes of ε are corresponds to the compressive and tensile stress, respectively.Table 1 Crystallite sizes, d-spacing, micro-strains, and binding energies of the PdO, NiO, and PdO–NiO.Full size tableFurthermore, the chemical bonding and functional groups were analyzed by FTIR spectrometer. Figure 1c show FTIR spectra of the synthesized NiO, PdO and PdO–NiO nanocomposite. Similar spectra were observed for the three catalyst, the absorption peaks at high-frequency region 3200–3400 cm−1 belongs to O–H stretching vibration of the water molecules, due to surface adsorption phenomenon. Furthermore, three absorption peaks appeared at 1398.2, 1237.8 and 1057.8 cm−1, which ascribed to the C–O, CH2 and C=O stretching vibrations, which is well associated with XPS analysis data. The metal oxide bonding peaks appeared in the frequency range of 480.2–702.6 cm−1. Hence the Pd–O and Ni–O stretching frequency in the PdO doped NiO sample confirmed the formation of hybrid PdO–NiO nanocomposite22. After that, the thermal stability of the PdO–NiO nanocomposite was studied by TGA analysis. Figure 1d shows the thermogram of the PdO–NiO nanocomposite. The first weight loss (7%) started in the temperature range of 65 to 180 °C. Due to the H2O molecules, evaporation and then the sustainable weight loss of around 10% was observed in the range of 187 to 574 °C. Beyond 600 °C a significant weight loss 20% was observed, which may be assigned to the unreacted CO3 combustion23. Hence, the XRD, FTIR and TGA spectral studies confirmed the formation of hybrid PdO–NiO nanocomposite.The electronic state and chemical bonding of the PdO doped NiO composite was analyzed by using XPS spectra. Figure 2 shows the XPS spectra of the PdO–NiO nanocomposite, the broad scan spectrum (Fig. SI. 1) of the PdO–NiO, which show the existence of the Pd (3d), Ni (2p), O (1s) and C(1s) elements. The deconvoluted Pd 3d XPS spectrum in Fig. 2a shows two major peaks at a binding energy of 336.4 and 342.2 eV corresponds to spin–orbit doublets of Pd 3d5/2 and Pd 3d3/2, respectively, which confirmed Pd2+ ions in the form of PdO in the PdO–NiO nanocomposite24,25. In addition, the satellite peak of Pd species appeared at a binding energy of 339.2 eV and 345.3 eV. In Ni 2p spectra (Fig. 2b), Ni 2p3/2 and Ni 2p3/2 spin–orbit doublets peaks were observed at 854.9 eV and 872.5 eV, which corresponded to Ni–O and Ni–OH, respectively and their corresponding satellite peaks are located at a binding energy of 867.2 eV and 879.1 eV26,27. Furthermore, O 1s spectra (Fig. 2c) show the two peaks at a binding energy of 529.3 eV and 534.2 eV, which ascribed M–O and M–OH species. The obtained XPS spectra of the PdO–NiO nanocomposite are well associated with the XRD and EDX analysis.Figure 2High resolution X-ray photoelectron spectroscopy (a) Pd 3d, (b) Ni 2p, (c) O 1s and (d) C 1s spectra of PdO–NiO nanocomposite.Full size imageThe morphology feature and elemental composition of the prepared NiO, PdO and PdO–NiO nanocomposite was scrutinised by FE-SEM. Figure 3 shows the SEM morphology images of NiO, PdO and PdO–NiO nanocomposite at low and high magnification. Pure metal oxides NiO and PdO samples in Fig. 3a–d shows the porous crystalline morphology with high purity of the respective elements. On the other hand, PdO doped NiO sample in Fig. 3e,f shows the uniform, monodisperse, spherical crystalline morphology. Which confirms that PdO uniformly distributed with NiO. Hence, the PdO doping enhances the surface area of the catalyst. In addition, the elemental composition and elemental mapping was analysed for PdO–NiO sample and shown in Fig. 3g,h. The EDX spectra and elemental mapping clearly confirms the presence of the Pd, Ni and O elements in the composite with high purity.Figure 3FESEM images (a,b) NiO (c,d) PdO, (e,f) PdO–NiO and (g,f) EDS spectra and elemental mapping of PdO–NiO nanocomposite.Full size imageThe detailed morphology and particle size distribution of the PdO–NiO NPs was measured by HR-TEM and the results are presented in Fig. 4. Figure 4a–c shows the typical HRTEM images of the as-synthesized PdO–NiO nanocomposite. The obtained TEM images confirmed the uniform distribution of the spherical PdO–NiO NPs, which agrees with FESEM results. In Fig. 4c (inset), the histogram reveals that formed PdO–NiO nanoparticles are uniformly distributed with an average particle size of about 9.64 ± 2.1 nm, which is well associated with XRD crystallite size. Furthermore, the SAED pattern was analyzed to understand the crystallinity and the crystal quality of the PdO–NiO nanoparticles are shown in Fig. 4d. The clear ring-like structure suggests the polycrystalline nature of PdO–NiO. The obtained diffraction rings d-spacing values are corresponding to the (101), (012), (110), (113) and (202) planes of the NiO nanoparticles. Figure 4e shows the lattice fringes of the PdO doped NiO nanoparticles. The fringes show the lattice planes for both metal oxides. The interplanar d-spacing value of 0.1997 nm to correspond to the (012) plane of the NiO phase and the d-spacing value of 0. 2145 nm to correspond to the (110) plane of the PdO in the composite. Which is well correlated with the XRD d-spacing values. Elemental mapping in Fig. 4f shows the presence of Ni, Pd and O elements with uniform distribution as similar as SEM mapping. The morphology results of the synthesized catalysts are well associated with XRD, FTIR and XPS analysis.Figure 4High resolution transmission electron microscopy HRTEM images (a–c), (d) SAED pattern, (e) interplanr spacing and (f) elemental mapping of PdO–NiO nanocomposite.Full size imageUV–Vis absorption spectra were analyzed for the as-synthesized catalysts NiO, PdO and PdO–NiO and the respective results are presented in Fig. 5a. The absorption spectra show the strongest absorption maxima at 234.8 nm for all three catalysts. In addition, the characteristic absorption band of NiO and PdO were observed at 338.2 nm and 422.1 nm respectively23, on the other hand, no characteristic absorption band was observed for PdO–NiO sample. Furthermore, the bandgap energy was calculated for three catalysts by using the Schuster-Kubelka–Munk function.$$(alpha {text{h}}nu ),{text{n}} = {text{A}}({text{h}}nu – {text{Eg}})$$
(1)
Figure 5(a) UV–Vis absorption spectra and (b) Plot of (αhν)2 Vs hν for NiO, PdO and PdO–NiO nanocomposite (Inset shows the bandgap energy of the catalyst).Full size imageThe bandgap energy (Eg) was achieved by extrapolating against the photon energy and the obtained results are shown in Fig. 5b. The calculated bandgap (Eg) of NiO, PdO and PdO–NiO are 4.05 eV, 3.84 eV and 4.24 eV, respectively25 (Fig. 5b, inset). The PdO doping with NiO increases its bandgap value. This suggests that the PdO interface and NiO interface are closely combined in the composite. The obtained band gap value of the catalysts is much higher than the reported bandgap energy. The bandgap energy is highly dependent on the particle’s size. The bandgap energy increases with decreasing particle size, which confirmed that the synthesized catalysts are in nanoscale. The bandgap energy (Eg) of the PdO–NiO catalyst is well associated with FESEM and TEM results.The photoluminescence (Pl) spectra of NiO, PdO and PdO–NiO materials were measured at 325 nm excitation wavelength and presented in Fig. 6. Figure 6a shows the Pl spectra of the pure NiO, PdO and PdO–NiO nanocomposite. The blue/violet emission was observed for all three samples at 364 nm due to the excitation of 3d8 electrons of Ni2+ ions from the conduction band to the valence band24. From Fig. 6a, it can be seen that the intensity of the PdO–NiO nanocomposite is lower than the pure NiO and PdO, which indicated the higher electron transfer between the NiO and PdO, which is well correlated with electrochemical results. The deconvoluted PL spectra of NiO, PdO and PdO–NiO materials are shown in Fig. 6b–d; four peaks have been fitted for each sample as shown in Fig. 6b–d. The UV emission at 364 nm (3.4 eV) corresponds to the near band edge (NBE) excitation of NiO25. The obtained PL spectra confirmed that the PdO–NiO nanocomposite has more conductivity than the pure metal oxides.Figure 6(a) PL spectra and (b–d) deconvoluted spectra of NiO, PdO and PdO–NiO nanocomposite respectively.Full size imageThe electrode kinetics of the NiO, PdO and PdO–NiO modified GC electrode was explored in 1 M KOH at different scan rate variation at room temperature. In addition, the resistance of the aforesaid electrodes was monitored in impedance analysis and shown in Fig. 7. Figure 7a, the NiO/GC show a pair of well-defined redox peaks at around 0.49 V and 0.44 V respectively, which corresponding to the reversible reaction between Ni2+ and Ni3+26. In addition, the redox peak currents linearly increase with increasing scan rate.Figure 7(a–c) Cyclic voltammetry and (d) EIS curves of NiO, PdO and PdO–NiO nanocomposite in 1 M KOH electrolyte solution.Full size imageFigure 7b shows CV pattern of PdO/GC electrode in 1 M KOH solution, which sows poor peaks palladium oxide and palladium reduction peak at 0.52 V and 0.53 V respectively due to the formation of oxyhydroxide on the electrode surface in basic medium. Whereas the PdO–NiO NPs modified GC electrode in Fig. 7c show well-defined Ni2+ and Ni3+ kinetics with eightfold higher peak current. Which confirms the Efficient electron transfer between NiO and PdO in the composite. Which is well associated with PL results. Furthermore, the impedance spectra were achieved for three electrodes at fixed over potential (500 mV s−1) in 1 M KOH electrolyte and presented in Fig. 7d. In Fig. 7d the Rct value of the pure metal oxides NiO/GC and PdO/GC electrode were obtained as 236 Ω and 2702 Ω respectively. Whereas the bimetal oxide PdO–NiO/GC show 425.7 Ω, which is lower than the pure PdO. Due to the superior electron transformation between each metal oxide.Catalytic reduction of Azo compoundsAzo compounds are highly toxic to the environment as well as human beings. Especially, nitrophenols are listed as the topmost hazardous chemical in the world. Hence the reduction of nitrophenols gains the most attention. Generally, the nitrophenol reduction reaction is thermodynamically favourable (E0 = − 0.76 V) at optimized conditions, whereas the NaBH4 acts as a reducing agent (E0 = − 1.33 V)27,28. However, the reduction rate is prolonged without the catalyst due to the kinetic barrier between the reducing agent and reactant. Hence, the catalytic reduction nitrophenols are a good way to convert to non-toxic aminophenol (AP) with the presence of NaBH4 as a reducing agent. The reduction reaction was easily monitored with a UV–Vis spectrometer. It is known that NaBH4 alone cannot reduce the nitrophenols into aminophenol’s. As shown in Fig. SI. 2 the fresh nitrophenols (NP, DNP and TNP) absorption peak appeared at 300–370 nm respectively. When the addition of reducing agent, the peak was shifted to 402–450 nm28. In addition, the solution color was turned light yellow to deep yellow, due to the formation of corresponding nitrophenolate ions in basic solution. However, no reduction was achieved over 2 h, indicating that nitrophenolate ions were very stable with NaBH4. Furthermore, the catalytic activity of the NiO was explored with three nitrophenols and shown in Fig. SI. 3. The pure NiO exhibits a poor catalytic reduction of nitro compounds. In contrast, the PdO–NiO catalyst show excellent activity on the nitrophenols, as shown in Fig. 8.Figure 8Catalytic activity and kinetic rate of PdO–NiO nanocomposite on reduction of nitrophenols with NaBH4 solution (a,b) NP, (c,d) DNP and (e,f) TNP.Full size imageIt can be seen that the nitrophenolate peak absorbance at 400 nm gradually decreases with reaction time, which confirmed that PdO–NiO promotes the electron and hydrogen transfer between the reactant. Due to the higher active sites of the PdO–NiO. The present PdO–NiO catalyst completes the reduction reaction of NP, DNP and TNP within 10, 13, 25 min respectively. In addition, the aminophenol absorption peak appeared around 300 nm for all three nitrophenols. On the other hand, the deep yellow solution turned colorless, indicating the formation of aminophenol. The rate constant κapp for each nitrophenol was calculated from the plot of ln (At/Ao) Vs. time. The proposed PdO–NiO catalyst exhibits excellent rate constant 0.1667, 0.0997, 0.0686 min−1 for NP, DNP and TNT, respectively, which is the higher rate constant than the previously reported catalyst (Table SI. 2). Generally, the reduction mechanism of nitrophenols to aminophenols follows many intermediate steps from nitro to nitroso and then to hydroxylamine and to final aminophenol. For these reaction required both electron transfer and active hydrogen atoms. Here, the BH4− ions produce the active hydrogen atoms on the surface of the catalyst and subsequently, the PdO–NiO catalyst enhances the electron transfer. As a result, the reduction of NP could be efficiently accelerated by the PdO–NiO catalyst. Furthermore, comparison of the catalytic reduction performance of nitrophenols with varies catalyst are shown in Table SI. 2. The reduction mechanism of the nitrophenols with PdO–NiO catalyst with NaBH4 as shown in Fig. SI. 6.Furthermore, the catalytic activity of PdO–NiO composite was explored by the reduction of organic azo dye compounds such as Methylene blue (MB), Rhodamine B (RhB) and Methyl orange (MO) with the addition of NaBH4 in the presence of PdO–NiO catalyst29. The reduction of each dye was monitored at different absorption peaks, as shown in Fig. 9. The intensity of each dye at respective wavelengths linearly reduced with time in the presence of the PdO–NiO. In addition, the reduction rate κapp for each dye was calculated from the plot of ln (At/Ao) Vs. time. PdO–NiO catalyst exhibited excellent reduction rate as 0.099, 0.0416 and 0.0896 min−1 for MB, RhB and MO, respectively, superior catalytic activity than previous reports. In addition, the azo dye solution turned into colourless, indicating the complete reduction occurs in the presence of PdO–NiO. The pure NiO catalyst exhibits poor reduction activity towards azo dyes with the presence of NaBH4 (Fig. SI. 4). Furthermore, comparison of the catalytic reduction performance azo dyes with varies catalyst are shown in Table. SI. 3. Additionally, the reduction of the mixture of nitrophenols (NP, DNP and TNP) and azo dyes (MB, RhB and MO) was tested with PdO–NiO nanocomposite and obtained results are shown in Fig. 10. Initially, the mixture of azo compounds is formed dark solution then rapidly turned into a colourless and became a transparent solution with the addition of PdO–NiO in the presence of NaBH430. The complete azo compounds reduction was achieved within 8 min. Hence, the proposed PdO–NiO is a promising catalyst for wastewater treatment. In addition, the effect of the catalyst loading on the reduction of mixture of azo compounds were studied with different loading amount of PdO–NiO catalyst (3–10 mg) and shown in the Table. SI. 4. Which show that the reduction rate increased with loading amount of the catalyst. In addition, the catalytic reduction performance of toxic azo compounds by various catalysts are shown in Table. 2. PdO–NiO catalyst exhibit superior reduction performance than the previously reported catalyst.Figure 9Catalytic activity and kinetic rate of PdO–NiO nanocomposite on reduction of azo dyes with NaBH4 solution (a,b) MB, (c,d) RhB and (e,f) MO.Full size imageFigure 10Reaction progress of an azo compounds mixture (4-NP, 2,4-DNP, 2,4,6-TNP, MB, RhB, MO) with PdO–NiO and NaBH4. Conditions: Dye: 100 ppm, 25 ml, Nitrophenol: 0.12 mM, 25 ml, NaBH4: 0.1 M, 5 ml and catalyst: 3 mg.Full size imageTable 2 Comparison of catalytic reduction performance of toxic azo compounds by various catalysts.Full size tableFurthermore, the reduction mechanism of azo dyes over PdO–NiO catalyst with reducing agent shown in Fig. SI. 7.After the complete reduction reaction, the catalyst property was analyzed to understand the stability of PdO–NiO. In Fig. 11a, FTIR spectra showed no noticeable changes before and after catalytic reduction of mixture reduction. Additionally, the SEM image (Fig. 11b) also showed no changes in the morphology of the PdO–NiO. In addition, HR-TEM image (Fig. 11c) was also analyzed to study the change in the particles size after catalytic reduction, show no considerable change in the particle size. Which proved that PdO–NiO is highly stable in the reduction conditions.Figure 11(a) FTIR spectra of PdO–NiO nanocomposite before and after reducing the mixture of azo compounds (b) FE-SEM image (c) HR-TEM image of PdO–NiO after reducing the mixture of azo compounds.Full size image More