Ecology

Lee, K., Khanal, S. & Bakshi, B. R. Techno-ecologically synergistic food–energy–water systems can meet human and ecosystem needs. Energy Environ. Sci. 14, 3700–3716 (2021).Article 

Google Scholar 
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).CAS 
Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) 1–35 (Cambridge Univ. Press, 2022).Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).CAS 
Article 

Google Scholar 
Bashir, M. A. et al. Co-culture of rice and aquatic animals: an integrated system to achieve production and environmental sustainability. J. Clean. Prod. 249, 119310 (2020).Article 

Google Scholar 
Dong, S. et al. Evaluation of the trophic structure and energy flow of a rice–crayfish integrated farming ecosystem based on the Ecopath model. Aquaculture 539, 736626 (2021).Article 

Google Scholar 
Polovina, J. J. Model of a coral reef ecosystem—I. The ECOPATH model and its application to French Frigate Shoals. Coral Reefs 3, 1–11 (1984).Article 

Google Scholar 
Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).Article 

Google Scholar 
Fath, B. D. et al. Ecological network analysis metrics: the need for an entire ecosystem approach in management and policy. Ocean Coast. Manag. 174, 1–14 (2019).Article 

Google Scholar 
Diffendorfer, J. E., Richards, P. M., Dalrymple, G. H. & DeAngelis, D. L. Applying linear programming to estimate fluxes in ecosystems or food webs: an example from the herpetological assemblage of the freshwater Everglades. Ecol. Model. 144, 99–120 (2001).Article 

Google Scholar 
Bolton, S. in Encyclopedia of Ecology 2nd edn, Vol. 4 (ed. Fath, B. D.) 493–497 (Elsevier, 2019).Galaitsi, S. E., Keisler, J. M., Trump, B. D. & Linkov, I. The need to reconcile concepts that characterize systems facing threats. Risk Anal. 41, 3–15 (2021).CAS 
Article 

Google Scholar 
Lao, A., Cabezas, H., Orosz, A., Friedler, F. & Tan, R. Socio-ecological network structures from process graphs. PLoS ONE 15, e0232384 (2020).CAS 
Article 

Google Scholar 
Friedler, F., Aviso, K. B., Bertok, B., Foo, D. C. Y. & Tan, R. R. Prospects and challenges for chemical process synthesis with P-graph. Curr. Opin. Chem. Eng. 26, 58–64 (2019).Article 

Google Scholar 
Heymans, J. J. et al. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331, 173–184 (2016).Article 

Google Scholar 
Hu, L. et al. Can the co-cultivation of rice and fish help sustain rice production? Sci. Rep. 6, 28728 (2016).CAS 
Article 

Google Scholar 
He, M., Liu, F. & Wang, F. Resource utilization, competition and cannibalism of the red swamp crayfish Procambarus clarkii in integrated rice-crayfish culture without artificial diets. Aquac. Rep. 20, 100644 (2021).Article 

Google Scholar 
Xu, Q. et al. Conversion from rice–wheat rotation to rice–crayfish coculture increases net ecosystem service values in Hung-tse Lake area, east China. J. Clean. Prod. 319, 128883 (2021).Article 

Google Scholar 
Kurth, M. et al. A portfolio decision analysis approach to support energy research and development resource allocation. Energy Policy 105, 128–135 (2017).Article 

Google Scholar 
Friedler, F., Pimentel Lozada, J. & Orosz, Á. P-Graphs for Process Systems Engineering: Mathematical Models and Algorithms (Springer Nature, 2022).P-Graph (accessed 10 August 2021); www.p-graph.org More

Aguiar-Melo C, Zanella CM, Goetze M, Palma-Silva C, Hirsch LD, Neves B et al. (2019) Ecological niche modeling and a lack of phylogeographic structure in Vriesea incurvata suggest historically stable areas in the southern Atlantic Forest. Am J Bot https://doi.org/10.1002/ajb2.1317Bicudo MOP, Ribani RH, Beta T (2014) Anthocyanins, phenolic acids and antioxidant properties of juçara fruits (Euterpe edulis M.) along the on-tree ripening process. Plant Foods Hum Nutr https://doi.org/10.1007/s11130-014-0406-0Blengini IAD, Cintra MAMU, Caiafa AN (2015) Proposta de Unidade de Conservação da Serra da Jiboia. Gambá, Salvador, BA, https://www.gamba.org.br/wp-content/uploads/2016/03/Proposta-Final.pdf Accessed 05 May 2022Bourscheid K (2011) Euterpe edulis—Palmito juçara. In: Coradin L, Siminski A, Câmara, Reis A (Eds) Espécies nativas da flora brasileira de valor econômico atual ou potencial: plantas para o futuro – Região Sul. Ministério do Meio Ambiente, Brasília, D, p 179–183
Google Scholar 
Cabanne GS, d’Horta FM, Sari EHR, Santos FR, Miyaki CY (2008) Nuclear and mitochondrial phylogeography of the Atlantic forest endemic Xiphorhynchus fuscus (Aves: Dendrocolaptidae): Biogeography and systematics implications Molecular. Mol Phylogenet Evol https://doi.org/10.1016/j.ympev.2008.09.013Cabanne GS, Santos FR, Miyaki CY (2007) Phylogeography of Xiphorhynchus fuscus (Passeriformes, Dendrocolaptidae): vicariance and recent demographic expansion in southern Atlantic forest. Biol J Linn Soc https://doi.org/10.1111/j.1095-8312.2007.00775.xCâmara IG (2003) Brief history of conservation in the Atlantic Forest. In: Galindo Leal C, Câmara IG (Eds.) The Atlantic Forest of South America: Biodiversity Status, Threats, and Outlook. CABS and Island Press, Washington, p 31–42
Google Scholar 
Carnaval AC, Moritz C (2008) Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic Forest J Biogeogr https://doi.org/10.1111/j.1365-2699.2007.01870.xCarnaval AC, Moritz C, Hickerson M, Haddad C, Rodrigues M (2009) Stability predicts diversity in the Brazilian Atlantic Forest hotspot. Science https://doi.org/10.1126/science.1166955Carnaval AC, Waltari E, Rodrigues MT, Rosauer D, VanDerWa J, Damasceno R et al. (2014) Prediction of phylogeographic endemism in an environmentally complex biome. Proc R Soc Lond https://doi.org/10.1098/rspb.2014.1461Carvalho CDS, Garcia C, Lucas MS, Jordano P, Cortes MC (2021) Extant fruit‐eating birds promote genetically diverse seed rain, but disperse to fewer sites in defaunated tropical forests. J Ecol https://doi.org/10.1111/1365-2745.13534Carvalho CS, Ballesteros-Mejia L, Ribeiro MC, Côrtes MC, Santos AS, Collevatti RG (2017) Climatic stability and contemporary human impacts affect the genetic diversity and conservation status of a tropical palm in the Atlantic Forest of Brazil Conserv Genet https://doi.org/10.1007/s10592-016-0921-7Carvalho CS, Galetti M, Colevatti RG, Jordano P (2016) Defaunation leads to microevolutionary changes in a tropical palm. Sci Rep https://doi.org/10.1038/srep31957Carvalho CS, Ribeiro MC, Côrtes MC, Galetti M, Collevatti RG (2015) Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm. Heredity https://doi.org/10.1038/hdy.2015.30Carvalho MS, Noia LR, Ferreira MFS, Ferreira A (2019) DNA de alta qualidade isolado a partir do córtex de Euterpe edulis Mart. (Arecaceae). Cienc Florest https://doi.org/10.5902/1980509824130Chávez-Pesqueira M, Núñez-Farfán J (2016) Genetic diversity and structure of wild populations of Carica papaya in Northern Mesoamerica inferred by nuclear microsatellites and chloroplast markers. Ann Bot https://doi.org/10.1093/aob/mcw183Cheng H, Sinha A, Cruz FW, Wang X, Edwards RL, d’Horta FM et al. (2013) Climate change patterns in Amazonia and biodiversity. Nat Commun https://doi.org/10.1038/ncomms2415Chhatre VE, Emerson KJ (2017) StrAuto: automation and parallelization of STRUCTURE analysis. BMC Bioinform https://doi.org/10.1186/s12859-017-1593-0Chybicki IJ, Burczyk J (2009) Simultaneous Estimation of Null Alleles and Inbreeding Coefficients. J Hered https://doi.org/10.1093/jhered/esn088Collevatti RG, Lima-Ribeiro MS, Terribile LC et al. (2014) Recovering species demographic history from multi-model inference: the case of a Neotropical savanna tree species. BMC Evol Biol https://doi.org/10.1186/s12862-014-0213-0Côrtes MC, Uriarte M, Lemes MR, Gribel R, Kress WJ, Smouse PE et al. (2013) Low plant density enhances gene dispersal in the Amazonian understory herb Heliconia acuminata. Mol Ecol https://doi.org/10.1111/mec.12495Cortez MBS, Sforça DA, Alves FM, Vidal JD, Alves-Pereira A, Mori GM, Andreotti IA et al. (2019) Elucidating the Clusia criuva species ‘complex’: cryptic taxa can exhibit great genetic and geographical variation. Biol J Linn Soc https://doi.org/10.1093/botlinnean/boz004Costa PC, Lorenz-Lemke AP, Furini PR, Honorio Coronado EN, Kjellberg F, Pereira RA (2017) The phylogeography of two disjunct Neotropical Ficus (Moraceae) species reveals contrasted histories between the Amazon and the Atlantic Forests. Biol J Linn Soc https://doi.org/10.1093/botlinnean/box056d’Horta FM, Cabanne GS, Meyer D, Miyaki CY (2011) The genetic effects of Late Quaternary climatic changes over a tropical latitudinal gradient: diversification of an Atlantic Forest passerine. Mol Ecol https://doi.org/10.1111/j.1365-294X.2011.05063.xEarl DA, Von Holdt BM (2011) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet Resources https://doi.org/10.1007/s12686-011-9548-7Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol https://doi.org/10.1111/j.1365-294X.2005.02553.xExcoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evol Bioinform PMCID: PMC2658868Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics PMCID: PMC1462648Fantini AC, Guries R, Ribeiro RJ (2000) Produção de palmito (Euterpe edulis Martius Arecaceae) na Floresta Ombrófila Densa: potenciais, problema e possíveis soluções. In: Reis MS, Reis A (Eds.) Euterpe edulis Martius (Palmiteiro) Biologia, Conservação e Manejo. Herbário Barbosa Rodrigues, Itajaí, p 256–280
Google Scholar 
Fundação Instituto Brasileiro de Geografia e Estatística (1993) Recursos naturais e meio ambiente: Uma visão do Brasil. Fundação Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro
Google Scholar 
Gaiotto FA, Brondani RPV, Grattapaglia D (2001) Microsatellite markers for heart of palm–Euterpe edulis and E, oleracea Mart, (Arecaceae). Mol Ecol Notes https://doi.org/10.1046/j.1471-8278.2001.00036.xGaiotto FA, Grattapaglia D, Vencovsky V (2003) Genetic structure, mating system, and long-distance gene flow in heart of palm (Euterpe edulis Mart.). J Hered 94(5):399–406. https://doi.org/10.1093/jhered/esg087CAS 
Article 
PubMed 

Google Scholar 
Galetti M, Fernandez JC (1998) Palm heart harvesting in the Brazilian Atlantic forest: changes in industry structure and the illegal trade. J Appl Ecol https://doi.org/10.1046/j.1365-2664.1998.00295.xGaletti M, Guevara R, Côrtes MC, Fadini R, Von Mattes S, Leite AB et al. (2013) Functional extinction of birds drives rapid evolutionary changes in seed size. Science https://doi.org/10.1126/science.1233774Gatti MG, Campanello PI, Montti LF, Goldstein G (2008) Frost resistance in the tropical palm Euterpe edulis and its pattern of distribution in the Atlantic Forest of Argentina. For Ecol Manag https://doi.org/10.1016/j.foreco.2008.05.012Ghazoul J (2005) Pollen and seed dispersal among dispersed plants. Biological Reviews Cambridge Philosophical Society. https://doi.org/10.1017/S1464793105006731Goudet J (2002) FSTAT: a program to estimate and test gene diversities and fixation indices (version 2.9.3.2). http://www2.unil.ch/popgen/softwares/fstat.htmGugger PF, Ikegami M, Sork VL (2013) Influence of late Quaternary climate change on present patterns of genetic variation in valley oak, Quercus lobata. Mol Ecol https://doi.org/10.1111/mec.12317Hardy OJ, Charbonnel N, Fréville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics https://doi.org/10.1093/genetics/163.4.1467Hardy OJ, Vekemans X (2002) SPAGEDI: a versatile computer program to analyses spatial genetic structure at the individual or population levels. Mol Ecol Notes https://doi.org/10.1046/j.1471-8286.2002.00305.xHenderson A, Galeano G, Bernal R (1995) Field guide to the palms of the Americas. Princeton University Press, Princeton, NJ, p 352
Google Scholar 
Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature https://doi.org/10.1038/35016000Hulce D, Li X, Snyder-Leiby T, Johathan Liu CS (2011) GeneMarker® Genotyping Software: tools to increase the statistical power of DNA fragment analysis. J Biomol Screen PMCID: PMC3186482Joly C, Aidar M, Klink CA, McGrath DG, Moreira AG, Moutinho P et al. (1999) Evolution of the Brazilian phytogeography classification systems: implications for biodiversity conservation. Ciên e Cul 51:331–348
Google Scholar 
Konzen ER, Martins, MP (2017) Contrasting levels of genetic diversity among populations of the endangered tropical palm Euterpe edulis Martius, Cerne https://doi.org/10.1590/01047760201723012237.Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour https://doi.org/10.1111/1755-0998.12387Lauterjung MB, Montagna T, Bernardi AP, Silva JZ, Freitas NCC, Steiner, F et al. (2019) Temporal changes in population genetics of six threatened Brazilian plant species in a fragmented landscape. For Ecol Manag https://doi.org/10.1016/j.foreco.2018.12.058Leitman P, Judice DM, Barros FSM, Prieto PV (2013) Arecaceae, In: Martinelli G, Moraes MA (org) Livro Vermelho da Flora do Brasil. CNCFlora, Rio de Janeiro, pp 187–195Lewis PO, Zaykin D (2002) Genetic data analysis: Computer program for the analysis of allelic data. http://phylogeny.uconn.edu/software/Martins FM (2011) Historical biogeography of the Brazilian Atlantic forest and the Carnaval—Moritz model of Pleistocene refugia: what do phylogeographical studies tell us? Biol. J Linn Soc https://doi.org/10.1111/j.1095-8312.2011.01745.xMartins FM, Ditchfield AD, Meyer D, Morgante JS (2007) Mitochondrial DNA phylogeography reveals marked population structure in the common vampire bat, Desmodus rotundus (Phyllostomidae). J Zoolog Syst Evol https://doi.org/10.1111/j.1439-0469.2007.00419.xNovello M, Viana JPG, Alves-Pereira A, Silvestre EA, Nunes HF, Pinheiro JB et al. (2017) Genetic conservation of a threatened Neotropical palm through community-management of fruits in agroforests and second-growth forests. For Ecol Manag https://doi.org/10.1016/j.foreco.2017.06.059Oliveira-Filho A, Fontes M (2000) Patterns of floristic differentiation among Atlantic forests in southeastern Brazil and the influence of climate. Biotropica https://doi.org/10.1111/j.1744-7429.2000.tb00619.xOrtego J, Riordan EC, Gugger PF, Sork VL (2012) Influence of environmental heterogeneity on genetic diversity and structure in an endemic southern Californian oak. Mol Ecol https://doi.org/10.1111/j.1365-294X.2012.05591.xPalma-Silva C, Lexer C, Paggi GM, Barbará T, Bered F, BodaneseZanettini MH (2009) Range-wide patterns of nuclear and chloroplast DNA diversity in Vriesea gigantea (Bromeliaceae), a neotropical forest species. Heredity 103:503–512CAS 
Article 

Google Scholar 
Pauls SU, Nowak C, Bálint M, Pfenninger M (2013) The impact of global climate change on genetic diversity within populations and species. Mol Ecol https://doi.org/10.1111/mec.12152Petit RJ, Csaiklb UM, Bordácsbc S, Burgb K, Coartd E, Cottrelle J et al. (2002) Chloroplast DNA variation in European white oaks. Phylogeography and patterns of diversity based on data from over 2600 populations. For Ecol Manag https://doi.org/10.1016/S0378-1127(01)00645-4Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics https://doi.org/10.1093/genetics/155.2.945Pritchard JK, Wen X, Falush D (2010) Documentation for structure software: Version o2.3. http://web.stanford.edu/group/pritchardlab/structure.htmlRambaut A, Drummond AJ (2007) TRACER version 1.4. http://beast.bio.ed.ac.uk/Tracer. Accessed 05 May 2022Reis A, Kageyama PY (2000) Dispersão de sementes de Euterpe edulis Martius Palmae. In: Reis MS, Reis A (Eds.) Euterpe edulis Martius (Palmiteiro): biologia, conservação e manejo. Herbário Barbosa Rodrigues, Itajaí, p 60–92
Google Scholar 
Ribeiro MC, Metzger JP, Martensen AC, FPonzoni FJ, Hirota MM (2009) The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation. Biol Conserv https://doi.org/10.1016/j.biocon.2009.02.021Santos AS, Cazetta E, Morante Filho JC, Baumgarten J, Faria D, Gaiotto FA (2015) Lessons from a palm: genetic diversity and structure in anthropogenic landscapes from Atlantic Forest, Brazil. Conserv Genet https://doi.org/10.1007/s10592-015-0740-2Soares LASS, Cazetta E, Santos LR, França DS, Gaiotto FA (2019). Anthropogenic disturbances eroding the genetic diversity of a threatened palm tree: a multiscale approach. Front Genet https://doi.org/10.3389/fgene.2019.01090Szpiecha ZA, Rosenberga NA (2011) On the size distribution of private microsatellite alleles. Theor Popul Biol https://doi.org/10.1016/j.tpb.2011.03.006Thomé MTC, Zamudio KR, Giovanelli JGR, Haddad CFB, Baldissera Jr FA, Alexandrino J (2010) Phylogeography of endemic toads and post-Pliocene persistence of the Brazilian Atlantic Forest Mol Phylogenet Evol https://doi.org/10.1016/j.ympev.2010.02.003Turchetto-Zolet AC, Pinheiro F, Salgueiro F, Palma-Silva C (2013) Phylogeographical patterns shed light on evolutionary process in South America. Mol Ecol https://doi.org/10.1111/mec.12323Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) Micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes https://doi.org/10.1111/j.1471-8286.2004.00684.xWilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics https://doi.org/10.1093/genetics/163.3.1177Zurbuchen A, Landert L, Klaiber J, Müller A, Hein S, Dorn S (2010) Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Conserv Biol https://doi.org/10.1016/j.biocon.2009.12.003 More

Schulz, H. N. et al. Dense populations of a giant sulfur bacterium in Namibian shelf sediments. Science 284, 493–495. https://doi.org/10.1126/science.284.5413.493%JScience (1999).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Brüchert, V. et al. Biogeochemical and physical control on shelf anoxia and water column hydrogen sulphide in the Benguela coastal upwelling system off Namibia. In Past and Present Water Column Anoxia (ed. Neretin, L. N.) 161–193 (Springer, 2006).Chapter 

Google Scholar 
Currie, B., Utne-Palm, A. C. & Salvanes, A. G. V. Winning ways with hydrogen sulphide on the Namibian shelf. Front. Mar. Sci. 5, 341. https://doi.org/10.3389/fmars.2018.00341 (2018).Article 

Google Scholar 
Emeis, K. C. et al. Shallow gas in shelf sediments of the Namibian coastal upwelling ecosystem. Cont. Shelf Res. 24, 627–642 (2004).ADS 
Article 

Google Scholar 
Eisenbarth, S. & Zettler, M. L. Diversity of the benthic macrofauna off northern Namibia from the shelf to the deep sea. J. Mar. Syst. 155, 1–10 (2016).Article 

Google Scholar 
Zettler, M. L., Bochert, R. & Pollehne, F. Macrozoobenthos diversity in an oxygen minimum zone off northern Namibia. Mar. Biol. 156, 1949–1961. https://doi.org/10.1007/s00227-009-1227-9 (2009).CAS 
Article 

Google Scholar 
Cary, S. C., Vetter, R. D. & Felbeck, H. Habitat characterization and nutritional strategies of the endosymbiont-bearing bivalve Lucinoma aequizonata. Mar. Ecol. Prog. Ser. 55, 31–45 (1989).ADS 
Article 

Google Scholar 
Le Pennec, M., Beninger, P. G. & Herry, A. Feeding and digestive adaptations of bivalve molluscs to sulphide-rich habitats. Comp. Biochem. Physiol. A Physiol. 111, 183–189. https://doi.org/10.1016/0300-9629(94)00211-B (1995).Article 

Google Scholar 
Taylor, J. D. & Glover, E. A. Functional anatomy, chemosymbiosis and evolution of the Lucinidae. Geol. Soc. Lond. Spec. Publ. 177, 207–225. https://doi.org/10.1144/GSL.SP.2000.177.01.12 (2000).ADS 
Article 

Google Scholar 
Lim, S. J. et al. Extensive thioautotrophic gill endosymbiont diversity within a single Ctena orbiculata (Bivalvia: Lucinidae) population and implications for defining host-symbiont specificity and species recognition. MSystems 4, e00280. https://doi.org/10.1128/mSystems.00280-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
König, S. et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat. Microbiol. 2, 16193. https://doi.org/10.1038/nmicrobiol.2016.193 (2016).CAS 
Article 
PubMed 

Google Scholar 
Petersen, J. M. et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat. Microbiol. 2, 16195. https://doi.org/10.1038/nmicrobiol.2016.195 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Osvatic, J. T. et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc. Natl. Acad. Sci. 118, e2104378118. https://doi.org/10.1073/pnas.2104378118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lim, S. J. et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 13, 902–920. https://doi.org/10.1038/s41396-018-0318-3 (2019).CAS 
Article 
PubMed 

Google Scholar 
Taylor, J., Glover, E. & Williams, S. Diversification of chemosymbiotic bivalves: Origins and relationships of deeper water Lucinidae. Biol. J. Lin. Soc. 111, 401–420. https://doi.org/10.1111/bij.12208 (2014).Article 

Google Scholar 
Taylor, J. & Glover, E. Biology, Evolution and Generic Review of the Chemosymbiotic Bivalve Family Lucinidae (Ray Society, 2021).
Google Scholar 
Nagel, B. et al. N-cycling and balancing of the N-deficit generated in the oxygen minimum zone over the Namibian shelf-An isotope-based approach. J. Geophys. Res. Biogeosci. 118, 361–371. https://doi.org/10.1002/jgrg.20040 (2013).ADS 
CAS 
Article 

Google Scholar 
Neumann, A. & Flohr, A. The bivalve Lembulus bicuspidatus may enhance denitrification in shelf sediment at the Angola-Benguela Frontal Zone. Afr. J. Mar. Sci. 40, 91–96. https://doi.org/10.2989/1814232X.2018.1437774 (2018).Article 

Google Scholar 
Sampaio, L., Rodrigues, A. M. & Quintino, V. Carbon and nitrogen stable isotopes in coastal benthic populations under multiple organic enrichment sources. Mar. Pollut. Bull. 60, 1790–1802. https://doi.org/10.1016/j.marpolbul.2010.06.003 (2010).CAS 
Article 
PubMed 

Google Scholar 
Sakko, A. L. The influence of the Benguela upwelling system on Namibia’s marine biodiversity. Biodivers. Conserv. 7, 419–433. https://doi.org/10.1023/A:1008867310010 (1998).Article 

Google Scholar 
Levin, L. A., Mendoza, G. F., Konotchick, T. & Lee, R. Macrobenthos community structure and trophic relationships within active and inactive Pacific hydrothermal sediments. Deep Sea Res. II 56, 1632–1648. https://doi.org/10.1016/j.dsr2.2009.05.010 (2009).ADS 
CAS 
Article 

Google Scholar 
Soto, L. A. Stable carbon and nitrogen isotopic signatures of fauna associated with the deep-sea hydrothermal vent system of Guaymas Basin, Gulf of California. Deep Sea Res. II 56, 1675–1682. https://doi.org/10.1016/j.dsr2.2009.05.013 (2009).ADS 
CAS 
Article 

Google Scholar 
Weems, J., Iken, K., Gradinger, R. & Wooller, M. J. Carbon and nitrogen assimilation in the Bering Sea clams Nuculana radiata and Macoma moesta. J. Exp. Mar. Biol. Ecol. 430, 32–42. https://doi.org/10.1016/j.jembe.2012.06.015 (2012).CAS 
Article 

Google Scholar 
Ferrier-Pagès, C. & Leal, M. C. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol. Evol. 9, 723–740. https://doi.org/10.1002/ece3.4712 (2019).Article 
PubMed 

Google Scholar 
DavySimon, K., Allemand, D. & WeisVirginia, M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261. https://doi.org/10.1128/MMBR.05014-11 (2012).CAS 
Article 

Google Scholar 
Ferrier-Pagès, C. et al. Summer autotrophy and winter heterotrophy in the temperate symbiotic coral Cladocora caespitosa. Limnol. Oceanogr. 56, 1429–1438. https://doi.org/10.4319/lo.2011.56.4.1429 (2011).ADS 
Article 

Google Scholar 
Berg, C. J. & Alatalo, P. Potential of chemosynthesis in molluscan mariculture. Aquaculture 39, 165–179. https://doi.org/10.1016/0044-8486(84)90264-3 (1984).CAS 
Article 

Google Scholar 
Dando, P. R. & Southward, A. J. Chemoautotrophy in bivalve molluscs of the genus Thyasira. J. Mar. Biol. Assoc. U.K. 66, 915–929. https://doi.org/10.1017/S0025315400048529 (1986).CAS 
Article 

Google Scholar 
Spiro, B., Greenwood, P. B., Southward, A. J. & Dando, P. R. 13C/12C ratios in marine invertebrates from reducing sediments: Confirmation of nutritional importance of chemoautotrophic endosymbiotic bacteria. Mar. Ecol. Prog. Ser. 28, 233–240 (1986).ADS 
CAS 
Article 

Google Scholar 
Fisher, C. R. Chemoautotrophic and methanotrophic symbioses in marine invertebrates. Rev. Aquat. Sci. 2, 399–436 (1990).CAS 

Google Scholar 
Duperron, S., Fiala-Medioni, A., Caprais, J. C., Olu, K. & Sibuet, M. Evidence for chemoautotrophic symbiosis in a Mediterranean cold seep clam (Bivalvia: Lucinidae): Comparative sequence analysis of bacterial 16S rRNA, APS reductase and RubisCO genes. FEMS Microbiol. Ecol. 59, 64–70. https://doi.org/10.1111/j.1574-6941.2006.00194.x (2007).CAS 
Article 
PubMed 

Google Scholar 
Zanzerl, H., Salvo, F., Jones, S. W. & Dufour, S. C. Feeding strategies in symbiotic and asymbiotic thyasirid bivalves. J. Sea Res. 145, 16–23. https://doi.org/10.1016/j.seares.2018.12.005 (2019).ADS 
Article 

Google Scholar 
Descolas-Gros, C. & Fontugne, M. R. Carbon fixation in marine phytoplankton: Carboxylase activities and stable carbon-isotope ratios; physiological and paleoclimatological aspects. Mar. Biol. 87, 1–6. https://doi.org/10.1007/BF00396999 (1985).CAS 
Article 

Google Scholar 
Brooks, J. M. et al. Deep-sea hydrocarbon seep communities: Evidence for energy and nutritional carbon sources. Science 238, 1138. https://doi.org/10.1126/science.238.4830.1138 (1987).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Conway, N., Capuzzo, J. M. & Fry, B. The role of endosymbiotic bacteria in the nutrition of Solemya velum: Evidence from a stable isotope analysis of endosymbionts and host. Limnol. Oceanogr. 34, 249–255. https://doi.org/10.4319/lo.1989.34.1.0249 (1989).ADS 
CAS 
Article 

Google Scholar 
Conway, N. M., Howes, B. L., McDowell Capuzzo, J. E., Turner, R. D. & Cavanaugh, C. M. Characterization and site description of Solemya borealis (Bivalvia; Solemyidae), another bivalve-bacteria symbiosis. Mar. Biol. 112, 601–613. https://doi.org/10.1007/BF00346178 (1992).Article 

Google Scholar 
Rau, G. H. Low 15N/14N in hydrothermal vent animals: Ecological implications. Nature 289, 484. https://doi.org/10.1038/289484a0 (1981).ADS 
CAS 
Article 

Google Scholar 
Kennicutt, M. C. et al. Stable isotope partitioning in seep and vent organisms: Chemical and ecological significance. Chem. Geol. Isot. Geosci. Sect. 101, 293–310. https://doi.org/10.1016/0009-2541(92)90009-T (1992).CAS 
Article 

Google Scholar 
Lee, R. W. & Childress, J. J. Assimilation of inorganic nitrogen by marine invertebrates and their chemoautotrophic and methanotrophic symbionts. Appl. Environ. Microbiol. 60, 1852–1858. https://doi.org/10.1128/AEM.60.6.1852-1858.1994 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140. https://doi.org/10.1016/0016-7037(84)90204-7 (1984).ADS 
CAS 
Article 

Google Scholar 
Zanden, M. J. V. & Rasmussen, J. B. Variation in δ15N and δ13C trophic fractionation: Implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061–2066. https://doi.org/10.4319/lo.2001.46.8.2061 (2001).ADS 
Article 

Google Scholar 
Nagel, B., Gaye, B., Lahajnar, N., Struck, U. & Emeis, K.-C. Effects of current regimes and oxygenation on particulate matter preservation on the Namibian shelf: Insights from amino acid biogeochemistry. Mar. Chem. 186, 121–132. https://doi.org/10.1016/j.marchem.2016.09.001 (2016).CAS 
Article 

Google Scholar 
Holmes, M. E. et al. Stable nitrogen isotopes in Angola Basin surface sediments. Mar. Geol. 134, 1–12. https://doi.org/10.1016/0025-3227(96)00031-X (1996).ADS 
CAS 
Article 

Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 (2002).Article 

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

Google Scholar 
Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750. https://doi.org/10.4319/lom.2009.7.740 (2009).CAS 
Article 

Google Scholar 
Glibert, P. M., Middelburg, J. J., McClelland, J. W. & Jake Vander Zanden, M. Stable isotope tracers: Enriching our perspectives and questions on sources, fates, rates, and pathways of major elements in aquatic systems. Limnol. Oceanogr. 64, 950–981. https://doi.org/10.1002/lno.11087 (2019).ADS 
CAS 
Article 

Google Scholar 
Mompeán, C., Bode, A., Gier, E. & McCarthy, M. D. Bulk vs amino acid stable N isotope estimations of metabolic status and contributions of nitrogen fixation to size-fractionated zooplankton biomass in the subtropical N Atlantic. Deep Sea Res. I 114, 137–148. https://doi.org/10.1016/j.dsr.2016.05.005 (2016).CAS 
Article 

Google Scholar 
Steinkopf, M. Trophische Strukturen des Mesozooplanktons im Benguela Auftriebsgebiet vor Namibia (Universität Rostock, 2018).
Google Scholar 
Sigman, D. & Fripiat, F. Nitrogen isotopes in the Ocean. In Encyclopedia of Ocean Sciences 3rd edn, Vol. 263 (eds Cochran, J. K. et al.) 268 (Academic Press, 2019).
Google Scholar 
Nagel, B. et al. Nutrients and δ15N measured in water samples in the oxygen minimum zone over the Namibian shelf during the Meteor campaign M76–2 in 2008. PANGAEA. https://doi.org/10.1594/PANGAEA.892369 (2018).Granger, J., Sigman, D. M., Rohde, M. M., Maldonado, M. T. & Tortell, P. D. N and O isotope effects during nitrate assimilation by unicellular prokaryotic and eukaryotic plankton cultures. Geochim. Cosmochim. Acta 74, 1030–1040 (2010).ADS 
CAS 
Article 

Google Scholar 
Prokopenko, M. G., Hammond, D. E. & Stott, L. Lack of isotopic fractionation of δ 15N of organic matter during long-term diagenesis in marine sediments, ODP Leg 202, Sites 1234 and 1235. In Proc. Ocean Drilling Program(eds. R. Tiedemann, A. C. Mix, C. Richter and W. F. Ruddiman) 22 (2006).Prokopenko, M. G. et al. Nitrogen cycling in the sediments of Santa Barbara basin and Eastern Subtropical North Pacific: Nitrogen isotopes, diagenesis and possible chemosymbiosis between two lithotrophs (Thioploca and Anammox)—“Riding on a glider”. Earth Planet. Sci. Lett. 242, 186–204 (2006).ADS 
CAS 
Article 

Google Scholar 
Robinson, R. S. et al. A review of nitrogen isotopic alteration in marine sediments. Paleoceanography 27, 4203. https://doi.org/10.1029/2012PA002321 (2012).ADS 
Article 

Google Scholar 
Hoch, M. P., Fogel, M. L. & Kirchman, D. L. Isotope fractionation during ammonium uptake by marine microbial assemblages. Geomicrobiol. J. 12, 113–127. https://doi.org/10.1080/01490459409377977 (1994).CAS 
Article 

Google Scholar 
Grasshoff, K. et al. (eds) Methods of Seawater Analysis 3rd edn. (Wiley, 2009).
Google Scholar 
Hofmann, D., Gehre, M. & Jung, K. Sample preparation techniques for the determination of natural 15N/14N variations in amino acids by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). Isot. Environ. Health Stud. 39, 233–244. https://doi.org/10.1080/1025601031000147630 (2003).CAS 
Article 

Google Scholar 
Veuger, B., Middelburg, J. J., Boschker, H. T. S. & Houtekamer, M. Analysis of 15N incorporation into D-alanine: A new method for tracing nitrogen uptake by bacteria. Limnol. Oceanogr. Methods 3, 230–240. https://doi.org/10.4319/lom.2005.3.230 (2005).CAS 
Article 

Google Scholar 
Loick-Wilde, N. et al. Stratification, nitrogen fixation, and cyanobacterial bloom stage regulate the planktonic food web structure. Glob. Change Biol. 25, 794–810. https://doi.org/10.1111/gcb.14546 (2019).ADS 
Article 

Google Scholar 
Chikaraishi, Y., Ogawa, N. O., Doi, H. & Ohkouchi, N. 15N/14N ratios of amino acids as a tool for studying terrestrial food webs: A case study of terrestrial insects (bees, wasps, and hornets). Ecol. Res. 26, 835–844. https://doi.org/10.1007/s11284-011-0844-1 (2011).Article 

Google Scholar 
Chikaraishi, Y. et al. High-resolution food webs based on nitrogen isotopic composition of amino acids. Ecol. Evol. 4, 2423–2449. https://doi.org/10.1002/ece3.1103 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Eglite, E. et al. Strategies of amino acid supply in mesozooplankton during cyanobacteria blooms: A stable nitrogen isotope approach. Ecosphere 9, e02135. https://doi.org/10.1002/ecs2.2135 (2018).Article 

Google Scholar 
Fujii, T. et al. Organic carbon and nitrogen isoscapes of reef corals and algal symbionts: Relative influences of environmental gradients and heterotrophy. Microorganisms 8, 1221. https://doi.org/10.3390/microorganisms8081221 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Ferrier-Pagès, C. et al. Tracing the trophic plasticity of the coral–dinoflagellate symbiosis using amino acid compound-specific stable isotope analysis. Microorganisms 9, 182. https://doi.org/10.3390/microorganisms9010182 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hannides, C. C. S., Popp, B. N., Landry, M. R. & Graham, B. S. Quantification of zooplankton trophic position in the North Pacific Subtropical Gyre using stable nitrogen isotopes. Limnol. Oceanogr. 54, 50–61. https://doi.org/10.4319/lo.2009.54.1.0050 (2009).ADS 
CAS 
Article 

Google Scholar 
Maeda, T. et al. Algivore or phototroph? Plakobranchus ocellatus (Gastropoda) continuously acquires kleptoplasts and nutrition from multiple algal species in nature. PLoS ONE 7, e42024. https://doi.org/10.1371/journal.pone.0042024 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pjevac, P. et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front. Microbiol. 12, 1069 (2021).Article 

Google Scholar 
Brettin, T. et al. RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365. https://doi.org/10.1038/srep08365 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Steffan, S. A. et al. Unpacking brown food-webs: Animal trophic identity reflects rampant microbivory. Ecol. Evol. 7, 3532–3541. https://doi.org/10.1002/ece3.2951 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson, J. J. & Cavanaugh, C. M. Expression of form I and form II Rubisco in chemoautotrophic symbioses: Implications for the interpretation of stable carbon isotope values. Limnol. Oceanogr. 40, 1496–1502. https://doi.org/10.4319/lo.1995.40.8.1496 (1995).ADS 
CAS 
Article 

Google Scholar 
Fry, B. Stable Isotope Ecology (Springer, 2006).Book 

Google Scholar 
Emeis, K. et al. pCO2 underway data from the Benguela upwelling system in southeastern South Atlantic Ocean. PANGAEA. https://doi.org/10.1594/PANGAEA.880406 (2017).Mook, W. G., Bommerson, J. C. & Staverman, W. H. Carbon isotope fractionation between dissolved bicarbonate and gaseous carbon dioxide. Earth Planet. Sci. Lett. 22, 169–176 (1974).ADS 
CAS 
Article 

Google Scholar 
Goericke, R., Montoya, J. & Fry, B. Physiology and isotopic fractionation in algae and cyanobacteria. In Stable Isotopes in Ecology and Environmental Science (eds Kajtah, K. & Michener, R. H.) 187–221 (Blackwell, 1994).
Google Scholar 
Duplessis, M. R., Dufour, S. C., Blankenship, L. E., Felbeck, H. & Yayanos, A. A. Anatomical and experimental evidence for particulate feeding in Lucinoma aequizonata and Parvilucina tenuisculpta (Bivalvia: Lucinidae) from the Santa Barbara Basin. Mar. Biol. 145, 551–561. https://doi.org/10.1007/s00227-004-1350-6 (2004).Article 

Google Scholar 
Lopez, G. R. & Levinton, J. S. Ecology of deposit-feeding animals in marine Sediments. Q. Rev. Biol. 62, 235–260. https://doi.org/10.1086/415511 (1987).Article 

Google Scholar 
Brüchert, V. et al. Regulation of bacterial sulfate reduction and hydrogen sulfide fluxes in the central Namibian coastal upwelling zone. Geochim. Cosmochim. Acta 67, 4505–4518 (2003).ADS 
Article 

Google Scholar 
Schukat, A., Auel, H., Teuber, L., Lahajnar, N. & Hagen, W. Complex trophic interactions of calanoid copepods in the Benguela upwelling system. J. Sea Res. 85, 186–196. https://doi.org/10.1016/j.seares.2013.04.018 (2014).ADS 
Article 

Google Scholar 
McCarthy, M. D., Benner, R., Lee, C. & Fogel, M. L. Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744. https://doi.org/10.1016/j.gca.2007.06.061 (2007).ADS 
CAS 
Article 

Google Scholar 
Zbinden, M. et al. Epsilonproteobacteria as gill epibionts of the hydrothermal vent gastropod Cyathermia naticoides (North East-Pacific Rise). Mar. Biol. 162, 435–448. https://doi.org/10.1007/s00227-014-2591-7 (2015).CAS 
Article 

Google Scholar 
Whitlatch, R. B. & Obrebski, S. Feeding selectivity and coexistence in two deposit-feeding gastropods. Mar. Biol. 58, 219–225. https://doi.org/10.1007/BF00391879 (1980).Article 

Google Scholar 
Connor, M. S. & Robert, K. E. Selective grazing by the mud snail Ilyanassa obsoleta. Oecologia 53, 271–275 (1982).ADS 
Article 

Google Scholar 
Feller, R. J. Dietary immunoassay of Ilyanassa obsoleta, the eastern mud snail. Biol. Bull. 166, 96–102. https://doi.org/10.2307/1541433 (1984).Article 

Google Scholar 
Kelaher, B. P., Levinton, J. S. & Matthew Hoch, J. Foraging by the mud snail, Ilyanassa obsoleta (Say), modulates spatial variation in benthic community structure. J. Exp. Mar. Biol. Ecol. 292, 139–157. https://doi.org/10.1016/S0022-0981(03)00183-7 (2003).Article 

Google Scholar 
Montoya, J. P. Natural abundance of 15N in marine planktonic ecosystems. In Stable Isotopes in Ecology and Environmental Science (eds Michener, R. & Lajtha, K.) 176–201 (Blackwell Publishing Ltd, 2007).Chapter 

Google Scholar 
Checkley, D. M. & Miller, C. A. Nitrogen isotope fractionation by oceanic zooplankton. Deep Sea Res. A Oceanogr. Res. Pap. 36, 1449–1456. https://doi.org/10.1016/0198-0149(89)90050-2 (1989).ADS 
CAS 
Article 

Google Scholar 
Nelson, D. C. & Fisher, C. R. Chemoautotrophic and methanotrophic endosymbiotic bacteria at deep-sea vents and seeps. In The Microbiology of Deep-Sea Hydrothermal Vents (ed. Karl, D. M.) 125–167 (CRC Press, 1995).
Google Scholar 
Cardini, U. et al. Chemosymbiotic bivalves contribute to the nitrogen budget of seagrass ecosystems. ISME J. 13, 3131–3134. https://doi.org/10.1038/s41396-019-0486-9 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lee, R. W., Robinson, J. J. & Cavanaugh, C. M. Pathways of inorganic nitrogen assimilation in chemoautotrophic bacteria-marine invertebrate symbioses: Expression of host and symbiont glutamine synthetase. J. Exp. Biol. 202, 289 (1999).CAS 
Article 

Google Scholar 
Hentschel, U. & Felbeck, H. Nitrate respiration in chemoautotrophic symbionts of the bivalve Lucinoma aequizonata is not regulated by oxygen. Appl. Environ. Microbiol. 61, 1630–1633 (1995).ADS 
CAS 
Article 

Google Scholar 
Sacks, L. E. & Barker, H. A. The influence of oxygen on nitrate and nitrite reduction. J. Bacteriol. 58, 11–22. https://doi.org/10.1128/JB.58.1.11-22.1949 (1949).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Mahdavi, A. et al. Proc. Natl Acad. Sci. USA 105, 2307–2312 (2008).CAS 
Article 

Google Scholar 
Levin, A. et al. Nat. Rev. Chem. 4, 615–634 (2020).CAS 
Article 

Google Scholar 
Graedel, T. E. Annu. Rev. Energy Environ. 21, 69–98 (1996).Article 

Google Scholar 
Erkman, S. J. Clean. Prod. 5, 1–10 (1997).Article 

Google Scholar 
Lao, A. R., Aviso, K. B., Cabezas, H. & Tan, R. R. Nat. Sustain. https://doi.org/10.1038/s41893-022-00912-w (2022).Benke, K. & Tomkins, B. Sustain. Sci. Practice Policy 13, 13–26 (2017).
Google Scholar 
Treich, N. Environ. Resource Econ. 79, 33–61 (2021).Article 

Google Scholar 
Liu, J., Caspersen, S. & Yong, J. W. H. Elife 11, e77202 (2022).Article 

Google Scholar 
Friedler, F., Tarján, K., Huang, Y. W. & Fan, L. T. Chem. Eng. Sci. 47, 1973–1988 (1992).CAS 
Article 

Google Scholar 
Sait, S. M., Liu, W.-C., Thompson, D. J., Godfray, H. C. J. & Begon, M. Nature 405, 448–450 (2000).CAS 
Article 

Google Scholar 
Nelson, M. Space Sci. Technol. 2021, 8067539 (2021).Article 

Google Scholar  More

Truffles are socially and economically important in parts of Croatia. They can be worth up to €5,000 (US$5,300) per kilogram. The truffle industry and related tourism provides jobs, supplements incomes and boosts local economies. It’s not just about money, however; many people just love being out in the forest looking for them.My fascination with fungi began at the age of six, when my father and grandfather began taking me out to hunt for game and to collect mushrooms near our home in Istria. Today, I focus mainly on truffles and other hypogeous fungi, which produce their fruiting bodies underground. I spend 50–100 days a year in the field with my dogs, collecting samples and data on the life cycles, ecology and geographical spread of fungi across Croatia. Here, I’m with my dog Masha. I love the work.Thirty years ago, rainfall used to be more predictable across the year in Istria. Now, the climate is more extreme, and includes droughts. Truffles require a specific amount of water to grow. And warm winters have increased the population of wild boars, which damage the soil and eat the truffles. The truffles are becoming harder to find.Truffle plantations could take the pressure off natural habitats. There, the soil water content can be controlled, agricultural methods can be used to enhance production and boars can be kept out. We’re studying the viability of farming black truffles, in part by experimenting with different ways to inoculate tree seedlings with their spores.We’re using DNA barcoding to identify fungi in soil from their spores and root-like mycelium in protected areas. We’re finding that there are often many more species present than previously thought.Our comparisons of areas with and without truffles could help to reveal why they grow in some areas but not others. Our work is also helping to show the importance of biodiversity in places such as the Adriatic islands of Brijuni National Park. More

Site descriptionThe six long-term fertilisation field experiments were located across a latitudinal gradient in China from north to south, spanning three climate zones from the middle temperate zone to the subtropical zone. These sites were chosen to represent the three main agricultural production areas in China. The soils at the sites were black soils (Udolls or Typic Hapludoll according to USDA Soil Taxonomy, BSA) in Hailun (Heilongjiang Province) and Changchun (Jilin Province); fluvo-aquic soils (Aquic Inceptisol according to USDA Soil Taxonomy, FSA) in Yucheng (Shandong Province) and Yuanyang (Henan Province); and red soils (Ultisols according to USDA Soil Taxonomy, RSA) in Jinxian (Jiangxi Province) and Qiyang (Hunan Province). The two most distant sites (from Hailun to Qiyang) were more than 2,500 km apart, and the two closest test sites (from Yucheng to Yuanyang) were at least 300 km apart. Three treatments, i.e., no fertiliser (Control), chemical fertiliser N, P, and K (NPK), and organic manure plus chemical fertiliser (NPKM), have been applied in triplicate plots in each field for 29 years or more. Climate data corresponding to the sampling site coordinates were obtained from the China Meteorological Data Network (http://data.cma.cn/). Further details of experimental sites are provided in Supplementary Data 1.Sample collectionSampling was performed during the silking-maturity period of maize in 2019 and 2020, with the exact date of sampling depending on the developmental stage of plants at each location (Supplementary Data 2). In the FSA and RSA soils, three individual maize plants were selected from each subplot (a total of 27 maize plants per site). From each maize plant, we collected the following compartments in the field: mixed leaves, xylem sap, stem, roots, bulk soil. The mixed leaves sample consisted of the 2nd, 4th, and 6th leaves, which were removed from the plant stem using ethanol-sterilised scissors. To collect xylem sap, a proxy for xylem, we cut off the stem mid-way between the 2nd and 3rd node from the base of the plant, and sterilised absorbent cotton in sterilised bags was placed on the cut end of the shoot (see Supplementary Movie 1 for details of this procedure). Meanwhile, we inserted a steel stick (sterilised, 2 cm diameter, 30 cm length) into the soil at each subplot to simulate the collection of xylem sap and check for contaminants during the field operations (Supplementary Fig. 11). The stem sample consisted of the upper region between the 2nd and 3rd nodes, collected into sterilised bags. To collect root samples, we shook whole roots vigorously to remove all loose soil. The roots and root-adhered soil particles were collected for further separation of the roots and rhizosphere soil in the laboratory. The bulk soil sample was collected from between the rows of maize plants. At the sites with BSA soils, we only collected one individual maize plant from each subplot (a total of nine maize plants per site), because these two long-term experiments have strict requirements for sampling to avoid large-scale damage to the entire test field. Thus, for each plant, the 1st and 2nd, the 3rd and 4th, and the 5th and 6th leaves were collected as three replicates. Similarly, fine roots (< 2 mm) and thick roots ( > 2 mm) were collected separately. Except for this difference, the other operations were the same as those at the other four experimental sites. All samples were placed on ice for transport and further processing within 48 h. The soil parameters of pH, total C (TC) and N (TN), ammonium (NH4+) and nitrate (NO3−), and soil available P (AP) and K (AK) are listed in Supplementary Table 1.Sample processingTo recover the xylem sap absorbed in the cotton, each cotton ball was placed into a 50-mL sterile centrifuge tube with a filter and centrifuged at 6000 × g for 5 min. The collected sap was divided into two parts; one part was used for bacterial isolation, and the other part (stored at −80 °C) was used for culture-independent bacterial 16 S rRNA gene profiling.Processing of root-associated samplesWe used a modified protocol15 to separate the microbiome living on the plant surface (epiphytes) from the microbiome living within the plant (endophytes). Briefly, 5 g root tissue was weighed into a 100 mL conical flask containing 80 mL sterile PBS and 5 μL Tween 80. The mixture was vortexed, and the liquid was collected as the rhizosphere (root epiphyte) sample. To extract rhizosphere DNA, the sample was centrifuged at 10,000 × g for 5 min, and then 500 mg of the resulting tight pellet containing fine sediment and microorganisms was placed in a Lysing Matrix E tube (supplied in the FastDNA™ Spin Kit for Soil). To obtain endophytes from the root samples, the roots were washed with fresh PBS until the buffer was clear after vortexing. The roots were then sonicated using an ultrasonic cell disruptor (Scientz JY 88- IIN, Ningbo Scientz Biotechnology Co., Ltd., Zhejiang, China) at a low frequency for 10 min (30-s bursts followed by 30-s rests).Processing of leaf-associated samplesThe method for washing leaf samples was similar to that used to wash the roots, except for an additional step before collecting the phyllosphere. Each leaf sample (5 g) was added to a conical flask containing sterile PBS, which was subjected to two 5-min treatments in an ultrasound bath (25 °C, 40 KHZ), with vortexing for 30 s between the two ultrasonication treatments. This procedure released most of the phyllosphere microbes from the leaves. Then, the filtrate was collected and the leaves were washed again using the above procedure. Phyllosphere samples were collected by centrifugation (at 10,000 × g for 20 min) of the accumulated filtrate and were resuspended in 1 mL sodium phosphate buffer (FastDNA™ Spin Kit for Soil) before being transferred to Lysing Matrix E tubes (FastDNA™ Spin Kit for Soil). Finally, the leaf and stem samples were washed and sonicated in the same way as the roots. Sonicated root, leaf, and stem samples were snap-frozen in liquid N2 and stored at −80 °C until analysis.DNA extraction, PCR amplification and sequencingTotal DNAs were extracted from the aforementioned samples with a FastDNA™ Spin Kit for Soil (MP Biomedicals, Solon, OH, USA) following the manufacturer’s instructions. The DNA concentration and purity were measured using a NanoDrop2000 spectrophotometer (NanoDrop2000, Thermo Fisher Scientific, Waltham, MA, USA). The DNAs extracted from soils (bulk and rhizosphere soil) were diluted 10-fold. For 16 S rRNA gene libraries, the V5–V7 region was amplified using the primers 799 F and 1193 R (Supplementary Table 5). Each DNA template was amplified in triplicate (together with a water control) in a 25-μL reaction volume. The PCR conditions were as follows: 12.5 µL 2× EasyTaq PCR SuperMix (TransGen Biotech, Beijing, China), 1.25 µL forward primers (10 µM), 1.25 µL barcoded reverse primers (10 µM), 1.25 µL template DNA, and 8.75 µL ddH2O. The PCR amplification program was as follows: 94 °C for 3 min; 28 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 90 s; and 72 °C for 90 s. The products were stored at 4 °C until use. After mixing the triplicate PCR products of each sample, the bacterial 16 S rRNA gene amplicons were extracted from a 1% agarose gel using a Gel Extraction Kit (Omega Bio-tek Inc., Norcross, GA, USA). The DNAs were measured using a Quant-iT™ PicoGreen™ dsDNA Assay kit (Thermo Fisher Scientific) and pooled in equimolar concentrations. Sequencing libraries were generated using an Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA, USA) and were sequenced on the NovaSeq-PE 250 platform (Illumina).16 S rRNA gene amplicon sequence processingPaired-end reads were checked by FastQC v.0.10.137 and merged using the USEARCH 11.0.66738 fastq_mergepairs script. Reads were assigned and demultiplexed to each sample according to the unique barcodes by QIIME 1.9.139. After removing barcodes and primers, low-quality reads were filtered and non-redundant reads were identified using VSEARCH 2.12.040. Unique reads with ≥ 97% similarity were assigned to the same OTU. Representative sequences were selected using UPARSE41 and the classify.seqs command in mothur42 was used to taxonomically classify each OTU with reference to the SILVA 138 database43. The OTUs classified as host plastids, cyanobacteria, and others not present in our samples were removed from the dataset.Statistical analysisAlpha and beta-diversity analyses were conducted with R script as described in previous studies44 and on the QIIME239 platform. PerMANOVA analyses were performed using the ‘Adonis’ function implemented in the vegan package45 of R. We used the microbial source-tracking method FEAST46 to determine the potential origin of the microbiota inhabiting the various compartments of maize plants. Bacterial OTUs were assigned into multiple functional groups using FAPROTAX v.1.2.147. All analyses were conducted in the R Environment48 except for beta-diversity analyses. All plots were generated with ggplot249 and GraphPad Prism 8.0.0 (GraphPad Software, San Diego, CA, USA, www.graphpad.com).Distance-decay relationship analysesWe conducted distance-decay relationship analyses to assess the relationship between the similarity of communities in individual plant compartments and spatial distance, edaphic distance, and climatic distance. We used the Geosphere package to calculate the geographic distances in km from the latitude and longitude coordinates, and calculated edaphic and climatic distances separately as the Euclidian distance. We used the ‘vegdist’ function in the vegan package to calculate the Bray–Curtis similarity of microbial communities. Here, the variation in the slope of distance decay reflects the degree to which the similarity of microbial communities in plant compartments varies with environmental distances. The relationships between the Bray–Curtis similarity of each compartment and specific soil or climatic factors were determined by calculating Pearson’s correlation (r) values. The significance of r values was assessed with the Mantel test implemented in the vegan package.Differential abundance testingA negative binomial generalised linear model was implemented with the edgeR package50 to detect differences in OTU abundance among samples. We compared individual plant compartments (RS, RE, VE, SE, LE, and P) against bulk soil (BS) and conducted pairwise comparisons among fertilisation treatments. For each comparison, after constructing the DGEList object and filtering out low counts, the calcNormFactors function was used to obtain normalisation factors and the estimateDisp function was used to estimate tagwise, common, and trended dispersions. We then used the glmFit function to test the differential OTU abundance. The corresponding P values were corrected for multiple tests using FDR with α = 0.05.Core taxa selectionWe used the UpSetR package51 to visualise the OTUs that overlapped among all plant compartments and soils. The overlapping OTUs were defined as those detected in at least one sample from each compartment. We further identified the union of overlapped OTUs and enriched OTUs in the xylem using the EVenn online tool52. Importantly, abundance–occupancy analyses were conducted to identify the core OTUs across environmental gradients. We calculated occupancy with the most conservative approach, which restricted the core to only those OTUs that were detected in all xylem sap samples (i.e., occupancy=1).Triple-qPCR to verify FAPROTAX resultsTo detect contamination with plant organelles, sample DNAs were amplified using the universal primer pair 799 F/1193R53, which amplifies both bacterial 16 S and mitochondrial 18 S rRNA but not chloroplast sequences; the mitochondrial-specific primer pair mito1345F/mito4130R54,55, which only amplifies mitochondrial 18 S rRNA, and the nifH gene primers PolF/PolR56 (Supplementary Table 5). To reflect the potential N-fixation of bacterial communities in each plant compartment, the following ratio was calculated:$${{{{{rm{Relative}}}}}},{{{{{rm{nitrogen}}}}}},{{{{{rm{fixation}}}}}},{{{{{rm{potential}}}}}}=frac{{nifH},{{{{{rm{gene}}}}}}}{{{{{{rm{bacterial}}}}}},{{{{{rm{16S}}}}}},{{{{{rm{and}}}}}},{{{{{rm{mitochondrial}}}}}},{{{{{rm{18S}}}}}},{{{{{rm{rRNA}}}}}}-{{{{{rm{mitochondrial}}}}}},{{{{{rm{18S}}}}}},{{{{{rm{rRNA}}}}}}}$$
(1)
All qPCR assays were run on a QuantStudio 6 Flex using SYBR Green Pro Taq HS Premix (Accurate Biotechnology, Changsha, China) in a 20 µL volume containing 200 nM of each primer and approximately 50 ng DNA per reaction. All three primer sets were amplified in a three-step qPCR57 run at 95 °C for 15 s, 55 °C for 30 s and 72 °C for 40 s for 40 cycles followed by a melting curve analysis. The amplification efficiency varied from 83 to 117%.Isolation of bacteria from xylem sapTo isolate strains, four gradient dilutions (10−3, 10−4, 10−5, and 10−6) of xylem sap were incubated on TSB, R2A, and Ashby’s Nitrogen-Free Agar media for 5–7 days at 30 °C (Supplementary Data 9). After incubation, colonies were selected based on their character and colony morphology and were purified by triple serial colony isolation. The isolates were subjected to Sanger sequencing and identified on the basis of PCR analyses with 27 F and 1492 R primers, and alignment against reference. 16S rRNA gene sequences using the BLAST algorithm. Isolates belonging to the core taxa were identified by comparing the 16 S rRNA V5–V7 regions against the highly abundant OTUs ( > 0.01%) using UCLUST with 98.65% similarity;58 this threshold has been reported to accurately distinguish two species. Cladograms were visualised by iTOL.v6.459. The isolated cultures were stored in 30% (v/v) glycerol.Nitrogen-fixing capacity of bacterial isolatesWe used three different methods to evaluate the N-fixing capacity of bacterial isolates. First, we observed growth on Ashby’s N-Free medium, and documented which strains grew well after streaking of diluted cultures. Then, these strains were analysed by PCR to detect nifH with the PolF/PolR primer set56. The positive control was the N-fixing strain, Azotobacter chroococcum ACCC10006 (Agricultural Culture Collection of China). Strains that did not yield a PCR product with this primer set were analysed using other nitrogenase gene primers including nifH-F/nifH-R60 primers and the nested PCR primers FGPH19/PolR (outer primers) and PolF/AQER (inner primers)56 (Supplementary Table 5). We also conducted acetylene reduction assays (ARA)61,62,63 to quantify the nitrogenase activity of putative N-fixing strains. Each tested strain was initially incubated overnight in TSB medium and then washed twice with sterile 0.9% NaCl solution. After centrifugation and re-suspension, the bacterial pellet was added to a 20 mL serum vial containing 5 mL Dobereiner’s N-free liquid medium (Supplementary Data 9), reaching a final OD600 of ~0.1. The vials were first flushed with argon to evacuate air, and then 1% and 10% of the headspace was replaced with pure and fresh O2 and C2H2, respectively. After incubation at 30 °C for 12 h, the gas phase was analysed with a gas chromatograph (Agilent Technologies 6890 N). Data are presented as mean values from five replicate cultures. To test the hypothesis that the core non-N-fixers might assist N-fixation by modifying the oxygen concentration, the nitrogenase activity was measured as described above except that the headspace atmosphere in the sealed vial was not adjusted to 1% O2 by flushing with argon gas so that the initial oxygen concentration was that found in ambient air.Draft whole-genome sequencing of cross-referenced core strainsIsolated genomic DNA was extracted with a TIANamp Bacteria DNA Kit (Tiangen Biotech, Beijing, China). The purified genomic DNA was used to construct a sequencing library, which was generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, Beverly, MA, USA) following the manufacturer’s recommendations. Pooled libraries were sequenced on the NovaSeq-PE 150 platform. After trimming low-quality reads by fastq64, the clean reads were assembled into draft genomes (excluding contigs of < 300 bp) by SPAdes 3.13.165. Gene prediction and annotation were performed by NCBI PGAP66, and the putative genes were further annotated by searching against the eggNOG database67 by emaper. The functional mapping and analysis pipeline (FMAP 0.15)68 was used to align the filtered reads using BLAST against a KEGG Filtered UniProt69 reference cluster (e  70%) and to calculate the number of reads mapping to each KEGG Orthologous group (KO). Other data manipulation was performed using perl scripts developed in-house.Potted plant experimentTwo potted plant experiments were performed to (1) reproduce the endophytic behaviour of isolates from xylem sap; and (2) verify their N-fixation potential in maize. We constructed SynComs consisting of two diazotrophs (K. variicola MNAZ1050 and Citrobacter sp. MNAZ1397) and two non-N-fixers (Acinetobacter sp. ACZLY512 and R. epipactidis YCCK550) based on a N-fixing capacity test. Each individual strain was cultured overnight in TSB medium at 30 °C and 180 rpm, then cells were collected by centrifugation and the pellet was suspended in sterile 0.9% NaCl solution. Four bacterial suspensions were mixed in equal amounts to a final OD600 of ~0.2.The potted plants were grown in plastic pots filled with loose a soilless mixture consisting of perlite and vermiculite (sterilised by autoclaving). The nutrients needed for plant growth were added as base fertilisers (Supplementary Data 10). The seeds of maize “Zhengdan 958” were surface-disinfected for 15 min with sodium hypochlorite (approximately 2% active chlorine, with 200 μL Tween 80) and washed for 5 min, five times, with sterile water. The final rinse water (100 μL) was spread on TSA medium to check for other attached bacteria. Seeds were allowed to germinate, and then germinated seeds with similar primary root lengths were selected for inoculation with SynComs. Maize plants were grown in a greenhouse under a 16-h light/8-h photoperiod at 30 °C/25 °C (day/night).Colonisation of maize tissues by GFP-tagged SynComsTo verify the endophytic behaviour, each of the four members of SynComs was tagged with green fluorescent protein (GFP) (vector pCPP6529-GFPuv). One GFP-tagged and three other wild bacterial suspensions were mixed as described above, giving a total of four different GFP-tagged combinations. The control was SynComs with no GFP tags. For inoculation, maize seedlings with the endosperm removed were soaked in four GFP-tagged combined solutions for 30 min. The same bacterial suspension was applied to the potted plants at days 10 and 30 after transplanting. After 63 days, stem samples from between the 2nd and 3rd nodes were surface-sterilised with 70% ethanol, collected, and then sectioned using a Leica VT 1000 S vibratome (Leica, Nussloch, Germany). Thin sections (60 μm) and xylem sap were observed under a confocal laser scanning microscope (CLSM, Zeiss LSM 880 confocal microscope, Jena, Germany). 15N isotope dilution methodTo verify the N-fixation potential of endophytes in maize, the N fertiliser was replaced with 15N-labelled (NH4)2SO4 (30 % 15N atom, Shanghai Research Institute of Chemical Industry, China). Maize seedlings with endosperm removed were soaked in a bacterial suspension of SynComs (Treatment group) or autoclaved SynComs (Control group) for 30 min. The same SynComs, active or autoclaved, were re-applied to the potted plants at 10 and 30 days after transplanting, as described above. The roots, stems and leaves were harvested separately from each treatment (four replicates) on day 65. The roots were washed with deionised water to remove adhering isotope residues. The N content and 15N enrichment of plant tissue were determined using Elementar vario PYRO cube elemental analyser (Vario PYRO Cube, Elementar, Hanau, Germany) and Isoprime 100 isotope mass spectrometer (Isoprime, Cheadle, United Kingdom). The plants inoculated with autoclaved SynComs were used as the reference to calculate BNF with the following equations34:$$% {{{{{rm{Ndfa}}}}}}=(1{{mbox{-}}}{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{I}}}}}}}/{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{UI}}}}}}})times 100$$ (2) $${{{{{{rm{N}}}}}}}_{2}{{mbox{-}}}{{{{{rm{fixed}}}}}}=left(1-{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{I}}}}}}}/{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{UI}}}}}}}right)times {{{{{{rm{N; yield}}}}}}.}_{{{{{{rm{I}}}}}}}$$ (3) where %Ndfa is the percentage of N derived from air, %15Na.e. (%15N atom excess) is the enrichment in plants inoculated with SynComs (I) and autoclaved SynComs (UI), N2-fixed is N derived from air, and N yield.I is the total N content of the whole inoculated plant.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Smith, K. G. V. Manual of Forensic Entomology (Trustees of the British Museum, 1986).
Google Scholar 
Catts, E. P. & Goff, M. L. Forensic entomology in criminal investigations. Annu. Rev. Entomol. 37, 253–272. https://doi.org/10.1146/annurev.en.37.010192.001345 (1992).CAS 
Article 
PubMed 

Google Scholar 
Charabidze, D., Gosselin, M. & Hedouin, V. Use of necrophagous insects as evidence of cadaver relocation: Myth or reality?. PeerJ 5, e3506. https://doi.org/10.7717/peerj.3506 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Matuszewski, S., Szafałowicz, M. & Jarmusz, M. Insects colonising carcasses in open and forest habitats of Central Europe: Search for indicators of corpse relocation. Forens. Sci. Int. 231, 234–239. https://doi.org/10.1016/j.forsciint.2013.05.018 (2013).Article 

Google Scholar 
Tarone, A. M. & Sanford, M. R. Is PMI the hypothesis or the null hypothesis?. J. Med. Entomol. 54, 1109–1115. https://doi.org/10.1093/jme/tjx119 (2017).Article 
PubMed 

Google Scholar 
Grassberger, M. & Frank, C. Initial study of arthropod succession on pig carrion in a central European urban habitat. J. Med. Entomol. 41, 511–523. https://doi.org/10.1603/0022-2585-41.3.511 (2004).CAS 
Article 
PubMed 

Google Scholar 
Matuszewski, S., Bajerlein, D., Konwerski, S. & Szpila, K. An initial study of insect succession and carrion decomposition in various forest habitats of Central Europe. Forens. Sci. Int. 180, 61–69. https://doi.org/10.1016/j.forsciint.2008.06.015 (2008).Article 

Google Scholar 
Amendt, J. et al. Best practice in forensic entomology—standards and guidelines. Int. J. Legal Med. 121, 90–104. https://doi.org/10.1007/s00414-006-0086-x (2007).Article 
PubMed 

Google Scholar 
Higley, L. G. & Haskell, N. In Forensic Entomology. The Utility of Arthropods in Legal Investigations (eds Byrd, J. H. & Castner, J. L.) 389–407 (CRC Press, 2010).
Google Scholar 
Amendt, J., Richards, C. S., Campobasso, C. P., Zehner, R. & Hall, M. J. R. Forensic entomology: Applications and limitations. Forens. Sci. Med. Pathol. 7, 379–392. https://doi.org/10.1007/s12024-010-9209-2 (2011).CAS 
Article 

Google Scholar 
Ikemoto, T. & Takai, K. A new linearized formula for the law of total effective temperature and the evaluation of line-fitting methods with both variables subject to error. Environ. Entomol. 29, 671–682. https://doi.org/10.1603/0046-225x-29.4.671 (2000).Article 

Google Scholar 
Picard, C. J. et al. Increasing precision in development-based postmortem interval estimates: What’s sex got to do with it?. J. Med. Entomol. 50, 425–431. https://doi.org/10.1603/me12051 (2013).Article 
PubMed 

Google Scholar 
Frątczak-Łagiewska, K. & Matuszewski, S. Sex-specific developmental models for Creophilus maxillosus (L.) (Coleoptera: Staphylinidae): Searching for larger accuracy of insect age estimates. Int. J. Legal Med. 132, 887–895. https://doi.org/10.1007/s00414-017-1713-4 (2018).Article 
PubMed 

Google Scholar 
Matuszewski, S. & Frątczak-Łagiewska, K. Size at emergence improves accuracy of age estimates in forensically-usefull beetle Creophilus maxillosus L. (Staphylinidae). Sci. Rep. https://doi.org/10.1038/s41598-018-20796-1 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gruszka, J. & Matuszewski, S. Estimation of physiological age at emergence based on traits of the forensically useful adult carrion beetle Necrodes littoralis L. (Silphidae). Forens. Sci. Int. 314, 110407. https://doi.org/10.1016/j.forsciint.2020.110407 (2020).Article 

Google Scholar 
Matuszewski, S. Post-mortem interval estimation based on insect evidence: Current challenges. Insects 12, 314. https://doi.org/10.3390/insects12040314 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bajerlein, D., Taberski, D. & Matuszewski, S. Estimation of postmortem interval (PMI) based on empty puparia of Phormia regina (Meigen) (Diptera: Calliphoridae) and third larval stage of Necrodes littoralis (L.) (Coleoptera: Silphidae)—advantages of using different PMI indicators. J. Forens. Legal Med. 55, 95–98. https://doi.org/10.1016/j.jflm.2018.02.008 (2018).CAS 
Article 

Google Scholar 
Greenberg, B. Flies as forensic indicators. J. Med. Entomol. 28, 565–577. https://doi.org/10.1093/jmedent/28.5.565 (1991).CAS 
Article 
PubMed 

Google Scholar 
Catts, E. P. Problems in estimating the postmortem interval in death investigations. J. Agric. Entomol. 9, 245–255 (1992).
Google Scholar 
Payne, J. A. A summer carrion study of the baby pig Sus scrofa Linnaeus. Ecology 46, 592–602 (1965).Article 

Google Scholar 
Ives, A. R. Aggregation and coexistence in a carrion fly community. Ecol. Monogr. 61, 75–94 (1991).Article 

Google Scholar 
Slone, D. H. & Gruner, S. V. Thermoregulation in larval aggregations of carrion-feeding blow flies (Diptera: Calliphoridae). J. Med. Entomol. 44, 516–523. https://doi.org/10.1603/0022-2585(2007)44[516:tilaoc]2.0.co;2 (2007).CAS 
Article 
PubMed 

Google Scholar 
Kulshrestha, P. & Satpathyb, D. K. Use of beetles in forensic entomology. Forens. Sci. Int. 120, 15–17. https://doi.org/10.1016/s0379-0738(01)00410-8 (2001).CAS 
Article 

Google Scholar 
Matuszewski, S., Bajerlein, D., Konwerski, S. & Szpila, K. Insect succession and carrion decomposition in selected forests of Central Europe. Part 2: Composition and residency patterns of carrion fauna. Forens. Sci. Int. 195, 42–51. https://doi.org/10.1016/j.forsciint.2009.11.007 (2010).Article 

Google Scholar 
Kadej, M., Szleszkowski, Ł, Thannhäuser, A. & Jurek, T. A mummified human corpse and associated insects of forensic importance in indoor conditions. Int. J. Legal Med. 134, 1963–1971 (2020).Article 

Google Scholar 
Mashaly, A., Al-Khalifa, M., Al-Qahtni, A. & Alshehri, A. Analysis of insects colonised on human corpses during autopsy in Riyadh Saudi Arabia. Entomol. Res. 50, 351–360. https://doi.org/10.1111/1748-5967.12441 (2020).Article 

Google Scholar 
Meira, L. M. R., Barbosa, T. M., Jales, J. T., Santos, A. N. & Gama, R. A. Insects associated to crime scenes in the northeast of Brazil: Consolidation of collaboration between entomologists and criminal investigation institutes. J. Med. Entomol. 57, 1012–1020. https://doi.org/10.1093/jme/tjaa040 (2020).CAS 
Article 
PubMed 

Google Scholar 
Wang, M. et al. Forensic entomology application in China: Four case reports. J. Forensic Leg. Med. 63, 40–47. https://doi.org/10.1016/j.jflm.2019.03.001 (2019).Article 
PubMed 

Google Scholar 
Moemenbellah-Fard, M. D., Keshavarzi, D., Fereidooni, M. & Soltani, A. First survey of forensically important insects from human corpses in Shiraz, Iran. J. Forens. Legal Med. 54, 62–68. https://doi.org/10.1016/j.jflm.2017.12.016 (2018).Article 

Google Scholar 
Bonacci, T., Vercillo, V. & Benecke, M. Dermestes frischii and D. undulatus (Coleoptera: Dermestidae) on a Human Corpse in Southern Italy: First Report*. Roman. J. Legal Med. 25, 180–184. https://doi.org/10.4323/rjlm.2017.180 (2017).Article 

Google Scholar 
Charabidze, D., Vincent, B., Pasquerault, T. & Hedouin, V. The biology and ecology of Necrodes littoralis, a species of forensic interest in Europe. Int. J. Legal Med. 130, 273–280. https://doi.org/10.1007/s00414-015-1253-8 (2016).Article 
PubMed 

Google Scholar 
Charabidze, D., Colard, T., Vincent, B., Pasquerault, T. & Hedouin, V. Involvement of larder beetles (Coleoptera: Dermestidae) on human cadavers: a review of 81 forensic cases. Int. J. Legal Med. 128, 1021–1030. https://doi.org/10.1007/s00414-013-0945-1 (2014).Article 
PubMed 

Google Scholar 
Arnaldos, M. I., Garca, M. D., Romera, E., Presa, J. J. & Luna, A. Estimation of postmortem interval in real cases based on experimentally obtained entomological evidence. Forens. Sci. Int. 149, 57–65. https://doi.org/10.1016/j.forsciint.2004.04.087 (2005).CAS 
Article 

Google Scholar 
Matuszewski, S. & Mądra-Bielewicz, A. Post-mortem interval estimation based on insect evidence in a quasi-indoor habitat. Sci. Justice 59, 109–115. https://doi.org/10.1016/j.scijus.2018.06.004 (2019).Article 
PubMed 

Google Scholar 
Midgley, J. M. & Villet, M. H. Development of Thanatophilus micans (Fabricius 1794) (Coleoptera: Silphidae) at constant temperatures. Int. J. Legal Med. 123, 103–108. https://doi.org/10.1007/s00414-008-0280-0 (2009).Article 
PubMed 

Google Scholar 
Velásquez, Y. & Viloria, A. L. Effects of temperature on the development of the Neotropical carrion beetle Oxelytrum discicolle (Brullé, 1840) (Coleoptera: Silphidae). Forens. Sci. Int. 185, 107–109. https://doi.org/10.1016/j.forsciint.2008.12.020 (2009).Article 

Google Scholar 
Ridgeway, J. A., Midgley, J. M., Collett, I. J. & Villet, M. H. Advantages of using developmental models of the carrion beetles Thanatophilus micans (Fabricius), and T. mutilatus (Castelneau) (Coleoptera: Silphidae) for estimating minimum post mortem intervals, verified with case data. Int. J. Legal Med. 128, 207–220. https://doi.org/10.1007/s00414-013-0865-0 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jakubec, P., Qubaiová, J., Novák, M. & Růžička, J. Developmental Biology of Forensically Important Beetle, Necrophila (Calosilpha) brunnicollis (Coleoptera: Silphidae). J. Med. Entomol. 58, 64–70. https://doi.org/10.1093/jme/tjaa170 (2020).Article 

Google Scholar 
Montoya-Molina, S. et al. Developmental Models of the Forensically Important Carrion Beetle, Thanatophilus sinuatus (Coleoptera: Silphidae). J. Med. Entomol. https://doi.org/10.1093/jme/tjaa255 (2020).Article 

Google Scholar 
Montoya-Molina, S. et al. Developmental models of the carrion beetle Thanatophilus rugosus (Linnaeus, 1758) (Coleoptera: Silphidae). Sci. Rep. https://doi.org/10.1038/s41598-021-98833-9 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Martín-Vega, D., Díaz-Aranda, L. M., Baz, A. & Cifrián, B. Effect of temperature on the survival and development of three forensically relevant Dermestes species (Coleoptera: Dermestidae). J. Med. Entomol. 54, 1140–1150 (2017).Article 

Google Scholar 
Wang, Y. et al. Development of Dermestes tessellatocollis Motschulsky under different constant temperatures and its implication in forensic entomology. Forens. Sci. Int. https://doi.org/10.1016/j.forsciint.2021.110723 (2021).Article 

Google Scholar 
Jakubec, P. Thermal summation model and instar determination of all developmental stages of necrophagous beetle, Sciodrepoides watsoni (Spence) (Coleoptera: Leiodidae: Cholevinae). PeerJ https://doi.org/10.7717/peerj.1944 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Development of the forensically important beetle Creophilus maxillosus (Coleoptera: Staphylinidae) at constant temperatures. J.Med. Entomol. https://doi.org/10.1093/jme/tjw193 (2016).Article 
PubMed 

Google Scholar 
Frątczak-Łagiewska, K., Grzywacz, A. & Matuszewski, S. Development and validation of forensically useful growth models for Central European population of Creophilus maxillosus L. (Coleoptera: Staphylinidae). Int. J. Legal Med. https://doi.org/10.1007/s00414-020-02275-3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hu, G. et al. Development of Necrobia rufipes (De Geer, 1775) (Coleoptera: Cleridae) under constant temperatures and its implication in forensic entomology. Forens. Sci. Int. 311, 110275. https://doi.org/10.1016/j.forsciint.2020.110275 (2020).CAS 
Article 

Google Scholar 
Wang, Y. et al. Temperature-dependent development of Omosita colon at constant temperature and its implication for PMI min estimation. J. Forens. Leg. Med. 72, 101946. https://doi.org/10.1016/j.jflm.2020.101946 (2020).Article 

Google Scholar 
Anton, E., Niederegger, S. & Beutel, R. G. Beetles and flies collected on pig carrion in an experimental setting in Thuringia and their forensic implications. Med. Vet. Entomol. 25, 353–364. https://doi.org/10.1111/j.1365-2915.2011.00975.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Bonacci, T. et al. First report of the presence of Necrodes littoralis (L) (Coleoptera: Silphidae) on a human corpse in Italy. J. Forens. Sci. 66, 2511–2514. https://doi.org/10.1111/1556-4029.14821 (2021).Article 

Google Scholar 
Matuszewski, S., Bajerlein, D., Konwerski, S. & Szpila, K. Insect succession and carrion decomposition in selected forests of Central Europe. Part 1: Pattern and rate of decomposition. Forens. Sci. Int. 194, 85–93. https://doi.org/10.1016/j.forsciint.2009.10.016 (2010).Article 

Google Scholar 
Matuszewski, S., Konwerski, S., Frątczak, K. & Szafałowicz, M. Effect of body mass and clothing on decomposition of pig carcasses. Int. J. Legal Med. 128, 1039–1048. https://doi.org/10.1007/s00414-014-0965-5 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Gruszka, J. et al. Patterns and mechanisms for larval aggregation in carrion beetle Necrodes littoralis (Coleoptera: Silphidae). Anim. Behav. 162, 1–10. https://doi.org/10.1016/j.anbehav.2020.01.011 (2020).Article 

Google Scholar 
Saloña-Bordas, M. I. & Perotti, M. A. First contribution of mites (Acari) to the forensic analysis of hanged corpses: A case study from Spain. Forens. Sci. Int. https://doi.org/10.1016/j.forsciint.2014.08.005 (2014).Article 

Google Scholar 
Dekeirsschieter, J., Frederickx, C., Verheggen, F., Boxho, P. & Haubruge, E. Forensic entomology investigations from doctor marcel leclercq (1924–2008): A review of cases from 1969 to 2005. J. Med. Entomol. 50, 935–954. https://doi.org/10.1603/me12097 (2013).CAS 
Article 
PubMed 

Google Scholar 
Lutz, L., Zehner, R., Verhoff, M. A., Bratzke, H. & Amendt, J. It is all about the insects: A retrospective on 20 years of forensic entomology highlights the importance of insects in legal investigations. Int. J. Legal Med. 135, 2637–2651. https://doi.org/10.1007/s00414-021-02628-6 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Matuszewski, S. & Szafałowicz, M. Temperature-dependent appearance of forensically useful beetles on carcasses. Forens. Sci. Int. 229, 92–99. https://doi.org/10.1016/j.forsciint.2013.03.034 (2013).Article 

Google Scholar 
Matuszewski, S. & Mądra-Bielewicz, A. Validation of temperature methods for the estimation of pre-appearance interval in carrion insects. Forens. Sci. Med. Pathol. 12, 50–57. https://doi.org/10.1007/s12024-015-9735-z (2016).Article 

Google Scholar 
Dekeirsschieter, J. Etude des interactions entre l’entomofaune et un cadavre: approches biologique, comportementale et chémo-écologique du coléoptère nécrophage, Thanatophilus sinuatus Fabricius (Col., Silphidae) PhD thesis, Gembloux Agro-Bio Tech – University of Liège, (2012).Matuszewski, S., Bajerlein, D., Konwerski, S. & Szpila, K. Insect succession and carrion decomposition in selected forests of Central Europe. Part 3: Succession of carrion fauna. Forens. Sci. Int. 207, 150–163. https://doi.org/10.1016/j.forsciint.2010.09.022 (2011).Article 

Google Scholar 
Matuszewski, S. et al. Effect of body mass and clothing on carrion entomofauna. Int. J. Legal Med. 130, 221–232. https://doi.org/10.1007/s00414-015-1145-y (2016).Article 
PubMed 

Google Scholar 
Gruszka, J. & Matuszewski, S. Insect rearing protocols in forensic entomology: benefits from collective rearing of larvae in a carrion beetle Necrodes littoralis L. (Silphidae). PLoS ONE 16, e0260680. https://doi.org/10.1371/journal.pone.0260680 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Midgley, J. M. & Villet, M. H. Effect of the killing method on post-mortem change in length of larvae of Thanatophilus micans (Fabricius 1794) (Coleoptera: Silphidae) stored in 70% ethanol. Int. J. Legal Med. 123, 103–108. https://doi.org/10.1007/s00414-008-0260-4 (2009).Article 
PubMed 

Google Scholar 
Tauchman, S. J., Lorch, J. M., Orth, A. P. & Goodman, W. G. Effects of stress on the hemolymph juvenile hormone binding protein titers of Manduca sexta. Insect Biochem. Mol. Biol. 37, 847–854. https://doi.org/10.1016/j.ibmb.2007.05.015 (2007).CAS 
Article 
PubMed 

Google Scholar 
Frątczak-Łagiewska, K. & Matuszewski, S. The quality of developmental reference data in forensic entomology: Detrimental effects of multiple, in vivo measurements in Creophilus maxillosus L. (Coleoptera: Staphylinidae). Forens. Sci. Int. 298, 316–322. https://doi.org/10.1016/j.forsciint.2019.02.059 (2019).Article 

Google Scholar 
Frątczak, K. & Matuszewski, S. Instar determination in forensically useful beetles Necrodes littoralis (Silphidae) and Creophilus maxillosus (Staphylinidae). Forens. Sci. Int. 241, 20–26. https://doi.org/10.1016/j.forsciint.2014.04.026 (2014).Article 

Google Scholar 
Newton, A. F. J. In Immature insects Vol. 2 (ed. Stehr, F. W.) 339–341 (Kendall/Hunt, 1991).
Google Scholar 
Villet, M. H. An inexpensive geometrical micrometer for measuring small, live insects quickly without harming them. Entomol. Exp. Appl. 122, 279–280. https://doi.org/10.1111/j.1570-7458.2006.00520.x (2007).Article 

Google Scholar  More

Characterization of EFM adsorbentBET analysisThe surface properties of newly prepared adsorbent and its components were investigated through nitrogen adsorption–desorption isotherms. Table 1 presents the results of the adsorbent the textural properties assessment.Table 1 EFM adsorbent and raw materials used (magnetite, eggshell and fly ash)—specific surface area determinate by Brunauer–Emmett–Teller theory (BET).Full size tableFrom the quantitative data reported in Table 1 it can be observed that eggshell BET/N2 surface area is 0.67 m2/g, value similar to that in the literature15,31,40.According to Table 1 the BET/N2 specific surface area for fly ash, is 4.961 m2/g. Apparently the value obtained in this study seems higher than in the data reported in the literature (0.414 m2/g). However, it should be noted that both the type of ash used and the experimental conditions of this study differ from the data reported in the literature16,22.As expected, very different values were obtained for the specific surface of M1 (25.196 m2/g) and respectively 26.866 m2/g for M2, which correspond to the adsorbent prepared in the two molar ratios studied. This difference can be justified by the variation of the ratio between eggshell and fly ash in M1 and M2, respectively.Physical properties of adsorbent (pore size, pore volume) were investigated using the low-temperature (77 K) nitrogen adsorption–desorption isotherms. As presented in Fig. 1 the isotherms of EFM fitted in a type II isotherm with a H3 hysteresis loop, indicating a macroporous structure for the both adsorbent molar ratio (M1 and M2)38.Figure 1The nitrogen adsorption–desorption isotherms for EFM adsorbent.Full size imageXRD studiesThe mineralogical compositions of the raw materials as well as of the adsorbent were studied through XRD analysis. The size of the crystalline domains was evaluated by means of the Debye–Scherrer formula (Eq. 14)41,$$D = frac{0.89lambda }{{beta cos left( theta right)}}$$
(14)
where (lambda) is the X-ray wavelength of Cu K-α ((lambda = 0.15406;{text{nm}})), (beta) is the full width at half maximum in radians and (theta) is the Bragg angle.From the most prominent peak, one gets D = 21.6 nm.The XRD spectrum of magnetite sample (Fig. 2) shows the diffraction peaks of the well crystallized spinel phase magnetite Fe3O4 (COD 9005837) with average crystallite size of 21.6 nm. In the XRD spectrum of eggshell sample (Fig. 3) are recorded the diffraction peaks of the single phase well crystallized calcite CaCO3 (COD 9000965) with mean crystallite size of 125.8 nm15,40.Figure 2XRD spectra of magnetite.Full size imageFigure 3XRD spectra of eggshell.Full size imageThe XRD spectrum (Fig. 4) shows that the fly ash sample has a complex composition. Four crystalline phases have been identified whose characteristics are presented in Table 2.Figure 4XRD spectra of fly ash sample.Full size imageTable 2 The phase compositions of fly ash sample.Full size tableFrom the data presented in the Table 2 indicates that fly ash sample used in the preparation of the adsorbent is not non-hazardous solid waste22.In the XRD spectra of M1 (Fig. 5a) are visible the diffraction peaks characteristic of the crystalline phases existing in the eggshell (calcite CaCO3), the fly ash (but only those of the phase with more intense peaks, quartz SiO2), and magnetite (Fe3O4). Because the eggshell is mixed in a larger proportion than the other two components, the CaCO3 peaks are the most intense.Figure 5(a) XRD spectra of M1. (b) XRD spectra of M2. (c) XRD spectra of magnetite, eggshell, fly ash, M1 and M2.Full size imageAnalyzing the XRD spectrum obtained for M2 (Fig. 5b), the crystalline phases that can be identified are: magnetite Fe3O4, calcite CaCO3, quartz SiO2, and corundum Al2O3. The most intense are the magnetite peaks. In this sample, because the ash is mixed in a larger proportion, the SiO2 peaks are more intense and another crystalline phase present in it (Al2O3) becomes visible in the spectrum.Figure 5c shows the overlapping XRD spectra of M1, M2 and raw materials (magnetite, fly ash and eggshell). In both M1 and M2, the phase peaks of individual components can be observed.However, due to the different materials crystallinity, only the most intense peaks appear by overlap. Also, the phase diffraction lines (Figs. 2, 3, 4, 5) are no longer visible.SEM micrographsThe surface morphology and particle size of raw materials and adsorbent were investigated through SEM technique. The micrographs are presented in Figs. 6, 7, 9, 10, 11, 12, 13 and 14.Figure 6Two-dimensional image of the magnetite particle obtained by the SEM technique.Full size imageFigure 7Two-dimensional image of the ash fly particle obtained by the SEM technique.Full size imageThe SEM image of magnetite (Fig. 6) suggest that particles are of nanometric dimensions (with the average size about 21 nm), uniform and with a cubic structure25,42,43.Figure 7 shows that the fly ash particles are of porous spherical shapes with different sizes as well as porous irregularly or angularly shaped particles16,44. As can be seen in (Fig. 7) the surface of the ash sphere is irregular and has streaks due to the mechanical and thermal stress.Figure 8 presents the elemental composition of the ash fly determined through EDX analysis.Figure 8EDS spectra of fly ash sample.Full size imageAccording to the data from EDX (Fig. 8), there are only seven elements which are predominant in sample: aluminum, iron, magnesium, calcium, silica, oxygen and sulphur16,45.SEM micrograph of eggshell sample (Fig. 9a,b) indicates a different size (about 100 nm) irregular crystal on multihole surface structure36,40.Figure 9(a) Two-dimensional image of the eggshell particle obtained by the SEM technique. (b) Two-dimensional image of the eggshell particle obtained by the SEM technique.Full size imageThe morphology of M1 (Fig. 10a,b) indicates the presence of agglomerations of particles of different sizes in the nano field, spherical shape, cubic shaped and irregular crystal structure sizes, suggesting a good connectivity between them.Figure 10(a) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 3 µm). (b) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 5 µm).Full size imageAlso, the (Fig. 10b) indicates that the cubic-shaped particles characteristic of magnetite (Fig. 6) loaded into the pores of the ash and eggshell particles.The Fig. 11 shows the live map for M1 and the distribution of the identified elements.Figure 11SEM M1- Live map.Full size imageThe SEM micrograph of M2 (Fig. 12a,b) the same agglomerations of particles of different nano-sizes, spherical shape, cubic shaped and irregular crystal structure sizes are observed as in the case of SEM graph for M1.Figure 12(a) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 3 µm). (b) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 5 µm).Full size imageThe Fig. 13 shows the live map for M2 and the distribution of the identified elements.Figure 13M2 SEM—Live map.Full size imageThe comparative analysis of the Fig. 13 showing Live map for M2 and M1 (Fig. 11) highlights the presence of differences regarding the proportion of identification elements in the two samples, due to the different molar ratio between eggshell and ash.In the Fig. 14a can be observed a larger number of spherical particles characteristic of district heating ash, as a result of the change in the ratio between the two wastes (eggshell:ash = 3:1), loaded with magnetite particles. At the same time, in SEM the micrograph for M1 (Fig. 14b) is much more obvious the multihole structure of the eggshell.Figure 14(a) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 30 µm). (b) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 50 µm).Full size imageThe analysis of the SEM micrograph (Fig. 14a) of the M1 sample (in which the eggshell component is predominant) indicates that the multi porous structure of the eggshell is loaded with both the cubic-shaped particles of the magnetite and the spherical ones belonging to the ash sample. This aspect is much more visible in the case of SEM micrograph of the M2 sample (Fig. 14b), considering the fact that in this ash is found in the majority proportion (magnetite:eggshell:ash = 1:1:3).This result suggests that through the procedure of mechanical alloying in the mill with high energy balls were achieved simultaneously:

1.

reducing the particle size of magnetite, ash and eggshell;

2.

individual functionalization of each waste (eggshell, ash) with magnetite particles;

3.

a new, nanosized material in which the double functionalization of the eggshell with ash particles functionalized with magnetite was achieved simultaneously with the loading of the pores of the eggshell surface with the magnetite particles.

By modifying the structure of the two wastes from the composition of the newly obtained material (decreasing the number of pores) leads to increased surface areas, confirmed by the results of the BET analysis (Table 1) and implicit sorption sites suggesting an improvement of adsorbent properties.FT-IR studiesFigure 15 shows the IR spectra for EFM adsorbent raw materials (magnetite, fly ash and eggshell).Figure 15IR spectra for adsorbent raw material samples (magnetite, eggshell and fly ash).Full size imageFT-IR spectra for EFM engineered adsorbent are presented in Fig. 16.Figure 16FT-IR spectra of adsorbent (both molar ratios: M1 and M2) and its raw materials.Full size imageThe FT-IR spectra for adsorbent (at the both molar ration: M1 and M2) presents the vibrational bands characteristic of magnetite at 589 and at 432 cm−1associated with Fe–O stretch vibration46. The peaks assigned to the fly ash component: at 588 cm-1 Ca O group, at about 670 cm−1 attributed to the Al–O–Al bending vibration, at 1100 cm−1 is associated with X–O (X = Al, Si) and asymmetric stretching vibrations and band at 830 cm−1 specific to AlO4 coordination16,22,47,48,49. In addition, in the adsorbent FTIR spectra (Fig. 16) were found the characteristic IR bands eggshell component (Fig. 15). Thus, peak at 712 cm−1 (correspond to CaO stretching vibration), peaks at 875 and 1423 are attributed to C–O stretching vibration. The bands at 1798 and 2515 cm−1 are associated with O–C–O and peaks at 2875 respectively at 2981 cm−1 are due to CH– symmetric and asymmetric stretching vibration15,31,50. The position of O–H peak at 3740 cm−1 indicates the presence of moisture and water molecules15,22. As expected, the intensity of the peaks differs in M1 and M2, due to the different molar ratio between two of the raw materials that are part of the adsorbent component (fly ash and eggshell). These results are in close agreement with the literature and theoretical values confirms the presence of magnetite, fly ash and eggshell in adsorbent (at both molar ration: M1 and M2).Thermogravimetric analysisFigure 17a presents the thermal analysis results for fly ash sample.Figure 17(a) Thermogravimetric analysis of the fly ash sample in the range of 30–500 °C with a heating rate of 10 °C/min in open aluminum crucibles in the air atmosphere. (b) Thermogravimetric analysis of the eggshell with a heating rate of 10 °C/min up to 500 °C.Full size imageThe thermal analysis performed in the interval 30–500 °C highlighted two stages of decomposition. The first stage takes place in the range of 30–49 °C with a loss of 0.22% of the sample mass. This decomposition can be attributed to water loss. This process is visible in the DTG curve with a maximum at 45.5 °C, but also on the Heat Flow curve with a maximum at the same temperature and characterized by an exothermic process with ΔH = − 12.44 J/g. The second process presents a continuous thermal decomposition with a maximum observable on the DTG and HF curve at 480 °C, characterized by an exothermic effect. The decomposition does not end in the studied interval. The total weight loss is 2% of the sample mass16.The thermal analysis, in the range of 30–500 °C, performed for the eggshell sample (Fig. 17b), revealed a complex thermal decomposition. This decomposition has several stages that are difficult to separate. It is known that, in addition to inorganic calcium carbonate compounds, in the eggshell are present a multitude of organic components such as: proteins as main constituents, small amounts of carbohydrates and lipids51,52.At the same time, uronic acid is also present, which plays an important role in the resistance of the shell, such as sialic acid in very low concentration and two glycosaminoglycans, including hyaluronic acid, as well as a copolymer consisting of chondroitin sulfate-dermatan sulfate. There is also limited information on variations in nitrogen concentrations and the amino acid composition of the eggshell. A better understanding of the chemicals present in the composition of the eggshell is very important for its application in various fields, including for the purpose of absorbent material.The analysis of the TG curve highlights three hardly separable decomposition stages, the last of which is characterized by a complex multistage decomposition process. It is observed that in the interval 30–100 °C which can be attributed to dehydration, followed by the loss of crystallization water in the range 100–266 °C (4.8% of the sample mass) and then the complex decomposition of organic components in different stages depending on their stability until at 500 °C15.The last decomposition stage results in the loss of 80% of the total mass of the sample. Over 500 °C the decomposition of the inorganic component takes place, namely Ca carbonate. It can be seen that in the analyzed sample the weight of inorganic component is relatively small, namely 12.2% of the sample mass. During the decomposition stage from the interval 266–500 °C several maxima are observed on the DTG curve, which led us to conclude that simultaneous decompositions of several organic compounds take place, observing maxima at 345, 363, 374, 403, 408, 412, 430, 466 and 470 °C. The same main decomposition steps are faintly visible and the HF curve with processes in most cases exothermic. At temperatures higher than 266 °C and on this curve are visible several processes, most of which are exothermic, which can be attributed to the oxidation of organic compounds and their decomposition. The residue left after the thermogravimetric study (performed up to 500 °C) is calcium carbonate15,53.Subsequently, the mixture of the two wastes was analyzed in the two molar ratios:eggshell:fly ash = 3:1 and eggshell:fly ash = 1:3, respectively.The profile of thermogravimetric analysis for the binary mixture eggshell:ash fly in a 1:3 mass ratio performed in the range of 30–500 °C is depicted in Fig. 18.Figure 18Thermogravimetric analysis for eggshell: fly ash binary mixture in a 1: 3 mass ratios obtained in the range of 30–500 °C.Full size imageThe thermal analysis performed in the case of the binary mixture of eggshell and ash, in a 1:3 molar ratios, highlights the decomposition stages of the two components. Namely, the stage of water loss within the ash is visible, to which is added the loss of moisture observed in the case of the eggshell. On the HF flow is visible the exothermic process with a maximum of 45 °C and a ΔH = − 17.023 J/g which represents a sum of the two processes mentioned above. Two other processes are visible on the TG curve, one in the temperature range, 51–213 °C, with a mass loss of 0.27% of the sample mass. Then followed by a loss of 1.74% in the temperature range 213–405 °C. The thermal decomposition continues even above this temperature and the decomposition process was not completed in the studied temperature range.A thermogravimetric study was performed for the same binary mixture but in the eggshell:fly ash = 3:1 molar ratio. The results are presented in the next figure (Fig. 19).Figure 19Thermogravimetric analysis for eggshell: fly ash binary mixture in a 3:1 mass ratio obtained in the range of 30–500 °C.Full size imageIn the case of the thermal analysis of the binary mixture of eggshell and ash in a 3:1 molar ratio, the decomposition stages and the thermal behaviour of the individual components in correlation with the mixing ratio are very clearly visible.Magnetic measurementsThe magnetic properties of the samples: magnetite, M1 and M2 were investigated with an induction hysteresis-graph at low frequency driving field (50 Hz)54. And the hysteresis loops are presented in Figs. 20, 21 and 22. It was found that the samples reveal ferromagnetic behaviour and from the measured hysteresis loops the saturation magnetization ((sigma_{S})), the coercive field (Hc) and the remnant magnetization ((sigma_{R})) were determined. The results are presented in Table 3.Figure 20The hysteresis loop of sample M2.Full size imageFigure 21The hysteresis loop of sample M1.Full size imageFigure 22The hysteresis loop of magnetite.Full size imageTable 3 The values of coercive field (Hc) and remnant magnetization ((sigma_{R})) of M1, M2 and magnetite sample.Full size tableAs expected, the largest value of the saturation magnetization is that of the sample consisting entirely of magnetite. By diminishing the content of ashes from the thermal power station (from three parts in M2 sample to one part in sample M1) a small increase of the saturation magnetization was observed, from 14.06 to 15.12 emu/g (see Table 3). This can be explained by the presence of diamagnetic compounds within the ashes of the thermal station, the decrease of which led to the increase in the saturation magnetization of the sample M1, as compared to the sample M2. All three samples have small values of the remnant ratio, (sigma_{R} /sigma_{S}), which is an indication of the ease with which the magnetization reorients to the nearest easy axis magnetization direction after the remove of magnetic field.The dependencies on frequency of the complex magnetic permeability of the samples, (mu left( f right) = mu^{prime}left( f right) – imu^{primeprime}left( f right)), measured at room temperature, over the frequency range 3 kHz to 2 MHz are presented in Fig. 23. The measurements were performed using an Agilent LCR-meter (E-4980A type) in conjunction with a coil containing a vial in which the samples were placed. Details on the method of measurements of the real, (mu^{prime}left( f right)) and imaginary, (mu^{primeprime}left( f right)) components of the complex magnetic permeability are given in a previous study55.Figure 23Frequency dependence of the magnetite, M1, M2 of the complex magnetic permeability.Full size imageIn the frequency range in which the measurements were made, samples M1 and magnetite exhibits visible relaxation peaks of (mu^{primeprime}left( f right)), at the frequency of 30 kHz. Even if the M1 sample and the M2 sample have the same amount of magnetite, due to the diamagnetic compounds in the fly ash, the relaxation peak of the M1 sample is very attenuated (little visible, almost missing).Given the small size of the magnetite particles in the samples (on the order of tens of nanometers), they do not have a multi-domain magnetic structure. Thus, the only magnetic relaxation process, measurable in the radio frequency field, is the Neel relaxation process. The Néel relaxation time, (tau_{N}) is given by Eq. (15)56$$tau_{N} = tau_{0} exp left( {frac{Kv}{{k_{B} T}}} right)$$
(15)
where K is the effective anisotropy constant of the material from which the magnetic particles are made of, v is the magnetic volume of particles, kB is the Boltzmann’s constant, T is the temperature and (tau_{0}) is a constant in order of 10–9 s56.Assuming that the frequency dependence of the complex magnetic permeability,(mu left( f right) = mu^{prime}left( f right) – imu^{primeprime}left( f right)) obeys the Debye dispersion relations, then the frequency corresponding to the maximum of (mu^{primeprime}left( f right)) is correlated with the relaxation time by the relation, (2pi {kern 1pt} ftau = 1). For measurements at room temperature, with f = 30 kHz, the magneto-crystalline anisotropy constant of magnetite, K = 1.1 × 104 J m−3 and (tau = tau_{N}), under assumption of spherical shape of particles, one gets a magnetic diameter of the magnetite particles, d = 18.2 nm. This value compares favourably with the values measured by SEM and X-ray diffraction.Adsorption propertiesEffect of adsorbent dosageFigure 24a and b show the relationships between different material dosage and the cadmium removal efficiency and respectively adsorption capacity.Figure 24(a) The relationship between different material dosage and the cadmium removal efficiency. (b) The relationship between different material dosage and the cadmium adsorption capacity.Full size imageAccording to the Fig. 24a and b, cadmium removal efficiency and adsorption capacity depending on the amount of adsorbent used shows an upward trend (for quantities between 0.05 and 0.25 g), reaches a maximum of 0.25 g adsorbent (99.9% and 75.48 mg/g for M1 and respectively 99.8% and 75.46 mg/g for M2), after which both removal efficiency and heavy metal adsorption capacity gradually decrease with the increase in the adsorbent dose (0.3 g). These results suggest that the increased amount of adsorbent provides a supplement to the free active sites, but after reaching equilibrium, it leads to the formation of agglomerations and consequently to a decrease in the number of available active sites22,23.Effect of initial concentration on cadmium removal efficiencyFigure 25a shows the influence of heavy metal initial concentration on cadmium removal efficiency. It can be seen that removal efficiency shows an upward trend simultaneously with the increase of the initial cadmium concentration in the range 0–33.5 mg/L. The maximum removal efficiency (99,9% for M1 and respectively 99.8% for M2) was reached at a concentration of 28.5 mg/L, after which the decrease in cadmium removal efficiency begins.Figure 25(a) Relationship between initial concentration and removal efficiency (%). (b) Relationship between initial concentration and adsorption capacity (mg/g).Full size imageAccording to the Fig. 25b, in the same cadmium concentration range (0–33.5 mg/L), the adsorption capacity shows a similar trend, reaching a maximum at 28.5 mg/L (75.48 mg/g for M1 and respectively 74.46 mg/g for M2), and after which gradually decreases.These results indicate the initially an increase in the concentration of heavy metal causes an increase in the amount of Cd2 + ions and implicitly in the possibility of interaction with the active sites of the EFM adsorbent. And after reaching equilibrium, the amount of available metal ions is disproportionate compared to the decreasing number of free sites in the adsorbent, causing a decrease in the adsorption efficiency of the new engineered magnetic adsorbent used in the study15,57.Effect of pHThe wastewater pH is one of the top parameters with highly influence on the adsorption process efficiency having impact direct on the adsorption rate and adsorption capacity as fluctuations in the pH value of the solute induce changes in the degree of ionization of the adsorptive species and the of adsorbent surface23,57.In this study was investigated the pH influence toward the cadmium removal using the prepared material in the pH range of 3.0–7.0, to avoid the precipitation of Cd(OH)2 at pH values  > 715.According to the experimental results presented in Fig. 26a and b, the increase in pH value (between pH 3 and pH 6) leads to a significant increase in adsorption efficiency and adsorption capacity. The adsorption efficiency and adsorption capacity reach a maximum value (99.9% and 75.48 mg/g for M1 and respectively 99.8% and 75.46 mg/g for M2) at pH 6.5, after which it decreases slightly. This could be explained as follows: at low pH values is a competition between protons and Cd2+ to occupy the active sites of the adsorbent, even if they are available in large numbers. An increase in pH simultaneously leads to a decrease in the competition of protons and electrostatic repulsion forces, which induces an increase in cadmium removal efficiency. At pH  > 6.5 the removal efficiency begins to decrease as increased hydroxyl ion generation occurs to the detriment of Cd2+ ions. Therefore, the optimal pH 6.5 was chosen for subsequent experiments15,16,25,58,59.Figure 26(a) Effect of pH variation on cadmium removal efficiency. (b) Effect of pH variation on adsorption capacity.Full size imageEffect of contact timeFigure 27a showed the relationship diagram between the contact time and cadmium adsorption capacity.Figure 27(a) Effect pf contact time on cadmium adsorption capacity (mg/g). (b) Effect of contact time on cadmium removal efficiency (%).Full size imageIt can be observed from the Fig. 27a and b that the increase of the contact time determines an increase of the adsorption capacity and of the removal efficiency respectively. Both reached the maxima at 120 min The maximum of cadmium adsorption capacity was 75.48 mg/g for M1 respectively 75.46 mg/g for M2, and the maximum of removal efficiency was 99.9% for M1 and respectively 99.8% for M232.This performance can be attributed to the higher surface, the microporous structure that results from the experimental conditions of this study15.The analysis of this diagram indicates that the adsorption of cadmium takes place in three distinct phases:

1.

0–90 min, characterized by adsorption is fast due to the large number of active sites available on the surface of the adsorbent.

2.

the second phase, 90–120 min, the adsorption is slower which can be attributed to the diminution of the free adsorbent active sites;

3.

phase three:120–330 min, corresponds to the time interval in which there are no more free sites on the surface of the adsorbent and the adsorption has reached equilibrium.

According to the experimental results, optimum time in which the adsorption reaches an equilibrium is 120 min and was selected for the next investigations16,60.Effect of temperature on absorption processTemperature represents a key parameter in adsorption process. Therefore, the influence of temperature on cadmium adsorption on prepared material in the two different molar ratios (both M1 and M2) was investigated in the range of 5–50 °C (278.15–323.15 K). The cadmium removal efficiency and adsorption capacity increase first and then a very slight decrease occurs with the increase of temperature (Fig. 28a,b).Figure 28(a) Relationship between temperature and heavy metal removal efficiency. (b) Relationship between temperature and heavy metal adsorption capacity.Full size imageAt 25 °C the maximum removal efficiency is reached (99.89% for M1 and respectively 99.64% for M2). At the same temperature the heavy metal adsorption capacity is maximum of 75.48 mg/g for M1 and 75.43 mg/g for M2. This can be explained by the fact that within this temperature range indicated the favorability for the heavy metal mobilization and thus contact between cadmium and active sites from adsorbent. The relationship between temperature and cadmium adsorption effect indicates that in the range of 5–25 °C the cadmium absorption on prepared material is an endothermic process (physical adsorption). At 25–50 °C the adsorption process becomes exothermic and chemisorption occurs. However, the removal efficiency remains very high even at a temperature of 50 °C (98.78% for M1 and respectively 97.74% for M2).Comparison of cadmium removal efficiency for with other adsorbentsA comparison of cadmium removal efficiency of the newly engineered adsorbent (EFM) with other adsorbents reported in literature is presented in the next table (Table 4).Table 4 Comparison of the removal efficiency of newly nanosized magnetic adsorbent (at both molar ratios: M1 and M2) with the one reported in the literature (selected study) for some adsorbent materials that use the similar waste.Full size tableThe performance of the nanosized adsorbent EFM (at both molar ratios) can be attributed to the higher surface area, the microporous structure that results from the experimental conditions of this study15.Comparison of cadmium removal efficiency with the raw materialsThe removal efficiency of EFM adsorbent compared to that of its raw materials (fly ash, eggshell and magnetite) was investigated as the effect of contact time on the adsorption process. The relationship between the removal efficiency and contact time is presented in Fig. 29. It can be observed that there is an increase in the efficiency of removing heavy metal for all five investigated adsorbents (eggshell, ash, magnetite, M1 and M2) with a maximum of two hours of contact. According to the experimental results presented in Fig. 29, the best cadmium removal efficiency was obtained for M1 (99.89%) and 99.80% for M2, followed by eggshell (95.23%), fly ash (76.31%) and magnetite (71.44%).Figure 29Relationship between adsorbents removal efficiency and contact time.Full size imageThen, a very slight decrease occurs with the increase in contact time. These results confirm the cadmium removal efficiency dependence on the specific surface area and pores (number of available active sites) of the adsorbent used (Table 1).The maximum cadmium removal efficiencies determined experimentally in this study for the raw materials (eggshell, ash and magnetite) corroborated with the data reported in the literature15,25,33,61.Adsorption IsothermsThe absorption mechanism evaluation can be performed through an isotherm adsorbent study. The equilibrium isotherm plays a key role in the investigation of the adsorption behaviour.Due to their simplicity and convenient accuracy, Langmuir and Freundlich’s models are the most commonly used to adjust an adsorption process.Langmuir models provides information on the interaction between the solute and the monolayer surface of the adsorbent. The main working hypotheses of this model are: (1) adsorbent surface consists of uniform, identical sites distributed on the surface of adsorbent (2) adsorbent process takes place only on the surface of the adsorbent and (3) no contact between adsorbed molecules on the surface of the adsorbent.Freundlich model is appropriate to monolayer and multilayer adsorption processes on multiphase surfaces. This isotherm gives an expression on adsorbent surface heterogeneity and the variation in the heat of adsorption process. The applicability of the Freundlich model is limited by adsorption processes that take place at high pressures, but this restriction does not apply to the Langmuir model.These two adsorption isotherm models were applied in order to identify and implement an optimal model that adequately reproduces the experimental results obtained in this study were employed to study the mechanism of cadmium adsorption on the prepared material60,62.The parameters calculated as well the coefficient of correlation (R2) for both Langmuir and Freundlich models are presented in the Table 5.Table 5 Parameters of adsorption Langmuir and Freundlich isotherms for cadmium adsorption.Full size tableAs shown in the Table 5 both models fitted well for the experimental results. The maximum capacities calculated are close to the values for each component of the prepared adsorbent material (magnetite, eggshell and fly ash) and maximum capacities obtained at equilibrium (Table 5)22,23,57,63,64. However, according to the values of the correlation coefficient, R2, the behaviour of cadmium absorption suits better with Freundlich model (the higher correlation coefficient) suggests that the adsorption for cadmium ions was a multi-molecular layer adsorption process. The values for the Langmuir constant, KL, or equilibrium parameter for absorbent (the both molar ratios, M1 respectively M2) falls within the range 0  1 indicated a favourable adsorption process. Moreover, Freundlich dimensionless constant n values having greater than 1 suggests a favourable adsorption process that occurs on the investigated EFM adsorbent heterogeneous surfaces62,65,66.Adsorption kinetic studyThe kinetic models provide information on the efficiency of the adsorbent, the dynamic parameters (rate, time, etc.) of the adsorption process. The cadmium adsorption process on the prepared material was investigated employing linear and non-linear of pseudo-first-order (Eq. 6) pseudo-second-order (Eq. 7) and intraparticle diffusion models (Eq. 8) to fit the obtained experimental adsorption data. The Fig. 30a–c depicted the plots of the first-order, second-order and intraparticle diffusion models for the cadmium adsorption on nano-engineered adsorbent (EFM).Figure 30(a) Pseudo first-order model fitting diagram. (b) Pseudo second-order model fitting diagram. (c) Intraparticle diffusion model fitting diagram.Full size imageThe kinetic parameters were obtained from the slope and intercept of the fitting plots of adsorption reaction models: pseudo first-order model (the correlation between log(qe-qt) against time), respectively the pseudo second-order model (correlation between t/qt function on time) and adsorption diffusion model: intraparticle diffusion model (the plot as function of ({t}^{1/2})).The results of fitting parameters on these kinetic models are presented in Table 6.Table 6 Kinetic parameters for cadmium adsorption on nano-engineered adsorbent (EFM) at both molar rations (M1 and M2).Full size tableAccording to the data obtained in Table 6, the coefficients of adsorption reaction models have both values close to one, slightly differing only at the fourth decimal. It could suggest that cadmium removal is achieved through a physical and chemical adsorption process. It must be noted that were obtained higher values for the correlation coefficient (R2) and the calculated adsorption capacity value is very similar to those determined experimentally in the case of the pseudo-second-order kinetics model. Therefore, pseudo-second-order kinetic model was more suitable to describe the adsorption process. This indicating a chemical adsorption is assumed as the rate-limiting step for the cadmium adsorption on prepared material, involving an electron exchange between adsorbent and adsorbate (cadmium occurs with formation of strong chemical bonding)23.According to the Fig. 30c the allure of intraparticle diffusion model includes three regions. The first region corresponds to a boundary diffusion (cadmium diffusion on the prepared material exterior surface). The second region is related to heavy metal intraparticle diffusion into the pores of nano-engineered adsorbent (EFM). The third region represent the cadmium adoption into the interior site of the EFM. Since, the slope of the three regions gradually decreases (Ki1  > Ki2  > Ki3) is assumed that boundary diffusion is the limiting region, followed by intraparticle diffusion15,67. The results indicate that beginning of the adsorption process cadmium ions can be quickly bound on the prepared material exterior surface. In the intraparticle diffusion process (second region) there is a gradual decrease in adsorption at the sites on the adsorbent surface (adsorption capacity reaches the maximum value). Then, cadmium adsorption takes place on the available sites inside the adsorbent, generating significant mass transfer resistance and reaching the adsorption equilibrium and the adsorption rate gradually decreases68,69,70.The adsorption models used provide information on both the performance of the prepared material and a perspective of the adsorption mechanism.Thermodynamical studyThe Gibbs free energy in adsorption process was calculated according to the corresponding equation (Eq. 10). The thermodynamic parameters ΔS and ΔH were obtained from the slope and intercept of the adsorption thermodynamic curve. The obtained results are presented in next table (Table 7).Table 7 Thermodynamic parameters for the cadmium adsorption on adsorbent.Full size tableFrom these data obtained (Table 7) can be found that the free energy variation value of the adsorption process has negative values (ΔG  α lower than 0.05 (α = 0.05), which suggests that between the M1 and M2 there are not statistically significant differences. More