Philippa Kaur

Zaiss, M. M. & Harris, N. L. Interactions between the intestinal microbiome and helminth parasites. Parasite Immunol. 38, 5–11 (2016).CAS 
PubMed 
Article 

Google Scholar 
Jhan, K. Y. et al. Angiostrongylus cantonensis causes cognitive impairments in heavily infected BALB/c and C57BL/6 mice. Parasites Vectors. 13, 405. https://doi.org/10.1186/s13071-020-04230-y (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boillat, M. et al. Neuroinflammation-associated aspecific manipulation of mouse predator fear by Toxoplasma gondii. Cell Rep. 30, 320–334 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brombacher, T. M. et al. Nippostrongylus brasiliensis infection leads to impaired reference memory and myeloid cell interference. Sci. Rep. 8, 2958. https://doi.org/10.1038/s41598-018-20770-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kavaliers, M. & Colwell, D. D. Reduced spatial learning in mice infected with the nematode Heligmosomoides polygyrus. Parasitology 110(Pt 5), 591–597 (1995).PubMed 
Article 

Google Scholar 
Pan, S. C. et al. Cognitive and microbiome impacts of experimental Ancylostoma ceylanicum hookworm infections in hamsters. Sci. Rep. 9, 7868. https://doi.org/10.1038/s41598-019-44301-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Blecharz-Klin, K. et al. Infection with intestinal helminth (Hymenolepis diminuta) impacts exploratory behavior and cognitive processes in rats by changing the central level of neurotransmitters. PLoS Pathog. 18, e1010330–e1010330. https://doi.org/10.1371/journal.ppat.1010330 (2022).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Braithwaite, V. et al. Spatial and discrimination learning in rodents infected with the nematode Strongyloides ratti. Parasitology 117(Pt 2), 145–154 (1998).PubMed 
Article 

Google Scholar 
Sharma, S., Rakoczy, S. & Brown-Borg, H. Assessment of spatial memory in mice. Life Sci. 87, 521–536 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vorhees, C. V. & Williams, M. T. Assessing spatial learning and memory in rodents. ILAR J. 55, 310–332 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pelletier, F., Page, K. A., Ostiguy, T. & Festa-Bianchet, M. Fecal counts of lungworm larvae and reproductive effort in bighorn sheep. Ovis canadensis. Oikos. 110, 473–480 (2005).Article 

Google Scholar 
Odiere, M. R., Koski, K. G., Weiler, H. A. & Scott, M. E. Concurrent nematode infection and pregnancy induce physiological responses that impair linear growth in the murine foetus. Parasitology 137, 991–1002 (2010).CAS 
PubMed 
Article 

Google Scholar 
Fitzgerald, E., Hor, K. & Drake, A. J. Maternal influences on fetal brain development: The role of nutrition, infection and stress, and the potential for intergenerational consequences. Early Hum. Dev. 150, 105190. https://doi.org/10.1016/j.earlhumdev.2020.105190 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boksa, P. Effects of prenatal infection on brain development and behavior: a review of findings from animal models. Brain Behav. Immun. 24, 881–897 (2010).PubMed 
Article 

Google Scholar 
Akitake, Y. et al. Moderate maternal food restriction in mice impairs physical growth, behavior, and neurodevelopment of offspring. Nutr. Res. 35, 76–87 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hammelrath, L. et al. Morphological maturation of the mouse brain: An in vivo MRI and histology investigation. Neuroimage 125, 144–152 (2016).PubMed 
Article 

Google Scholar 
Wills, T., Muessig, L. & Cacucci, F. The development of spatial behaviour and the hippocampal neural representation of space. Philos. Trans. R. Soc. B: Biol. Sci. 369, 20130409. https://doi.org/10.1098/rstb.2013.0409 (2014).Article 

Google Scholar 
Travaglia, A., Steinmetz, A. B., Miranda, J. M. & Alberini, C. M. Mechanisms of critical period in the hippocampus underlie object location learning and memory in infant rats. Learn Mem. 25, 176–182 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McHail, D. G., Valibeigi, N. & Dumas, T. C. A Barnes maze for juvenile rats delineates the emergence of spatial navigation ability. Learn Mem. 25, 138–146 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Bliss, T. V. P., Collingridge, G. L., Morris, R. G. M. & Reymann, K. G. Long-term potentiation in the hippocampus: Discovery, mechanisms and function. Neuroforum 24, A103–A120 (2018).Article 

Google Scholar 
Schiller, D. et al. Memory and space: Towards an understanding of the cognitive map. J. Neurosci. 35, 13904–13911 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, J. J. & Diamond, D. M. The stressed hippocampus, synaptic plasticity and lost memories. Nat. Rev. Neurosci. 3, 453–462 (2002).CAS 
PubMed 
Article 

Google Scholar 
Jiang, P. et al. The persistent effects of maternal infection on the offspring’s cognitive performance and rates of hippocampal neurogenesis. Prog. Neuropsychopharmacol. Biol. Psychiatry. 44, 279–289 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wallace, K. L. et al. Interleukin-10/Ceftriaxone prevents E. coli-induced delays in sensorimotor task learning and spatial memory in neonatal and adult Sprague-Dawley rats. Brain. Res. Bull. 81, 141–148 (2010).Shi, L., Fatemi, S. H., Sidwell, R. W. & Patterson, P. H. Maternal influenza infection causes marked behavioral and pharmacological changes in the offspring. J. Neurosci. 23, 297–302 (2003).PubMed 
PubMed Central 
Article 

Google Scholar 
Denenberg, V. H. Open-field behavior in the rat: what does it mean?. Ann. N. Y. Acad. Sci. 159, 852–859 (1969).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Vogel-Ciernia, A. & Wood, M. A. Examining object location and object recognition memory in mice. Curr. Protoc. Neurosci. 69, 8.31.1–17 (2014).Denninger, J. K., Smith, B. M. & Kirby, E. D. Novel object recognition and object location behavioral testing in mice on a budget. J. Vis. Exp. https://doi.org/10.3791/58593 (2018).Article 
PubMed 

Google Scholar 
Krüger, H.-S., Brockmann, M. D., Salamon, J., Ittrich, H. & Hanganu-Opatz, I. L. Neonatal hippocampal lesion alters the functional maturation of the prefrontal cortex and the early cognitive development in pre-juvenile rats. Neurobiol. Learn. Mem. 97, 470–481 (2012).PubMed 
Article 

Google Scholar 
Cruz-Sanchez, A. et al. Developmental onset distinguishes three types of spontaneous recognition memory in mice. Sci. Rep. 10, 10612. https://doi.org/10.1038/s41598-020-67619-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sunyer, B., Patil, S., Hoger, H. & Lubec, G. Barnes maze, a useful task to assess spatial reference memory in the mice. Nat. Protoc. https://doi.org/10.1038/nprot.2007.390 (2007).Article 

Google Scholar 
Schenk, F. Development of place navigation in rats from weaning to puberty. Behav. Neural Biol. 43, 69–85 (1985).CAS 
PubMed 
Article 

Google Scholar 
Brown, R. W. & Kraemer, P. J. Ontogenetic differences in retention of spatial learning tested with the Morris water maze. Dev. Psychobiol. 30, 329–341 (1997).CAS 
PubMed 
Article 

Google Scholar 
Batinić, B. et al. Lipopolysaccharide exposure during late embryogenesis results in diminished locomotor activity and amphetamine response in females and spatial cognition impairment in males in adult, but not adolescent rat offspring. Behav. Brain Res. 299, 72–80 (2016).PubMed 
Article 
CAS 

Google Scholar 
Lante, F. et al. Neurodevelopmental damage after prenatal infection: role of oxidative stress in the fetal brain. Free Radic. Biol. Med. 42, 1231–1245 (2007).CAS 
PubMed 
Article 

Google Scholar 
Wang, H. et al. Age- and gender-dependent impairments of neurobehaviors in mice whose mothers were exposed to lipopolysaccharide during pregnancy. Toxicol. Lett. 192, 245–251 (2010).CAS 
PubMed 
Article 

Google Scholar 
Yagi, S. & Galea, L. A. M. Sex differences in hippocampal cognition and neurogenesis. Neuropsychopharmacology 44, 200–213 (2019).PubMed 
Article 

Google Scholar 
Vuoksimaa, E. et al. Brain structure mediates the association between height and cognitive ability. Brain Struct. Func. 223, 3487–3494 (2018).Article 

Google Scholar 
Harris, M. A., Brett, C. E., Deary, I. J. & Starr, J. M. Associations among height, body mass index and intelligence from age 11 to age 78 years. BMC Geriatr. 16, 167. https://doi.org/10.1186/s12877-016-0340-0 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Pereira, V. H. et al. Adult body height is a good predictor of different dimensions of cognitive function in aged individuals: A cross-sectional study. Front. Aging Neurosci. 8, 1. https://doi.org/10.3389/fnagi.2016.00217 (2016).Case, A. & Paxson, C. Stature and status: Height, ability, and labor market outcomes. J. Polit. Econ. 116, 499–532 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Frick, K. M., Kim, J., Tuscher, J. J. & Fortress, A. M. Sex steroid hormones matter for learning and memory: estrogenic regulation of hippocampal function in male and female rodents. Learn Mem. 22, 472–493 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qiu, L. R. et al. Mouse MRI shows brain areas relatively larger in males emerge before those larger in females. Nat. Commun. 9, 2615. https://doi.org/10.1038/s41467-018-04921-2 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Towe, A. L. & Mann, M. D. Brain size/body length relations among myomorph rodents. Brain Behav. Evol. 39, 17–23 (1992).CAS 
PubMed 
Article 

Google Scholar 
Perepelkina, O. V., Tarasova, A. Y., Ogienko, N. A., Lil’p, I. G. & Poletaeva, I. I. Brain weight and cognitive abilities of laboratory mice. Biol. Bull. Rev. 10, 91–101 (2020).Odiere, M. R., Scott, M. E., Weiler, H. A. & Koski, K. G. Protein deficiency and nematode infection during pregnancy and lactation reduce maternal bone mineralization and neonatal linear growth in mice. J. Nutr. 140, 1638–1645 (2010).CAS 
PubMed 
Article 

Google Scholar 
Sánchez, M. B. et al. Leishmania amazonensis infection impairs reproductive and fetal parameters in female mice. Rev. Argent. Microbiol. 53, 194–201 (2021).PubMed 

Google Scholar 
Haque, M., Koski, K. G. & Scott, M. E. Maternal gastrointestinal nematode infection up-regulates expression of genes associated with long-term potentiation in perinatal brains of uninfected developing pups. Sci. Rep. 9, 4165. https://doi.org/10.1038/s41598-019-40729-w (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gregory, R. D., Montgomery, S. S. J. & Montgomery, W. I. Population biology of Heligmosomoides polygyrus (Nematoda) in the wood mouse. J. Anim. Ecol. 61, 749–757 (1992).Article 

Google Scholar 
Reynolds, L. A., Filbey, K. J. & Maizels, R. M. Immunity to the model intestinal helminth parasite Heligmosomoides polygyrus. Semin. Immunopathol. 34, 829–846 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maizels, R. M. et al. Immune modulation and modulators in Heligmosomoides polygyrus infection. Exp. Parasitol. 132, 76–89 (2012).CAS 
PubMed 
Article 

Google Scholar 
Haque, M., Starr, L. M., Koski, K. G. & Scott, M. E. Differential expression of genes in fetal brain as a consequence of maternal protein deficiency and nematode infection. Int. J. Parasitol. 48, 51–58 (2018).CAS 
PubMed 
Article 

Google Scholar 
Hsueh, P.-T. et al. Immune imbalance of global gene expression, and cytokine, chemokine and selectin levels in the brains of offspring with social deficits via maternal immune activation. Genes Brain Behav. 17, e12479. https://doi.org/10.1111/gbb.12479 (2018).Steimer, T. The biology of fear- and anxiety-related behaviors. Dialogues Clin. Neurosci. 4, 231–249 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Ricceri, L., Colozza, C. & Calamandrei, G. Ontogeny of spatial discrimination in mice: A longitudinal analysis in the modified open-field with objects. Dev. Psychobiol. 37, 109–118 (2000).CAS 
PubMed 
Article 

Google Scholar 
Howland, J. G., Cazakoff, B. N. & Zhang, Y. Altered object-in-place recognition memory, prepulse inhibition, and locomotor activity in the offspring of rats exposed to a viral mimetic during pregnancy. Neuroscience 201, 184–198 (2012).CAS 
PubMed 
Article 

Google Scholar 
Ito, H. T., Smith, S. E. P., Hsiao, E. & Patterson, P. H. Maternal immune activation alters nonspatial information processing in the hippocampus of the adult offspring. Brain Behav. Immun. 24, 930–941 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer, U. et al. The time of prenatal immune challenge determines the specificity of inflammation-mediated brain and behavioral pathology. J. Neurosci. 26, 4752–4762 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer, U. et al. Adult behavioral and pharmacological dysfunctions following disruption of the fetal brain balance between pro-inflammatory and IL-10-mediated anti-inflammatory signaling. Mol. Psychiatry. 13, 208–221 (2008).CAS 
PubMed 
Article 

Google Scholar 
Chapillon, P. & Roullet, P. Use of proximal and distal cues in place navigation by mice changes during ontogeny. Dev. Psychobiol. 29, 529–545 (1996).CAS 
PubMed 
Article 

Google Scholar 
Variyam, E. P. & Banwell, J. G. Hookworm disease: Nutritional implications. Rev. Infect. Dis. 4, 830–835 (1982).CAS 
PubMed 
Article 

Google Scholar 
Bansemir, A. D. & Sukhdeo, M. V. The food resource of adult Heligmosomoides polygyrus in the small intestine. J. Parasitol. 80, 24–28 (1994).CAS 
PubMed 
Article 

Google Scholar 
Starr, L. M., Scott, M. E. & Koski, K. G. Protein deficiency and intestinal nematode infection in pregnant mice differentially impact fetal growth through specific stress hormones, growth factors, and cytokines. J. Nutr. 145, 41–50 (2015).CAS 
PubMed 
Article 

Google Scholar 
Herring, C. M., Bazer, F. W., Johnson, G. A. & Wu, G. Impacts of maternal dietary protein intake on fetal survival, growth, and development. Exp. Biol. Med. (Maywood). 243, 525–533 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Starr, L. M., Odiere, M. R., Koski, K. G. & Scott, M. E. Protein deficiency alters impact of intestinal nematode infection on intestinal, visceral and lymphoid organ histopathology in lactating mice. Parasitology 141, 801–813 (2014).CAS 
PubMed 
Article 

Google Scholar 
Bastian, T. W., von Hohenberg, W. C., Mickelson, D. J., Lanier, L. M. & Georgieff, M. K. Iron deficiency impairs developing hippocampal neuron gene expression, energy metabolism, and dendrite complexity. Dev. Neurosci. 38, 264–276 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bastian, T. W., Rao, R., Tran, P. V. & Georgieff, M. K. The effects of early-life iron deficiency on brain energy metabolism. Neurosci. Insights. 15, 2633105520935104. https://doi.org/10.1177/2633105520935104 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gould, J. M. et al. Mouse maternal protein restriction during preimplantation alone permanently alters brain neuron proportion and adult short-term memory. Proc. Natl. Acad. Sci. 115, E7398–E7407. https://doi.org/10.1073/pnas.1721876115 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Radlowski, E. & Johnson, R. Perinatal iron deficiency and neurocognitive development. Front. Hum. Neurosci. 7, 585 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Rytych, J. L. et al. Early life iron deficiency impairs spatial cognition in neonatal piglets. J. Nutr. 142, 2050–2056 (2012).CAS 
PubMed 
Article 

Google Scholar 
Snyder, J. S., Hong, N. S., McDonald, R. J. & Wojtowicz, J. M. A role for adult neurogenesis in spatial long-term memory. Neuroscience 130, 843–852 (2005).CAS 
PubMed 
Article 

Google Scholar 
Brązert, M. et al. Expression of genes involved in neurogenesis, and neuronal precursor cell proliferation and development: Novel pathways of human ovarian granulosa cell differentiation and transdifferentiation capability in vitro. Mol. Med. Rep. 21, 1749–1760 (2020).PubMed 
PubMed Central 

Google Scholar 
van Praag, H., Christie, B. R., Sejnowski, T. J. & Gage, F. H. Running enhances neurogenesis, learning, and long-term potentiation in mice. Proc. Natl. Acad. Sci. U.S.A. 96, 13427–13431 (1999).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. et al. Regular treadmill running improves spatial learning and memory performance in young mice through increased hippocampal neurogenesis and decreased stress. Brain. Res. 1531, 1–8 (2013).MathSciNet 
CAS 
PubMed 
Article 

Google Scholar 
Odiere, M. R., Scott, M. E., Leroux, L. P., Dzierszinski, F. S. & Koski, K. G. Maternal protein deficiency during a gastrointestinal nematode infection alters developmental profile of lymphocyte populations and selected cytokines in neonatal mice. J. Nutr. 143, 100–107 (2013).CAS 
PubMed 
Article 

Google Scholar 
El Ahdab, N., Haque, M., Madogwe, E., Koski, K. G. & Scott, M. E. Maternal nematode infection upregulates expression of Th2/Treg and diapedesis related genes in the neonatal brain. Sci. Rep. 11, 22082. https://doi.org/10.1038/s41598-021-01510-0 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hein, A. M. et al. Sustained hippocampal IL-1β overexpression impairs contextual and spatial memory in transgenic mice. Brain Behav. Immun. 24, 243–253 (2010).CAS 
PubMed 
Article 

Google Scholar 
Derecki, N. C. et al. Regulation of learning and memory by meningeal immunity: A key role for IL-4. Exp. Med. 207, 1067–1080 (2010).CAS 
Article 

Google Scholar 
Brombacher, T. M. et al. IL-4R alpha deficiency influences hippocampal-BDNF signaling pathway to impair reference memory. Sci. Rep. 10, 16506. https://doi.org/10.1038/s41598-020-73574-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mizuno, M., Yamada, K., Olariu, A., Nawa, H. & Nabeshima, T. Involvement of brain-derived neurotrophic factor in spatial memory formation and maintenance in a radial arm maze test in rats. J. Neurosci. 20, 7116–7121 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miranda, M., Morici, J. F., Zanoni, M. B. & Bekinschtein, P. Brain-derived neurotrophic factor: A key molecule for memory in the healthy and the pathological brain. Front. Cell. Neurosci. 13. https://doi.org/10.3389/fncel.2019.00363 (2019).Williamson, L. L. et al. Got worms? Perinatal exposure to helminths prevents persistent immune sensitization and cognitive dysfunction induced by early-life infection. Brain Behav. Immun. 51, 14–28 (2016).CAS 
PubMed 
Article 

Google Scholar 
McKay, D. M. The immune response to and immunomodulation by Hymenolepis diminuta. Parasitology 137, 385–394 (2010).CAS 
PubMed 
Article 

Google Scholar 
Meyer, U., Feldon, J. & Fatemi, S. H. In-vivo rodent models for the experimental investigation of prenatal immune activation effects in neurodevelopmental brain disorders. Neurosci. Biobehav. Rev. 33, 1061–1079 (2009).CAS 
PubMed 
Article 

Google Scholar 
Johnston, C. J. C. et al. Cultivation of Heligmosomoides polygyrus: an immunomodulatory nematode parasite and its secreted products. J. Vis. Exp. e52412–e52412. https://doi.org/10.3791/52412 (2015).Valanparambil, R. M. et al. Production and analysis of immunomodulatory excretory-secretory products from the mouse gastrointestinal nematode Heligmosomoides polygyrus bakeri. Nat. Protoc. 9, 2740–2754 (2014).CAS 
PubMed 
Article 

Google Scholar 
Murai, T., Okuda, S., Tanaka, T. & Ohta, H. Characteristics of object location memory in mice: Behavioral and pharmacological studies. Physiol. Behav. 90, 116–124 (2007).CAS 
PubMed 
Article 

Google Scholar 
Patil, S. S., Sunyer, B., Hoger, H. & Lubec, G. Evaluation of spatial memory of C57BL/6J and CD1 mice in the Barnes maze, the Multiple T-maze and in the Morris water maze. Behav. Brain. Res. 198, 58–68 (2009).PubMed 
Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).Wickham, H. ggplot2: Elegant graphics for data analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
Lazic, S. E. The problem of pseudoreplication in neuroscientific studies: Is it affecting your analysis?. BMC Neurosci. 11, 5. https://doi.org/10.1186/1471-2202-11-5 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Vol. 1–574 (2009).Bates, D., Maechler, M., Bolker, B. & Steve, W. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Fox, J. & Sanford, W. An R Companion to Applied Regression. 3 edn, (Sage, 2019).emmeans: Estimated marginal means, aka least-squares means v. 1.4.8 (R package, 2020).RVAideMemoire: Testing and plotting procedures for biostatistics. v. 0.9-78 (R package, 2020).DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models v. 0.3.3.0 (R package, 2020).Delignette-Muller, M. L. & Dutang, C. fitdistrplus: An R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015).Article 

Google Scholar  More

Plant biomarker distributionsPlant lipid biomarkers are widely used to reconstruct vegetation and (hydro)climate in ancient environments, and previous studies at Olduvai15,17 have used biomarkers in spatiotemporal landscape reconstructions, although not at the resolution of this study (i.e.,  More

Eid, E. M., Galal, T. M. & El-Bebany, A. F. Prediction models for monitoring heavy metals accumulation by wheat (Triticum aestivum L.) plants grown in soil amended with different rates of sewage sludge. Int. J. Phytoremediation 22(10), 1000–1008 (2020).CAS 
PubMed 
Article 

Google Scholar 
Singh, R. P. & Agrawal, M. Effect of different sewage sludge applications on growth and yield of Vigna radiata L. field crop: Metal uptake by plant. Ecol. Eng. 36(7), 969–972 (2010).Article 

Google Scholar 
Wu, N., Su-mei, L., Gui-ling, Z. & Hong-mei, Z. Anthropogenic impacts on nutrient variability in the lower yellow river. Sci. Tot. Environ. 755, 142488 (2021).CAS 
Article 

Google Scholar 
Jacob, J. M. et al. Biological approaches to tackle heavy metal pollution: a survey of literature. J. Environ. Manag. 217, 56–70 (2018).CAS 
Article 

Google Scholar 
Gharib, F. A., Mansour, K. H., Ahmed, E. Z. & Galal, T. M. Heavy metals concentration, and antioxidant activity of the essential oil of the wild mint (Mentha longifolia L.) in the Egyptian watercourses. Int. J. Phytoremediation 23(6), 641–651 (2020).PubMed 

Google Scholar 
Dai, T. et al. Dynamics of coastal bacterial community average ribosomal RNA operon copy number reflect its response and sensitivity to ammonium and phosphate. Environ. Pollut. 260, 113971 (2020).CAS 
PubMed 
Article 

Google Scholar 
Chowdhury, A. H., Chowdhury, T. & Rahman, A. Heavy metal accumulation in tomato and cabbage grown in some industrially contaminated soils of Bangladesh. J. Bangladesh Agric. Univ. 17(3), 288–294 (2019).Article 

Google Scholar 
Hu, S., Liu, L., Zuo, S., Ali, M. & Wang, Z. Soil salinity control and cauliflower quality promotion by intercropping with five turfgrass species. J. Clean Prod. 266, 121991 (2020).CAS 
Article 

Google Scholar 
Hu, W. Y., Zhang, Y. X., Huang, B. & Teng, Y. Soil environmental quality in greenhouse vegetable production systems in eastern China: current status and management strategies. Chemosphere 170, 183–195 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Qin, S. et al. Toxicity of cadmium and its competition with mineral nutrients for uptake by plants: a review. Pedosphere 30(2), 168–180 (2020).Article 

Google Scholar 
Kumar, V., Thakur, K. R. & Kumar, P. Assessment of heavy metals uptake by cauliflower (Brassica oleracea Var. botrytis) grown in integrated industrial effluent irrigated soils: a prediction modeling study. Sci. Hortic. 257, 108682 (2019).CAS 
Article 

Google Scholar 
Paltseva, A., Cheng, Z., Egendorf, S. & Groffman, P. Remediation of an urban garden with elevated levels of soil contamination. Sci Total Environ. 722, 137965 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ma, L., Liu, Y., Wu, Y., Wang, Q. & Zhou, Q. The effects and health risk assessment of cauliflower co-cropping with Sedum alfredii in cadmium contaminated vegetable field. Environ. Pollut. 268, 115869 (2021).CAS 
PubMed 
Article 

Google Scholar 
Dong, Q., Fei, L., Wang, C., Hu, S. & Wang, Z. Cadmium excretion via leaf hydathodes in tall fescue and its phytoremediation potential. Environ. Pollut. 252, 1406–1411 (2019).CAS 
PubMed 
Article 

Google Scholar 
Perveen, S., Ihsanullah, I., Shah, Z., Shah, W. S. S. & Shah, H. H. Study on accumulation of heavy metals in vegetables receiving sewage water. J. Chem. Soc. Pak. 33, 220 (2011).CAS 

Google Scholar 
Galal, T. M., Sheded, Z. A. & Hassan, L. M. Hazards assessment of the intake of trace metals by common mallow (Malva parviflora L.) growing in polluted soils. Int. J. Phytoremediation 21(14), 1397–1406 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ogundeji, A. O. et al. Eggplant by grafting enhanced with biochar recruits specific microbes for disease suppression of Verticillium Wilt. Appl. Soil Ecol. 163, 103912 (2021).Article 

Google Scholar 
Mauro, R. P. et al. Recovery of eggplant field waste as a source of phytochemicals. Sci. Hort. 261, 109023 (2020).CAS 
Article 

Google Scholar 
Martini, S., Conte, A., Cattivelli, A. & Tagliazucchi, D. Domestic cooking methods affect the stability and bioaccessibility of dark purple eggplant (Solanum melongena) phenolic compounds. Food Chem. 341, 128298 (2021).CAS 
PubMed 
Article 

Google Scholar 
Gürbüz, N., Uluişik, S., Frary, A., Frary, A. & Doğanlar, S. Health benefits and bioactive compounds of eggplant. Food Chem. 268, 602–610 (2018).PubMed 
Article 
CAS 

Google Scholar 
FAOSTAT. Food and Agriculture Organization of the United Nations. http://faostat.fao.org/site/567/default.aspx#ancor (2014).Asgari, K. & Cornelis, W. M. Heavy metal accumulation in soils and grains, and health risks associated with use of treated municipal wastewater in subsurface drip irrigation. Environ. Monit. Assess. 187, 410 (2015).PubMed 
Article 
CAS 

Google Scholar 
IWMI. (International Water Management Institute) Global water scarcity study. From http://www.iwmi.cgiar.org/home/wsmap.htm (2000).Oweis, T. & Hachum, A. Water harvesting for improved rainfed agriculture in the dry environments. In Rainfed Agriculture: Unlocking the Potential (eds Wani, S. P. et al.) 164–181 (CABI, 2009).Chapter 

Google Scholar 
Shehata, H. S. & Galal, T. M. Trace metal concentration in planted cucumber (Cucumis sativus L.) from contaminated soils and its associated health risks. J. Cons. Prot. Food Safe. 15, 205–217 (2020).CAS 
Article 

Google Scholar 
Elawa, O. E. Impact assessment of industrial pollution on some economic plants south of Cairo Province, Egypt. M.Sc. Thesis, Helwan Uni Cairo, Egypt 194 pp (2015).Umbriet, W. W. et al. Monometric Technique, A Manual Description Method, Applicable to Study of Desiring Metabolism 239 (Burgess Publishing Company, 1959).
Google Scholar 
Lowry, O. H., Rosebrough, N. J., Farr, A. L. & Randall, R. J. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193, 265–275 (1951).CAS 
PubMed 
Article 

Google Scholar 
Allen, S. E. Chemical Analysis of Ecological Materials (Blackwell Scientific Publications, 1989).
Google Scholar 
Liu, R. L., Li, S. T., Wang, X. B. & Wang, M. Contents of heavy metal in commercial organic fertilizers and organic wastes. J. Agro-Environ. Sci. 24, 392–397 (2005).CAS 

Google Scholar 
Lu, X. W. et al. Risk assessment of toxic metals in street dust from a medium-sized industrial city of China. Ecotoxicol. Environ. Safe. 106, 154–163 (2014).CAS 
Article 

Google Scholar 
Eid, E. M., Alrumman, S. A., Galal, T. M. & El-Bebany, A. F. Regression models for monitoring trace metal accumulations by Faba sativa Bernh. plants grown in soils amended with different rates of sewage sludge. Sci. Rep. 9, 5443 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Khan, S. et al. Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environ. Pollut. 152, 686–692 (2008).CAS 
PubMed 
Article 

Google Scholar 
Rattan, R. K., Datta, S. P., Chhonkar, P. K., Suribabu, K. & Singh, A. K. Long-term impact of irrigation with sewage effluents on heavy metals content in soils, crops and groundwater: a case study. Agric. Ecosyst. Environ. 109, 310–322 (2005).CAS 
Article 

Google Scholar 
US-EPA. Environmental Protection Agency, Region 9, Preliminary remediation goals (2012).SPSS. SPSS Version 20.0. SPSS Inc, 233 S Wacker Drive, Chicago, Illinois (2012).Al-jaboobi, M. et al. Evaluation of heavy metals pollution in groundwater, soil and some vegetables irrigated with wastewater in the Skhirat Region, Morocco. J. Mater Environ. Sci. 5(3), 961–966 (2014).
Google Scholar 
Rangamani, T., Rao, K. V. & Srinivas, T. Evaluation of heavy metals in water, soil and cauliflower in Penamaluru Mandal in Vijayawada. Int. J. Multidiscip. Adv. Res. Trends 1(3), 242–248 (2017).
Google Scholar 
Galal, T. M. Health hazards and heavy metals accumulation by summer squash (Cucurbita pepo L.) cultivated in contaminated soils. Environ. Monit. Assess. 188(7), 134 (2016).MathSciNet 
Article 
CAS 

Google Scholar 
Eid, E. M. et al. Evaluation of newly reclaimed areas in Abha, Saudi Arabia, for cultivation of the Phaseolus vulgaris leguminous crop under sewage sludge amendment. J. Cons. Prot. Food. Safe. https://doi.org/10.1007/s00003-020-01311-z (2021).Article 

Google Scholar 
Galal, T. M., Hassan, L. M. & Elawa, O. E. Impact Assessment of Industrial Pollution on Important Food Crops (Lambert Academic Publishing Gmbh&Co.KG, 2019).
Google Scholar 
WHO/FAO (2013) Guidelines for the Safe Use of Wastewater and foodstuff; 2(1), 14 ppGalal, T. M., Khalafallah, A. A., Elawa, O. E. & Hassan, L. M. Human health risks from consuming cabbage (Brassica oleracea L. var. capitata) grown on wastewater irrigated soil. Int. J. Phytoremediation 20(10), 1007–1016 (2018).CAS 
PubMed 
Article 

Google Scholar 
Chaturvedi, R., Favas, P., Pratas, J., Varun, M. & Paul, M. S. Ecotoxicology and environmental safety assessment of edibility and effect of arbuscular mycorrhizal fungi on Solanum melongena L. grown under heavy metal (Loid) contaminated soil. Ecotoxicol. Environ. Safe. 148, 318–326 (2018).CAS 
Article 

Google Scholar 
Ai, P., Jin, K., Alengebawy, A., Elsayed, M. & Meng, L. Effect of application of different biogas fertilizer on eggplant production: analysis of fertilizer value and risk assessment. Environ. Technol. Innov. 19(13), 101019 (2020).Article 

Google Scholar 
Ekmekçi, Y., Tanyolaç, D. & Ayhan, B. Effects of cadmium on antioxidant enzyme and photosynthetic activities in leaves of two maize cultivars. Plant Physiol. 165, 600–611 (2008).Article 
CAS 

Google Scholar 
Hadi, F., Bano, A. & Fuller, M. P. The improved phytoextraction of lead (Pb) and the growth of maize (Zea mays L.): the role of plant growth regulators (GA3 and IAA) and EDTA alone and in combinations. Chemosphere 80, 457–462 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Li, G. et al. A multi-level analysis of China’s phosphorus flows to identify options for improved management in agriculture. Agric. Syst. 144, 87–100 (2016).Article 

Google Scholar 
Farahat, E. A., Galal, T. M., Elawa, O. E. & Hassan, L. M. Health risk assessment and growth characteristics of wheat and maize crops irrigated with contaminated wastewater. Environ. Monit. Assess. 189, 535 (2017).PubMed 
Article 
CAS 

Google Scholar 
Ghazi, S. M., Galal, T. M. & Husein, K. H. Monitoring Water Pollution in the Egyptian Watercourses: A Phytoremediation Approach (Lap Lambert Academic Publishing, 2019).
Google Scholar 
Körpe, D. A. & Aras, S. Evaluation of copper-induced stress on eggplant (Solanum melongena L.) seedlings at the molecular and population levels by use of various biomarkers. Mutat. Res. 719, 29–34 (2011).PubMed 
Article 
CAS 

Google Scholar 
Nagajyoti, P., Lee, K. & Sreekanth, T. Heavy metals, occurrence and toxicity for plants: a review. Environ. Chem. Lett. 8, 199–216 (2010).CAS 
Article 

Google Scholar 
Ai, S. et al. Temporal variations and spatial distributions of heavy metals in a wastewater-irrigated soil-eggplant system and associated influencing factors. Ecotoxicol. Environ. Safe. 153, 204–214 (2018).CAS 
Article 

Google Scholar 
Vecchia, F. D. et al. Morphogenetic, ultrastructural and physiological damages suffered by submerged leaves of Elodea canadensis exposed to cadmium. Plant Sci. 168, 329–338 (2005).Article 
CAS 

Google Scholar 
Rizwan, M. et al. A critical review on effects, tolerance mechanisms and management of cadmium in vegetables. Chemosphere 182, 90–105 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kupper, H. & Andresen, E. Mechanisms of metal toxicity in plants. Metallomics 8, 269–285 (2016).PubMed 
Article 

Google Scholar 
Piotrowska, A., Bajguz, A., Godlewska, B., Czerpak, R. & Kaminska, M. Jasmonic acid as modulator of lead toxicity in aquatic plant Wolffia arrhiza (Lamnaceae). Environ. Exp. Bot. 66, 507–513 (2009).CAS 
Article 

Google Scholar 
Singh, R. et al. Lead bioaccumulation potential of an aquatic macrophyte Najas indica are related to antioxidant system. Bioresour. Technol. 101, 3025–3032 (2010).CAS 
PubMed 
Article 

Google Scholar 
Collado-González, J., Piñero, M. C., Otálora, G. & López-Marín, J. Exogenous spermidine modifies nutritional and bioactive constituents of cauliflower (Brassica oleracea var. botrytis L.) florets under heat stress. Sci. Hortic. 277, 109818 (2021).Article 
CAS 

Google Scholar 
Al Jassir, M., Shaker, A. & Khaliq, M. Deposition of heavy metals on green leafy vegetables sold on roadsides of Riyadh City, Saudi Arabia. Bull. Environ. Contam. Toxicol. 75, 1020–1027 (2005).CAS 
PubMed 
Article 

Google Scholar 
ur Rehman, K. et al. Ecological risk assessment of heavy metals in vegetables irrigated with groundwater and wastewater: the particular case of Sahiwal District in Pakistan. Agric. Water Manag. 226, 105816 (2019).Article 

Google Scholar 
Misra, S. G. & Mani, D. Soil pollution (Ashish Publishing House, 1991).
Google Scholar 
Jolly, Y. N., Islam, A. & Akbar, S. Transfer of metals from soil to vegetables and possible health risk assessment. Springerplus 2, 385 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhou, H. et al. Accumulation of heavy metals in vegetable species planted in contaminated soils and the health risk assessment. Int. J. Environ. Res. Public Health 13(3), 289 (2016).PubMed Central 
Article 
CAS 

Google Scholar 
Rehman, Z., Hussain, R. A., Jabeen, S. & Ali, S. Heavy metals (Cd, Pb & Zn) accumulation in cauliflower (Brassica oleracea var. botyris) and associated health risks assessment in three districts of Punjab Pakistan. Biol. Pak. 64(1), 105–110 (2018).
Google Scholar 
Saeedifar, F., Ziarati, P. & Ramezan, Y. Nitrate and heavy metal contents in eggplant (Solanum melongena) cultivated in the farmlands in the south of Tehran-Iran. Int. J. Farm Allied Sci. 3, 60–65 (2014).Article 

Google Scholar 
Khanal, B. R., Shah, S. C., Sah, S. K. & Shriwastav, C. P. Heavy metals accumulation in cauliflower (Brassica oleracea L. var. botrytis) grown in brewery sludge amended sandy loam soil. Int. J. Agric. Sci. Technol. 2(3), 87–92 (2014).
Google Scholar  More

Sætre, G.-P. et al. Single origin of human commensalism in the house sparrow. J. Evol. Biol. 25, 788–796 (2012).PubMed 
Article 

Google Scholar 
Anderson, T. Biology of the Ubiquitous House Sparrow: From Genes to Populations (Oxford University Press, 2006). https://doi.org/10.1093/acprof:oso/9780195304114.001.0001.Book 

Google Scholar 
Johnston, R. F. Synanthropic Birds of North America. In Avian Ecology and Conservation in an Urbanizing World (eds Marzluff, J. M. et al.) 49–67 (Springer, 2001). https://doi.org/10.1007/978-1-4615-1531-9_3.Chapter 

Google Scholar 
Shaw, L. M., Chamberlain, D., Conway, G. & Toms, M. Spatial distribution and habitat preferences of the House Sparrow Passer domesticus in urbanised landscapes. (2011).Guetté, A., Gaüzère, P., Devictor, V., Jiguet, F. & Godet, L. Measuring the synanthropy of species and communities to monitor the effects of urbanization on biodiversity. Ecol. Indic. 79, 139–154 (2017).Article 

Google Scholar 
Slusher, M. J. et al. Are passerine birds reservoirs for influenza A viruses?. J. Wildl. Dis. 50, 792–809 (2014).PubMed 
Article 

Google Scholar 
Veen, J. et al. Ornithological data relevant to the spread of Avian Influenza in Europe (phase 2): further identification and first field assessment of Higher Risk Species. (2007).Caron, A., Cappelle, J. & Gaidet, N. Challenging the conceptual framework of maintenance hosts for influenza A viruses in wild birds. J. Appl. Ecol. 54, 681–690 (2017).Article 

Google Scholar 
Olsen, B. et al. Global patterns of influenza A virus in wild birds. Science 312, 384–388 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Brown, J. D., Stallknecht, D. E., Berghaus, R. D. & Swayne, D. E. Infectious and lethal doses of H5N1 highly pathogenic avian influenza virus for house sparrows (Passer Domesticus) and rock pigeons (Columbia Livia). J. Vet. Diagn. Invest. 21, 437–445 (2009).PubMed 
Article 

Google Scholar 
Forrest, H. L., Kim, J.-K. & Webster, R. G. Virus shedding and potential for interspecies waterborne transmission of highly pathogenic H5N1 influenza virus in sparrows and chickens. J. Virol. 84, 3718–3720 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nemeth, N. M., Thomas, N. O., Orahood, D. S., Anderson, T. D. & Oesterle, P. T. Shedding and serologic responses following primary and secondary inoculation of house sparrows (Passer domesticus) and European starlings (Sturnus vulgaris) with low-pathogenicity avian influenza virus. Avian Pathol. 39, 411–418 (2010).PubMed 
Article 

Google Scholar 
Yamamoto, Y., Nakamura, K., Yamada, M. & Mase, M. Pathogenesis in Eurasian tree sparrows inoculated with H5N1 highly pathogenic avian influenza virus and experimental virus transmission from tree sparrows to chickens. Avian Dis. 57, 205–213 (2013).PubMed 
Article 

Google Scholar 
Ellis, J. W. et al. Avian influenza A virus susceptibility, infection, transmission, and antibody kinetics in European starlings. PLOS Pathog. 17, e1009879 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gutiérrez, R. A., Sorn, S., Nicholls, J. M. & Buchy, P. Eurasian tree sparrows, risk for H5N1 virus spread and human contamination through buddhist ritual: An experimental approach. PLoS ONE 6, e28609 (2011).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Caron, A., Cappelle, J., Cumming, G. S., de Garine-Wichatitsky, M. & Gaidet, N. Bridge hosts, a missing link for disease ecology in multi-host systems. Vet. Res. 46, 1–11 (2015).CAS 
Article 

Google Scholar 
Guinat, C. et al. Duck production systems and highly pathogenic avian influenza H5N8 in France, 2016–2017. Sci. Rep. 9, 1–9 (2019).CAS 
Article 

Google Scholar 
EFSA et al. Scientific report Avian influenza overview October 2016–August 2017. EFSA J. 15, 101 (2017).
Google Scholar 
EFSA et al. Scientific report: Avian influenza overview December 2020–February 2021. EFSA J. 19, 74 (2021).
Google Scholar 
Le Bouquin, S. et al. L’épisode d’influenza aviaire en France en 2015–2016: Situation épidémiologique au 30 juin 2016. Bull. Epidémiologique Santé Anim. Aliment.—DGAL—Anses 1–7 (2016).EFSA et al. Avian influenza overview December 2021–March 2022. EFSA J. 20, e07289 (2022).
Google Scholar 
DGAL. Arrêté du 8 février 2016 relatif aux mesures de biosécurité applicables dans les exploitations de volailles et d’autres oiseaux captifs dans le cadre de la prévention contre l’influenza aviaire. AGRG1603907A, (2016).Koch, G. & Elbers, A. R. W. Outdoor ranging of poultry: A major risk factor for the introduction and development of high-pathogenicity avian influenza. NJAS—Wagening. J. Life Sci. 54, 179–194 (2006).Article 

Google Scholar 
Delpont, M. et al. Biosecurity practices on foie gras duck farms Southwest France. Prev. Vet. Med. 158, 78–88 (2018).PubMed 
Article 

Google Scholar 
Bicout, J. D., Artois, M., Musseau, R., Caparros, O. & Lubac, S. Which wild birds are potentially at risk for contacts between wild avifauna and with poultry? in 9èmes Journées de la Recherche Avicole, Tours, France 5pp (World’s Poultry Science Association (WPSA), 2011).Gotteland, C., Lubac, S. & Bicout, D. Où trouve-t-on les oiseaux sauvages aux alentours des élevages? Risque de contact oiseaux sauvages et volailles. Epidemiol. Sante Anim. 55, 103–115 (2009).
Google Scholar 
Lubac, S., Musseau, R., Caparros, O., Artois, M. & Bicout, D. J. Interactions entre l’avifaune sauvage et les élevages de volailles: Quel risque épidémiologique vis à vis de l’Influenza aviaire ?. Innov. Agron. 25, 299–312 (2012).
Google Scholar 
Burns, F. et al. Abundance decline in the avifauna of the European Union reveals cross-continental similarities in biodiversity change. Ecol. Evol. 0, 1–14 (2021).Jeliazkov, A. et al. Impacts of agricultural intensification on bird communities: New insights from a multi-level and multi-facet approach of biodiversity. Agric. Ecosyst. Environ. 216, 9–22 (2016).Article 

Google Scholar 
Chiatante, G., Pellitteri-Rosa, D., Torretta, E., Nonnis Marzano, F. & Meriggi, A. Indicators of biodiversity in an intensively cultivated and heavily human modified landscape. Ecol. Indic. 130, 108060 (2021).Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information System. (QGIS Association, 2022).Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Bacigalupo, S. A., Dixon, L. K., Gubbins, S., Kucharski, A. J. & Drewe, J. A. Towards a unified generic framework to define and observe contacts between livestock and wildlife: A systematic review. PeerJ 8, e10221 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, X., Li, Y., Chwang, A. T. Y., Ho, P. L. & Seto, W. H. How far droplets can move in indoor environments: revisiting the Wells evaporation-falling curve. Indoor Air 17, 211–225 (2007).CAS 
PubMed 
Article 

Google Scholar 
Zuo, Z. et al. Association of airborne virus infectivity and survivability with its carrier particle size. Aerosol Sci. Technol. 47, 373–382 (2013).ADS 
CAS 
Article 

Google Scholar 
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pons, P. & Latapy, M. Computing Communities in Large Networks Using Random Walks. in Computer and Information Sciences : ISCIS 2005 284–293 (Springer, 2005). https://doi.org/10.1007/11569596_31.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).
Google Scholar 
Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: Estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).ADS 
Article 

Google Scholar 
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge, 1988). https://doi.org/10.4324/9780203771587.Book 
MATH 

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

Google Scholar 
Bates, D. et al. lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. (2022).Bartoń, K. MuMIn: Multi-Model Inference. (2022).Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Lefcheck, J., Byrnes, J. & Grace, J. piecewiseSEM: Piecewise Structural Equation Modeling. (2020).Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
UICN France, MNHN, LPO BirdLife France, SEOF & ONCFS. La Liste rouge des espèces menacées en France—Chapitre Oiseaux de France métropolitaine. (2016).Bestman, M., de Jong, W., Wagenaar, J.-P. & Weerts, T. Presence of avian influenza risk birds in and around poultry free-range areas in relation to range vegetation and openness of surrounding landscape. Agrofor. Syst. 92, 1001–1008 (2018).Article 

Google Scholar 
Scott, A. B. et al. Wildlife presence and interactions with chickens on australian commercial chicken farms assessed by camera traps. Avian Dis. 62, 65–72 (2018).PubMed 
Article 

Google Scholar 
Scherer, A. L., de Scherer, J. F. M., Petry, M. V. & Sander, M. Occurrence and interaction of wild birds at poultry houses in southern Brazil. Rev. Bras. Ornitol.: Braz. J. Ornithol. 19, 74–79 (2011).
Google Scholar 
Burns, T. E. et al. Use of observed wild bird activity on poultry farms and a literature review to target species as high priority for avian influenza testing in 2 regions of Canada. Can. Vet. J. 53, 158–166 (2012).PubMed 
PubMed Central 

Google Scholar 
Elbers, A. R. W. & Gonzales, J. L. Quantification of visits of wild fauna to a commercial free-range layer farm in the Netherlands located in an avian influenza hot-spot area assessed by video-camera monitoring. Transbound. Emerg. Dis https://doi.org/10.1111/tbed.13382 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Craft, M. E. Infectious disease transmission and contact networks in wildlife and livestock. Philos. Trans. R. Soc. B. Biol. Sci. 370, 20140107 (2015).Article 

Google Scholar 
Clergeau, P., Savard, J.-P.L., Mennechez, G. & Falardeau, G. Bird abundance and diversity along an urban-rural gradient: A comparative study between two cities on different continents. The Condor 100, 413–425 (1998).Article 

Google Scholar 
Le Gall-Ladevèze, C. et al. Detection of a novel enterotropic Mycoplasma gallisepticum-like in European starling (Sturnus vulgaris) around poultry farms in France. Transbound. Emerg. Dis. 0, 1–12 (2021).Shriner, S. A. & Root, J. J. A review of avian influenza A virus associations in synanthropic birds. Viruses 12, 1209 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Shriner, S. A. et al. Surveillance for highly pathogenic H5 avian influenza virus in synanthropic wildlife associated with poultry farms during an acute outbreak. Sci. Rep. 6, 36237 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davies, N. B. Food, flocking and territorial behaviour of the pied wagtail (Motacilla alba yarrellii Gould) in winter. J. Anim. Ecol. 45, 235–253 (1976).Article 

Google Scholar 
Snow, D. W., Perrins, C. M. & Gillmor, R. The birds of the western palaearctic. Vol. 2, Passerines. vol. 2 (Oxford University Press, 1998).Rigal, S. et al. Biotic homogenisation in bird communities leads to large-scale changes in species associations. Oikos 2022, e08756 (2022).Article 

Google Scholar 
Dalziel, A. E., Delean, S., Heinrich, S. & Cassey, P. Persistence of low pathogenic influenza A virus in water: A systematic review and quantitative meta-analysis. PLoS ONE 11, e0161929 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Keeler, S. P., Dalton, M. S., Cressler, A. M., Berghaus, R. D. & Stallknecht, D. E. Abiotic factors affecting the persistence of avian influenza virus in surface waters of waterfowl habitats. Appl. Environ. Microbiol. 80, 2910–2917 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Marois, C., Dufour-Gesbert, F. & Kempf, I. Polymerase chain reaction for detection of mycoplasma gallisepticum in environmental samples. Avian Pathol. 31, 163–168 (2002).PubMed 
Article 

Google Scholar 
Blagodatski, A. et al. Avian influenza in wild birds and poultry: dissemination pathways, monitoring methods, and virus ecology. Pathogens 10, 630 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Stoffolano, J. G. Jr. & Geden, C. J. Succession of manure arthropods at a poultry farm in massachusetts, USA, With observations on carcinops pumilio (Coleoptera: Histeridae) sex ratios, ovarian condition, and body size1. J. Med. Entomol. 24, 212–220 (1987).Article 

Google Scholar 
Ushio, M. et al. Demonstration of the potential of environmental DNA as a tool for the detection of avian species. Sci. Rep. 8, 1–10 (2018).CAS 
Article 

Google Scholar 
Fontaine, B. et al. Suivi des oiseaux communs en France 1989–2019 : 30 ans de suivis participatifs—Executive summary of the 2019 common birds monitoring report. https://inpn.mnhn.fr/actualites/lire/12721/bilan-des-30-ans-du-suivi-temporel-des-oiseaux-communs-stoc (2020).Seamans, T. & Gosser, A. Bird dispersal techniques. in Wildlife Damage Management Technical Series 12pp (USDA, APHIS, WS National Wildlife Research Center, 2016). https://doi.org/10.32747/2016.7207730.ws.Elbers, A. R. W. & Gonzales, J. L. Efficacy of an automated laser for reducing wild bird visits to the free range area of a poultry farm. Sci. Rep. 11, 12779 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Conover, M. R. & Perito, J. J. Response of starlings to distress calls and predator models holding conspecific prey. Z. Für Tierpsychol. 57, 163–172 (1981).Article 

Google Scholar 
Aubin, T. Synthetic bird calls and their application to scaring methods. Ibis 132, 290–299 (1990).Article 

Google Scholar 
Guinat, C. et al. Biosecurity risk factors for highly pathogenic avian influenza (H5N8) virus infection in duck farms France. Transbound. Emerg. Dis. 67, 2961–2970 (2020).PubMed 
Article 

Google Scholar 
Gaide, N. et al. Viral tropism and detection of clade 2.3.4.4b H5N8 highly pathogenic avian influenza viruses in feathers of ducks and geese. Sci. Rep. 11, 5928 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Spekreijse, D., Bouma, A., Koch, G. & Stegeman, A. Quantification of dust-borne transmission of highly pathogenic avian influenza virus between chickens. Influenza Other Respir. Viruses 7, 132–138 (2013).PubMed 
Article 

Google Scholar 
Torremorell, M. et al. Investigation into the airborne dissemination of H5N2 highly pathogenic avian influenza virus during the 2015 spring outbreaks in the midwestern United States. Avian Dis. 60, 637–643 (2016).PubMed 
Article 

Google Scholar 
Caron, A., Grosbois, V., Etter, E., Gaidet, N. & de Garine-Wichatitsky, M. Bridge hosts for avian influenza viruses at the wildlife/domestic interface: An eco-epidemiological framework implemented in southern Africa. Prev. Vet. Med. 117, 590–600 (2014).CAS 
PubMed 
Article 

Google Scholar  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

Day, K. R. & Leather, S. Threats to forestry by insect pests in Europe. In Forests and Insects. (eds. Watt, A. D., Stork, N. E. & Hunter, M. D.) 177–205 (Chapman and Hall, 1997).Långström, B. & Day, K.R. Damage, control and management of weevil pests, especially Hylobius abietis. In Bark and Wood Boring Insects in Living Trees in Europe, a Synthesis (eds. Lieutier, F. et al.) 415–444 (Springer, 2007).Nordlander, G., Hellqvist, C., Johansson, K. & Nordenhem, H. Regeneration of European boreal forests: Effectiveness of measures against seedling mortality caused by the pine weevil Hylobius abietis. For. Ecol. Manag. 262, 2354–2363 (2011).Article 

Google Scholar 
Leather, S. R., Day, K. R. & Salisbury, A. The biology and ecology of the large pine weevil, Hylobius abietis (Coleoptera: Curculionidae): A problem of dispersal?. Bull. Entomol. 89, 3–16 (1999).Article 

Google Scholar 
Moore, R. Managing the threat to restocking posed by the large pine weevil, Hylobius abietis: the importance of time of felling of spruce stands. Forestry Commission Information https://www.forestresearch.gov.uk/documents/323/fcin061_OIvSOQX.pdf (2004). Accessed 29 May 2022.Nordlander, G., Nordenhem, H. & Bylund, H. Oviposition patterns of the pine weevil Hylobius abietis. Entomol. Exp. Appl. 85, 1–9 (1997).Article 

Google Scholar 
Örlander, G., Nilsson, U. & Nordlander, G. Pine weevil abundance on clearcuts of different ages: A 6-year study using pitfall traps. Scand. J. For. Res. 12, 225–240 (1997).Article 

Google Scholar 
Nordlander, G., Hellqvist, C. & Hjelm, K. Replanting conifer seedlings after pine weevil emigration in spring decreases feeding damage and seedling mortality. Scand. J. For. Res. 32, 60–67 (2017).Article 

Google Scholar 
Fedderwitz, F., Björklund, N., Ninkovic, V. & Nordlander, G. Does the pine weevil (Hylobius abietis) prefer conifer seedlings over other main food sources?. Silva Fenn. 52, 9946 (2018).Article 

Google Scholar 
Day, K. R., Nordlander, G., Kenis, M., Halldórsson, G. General biology and life cycles of bark weevils. In Bark and Wood Boring Insects in Living Trees in Europe, a Synthesis (eds. Lieutier, F., et al.) 331–349 (Springer, 2007).Willoughby, I., Moore, R. & Nisbet, T. Interim guidance on the integrated management of Hylobius abietis in UK forestry. Forest Research Research Note, https://www.forestresearch.gov.uk/documents/607/FR_InterimguidanceonmanagementHylobiusabietis_2017.pdf (2017). Accessed 29 May 2022.Eidmann, H. H. & Lindelöw, A. Estimates and measurements of pine weevil feeding on conifer seedlings: Their relationships and application. Can. J. For. Res. 27, 1068–1073 (1997).Article 

Google Scholar 
Dillon, A. B., Moore, C. P., Downes, M. J. & Griffin, C. T. Evict of infect? Managing populations of the large pine weevil, Hylobius abietis, using bottom-up and topdown approach. For. Ecol. Manag. 255, 2634–2642 (2008).Article 

Google Scholar 
Moore, R., Brixey, J. M. & Milner, A. D. Effect of time of year on the development of immature stages of the large pine weevil (Hylobius abietis L.) in stumps of Sitka spruce (Picea sitchensis Carr.) and influence of felling date on their growth, density and distribution. J. Appl. Entomol. 128, 167–176 (2004).Article 

Google Scholar 
Inward, D. J. G., Wainhouse, D. & Peace, A. The effect on temperature on the development and life cycle regulation of the pine weevil Hylobius abietis and potential impacts of climate change. Agric. For. Entomol. 14, 348–357 (2012).Article 

Google Scholar 
Tudoran, A., Oltean, I. & Varga, M. Control and management of the pine weevil Hylobius abietis L. Bull. UASVM Hortic. 76, 1 (2019).
Google Scholar 
Wainhouse, D., Inward, G. & Morgan, G. Modelling geographical variation in voltinism of Hylobius abietis under climate change and implications for management. Agric. For. Entomol. 16, 136–146 (2014).Article 

Google Scholar 
Galko, J., Vakula, J., Kunca, A., Rell, S. & Gubka, A. Slovak technical standard no. 48 2712, Ochrana lesa proti tvrdoňom a lykokazom na sadeniciach (Forest protection against large pine weevil and bark beetles from genus Hylastes on seedlings), Slovak Office of Standards, Metrology and Testing, Bratislava (2016).Lalík, M. et al. Non-pesticide alternatives for reducing feeding damage caused by the large pine weevil (Hylobius abietis L.). Ann. Appl. Biol. 177, 132–142 (2020).Article 

Google Scholar 
Tudoran, A., Nordlander, G., Karlberg, A. & Puentes, A. A major forest insect pest, the pine weevil Hylobius abietis, is more susceptible to Diptera-than Coleoptera-targeted Bacillus thuringiensis strains. Pest Manag. Sci. 77, 1303–1315 (2020).Article 

Google Scholar 
Lalík, M. et al. Ecology, management and damage by the large pine weevil (Hylobius abietis) (Coleoptera: Curculionidae) in coniferous forests within Europe. Centr. Eur. For. J. 67, 91–107 (2021).
Google Scholar 
Örlander, G. & Nilsson, U. Effect of reforestation methods on pine weevil (Hylobius abietis) damage and seedling survival. Scand. J. For. Res. 14, 341–354 (1999).Article 

Google Scholar 
Petersson, M. & Örlander, G. Effectiveness of combinations of shelterwood, scarification, and feeding barriers to reduce pine weevil damage. Can. J. For. Res. 33, 64–73 (2003).Article 

Google Scholar 
Hardy, C., Sayyed, I., Leslie, A. D. & Dittrich, A. D. Effectiveness of insecticides, physical barriers and size of planting stock against damage by the pine weevil (Hylobius abietis). Crop Prot. 137, 105307 (2020).CAS 
Article 

Google Scholar 
Harvey, C. D., Williams, C. D., Dillon, A. B. & Griffin, C. T. Inundative pest control: How risky is it? A case study using entomopathogenic nematodes in a forest ecosystem. For. Ecol. Manag. 380, 242–251 (2016).Article 

Google Scholar 
Willoughby, I. H. et al. Are there viable chemical and non-chemical alternatives to the use of conventional insecticides for the protection of young trees from damage by the large pine weevil Hylobius abietis L. in UK forestry?. Forestry 93, 694–712 (2020).Article 

Google Scholar 
Eriksson, S., Wallertz, K. & Karlsson, A.-B. Test av mekaniska plantskyddmot snytbaggar i omarkberedd ochmarkberedd mark, anlagt våren 2015. Sveriges Lantbruksuniversitet Rapport 16. (2018) https://pub.epsilon.slu.se/15698/1/eriksson_s_et_al_181010.pdf (accessed 8 Dec 2021).Swedish Forest Agency [Skogsstyrelsen] Forest seedlings delivered for planting 2020. Sveriges Officiella Statistik, Statistiska Meddelanden JO 0313 SM 2001. (2021) https://www.skogsstyrelsen.se/globalassets/statistik/statistiska-meddelanden/sm-levererade-skogsplantor-2020.pdf (accessed 8 Dec 2021).Petersson, M., Örlander, G. & Nilsson, U. Feeding barriers to reduce damage by pine weevil (Hylobius abietis). Scand. J. For. Res. 19, 48–59 (2004).Article 

Google Scholar 
Petersson, M., Eriksson, S. & Zetterqvist, F. Mekaniska plantskydd mot snytbaggeskador, anlagt 2003. Slutrapport SLU, Asa försökspark, Rapport, 3. (2006) https://docplayer.se/137316277-Mekaniska-plantskydd-mot-snytbaggeskador-anlagt-2006.html (accessed 10 Dec 2021).Nordlander, G., Nordenhem, H. & Hellqvist, C. A flexible sand coating (Conniflex) for the protection of conifer seedlings against damage by the pine weevil Hylobius abietis. Agric. For. Entomol. 11, 91–100 (2009).Article 

Google Scholar 
Leslie, K. & Liddon, T. An integrated pest management strategy for Hylobius—the holy grail of forestry? Forest and Timber News, April 2017, 44–45. https://www.confor.org.uk/media/246596/integrated-pest-managment-strategy-for-hylobius-april-2017.pdf (2017). Accessed 29 May 2022.Moore, R., Willoughby, I. H., Moffat, A. J. & Forster, J. Acetamiprid, chlorantraniliprole, and in some situations the physical barriers MultiPro® or Kvaae® wax, can be alternatives to traditional synthetic pyrethroid insecticides for the protection of young conifers from damage by the large pine weevil Hylobius abietis L. Scand. J. For. Res. 36, 230–248 (2021).Article 

Google Scholar 
Willoughby, I. H. et al. Are there viable chemical and non-chemical alternatives to the use of conventional insecticides for the protection of young trees from damage by the large pine weevil Hylobius abietis L. in UK forestry?. Forestry 93, 694–712 (2020).Article 

Google Scholar 
Galko, J., Kunca, A., Gubka, A. & Vakula, J. Predstavenie nového spôsobu ošetrenia sadeníc voskom ako účinnej ochrany pred tvrdoňom smrekovým (Introduction of a new method of wax seedling treatment as an effective protection against large pine weevil). In Actual Problems in Forest Protections 2013 (Conference Proceedings), National Forest Centre, Zvolen, vol. 22 (ed. Kunca, A.) 86–89. (2013) http://www.los.sk/data/DownloadHandler.ashx?id=38&filename=15.pdf (accessed 1 Dec 2021).Galko, J. et al. Vyhodnotenie experimentov voskom ošetrených sadeníc, ako mechanickej ochrany proti tvrdoňovi smrekovému a návrh technologického postupu voskovania (Evaluation of experiments of wax-treated seedlings as mechanical protection against large pine weevil and design of technological process of waxing). In Actual Problems in Forest Protections 2015 (Conference Proceedings), National Forest Centre, Zvolen, vol. 24 (ed. Kunca, A.) 21–30. (2015) http://www.los.sk/data/DownloadHandler.ashx?id=512&filename=Galko.4-2015.pdf (accessed 8 June, 2021).Lalík, M. Modern biotechnological control of pine weevil (Hylobius abietis). Ph.D. thesis. Czech University of Life Sciences Prague (2021).Rell, S. Alternative methods of seedling protection against the large pine weevil (Hylobius abietis). PhD. thesis Technical University Zvolen. (2018).Norsk Wax, Substitution of insecticides with wax. (2016) http://www.metla.fi/tapahtumat/2016/taimitarhapaivat/Pettersen.pdf (accessed 29 Nov 2021).Wax information. https://www.norsk-wax.no/forestry (accessed 28 Nov 2021).Watson, P. G. Influence of insecticide, wax and biofungicide treatments, applied to Pinus sylvestris and Picea abies, on the olfactory orientation of the large pine weevil, Hylobius abietis L. (Coleoptera: Curculionidae). Agric. For. Entomol. 1, 171–177 (1999).Article 

Google Scholar 
Härlin, C. & Eriksson, S. Test av mekaniska plantskydd mot snytbaggar i omarkberedd och markberedd mark, anlagt våren 2012. Slutrapport. SLU, Enheten för skoglig fältforskning, Rapport 12. (2016) https://pub.epsilon.slu.se/13059/7/harlin_c_eriksson_s_160223.pdf (accessed 1 Dec 2021).Glue information. https://www.moudry-cz.com/cs/vermifix-lepidlo-ve-spreji-na-ochranu-stromu-a-rostlin/ (accessed 27 Nov 2021).Azeem, M. et al. Chemical composition and antifeedant activity of some aromatic plants against pine weevil (Hylobius abietis). Ann. Appl. Biol. 177, 121–131 (2020).CAS 
Article 

Google Scholar 
Unelius, C., Bohman, B. & Nordlander, G. Comparison of phenylacetates with benzoates and phenylpropanoates as antifeedants for the pine weevil, Hylobius abietis. J. Agric. Food Chem. 66, 11797–11805 (2018).CAS 
Article 

Google Scholar 
Fedderwitz, F., Björklund, N., Anngren, R., Lindström, A. & Nordlander, G. Can methyl jasmonate treatment of conifer seedlings be used as a tool to stop height growth in nursery forest trees?. New For. 51, 379–394 (2020).Article 

Google Scholar 
Chen, Y., Bylund, H., Björkman, C., Fedderwitz, F. & Puentes, A. Seasonal timing and recurrence of methyl jasmonate treatment influence pine weevil damage to Norway spruce seedlings. New For. 52, 431–448 (2021).Article 

Google Scholar 
Tahvonen, O., Pukkala, T., Laiho, O., Lähde, E. & Niinimäki, S. Optimal forest management of uneven-aged Norway spruce stands. For. Ecol. Manag. 260, 106–115 (2010).Article 

Google Scholar 
Saniga, M. & Kucbel, S. Prírode blízke pestovanie lesa. Technická univerzita vo Zvolene, Zvolen. ISBN 978-80-228-2411-8 (2012).Vencúrik, J., Jaloviar, P., Saniga, M. & Kucbel, S. Rast prirodzenej a umelej obnovy vo vybraných rekonštruovaných smrekových porastoch Oravských Beskýd: vedecká monografia. Technická univerzita vo Zvolene, Zvolen. ISBN 978-80-228-3017-1 (2017).Kunca, A. et al. Salvage felling in the Slovak Republic’s forests during the last twenty years (1998–2017). Cent. Eur. For. J. 65, 3–11 (2019).
Google Scholar 
Vakula, J. et al. Influence of selected factors on bark beetle outbreak dynamics in the Western Carpathians. Cent. Eur. For. J. 61, 149–156 (2015).
Google Scholar 
Barta, M. et al. Hypocrealean fungi associated with Hylobius abietis in Slovakia, their virulence against weevil adults and effect on feeding damage in laboratory. Forests 10, 634 (2019).Article 

Google Scholar 
Lalík, M. et al. Simple is best: Pine twigs are better than artificial lures for trapping of pine weevils in pitfall traps. Forests 10, 642 (2019).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.2. (2020) https://CRAN.R-project.org/package=dplyr (accessed 23 Nov 2021).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Petersson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: A semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2019).MathSciNet 
Article 

Google Scholar 
Lenth, R. V. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2021) https://CRAN.R-project.org/package=emmeans (accessed 23 Nov 2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.4.5. (2022) https://CRAN.R-project.org/package=DHARMa.Fay, M. P. Applied Statistical Hypothesis Tests. R package version 0.9.6. (2020) https://CRAN.R-project.org/package=asht (accessed 15 Nov 2021).Eriksson, S., Karlsson, A. & Härlin, C. Test av mekaniska plantskydd mot snytbaggar i omarkberedd och markberedd mark, anlagt våren 2013. Slutrapport Sveriges lantbruksuniversitet, Report, 15. (2017)https://pub.epsilon.slu.se/14040/7/eriksson_s_et_al_170214.pdf (accessed 17 Nov 2021).Härlin, C. & Eriksson, S. Test av mekaniska plantskydd och insekticider mot snytbaggar, anlagt våren 2010. Slutrapport. SLU, Enheten för skoglig fältforskning, Rapport 7. (2013) https://pub.epsilon.slu.se/10901/7/harlin_c_eriksson_s_131121.pdf (accessed 22 Nov 2021).Härlin, C. & Eriksson, S. Test av mekaniska plantskydd och insekticider mot snytbaggar i omarkberedd och markberedd mark, anlagt våren 2011. Slutrapport. SLU, Enheten för skoglig fältforskning, Rapport 10. (2014) https://pub.epsilon.slu.se/11603/7/harlin_c_etal_141023.pdf (accessed 19 Nov 2021).Öhrn, P. & Nordlander, G. WeevilSTOP—Field activities in Sweden 2015. (2015) https://www.weevilstop.com/project-results (accessed 30 Oct 2021).Final Report Summary—WEEVIL STOP https://cordis.europa.eu/project/id/315404/reporting/fr (accessed 3 Dec 2021).Moore, R., 2018. Hylobius Management Support System (MSS). https://www.forestresearch.gov.uk/tools-and-resources/tree-healthand-protection-services/hylobius-management-support-system/.Olenici, N., Bouriaud, O. & Manea, I. A. Efficient conifer seedling protection against pine weevil damage using neonicotinoids. Baltic For. 24, 201–209 (2018).
Google Scholar 
Viiri, H., Tuomainen, A. & Tervo, L. Persistence of deltamethrin against Hylobius abietis on Norway spruce seedlings. Scand. J. For. Res. 22, 128–135 (2007).Article 

Google Scholar 
Lalík, M. et al. Potential of Beauveria bassiana application via a carrier to control the large pine weevil. Crop Prot. 143, 105563 (2021).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

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