Ecology

Rands, M. R. et al. Biodiversity conservation: challenges beyond 2010. Science 329, 1298–1303 (2010).CAS 
PubMed 
Article 

Google Scholar 
Diaz, S., Fargione, J., Chapin, F. S. III & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).CAS 
PubMed 
Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 
Article 

Google Scholar 
Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).CAS 
PubMed 
Article 

Google Scholar 
Bascompte, J., García, M. B., Ortega, R., Rezende, E. L. & Pironon, S. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).CAS 
PubMed 
Article 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 
Article 

Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, D., Miller, J. E. D. & Harrison, S. Climate drives loss of phylogenetic diversity in a grassland community. Proc. Natl Acad. Sci. USA 116, 19989–19994 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).CAS 
PubMed 
Article 

Google Scholar 
Xue, K. et al. Annual removal of aboveground plant biomass alters soil microbial responses to warming. mBio https://doi.org/10.1128/mBio.00976-16 (2016).Zhou, J. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110 (2012).CAS 
Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 

Google Scholar 
Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).PubMed 
Article 

Google Scholar 
Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).Article 

Google Scholar 
Guo, X. et al. Climate warming accelerates temporal scaling of grassland soil microbial biodiversity. Nat. Ecol. Evol. 3, 612–619 (2019).PubMed 
Article 

Google Scholar 
Yuan, M. M. et al. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change 11, 343–348 (2021).Article 

Google Scholar 
Thakur, M. P. et al. Climate warming promotes species diversity, but with greater taxonomic redundancy, in complex environments. Sci. Adv. 3, e1700866 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Xu, X., Sherry, R. A., Niu, S., Li, D. & Luo, Y. Net primary productivity and rain‐use efficiency as affected by warming, altered precipitation, and clipping in a mixed‐grass prairie. Glob. Change Biol. 19, 2753–2764 (2013).Article 

Google Scholar 
Luo, Y., Sherry, R., Zhou, X. & Wan, S. Terrestrial carbon‐cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest. Glob. Change Biol. Bioenergy 1, 62–74 (2009).CAS 
Article 

Google Scholar 
Chen, M.-M. et al. Effects of soil moisture and plant interactions on the soil microbial community structure. Eur. J. Soil Biol. 43, 31–38 (2007).CAS 
Article 

Google Scholar 
Zhou, J. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 
Article 

Google Scholar 
DeBruyn, J. M., Nixon, L. T., Fawaz, M. N., Johnson, A. M. & Radosevich, M. Global biogeography and quantitative seasonal dynamics of Gemmatimonadetes in soil. Appl. Environ. Microbiol. 77, 6295–6300 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Horn, D. J. et al. Soil microbial responses to increased moisture and organic resources along a salinity gradient in a polar desert. Appl. Environ. Microbiol. 80, 3034–3043 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Van Nuland, M. E. et al. Warming and disturbance alter soil microbiome diversity and function in a northern forest ecotone. FEMS Microbiol. Ecol. 96, fiaa108 (2020).CAS 
PubMed 
Article 

Google Scholar 
Reimer, L. C. et al. Bac Dive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res. 50, D741–D746 (2022).CAS 
PubMed 
Article 

Google Scholar 
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ning, D. et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in respoÿnse to warming. Nat. Commun. 11, 4717 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tiedje, J. M. et al. Microbes and climate change: a research prospectus for the future. MBio, e00800-22 (2022). doi:10.1128/mbio.00800-22 (2022).Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. USA 112, 15684–15689 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, D., Zhou, X., Wu, L., Zhou, J. & Luo, Y. Contrasting responses of heterotrophic and autotrophic respiration to experimental warming in a winter annual‐dominated prairie. Glob. Change Biol. 19, 3553–3564 (2013).
Google Scholar 
Sherry, R. A. et al. Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie. Glob. Change Biol. 14, 2923–2936 (2008).Article 

Google Scholar 
Catchpole, W. & Wheeler, C. Estimating plant biomass: a review of techniques. Aust. J. Ecol. 17, 121–131 (1992).Article 

Google Scholar 
McLean, E. in Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (ed Page, A. L.) 199–224 (1982).Buyer, J. S. & Sasser, M. High throughput phospholipid fatty acid analysis of soils. Appl. Soil Ecol. 61, 127–130 (2012).Article 

Google Scholar 
Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wu, L. et al. Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis. BMC Microbiol. 15, 125 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhou, J. et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 5, 1303–1313 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).CAS 
PubMed 
Article 

Google Scholar 
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Munoz, R. et al. Release LTPs104 of the all-species living tree. Syst. Appl. Microbiol. 34, 169–170 (2011).PubMed 
Article 

Google Scholar 
Nuccio, E. E. et al. Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology 97, 1307–1318 (2016).PubMed 
Article 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).CAS 
PubMed 
Article 

Google Scholar 
Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (Babraham Bioinformatics, 2010).Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 
PubMed 
Article 

Google Scholar 
Patel, R. K. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE 7, e30619 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 

Google Scholar 
Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 

Google Scholar 
R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2014).Oksanen, J. et al. Package ‘vegan’. Community Ecology Package v.2 (2013).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 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2018).Barton, K. & Barton, M. K. Package ‘MuMIn’ v.1.18 (2015).Carver, R. Practical Data Analysis with JMP (SAS Institute, 2019).Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
Rosseel, Y. lavaan: An R Package for Structural Equation Modeling and More v.0.4-9 (BETA) (Ghent University, 2011). 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

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

This study reveals that various measures of bee diversity-including abundance, richness, and assemblage patterns are influenced by vane color and light reflectance patterns when passively sampling bees with vane traps. In particular, brightly colored vanes with higher light reflectance within 400–600 nm range attracted a greater diversity of bees in traps placed in a livestock pasture ecosystem. Effectiveness of blue and yellow vane traps had been compared previously in different ecosystems, for instance in apple orchards17, both woodland and open agriculture farmland13, and adjacent to Helianthus spp. (Asteraceae) field27. In all these studies, blue vane trap captured more bee species and 5–6 times more individuals compared to yellow vane trap.In the current study, we assessed a different design and size of vanes and a wider array of vane colors and reflectance patterns attached to sample collection jars. In particular, we used bright blue and yellow vanes that were made of plastic sheets covered with a micro-prismatic retro-reflective sheeting that provides better daytime and nighttime brightness as well as high visibility and durability. These vanes showed higher light reflectance and captured the most bees and bee species in this study (Table 2). Similar material was used on red vanes as well, but the light reflectance from those vanes was relatively lower, and as a result captured fewer bees. Traps with bright blue vanes performed especially well in terms of rates of bee capture (Fig. 2; 11.1 bees per trap per sampling date) and rates of species accumulation (Fig. 3). Bright yellow traps exhibited the second highest values for capture rates (Fig. 2; 6.6 bees per trap per sampling date) and species accumulation (Fig. 3), but these rates were not deemed significantly different from some other colors in which the reflective sheeting was not used, such as dark yellow, dark blue and purple.Bees use visual clues for detection, recognition, and memorization of floral resources in the foraging landscape7,28. The intensity of light reflected from different colors of vanes in traps affect number of bees attracted toward the trap10. Most bees can recognize colors that fall between 300 to 600 nm visual spectrums29. While the information related to the vision of many solitary and wild bees is not available, in the case of honey bees (Apis mellifera), color vision is trichromatic with highly sensitive photoreceptors at 344 nm (ultraviolet), 436 nm (blue) and 544 nm (green)30.In this study, colored vanes at a higher light reflectance between 400 to 600 nm attracted the highest number bee species in these passive traps. Capture rate differed among traps with different colored vanes in the current study, which can be explained by sensitivity of visual spectrum of bees and variation in the light reflectance of vanes of these traps. For example, bright blue vanes had two peaks of higher light reflectance, initially in 450–455 nm range and second peak with  > 800 nm. Such higher reflectance peak within the optimal range of bee vision may have played an important role in attracting abundant and diverse bee species to these passive traps. Similarly, bright yellow captured second largest number of bees, also had higher light reflectance peak within 600 nm but gradually decreased with increasing wavelength. Though bees have color spectrum from UV to orange31, they are sensitive to color spectrum between blue, green and ultraviolet32, which is a type of trichromatic vision system28. In one study33, red color vanes showed relatively lower light reflectance within 600 nm range, but had higher reflectance later in the spectrum, and this could be a reason why a low number of bees were collected in the traps. Past research showed contradictory views regarding the ability of bees to perceive red color. For instance, an early researcher in this field33, reported that bees recognize red color objects; however, other researchers had reported inability of bees to perceive34 or discriminate red from other colors35,36. It was argued that the bees see up to 650 nm in the visual spectrum and may not miss red colored flowers while foraging. However, other factors such as background (vegetation) color could also be contributing to bees’ ability to navigate different vane or flower colors in a livestock pasture landscape. Generally bees use color contrast to locate flower source, and hence neutral colors such as white are usually ignored29. Ultraviolet signal can make flowers more or less attractive to bees depending on whether it increases or decreases color contrast37. For example, UV color component in yellow38 and red39 flower increases chromatic contrast of these colored flowers with their background contributing attractiveness to the flowers. However, UV-reflecting white flowers decreases attractiveness for bees40.Different species of bees responded to different colors of vane traps. Out of the 49 bee species collected in this study, only nine bee species were found in all vane color types, whereas 14 species were found in only one trap color. For instance, out of five bumble bee species, two were found in all six vane colors, one was found in five colors, and two species (Bombus bimaculatus and B. fervidus) were only found in the traps with bright blue vanes. Many of the species that were only found in one trap color- Calliopsis andreniformis (1, bright yellow), Ceratina dupla (1, bright yellow), Diadasia afflicta (1, bright blue), Diadasia enavata (1, dark blue), Halictus rubicundus (1, dark yellow), Hylaeus mesillae (1, red), Lasioglossum tegulare (1, bright blue), Lasioglossum trigeminum (1, purple), Megachile montivaga (1, dark yellow), Melitoma taurea (1, bright blue), Svastra atripes (1, bright blue), and Triepeolus lunatus (1, dark yellow) were singletons and it was impossible to know if this represented a true preference or pattern. Our analysis of assemblage patterns after aggregating bees at the genus level, did show a gradient-like response in bee-color associations (Fig. 4), ranging from dark blue to yellows (with no strong associations found with red vanes). These patterns may be used to guide future (passive trap-based) sampling efforts to monitor bee diversity or to target specific bee species in livestock pastures or other ecosystems. While the bright blue and yellow traps with reflective sheeting were particularly attractive to bees, dark blue and purple traps also had relatively high levels of abundance and richness and collected higher number of Melissodes. Purple, as a color, is less commonly used than blue and yellow traps in bee monitoring. While this study shows that purple may be a viable option for bee collection, it’s similar assemblage pattern (Fig. 4) and low level of complementarity with dark blue traps (Table 2) suggests that it may be redundant with blue traps that are already commonly used. Differences in species- and sex-specific associations of bees with different colors of sampling traps had also been reported in previous studies41.Most of the bees collected in the current study were from Halictidae family (77.6%) followed by Apidae. However, few bee species in the families Andrenidae, Colletidae, and Megachilidae were collected. Consistent with our findings, others42 reported that bees of the Halictidae family were the most abundant bees in rangeland of Texas. The most common species found in this study were Au. aurata, L. disparile, L. imitatum, and Ag. texanus). In our previous studies we have found similar bee diversity in this study region18. Pollinator species richness and diversity as well as population distribution in livestock pasture vary during the season43. Mid-July to mid-August is the latter half of the summer season in the Southeastern USA, and the sampling period may have missed bee species that emerge earlier in the season and are reported in other studies42,43.Overall, the findings of this study showed that the wild bees responded differently to passive traps with colored vanes of different light wavelength and reflectivity when deployed in a livestock pasture ecosystem. Among six different colors of vanes (dark blue, bright blue, dark yellow, bright yellow, purple and red), the bright blue traps captured the highest number of individuals and species of bees. This could be due to an appropriate match between the visual spectrum of bees and the light reflectance spectrum of vanes, which were made of a micro-prismatic retro-reflective material. Bees responded similarly to traps with other colors of vanes, except for red vane traps, which captured the lowest number of bees. The findings of this study would be useful in understanding bee vision and responses to passive traps, and, such information would help in optimizing bee sampling methods for future monitoring efforts. 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

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

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