Philippa Kaur

Coffaro, G. & Bocci, M. Resources competition between Ulva rigida and Zostera marina: A quantitative approach applied to the Lagoon of Venice. Ecol. Model. 102(1), 81–95 (1997).CAS 
Article 

Google Scholar 
Araújo, C. V. et al. Feeding niche preference of the mudsnail Peringia ulvae. Mar. Freshw. Res. 66(7), 573–581 (2015).Article 

Google Scholar 
Whitfield, A. K. The role of seagrass meadows, mangrove forests, salt marshes and reed beds as nursery areas and food sources for fishes in estuaries. Rev. Fish Biol. Fish. 27(1), 75–110 (2017).Article 

Google Scholar 
Su, L. et al. The occurrence of microplastic in specific organs in commercially caught fishes from coast and estuary area of east China. J. Hazard. Mater. 365, 716–724 (2019).CAS 
PubMed 
Article 

Google Scholar 
Benassai, G. Introduction to Coastal Dynamics and Shoreline Protection (Wit Press, 2006).
Google Scholar 
Decho, A. W. Microbial biofilms in intertidal systems: An overview. Cont. Shelf Res. 20(10–11), 1257–1273 (2000).ADS 
Article 

Google Scholar 
Thompson, C. E., Amos, C. L. & Umgiesser, G. A comparison between fluid shear stress reduction by halophytic plants in Venice Lagoon, Italy and Rustico Bay, Canada—Analyses of in situ measurements. J. Mar. Syst. 51(1–4), 293–308 (2004).Article 

Google Scholar 
Neumeier, U. & Amos, C. L. Turbulence reduction by the canopy of coastal Spartina salt-marshes. J. Coast. Res. 53, 433–439 (2006).
Google Scholar 
Black, K. S., Tolhurst, T. J., Paterson, D. M. & Hagerthey, S. E. Working with natural cohesive sediments. J. Hydraul. Eng. 128(1), 2–8 (2002).Article 

Google Scholar 
Paterson, D. M. Short-term changes in the erodibility of intertidal cohesive sediments related to the migratory behavior of epipelic diatoms. Limnol. Oceanogr. 34(1), 223–234 (1989).ADS 
Article 

Google Scholar 
Tolhurst, T.J., Jesus, B., Brotas, V. & Paterson, D.M. Diatom migration and sediment armouring—An example from the Tagus Estuary, Portugal. in Migrations and Dispersal of Marine Organisms. 183–193. (Springer, 2003).Tinoco, R. O. & Coco, G. Observations of the effect of emergent vegetation on sediment resuspension under unidirectional currents and waves. Earth Surf. Dyn. 2(1), 83 (2014).ADS 
Article 

Google Scholar 
Chen, Y. et al. Differential sediment trapping abilities of mangrove and saltmarsh vegetation in a subtropical estuary. Geomorphology 318, 270–282 (2018).ADS 
Article 

Google Scholar 
Cozzolino, L., Nicastro, K. R., Zardi, G. I. & Carmen, B. Species-specific plastic accumulation in the sediment and canopy of coastal vegetated habitats. Sci. Total Environ. 723, 138018 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Widdows, J., Pope, N. D. & Brinsley, M. D. Effect of Spartina anglica stems on near-bed hydrodynamics, sediment erodability and morphological changes on an intertidal mudflat. Mar. Ecol. Prog. Ser. 362, 45–57 (2008).ADS 
Article 

Google Scholar 
Marion, C., Anthony, E. J. & Trentesaux, A. Short-term (≤ 2 yrs) estuarine mudflat and saltmarsh sedimentation: High-resolution data from ultrasonic altimetery, rod surface-elevation table, and filter traps. Estuar. Coast. Shelf Sci. 83(4), 475–484 (2009).ADS 
Article 

Google Scholar 
Coulombier, T., Neumeier, U. & Bernatchez, P. Sediment transport in a cold climate salt marsh (St. Lawrence Estuary, Canada), the importance of vegetation and waves. Estuar. Coast. Shelf Sci. 101, 64–75 (2012).ADS 
Article 

Google Scholar 
Neumeier, U. & Ciavola, P. Flow resistance and associated sedimentary processes in a Spartina maritima salt-marsh. J. Coast. Res. 20(2), 435–447 (2002).
Google Scholar 
Yao, W. et al. Micro-and macroplastic accumulation in a newly formed Spartina alterniflora colonized estuarine saltmarsh in southeast China. Mar. Pollut. Bull. 149, 110636 (2019).CAS 
Article 

Google Scholar 
Fok, L. & Cheung, P. K. Hong Kong at the Pearl River Estuary: A hotspot of microplastic pollution. Mar. Pollut. Bull. 99(1–2), 112–118 (2015).CAS 
PubMed 
Article 

Google Scholar 
Weinstein, J. E., Crocker, B. K. & Gray, A. D. From macroplastic to microplastic: Degradation of high-density polyethylene, polypropylene, and polystyrene in a salt marsh habitat. Environ. Toxicol. Chem. 35(7), 1632–1640 (2016).CAS 
PubMed 
Article 

Google Scholar 
Willis, K. A., Eriksen, R., Wilcox, C. & Hardesty, B. D. Microplastic distribution at different sediment depths in an urban estuary. Front. Mar. Sci. 4, 419 (2017).Article 

Google Scholar 
Stead, J. L. et al. Identification of tidal trapping of microplastics in a temperate salt marsh system using sea surface microlayer sampling. Sci. Rep. 10(1), 1–10 (2020).Article 
CAS 

Google Scholar 
Friend, P. L., Ciavola, P., Cappucci, S. & Santos, R. Bio-dependent bed parameters as a proxy tool for sediment stability in mixed habitat intertidal areas. Cont. Shelf Res. 23(17–19), 1899–1917 (2003).ADS 
Article 

Google Scholar 
Hurley, R., Woodward, J. & Rothwell, J. J. Microplastic contamination of river beds significantly reduced by catchment-wide flooding. Nat. Geosci. 11(4), 251–257 (2018).ADS 
CAS 
Article 

Google Scholar 
Ockelford, A., Cundy, A. & Ebdon, J. E. Storm response of fluvial sedimentary microplastics. Sci. Rep. 10(1), 1–10 (2020).Article 
CAS 

Google Scholar 
Wang, J. Q. et al. Bioturbation of burrowing crabs promotes sediment turnover and carbon and nitrogen movements in an estuarine salt marsh. Ecosystems 13(4), 586–599 (2010).CAS 
Article 

Google Scholar 
Soulsby, R.L.. The bottom boundary layer of shelf seas. in Elsevier Oceanography Series. Vol. 35. 189–266. (Elsevier, 1983).Thompson, C. E., Amos, C. L., Lecouturier, M. & Jones, T. E. R. Flow deceleration as a method of determining drag coefficient over roughened flat beds. J. Geophys. Res. Oceans 109, C3 (2004).
Google Scholar 
Chirol, C. et al. The influence of bed roughness on turbulence: Cabras Lagoon, Sardinia, Italy. J. Mar. Sci. Eng. 3(3), 935–956 (2015).Article 

Google Scholar 
Kassem, H., Sutherland, T. F. & Amos, C. L. Hydrodynamic controls on the particle size of resuspended sediment from sandy and muddy substrates in British Columbia, Canada. J. Coast. Res. 37, 691 (2021).CAS 
Article 

Google Scholar 
Nepf, H. M. Flow and transport in regions with aquatic vegetation. Annu. Rev. Fluid Mech. 44, 123–142 (2012).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Bouma, T. J. et al. Density-dependent linkage of scale-dependent feedbacks: A flume study on the intertidal macrophyte Spartina anglica. Oikos 118(2), 260–268 (2009).Article 

Google Scholar 
Amos, C. L. et al. The stability of tidal flats in Venice Lagoon—The results of in-situ measurements using two benthic, annular flumes. J. Mar. Syst. 51(1–4), 211–241 (2004).Article 

Google Scholar 
Amos, C. L., Feeney, T., Sutherland, T. F. & Luternauer, J. L. The stability of fine-grained sediments from the Fraser River Delta. Estuar. Coast. Shelf Sci. 45(4), 507–524 (1997).ADS 
CAS 
Article 

Google Scholar 
Tolhurst, T.J., Gust, G., & Paterson, D.M. The influence of an extracellular polymeric substance (EPS) on cohesive sediment stability. in Proceedings in Marine Science. Vol. 5. 409–425. (Elsevier, 2002).Brückner, M. Z. et al. Benthic species as mud patrol-modelled effects of bioturbators and biofilms on large-scale estuarine mud and morphology. Earth Surf. Proc. Land. 46(6), 1128–1144 (2021).ADS 
Article 

Google Scholar 
Ferdowsi, B., Ortiz, C. P., Houssais, M. & Jerolmack, D. J. River-bed armouring as a granular segregation phenomenon. Nat. Commun. 8(1), 1–10 (2017).CAS 
Article 

Google Scholar 
Andersen, T. J., Jensen, K. T., Lund-Hansen, L., Mouritsen, K. N. & Pejrup, M. Enhanced erodibility of fine-grained marine sediments by Hydrobia ulvae. J. Sea Res. 48(1), 51–58 (2002).ADS 
Article 

Google Scholar 
Orvain, F., Sauriau, P. G., Sygut, A., Joassard, L. & Le Hir, P. Interacting effects of Hydrobia ulvae bioturbation and microphytobenthos on the erodibility of mudflat sediments. Mar. Ecol. Prog. Ser. 278, 205–223 (2004).ADS 
Article 

Google Scholar 
Orvain, F., Sauriau, P. G., Bacher, C. & Prineau, M. The influence of sediment cohesiveness on bioturbation effects due to Hydrobia ulvae on the initial erosion of intertidal sediments: A study combining flume and model approaches. J. Sea Res. 55(1), 54–73 (2006).ADS 
Article 

Google Scholar 
Widdows, J. et al. Inter-comparison between five devices for determining erodability of intertidal sediments. Cont. Shelf Res. 27(8), 1174–1189 (2007).ADS 
Article 

Google Scholar 
Amos, C. L. et al. The stability of a mudflat in the Humber estuary, South Yorkshire, UK. Geol. Soc. Lond. Spec. Publ. 139(1), 25–43 (1998).ADS 
Article 

Google Scholar 
Tolhurst, T. J., Black, K. S. & Paterson, D. M. Muddy sediment erosion: Insights from field studies. J. Hydraul. Eng. 135(2), 73–87 (2009).Article 

Google Scholar 
Quaresma, V. D. S., Bastos, A. C. & Amos, C. L. Sedimentary processes over an intertidal flat: A field investigation at Hythe flats, Southampton Water (UK). Mar. Geol. 241(1–4), 117–136 (2007).ADS 
Article 

Google Scholar 
Helcoski, R., Yonkos, L. T., Sanchez, A. & Baldwin, A. H. Wetland soil microplastics are negatively related to vegetation cover and stem density. Environ. Pollut. 256, 113391 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rochman, C. M. et al. Classify plastic waste as hazardous. Nature 494(7436), 169–171 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barboza, L. G. A., Vethaak, A. D., Lavorante, B. R., Lundebye, A. K. & Guilhermino, L. Marine microplastic debris: An emerging issue for food security, food safety and human health. Mar. Pollut. Bull. 133, 336–348 (2018).CAS 
PubMed 
Article 

Google Scholar 
de Barros, M. S. F., dos Santos Calado, T. C., Silva, A. S. & dos Santos, E. V. Ingestion of plastic debris affects feeding intensity in the rocky shore crab Pachygrapsus transversus Gibbes 1850 (Brachyura: Grapsidae). Int. J. Biodivers. Conserv. 12(1), 113–117 (2020).
Google Scholar 
Villagran, D. M., Truchet, D. M., Buzzi, N. S., Lopez, A. D. F. & Severini, M. D. F. A baseline study of microplastics in the burrowing crab (Neohelice granulata) from a temperate southwestern Atlantic estuary. Mar. Pollut. Bull. 150, 110686 (2020).CAS 
PubMed 
Article 

Google Scholar 
Townend, I. A Conceptual Model of Southampton Water. Vol 1. (Tech. Rep.). ABPmer report.. http://www.estuary-guide.net/pdfs/southampton_water_case_study.pdf. Accessed 21 May 2008 (ABP Marine Environmental Research Ltd., 2008).Amos, C. L., Grant, J., Daborn, G. R. & Black, K. Sea carousel—A benthic, annular flume. Estuar. Coast. Shelf Sci. 34(6), 557–577 (1992).ADS 
Article 

Google Scholar 
Thompson, C. E., Amos, C. L., Jones, T. E. R. & Chaplin, J. The manifestation of fluid-transmitted bed shear stress in a smooth annular flume-a comparison of methods. J. Coast. Res. 1, 1094–1103 (2003).
Google Scholar 
Buls, T., Anderskouv, K., Friend, P. L., Thompson, C. E. & Stemmerik, L. Physical behaviour of Cretaceous calcareous nannofossil ooze: Insight from flume studies of disaggregated chalk. Sedimentology 64(2), 478–507 (2017).Article 

Google Scholar 
Tuprakay, S., Usahanunth, N. & Tuprakay, S. R. A study bakelite plastics waste from industrial process in concrete products as aggregate. Int. J. Struct. Civ. Eng. Res. 6(4), 7 (2017).
Google Scholar 
Thompson, C. E. L., Couceiro, F., Fones, G. R. & Amos, C. L. Shipboard measurements of sediment stability using a small annular flume—Core mini flume (CMF). Limnol. Oceanogr. Methods 11(11), 604–615 (2013).Article 

Google Scholar 
Kassem, H., Thompson, C. E., Amos, C. L. & Townend, I. H. Wave-induced coherent turbulence structures and sediment resuspension in the nearshore of a prototype-scale sandy barrier beach. Cont. Shelf Res. 109, 78–94 (2015).ADS 
Article 

Google Scholar 
Kassem, H. et al. Observations of nearbed turbulence over mobile bedforms in combined, collinear wave-current flows. Water 12(12), 3515 (2020).CAS 
Article 

Google Scholar 
Elgar, S., Raubenheimer, B. & Guza, R. T. Quality control of acoustic Doppler velocimeter data in the surfzone. Meas. Sci. Technol. 16(10), 1889 (2005).ADS 
CAS 
Article 

Google Scholar 
Goring, D. G. & Nikora, V. I. Despiking acoustic Doppler velocimeter data. J. Hydraul. Eng. 128(1), 117–126 (2002).Article 

Google Scholar 
Mori, N., Suzuki, T. & Kakuno, S. Noise of acoustic Doppler velocimeter data in bubbly flows. J. Eng. Mech. 133(1), 122–125 (2007).
Google Scholar 
Stapleton, K. R. & Huntley, D. A. Seabed stress determinations using the inertial dissipation method and the turbulent kinetic energy method. Earth Surf. Proc. Land. 20(9), 807–815 (1995).ADS 
Article 

Google Scholar 
Dyer, K. Estuaries, A Physical Introduction. 2nd edn. https://doi.org/10.2307/1797104 (Wiley, 1997). More

Deli, J., Matus, Z. & Tóth, G. Carotenoid composition in the fruits of asparagus officinalis. J. Agric. Food Chem. 48, 2793–2796 (2000).CAS 
PubMed 

Google Scholar 
Howard, L. R., Talcott, S. T., Brenes, C. H. & Villalon, B. Changes in phytochemical and antioxidant activity of selected pepper cultivars (Capsicum species) as influenced by maturity. J. Agric. Food Chem. 48, 1713–1720 (2000).CAS 
PubMed 

Google Scholar 
Odgerel, B. & Tserendulam, D. Effect of Chlorella as a biofertilizer on germination of wheat and barley grains. P. Mongolian Acad. Sci. 56(4), 26–31 (2016).
Google Scholar 
Sun, R. B., Guo, X. S., Wang, D. Z. & Chua, H. Y. Effects of long-term application of chemical and organic fertilizers on the abundance of microbial communities involved in the nitrogen cycle. Appl. Soil Ecol. 95(6), 171–178 (2015).
Google Scholar 
Yu, Y. Q., Luo, Z. B., Fu, H. & Jin, Y. Effect of balanced nutrient fertilizer: A case study in Pinggu District, Beijing China. Sci. Total Environ. 754, 1–8 (2021).
Google Scholar 
Ahmad, P. et al. Role of transgenic plants in agriculture and biopharming. Biotech. Adv. 30, 524–540 (2012).CAS 

Google Scholar 
Sherlock, R. & Morrey, J. D. Ethical Issues in Biotechnology (Rowman and Littlefield. Publishers, Inc., 2002).
Google Scholar 
Schiavon, M., Ertani, A. & Nardi, S. Effects of an alfalfa protein hydrolysate on the gene expression and activity of enzymes of TCA cycle and N metabolism in Zea mays L. J. Agric. Food Chem. 56, 11800–11808 (2008).CAS 
PubMed 

Google Scholar 
Muscolo, A., Sidari, M. & Nardi, S. Humic substance: relationship between structure and activity. Deeper information suggests univocal findings. J. Geochem. Explor. 129, 57–63 (2013).CAS 

Google Scholar 
Nardi, S., Carletti, P., Pizzeghello, D., Muscolo, A. Biological activities of humic substances. In Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems. PART I. Fundamentals and Impact of Mineral-Organic-Biota Interactions on the Formation, Transformation, Turnover, and Storage of Natural Nonliving Organic Matter (NOM). (Ed. Senesi, N., Xing, B., Huang, P.M.) 301–335 (John Wiley and Sons, Hoboken, 2009).Ertani, A., Nardi, S. & Altissimo, A. Review: long-term research activity on the biostimulant properties of natural origin compounds. Acta Hort. 1009, 181–188 (2013).
Google Scholar 
Ertani, A. et al. Biostimulant activity of two protein hydrolysates on the growth and nitrogen metabolism in maize seedlings. J. Plant Nutr. Soil Sci. 172, 237–244 (2009).CAS 

Google Scholar 
Vaccaro, S. et al. Effect of a compost and its water-soluble fractions on key enzymes of nitrogen metabolism in maize seedlings. J. Agric. Food Chem. 57, 11267–11276 (2009).CAS 
PubMed 

Google Scholar 
Azcona, I. et al. Growth and development of pepper are affected by humic substances derived from composted sludge. J. Plant Nutr. Soil Sci. 174, 916–924 (2011).CAS 

Google Scholar 
Schiavon, M. et al. High molecular size humic substances enhance phenylpropanoid metabolism in maize (Zea mays L.). J. Chem. Ecol. 36, 662–669 (2010).CAS 
PubMed 

Google Scholar 
Ertani, A., Schiavon, M., Muscolo, A. & Nardi, S. Alfalfa plant-derived biostimulant stimulates short-term growth of salt stressed Zea mays L. plants. Plant Soil 364, 145–158 (2013).CAS 

Google Scholar 
Pascual, I., Azcona, I., Morales, F., Aguirreolea, J. & Sanchez-Diaz, M. Growth, yield and physiology of verticillium-inoculated pepper plants treated with ATAD and composted sewage sludge. Plant Soil 319, 291–306 (2009).CAS 

Google Scholar 
Pascual, I. et al. Growth, yield and fruit quality of pepper plants amended with two sanitized sewage sludges. J. Agric. Food Chem. 58, 6951–6959 (2010).CAS 
PubMed 

Google Scholar 
Kim, M. J., Shim, C. K., Kim, Y. K., Ko, B. G. & Kim, B. H. Effect of Biostimulator Chlorella fusca on improving growth and qualities of Chinese Chives and Spinach in organic farm. Plant Pathol. J. 34(6), 567–574 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pulz, O. & Gross, W. Valuable products from biotechnology of microalgae. Appl. Microbiol. Biot. 65, 635–648 (2004).CAS 

Google Scholar 
Kim, S. J., Ko, E. J., Hong, J. K. & Jeun, Y. C. Ultrastructures of Colletotrichum orbiculare in cucumber leaves expressing systemic acquired resistance mediated by Chlorella fusca. Plant Pathol. J. 34(2), 113–120 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Faheed, F. A. & Abd-El Fattah, Z. Effect of Chlorella vulgaris as bio-fertilizer on growth parameters and metabolic aspects of Lettuce Plant. J. Agri. Soc. Sci. 4, 165–169 (2008).
Google Scholar 
Agwa, O. K., Ogugbue, C. J. & Williams, E. E. Field evidence of Chlorella vulgaris potentials as a biofertilizer for Hibiscus esculentus. Int. J. Agric. Res. 12(4), 181–189 (2017).CAS 

Google Scholar 
Ördög, V. et al. Screening microalgae for some potentially useful agricultural and pharmaceutical secondary metabolites. J. Appl. Physicol. 16, 309–401 (2004).
Google Scholar 
Stirk, W. A., Novák, O., Strnad, M. & van Staden, J. Cytokinins in macroalgae. Plant Growth Regul. 41, 13–24 (2003).CAS 

Google Scholar 
Kholssi, R., Marks, E. A. N., Montero, J. M. O., Debdoubi, A. & Rad, C. Biofertilizing effect of Chlorella sorokiniana suspensions on wheat growth. J. Plant Growth Regul. 38, 644–649 (2019).CAS 

Google Scholar 
Stirk, W. A., Ördög, V., Van Staden, J. & Jäger, K. Cytokinin-and auxin-like activity in Cyanophyta and microalgae. J. Appl. Phycol. 14, 215–221 (2002).CAS 

Google Scholar 
Park, E. R., Jo, J. O., Kim, S. M., Lee, M. Y. & Kim, K. S. Volatile flavor component of leek (Allium tuberosum Rotter). J. Korean Soc. Food Sci. Nutr. 27, 563–567 (1998) ((in Korean)).CAS 

Google Scholar 
Jin, H. et al. Ultrahigh-cell-density heterotrophic cultivation of the unicellular green microalga Scenedesmus acuminatus and application of the cells to photoautotrophic culture enhance biomass and lipid production. Biotechnol. Bioeng. 117, 96–108 (2020).CAS 
PubMed 

Google Scholar 
Kim, M. J., Shim, C. K., Kim, Y. K., Hong, S. J. & Kim, S. C. Isolation and morphological identification of fresh water green algae from organic farming habitats in Korea. Korean J. Org. Agric. 22, 743–760 (2014).
Google Scholar 
Li, L., Tian, S. L., Jiang, J. & Wang, Y. Regulation of nitric oxide to Capsicum under lower light intensities. S. Afr. J. Bot. 132, 268–276 (2020).CAS 

Google Scholar 
Cho, Y. Y., Oh, S. B., Oh, M. M. & Son, J. E. Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Sci. Hortic-Amst. 111, 330–334 (2007).
Google Scholar 
Oster, U., Tanaka, R., Tanaka, A. & Rudiger, W. Cloning and functional expression of gene encoding the key enzyme for chlorophyll b biosynthesis (CAO) from Arabidopsis thaliana. Plant J. 21(3), 305–310 (2000).CAS 
PubMed 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72(1–2), 248–254 (1976).CAS 
PubMed 

Google Scholar  More

MaterialsWeinan city is located in the middle reaches of the Yellow River and in the southern part of the Loess Plateau (34°13’–35°52’N, 108°58’–110°35’E) (Fig. 1). It has a temperate semihumid, semiarid climate. The modern MAT observations indicate a value of 13.8 °C, and MAP is 570 mm; these values were obtained from the China meteorological data network, comprising the meteorological data of 2000–2015 (http://data.cma.cn/). Weinan has four distinct seasons, with hot and rainy conditions occurring in the same season. Much of the annual precipitation falls from June to August. The Weinan profile contains 42.8 m of loess–paleosol sequences (LPSs), including five paleosol layers from S0–S4 and five loess layers from L1–L5 and covering five glacial–interglacial cycles. The sampling method involved collecting one sample every 10 cm without interruption. A total of 427 samples were collected from this profile.Modern brGDGTs dataset and MAP datasetPreviously published brGDGTs data from surface soil samples were extracted using an established brGDGT-MAP model. The surface soil samples contain various types of soil and cover nearly all climatic and latitudinal zones. These datasets contain 712 surface soil samples, which all have separated 5-methyl and 6-methyl brGDGTs isomers (Table 1). To reduce the errors in collecting data from different laboratories, the MAP datasets we entered into the brGDGT-MAP model were all published in their previous studies, and we calculated the fractional abundances of each brGDGTs compound for each sample (Table 1), although there were no data regarding changes in soil occurring based on the brGDGTs indices among various laboratories. To eliminate and test the error of the previous MAP dataset, in this study, we also extracted each soil site’s multiyear MAP (1990–2020) through TerraClimate, which is a dataset of high-spatial-resolution monthly climate for global terrestrial surfaces (1/24°, ∼4 km)48. TerraClimate datasets reveal significant advances in the overall mean absolute error and enhance spatial realism compared with coarser resolution gridded datasets. Supplementary Fig. 3 shows that the two MAP datasets have high correlations, with only a few sites exhibiting large deviations. In this study, we entered these two MAP datasets into the DLNN model to obtain the most suitable DLNN-MAP model.Grain-size and magnetic susceptibility measurementsSamples at 10-cm intervals were dried in an oven at 40 °C for 3 days. Then, 0.2 g of each sample was weighed using a clean beaker with an electronic balance. Then, 10 ml of 30% H2O2 and 10 ml 10% HCl were added to remove organic matter and carbonate, respectively. Before the grain-size measurement, 0.05 mol/L (NaPO3)6 was added, and the solutions were placed in an ultrasonic machine for 10 min. The magnetic susceptibility of the samples were measured with an MS-2B Bartington meter. The grain-size was measured using a Mastersizer 2000 produced by Marvern Company in the UK, with an error of less than 1%.ChronologyWe used the ages of LPS control points on the Loess Plateau to obtain the age of each sample in the Weinan profile40. We used the magnetic susceptibility as an indicator of the accumulation rate39 combined with the U–230Th-dated oxygen isotope records from Sanbao caves in central China14. Each sample’s magnetic susceptibility was analyzed at 10-cm intervals (Supplementary Fig. 7). The calculation was as follows:$${T}_{{{{{{rm{m}}}}}}}={T}_{1}+frac{left({sum }_{i=1}^{m}{a}_{i}{s}_{i}right)left({T}_{2}-{T}_{1}right)}{{sum }_{i=1}^{n}{a}_{i}{s}_{i}}$$
(1)
where T1 and T2 indicate the ages of the control points, ai indicates the thickness of the layer, and si indicates the magnetic susceptibility of the layer.GDGTs analysisLipids in a total of 238 LPS samples were extracted, including the 198 samples reported in ref. 49. Forty samples at depths from 34.9 m to 43.7 m were selected every 20 cm intervals from the Weinan profile, and dried in an oven at 40 °C for 3 days. Afterward, the loess and paleosol samples were ground into powder and passed through a 60-mesh sieve. Each sample was weighed and extracted with 80 ml of methanol: dichloromethane (DCM) (1:9, v/v) using accelerated solvent extractors (ASE 100 or 150, Dionex, USA). The temperature and pressure were set at 100 °C and 1400 psi, respectively. Then, the lipid extracts were condensed in a rotary evaporator at 40 °C and separated into apolar and polar fractions on a flash silica gel column (0.7 cm i.d. and 1.5 g activated silica gel) chromatography using n-hexane and methanol as eluents, respectively. All polar components were passed through a 0.45-µm PTFE syringe filter. All apolar and polar compositions were dried under a gentle stream of nitrogen gas.The GDGTs were analyzed by using an Agilent 1200 series liquid chromatography-atmospheric pressure chemical ionization-6460A triple quadrupole mass spectrometry (LC-APCI-MS/MS). Ten microlitres of C46 GTGTs (0.001157 μg/μl) were added to each polar fraction, and the samples were then dissolved in 300 μl of n-hexane: iso-isopropanol (IPA) (98.2:1.8, v/v)). Two silica gel columns in series (150 mm × 2.1 mm, 1.9 μm, Thermo Finnigan; USA) were used for the separation of 5-methyl and 6-methyl brGDGTs, with the column temperature kept at 40 °C. The mass spectrometry settings were as follows: the vaporizer pressure 60 psi, the vaporizer temperature 400 °C, the flow rate of dry gas (N2) 6 l/min, drying gas temperature 200 °C, the capillary voltage 3500 V, the corona current 5 μA (∼3200 V), and a single-ion monitoring mode (SIM) was used50, targeting the protonated molecular ions ([M + H]+) 1304, 1302, 1300, 1298, 1296, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1020, 1018, and 744.The MATmr proxy was calculated to identify the changes that occurred in the mean annual temperature in the Weinan section over the last 430 ka. The calculation was as follows24,51.$${{MAT}}_{{mr}} =7.17+17.1*[{Ia}]+25.9*[{Ib}]+34.4*[{Ic}]-28.6*[{IIa}],(n=222,,{R}^{2} \ =0.68,; {RM}{SE}=4.6 {deg} {{{rm{C}}}},,P ; < ; 0.01)$$ (2) $${{MAT}}_{{mr}}=5.58+17.91*[{Ia}]-18.77*[{IIa}]$$ (3) $${MAT}({SSM})= 20.9-13.4*[{IIa}+{IIa}^{{prime}}]-17.2*[{IIIa}+{IIIa}^{{prime}}]\ -17.5*[{IIb}+{IIb}^{{prime}}]+11.2*[{Ib}]$$ (4) $${MAAT}=0.81-5.67*{CBT}+31.0*{MBT}^{{prime}}$$ (5) The soil pH was calculated using the following formulas24.$${pH}=7.15+1.59*{CBT}^{{prime}}(n=221,,{R}^{2}=0.85,,{RMSE}=0.52,, P , < ,0.0001)$$ (6) $${{CBT}}^{{prime} }=-{{log }}frac{{Ic}+{II}{a}^{{prime}}+{II}{b}^{{prime}}+{{IIc}}^{{prime} }+{{IIIa}}^{{prime} }+{III}{b}^{{prime} }+{{IIIc}}^{{prime} }}{{Ia}+{Ib}+{Ic}}$$ (7) SWC is well correlated with MBT’ when IR6ME  > 0.5, and these proxies were calculated using the following expressions:$${{MBT}}^{{prime} }=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{IIa}+{{IIa}}^{{prime} }+{IIb}+{{IIb}}^{{prime} }+{IIc}+{{IIc}}^{{prime} }+{IIIa}+{IIIa}^{prime} )}$$
(8)
$${{IR}}_{6{ME}}=frac{sum (C6-{methylated; brGDGTs})}{sum {brGDGTs}}$$
(9)
$${{MBT}^{prime} }_{6{ME}}=frac{({Ia}+{Ib}+{Ic})}{({Ia}+{Ib}+{Ic}+{{IIa}}^{{prime} }+{{IIb}}^{{prime} }+{{IIc}}^{{prime} }+{IIIa}^{prime} )}$$
(10)
where the Roman numerals indicate different brGDGTs structures (Supplementary Fig. 1).Principal component analysis (PCA)CANOCO version 5 software was utilized to reveal the relationships among various environmental factors. The first PCA figure (Fig. 3a) was generated for the environmental factors MAT, MAPc, SWC, and pH. These variables are based on the same dataset (238 LSPs samples from Weinan profile) without any data transformation. The second PCA figure (Fig. 3b) was generated for the environmental factors MAT, MAP (based on 10Be), SWC and pH, which were all in the transition of the glacial–interglacial after 430 ka BP on the CLP. As the two LSPs profile had similar sedimentation rates, we obtained the same chronological control through linear interpolation in those transition periods. All datasets passed the Gaussian distribution test in this study.Cross wavelet analysisCompared with wavelet special analysis, cross wavelet analysis is even more complicated. The wavelet cross-spectrum can be defined as follows:$${CS}left(b,, a right)={m}_{1c}(b,, a){m}_{2c}(b,, a)$$
(11)
where ({m}_{1c}) and ({m}_{2c}) describe the covarying fractions of the overall spectra given by:$${m}_{1}left(b,, a right)={m}_{1c}left(b,, a right)+{m}_{1i}(b,, a)$$
(12)
$${m}_{2}left(b,, a right)={m}_{2c}left(b,, a right)+{m}_{2i}left(b,, a right)$$
(13)
where ({m}_{1i}) and ({m}_{2i}) are independent contributions to the variance.Overall, this is a multipart function that may be decomposed into amplitude and phase:$${CS}left(b,, a right)={{{{{rm{|}}}}}}{CS}left(b,, a right){{{{{rm{|}}}}}}{{exp }}(i;{{arg }}({CS}(b,, a)))$$
(14)
In this study, a and b represent the array of reconstructed MAPc and the Sanbao speleothem δ18O, respectively.Multiple regression linear modelTo compare the precision of the DLNN-MAP model we established, we set up a multiple regression linear model based on all 6-methyl brGDGTs except Ib. The basis of the model is defined as:$$y=a+{b}_{1}{x}_{1}+{b}_{2}{x}_{2}ldots+{b}_{n}{x}_{n}$$
(15)
where y represents MAP, x represents all 6-methyl brGDGTs and Ia and Ic, and a, b1, b2…bn represent the partial regression coefficients. n represents the number of 6-methyl we entered into the model (in this study, n = 8).The multiple correlation coefficient (R) was defined as follows:$$R=sqrt{frac{{sum }_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}}{{sum }_{i=1}^{n}{({y}_{i}-bar{y})}^{2}}}$$
(16)
where ({y}_{i}) represents the actual observed value, ({hat{y}}_{i}) represents the calculation value and (bar{y}) represents the mean value of all observed data.The t statistic is used to test the validity of regression coefficients, and it can be defined as follows:$${t}_{{b}_{j}}=frac{{b}_{j}}{{s}_{{b}_{j}}}$$
(17)
$${s}_{{b}_{j}}=sqrt{{p}_{{jj}}}*s$$
(18)
$$s=sqrt{frac{1}{n-m-1}mathop{sum }limits_{i=1}^{n}{({y}_{i}-{hat{y}}_{i})}^{2}}$$
(19)
$$P={({p}_{{jj}})}^{-1}=mathop{sum }limits_{i=1}^{n}({x}_{{ij}}-{bar{x}}_{j})({x}_{{ik}}-{bar{x}}_{k})$$
(20)
where ({b}_{j}) represents the regression coefficient of ({x}_{j}), n represents the number of samples and m represents the number of variables.The F statistic is used to test the linear relationship of variables and can be defined as follows:$$F=frac{1}{m{s}^{2}}mathop{sum }limits_{i=1}^{n}{({hat{y}}_{i}-bar{y})}^{2}$$
(21)
The variance inflation factor (VIF) is used to measure collinearity between variables and can be defined as follows:$${{VIF}}_{j}=frac{1}{1-{R}_{j}^{2}}$$
(22)
As shown in Supplementary Fig. 5, we found no obvious collinearity between different variables. However, there are fewer contributions in IIc’, IIIa’, IIIb’, and IIIc’ in the multiple regression linear model we established, and the value of t cannot attain the 95% confidence level (Table 2). The results of both the training dataset and extrapolated experimental dataset (Supplementary Fig. 6), although they seem good (R2 = 0.44 and 0.46, respectively), still have a considerable gap compared with the DLNN-MAP model. Especially when MAP  > 1500 mm, the multiple linear model is unable to forecast the real MAP. These results all indicate that the MAP influence on the brGDGTs compounds is not a simple linear relationship; instead, we suggest that there are complex pilot processes between them.Table 2 List of the parameters of the multiple linear modelFull size tableDLNN modelsDLNNs usually contain an input layer, a few hidden layers, and an output layer. A conceptual structure of the DLNN model was established for forecasting MAP values. The first layer accepts input signals that are various combinations of brGDGTs. The relationships among different variables are processed and analyzed in the hidden layers. The final class output is presented in the output layer; in this study, the output is the MAP reconstruction at the study site.The rectified linear unit (ReLU) activation function is applied in each neuron of the hidden layer, which is computationally simpler than the traditionally applied sigmoid function. The function of the ReLU activation function is given as follows:$$fleft(xright)=left{begin{array}{c}x,, x , > , 0 \ 0,, x , le , 0end{array}right.={{{{{rm{max }}}}}}(0,, x)$$
(23)
where x represents an input signal to a neuron and f represents the activation function.Furthermore, the bias between the measured and forecasted output values is reflected by the loss function. The loss function applied herein is the MAE (mean absolute error), which is given as follows:$${MAE}=frac{1}{N}mathop{sum }limits_{i=1}^{n}{{{{{rm{|}}}}}}T-Y{{{{{rm{|}}}}}}$$
(24)
where N is the number of training data points, and T and Y represent the measured output value and the forecasted class value, respectively.To realize the backpropagation framework, the derivative of the ReLU activation function needs to be acquired. According to the definition of the ReLU, the derivative is shown as follows.$$f{^prime} left(xright)=left{begin{array}{c}1,; x , > , 0 \ 0,; x , le , 0end{array}right.$$
(25)
Given a minibatch of m training samples obtained from the training set {x(1), x(2)…, x(m)} and their corresponding targets T(i) (i = 1,2…, m), the gradient used in the backpropagation framework is shown as follows:$$f=frac{1}{m}mathop{sum }limits_{i=1}^{n}frac{partial L}{partial w}$$
(26)
where L is the loss function; w represents the network weights; and n = 1 is the number of output values (MAP).In addition, considering that the adaptive moment estimation algorithm (Adam) was proven to be an effective neural network training method with a fast convergence speed and great classification performance, we applied this algorithm to train the DLNN model for MAP forecasting in this study. Adam has two biased equations, which are shown as follows:$$a={rho }_{1}a+(1-{rho }_{1})g$$
(27)
$$b={rho }_{2}b+(1-{rho }_{2})gtimes g$$
(28)
where ({rho }_{1}=0.9) and ({rho }_{2}=0.999) are exponential decay rates; g is the gradient; and (times) represents an elementwise product operator.After this calculation, the correct biases in the above two moments are given as follows:$${a}_{c}=frac{a}{1-{rho }_{1}^{t}}$$
(29)
$${b}_{c}=frac{b}{1-{rho }_{2}^{t}}$$
(30)
where t represents the current time step.Moreover, the update of the network weights is shown as follows:$${triangle }_{w}=-lambda frac{{a}_{c}}{sqrt{{b}_{c}}+epsilon }$$
(31)
where (lambda=0.001) represents the learning rates and (epsilon={10}^{-8}) is a constant used to ensure numerical stability.Eventually, the DLNN parameters can be updated according to the following formula.$$w ,=, w ,+, {triangle }_{w}$$
(32)
brGDGT-MAP modelsWe entered 9 brGDGTs compounds (all 6-methyl brGDGTs; each compound entered in the model is the percentage of all brGDGTs in the surface soil) into the input layer of the DLNN; these compounds are closely related to soil moisture. Then, we selected 533 surface soil samples as the training dataset and 179 surface soil samples as the validation dataset, both of which satisfied the principle of randomness. We assessed the precision of the model using forecast data R2 and root mean square error (RMSE) values.Through several parameters applied in the DLNN model, we found that the frequency of training and the number of neurons play the most significant roles in the brGDGT-MAP models. In addition, four hidden layers containing the other DLNN parameters allow the model to become more stable (detailed parameters are shown in Supplementary Fig. 7). To test the best frequency of training and the number of neurons in each hidden layer, we set a series of gradients to test the model to find the most suitable combination. As shown in Supplementary Fig. 8, for the frequency of training, we set the minimum and maximum training times to 1000 and 1500, respectively, with 100 times as the interval. We also set the numbers of neurons from 160 to 260 with a 20-neuron interval.Testing the weights of different compounds in the DLNN model and determining whether it was essential to eliminate some compounds that may make the dataset redundant were also required. Based on the model in which the Ib parameter was removed, we also set a series of experiments to test the effects of the different 6-methyl isomers on the predicted MAP values. Then, we made seven attempts to test the forecast effect of the brGDGT-MAP models by removing the Ic, IIa’, IIb’, IIc’, IIIa’, IIIb’, and IIIc’ parameters (Supplementary Fig. 9). Then, we obtained the best brGDGT-MAP model (Supplementary Fig. 10).Comparison of various ANN structuresTo improve the accuracy of our brGDGT-MAP models and the models’ universality, we also tested more complex ANN structures and then compared them with our DLNN models.RNNA recurrent neuron network (RNN) is an artificial neural network in which nodes are directionally connected into loops. The essential feature of RNN is that there are both internal feedback connections and feedforward connections between processing units. The inner structure of RNN is similar to that of the human brain, which can learn to transform a lifetime of sensory input streams into an efficient sequence of motor outputs (Supplementary Fig. 11a). Therefore, the basis of the RNN is defined as follows:$${h}_{t}=fleft(U ,*, {X}_{t}+W ,*, {h}_{t-1}right)$$
(33)
$${o}_{t}={softmax}(V ,{h}_{t})$$
(34)
where Xt represents the input value at time t; ot represents the output value at time t; ht represents the memory value at time t; and U, V, and W are the parameters of this network. For the motivative function, we chose softmax.LSTMLong short-term memory networks (LSTM) are a special type of RNN that can learn long-term dependence and contain three gates (forget gate, input gate and output gate) and one memory cell. The horizontal line above the box is called the cell state, and it acts as a conveyor belt to control the flow of information to the next moment (Supplementary Fig. 11b). Therefore, the basis of LSTM is defined as follows:$${C}_{t}={f}_{t}*{C}_{t-1}+{i}_{t}*{widetilde{C}}_{t}$$
(35)
where ({C}_{t-1}) represents the knowledge state of the model at time t − 1 and ({widetilde{C}}_{t}) represents the newly acquired information after entering new observations. ({f}_{t}) and ({i}_{t}) represent the weight parameters of ({C}_{t-1}) and ({widetilde{C}}_{t}), respectively.$${f}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{f})$$
(36)
$${i}_{t}=sigma ({W}_{f}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{i})$$
(37)
$$kern0.9pc {widetilde{C}}_{t}={{tanh }}({W}_{c}cdot left[{h}_{t-1},, {x}_{t}right]+{b}_{c})$$
(38)
where ({h}_{t-1}) represents the output value at time t − 1 and ({x}_{t}) represents the new input value at time t. ({W}_{f}) represents the motivative function in this study. We used tanh as the motivative function when our model was learning. Each new input may not have a positive impact on the machine, but it may also have a negative impact., ({b}_{f}), ({b}_{i}) and ({b}_{c}) represent the random disturbances (white noise).GRUAs mentioned above, the LSTM is proposed to overcome RNN’s inability to address remote dependence and the gate recurrent unit (GRU), a variant of the LSTM, keeps the effect of the LSTM while making the structure simpler.Compared with the LSTM, the GRU only has two gates (update (zt) and reset (rt) gates). The update gate is used to control the degree to which the state information at the previous moment is brought into the current state. The larger the value of the update gate is, the more state information at the previous moment is brought in. The reset gate is used to control the degree to which the state information at the previous moment is ignored (Supplementary Fig. 11c). Therefore, the basis of the LSTM is defined as follows:$${r}_{t}=sigma ({W}_{r}cdot [{h}_{t-1},, {x}_{t}])$$
(39)
$${z}_{t}=sigma ({W}_{z}cdot [{h}_{t-1},, {x}_{t}])$$
(40)
$${widetilde{h}}_{t}={tanh }({W}_{widetilde{h}}cdot [{{r}_{t}*h}_{t-1},, {x}_{t}])$$
(41)
$${h}_{t}=left(1-{z}_{t}right)*{{r}_{t}*h}_{t-1}+{z}_{t}*{widetilde{h}}_{t}$$
(42)
$${y}_{t}=sigma ({W}_{o}cdot {h}_{t})$$
(43)
where [] represents the connection of two vectors and * represents the multiplication of matrix elements. The xt and yt represent the input and output values at time t, respectively.It can be seen from the above formula that the parameters to be learned are the weight parameters of Wr, Wz, Wh, and Wo. The first three weights are spliced; therefore, they need to be segmented during learning. These can be defined as follows:$${W}_{r}={W}_{{rx}}+{W}_{{rh}}$$
(44)
$${W}_{z}={W}_{{zx}}+{W}_{{zh}}$$
(45)
$${W}_{widetilde{h}}={W}_{widetilde{h}x}+{W}_{widetilde{h}h}$$
(46)
As we can find in the RNN, LSTM, and GRU models we reconstructed (Supplementary Fig. 12), the training datasets all show extraordinarily high R2 values (0.99, 0.99, and 0.99, respectively) and low RMSE values (0.36, 0.23, and 0.16, respectively). However, the validation datasets do not show good prediction ability compared with the DLNN. These results indicate that the two ANN structures are not suitable for MAP prediction based on brGDGTs, although their inner structures are more complex than those of the DLNN. The reason we suggested is that the RNN, LSTM and GRU are more appropriate to the massive amounts of data and the data that have obvious spatiotemporal characteristics. The great prediction precision in the training dataset and the poor performance in the extrapolated datasets indicate that the models based on the RNN, LSTM and GRU have significant overfitting. As a result, compared with other ANN structures, we concluded that our DLNN model is the most suitable one to forecast MAP based on brGDGTs.Environmental indicators of n-alkanes proxiesLong-chain n-alkanes in plant leaf waxes are universal in terrestrial environments and can deliver signals of variations in plant sources and past climate. They are widely distributed in surface soils and Quaternary sediments, especially in LPSs. In this study, due to the insufficient samples in Weinan profile, we only analyzed n-alkanes components for 40 LPS samples, which contain ages between 340 and 430 ka BP.Instrumental measurementsFor the apolar fractions, a total of 40 samples in this study, mainly containing n-alkanes, were all investigated utilizing a Shimadzu 2010 gas chromatograph (GC) equipped with a flame ionization detector (FID) and a DB-5 fused silica capillary column (60 m (times) 0.32 mm (times) 0.25 μm film thickness) with helium as the carrier gas. The temperature of the GC oven was enhanced from 70 to 300 °C at a rate of 3 °C/min. Then, this temperature (300 °C) was maintained for 30 min. Finally, the concentrations of the n-alkane homologs were evaluated by assessing the peak area of the n-alkanes to that of the internal standard (cholane).Long-term paleoclimatic changeThe carbon preference index (CPI) evaluates the relative abundances of odd vs. even-numbered n-alkanes. The CPI increases as the environmental aridity increases. The CPI indicated warm–wet periods and cold-dry periods in paleoclimate and corresponded well with the loess–paleosol cycle52. The average chain length (ACL) value is the weighted average of the different carbon chain lengths. The lower ACL value corresponds to the lower temperature in the research of LPSs. The variations in the ACL value have good coordination with the magnetic susceptibility and particle size. The n-alkane CPI53 and ACL54 are calculated as follows:$${CPI}(1)=frac{({C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31})+({C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33})}{2({C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32})}$$
(47)
$${CPI}left(2right)=frac{1}{2}left(frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{24}+{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}}+frac{{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}+{C}_{33}}{{C}_{26}+{C}_{28}+{C}_{30}+{C}_{32}+{C}_{34}}right)$$
(48)
$${ACL}=frac{{23C}_{23}+{25C}_{25}+{27C}_{27}+{29C}_{29}+31{C}_{31}+{33C}_{33}}{{C}_{23}+{C}_{25}+{C}_{27}+{C}_{29}+{C}_{31}{+C}_{33}}$$
(49)
Figure 13 shows the variations in CPI (Supplementary Fig. 13a) and ACL (Supplementary Fig. 13b) values in the Weinan profile from 340 to 430 ka BP. Compared with the MAP (Supplementary Fig. 13c) and SWC (Fig. 2e) reconstructions based on brGDGTs, we found that they all had a peak at ∼350 ka BP, which indicates relatively high soil moisture at approximately 350 ka BP.MAP reconstruction in the XRD sectionIn this section, we test the brGDGT-MAP model in the Xiangride (XRD) profile, which is located in the margin region of the East Asian monsoon (Fig. 1). With robust chronological control, we reconstructed the rainfall changes in 7000 years BP (Supplementary Fig. 14b). We found that MAP was ∼200 mm in the late Holocene, which approaches multiple modern observations in this region (180 mm). Moreover, we suggest that this region experienced the most humid period in the mid-Holocene, when the rainfall reached 600 mm. Afterward, the precipitation declined from 6000 to 4000 years BP and then increased and reached a peak value at ∼3000 years BP. Then, it had a drought trend until modern times.We discovered that our brGDGT-MAP model could precisely capture rainfall dynamics based on the Weinan profile (Supplementary Fig. 14a) and XRD profile (Supplementary Fig. 14b). Combined with the most acceptable rainfall records in the Holocene (i.e., 10Be (Supplementary Fig. 14c), pollen in Gonghai (Fig. 1 and Supplementary Fig. 14d), and Dongge cave δ18O (Fig. 1 and Supplementary Fig. 14e)), we found the same precipitation peak values in the early Holocene and mid-Holocene. In addition, they all revealed a drought trend throughout the whole Holocene. We suggest that brGDGTs can become a robust proxy to reconstruct precipitation in the Holocene. More

Falkowski PG. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 1994;39:235–58.CAS 
PubMed 
Article 

Google Scholar 
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237.CAS 
PubMed 
Article 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bell W, Mitchell R. Chemotactic and growth response of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.Article 

Google Scholar 
Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 
Article 

Google Scholar 
Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nat Rev Microbiol. 2007;5:782–91.CAS 
PubMed 
Article 

Google Scholar 
Meyer N, Bigalke A, Kaulfuß A, Pohnert G. Strategies and ecological roles of algicidal bacteria. FEMS Microbiol Rev. 2017;41:880–99.CAS 
PubMed 
Article 

Google Scholar 
Windler M, Bova D, Kryvenda A, Straile D, Gruber A, Kroth PG. Influence of bacteria on cell size development and morphology of cultivated diatoms. Phycol Res. 2014;62:269–81.Article 

Google Scholar 
Buhmann MT, Schulze B, Forderer A, Schleheck D, Kroth PG. Bacteria may induce the secretion of mucin-like proteins by the diatom Phaeodactylum tricornutum. J Phycol. 2016;52:463–74.CAS 
PubMed 
Article 

Google Scholar 
van Tol HM, Amin SA, Armbrust EV. Ubiquitous marine bacterium inhibits diatom cell division. ISME J. 2017;11:31–42.PubMed 
Article 
CAS 

Google Scholar 
Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signaling between a cosmopolitan phytoplankton and associated bacteria. Nature 2015;522:98–101.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci. 2015;112:453–7.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol 2017;19:3500–13.CAS 
PubMed 
Article 

Google Scholar 
Grossart H-P, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 
Article 

Google Scholar 
Crenn K, Duffieux D, Jeanthon C. Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short- and long-term associations. Front Microbiol. 2018;9:2879.PubMed 
PubMed Central 
Article 

Google Scholar 
Behringer G, Ochsenkühn MA, Fei C, Fanning J, Koester JA, Amin SA. Bacterial communities of diatoms display strong conservation across strains and time. Front Microbiol. 2018;9:659.PubMed 
PubMed Central 
Article 

Google Scholar 
Schäfer H, Abbas B, Witte H, Muyzer G. Genetic diversity of ‘satellite’ bacteria present in cultures of marine diatoms. FEMS Microbiol Ecol 2002;42:25–35.PubMed 

Google Scholar 
Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci. 2020;117:3656–3662.Stock W, Blommaert L, De Troch M, Mangelinckx S, Willems A, Vyverman W, et al. Host specificity in diatom-bacteria interactions alleviates antagonistic effects. FEMS Microbiol Ecol. 2019;95:fiz171.Segev E, Wyche TP, Kim KH, Petersen J, Ellebrandt C, Vlamakis H, et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife. 2016;5:e17473.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wagner-Döbler I, Ballhausen B, Berger M, Brinkhoff T, Buchholz I, Bunk B, et al. The complete genome sequence of the algal symbiont Dinoroseobacter shibae: a hitchhiker’s guide to life in the sea. ISME J. 2010;4:61–77.PubMed 
Article 
CAS 

Google Scholar 
Frank O, Michael V, Päuker O, Boedeker C, Jogler C, Rohde M, et al. Plasmid curing and the loss of grip – the 65-kb replicon of Phaeobacter inhibens DSM 17395 is required for biofilm formation, motility and the colonization of marine algae. Syst Appl Microbiol. 2015;38:120–7.CAS 
PubMed 
Article 

Google Scholar 
Paul C, Pohnert G. Interactions of the algicidal bacterium Kordia algicida with diatoms: regulated protease excretion for specific algal lysis. PLoS One. 2011;6:e21032.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stock F, Bilcke G, De Decker S, Osuna-Cruz CM, Van den Berge K, Vancaester E, et al. distinctive growth and transcriptional changes of the diatom Seminavis robusta in response to quorum sensing related compounds. Front Microbiol. 2020;11:1240.PubMed 
PubMed Central 
Article 

Google Scholar 
Guillard RRL Culture of Phytoplankton for Feeding Marine Invertebrates. In: Smith WL, Chanley MH, editors. Culture of marine invertebrate animals: proceedings — 1st conference on culture of marine invertebrate animals greenport. Boston, MA: Springer US; 1975. p. 29–60.Rasband WS (2016). ImageJ, U.S. National Institutes of Health, Bethesda, MD, USA. Available at: http://imagej.nih.gov/ij/, 1997–2015.DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith F. Colorimetric method for determination of sugars and related substances. Anal Chem. 1956;28:350–6.CAS 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.Article 
CAS 

Google Scholar 
Alexa A, Rahnenfuhrer J (2021). topGO: Enrichment analysis for gene ontology. R package version 2.46.0.Csardi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.Wei Q, Khan IK, Ding Z, Yerneni S, Kihara D. NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinform. 2017;18:177.Article 
CAS 

Google Scholar 
Shapiro HM (2003). Physical parameters and their uses. In: Shapiro HM (ed). Practical Flow Cytometry. John Wiley & Sons, Inc.: New York, NY, USA, pp. 273-85.Clercq AD, Inzé D. Cyclin-dependent kinase inhibitors in yeast, animals, and plants: a functional comparison. Crit Rev Biochem Mol Biol. 2006;41:293–313.PubMed 
Article 
CAS 

Google Scholar 
Zinser ER. The microbial contribution to reactive oxygen species dynamics in marine ecosystems. Environ Microbiol Rep. 2018;10:412–27.CAS 
PubMed 
Article 

Google Scholar 
Whalen KE, Kirby C, Nicholson RM, O’Reilly M, Moore BS, Harvey EL. The chemical cue tetrabromopyrrole induces rapid cellular stress and mortality in phytoplankton. Sci Rep. 2018;8:15498.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sheyn U, Rosenwasser S, Ben-Dor S, Porat Z, Vardi A. Modulation of host ROS metabolism is essential for viral infection of a bloom-forming coccolithophore in the ocean. ISME J. 2016;10:1742–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finkel ZV, Irwin AJ, Schofield O. Resource limitation alters the ¾ size scaling of metabolic rates in phytoplankton. Mar Ecol Prog Ser. 2004;273:269–80.Article 

Google Scholar 
De Troch M, Chepurnov V, Gheerardyn H, Vanreusel A, Ólafsson E. Is diatom size selection by harpacticoid copepods related to grazer body size? J Exp Mar Biol Ecol. 2006;332:1–11.Article 

Google Scholar 
Finkel ZV. Light absorption and size scaling of light-limited metabolism in marine diatoms. Limnol Oceanogr. 2001;46:86–94.CAS 
Article 

Google Scholar 
Wilhelm T, Said M, Naim V. DNA replication stress and chromosomal instability: dangerous liaisons. Genes. 2020;11:642.CAS 
PubMed Central 
Article 

Google Scholar 
Gelot C, Magdalou I, Lopez BS. Replication stress in mammalian cells and its consequences for mitosis. Genes. 2015;6:267–98.Vogt E, Kirsch-Volders M, Parry J, Eichenlaub-Ritter U. Spindle formation, chromosome segregation and the spindle checkpoint in mammalian oocytes and susceptibility to meiotic error. Mutat. Res. – Genet. Toxicol. Environ. Mutagen. 2008;651:14–29.CAS 

Google Scholar 
Van de Meene AML, Pickett-Heaps JD. Valve morphogenesis in the centric diatom Rhizosolenia setigera (Bacillariophyceae, Centrales) and its taxonomic implications. Eur J Phycol. 2004;39:93–104.Article 

Google Scholar 
Pollara SB, Becker JW, Nunn BL, Boiteau R, Repeta D, Mudge MC, et al. Bacterial quorum-sensing signal arrests phytoplankton cell division and impacts virus-induced mortality. mSphere. 2021;6:e00009–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Dassow P, Petersen TW, Chepurnov VA, Virginia Armbrust E. Inter- and intraspecific relationships between nuclear DNA content and cell size in selected members of the centric diatom genus Thalassiosira (Bacillariophyceae). J Phycol. 2008;44:335–49.Article 
CAS 

Google Scholar 
Pokrzywinski KL, Tilney CL, Warner ME, Coyne KJ. Cell cycle arrest and biochemical changes accompanying cell death in harmful dinoflagellates following exposure to bacterial algicide IRI-160AA. Sci Rep. 2017;7:45102.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durkin CA, Mock T, Armbrust EV. Chitin in diatoms and its association with the cell wall. Eukaryot Cell. 2009;8:1038.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wildermuth MC. Modulation of host nuclear ploidy: a common plant biotroph mechanism. Curr Opin Plant Biol. 2010;13:449–58.CAS 
PubMed 
Article 

Google Scholar 
Cho J-C, Giovannoni SJ. Croceibacter atlanticus gen. nov., sp. nov., A Novel Marine Bacterium in the Family Flavobacteriaceae. Syst Appl Microbiol. 2003;26:76–83.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Morris JJ, Lenski RE, Zinser ER. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio. 2012;3:e00036–12.PubMed 
PubMed Central 
Article 

Google Scholar 
Ndhlovu A, Durand PM, Ramsey G. Programmed cell death as a black queen in microbial communities. Mol Ecol. 2021;30:1110–9.CAS 
PubMed 
Article 

Google Scholar 
Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
PubMed 
Article 

Google Scholar 
Sengupta A, Carrara F, Stocker R. Phytoplankton can actively diversify their migration strategy in response to turbulent cues. Nature 2017;543:555–8.CAS 
PubMed 
Article 

Google Scholar 
Levy SF, Ziv N, Siegal ML. Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol. 2012;10:e1001325.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
PubMed 
Article 

Google Scholar 
Blair PM, Land ML, Piatek MJ, Jacobson DA, Lu T-YS, Doktycz MJ, et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 2018;3:e00045–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Helfrich EJN, Vogel CM, Ueoka R, Schäfer M, Ryffel F, Müller DB, et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat Microbiol. 2018;3:909–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Long RA, Qureshi A, Faulkner DJ, Azam F. 2-n-Pentyl-4-quinolinol produced by a marine Alteromonas sp. and its potential ecological and biogeochemical roles. Appl Environ Microbiol. 2003;69:568–76.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calabrese EJ. Hormesis: from mainstream to therapy. Cell Commun Signal. 2014;8:289–91.Article 

Google Scholar 
Chen WM, Sheu FS, Sheu SY. Novel l-amino acid oxidase with algicidal activity against toxic cyanobacterium Microcystis aeruginosa synthesized by a bacterium Aquimarina sp. Enzym Microb Technol. 2011;49:372–9.CAS 
Article 

Google Scholar 
El-Aouar Filho RA, Nicolas A, De Paula Castro TL, Deplanche M, De Carvalho Azevedo VA, Goossens PL, et al. Heterogeneous family of cyclomodulins: smart weapons that allow bacteria to hijack the eukaryotic cell cycle and promote infections. Front Cell Infect Microbiol. 2017;7:364.Ricci V, Giannouli M, Romano M, Zarrilli R. Helicobacter pylori gamma-glutamyl transpeptidase and its pathogenic role. World J Gastroenterol. 2014;20:630–8.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Teeling H, Fuchs Bernhard M, Becher D, Klockow C, Gardebrecht A, Bennke Christin M, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 2012;336:608–11.CAS 
PubMed 
Article 

Google Scholar  More

Spatial–temporal characteristics of land use changeAs shown in Fig. 2, cultivated land was the dominant land use type in Shandong Province during the past 40 years, which accounted for 69.86% (1980), 69.98% (1990), 69.25% (2000), 68.00% (2010) and 66.88% (2020) respectively. Moreover, it was found that the area of cultivated land, forest land, grassland, unused land and ocean gradually decreased, whereas the water area and URL (urban and rural industrial and mining residential land) increased obviously. In particular, grassland decreased by 7542.87 km2 in the past 40 years with a decline rate of 37.18%, which was much higher than cultivated land and forest land. This phenomenon was attributed to the fact that cultivated land and forest land were less susceptible to encroachment as their high vegetation coverage, while grassland was easily occupied by other land types. The serious occupation by other land types has led to a significant reduction in unused land with a very high decline ratio of 64.32% from 2010 to 2020. In contrast to unused land, URL increased significantly at this period (Fig. 3), which was due to the rapidly economic development.Figure 2Land use type map of Shandong Province from 1980 to 2020.Full size imageFigure 3Sankey diagram of land use transfer in different periods.Full size imageThe total area of land use conversion in Shandong Province was 86,909 km2 during the past 40 years, the most drastic change was observed from 2010 to 2020. On the one hand, the major project of new and old kinetic energy conversion in Shandong Province had been implemented since 2000, which led to the expansion of urban land and dramatic changes in land use patterns. On the other hand, social, economic, technological and other factors had a direct impact on land use change by influencing people’s decision-making on land use (e.g., demand for land products, investment in land, protection of land resources, etc.)45,46,47,48. Statistics showed that GDP (Gross Domestic Product) and population density of Shandong Province had increased significantly since 21st century. The GDP of 2010–2020 was about 10 times that of 1980–2000 and population density had also increased by 1.4 times (Data from: Shandong statistical yearbook, http://tjj.shandong.gov.cn/col/col6279/index.html). As the most direct reflection of human activities, land use change was obviously affected by factors such as agricultural cultivation, industrial and mining construction, and urbanization driven by population growth49,50.The most significant changes of land use type were URL (increased by 17.75%), grassland (decreased by 8.72%) and cultivated land (decreased by 7.26%) over the past forty years. URL was mostly converted from cultivated land (26,306 km2) and grassland (1684 km2), which reflected the serious situation of occupying cultivated land in the process of urbanization in Shandong Province. It was caused by tight land use scale and relatively flat terrain of grassland. Besides, the range of land use type in the four periods also exhibited great variations. The conversion of land use from 1980 to 1990 was concentrated in the Yellow River Delta, Laizhou Bay and Weishan Lake, for the same as 1990–2000. At the period of 2000 to 2010, the conversion types concentrated in Bohai Bay and Yellow River Delta. The land use conversion was violent and widely distributed from 2010 to 2020, which was different from previous periods from 1980 to 2010. The conversion of cultivated land → URL and URL → cultivated land were widely distributed in Shandong Province, while another conversion of grassland → cultivated land and forest → cultivated land were concentrated in the Central and South Shandong Mountains and Jiaodong Hills. In addition, the conversion of cultivated land → water area and URL → water area were concentrated in Bohai Bay, Yellow River Delta and Laizhou Bay. Ample water, flat terrain and fertile soils in these bays and deltas facilitates agricultural cultivation and other productive activities. Therefore, the conversion of land use types from 1980 to 2010 was mainly concentrated here (Fig. 4). Specifically, the conversion of water area → URL was 1083 km2 from 1980 to 1990, unused land → water area was 925 km2 from 1990 to 2000, cultivated land → water area was 687 km2 from 2000 to 2010. However, the pattern of land use change dominated by natural factors has been broken in the process of increasing demand for social development and continuous advancement of science and technology. The conversion of land use types has become more dispersed in spatial distribution and the types of conversion have become more diverse.Figure 4Spatial distribution map of land use conversion types in different periods.Full size imageIn fact, one issue of concern in the early exploitation of water was the ecological problems caused by over-exploitation. For example, the cut-off of the Yellow River downstream made it difficult to guarantee the water security of industrial and agricultural production and residential life in the areas along the way. At the same time, the safety of coastal ecosystems was threatened and the phenomenon of soil salinization had become more serious. To alleviate these problems, government and the public have taken a series of measures such as establishing the Yellow River Delta National Nature Reserve was established in 1992, returning farmland to lakes and wetlands, and improving the landscape pattern of rivers and lakes by carrying out ecological treatment in the coastal zone of rivers and lakes51,52. By 2020, the area of water has increased by 50% compared to 1980, while many ecological security issues have been mitigated.Spatial–temporal characteristics of habitat degradationThe spatial–temporal variation of land use types were conducted to explore the variation trend of its habitat quality in Shandong Province. The InVEST-HQ was applied to obtain layers of habitat degradation in different periods. According to the interval range of 0–0.03, 0.03–0.07 and 0.07–0.18, habitat degradation was divided into three levels: slight, moderate and high degradation35,38.As shown in Fig. 5, the habitat quality in Shandong Province was dominated by moderate degradation, with the proportion of 73.30% (1980), 73.25% (1990), 72.49% (2000), 70.45% (2010) and 64.33% (2020), respectively. The spatial pattern of habitat quality was consistent with cultivated land, indicating that cultivated land who was affected by natural and anthropogenic activities exhibited moderate degradation. The proportion of moderate degradation has decreased due to cultivated land have been encroached upon for construction in the process of development, thus habitat degradation has become more and more serious. Although some of the moderate degraded areas were also converted to slight degraded areas, the area of conversion was very small compared to its conversion to high degraded areas.Figure 5Distribution map of habitat degradation in Shandong Province from 1980 to 2020.Full size imageThe proportion of slight degradation ranges from 22.38% to 24.89%, it was concentrated in the Yellow River Delta, the Central and South of Shandong Mountains, Weishan Lake and Jiaodong Hills, which was less disturbed by human activities. Compared with 1980, the proportion of slight degraded areas increased marginally in 2020, and its change was a fluctuating process. The proportion of slight degraded areas decreased from 1980 to 1990, and its proportion slowly increased from 1990 to 2020. This dynamic change process could be verified according to the spatial distribution characteristics in the Yellow River Delta. The habitat quality of the Yellow River Delta, which originally showed slight degradation, showed high degradation in 1990, 2000 and 2010.The proportion of high degradation ranges from 4.03% to 10.78%, which was concentrated in the built-up area of the city where human activities were more intensive. The proportion of high degraded areas has been increasing, indicating that the habitat has been degraded severely and its quality has declined. As the proportion of high degraded areas raised, two patterns of their spatial distribution also emerged. First spatial pattern was concentrated in urban built-up areas because of the high degree of human exploitation of land, which led to significant habitat degradation. The second pattern was a circle structure with “slight degradation” as the center and “high degradation-moderate degradation-slight degradation” outward, which was similar to the spatial distribution structure of habitat degradation in Fujian Province studied by Li et al.40. The circle structure was formed in 2010, and the distribution range was significantly expanded in 2020. The reason for the formation was that the built-up land in the city center has been severely damaged, and the possibility of re-degradation was reduced, instead showing “slight degradation”. However, the adjacent urban areas were more threatened and severely degraded, presenting “high degradation”. With the increase of distance, habitat threat and degradation decreased gradually, displaying “slight degradation”.Spatial–temporal evolution characteristics of habitat qualityThe InVEST-HQ was used to obtain layers of habitat quality in different periods. As summarized in Table 4, habitat quality was divided into five levels by the interval range: low (0–0.2), relatively low (0.2–0.4), medium (0.4–0.6), relative high (0.6–0.8), and high (0.8–1.0)35,38.Table 4 The proportion of habitat quality level at different periods in Shandong Province.Full size tableOur study concluded that the level of habitat quality in Shandong Province declined from 1980 to 2020.The results showed an overall decline of 4.75% in Shandong Province. Among them, the most significant rate of decline was observed in 2010–2020 (1.86%), which was similar to the phase change characteristics of land use types. At this period, the “Development Plan of Yellow River Delta Efficient Ecological Economic Zone” and the “Development Plan of Shandong Peninsula Blue Economic Zone” have become national development strategies. The demonstration area of “Bohai granary” and the restructuring of steel industry were carried out simultaneously. Meanwhile, the Beijing-Shanghai high-speed railway (Shandong section), Qingdao Jiaozhou Bay Bridge, Jiaozhou Bay Tunnel have strengthened the connection between Shandong Province and the outside world. As a result, rapid development has led to a rapid decline in the quality of its habitat. The rate of decline in 1980–1990 (1.43%) and 2000–2010 (1.42%) was comparable and the rate of decline in 1990–2000 was the lowest at 0.12%, which was significantly related to the development level of cities in each period. The period of 1980–1990 and 2000–2010 were in the initial and rapid promotion stages of reform and opening-up respectively. The initial stage was led by rural reform, and urban reform was launched on a pilot basis. The rapid advancement stage was led by urban reform, and economic development entered a healthy track of steady progress. Therefore, the proportion of habitat quality changes in the two periods was comparable. The period of 1990–2000 was in the exploration and transition stage of reform and opening-up, whose development process was relatively stable, resulting in the lowest rate of change in habitat quality.The average value of habitat quality in Shandong Province was 1980 (0.5091), 1990 (0.5018), 2000 (0.5012), 2010 (0.4941) and 2020 (0.4849), which decreased during the entire period. Habitat quality was dominated by medium-level throughout the whole period, with the proportion in 1980 (68.95%), 1990 (68.54%), 2000 (67.74%), 2010 (66.37%) and 2020 (65.47%). The land type in this category was mainly cultivated land (Fig. 6), which was continuous encroachment during the study period, resulting in a decrease in the percentage of medium-level habitat quality. From 1980 to 2020, the percentage of low-level habitat quality increased from 12.67% to 17.44%, and the relatively low-level decreased from 0.46% to 0.23%. The main reason was the continuous increasing of construction land and the degree of habitat threat led to the decreasing of habitat suitability. Therefore, the area of low-level habitat quality showed an increasing trend. Low and relative low-level habitat quality areas were concentrated in the urban areas of coastal and inland cities, and the Yellow River Delta. Urban areas, with a large scale of industry, commerce and population, also have a high level of urbanization. The original natural habitat has been modified during the development process, which resulted low-level habitat quality. The habitat quality of the Yellow River Delta was dynamic. The low-level pattern formed by early over-exploitation was improved in later conservation and development. The proportion of high-level habitat quality increased from 11.64% to 12.98%, and the relatively high-level decreased from 6.28% to 3.88%. In terms of spatial distribution, it was concentrated in the Central and South Shandong Mountains, Jiaodong Hills, the Yellow River Delta (2020), Weishan Lake and Wulian Mountain. These areas were dominated by mountains and well-protected water, which had high habitat suitability and were less stressed by surrounding construction land, thus maintaining high-level habitat quality. The increase of high-level habitat quality was due to the influence of water with high habitat suitability, which expanded a lot in the past 40 years, leading to the spread of high-level regional habitat quality, especially in the Yellow River Delta.Figure 6Distribution map of habitat quality in Shandong Province from 1980 to 2020.Full size imageThe value of Moran’s I was 0.3935 (1980), 0.3852 (1990), 0.4031 (2000), 0.4186 (2010) and 0.4644 (2020), respectively, which revealed that the spatial agglomeration of habitat quality in Shandong Province was characterized by agglomeration, and the trend of agglomeration increased obviously after 2000.As shown in Fig. 7, the habitat quality in Shandong Province exhibited obvious spatial heterogeneity, and spatial distribution of cold and hot spot was consistent with the topographic features. Hot spot (high-value area of habitat quality) presented “two primary and two secondary + Yellow River Delta”. Two primary hot spots distributed in the Central and South Shandong Mountains and the Jiaodong Hills, the two secondary hot spots located in Weishan Lake and Wulian Mountain. The formation of above hot spot was mainly due to high altitudes or steep slopes conferred favorable habitat quality, which was associated with the accessibility of human activities. Human accessibility at high altitudes or steep slopes was limited, so it was unlikely to cause major interference with the original environment53,54. However, the formation of other hot spot in Yellow River Delta was due to protective human activities. Cold spot (low-value area of habitat quality) was scattered in the northwestern Plain of Shandong Province, provincial capital metropolitan area and peninsula urban agglomeration which was dominated by cultivated land and built-up land in the cities that was affected by agricultural cultivation and industrial activities.Figure 7Distribution map of hot and cold spots of habitat quality in Shandong Province from 1980 to 2020.Full size imageOverall, the spatial distribution pattern of habitat quality in Shandong Province was relatively stable and affected by many factors, among which land use change was the most important one9,40,55. The most dominant land type in Shandong Province was cultivated land, which was concentrated in the northwest plain. Influenced by agricultural farming, the habitat quality of cultivated land presented medium-level category. At the same time, the habitat quality of some cultivated land has decreased due to the influence of construction land intrusion. The high vegetation coverage and rich species diversity of mountains and hills make their natural habitat quality superior. With the development of urban economy, the scale of construction land in coastal lowlands as well as inland urban areas continued to expand. The increase in population density as well as the intensity of land use activities has led to the expansion of regional dehabitatization. In addition, the dynamic changes in the habitat quality of the Yellow River Delta indicated that differences in the degree of land use change led to a variety of impacts on habitat quality. Therefore, habitat quality improvement and ecological protection should be based on local regional resource endowments and follow the concept of comprehensive, coordinated and sustainable development. Administration should formulate differentiated ecological protection strategies. For urban land development, authorities should increase the intensive utilization of construction land, limit the development boundaries of urban land and increase the greening rate inside urban land, such as equipped with urban green space park and other ecological land. In order to ensure the efficiency of agricultural production in Shandong Province, authorities should pay special attention to the conservation of cultivated land and to the development of ecological agriculture56. For natural ecosystems such as forest and grassland, authorities should improve the natural reserve system57. The vegetation ecological restoration project should be carried out according to local conditions. Drawing on the effective experience of ecological changes in the Yellow River Delta, we would take it as a typical example in future development and adopt corresponding administrative methods to coordinate the relationship between economy and habitat quality and change the dilemma of low-level habitat quality areas. Therefore, it is necessary to implement reasonable and effective territorial space planning to achieve regional sustainable development. More

Hawkes, C. V., Bull, J. J. & Lau, J. A. Symbiosis and stress: how plant microbiomes affect host evolution. Philos. T. R. Soc. B. 375, 20190590 (2020).CAS 
Article 

Google Scholar 
Leopold, D. R. & Busby, P. E. Host Genotype and Colonist Arrival Order Jointly Govern Plant Microbiome Composition and Function. Curr. Biol. 30, 3260–3266 (2020).CAS 
PubMed 
Article 

Google Scholar 
Morella, N. M. et al. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc. Natl Acad. Sci. USA 117, 1148–1159 (2020).CAS 
PubMed 
Article 

Google Scholar 
Garcia, J. & Kao-Kniffin, J. Microbial Group Dynamics in Plant Rhizospheres and Their Implications on Nutrient Cycling. Front. Plant Sci. 9, 1516 (2018).Article 

Google Scholar 
Marschner P. Plant-Microbe Interactions in the Rhizosphere and Nutrient Cycling in Nutrient Cycling in Terrestrial Ecosystems (eds. Marschner, P. & Rengel, Z.) 159–183 (Springer, 2007).Vannier, N., Agler, M. & Hacquard, S. Microbiota-mediated disease resistance in plants. PLoS Pathog. 15, e1007740 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, 6584 (2019).
Google Scholar 
Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An Underground Revolution: Biodiversity and Soil Ecological Engineering for Agricultural Sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 
Article 

Google Scholar 
Dessaux, Y., Grandclemént, C. & Faure, D. Engineering the Rhizosphere. Trends Plant Sci. 21, 266–278 (2016).CAS 
PubMed 
Article 

Google Scholar 
Swenson, W., Wilson, D. S. & Elias, R. Artificial ecosystem selection. Proc. Natl Acad. Sci. USA 97, 9110–9114 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Panke-Buisse, K., Poole, A., Goodrich, J., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).CAS 
PubMed 
Article 

Google Scholar 
van den Bergh, B. et al. Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution. Microbiol. Mol. Biol. Rev. 82, e00008–e00018 (2018).PubMed 
PubMed Central 

Google Scholar 
Garcia, J. & Kao-Kniffin, J. Can dynamic network modelling be used to identify adaptive microbiomes? Funct. Ecol. 34, 2065–2074 (2020).Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
Wilson, D. & Wilson, E. Evolution “for the good of the group”. Am. Sci. 96, 380–389 (2008).Article 

Google Scholar 
de la Fuente Cantó, C. et al. An extended root phenotype: the rhizosphere, its formation and impacts on plant fitness. Plant J. 103, 951–964 (2020).PubMed 
Article 
CAS 

Google Scholar 
Sachs, J., Mueller, U., Wilcox, T. & Bull, J. The evolution of cooperation. Q. Rev. Biol. 79, 135–160 (2004).PubMed 
Article 

Google Scholar 
Harrington, K. & Sanchez, A. Eco-evolutionary dynamics of complex social strategies in microbial communities. Commun. Integr. Biol. 7, e28230 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rauch, J., Kondev, J. & Sanchez, A. Cooperators trade off ecological resilience and evolutionary stability in public goods games. J. R. Soc. Interface 14, 20160967 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Sexton, D. & Schuster, M. Nutrient limitation determines the fitness of cheaters in bacterial siderophore cooperation. Nat. Commun. 8, 230 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schmidt, J. E., Kent, A. D., Brisson, V. L. & Gaudin, A. C. M. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome 7, 146 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Turner, T. et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere. microbiome plants ISME J. 7, 2248–2258 (2013).CAS 
PubMed 

Google Scholar 
Jones, D. L., Nguyen, C. & Finlay, R. D. Carbon flow in the rhizosphere: carbon trading at the soil–root interface. Plant Soil 321, 5–33 (2009).CAS 
Article 

Google Scholar 
George, E., Marschner, H. & Jakobsen, I. Role of Arbuscular Mycorrhizal Fungi in Uptake of Phosphorus and Nitrogen From Soil. Crit. Rev. Biotechnol. 15, 257–270 (1995).Article 

Google Scholar 
Hodge, A. & Fitter Alastair, H. Substantial nitrogen acquisition by arbuscular mycorrhizal fungi from organic material has implications for N cycling. Proc. Natl Acad. Sci. USA 107, 13754–13759 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lambers, H. & Teste, F. P. Interactions between arbuscular mycorrhizal and non-mycorrhizal plants: do non-mycorrhizal species at both extremes of nutrient availability play the same game. Plant Cell Environ. 36, 1911–1915 (2013).PubMed 

Google Scholar 
Delaux, P. M. et al. Comparative phylogenomics uncovers the impact of symbiotic associations on host genome evolution. PLoS Genet. 10, e1004487 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Anas, M. et al. Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency. Biol. Res. 53, 47 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Madhaiyan, M. et al. Arachidicoccus rhizosphaerae gen. nov., sp. nov., a plant-growth-promoting bacterium in the family Chitinophagaceae isolated from rhizosphere soil. Int. J. Syst. Evol. Microbiol. 65, 578–586 (2015).CAS 
PubMed 
Article 

Google Scholar 
Song, H. et al. Environmental filtering of bacterial functional diversity along an aridity gradient. Sci. Rep. 9, 866 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Xia, L. C. et al. Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst. Biol. 5, S15 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Kuntal, B. K., Chandrakar, P., Sadhu, S. & Mandhi, S. S. ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J. 13, 442–454 (2019).PubMed 
Article 

Google Scholar 
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling Interactions in the Microbiome: A Network Perspective. Trends Microbiol. 25, 217–228 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hu, Q. et al. Network analysis infers the wilt pathogen invasion associated with non-detrimental bacteria. NPJ Biofilms Microbiomes 6, 8 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, K. et al. Cross-biome comparison of microbial association networks. Front. Microbiol. 6, 1200 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Rengel, Z. & Marschner, P. Nutrient availability and management in the rhizosphere: exploiting genotypic differences. N. Phytol. 168, 305–312 (2005).CAS 
Article 

Google Scholar 
Marschner, P. The Role of Rhizosphere Microorganisms in Relation to P Uptake by Plants in The Ecophysiology of Plant-Phosphorus Interactions (eds. White, P. & Hammond, J.) 165–167 (Springer, 2008).Repert, D., Underwood, J., Smith, R. & Song, B. Nitrogen cycling processes and microbial community composition in bed sediments in the Yukon River at Pilot Station. J. Geophys. Res. Biogeosci. 119, 2328–2344 (2014).CAS 
Article 

Google Scholar 
Rolletschek, H. et al. Ectopic expression of an amino acid transporter (VfAAP1) in seeds of Vicia narbonensis and pea increases storage proteins. Plant Physiol. 137, 1236–1249 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sanders, A. et al. AAP1 regulates import of amino acids into developing Arabidopsis embryos. Plant J. 59, 540–552 (2009).CAS 
PubMed 
Article 

Google Scholar 
Carter, A. M. & Tegeder, M. Increasing nitrogen fixation and seed development in soybean requires complex adjustments of nodule nitrogen metabolism and partitioning processes. Curr. Biol. 26, 2044–2051 (2016).CAS 
PubMed 
Article 

Google Scholar 
Meier, I. C. et al. Root exudation of mature beech forests across a nutrient availability gradient: the role of root morphology and fungal activity. N. Phytol. 226, 583–594 (2020).CAS 
Article 

Google Scholar 
Xu, Y., He, J., Cheng, W., Xing, X. & Li, L. Natural 15N abundance in soils and plants in relation to N cycling in a rangeland in Inner Mongolia. J. Plant Ecol. 3, 201–207 (2010).Article 

Google Scholar 
Henneron, L. et al. Rhizosphere control of soil nitrogen cycling: a key component of plant economic strategies. N. Phytol. 228, 1269–1282 (2020).CAS 
Article 

Google Scholar 
Hobbie, E. A. & Högberg, P. Nitrogen isotopes link mycorrhizal fungi and plants to nitrogen dynamics. N. Phytol. 196, 367–382 (2012).CAS 
Article 

Google Scholar 
Zhou, S. et al. Assessing nitrification and denitrification in a paddy soil with different water dynamics and applied liquid cattle waste using the 15N isotopic technique. Sci. Total Environ. 430, 93–100 (2012).CAS 
PubMed 
Article 

Google Scholar 
Fuertes-Mendizábal, T. et al. 15N Natural Abundance Evidences a Better Use of N Sources by Late Nitrogen Application in Bread Wheat. Front. Plant Sci. 9, 853 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Yoneyama, T., Omata, T., Nakata, S. & Yazaki, J. Fractionation of Nitrogen Isotopes during the Uptake and Assimilation of Ammonia by Plants. Plant Cell Physiol. 32, 1211–1217 (1991).CAS 

Google Scholar 
Vacheron, J. et al. Plant growth-promoting rhizobacteria and root system functioning. Front. Plant Sci. 4, 356 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Granada, C., Passaglia, L., de Souza, E. & Sperotto, R. Is Phosphate Solubilization the Forgotten Child of Plant Growth-Promoting Rhizobacteria? Front. Microbiol. 9, 2054 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Compant, S., Duffy, B., Nowak, J., Clément, C. & Barka, E. Use of Plant Growth-Promoting Bacteria for Biocontrol of Plant Diseases: Principles, Mechanisms of Action, and Future Prospects. Appl. Environ. Microbiol. 71, 4951 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berges, J.A., & Mulholland, M.R. Enzymes and nitrogen cycling in Nitrogen in the marine environment (eds. Capone, D., Bronk, D., Mulholland, M., & Carpenter, E.) 1385–1444 (Elsevier 2008).DeAngelis, K. M., Lindow, S. E. & Firestone, M. Bacterial quorum sensing and nitrogen cycling in rhizosphere soil. FEMS Microb. Ecol. 66, 197–207 (2008).CAS 
Article 

Google Scholar 
Evans, S., Martiny, J. & Allison, S. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 11, 176–185 (2017).PubMed 
Article 

Google Scholar 
Ron, R., Fragman-Sapir, O. & Kadmon, R. (2018). Dispersal increases ecological selection by increasing effective community size. Proc. Natl Acad. Sci. USA. 115, 11280–11285 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Busby, P. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sergaki, C., Lagunas, B., Lidbury, I., Gifford, M. & Schäfer, P. Challenges and Approaches in Microbiome Research: From Fundamental to Applied. Front. Plant Sci. 9, 1205 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Garcia, J. et al. Selection pressure on the rhizosphere microbiome alters nitrogen use efficiency and seed yield in Brassica rapa. National Center for Biotechnology Information Repository. https://www.ncbi.nlm.nih.gov/sra/PRJNA833111 (2022).Ruan, Q. et al. Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics 22, 2532–2538 (2006).CAS 
PubMed 
Article 

Google Scholar 
Pollet, T. et al. Prokaryotic community successions and interactions in marine biofilms: the key role of Flavobacteria. FEMS Microbiol. Ecol. 94, fiy083 (2018).
Google Scholar 
Durno, W. E. et al. Expanding the boundaries of local similarity analysis. BMC Genomics 14, S3 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Garcia, J. et al. Selection pressure on the rhizosphere microbiome alters nitrogen use efficiency and seed yield in Brassica rapa. https://doi.org/10.5281/zenodo.6800595 (2022). More

Thakur MP, van der Putten WH, Cobben MMP, van Kleunen M, Geisen S. Microbial invasions in terrestrial ecosystems. Nat Rev Microbiol. 2019;17:621–31.CAS 
PubMed 
Article 

Google Scholar 
Mooney HA, Cleland EE. The evolutionary impact of invasive species. Proc Natl Acad Sci USA. 2001;98:5446–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mallon CA, Elsas JDV, Salles JF. Microbial invasions: the process, patterns, and mechanisms. Trends Microbiol. 2015;23:719–29.CAS 
PubMed 
Article 

Google Scholar 
O’Brien S, Hodgson DJ, Buckling A. Social evolution of toxic metal bioremediation in Pseudomonas aeruginosa. Proc R Soc B Biol Sci. 2014;281:20140858.Walter J, Maldonado-Gómez MX, Martínez I. To engraft or not to engraft: an ecological framework for gut microbiome modulation with live microbes. Curr Opin Biotechnol. 2018;49:129–39.CAS 
PubMed 
Article 

Google Scholar 
van der Goot E, van Spronsen FJ, Falcão Salles J, van der Zee EA. A microbial community ecology perspective on the gut-microbiome-brain axis. Front Endocrinol. 2020;11:611.Article 

Google Scholar 
Williamson M, Fitter A. The varying success of invaders. Ecology. 1996;77:1661–6.Article 

Google Scholar 
Catford JA, Jansson R, Nilsson C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers Distrib. 2009;15:22–40.Article 

Google Scholar 
Simberloff D. The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst. 2009;40:81–102.Article 

Google Scholar 
Acosta F, Zamor RM, Najar FZ, Roe BA, Hambright KD. Dynamics of an experimental microbial invasion. Proc Natl Acad Sci USA. 2015;112:11594–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barney JN, Ho MW, Atwater DZ. Propagule pressure cannot always overcome biotic resistance: the role of density-dependent establishment in four invasive species. Weed Res. 2016;56:208–18.Article 

Google Scholar 
Ketola T, Saarinen K, Lindström L. Propagule pressure increase and phylogenetic diversity decrease community’s susceptibility to invasion. BMC Ecol. 2017;17:1–7.Article 

Google Scholar 
Vila JCC, Jones ML, Patel M, Bell T, Rosindell J. Uncovering the rules of microbial community invasions. Nat Ecol Evol. 2019;3:1162–71.PubMed 
Article 

Google Scholar 
Dressler MD, Conde J, Eldakar OT, Smith RP. Timing between successive introduction events determines establishment success in bacteria with an Allee effect. Proc R Soc B Biol Sci. 2019;286:20190598.CAS 
Article 

Google Scholar 
Jones ML, Rivett DW, Pascual-Garria A, Bell T. Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms. eLife. 2021;10:e71811.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rivett DW, Jones ML, Ramoneda J, Mombrikotb SB, Ransome E, Bell T. Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. Ecol Lett. 2018;21:516–24.PubMed 
Article 

Google Scholar 
Case TJ. Invasion resistance arises in strongly interacting species-rich model competition communities. Proc Natl Acad Sci USA. 1990;87:9610–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jousset A, Schulz W, Scheu S, Eisenhauer N. Intraspecific genotypic richness and relatedness predict the invasibility of microbial communities. ISME J. 2011;5:1108–14.PubMed 
PubMed Central 
Article 

Google Scholar 
Eisenhauer N, Scheu S, Jousset A. Bacterial diversity stabilizes community productivity. PLoS ONE. 2012;7:e34517.Wei Z, Yang T, Friman VP, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Comm. 2015;6:8413.CAS 
Article 

Google Scholar 
Amalfitano S, Coci M, Corno G, Luna GM. A microbial perspective on biological invasions in aquatic ecosystems. Hydrobiologia. 2015;746:13–22.Article 

Google Scholar 
Li SP, Tan J, Yang X, Ma C, Jiang L. Niche and fitness differences determine invasion success and impact in laboratory bacterial communities. ISME J. 2019;13:402–12.PubMed 
Article 

Google Scholar 
Baumgartner M, Pfrunder-Cardozo KR, Hall AR. Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples. Proc R Soc B Biol Sci. 2021;288:20203106.CAS 
Article 

Google Scholar 
Kurkjian HM, Akbari MJ, Momeni B. The impact of interactions on invasion and colonization resistance in microbial communities. PLoS Comp Biol. 2021;17:e1008643.CAS 
Article 

Google Scholar 
Tilman D. Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc Natl Acad Sci USA. 2004;101:10854–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Elsas JD, Chiurazzi M, Mallon CA, Elhottova D, Kristufek V, Salles JF. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA. 2012;109:1159–64.PubMed 
PubMed Central 
Article 

Google Scholar 
Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50.CAS 
PubMed 
Article 

Google Scholar 
Mitri S, Foster KR. The genotypic view of social interactions in microbial communities. Annu Rev Microbiol. 2013;43:247–73.
Google Scholar 
Kehe J, Ortiz A, Kulesa A, Gore J, Blainey PC, Friedman J. Positive interactions are common among culturable bacteria. Sci Adv. 2021;7:7159.Article 
CAS 

Google Scholar 
Connell JH, Slatyer RO. Mechanisms of succession in natural communities and their role in community stability and organization. Am Nat. 1977;111:1119–44.Article 

Google Scholar 
Debray R, Socolar Y, Kaulbach G, Guzman A, Hernandez CA, Curley R, et al. Priority effects in microbiome assembly. Nat Rev Microbiol. 2022;20:109–21.CAS 
PubMed 
Article 

Google Scholar 
Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun. 2016;7:11965.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Datta MS, Schwartzman J, Cermak N, Schmitz D, Barrere J, et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr Biol. 2019;29:1528–35.e6.CAS 
PubMed 
Article 

Google Scholar 
Mazumdar V, Amar S, Segrè D. Metabolic proximity in the order of colonization of a microbial community. PLoS ONE. 2013;8:e77617.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka M, Szabo R, Stocker R, Cordero OX. Trophic interactions and the drivers of microbial community assembly. Curr Biol. 2020;30:R1176–88.CAS 
PubMed 
Article 

Google Scholar 
Rickard AH, Gilbert P, High NJ, Kolenbrander PE, Handley PS. Bacterial coaggregation: an integral process in the development of multi-species biofilms. Trends Microbiol. 2003;11:94–100.CAS 
PubMed 
Article 

Google Scholar 
Kolenbrander PE, Palmer RJ, Periasamy S, Jakubovics NS. Oral multispecies biofilm development and the key role of cell–cell distance. Nat Rev Microbiol. 2010;8:471–80.CAS 
PubMed 
Article 

Google Scholar 
Monier JM, Lindow SE. Aggregates of resident bacteria facilitate survival of immigrant bacteria on leaf surfaces. Micro Ecol. 2005;49:343–52.Article 

Google Scholar 
Poza-Carrion C, Suslow T, Lindow S. Resident bacteria on leaves enhance survival of immigrant cells of Salmonella enterica. Phytopathology. 2013;103:341–51.PubMed 
Article 

Google Scholar 
Li M, Wei Z, Wang J, Jousset A, Friman V, Xu Y, et al. Facilitation promotes invasions in plant-associated microbial communities. Ecol Lett. 2019;22:149–58.PubMed 
Article 

Google Scholar 
Estrela S, Vila JCC, Lu N, Bajić D, Rebolleda-Gómez M, Chang CY, et al. Functional attractors in microbial community assembly. Cell Syst. 2022;13:29–42.e7.CAS 
PubMed 
Article 

Google Scholar 
Bertness MD, Callaway R. Positive interactions in communities. Trends Ecol Evol. 1994;9:191–3.CAS 
PubMed 
Article 

Google Scholar 
Zaneveld JR, McMinds R, Thurber RV. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:1–8.Article 
CAS 

Google Scholar 
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:3033.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen LTT, Broughton K, Osanai Y, Anderson IC, Bange MP, Tissue DT, et al. Effects of elevated temperature and elevated CO2 on soil nitrification and ammonia-oxidizing microbial communities in field-grown crop. Sci Total Environ. 2019;675:81–9.CAS 
PubMed 
Article 

Google Scholar 
Piccardi P, Vessman B, Mitri S. Toxicity drives facilitation between 4 bacterial species. Proc Natl Acad Sci USA. 2019;116:15979–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34.PubMed 
PubMed Central 
Article 

Google Scholar 
Urban MC, De Meester L. Community monopolization: Local adaptation enhances priority effects in an evolving metacommunity. Proc R Soc B Biol Sci. 2009;276:4129–38.Article 

Google Scholar 
Vanoverbeke J, Urban MC, De Meester L. Community assembly is a race between immigration and adaptation: eco-evolutionary interactions across spatial scales. Ecography. 2016;39:858–70.Article 

Google Scholar 
De Meester L, Vanoverbeke J, Kilsdonk LJ, Urban MC. Evolving perspectives on monopolization and priority effects. Trends Ecol Evol. 2016;31:136–46.PubMed 
Article 

Google Scholar 
Loeuille N, Leibold MA. Evolution in metacommunities: on the relative importance of species sorting and monopolization in structuring communities. Am Nat. 2008;171:788–99.PubMed 
Article 

Google Scholar 
Nadeau CP, Farkas TE, Makkay AM, Papke RT, Urban MC. Adaptation reduces competitive dominance and alters community assembly. Proc R Soc B Biol Sci. 2021;288:20203133.Article 

Google Scholar 
Faillace CA, Morin PJ. Evolution alters the consequences of invasions in experimental communities. Nat Ecol Evol. 2017;1:0013.Article 

Google Scholar 
Castledine M, Sierocinski P, Padfield D, Buckling A. Community coalescence: an eco-evolutionary perspective. Philos Trans R Soc B Biol Sci. 2020;375:20190252.Article 

Google Scholar 
Amor DR, Ratzke C, Gore J. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci Adv. 2020;6:eaay8676.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van der Gast CJ, Thompson IP. Effects of pH amendment on metal working fluid wastewater biological treatment using a defined bacterial consortium. Biotechnol Bioeng. 2005;89:357–66.PubMed 
Article 
CAS 

Google Scholar 
Saha R, Donofrio RS. The microbiology of metalworking fluids. Appl Microbiol Biotechnol. 2012;94:1119–30.CAS 
PubMed 
Article 

Google Scholar 
Ratzke C, Barrere J, Gore J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat Ecol Evol. 2020;4:376–83.PubMed 
Article 

Google Scholar 
Estrela S, Libby E, Van Cleve J, Débarre F, Deforet M, Harcombe WR, et al. Environmentally mediated social eilemmas. Trends Ecol Evol. 2019;34:6–18.PubMed 
Article 

Google Scholar 
Enke TN, Leventhal GE, Metzger M, Saavedra JT, Cordero OX. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat Commun. 2018;9:2743.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Furman O, Shenhav L, Sasson G, Kokou F, Honig H, Jacoby S, et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat Commun. 2020;11:1–13.Article 
CAS 

Google Scholar 
Coyte KZ, Rao C, Rakoff-Nahoum S, Foster KR. Ecological rules for the assembly of microbiome communities. PLoS Biol. 2021;19:e3001116.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Molinero N, Ruiz L, Sánchez B, Margolles A, Delgado S. Intestinal bacteria interplay with bile and cholesterol metabolism: implications on host physiology. Front Physiol. 2019;10:185.PubMed 
PubMed Central 
Article 

Google Scholar 
Ruiz L, Margolles A, Sánchez B. Bile resistance mechanisms in Lactobacillus and Bifidobacterium. Front Microbiol. 2013;4:396.PubMed 
PubMed Central 
Article 

Google Scholar 
Gérard P. Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens. 2013;3:14–24.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Amarnath K, Narla AV, Pontrelli S, Dong J, Caglar T, Taylor BR, et al. Stress-induced cross-feeding of internal metabolites provides a dynamic mechanism of microbial cooperation. bioRxiv. 2021. https://doi.org/10.1101/2021.06.24.449802.Fukami T, Beaumont HJ, Zhang XX, Rainey PB. Immigration history controls diversification in experimental adaptive radiation. Nature. 2007;446:436–9.CAS 
PubMed 
Article 

Google Scholar 
Bell T, Newman JA, Silverman BW, Turner SL, Lilley AK. The contribution of species richness and composition to bacterial services. Nature. 2005;436:1157–60.CAS 
PubMed 
Article 

Google Scholar 
Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
PubMed 
Article 

Google Scholar 
Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.Article 

Google Scholar  More

Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710 (2000).
Google Scholar 
Turbelin, A. J., Malamud, B. D. & Francis, R. A. Mapping the global state of invasive alien species: Patterns of invasion and policy responses. Glob. Ecol. Biogeogr. 26, 78–92 (2017).
Google Scholar 
Jackson, M. C. Interactions among multiple invasive animals. Ecology 96, 2035–2041 (2015).CAS 
PubMed 

Google Scholar 
Rodriguez, L. F. Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biol. Invasions 8, 927–939 (2006).
Google Scholar 
Duenas, M. A. et al. The role played by invasive species in interactions with endangered and threatened species in the United States: A systematic review. Biodivers. Conserv. 27, 3171–3183 (2018).
Google Scholar 
Weidenhamer, J. D. & Callaway, R. M. Direct and indirect effects of invasive plants on soil chemistry and ecosystem function. J. Chem. Ecol. 36, 59–69 (2010).CAS 
PubMed 

Google Scholar 
Bajwa, A. A., Chauhan, B. S., Farooq, M., Shabbir, A. & Adkins, S. W. What do we really know about alien plant invasion? A review of the invasion mechanism of one of the world’s worst weeds. Planta 244, 39–57 (2016).CAS 
PubMed 

Google Scholar 
Tallamy, D. W., Narango, D. L. & Mitchell, A. B. Do non-native plants contribute to insect declines?. Ecol. Entomol. 46, 729–742. https://doi.org/10.1111/een.12973 (2021).Article 

Google Scholar 
Bezemer, T. M., Harvey, J. A. & Cronin, J. T. Response of native insect communities to invasive plants. Annu. Rev. Entomol. 59, 119 (2014).CAS 
PubMed 

Google Scholar 
Cheng, F. & Cheng, Z. Research progress on the use of plant allelopathy in agriculture and the physiological and ecological mechanisms of allelopathy. Front. Plant Sci. 6, 1020 (2015).PubMed 
PubMed Central 

Google Scholar 
Kalisz, S., Kivlin, S. N. & Bialic-Murphy, L. Allelopathy is pervasive in invasive plants. Biol. Invasions 23, 367–371 (2021).
Google Scholar 
Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communities and ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Change Biol. 18, 1725–1737 (2012).ADS 

Google Scholar 
Zhang, P., Li, B., Wu, J. & Hu, S. Invasive plants differentially affect soil biota through litter and rhizosphere pathways: A meta-analysis. Ecol. Lett. 22, 200–210 (2019).ADS 
PubMed 

Google Scholar 
Dudareva, N., Klempien, A., Muhlemann, J. K. & Kaplan, I. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytol. 198, 16–32 (2013).CAS 
PubMed 

Google Scholar 
Clavijo McCormick, A. Can plant–natural enemy communication withstand disruption by biotic and abiotic factors?. Ecol. Evol. 6, 8569–8582 (2016).PubMed 
PubMed Central 

Google Scholar 
Bruce, T. J., Wadhams, L. J. & Woodcock, C. M. Insect host location: A volatile situation. Trends Plant Sci. 10, 269–274 (2005).CAS 
PubMed 

Google Scholar 
Clavijo McCormick, A., Unsicker, S. B. & Gershenzon, J. The specificity of herbivore-induced plant volatiles in attracting herbivore enemies. Trends Plant Sci. 17, 303–310 (2012).CAS 
PubMed 

Google Scholar 
Baldwin, I. T., Halitschke, R., Paschold, A., Von Dahl, C. C. & Preston, C. A. Volatile signaling in plant–plant interactions: “Talking trees” in the genomics era. Science 311, 812–815 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Kegge, W. & Pierik, R. Biogenic volatile organic compounds and plant competition. Trends Plant Sci. 15, 126–132 (2010).CAS 
PubMed 

Google Scholar 
Effah, E., Holopainen, J. K. & Clavijo McCormick, A. Potential roles of volatile organic compounds in plant competition. Perspect. Plant Ecol. Evol. Syst. 38, 58–63 (2019).
Google Scholar 
Kigathi, R. N., Weisser, W. W., Reichelt, M., Gershenzon, J. & Unsicker, S. B. Plant volatile emission depends on the species composition of the neighboring plant community. BMC Plant Biol. 19, 1–17 (2019).
Google Scholar 
Karban, R., Wetzel, W. C., Shiojiri, K., Pezzola, E. & Blande, J. D. Geographic dialects in volatile communication between sagebrush individuals. Ecology 97, 2917–2924 (2016).PubMed 

Google Scholar 
Wheeler, G. S., David, A. S. & Lake, E. C. Volatile chemistry, not phylogeny, predicts host range of a biological control agent of Old-World climbing fern. Biol. Control 159, 104636 (2021).CAS 

Google Scholar 
Li, N. et al. Manipulating two olfactory cues causes a biological control beetle to shift to non-target plant species. J. Ecol. 105, 1534–1546 (2017).CAS 

Google Scholar 
Buddenhagen, C. E. Broom Control Monitoring at Tongariro National Park (Department of Conservation Wellington, 2000).
Google Scholar 
Hayes, L. et al. Biocontrol of Weeds: Achievements to Date and Future Outlook. Ecosystem services in New Zealand-conditions and trends Vol. 2, 375–385 (Manaaki Whenua Press, 2013).
Google Scholar 
Bagnall, A. Heather at Tongariro. A study of a weed introduction. Tussock Grasslands Mt. Lands Inst. Rev 41, 17–21 (1982).
Google Scholar 
Chapman, H. M. & Bannister, P. The spread of heather, Calluna vulgaris (L.) Hull, into indigenous plant communities of Tongariro National Park. N. Z. J. Ecol. 7–16 (1990).Effah, E. et al. Effects of two invasive weeds on arthropod community structure on the Central Plateau of New Zealand. Plants 9, 919 (2020).CAS 
PubMed Central 

Google Scholar 
Keesing, V. F. Impacts of invasion on community structure: habitat and invertebrate assemblage responses to Calluna vulgaris (L.) Hull invasion, in Tongariro National Park, New Zealand, Massey University Palmerston North, New Zealand, (1995).Peterson, P. G., Fowler, S. V. & Barrett, P. Is the poor establishment and performance of heather beetle in Tongariro National Park due to the impact of parasitoids predators or disease. N. Z. Plant Prot. 57, 89–93. https://doi.org/10.30843/nzpp.2004.57.6977 (2004).Article 

Google Scholar 
Ajpark. The brands and the bees: trade marks and the mānuka challenge for honey businesses, https://www.ajpark.com/insights/the-brands-and-the-bees-trade-marks-and-the-manuka-challenge-for-honey-businesses/#:~:text=M%C4%81nuka%20is%20a%20taonga%20species,may%20be%20offensive%20to%20M%C4%81ori (2021).Effah, E. et al. Seasonal and environmental variation in volatile emissions of the New Zealand native plant Leptospermum scoparium in weed-invaded and non-invaded sites. Sci. Rep. 10, 1–11 (2020).
Google Scholar 
Effah, E., Min Tun, K., Rangiwananga, N. & Clavijo McCormick, A. Mānuka clones differ in their volatile profiles: Potential implications for plant defence, pollinator attraction and bee products. Agronomy 12, 169 (2022).CAS 

Google Scholar 
Effah, E. et al. Natural variation in volatile emissions of the invasive weed Calluna vulgaris in New Zealand. Plants 9, 283 (2020).CAS 
PubMed Central 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Ripley, B. et al. Package ‘mass’. Cran r 538, 113–120 (2013).
Google Scholar 
Chen, B. M., Liao, H. X., Chen, W. B., Wei, H. J. & Peng, S. L. Role of allelopathy in plant invasion and control of invasive plants. Allelopathy J 41, 155–166 (2017).
Google Scholar 
Ninkovic, V., Markovic, D. & Rensing, M. Plant volatiles as cues and signals in plant communication. Plant Cell Environ. 44, 1030–1043 (2021).CAS 
PubMed 

Google Scholar 
Holopainen, J. K. Multiple functions of inducible plant volatiles. Trends Plant Sci. 9, 529–533 (2004).CAS 
PubMed 

Google Scholar 
Rhoades, D. F. Responses of alder and willow to attack by tent caterpillars and webworms: evidence for pheromonal sensitivity of willows. In Plant Resistance to Insects (ed. Hedin, P. A.) 55–68 (American Chemical Society, 1983).
Google Scholar 
Hedin, P. A. Plant Resistance to Insects (American Chemical Society, 1983).
Google Scholar 
Heil, M. & Karban, R. Explaining evolution of plant communication by airborne signals. Trends Ecol. Evol. 25, 137–144 (2010).PubMed 

Google Scholar 
Barbosa, P. et al. Associational resistance and associational susceptibility: Having right or wrong neighbors. Annu. Rev. Ecol. Evol. Syst. 40, 1 (2009).
Google Scholar 
Kigathi, R. N., Weisser, W. W., Veit, D., Gershenzon, J. & Unsicker, S. B. Plants suppress their emission of volatiles when growing with conspecifics. J. Chem. Ecol. 39, 537–545 (2013).CAS 
PubMed 

Google Scholar 
Peñuelas, J. & Llusià, J. Influence of intra-and inter-specific interference on terpene emission by Pinus halepensis and Quercus ilex seedlings. Biol. Plant. 41, 139–143 (1998).
Google Scholar 
Ormeno, E., Fernandez, C. & Mévy, J.-P. Plant coexistence alters terpene emission and content of Mediterranean species. Phytochemistry 68, 840–852 (2007).CAS 
PubMed 

Google Scholar 
Himanen, S. J. et al. Birch (Betula spp.) leaves adsorb and re-release volatiles specific to neighbouring plants—A mechanism for associational herbivore resistance?. New Phytol. 186, 722–732 (2010).CAS 
PubMed 

Google Scholar 
Kessler, A. & Kalske, A. Plant secondary metabolite diversity and species interactions. Annu. Rev. Ecol. Evol. Syst. 49, 115–138 (2018).
Google Scholar 
Quintana-Rodriguez, E. et al. Plant volatiles cause direct, induced and associational resistance in common bean to the fungal pathogen Colletotrichum lindemuthianum. J. Ecol. 103, 250–260 (2015).CAS 

Google Scholar 
Loreto, F. & D’Auria, S. How do plants sense volatiles sent by other plants? Trends Plant Sci. (2021).Giordano, D., Facchiano, A., D’Auria, S. & Loreto, F. A hypothesis on the capacity of plant odorant-binding proteins to bind volatile isoprenoids based on in silico evidences. Elife 10, e66741 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ninkovic, V., Markovic, D. & Dahlin, I. Decoding neighbour volatiles in preparation for future competition and implications for tritrophic interactions. Perspect. Plant Ecol. Evol. Syst. 23, 11–17 (2016).
Google Scholar 
Kegge, W. et al. Red: far-red light conditions affect the emission of volatile organic compounds from barley (Hordeum vulgare), leading to altered biomass allocation in neighbouring plants. Ann. Bot. 115, 961–970 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gershenzon, J. Metabolic costs of terpenoid accumulation in higher plants. J. Chem. Ecol. 20, 1281–1328 (1994).CAS 
PubMed 

Google Scholar 
Anderson, P., Sadek, M., Larsson, M., Hansson, B. & Thöming, G. Larval host plant experience modulates both mate finding and oviposition choice in a moth. Anim. Behav. 85, 1169–1175 (2013).
Google Scholar 
Cunningham, J. P., Moore, C. J., Zalucki, M. P. & West, S. A. Learning, odour preference and flower foraging in moths. J. Exp. Biol. 207, 87–94 (2004).PubMed 

Google Scholar 
McCormick, A. C., Reinecke, A., Gershenzon, J. & Unsicker, S. B. Feeding experience affects the behavioral response of polyphagous gypsy moth caterpillars to herbivore-induced poplar volatiles. J. Chem. Ecol. 42, 382–393 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Proffit, M., Khallaf, M. A., Carrasco, D., Larsson, M. C. & Anderson, P. ‘Do you remember the first time?’ Host plant preference in a moth is modulated by experiences during larval feeding and adult mating. Ecol. Lett. 18, 365–374 (2015).PubMed 

Google Scholar 
Mayhew, P. J. Herbivore host choice and optimal bad motherhood. Trends Ecol. Evol. 16, 165–167 (2001).PubMed 

Google Scholar 
Jackson, T. et al. Anticipating the unexpected–managing pasture pest outbreaks after large-scale land conversion (New Zealand Grassland Association, 2012).Townsend, R. J., Dunbar, J. E. & Jackson, T. A. Flight behaviour of the manuka chafers, Pyronota festiva (Fabricius) and Pyronota setosa (Given) (Coleoptera: Melolonthinae), on the flipped soils of Cape Foulwind on the West Coast of New Zealand. N. Z. Plant Prot. 71, 255–261. https://doi.org/10.30843/nzpp.2018.71.175 (2018).Article 

Google Scholar 
Ferguson, C. M. et al. Quantifying the economic cost of invertebrate pests to New Zealand’s pastoral industry. N. Z. J. Agric. Res. 62, 255–315 (2019).
Google Scholar 
Cunningham, J. Can mechanism help explain insect host choice?. J. Evol. Biol. 25, 244–251 (2012).CAS 
PubMed 

Google Scholar 
Syrett, P., Smith, L. A., Bourner, T. C., Fowler, S. V. & Wilcox, A. A European pest to control a New Zealand weed: Investigating the safety of heather beetle, Lochmaea suturalis (Coleoptera: Chrysomelidae) for biological control of heather, Calluna vulgaris. Bull. Entomol. Res. 90, 169–178. https://doi.org/10.1017/S0007485300000286 (2000).CAS 
Article 
PubMed 

Google Scholar 
Fowler, S., Harman, H., Memmott, J., Peterson, P. & Smith, L. In Proceedings of the XII International Symposium on Biological Control of Weeds (eds Julien, M. H. et al.) 495–502.Fowler, S. V. et al. Investigating the poor performance of heather beetle, Lochmaea suturalis (Thompson) (Coleoptera: Chrysomelidae), as a weed biocontrol agent in New Zealand: Has genetic bottlenecking resulted in small body size and poor winter survival?. Biol. Control 87, 32–38 (2015).
Google Scholar 
Effah, E. et al. Herbivory and attenuated UV radiation affect volatile emissions of the invasive weed Calluna vulgaris. Molecules 25, 3200 (2020).CAS 
PubMed Central 

Google Scholar 
Pearson, D. E. & Callaway, R. M. Indirect nontarget effects of host-specific biological control agents: Implications for biological control. Biol. Control 35, 288–298 (2005).
Google Scholar 
Rand, T. A. & Louda, S. M. Exotic weed invasion increases the susceptibility of native plants to attack by a biocontrol herbivore. Ecology 85, 1548–1554. https://doi.org/10.1890/03-3067 (2004).Article 

Google Scholar  More