Philippa Kaur

Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).Article 
CAS 
PubMed 

Google Scholar 
Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).Article 
PubMed 

Google Scholar 
Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Environ. Resour. 28, 137–167 (2003).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
PubMed 

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

Google Scholar 
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).Article 
CAS 
PubMed 

Google Scholar 
Primack, R. B. et al. Biodiversity gains? The debate on changes in local- vs global-scale species richness. Biol. Conserv. 219, A1–A3 (2018).Article 

Google Scholar 
Vellend, M. The biodiversity conservation paradox. Am. Sci. 105, 94 (2017).Article 

Google Scholar 
Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).Article 

Google Scholar 
Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

Google Scholar 
Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).Article 
PubMed 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 
PubMed 

Google Scholar 
Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).Article 
ADS 
CAS 

Google Scholar 
Beck, J. J., Larget, B. & Waller, D. M. Phantom species: adjusting estimates of colonization and extinction for pseudo-turnover. Oikos 127, 1605–1618 (2018).Article 

Google Scholar 
Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).Article 

Google Scholar 
Avolio, M. L. et al. A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere 10, e02881 (2019).Article 

Google Scholar 
Diekmann, M. et al. Patterns of long‐term vegetation change vary between different types of semi‐natural grasslands in Western and Central Europe. J. Veg. Sci. 30, 187–202 (2019).Article 

Google Scholar 
Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gini, C. Il diverso accrescimento delle classi sociali e la concentrazione della ricchezza. Giornale degli Economisti38, 27–83 (1909).Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).Article 
PubMed 

Google Scholar 
Hundt, R. Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung (Wehry-Druck OHG, 2001).Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jansen, F., Bonn, A., Bowler, D. E., Bruelheide, H. & Eichenberg, D. Moderately common plants show highest relative losses. Conserv. Lett. 13, e12674 (2020).Article 

Google Scholar 
Bruelheide, H. et al. Using incomplete floristic monitoring data from habitat mapping programmes to detect species trends. Divers. Distrib. 26, 782–794 (2020).Article 

Google Scholar 
Sperle, T. & Bruelheide, H. Climate change aggravates bog species extinctions in the Black Forest (Germany). Divers. Distrib. 27, 282–295 (2020).Article 

Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).Article 
CAS 
PubMed 

Google Scholar 
Timmermann, A., Damgaard, C., Strandberg, M. T. & Svenning, J.-C. Pervasive early 21st-century vegetation changes across Danish semi-natural ecosystems: more losers than winners and a shift towards competitive, tall-growing species. J. Appl. Ecol. 52, 21–30 (2015).Article 

Google Scholar 
Milligan, G., Rose, R. J. & Marrs, R. H. Winners and losers in a long-term study of vegetation change at Moor House NNR: effects of sheep-grazing and its removal on British upland vegetation. Ecol. Indic. 68, 89–101 (2016).Baskin, Y. Winners and losers in a changing world. BioScience 48, 788–792 (1998).Article 

Google Scholar 
Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

Google Scholar 
Naaf, T. & Wulf, M. Habitat specialists and generalists drive homogenization and differentiation of temperate forest plant communities at the regional scale. Biol. Conserv. 143, 848–855 (2010).Article 

Google Scholar 
Heinrichs, S. & Schmidt, W. Biotic homogenization of herb layer composition between two contrasting beech forest communities on limestone over 50 years. Appl. Veg. Sci. 20, 271–281 (2017).Article 

Google Scholar 
Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J. Veg. Sci. 25, 113–121 (2014).Article 

Google Scholar 
Metzing, D. et al. Rote Liste und Gesamtartenliste der Farn- und Blütenpflanzen (Trachaeophyta) Deutschlands (Landwirtschaftsverlag, 2018).Poschlod, P. Geschichte der Kulturlandschaft (Ulmer, 2017).Sukopp, H. ‘Rote Liste’ der in der Bundesrepublik Deutschland gefährdeten Arten von Farn- und Blütenpflanzen. (1. Fassung). Nat. Landsch. 49, 315–322 (1974).
Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
PubMed 

Google Scholar 
Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Jandt, U., von Wehrden, H. & Bruelheide, H. Exploring large vegetation databases to detect temporal trends in species occurrences. J. Veg. Sci. 22, 957–972 (2011).Article 

Google Scholar 
Jones, F. A. M. & Magurran, A. E. Dominance structure of assemblages is regulated over a period of rapid environmental change. Biol. Lett. 14, 20180187 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chytrý, M., Tichý, L., Hennekens, S. M. & Schaminée, J. H. J. Assessing vegetation change using vegetation-plot databases: a risky business. Appl. Veg. Sci. 17, 32–41 (2014).Article 

Google Scholar 
Jandt, U. et al. ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany. Sci. Data, https://doi.org/10.1038/s41597-022-01688-6 (2022)Bohn, U. & Schniotalle, S. Hochmoor-, Grünland- und Waldrenaturierung im Naturschutzgebiet ‘Rotes Moor’/Hohe Rhön 1981–2001 (Landwirtschaftsverlag, 2008).Rosenthal, G. Erhaltung und Regeneration von Feuchtwiesen. Vegetationsökologische Untersuchungen auf Dauerflächen. Diss. Bot. 182, 1–283 (1992).
Google Scholar 
Schwabe, A. & Kratochwil, A. Pflanzensoziologische Dauerflächen-Untersuchungen im Bannwald ‘Flüh’ (Südschwarzwald) unter besonderer Berücksichtigung der Weidfeld-Sukzession. Standort Wald 49, 5–49 (2015).
Google Scholar 
Poschlod, P., Schreiber, K.-F., Mitlacher, K., Römermann, C. & Bernhardt-Römermann, M. in Landschaftspflege und Naturschutz im Extensivgrünland. 30 Jahre Offenhaltungsversuche Baden-Württemberg Vol. 97 (eds. Schreiber, K.-F. et al.) 243–288 (2009).Hennekens, S. M. & Schaminée, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sci. 12, 589–591 (2001).Article 

Google Scholar 
Chytrý, M. et al. EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Appl. Veg. Sci. 23, 648–675 (2020).Article 

Google Scholar 
Bruelheide, H., Tichý, L., Chytrý, M. & Jansen, F. Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R. Appl. Veg. Sci. 12, e12562 (2021).Jansen, F. & Dengler, J. GermanSL—eine universelle taxonomische Referenzliste für Vegetationsdatenbanken. Tuexenia 28, 239–253 (2008).
Google Scholar 
Wisskirchen, R. & Haeupler, H. Standardliste der Farn-und Blütenpflanzen Deutschlands (Ulmer, 1998).Jansen, F. & Dengler, J. Plant names in vegetation databases–a neglected source of bias. J. Veg. Sci. 21, 1179–1186 (2010).Article 

Google Scholar 
Wegener, U. Vegetationswandel des Berggrünlands nach Untersuchungen von 1954 bis 2016—Wege zur Erhaltung der Bergwiesen (Mountain grasslands vegetation change after research from 1954 to 2016—ways to preserve mountain meadows). Abh. Berichte Aus Dem Mus. Heine. 11, 35–101 (2018).
Google Scholar 
Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 
ADS 

Google Scholar 
Weiner, J. & Solbrig, O. T. The meaning and measurement of size hierarchies in plant populations. Oecologia 61, 334–336 (1984).Article 
ADS 
PubMed 

Google Scholar 
Signorell, A. et al. DescTools: tools for descriptive statistics. R version 0.99.32 https://CRAN.R-project.org/package=DescTools (2020).BiolFlor—a new plant-trait database as a tool for plant invasion ecology. Divers. Distrib. 10, 363–365 (2004).INSPIRE. D2.8.III.18 Data Specification on Habitats and Biotopes—Technical Guidelines https://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_HB_v3.0rc2.pdf (2013).Jandt, U. & Bruelheide, H. German Vegetation Reference Database (GVRD). Biodivers. Ecol. 4, 355–355 (2012).Article 

Google Scholar 
Sokal, R. R. & Rohlf, F. J. Biometry (Freeman, 1995).Chytrý, M., Tichý, L., Holt, J. & Botta‐Dukát, Z. Determination of diagnostic species with statistical fidelity measures. J. Veg. Sci. 13, 79–90 (2002).Article 

Google Scholar 
Gotelli, N. J. Null model analysis of species co‐occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

Google Scholar 
Pillar, V. D., Sabatini, F. M., Jandt, U., Camiz, S. & Bruelheide, H. Revealing the functional traits linked to hidden environmental factors in community assembly. J. Veg. Sci. 32, e12976 (2021).Sabatini, F. M., Jiménez‐Alfaro, B., Burrascano, S., Lora, A. & Chytrý, M. Beta‐diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes. Ecography 41, 1038–1048 (2018).Article 

Google Scholar 
MacArthur, R. On the relative abundance of species. Am. Nat. 94, 25–36 (1960).Article 

Google Scholar 
Prado, P. I., Miranda, M. D. & Chalom, A. sads: maximum likelihood models for species abundance distributions. R version 0.4.2. https://CRAN.R-project.org/package=sads (2018).Kuhn, G., Heinz, S. & Mayer, F. Grünlandmonitoring Bayern. Ersterhebung der Vegetation 2002–2008. Schriftenreihe LfL Bayer. Landesanst. Für Landwirtsch. 3, 1–161 (2011).
Google Scholar  More

Basic characteristics of river sedimentsA considerable variation was found in the distribution of clay (81 to 48.4 g kg−1), silt (145 to 656 g kg−1), and sand (38 to 821 g kg−1) particles among sediment materials. The associated coefficient of variations (CV) was 57, 59.5, and 41%, respectively. Statistical data related to the physicochemical properties of sediments and their main elements are reported in Table 2. The variations in particle size distribution located sediment material in seven textural classes ranging from loamy sand to silty clay. The high variability in particle size distribution suggests that different sets of geogenic and anthropogenic processes are enacted in the development and distribution of sediments in the rivers. The pH and CCE ranged from 7.4 to 8.2 and 31 to 251 g kg−1, respectively, indicating the dominancy of alkaline-calcareous condition. None of the sediment samples exhibited salinity conditions (EC  > 4 dS m−1) with EC in the range of 0.3 to 1.4 dS m−1. A relatively low range of OM was found in all samples ranging from 7 to 61 g kg−1 with a mean value of 19 g kg−1. This range of OM coincides with the corresponding values in regional soils47. Except for pH, other sediments properties demonstrated above 35% of CV illustrating a wide range of variability in sediments’ physicochemical properties across the study rivers.Table 2 Summary statistics of sediment properties.Full size tableThe highest concentration among major elements was observed in SiO2, varying between 37.5 and 55.2%, with a mean percentage of 44.9%. This element followed in magnitude by Al2O3 (8.9–15.9%), CaO (5–14.3%), Fe2O3 (4.8–10%), MgO (2.4–17.2%), K2O (1.2–3.1%), Na2O (0.68–2.7%), SO3 (0.01–4.8% g kg−1) (Table 2). Considering the semi-arid climatic condition of the study region, higher levels of SiO2 and lower levels of Al2O3 may indicate that the silicate minerals forming the sediments of the area have not been subjected to severe weathering processes. Likewise, the Na2/K2O ratio was greater than 1 in the majority of sediment samples, implying an enrichment of potassium feldspar and the relatively intense weathering of Na-bearing minerals in the region48,49. The CIA value was in the range of 64.9 to 85.7% with a mean percentage of 72.9%, representing a moderate chemical weathering intensity of lithological materials (65%  Pb  > Cu  > Cd which varied largely among the sampling points. The level of Zn, Cu, Cd, Pb, and Ni varied in the ranges of 32.6–87.5, 14.2–33.3, 0.42–4.8, 14.5–69.5, and 20.1–183.5 mg kg-1, respectively, for winter, and 35.3–92.5, 15.6–35.1, 0.47–5.1, 15.5–73.1, 23.2–188.3 mg kg−1 for summer. The obtained ranges are comparable with data found in previous studies in Asia4,54,55,54.Figure 2The comparison of the mean concentration of Zn, Cu, Cd, Pb, and Ni elements in the study rivers’ sediments during summer and winter. Different letters show significant differences in metal content among rivers pooled over seasons at P  More

Insect rearingThe cotton mealybugs used in this study were originally collected from Rose of Sharon, Hibiscus syriacus L. (Malvales: Malvaceae) in Jinhua, Zhejiang Province, China, in June 2016. They were maintained on fresh tomato plants (cv. Hezuo-903, Shanghai Changzhong Seeds Industry Co., Ltd, China) in a climatically controlled chamber maintained at 27 ± 1 °C, 75% relative humidity (RH), and a photoperiod of 14:10 (L:D). For detailed insect rearing and tomato cultivation methods see ref. 56.Scanning electron microscopy (SEM) of P. solenopsis waxSEM was used to observe changes in wax on the body surface of adult P. solenopsis females according to the methods of Huang et al.57. Briefly, collected insects were taped onto a stub and dried in an ion sputter (Hatachi, Tokyo, Japan) under a vacuum. After gold sputtering, the samples were observed using a TM-1000 SEM (Hatachi, Tokyo, Japan). Photos were scanned from the dorsal part of the third thoracic segment. Thirty insects were used for both RNAi-treated and control groups.Chemical composition analysis of mealybug waxA small soft brush was used to collect wax filaments from the body surface of P. solenopsis females. Prior to use, the brush was washed successively by 70% ethanol, sterile water, and 1× sterile phosphate-buffered saline (PBS, pH 7.4). The wax was collected into a clean chromatography vial for the following experiments. Two vials of wax, each collected from 1000 adult females, were dissolved in 1 ml of methanol and 1 ml of n-hexane, respectively. The vials were stirred gently for 3 min, kept at room temperature for 30 min, and then put into an S06H ultrasonic vibrator (Zealway, Xiamen, China) for 30 min to dissolve the wax sufficiently. The samples were analyzed on a TRACE 1310 (Thermo Scientific, Waltham, USA) gas chromatograph (GC) equipped with an ISQ single quadrupole MS and interfaced with the Chromeleon 7.2 data analysis system (Thermo Scientific, Waltham, USA), with a constant flow of helium at 1 ml/min. For each sample, a splitless injection of 1.0 μl was respectively made into a polar TG-WaxMS (Thermo Scientific, Waltham, USA) and a nonpolar TG-5MS (Thermo Scientific, Waltham, USA) 30 m × 0.25 mm × 0.25 μm capillary column. The temperature program for polar column samples was as follows: 40 °C for 2 min, then 5 °C/min to 240 °C, hold 10 min; the program for nonpolar column samples was: 40 °C for 2 min, then 5 °C/min to 300 °C, hold 5 min. Injector and detector temperatures were, respectively, set at 250 and 230 °C for polar column samples, and at 300 and 300 °C for nonpolar column samples. Mass detection for all samples was run under an EI mode with a 70 eV ionization potential and an effective m/z range of 35–450 at a scan rate of 5 scan/s. Chemical compounds were identified by mapping against the NIST database. The relative content of each compound was calculated by peak area which was determined using the Agilent MassHunter system.RNA extraction and RT-qPCRTotal RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer’s instructions, and RNA quality was accessed using agarose gel electrophoresis and a Biodrop μLite. 800 ng of total RNA was used for cDNA synthesis using the HiScript III RT SuperMixfor qPCR (+gDNA wiper) (Vazyme Biotech Co., Ltd., Nanjing, China), according to the manufacturer’s instructions. Quantitative RT-PCR (RT-qPCR) was conducted using an AriaMx real-time PCR system (Agilent Technologies, USA), using a 20 μl reaction containing 2 μl of 10-fold diluted cDNA, 0.8 μl of each primer, and 10 μl ChamQ SYBR Color qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). The RT-qPCR thermocycling protocol was 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The PsActin gene was used as an internal control. At least three biological replicates were used for each experiment. Quantitative variations were evaluated using the relative quantitative method (2−ΔΔCt)58.Transcriptome analysis of integumentary and non-integumentary tissuesTo obtain the integument and other tissues, adult P. solenopsis females were dissected in 1× sterile PBS (pH 7.4) on a sterile Petri dish. Dissected fresh tissues were directly used or frozen in liquid nitrogen and stored at −80 °C for follow-up experiments. We sequenced the transcriptomes of integumentary and non-integumentary tissues (all other tissues without integument) dissected from 150 adult females, with each sample being repeated in triplicate. mRNAs were purified from total RNA via oligo (dT) magnetic beads, and the fragmented mRNAs were then reverse transcribed into cDNA using random primers. Constructed pair-end libraries were sequenced using an Illumina HiSeq X Ten platform in Novogene (Beijing, China). After quality control, the clean RNA-Seq data of the six libraries were aligned with the P. solenopsis genome (http://v2.insect-genome.com/Organism/624) using HISTAT259. Then featureCounts60 and DESeq261 were used for the differential expression analysis of genes. The threshold for differentially expressed genes (DEGs) was defined by log2fold ≥ 1 or ≤−1 and a padj-value  More

Dorning, M. & Cipollini, D. Leaf and root extracts of the invasive shrub, Lonicera maackii, inhibit seed germination of three herbs with no autotoxic effects. Plant Ecol. 184, 287–296 (2006).Article 

Google Scholar 
Greer, M. J., Wilson, G. W., Hickman, K. R. & Wilson, S. M. Experimental evidence that invasive grasses use allelopathic biochemicals as a potential mechanism for invasion: Chemical warfare in nature. Plant Soil 385, 165–179 (2014).Article 
CAS 

Google Scholar 
Möhler, H., Diekötter, T., Herrmann, J. D. & Donath, T. W. Allelopathic vs. autotoxic potential of a grassland weed-evidence from a seed germination experiment. Plant Ecol. Divers. 11, 539–549 (2018).Article 

Google Scholar 
Callaway, R. M. & Aschehoug, E. T. Invasive plants versus their new and old neighbors: A mechanism for exotic invasion. Science 290, 521–523 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Niu, H. B., Liu, W. X., Wan, F. H. & Liu, B. An invasive aster (Ageratina adenophora) invades and dominates forest understories in China: Altered soil microbial communities facilitate the invader and inhibit natives. Plant Soil 294, 73–85 (2007).Article 
CAS 

Google Scholar 
Wardle, D. A., Karban, R. & Callaway, R. M. The ecosystem and evolutionary contexts of allelopathy. Trends Ecol. Evol. 26, 655–662 (2011).Article 
PubMed 

Google Scholar 
Meiners, S. J., Kong, C. H., Ladwig, L. M., Pisula, N. L. & Lang, K. A. Developing an ecological context for allelopathy. Plant Ecol. 213, 1221–1227 (2012).Article 

Google Scholar 
Liebhold, A. M., Brockerhoff, E. G., Kalisz, S., Nunez, M. A. & Wardle, D. A. Biological invasions in forest ecosystems. Biol. Invasions 19, 3437–3458 (2017).Article 

Google Scholar 
Liao, H. X. et al. Soil microbes regulate forest succession in a subtropical ecosystem in China: Evidence from a mesocosm experiment. Plant Soil 430, 277–289 (2018).Article 
CAS 

Google Scholar 
Wardle, D. A., Nilsson, M. C., Gallet, C. & Zackrisson, O. An ecosystem-level perspective of allelopathy. Biol. Rev. 73, 305–319 (2010).Article 

Google Scholar 
Hierro, J. L. & Callaway, R. M. Allelopathy and exotic plant invasion. Plant Soil 256, 29–39 (2003).Article 
CAS 

Google Scholar 
Uddin, M. N., Robinson, R. W., Buultjens, A., Harun, M. A. & Shampa, S. H. Role of allelopathy of Phragmites australis in its invasion processes. J. Exp. Mar. Biol. Ecol. 486, 237–244 (2017).Article 

Google Scholar 
Thiébaut, G., Tarayre, M. & Rodríguez-Pérez, H. Allelopathic effects of native versus invasive plants on one major invader. Front. Plant Sci. 2, 854 (2019).Article 

Google Scholar 
Smith, M., Cecchi, L., Skjøth, C. A., Karrer, G. & Šikoparijae, B. Common ragweed: A threat to environmental health in Europe. Environ. Int. 61, 115–126 (2013).Article 
CAS 
PubMed 

Google Scholar 
Montagnani, C., Gentili, R., Smith, M., Guarino, M. F. & Citterio, S. The worldwide spread, success, and impact of ragweed (Ambrosia spp.). Crit. Rev. Plant Sci. 36, 1–40 (2017).Article 

Google Scholar 
Zeng, K., Zhu, Y. Q. & Liu, J. X. Research progress on ragweed (Ambrosia). Acta Prataculturae Sin. 19, 212–219 (2010).
Google Scholar 
Jacobs, R. L. et al. Responses to ragweed pollen in a pollen challenge chamber versus seasonal exposure identify allergic rhinoconjunctivitis endotypes. J. Allergy Clin. Immun. 130, 122-127.e8 (2012).Article 
PubMed 

Google Scholar 
Lake, R. I. et al. Climate change and future pollen allergy in Europe. Environ. Health Perspect. 125, 385–391 (2017).Article 
PubMed 

Google Scholar 
Wang, J. J., Zhao, B. Y., Li, M. T. & Li, R. Ecological invasion plant-bitter weed (Ambrosia artemisiifolia) and integrated control strategy. Pratacultural Sci. 023, 71–75 (2006).CAS 

Google Scholar 
Deng, Z. Z., Bai, J. D., Zhao, C. Y. & Li, J. S. Advance in invasion mechanisms of Ambrosia artemisiifolia. Pratacultural Sci. 32, 54–63 (2015).
Google Scholar 
Dong, H. G. et al. Diffusion and intrusion features of Ambrosia artemisiifolia and Ambrosia trifida in Yili River Valley. J. Arid Land Resour. Environ. 31, 175–180 (2017).
Google Scholar 
Vink, J. P. et al. Glyphosate-resistant giant ragweed (Ambrosia trifida) control in dicamba-tolerant soybean. Weed Technol. 26, 422–428 (2012).Article 
CAS 

Google Scholar 
Simard, M. J. & Benoit, D. L. Effect of repetitive mowing on common ragweed (Ambrosia artemisiifolia L.) pollen and seed production. Ann. Agric. Environ. Med. 18, 55–62 (2011).PubMed 

Google Scholar 
Goplen, J. J. et al. Seedbank depletion and emergence patterns of giant ragweed (Ambrosia trifida) in Minnesota cropping systems. Weed Sci. 65, 52–60 (2017).Article 

Google Scholar 
Jurik, T. W. Population distributions of plant size and light environment of giant ragweed (Ambrosia trifida L.) at three densities. Oecologia 87, 539–550 (1991).Article 
ADS 
PubMed 

Google Scholar 
Patracchini, C., Vidotto, F. & Ferrero, A. Common ragweed (Ambrosia artemisiifolia) growth as affected by plant density and clipping. Weed Technol. 25, 268–276 (2011).Article 

Google Scholar 
Kazinczi, G. Ragweed seed bank in the soils of arable fields of Transdanubia, Hungary. Hung. Weed Res. Technol. 19(1), 21–36 (2018).
Google Scholar 
Essl, F. et al. Biological flora of the British Isles: Ambrosia artemisiifolia. J. Ecol. 103, 1069–1098 (2015).Article 

Google Scholar 
Goplen, J. J. Giant Ragweed (Ambrosia trifida) Seed Bank Dynamics and Management. (Master’s dissertation, University of Minnesota.) Retrieved from https://hdl.handle.net11299174767 (2015).Yoda, K. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. 14, 107–129 (1963).
Google Scholar 
Friedman, J. & Waller, G. R. Allelopathy and autotoxicity. Trends Biochem. Sci. 10, 47–50 (1985).Article 
CAS 

Google Scholar 
Weller, D. E. The interspecific size-density relationship among crowded plant stands and its implications for the −3/2 power rule of self-thinning. Am. Nat. 133, 20–41 (1989).Article 

Google Scholar 
Deng, J. et al. Autotoxicity of phthalate esters in tobacco root exudates: Effects on seed germination and seedling growth. Pedosphere 27, 1073–1082 (2017).Article 
CAS 

Google Scholar 
Sudatti, D. B., Duarte, H. M., Soares, A. R., Salgado, L. T. & Pereira, R. C. New ecological role of seaweed secondary metabolites as autotoxic and allelopathic. Front. Plant Sci. 11, 347 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Singh, H. P., Batish, D. & Kohil, R. Autotoxicity: Concepts, organisms, and ecological significance. Plant Sci. 18, 757–772 (1999).CAS 

Google Scholar 
Chon, S. U. et al. Effects of alfalfa leaf extracts and phenolic allelochemicals on early seedling growth and root morphology of alfalfa and barnyard grass. Crop Prot. 21, 1077–1082 (2002).Article 
CAS 

Google Scholar 
Chen, B. M., D’Antonio, C. M., Molinari, N. & Peng, S. L. Mechanisms of influence of invasive grass litter on germination and growth of coexisting species in California. Biol. Invasions 20, 1881–1897 (2018).Article 

Google Scholar 
Chen, L. C., Wang, S. L., Wang, P. & Kong, C. H. Autoinhibition and soil allelochemical (cyclic dipeptide) levels in replanted Chinese fir (Cunninghamia lanceolata) plantations. Plant Soil 374, 793–801 (2014).Article 
CAS 

Google Scholar 
Perry, L. G. et al. Retracted: Dual role for an allelochemical: catechin from Centaurea maculosa root exudates regulates conspecific seedling establishment. J. Ecol. 93, 1126–1135 (2005).Article 
CAS 

Google Scholar 
Yu, J. Q., Ye, S. F., Zhang, M. F. & Hu, W. H. Effects of root exudates and aqueous root extracts of cucumber (Cucumis sativus) and allelochemicals, on photosynthesis and antioxidant enzymes in cucumber. Biochem. Syst. Ecol. 31, 129–139 (2003).Article 
CAS 

Google Scholar 
Kong, C. H., Wang, P. & Xu, X. H. Allelopathic interference of Ambrosia trifida with wheat (Triticum aestivum). Agric. Ecosyst. Environ. 119, 416–420 (2007).Article 
CAS 

Google Scholar 
Béres, I., Kazinczi, G. & Narwal, S. S. Allellopathic plants. 4. Common ragweed (Ambrosia elatior L. syn. A. artemisiifolia). Allelopathy J. 9, 27–34 (2002).
Google Scholar 
Bauer, J. T., Shannon, S. M., Stoops, R. E. & Reynolds, H. L. Context dependency of the allelopathic effects of Lonicera maackii on seed germination. Plant Ecol. 213, 1907–1916 (2012).Article 

Google Scholar 
Renne, I. J., Sinn, B. T., Shook, G. W., Sedlacko, D. M. & Hierro, J. L. Eavesdropping in plants: Delayed germination via biochemical recognition. J. Ecol. 102, 86–94 (2014).Article 

Google Scholar 
Loydi, A., Donath, T. W., Eckstein, R. L. & Otte, A. Non-native species litter reduces germination and growth of resident forbs and grasses: Allelopathic, osmotic or mechanical effects?. Biol. Invasions 17, 581–595 (2014).Article 

Google Scholar 
Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006).Article 
CAS 
PubMed 

Google Scholar 
Bonea, D., Bonciu, E., Niculescu, M. & Olaru, A. L. The allelopathic, cytotoxic and genotoxic effect of Ambrosia artemisiifolia on the germination and root meristems of Zea mays. Caryologia 71, 24–28 (2017).Article 

Google Scholar 
Dadkhah, A. Allelopathic effect of sugar beet (Beta vulgaris) and eucalyptus (Eucalyptus camaldulensis) on seed germination and growth of Portulaca oleracea. Russ. Agric. Sci. 39, 117–123 (2013).Article 

Google Scholar 
Zheng, L. & Feng, Y. L. Allelopathic effects of Eupatorium adenophorum Spreng on. seed germination and seedling growth in ten herbaceous species. Acta Ecol. Sin. 25, 2782–2787 (2005).CAS 

Google Scholar 
Brückner, D. J. The allelopathic effect of ragweed (Ambrosia artemisiifolia L.) on the germination of cultivated plants. Novenytermeles 47, 635–644 (1998).
Google Scholar 
Qin, R. M. et al. The evolution of increased competitive ability, innate competitive advantages, and novel biochemical weapons act in concert for a tropical invader. New Phytol. 197, 979–988 (2012).Article 
PubMed 

Google Scholar 
Zheng, Y. L. et al. Integrating novel chemical weapons and evolutionarily increased competitive ability in success of a tropical invader. New Phytol. 205, 1350–1359 (2015).Article 
PubMed 

Google Scholar 
Kaushal, R., Verma, K. S. & Singh, K. N. Effect of Grewia optiva and Populus deltoides leachatesv on field crops. Allelopathy J. 11, 229–234 (2003).
Google Scholar 
Kumari, A. & Kohli, R. Autotoxicity of ragweed parthenium (Parthenium hysterophorus). Weed Sci. 35, 629–632 (1987).Article 

Google Scholar 
Einhellig, F. A. Allelopathy: Current status and future goals. In Allelopathy: Organisms, processes and applications (ed. Inderjit Dakshini, K. M. M.) 1–24 (Am Chem. Soc, Washington, 1995).
Google Scholar 
Hadack, F. Secondary metabolites as plant traits: Current assessment and future perspectives. Crit. Rev. Plant Sci. 21, 273–322 (2002).Article 

Google Scholar 
Rice, E. L. Biological Control of Weeds and Plant Diseases (Oklahomka Press, 1995).
Google Scholar 
Choi, B. et al. Common ragweed-derived phenolic compounds and their effects on germination and seedling growth of weed species. Weed Turfgrass Sci. 30, 396–404 (2010).
Google Scholar 
Friedman, J. & Waller, G. R. Seeds as allelopathic agents. Chem. Ecol. 9, 1107–1117 (1983).Article 
CAS 

Google Scholar 
Canals, R. M., Emeterio, L. S. & Peralta, J. Autotoxicity in Lolium rigidum: Analyzing the role of chemically mediated interactions in annual plant populations. J. Theor. Biol. 235, 402–407 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
San Emeterio, L., Damgaard, C. & Canals, R. M. Modelling the combined effect of chemical interference and resource competition on the individual growth of two herbaceous populations. Plant Soil 292, 95–103 (2007).Article 
CAS 

Google Scholar 
Dickerson, C. T. Studies on the germination, growth, development and control of Common Ragweed (Ambrosia artemisiifolia L.). PhD thesis, Cornell University, Ann Arbor (1968).Nuutinen, V. & Butt, K. R. Homing ability widens the sphere of influence of the earthworm Lumbricus terrestris L. Soil Biol. Biochem. 37, 805–807 (2005).Article 
CAS 

Google Scholar 
Favaretto, A., Scheffer-basso, S. M. & Perez, N. B. Autotoxicity in tough lovegrass (Eragrostis plana). Planta Daninha 35(35), e017164046 (2017).
Google Scholar 
Sinkkonen, A. Modelling the effect of autotoxicity on density-dependent phytotoxicity. J. Theor. Biol. 244, 218–227 (2007).Article 
ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Zhang, S. S., Shi, F. Q., Yang, W. Z., Xiang, Z. Y. & Duan, Z. L. Autotoxicity as a cause for natural regeneration failure in Nyssa yunnanensis and its implications for conservation. Isr. J. Plant Sci. 62, 187–197 (2015).Article 

Google Scholar 
Liu, Y. et al. Relationship between seed germination and invasion of Ambrosia artemisiifolia and A. trifida at different positions. Acta Ecol. Sin. 39, 9079–9088 (2019).

Google Scholar  More

Retrieval of E. coli plasmid sequencesAll E. coli sequences were downloaded from the NCBI FTP server in May 2020. To establish an initial collection of plasmids, only complete genomes with an associated plasmid were retained. All genomes were verified for belonging to the species E. coli using kmerfinder (https://cge.cbs.dtu.dk/services/KmerFinder/). Sequence type (ST) was determined via multi-locus sequence typing (MLST) based on the 7-gene Achtman scheme using pubMLST (https:/github.com/tseemann/mlst). Only genomes with exact matches were assigned for each ST and used for subsequent analysis. To ensure our sequences were sufficiently representative of E. coli pathogens expected in nature, a systematic literature search (see description below and Fig. S1) was conducted to establish an expected distribution of STs (Table S1). This information was used to update our initial collection to match the top 4 most prevalent STs (131, 11, 73, and 95). Specifically, to identify supplementary plasmid sequences, genome accession IDs were chosen from EnteroBase based on the following criteria: the strain was matched to the correct ST and had a high-quality genome sequence (based on N50  > 20,000 and the number of contigs  0.1, 2-tailed student t test). For the second method, all kanR plasmids were used, and instead changed the hosts such that DH5αPro cells were in competition with DH5αPro containing a spontaneous rifampicin-resistant mutant (rifR). Any rifR strain was quantified on rifampicin-containing plates, and the second strain was quantified by rifampicin CFU minus CFU obtained on blank plates. We established that rifR exhibited no fitness defects by (1) growth rates between the wild-type (WT) strain (W) and rifR (M) (Fig. S5D), and (2) directly competing the two control strains (Fig. S5E). In both cases, results were statistically indistinguishable (p  > 0.1, two-tailed student t test). KanR/cmR and WT/rifR experiments were each conducted in LB or M9CAG, respectively. In all cases, experiments were repeated with at least three independent biological replicates.Time-kill measurements in the presence of carbenicillinAll strains were grown as previously described. Time-kill experiments entailed hourly measurements of CFU in presence of carbenicillin at either 3.75 μg/mL (3x IC50) or 5 μg/mL (4x IC50) over a span of 2 or 3 h, including time 0. Specifically, overnight cultures were first diluted 1:100 into LB media containing 1 mM IPTG and 50 μg/mL kanamycin and sub-cultured for two hours in a 37 °C incubator with shaking at 250 rpm. Following this, cell density was adjusted as necessary to achieve a starting OD600 of ~0.15 in all cases. Adjusted subcultures were then aliquoted into a 96-well plate and the appropriate carbenicillin treatments were added directly to the well. Plates were sealed with a paper film and placed in a 37 °C incubator with shaking at 250 rpm. Initial collection for time=0 was acquired before carbenicillin treatment. Thereafter, 10 μL of culture was removed from the well every hour, 10-fold serial dilutions were performed and 10 μL was plated on blank LB agar with three technical replicates at each time point. Colonies were counted after plates were grown for 16 h in a 37 °C incubator to determine CFU. This procedure utilized 14 strains of DH5αPro transformed with kanR plasmids of interest – ctrl, katG, lpxM, yfbR, aroH, pld, fdtC, agp, eptC, arcA, argF, mmuM, ahr, and fabG. CFUs were averaged for all technical replicates, and experiments were conducted with at least three independent biological replicates.Oxygen consumption rateOxygen consumption rates (OCR) were obtained with the Resipher device from Lucid Scientific. The selected strains were grown overnight as previously described. Overnight cultures were resuspended in M9CAG media with 1 mM IPTG and 50 μg/mL kanamycin, and placed in 25 °C for one hour to initiate gene expression. Following this, cells were diluted 10x into M9CAG media containing kanamycin and IPTG, and 100 μL was aliquoted per well into a 96-well microtiter plate according to the manufacturer’s instructions. Plates were placed at 30 °C to minimize growth, and oxygen concentration (μM) was measured immediately thereafter. 24 wells were measured consisting of 6 technical replicates for each strain. Given the clear well-well variability (Fig. S8B, C), data shown are for one biological replicate. However, qualitative trends were consistently reproduced in multiple independent experiments.StatisticsIn all cases where t tests and ANOVA’s were used, data was first verified to be normally distributed using Kolmogorov test for normality. Otherwise, Mann-Whitney U-tests were conducted. For panels with multiple tests, Bonferroni correction was used to adjust the p values. To determine whether any metabolic category was significantly dependent on incompatibility groups, we implemented logistic regressions in MATLAB with the function fitglm. Random forest classification was used to establish the relative importance of prevalent metabolic genes and gene categories predicting the presence of antibiotic resistance genes. Chi-square tests were conducted to determine significant co-occurrence of individual antibiotic resistant and metabolism genes. Dissociative relationships were distinguished by the odds ratios from the chi-square tests. To investigate whether the strong associations and disassociations were driven by evolutionary constraints, or simply artifacts of a common ancestor, we re-ran our statistical analysis using Coinfinder [29] to take in our gene presence-absence data, along with the genome phylogeny, and compute the Bonferroni-corrected statistical likelihood of coincidence (either associations or dissociations), thereby accounting for evolutionary relatedness. More

Geisen S, Wall DH, van der Putten WH. Challenges and opportunities for soil biodiversity in the anthropocene. Curr Biol. 2019;29:R1036–44.Article 
CAS 
PubMed 

Google Scholar 
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.Article 
CAS 
PubMed 

Google Scholar 
Gossner MM, Lewinsohn TM, Kahl T, Grassein F, Boch S, Prati D, et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature. 2016;540:266–9.Article 
CAS 
PubMed 

Google Scholar 
Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967–72.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Alberti M, Correa C, Marzluff JM, Hendry AP, Palkovacs EP, Gotanda KM, et al. Global urban signatures of phenotypic change in animal and plant populations. Proc Natl Acad Sci USA. 2017;114:8951–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
El-Sabaawi R. Trophic structure in a rapidly urbanizing planet. Funct Ecol. 2018;32:1718–28.Article 

Google Scholar 
Yu S, Wu Z, Xu G, Li C, Wu Z, Li Z, et al. Inconsistent patterns of soil fauna biodiversity and soil physicochemical characteristic along an urbanization gradient. Front Ecol Evol. 2022;9:824004.Article 

Google Scholar 
Zambrano L, Aronson MFJ, Fernandez T. The consequences of landscape fragmentation on socio-ecological patterns in a rapidly developing urban area: a case study of the National Autonomous University of Mexico. Front. Environ Sci. 2019;7:152.
Google Scholar 
Wilson MC, Chen XY, Corlett RT, Didham RK, Ding P, Holt RD, et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc Ecol. 2016;31:219–27.Article 

Google Scholar 
Guilland C, Maron PA, Damas O, Ranjard L. Biodiversity of urban soils for sustainable cities. Environ Chem Lett. 2018;16:1267–82.Article 
CAS 

Google Scholar 
Dou Y, Kuang W. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Sci. Total Environ. 2020;706:135828.Article 
CAS 
PubMed 

Google Scholar 
Francini G, Hui N, Jumpponen A, Kotze D, Romantschuk M, Allen J, et al. Soil biota in boreal urban greenspace: responses to plant type and age. Soil Biol Biochem. 2018;118:145–55.Article 
CAS 

Google Scholar 
Corline NJ, Peek RA, Montgomery J, Katz JVE, Jeffres CA. Understanding community assembly rules in managed floodplain food webs. Ecosphere. 2021;12:e03330.Article 

Google Scholar 
Tripathi BM, Stegen JC, Kim M, Dong K, Adams JM, Lee YK. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 2018;12:1072–83.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu W, Graham EB, Dong Y, Zhong L, Zhang J, Qiu C, et al. Balanced stochastic versus deterministic assembly processes benefit diverse yet uneven ecosystem functions in representative agroecosystems. Environ Microbiol. 2021;23:391–404.Article 
CAS 
PubMed 

Google Scholar 
Thakur MP, Phillips HR, Brose U, De Vries FT, Lavelle P, Loreau M, et al. Towards an integrative understanding of soil biodiversity. Biol Rev. 2020;95:350–64.Article 
PubMed 

Google Scholar 
Bahram M, Kohout P, Anslan S, Harend H, Abarenkov K, Tedersoo L. Stochastic distribution of small soil eukaryotes resulting from high dispersal and drift in a local environment. ISME J. 2016;10:885–96.Article 
PubMed 

Google Scholar 
Luan L, Jiang Y, Cheng M, Dini-Andreote F, Sui Y, Xu Q, et al. Organism body size structures the soil microbial and nematode community assembly at a continental and global scale. Nat Commun. 2020;11:1–11.Article 

Google Scholar 
Isabwe A, Yang JR, Wang Y, Wilkinson DM, Graham EB, Chen H, et al. Riverine bacterioplankton and phytoplankton assembly along an environmental gradient induced by urbanization. Limnol Oceanogr. 2022;67:1943–58.Article 
CAS 

Google Scholar 
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 
PubMed 
PubMed Central 

Google Scholar 
Zinger L, Taberlet P, Schimann H, Bonin A, Boyer F, De Barba M, et al. Body size determines soil community assembly in a tropical forest. Mol Ecol. 2019;28:528–43.Article 
CAS 
PubMed 

Google Scholar 
Jiao S, Yang Y, Xu Y, Zhang J, Lu Y. Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China. ISME J. 2020;14:202–16.Article 
PubMed 

Google Scholar 
Jiao S, Chen W, Wei G. Biogeography and ecological diversity patterns of rare and abundant bacteria in oil‐contaminated soils. Mol Ecol. 2017;26:5305–17.Article 
CAS 
PubMed 

Google Scholar 
Wu W, Lu H-P, Sastri A, Yeh Y-C, Gong G-C, Chou W-C, et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 2018;12:485–94.Article 
PubMed 

Google Scholar 
Farjalla VF, Srivastava DS, Marino NA, Azevedo FD, Dib V, Lopes PM, et al. Ecological determinism increases with organism size. Ecology. 2012;93:1752–9.Article 
PubMed 

Google Scholar 
Carscadden KA, Emery NC, Arnillas CA, Cadotte MW, Afkhami ME, Gravel D, et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q Rev Biol. 2020;95:179–214.Article 

Google Scholar 
Beissinger SR. Ecological mechanisms of extinction. Proc Natl Acad Sci USA. 2000;97:11688–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Poiani KA, Richter BD, Anderson MG, Richter HE. Biodiversity conservation at multiple scales: functional sites, landscapes, and networks. Bioscience. 2000;50:133–46.Article 

Google Scholar 
Yang J, Zhang X, Jin X, Seymour M, Richter C, Logares R, et al. Recent advances in environmental DNA-based biodiversity assessment and conservation. Divers Distrib. 2021;27:1876–9.Article 

Google Scholar 
Breed MF, Harrison PA, Blyth C, Byrne M, Gaget V, Gellie NJC, et al. The potential of genomics for restoring ecosystems and biodiversity. Nat Rev Genet. 2019;20:615–28.Article 
CAS 
PubMed 

Google Scholar 
Department of Economic and Social Affairs (DESA). World Urbanization Prospects. The 2018 Revision. United Nations. 2019. https://population.un.org/wup/publications/Files/WUP2018-Report.pdf. Accessed 13 Mar 2022.Qiao Z, Wang B, Yao H, Li Z, Scheu S, Zhu Y-G, et al. Urbanization and greenspace type as determinants of species and functional composition of collembola communities. Geoderma. 2022;428:116175.Article 

Google Scholar 
Shrestha S, Cui S, Xu L, Wang L, Manandhar B, Ding S. Impact of land use change due to urbanisation on surface runoff using GIS-based SCS–CN Method: a case study of Xiamen City, China. Land. 2021;10:839.Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2022. Vienna, Austria. https://www.R-project.org/.Wickham. H ggplot2: elegant graphics for data analysis. Springer-Verlag New York, 2016.Kassambara A. ggpubr: ‘ggplot2’ based publication ready plots. 2020. https://CRAN.R-project.org/package=ggpubr.Morlon H, Chuyong G, Condit R, Hubbell S, Kenfack D, Thomas D, et al. A general framework for the distance–decay of similarity in ecological communities. Ecol Lett. 2008;11:904–17.Article 
PubMed 
PubMed Central 

Google Scholar 
Goslee S, D. Urban, Goslee, MS. ecodist: dissimilarity-based functions for rcological analysis. 2020. https://cran.r-project.org/web/packages/ecodist/index.html.Ofiţeru ID, Lunn M, Curtis TP, Wells GF, Criddle CS, Francis CA, et al. Combined niche and neutral effects in a microbial wastewater treatment community. Proc Natl Acad Sci USA. 2010;107:15345–50.Article 
PubMed 
PubMed Central 

Google Scholar 
Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.Article 
CAS 
PubMed 

Google Scholar 
Chen W, Ren K, Isabwe A, Chen H, Liu M, Yang J. Stochastic processes shape microeukaryotic community assembly in a subtropical river across wet and dry seasons. Microbiome. 2019;7:138.Article 
PubMed 
PubMed Central 

Google Scholar 
Chase JM, Kraft NJ, Smith KG, Vellend M, Inouye BD. Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere. 2011;2:1–11.Article 

Google Scholar 
Pandit SN, Kolasa J, Cottenie K. Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology. 2009;90:2253–62.Article 
PubMed 

Google Scholar 
Salazar G. EcolUtils: utilities for community ecology analysis. 2019. https://github.com/GuillemSalazar/EcolUtils.Kraft NJB, Adler PB, Godoy O, James EC, Fuller S, Levine JM. Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol. 2015;29:592–9.Article 

Google Scholar 
Cadotte MW, Tucker CM. Should environmental filtering be abandoned? Trends Ecol Evol. 2017;32:429–37.Article 
PubMed 

Google Scholar 
Leibold MA, McPeek MA. Coexistence of the niche and neutral perspectives in community ecology. Ecology. 2006;87:1399–410.Article 
PubMed 

Google Scholar 
Evans S, Martiny JB, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.Article 
PubMed 

Google Scholar 
Jiang Y, Liu M, Zhang J, Chen Y, Chen X, Chen L, et al. Nematode grazing promotes bacterial community dynamics in soil at the aggregate level. ISME J. 2017;11:2705–17.Article 
PubMed 
PubMed Central 

Google Scholar 
Douhan GW, Vincenot L, Gryta H, Selosse M-A. Population genetics of ectomycorrhizal fungi: from current knowledge to emerging directions. Fungal Biol. 2011;115:569–97.Article 
PubMed 

Google Scholar 
Granot I, Belmaker J. Niche breadth and species richness: correlation strength, scale and mechanisms. Glob Ecol Biogeogr. 2020;29:159–70.Article 

Google Scholar 
Sexton JP, Montiel J, Shay JE, Stephens MR, Slatyer RA. Evolution of ecological niche breadth. Annu Rev Ecol Evol Syst Annu Rev Ecol Evol S. 2017;48:183–206.Article 

Google Scholar 
Fraaije RGA, ter Braak CJF, Verduyn B, Verhoeven JTA, Soons MB. Dispersal versus environmental filtering in a dynamic system: drivers of vegetation patterns and diversity along stream riparian gradients. J Ecol. 2015;103:1634–46.Article 

Google Scholar 
Soininen J, McDonald R, Hillebrand H. The distance decay of similarity in ecological communities. Ecography. 2007;30:3–12.Article 

Google Scholar 
Zhang K, Delgado-Baquerizo M, Zhu Y-G, Chu H. Space is more important than season when shaping soil microbial communities at a large spatial scale. mSystems. 2020;5:e00783–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ma B, Dai Z, Wang H, Dsouza M, Liu X, He Y, et al. Distinct biogeographic patterns for archaea, bacteria, and fungi along the vegetation gradient at the continental scale in Eastern China. mSystems. 2017;2:e00174–16.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang J, Zhang T, Li L, Li J, Feng Y, Lu Q. The patterns and drivers of bacterial and fungal β-diversity in a typical dryland ecosystem of northwest China. Front Microbiol. 2017;8:2126.Article 
PubMed 
PubMed Central 

Google Scholar 
Kang L, Chen L, Zhang D, Peng Y, Song Y, Kou D, et al. Stochastic processes regulate belowground community assembly in alpine grasslands on the Tibetan Plateau. Environ Microbiol. 2021;24:179–94.Article 
PubMed 

Google Scholar 
Chen Q-L, Hu H-W, Yan Z-Z, Li C-Y, Nguyen B-AT, Sun A-Q, et al. Deterministic selection dominates microbial community assembly in termite mounds. Soil Biol Biochem. 2021;152:108073.Article 
CAS 

Google Scholar 
Huang S, Tucker MA, Hertel AG, Eyres A, Albrecht J. Scale-dependent effects of niche specialisation: the disconnect between individual and species ranges. Ecol Lett. 2021;24:1408–19.Article 
PubMed 

Google Scholar 
Rapacciuolo G, Blois JL. Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory. Ecography. 2019;42:1247–66.
Google Scholar 
van der Gast CJ. Microbial biogeography: the end of the ubiquitous dispersal hypothesis? Environ Microbiol. 2015;17:544–6.Article 
PubMed 

Google Scholar 
Levy-Booth DJ, Giesbrecht IJW, Kellogg CTE, Heger TJ, D’Amore DV, Keeling PJ, et al. Seasonal and ecohydrological regulation of active microbial populations involved in DOC, CO2, and CH4 fluxes in temperate rainforest soil. ISME J. 2019;13:950–63.Article 
CAS 
PubMed 

Google Scholar 
De Gannes V, Bekele I, Dipchansingh D, Wuddivira MN, De Cairies S, Boman M, et al. Microbial community structure and function of soil following ecosystem conversion from native forests to teak plantation forests. Front Microbiol. 2016;7:1976.Article 
PubMed 
PubMed Central 

Google Scholar 
Männistö M, Vuosku J, Stark S, Saravesi K, Suokas M, Markkola A, et al. Bacterial and fungal communities in boreal forest soil are insensitive to changes in snow cover conditions. FEMS Microbiol. 2018;94:fiy123.
Google Scholar 
Sakarika M, Spanoghe J, Sui Y, Wambacq E, Grunert O, Haesaert G, et al. Purple non‐sulphur bacteria and plant production: benefits for fertilization, stress resistance and the environment. Microb Biotechnol. 2020;13:1336–65.Article 
CAS 
PubMed 

Google Scholar 
Kernaghan G, Patriquin G. Diversity and host preference of fungi co-inhabiting Cenococcum mycorrhizae. Fungal Ecol. 2015;17:84–95.Article 

Google Scholar 
Lumibao CY, Kimbrough ER, Day RH, Conner WH, Krauss KW, Van Bael SA. Divergent biotic and abiotic filtering of root endosphere and rhizosphere soil fungal communities along ecological gradients. FEMS Microbiol. 2020;96:fiaa124.Article 
CAS 

Google Scholar 
Rueckert S, Betts EL, Tsaousis AD. The symbiotic spectrum: where do the gregarines fit? Trends Parasitol. 2019;35:687–94.Article 
PubMed 

Google Scholar 
Butaeva F, Paskerova G, Entzeroth R. Ditrypanocystis sp.(Apicomplexa, Gregarinia, Selenidiidae): the mode of survival in the gut of Enchytraeus albidus (Annelida, Oligochaeta, Enchytraeidae) is close to that of the coccidian genus Cryptosporidium. Tsitologiia. 2006;48:695–704.CAS 
PubMed 

Google Scholar 
Pavao-Zuckerman MA, Coleman DC. Urbanization alters the functional composition, but not taxonomic diversity, of the soil nematode community. Appl Soil Ecol. 2007;35:329–39.Article 

Google Scholar 
Gaspar C, Borges PA, Gaston KJ. Diversity and distribution of arthropods in native forests of the Azores archipelago. Arquipelago: Life Mar Sci. 2008;25:1–30.
Google Scholar 
Suter RB, Doyle G, Shane CM. Oviposition site selection by Frontinella pyramitela (Araneae, Linyphiidae). J Arachnol. 1987;15:349–54.Tian T, Ren Q, Fan J, Haseeb M, Zhang R. Too dry or too wet soils have a negative impact on larval pupation of fall armyworm. J Appl Entomol. 2022;146:196–202.Article 

Google Scholar 
Marczylo EL, Macchiarulo S, Gant TW. Metabarcoding of soil fungi from different urban greenspaces around Bournemouth in the UK. EcoHealth. 2021;18:315–30.Article 
PubMed 
PubMed Central 

Google Scholar 
Corline NJ, Peek RA, Montgomery J, Katz JVE, Jeffres CA. Understanding community assembly rules in managed floodplain food webs. Ecosphere. 2021;12:e03330.Article 

Google Scholar 
Schlägel UE, Grimm V, Blaum N, Colangeli P, Dammhahn M, Eccard JA, et al. Movement-mediated community assembly and coexistence. Biol Rev Camb Philos Soc. 2020;95:1073–96.Article 
PubMed 

Google Scholar 
Stubner S. Enumeration of 16S rDNA of desulfotomaculum lineage 1 in rice field soil by real-time PCR with SybrGreen™ detection. J Microbiol Methods. 2002;50:155–64.Article 
CAS 
PubMed 

Google Scholar 
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Toju H, Tanabe AS, Yamamoto S, Sato H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PloS One. 2012;7:e40863.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Abarenkov K, Henrik Nilsson R, Larsson KH, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi–recent updates and future perspectives. New Phytol. 2010;186:281–5.Article 
PubMed 

Google Scholar 
Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.Article 
CAS 
PubMed 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;41:D597–604.Article 
PubMed 
PubMed Central 

Google Scholar 
Porazinska DL, Giblin‐Davis RM, Faller L, Farmerie W, Kanzaki N, Morris K, et al. Evaluating high‐throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Res. 2009;9:1439–50.Article 
CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–96.Article 
PubMed 
PubMed Central 

Google Scholar 
Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:1–14.Article 

Google Scholar 
Porter TM, Hajibabaei M. Over 2.5 million COI sequences in GenBank and growing. PloS One. 2018;13:e0200177.Article 
PubMed 
PubMed Central 

Google Scholar  More

Sequencing and assembly of the H. schachtii genomeWe measured (Supplemental Fig. 1), sequenced (BioProject PRJNA722882), and assembled the genome of H. schachtii (population Bonn) using a combination of flow cytometry, Pacific Biosciences sequencing, and Illumina sequencing. H. schachtii has the largest genome (160–170 Mb) of any cyst nematode measured/sequenced to date (Supplementary Table 1). It was sequenced to 192-fold coverage using Pacific Biosciences sequencing (fragment n50 of 16 kb), and 144-fold coverage using Illumina sequencing (150 bp Paired-end reads). The final, polished, contamination-free (Supplemental Fig. 2), assembly (v1.2) included ~179 Mbp contained within 395 scaffolds: 90% of the sequence is contained on scaffolds longer than 281,463 bp (n = 154). The assembly is a largely complete haploid representation of the diploid genome, as evidenced by core eukaryotic genes being largely present, complete and single copy (CEGMA 93.15% complete with an average of 1.12 copies each, and BUSCO (Eukaryota odb9) 79% complete with 8.2% duplicated—Supplementary Table 2). Over three million variants were phased into haplotypes (2029 blocks, N50 239.5 kb, covering 94.7% of the reference) which can be used to predict true protein variants (Supplementary data 1), and 601 larger structural variants were identified (Supplementary data 2).The trans-kingdom, lifestage-specific, transcriptomes of H. schachtii and A. thaliana provide a holistic view of parasitismWe devised a sampling procedure to cover all major life stages/transitions of the parasitic life cycle to generate a simultaneous, chronological, and comprehensive picture of nematode gene expression, and infection-site-specific plant gene expression patterns. We sampled cysts and pre-infective second-stage juveniles (J2s), as well as infected segments of A. thaliana root and uninfected adjacent control segments of root at 10 hours post infection (hpi – migratory J2s, pre-establishment of the feeding site), 48 hpi (post establishment of the feeding site), 12 days post infection females (dpi – virgin), 12 dpi males (differentiated, pre-emergence, most if not all stopped feeding), and 24 dpi females (post mating), each in biological triplicate (Fig. 1A). We generated approximately nine billion pairs of 150 bp strand-specific RNAseq reads (Supplementary data 3) covering each stage in biological triplicate (for the parasite and the host): in the early stages of infection we generated over 400 million reads per replicate, to provide sufficient coverage of each kingdom.Fig. 1: Trans-kingdom, lifestage-specific, transcriptome of H. schachtii and A. thaliana.A Schematic representation of the life cycle of H. schachtii infecting A. thaliana, highlighting the 7 stages sampled in this study. For each stage, the average number of trimmed RNAseq read pairs per replicate is shown, with the proportion of reads mapping to either parasite or host in parentheses. B Principle components 1 and 2 for H. schachtii and A. thaliana expression data are plotted. Arrows indicate progression through the life cycle/real-time. Hours post infection (hpi), days post infection (dpi).Full size imageStrand-specific RNAseq reads originating from host and parasite were deconvoluted by mapping to their respective genome assemblies (H. schachtii v.1.2 and TAIR10). For the parasite, ~500 million Illumina RNAseq read pairs uniquely mapping to the H. schachtii genome were used to generate a set of 26,739 gene annotations (32,624 transcripts – detailed further in the next section), ~77% of which have good evidence of transcription in at least one lifestage (≥10 reads in at least one rep). Similarly for the host, ~2.8 billion Illumina RNAseq read pairs uniquely mapping to the A. thaliana genome show that ~77% of the 32,548 gene models have good evidence of transcription in at least one stage (≥10 reads in at least one rep, even though we only sampled roots). A principal component analysis of the host and parasite gene expression data offers several insights into the parasitic process. Principle component 1 (60% of the variance) and 2 (19% of the variance) of the parasite recapitulate the life cycle in PCA space (Fig. 1B). The 12 dpi female transcriptome is more similar to the 24 dpi female transcriptome than to the 12 dpi male transcriptome. Principle components 1 (75% of the variance) and 2 (10% of the variance) of the host show that the greatest difference between infected and uninfected plant tissue is at the early time points (10 hpi), and that the transcriptomes of infected and uninfected plant material converge over time, possibly due to systemic effects of infection. A 12 dpi male syncytium transcriptome is roughly intermediate between a control root transcriptome and a 12 dpi female syncytium transcriptome. Given that at this stage most if not all of the males will have ceased feeding, this could be due to inadequate formation of the feeding site, or regression of the tissue. In any case, by comparing both principal component analyses, we can see that what is a relatively small difference in the transcriptomes of the feeding sites of males and females is amplified to a relatively large difference in the transcriptomes of the males and females themselves (Fig. 1B).The consequences, and possible causes, of large-scale segmental duplication in the Heterodera lineageTo understand the evolutionary origin(s) of the relatively large number of genes in H. schachtii in particular, and Heterodera spp. in general, we analysed the abundance and categories of gene duplication in the predicted exome. Compared to a related cyst nematode, Globodera pallida (derived using comparable methodology and of comparable contiguity) the exomes of H. schachtii and H. glycines are characterised by a relatively smaller proportion of single-copy genes (as classified by MCSanX toolkit17, and a relatively greater proportion of segmental duplications (at least five co-linear genes with no >25 genes between them), with relatively similar proportions of dispersed duplications (two similar genes with >20 other genes between them), proximal duplications (two similar genes with  +0.5 or  More

This study quantitatively assessed waterbody loss due to urban expansion of large Chinese cities. We first extracted multi-temporal urban boundaries to determine the expansion of cities of over one million in population from 1990 to 2018. The monthly surface-water dataset was then used to identify surface waterbodies in the study period. Depending on the ratio of surface waterbody area to urban area, cities were further divided into three categories (i.e. water-abundant, water-medium, water-deficient). Finally, we quantified the rate of waterbody loss and evaluated the spatial and temporal variation of waterbody loss as a function of urban expansion and according to city type.GUB datasetThe Global Urban Boundary (GUB) dataset (http://data.ess.tsinghua.edu.cn) was used to determine urban expansion. GUB provides data on built-up areas over 30 years, with a spatial resolution of 30 m. In the GUB dataset, nonurban areas (such as green space and water space) surrounded by artificial impervious areas are filled within the urban boundary and removed by the algorithm, which is consistent with global mapping methods. The continuous urban boundary was demarcated by morphological image processing methods, which have an overall accuracy of over 90%. In this dataset, extensive water and forests are excluded, and the impervious surface within the urban boundaries accounts for about 60% of the total surface area47. Compared with urban boundaries obtained from night-time light, GUB better separates urban areas from surrounding nonurban areas.Monthly waterbody datasetWe selected the JRC Monthly Water History V1.3 dataset(https://global-surface-water.appspot.com/), which is available from the Google Earth Engine, as the basis for representing surface waterbodies48. This data collection, which was produced by using images from the Landsat series, contains 442 images of global monthly waterbody area from March 1984 to December 2020. In this dataset, the validation confirmed that fewer than 1% of waterbodies were incorrectly detected, and fewer than 5% of waterbodies were missed altogether. We chose this dataset due to the long-term spatial distribution of waterbodies and due to mountain shadows and urban-constructions masking, which reflects the real changes in waterbodies.Theoretical backgroundIt is well known that cities have high concentrations of population and resources and expand spatially during development. There are many different perspectives on the size of cities, and studies have mostly used urban density and population to characterize them. However, because it is challenging to standardize data sources and quality, there is no unified quantitative standard49. Urban construction has concentrated human activity and brought about changes in land types. Cities are also identified as physical spaces, which can be defined as the built environment50,51. The built environment, which includes structures like buildings, roads, and other artificial constructions, is sometimes referred to as a non-natural environment52.Rural is the antithesis of urban. As large cities have spread outward in developing nations like Asia, a transitional fringe has been created by the gradual blurring of the line separating urban and rural areas53. According to McGee, good locations, easy access, and sizable agricultural land all contribute to the development potential of large cities. Thus, between urban and rural areas, there are transitional areas of active spatial morphological change known as desakota33,54. The peri-urban areas, like desakota, are gradually developed and incorporated into original built-up urban areas in urbanization. The original landscape, which included agricultural land, vegetation, and waterbodies, gradually changed into an urban land use type, i.e. impervious surface, and thus the city continues to expand outwards. Waterbody, an essential ecological element, has been heavily developed or filled in during urbanization, which may present dangerous ecological risks. In this paper, we identified the urban boundaries based on physical space to explore the encroachment activities on waterbodies during the urbanization of large cities. We determined whether existing waterbodies were transformed into urban waterbodies or encroached upon and whether waterbodies were increased in the expansion of urban boundaries, thus proposing strategies for protecting waterbodies in the future.Extracting the extent of large Chinese cities from GUB datasetTo characterize urban expansion, GUB data are selected as the original data for urban boundary selection. The Chinese administrative scale of municipalities is not exclusively urban, but also includes rural areas. In our study, cities were defined as municipal districts excluding the vast countryside within the administrative boundaries of prefecture-level cities. We identified urban areas based on the physical boundaries from the perspective of remote sensing, which can precisely track urban expansion51.In this work, we selected 159 cities with a population of over one million in 2018 based on the average annual population of urban districts from the 2019 China City Statistical Yearbook (Fig. S1). Taiwan, Hong Kong, and Macau are omitted. According to statistics, China had 160 cities with populations exceeding one million in 2018. However, due to the lack of data for the built-up area in 1990, Guang’an was not included in the study. We thus obtained 159 cities from the GUB dataset. Due to numerous fragmented patches within the administrative boundary, the population identified the main urban areas, and max patch areas were comprehensively based on the urban boundaries. Through manual detection and adjustment of the map, we determined that the location of the extracted urban area was consistent with that of the municipal government, and the boundary was extracted for each period. We took the growth area as the expansion area, with the original area being the city at the onset of each period (Fig. S3).We used the average annual urban growth (AUG) rate to characterize the rate of urban expansion, as is widely done to evaluate urban expansion55,56. It is calculated as$${text{AUG}} = left[ {frac{{Land_{t1} }}{{Land_{t0} }}^{{frac{1}{t1 – t0}}} – 1} right] times 100% ,$$
where (Land_{t0}) and (Land_{t1}) represent the urban land area at time t0 and t1, where t0 and t1 are the start and end of the given study period.Identification of urban waterbodiesUrban waterbodies contain all the components of urban flow networks above the ground and include natural waterbodies such as lakes, rivers, streams, and wetlands and artificial waterbodies such as parks and ponds48. We identified all waterbodies existing within the urban boundary as urban waterbody. Considering urban expansion, urban waterbodies vary as urban boundary shift at different stages. Our study explored how the original waterbodies changed under urban expansion, including whether they were kept as urban waterbodies or encroached upon. Considering the dryness or wetness of each year, we used the data for 3 years (36 months) around each period (1990, 1995, 2000, 2005, 2010, 2015, and 2018) to describe the waterbody. Not all waterbodies could be detected for each month of the year; for example, freezing may prevent waterbodies from being detected. To cover seasonal and permanent waterbodies, we used the waterbody frequency index (WFI), which is calculated as the fraction of waterbody months within the 3 years to identify stable waterbodies pixel by pixel57. The spatial distribution of each waterbody was then mapped comprehensively for each period. By comparing the extracted waterbody with the long-time-series high-resolution remote-sensing images from Google Earth, we found that the extracted waterbodies fit the actual waterbody distribution quite well (Fig. S2):$$WFIleft( i right) = frac{WMleft( i right)}{{DMleft( i right)}}$$
where WFI(i) is the water occurrence for pixel i in the images before and after the given year, and i is the pixel number for the study area. WM(i) is the number of months during which the waterbody is detected in i pixel over the 3 years. DM(i) is the number of months during which the data are available in pixel i. If the waterbody frequency index of a pixel is greater than 25%, this pixel is considered as a waterbody; otherwise, it is not.City classification based on surface waterbodyCities with over one million in population may not be short of waterbodies, but significant differences remain in surface waterbody abundance. Due to large differences in city size, it is inappropriate to use waterbody area as a criterion. Considering the influence of urban expansion, we ranked 159 cities according to the indicator of waterbody fraction (WF), namely the fraction of the original surface water within the urban boundary in 2018. Waterbodies not impacted by urbanization were taken as the original surface waterbody, which used the average surface waterbody from 1985 to 1991 as baseline. We used the natural break method to divide cities into abundant, moderate, and deficient levels (referred to as Type I, Type II, and Type III, respectively) and evaluate the abundance of waterbodies in cities. Based on the waterbody fraction (WF) value, which is calculated as follows:$${text{WF}} = frac{{Water_{origin} }}{{Land_{2018} }}$$
where WF is used to judge the urban waterbody abundance in cities. (Water_{1990}) is the origin surface waterbody area (used the year in 1985–1991) in the urban boundary of 2018, (Land_{2018}) the urban land area in the urban boundary of 2018.Temporal characteristic of waterbody loss and gainTo understand the spatial–temporal features of surface waterbodies, we used five normalized indicators to compare waterbody variations between cities during urban expansion from the overall perspective and from the city perspective.The variation in original natural waterbodies reflects the intensity of the natural resource development in urban expansion. We summarized the reduction and preservation of original waterbodies in urban expansion areas with a population of over one million to represent the encroachment of urban expansion on waterbodies:$$WL = frac{{sum NWL_{t0_t1} left( i right)}}{{sum W_{t0} left( i right)}} times 100%$$$$WP = frac{{sum (W_{t0} left( i right) – NWL_{t0_t1} left( i right))}}{{sum W_{t0} left( i right)}} times 100%$$
where i labels the city within the 159 cities, WL and WP are the fractions of waterbody loss and preservation in urban expansion areas of all cities, (NWL_{t0_t1}) is the net waterbody loss during period t0–t1 (, and W_{t0}) is the natural waterbody in the urban expansion area at time t0.To estimate the net waterbody loss caused by urban expansion at various stages, we used the standardized indicator, annual average net waterbody loss rate (ANWL), to compare waterbody loss speeds over time. This indicator is independent of the difference in waterbody abundance and can be compared over time. Waterbody loss is one part of the impact of urbanization; the other is waterbody gain. We used the same method to evaluate the annual average net waterbody gain rate (ANWG). The formulas are$$A{text{NWL}} = frac{{NWL_{t0_t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$$$ANWG = frac{{NWG_{t0 – t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$
where NWL and NWG are the net waterbody loss and gain, respectively, and the other abbreviations are the same as above.Considering the direct impact of urban expansion, we used a normalized indicator, the average net waterbody loss velocity of urban expansion ((AWLV)), which refers to the amount of waterbody encroachment per unit urban expansion area. It quantifies the time-heterogeneity of waterbody loss due to urban expansion and is calculated as follows:$$AWLV = frac{{NWL_{t0_t1} }}{{Land_{t1} – Land_{t0} }}$$We calculated these indicators for the six expansion periods (1990–1995, 1995–2000, 2000–2005, 2005–2010, 2010–2015, and 2015–2018) (Fig. 3). In the study, if the waterbody pixel count is zero at the onset of the period, the indicator for the period is abnormal and thus excluded. More