Philippa Kaur

Hwang, H. Y. et al. Effect of cover cropping on the net global warming potential of rice paddy soil. Geoderma 292, 49–58 (2017).ADS 
CAS 
Article 

Google Scholar 
IPCC. Climate change 2013: The physical science basis. The Working Group I contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).
Google Scholar 
Cardoso, R. M., Soares, P. M. M., Lima, D. C. A. & Miranda, P. M. A. Mean and extreme temperatures in warming climate: EURO CORDEX and WRF regional climate high-resolution projection for Portugal. Clim. Dyn. 52, 129–157 (2019).Article 

Google Scholar 
Ding, T., Gao, H. & Li, W. J. Extreme high-temperature event in southern China in 2016 and the possible role of cross-equatorial flows. Int. J. Climatol. 38, 3579–3594 (2018).Article 

Google Scholar 
Escalas, A. et al. Functional diversity and redundancy across fish gut, sediment, and water bacterial communities. Environ. Microbiol. 19, 3268–3282 (2017).Article 

Google Scholar 
Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
Article 

Google Scholar 
Li, Y. B. et al. Serratia spp. Are responsible for nitrogen fixation fueled by As(III) oxidation, a novel biogeochemical process identified in mine tailings. Environ. Sci. Technol 56, 2033–2043 (2022).ADS 
Article 

Google Scholar 
Jia, M., Gao, Z. W., Gu, H. J., Zhao, C. Y. & Han, G. D. Effects of precipitation change and nitrogen addition on the composition, diversity, and molecular ecological network of soil bacterial communities in a desert steppe. PLoS ONE 16, e0248194. https://doi.org/10.1371/journal.pone.0248194 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Waghmode, T. R. et al. Response of nitrifier and denitrifier abundance and microbial community structure to experimental warming in an agricultural ecosystem. Front. Microbiol. 9, 474. https://doi.org/10.3389/fmicb.2018.00474 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hu, Y. L., Wang, S., Niu, B., Chen, Q. & Zhang, G. Effect of increasing precipitation and warming on microbial community in Tibetan alpine steppe. Environ. Res. 189, 109917. https://doi.org/10.1016/j.envres.2020.109917 (2020).CAS 
Article 
PubMed 

Google Scholar 
Li, H. et al. Responses of soil bacterial communities to nitrogen deposition and precipitation increment are closely linked with aboveground community variation. Microb. Ecol. 71, 974–989 (2016).CAS 
Article 

Google Scholar 
Wang, H. et al. Experimental warming reduced topsoil carbon content and increased soil bacterial diversity in a subtropical planted forest. Soil Biol. Biochem. 133, 155–164 (2019).CAS 
Article 

Google Scholar 
Haumann, F. A., Gruber, N. & Münnich, M. Sea-Ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances 1, e2019AV000132. https://doi.org/10.1029/2019AV000132 (2020).ADS 
Article 

Google Scholar 
Ji, F., Wu, Z. H., Huang, J. P. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Chang. 4, 462–466 (2014).ADS 
Article 

Google Scholar 
Sabri, N. S. A., Zakaria, Z., Mohamad, S. E., Jaafar, A. B. & Hara, H. Importance of soil temperature for the growth of temperate crops under a tropical climate and functional role of soil microbial diversity. Microbes Environ. 33, 144–150 (2018).Article 

Google Scholar 
McGrady-Steed, J. & Morin, P. T. Biodiversity, density compensation, and the dynamics of populations and functional groups. Ecology 81, 361–373 (2000).Article 

Google Scholar 
Jiang, L. Density compensation can cause no effect of biodiversity on ecosystem function. Oikos 116, 324–334 (2007).Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538. https://doi.org/10.1038/nrmicro283 (2012).CAS 
Article 
PubMed 

Google Scholar 
Gao, X. X. et al. Revegetation significantly increased the bacterial-fungal interactions in different successional stages of alpine grasslands on the Qinghai-Tibetan Plateau. CATENA 205, 105385. https://doi.org/10.1016/j.catena.2021.105385 (2021).CAS 
Article 

Google Scholar 
Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349. https://doi.org/10.1038/ncomms14349 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Pržulj, N. & Malod-Dognin, N. Network analytics in the age of big data. Science 353, 123–124 (2016).ADS 
Article 

Google Scholar 
Ratzke, C., Barrere, J. M. R. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).Article 

Google Scholar 
Fuhrman, J. A. Microbial community structure and its functional implications. Nature 45, 193–199 (2009).ADS 
Article 

Google Scholar 
Zhao, M. X., Cong, J., Cheng, J. M., Qi, Q. & Zhang, Y. G. Soil microbial community assembly and interactions are constrained by nitrogen and phosphorus in broadleaf forests of southern China. Forest 11, 285. https://doi.org/10.3390/f11030285 (2020).Article 

Google Scholar 
Wan, X. L. et al. Biogeographic patterns of microbial association networks in paddy soil within Eastern China. Soil Biol. Biochem. 142, 07696. https://doi.org/10.1016/j.soilbio.2019.107696 (2020).CAS 
Article 

Google Scholar 
Yuan, M. M., Guo, X., Wu, L., Zhang, Y. & Zhou, J. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change 11, 343–348 (2021).ADS 
Article 

Google Scholar 
Lassaletta, L. et al. Food and feed trade as a driver in the global nitrogen cycle: 50-year trends. Biogeochemistry 11, 225–241 (2014).Article 

Google Scholar 
Phoenix, G. K. et al. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Change Biol. 18, 1197–1215 (2012).ADS 
Article 

Google Scholar 
Nakaji, T., Fukami, M., Dokiya, Y. & Izuta, T. Effects of high nitrogen load on growth, photosynthesis and nutrient status of Cryptomeria japonica and Pinus densiflora seedlings. Trees-Struct. Funct. 15, 453–461 (2001).CAS 
Article 

Google Scholar 
Wang, H. Y. et al. Reduction in nitrogen fertilizer use results in increased rice yields and improved environmental protection. Int. J. Agric. Sustain. 15, 681–692 (2017).Article 

Google Scholar 
Zhou, X. G. & Wu, F. Z. Land-use conversion from open field to greenhouse cultivation differently affected the diversities and assembly processes of soil abundant and rare fungal communities. Sci. Total Environ. 788, 147751. https://doi.org/10.1016/j.scitotenv.2021.147751 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Guo, H. et al. Long-term nitrogen & phosphorus additions reduce soil microbial respiration but increase its temperature sensitivity in a Tibetan alpine meadow. Soil Biol. Biochem. 113, 26–34 (2017).CAS 
Article 

Google Scholar 
Zhang, C. et al. Effects of simulated nitrogen deposition on soil respiration components and their temperature sensitivities in a semiarid grassland. Soil Biol. Biochem. 75, 113–123 (2014).CAS 
Article 

Google Scholar 
Zhang, J. J. et al. Different responses of soil respiration and its components to nitrogen and phosphorus addition in a subtropical secondary forest. For. Ecosyst. 8, 37. https://doi.org/10.1186/s40663-021-00313-z (2021).Article 

Google Scholar 
Norse, D. & Ju, X. T. Environmental costs of China’s food security. Agric. Ecosyst. Environ. 209, 5–14 (2015).Article 

Google Scholar 
Xu, H. F., Du, H., Zeng, F. P., Song, T. Q. & Peng, W. X. Diminished rhizosphere and bulk soil microbial abundance and diversity across succession stages in Karst area, southwest China. Appl. Soil Ecol. 158, 103799. https://doi.org/10.1016/j.apsoil.2020.103799 (2020).Article 

Google Scholar 
Li, Y. B. et al. Arsenic and antimony co-contamination influences on soil microbial community composition and functions: Relevance to arsenic resistance and carbon, nitrogen, and sulfur cycling. Environ. Int. 153, 106522. https://doi.org/10.1016/j.envint.2021.106522 (2021).CAS 
Article 
PubMed 

Google Scholar 
Zhou, J. & Fong, J. J. Strong agricultural management effects on soil microbial community in a non-experimental agroecosystem. Appl. Soil Ecol. 165, 103970. https://doi.org/10.1016/j.apsoil.2021.103970 (2021).Article 

Google Scholar 
Bárcenas-Moreno, G., Gómez-Brandón, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: Comparison of fungi and bacteria in a laboratory experiment. Glob. Chang. Biol. 15, 2950–2957 (2009).ADS 
Article 

Google Scholar 
Tan, E. H., Zou, W., Zheng, Z., Yan, X. & Kao, S. J. Warming stimulates sediment denitrification at the expense of anaerobic ammonium oxidation. Nat. Clim. Change 10, 349–355 (2020).ADS 
CAS 
Article 

Google Scholar 
Supramaniam, Y., Chong, C. W., Silvaraj, S. & Tan, K. P. Effect of short term variation in temperature and water content on the bacterial community in a tropical soil. Appl Soil Ecol. 107, 279–289 (2016).Article 

Google Scholar 
Zhu, Y. Z., Li, Y. Y., Zheng, N. G., Chapman, S. J. & Yao, H. Y. Similar but not identical resuscitation trajectories of the soil microbial community based on either DNA or RNA after flooding. Agronomy 10, 502. https://doi.org/10.3390/agronomy10040502 (2020).CAS 
Article 

Google Scholar 
Donhauser, J., Qi, W., Bergk-Pinto, B. & Frey, B. High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high-mountain soils. Glob. Chang. Biol. 27, 1365–1386 (2021).ADS 
Article 

Google Scholar 
Santoyo, G., Hernandez-Pacheco, C., Hernandez-Salmeron, J. & Hernandez-Leon, R. The role of abiotic factors modulating the plant-microbe-soil interactions: Toward sustainable agriculture. A review. Span. J. Agric. Res. 15, e03R01-e11. https://doi.org/10.5424/sjar/2017151-9990 (2017).Article 

Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936. https://doi.org/10.1038/ncomms7936 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Corrigendum: Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
Article 

Google Scholar 
Ma, B., Wang, H., Dsouza, M., Lou, J. & Xu, J. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 10, 1891–1901 (2016).CAS 
Article 

Google Scholar 
Trivedi, C. et al. Losses in microbial functional diversity reduce the rate of key soil processes. Soil Biol. Biochem. 135, 267–274 (2019).CAS 
Article 

Google Scholar 
Melanie, K. et al. Effects of season and experimental warming on the bacterial community in a temperate mountain forest soil assessed by 16S rRNA gene pyrosequencing. FEMS Microbiol. Ecol. 82, 551–562 (2012).Article 

Google Scholar 
Zheng, H. F., Liu, Y., Chen, Y., Zhang, J. & Chen, Q. Short-term warming shifts microbial nutrient limitation without changing the bacterial community structure in an alpine timberline of the eastern Tibetan Plateau. Geoderma 360, 113985. https://doi.org/10.1016/j.geoderma.2019.113985 (2020).ADS 
CAS 
Article 

Google Scholar 
Finlay, B. J. & Cooper, J. L. Microbial diversity and ecosystem function. CEH Integrating Fund second progress report to the Director, Centre for Ecology and Hydrology Nov 1996–Sept (1997).Xing, X. Y. et al. Warming shapes nirS- and nosZ-type denitrifier communities and stimulates N2O emission in acidic paddy soil. Appl. Environ. Microbiol. 87, e02965-e3020. https://doi.org/10.1128/AEM.0296520 (2021).CAS 
Article 
PubMed Central 

Google Scholar 
Lin, Y. T., Whitman, W. B., Coleman, D. C., Jien, S. H. & Chiu, C. Y. Soil bacterial communities at the treeline in subtropical alpine areas. CATENA 201, 105205. https://doi.org/10.1016/j.catena.2021.105205 (2021).CAS 
Article 

Google Scholar 
Wang, J. C. et al. Impacts of inorganic and organic fertilization treatments on bacterial and fungal communities in a paddy soil. Appl. Soil Ecol. 112, 42–50 (2017).Article 

Google Scholar 
Chacón, J. M., Shaw, A. K. & Harcombe, W. R. Increasing growth rate slows adaptation when genotypes compete for diffusing resources. PLoS Comput. Biol. 16, e1007585. https://doi.org/10.1371/journal.pcbi.1007585 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 

Google Scholar 
Baath, E. Growth rates of bacterial communities in soils at varying pH: A comparison of the thymidine and leucine incorporation techniques. Microb. Ecol. 36, 316–327 (1998).CAS 
Article 

Google Scholar 
Qin, H. L. et al. Soil moisture and activity of nitrite- and nitrous oxide-reducing microbes enhanced nitrous oxide emissions in fallow paddy soils. Biol. Fertil. Soils 56, 53–67 (2020).CAS 
Article 

Google Scholar 
Chen, Z. et al. Impact of long term fertilization on the composition of denitrifier communities based on nitrite reductase analyses in a paddy soil. Microb. Ecol. 60, 850–861 (2010).CAS 
Article 

Google Scholar 
Wei, G. S. et al. Similar drivers but different effects lead to distinct ecological patterns of soil bacterial and archaeal communities. Soil Biol. Biochem. 144, 107759. https://doi.org/10.1016/j.soilbio.2020.107759 (2020).CAS 
Article 

Google Scholar 
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, A. L. Impact of wheat straw decomposition on successional patterns of soil microbial community structure. Soil Biol. Biochem. 41, 262–275 (2009).CAS 
Article 

Google Scholar 
Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton University Press, 1968).Book 

Google Scholar  More

China economy-wide material flow identification: system boundary, processes, and materialsThe first step is to define an economy, i.e., the economic (rather than geographical) territory of a country in which the activities and transactions of producer and consumer units are resident. Additionally, the period is a total of thirty-one years, from 1990 to 2020, for the following reasons: (1) statistics before 1990 are of poor quality and are insufficient to allow us to conduct analyses; and (2) so far, statistics have just recently been updated to cover the year of 2020. Furthermore, the analytical framework (hereinafter referred to as China EW-MFA) is developed to explore material utilisation and its environmental consequences within China’s economy.The general structure of China EW-MFA is depicted in Fig. 1, which comprises seven processes. (1) Input of extracted resources: domestic natural resources are extracted from the environment to the economy through human-controlled means. (2) Output of domestic processed materials: after being processed by manufacturers, materials are released from the economy into the environment in the form of by-products and residues, which can be classified by their destinations (i.e., air, land, and water) and pathways (dissipative use and losses). (3) Input and (4) output by cross-border trade: by imports and exports, materials are transported between China’s economy and the economies of the rest of the world. (5) Input and (6) output of balancing items (BI): sometimes, materials identified in the output processes are not considered by inputs, which needs to be balanced. For example, the utilisation of fossil energy materials by combustion causes the emission of carbon dioxide (CO2) into the air, which is identified as system output, but requirements of oxygen (O2) as system input are not counted. (7) Additions to the system: within the economy, materials would have been added to the economy in the form of buildings, infrastructures, durable goods, and household appliances, which are referred to as the net additions to stock (NAS).Fig. 1The general structure of China EW-MFA. To note, white data cells can be obtained directly from official statistics, whereas grey cells are estimated.Full size imageThe last step is to specify the materials concerned in each process. Four types (in blue boxes in Fig. 1) of natural materials are extracted and input into the economy in China, i.e., harvested biomass (33 items), mined metal ores (28 items), quarried non-metallic minerals (155 items), and mined fossil energy materials (6 items in 3 classes). Materials (green boxes) released into the air are greenhouse gases (e.g., CO2, methane (CH4), dinitrogen oxide (N2O)), air pollutants (e.g., particulate matter 10 (PM10), black carbon (BC)), and toxic contaminants of mercury (Hg) in divalent, gaseous elemental, and particulate forms. Those released into the water are inorganic matters (of nitrogen (N), phosphorus (P), Arsenic (As), and four heavy metals of lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr)) and organic matters of cyanide, petroleum, and volatile phenol. Materials released into the land are waste disposal in uncontrolled landfills, which are illegal in China. Some materials are dissipated by application, for example, fertilisers, compost, sewage sludge being applied to agricultural land, and pesticides being used to cultivate crops. Some would be unintentionally dissipated from abrasion, corrosion, erosion, and leakages. Materials (in red boxes) are BI, which includes the input of O2 and output of water vapour in the fossil energy material combustion process, the input of O2 and output of water vapour and CO2 in the respiration process of human and cultivated livestock, input and output of water in imported and exported beverages, and the output of water from domestically extracting crops.There are some messages needed to be mentioned: (1) Material of water is not included since its flow volume is more substantial than others, which needs to be independently analysed; (2) Activities of foreign tourists, cross-border transfer of emissions through natural media, etc. are excluded. (3) To be clear, we refer to a data cell as a specific flow process of a specific substance in a specific year, e.g., the number of cereals domestically extracted in 2020.Data acquisition: sources and collectionBased on our China EW-MFA, we first analyse accessibility, reliability, completeness, rules of redistribution, etc., for each data source (yellow boxes in Fig. 1), including China national database, China rural statistical yearbooks, USGS mineral yearbooks, etc. The complete list of data sources and descriptions are presented in Table 1. Then, we store the originally retrieved data source files in a semi- or unstructured format (e.g., CSV, PDF). Next, we manually collect these statistics and reorganise them according to China EW-MFA material types and processes. However, only a tiny part of retrieved statistics can be applied directly, as specified in black colour in Fig. 1.Table 1 Data sources and descriptions.Full size tableData compilation: parameter localisation and data estimationA few inconsistencies in statistics were noticed, which would result in data incompleteness. For example, the domestic extraction of vegetables has been accounted for and published since 1995, before which statistics are unavailable. The domestically harvested timber has been measured in the volume unit of cubic metres, which needs to be converted into the mass unit via density conversion factor. Therefore, acquired statistics have to be estimated, which are specified in grey colour in Fig. 1. The following section elaborates on each data cell’s estimation methods, localised parameters, references, etc. In our uploaded data files, the original statistics, data sources, and compilation methods (using formulas) are all implemented, as explained in the Data Records Section.

The input of natural resources by domestic extraction

Vegetables in crops: Statistics of vegetable production (WVegetables)16 during 1990–1994 are unavailable, which is estimated based on the relationship between the production yield (PYield) and areas (AVegetables), as shown in Eq. 1. Here, PYield is assumed to remain constant at 27.04 thousand tonnes per thousand hectares from 1990 to 1995, derived by dividing vegetable production (257,267 thousand tonnes) by areas (9,515 thousand hectares) in 1995.$${W}_{Vegetables}={P}_{Yield}times {A}_{Vegetables}$$
(1)

Nuts in crops: One of them is chestnuts. The chestnut production in 2020 is unavailable, which is assumed to be the same as in 2019.

Crop residues in biomass residues: They are referred to as that harvested production of crops that do not reach the market to be sold but are instead employed as raw materials for commercial purposes such as energy generation and livestock husbandry. This number (Wcrop residues) can be calculated by first determining the number of crop residues available from primary crop production (Wcrop) and the harvest factor (Pharvest factor), and then using the recovery rate (Precovery rate) to determine the number of crop residues used by the economy, as shown in Eq. 2. These parameters have been localized by previous studies17,18, which are adopted in this study, i.e., wheat (1.1 for Pharvest factor and 0.463 for Precovery rate), maize (1.2, 0.463), rice (0.9, 0.463), sugar cane (0.5, 0.9), beetroots (0.7, 0.9), tuber (0.5, 0.463), pulse (1.2, 0.7), cotton (3.4, 0.463), fibre crops (1.8, 0.463), silkworm cocoons (1.8, 0.463), and oil-bearing crops (1.8, 0.463).$${W}_{cropresidues}={W}_{crop}times {P}_{harvestfactor}times {P}_{recoveryrate}$$
(2)

Roughage of grazed biomass and fodder crops in biomass residues: In China, the grazed biomass for roughage includes annual forage and perennial forage, whereas fodder crops comprise straw feed, processed straw feed, and all other fodder crops. However, information19 on grazed biomass production is only accessible from 2006 to 2018, whereas fodder crop statistics are only available from 2015 to 2017. Equation 3 and Eq. 4 can be used to estimate unavailable statistics. To note, we assume that China’s domestic roughage supply structure has remained unaltered, which has two meanings. The proportion of total domestic roughage production (WDomestic production) in requirement (WRoughage requirement) has remained constant, while the proportion (PSupply fraction) of grazed biomass and fodder crop in domestic roughage production has been unchanged. The requirement (WRoughage requirement) is determined by the quantity of livestock (QLivestock) and their annual feeding amount (PAnnual intake). PAnnual intake (in tonnes per head per year) has been localised for each type of livestock4, with 4.5 for live cattle and buffaloes, 0.5 for sheep and goats, 3.7 for horses, and 2.2 for mules and asses.$${W}_{Roughagerequirement}={Q}_{Livestock}times {P}_{Annualintake}$$
(3)
$${W}_{Domesticproduction}={W}_{Roughagerequirement}times {P}_{Supplyfraction}$$
(4)

Timber in wood: As illustrated in Eq. 5, wood production16 is reported in volume units of cubic metres (VTimber), which need to be converted into mass units (WTimber) via density (PDensity). The parameter PDensity is assumed to be 0.58 tonnes per cubic metre, calculated by averaging 0.52 for coniferous types and 0.64 for non-coniferous ones4.$${W}_{Timber}={V}_{Timber}times {P}_{Density}$$
(5)

Non-ferrous metals in metal ores: Non-ferrous metal statistics are derived from two sources. China statistics20 are measured in gross ore (WMetal ores in gross ore) but are only available from 1999 to 2017, whereas the USGS statistics21 cover the period of 1990 to 2020 but they are measured in metal or concentrate content (WMetal ores in other units). Therefore, USGS statistics need to be converted with an empirical unit conversion factor (PUnit conversion factor) before being applied to estimate unavailable statistics reported by China, as shown in Eq. 6. Conversion factors are localised for each non-ferrous metal in each year from 2000 to 2017 by using USGS statistics divided by China statistics and then averaged after removing the highest value and the lowest value (i.e., trimmed mean). This factor could capture the general relationship between statistics from two separate sources, which can be used in other long time-series studies on resource management on a particular element in China.$${W}_{Metaloresingrossore}={W}_{Metaloresinotherunits}/{P}_{Unitconversionfactor}$$
(6)

Non-metallic minerals: The official China-specific information on non-metallic mineral domestic production is available between 1999 and 201720, the rest of which could be estimated from USGS statistics (1990–2020)21. Also, two differences in reporting standards are observed resulting from the material coverages and reporting units. China statistics contain eighty-eight materials in mineral ores, whereas the USGS only includes twenty in the concentrate unit. Therefore, a conversion factor is developed in this estimation, as shown in Eq. 7. This conversion factor is applied to the total amount of non-metallic mineral production, which is assumed to have been constant from 1990 to 1999 at 11.38% (1999) and 12.56% (2017) from 2017 to 2020.$${W}_{Mineralsingrossore}={W}_{Mineralsinotherunits}/{P}_{Conversionfactor}$$
(7)

Coal in fossil energy materials: Coal, mined in China, includes raw coal, peat, stone coal, and oil shale. Except for raw coal, statistics for the rest are only available from 1999 to 201720. The unavailable data (WOther coals) is estimated using Eq. 8 under the assumption that the structure of the coal supply in China barely changes. That is, the proportion (PSupply fraction) of peat, stone coal, and oil shale in raw coal production (WRaw coal) remains constant, so the 1999 proportion is applied to all years before that (earlier years of 1990–1998), while the 2017 proportion is used to the recent years between 2018 and 2020. For example, PSupply fraction for oil shale production was assumed to be 0.014% during 1990–1999, calculated by dividing raw coal production (1,250,000) by oil shale production (179) in 1999. PSupply fraction in the earlier and the recent years are 0.007% and 0.001% for peat, 0.203% and 0.031% for stone coal, and 0.014% and 0.067% for oil shale.

$${W}_{Othercoals}={W}_{Rawcoal}/{P}_{Supplyfraction}$$
(8)

The output of processed materials by release

Materials released into the air: In China, thirteen materials are released into the air, as shown in Fig. 1. The emission of sulphur dioxide (SO2) is reported in China environmental statistical yearbooks22,23, while the rest is specified in the EDGAR24. However, in EDGAR, statistics for recent years have not yet been updated, which are estimated with the value in the most recent year in our database. For example, nitrous oxide (NOx) records are only available for the years prior to 2016, with 26,365 thousand tonnes in 2015 and 26,837 in 2014. As a result of the observed decreasing trend in NOx emissions, NOx emission data for 2016–2020 is estimated to be 26,000 thousand tonnes. This estimate may be subjective due to constraints, but it would be aligned with European statistics, allowing for international comparisons. Data can be updated after the EDGAR statistics have been updated.

Materials released into the water: Ten principal materials have been found in China wastewater (both industrial and municipal) that are nitrogen (N), phosphorus (P), organic pollutants of petroleum, volatile phenol and cyanide, heavy metals of mercury (Hg), lead (Pb), cadmium (C·d), and the hexavalent chromium (Cr6+), and arsenic (As). Many statistics22,23 have been of poor quality (e.g., inconsistent material coverages between years). Given that the statistics of pollutants in industrial wastewater cover more periods and contain fewer abnormal observations, the total material emissions can be approximated from those of industrial wastewater. Equations 9 and 10 show the estimation processes. The materials in industrial wastewater (WIndustrial materials) are first identified using material mass concentration (PConcentration) and the weight of industrial wastewater (WIndustrial wastewater), and then the materials in total wastewater (WTotal materials) are identified using the proportion (PContribution) of materials in industrial wastewaters (WIndustrial materials) to the total. The assumption is that PConcentration and PContribution change gradually between years, which enables to use linear interpolation method to estimate unavailable parameters. Consider cyanide: its PConcentration was 23.61 (1‰ ppm) in 2005 and 37.31 in 2002, which was assumed to be 28.18 in 2004 and 32.74 in 2003. PConcentration was assumed to be 100% throughout the years for cyanide because all cyanide emissions in China are driven by industrial wastewater discharges. Later, the total material emissions can be derived by dividing the industrial wastewater mass by PConcentration.$${W}_{Industrialmaterials}={W}_{Industrialwastewater}times {P}_{Concentration}$$
(9)
$${W}_{Totalmaterials}={W}_{Industrialmaterials},/,{P}_{Contribution}$$
(10)

Materials released to the land: This is zero because uncontrolled landfills are illegal in China.

Materials dissipated by organic fertiliser use: In China, manure is the primary organic fertiliser, which is excreted by pigs, dairy cows, calves, sheep, horses, asses, mules, camels, chickens, and other animals. As shown in Eq. 11, the manure production (WManure) is estimated through the amounts of raised livestock (QLivestock, heads), the weight of daily manure production (PManure production, kilograms per head per day), the number of days they are raised (PFeeding period, in days per year), and the moisture content of their manure (PDry matter, %) for each type of animal. These parameters are region-specific, which have been localised by Chinese scholars25,26,27 and listed in Table 2.$${W}_{Manure}={Q}_{Livestock}times {P}_{Manureproduction}times {P}_{Feedingperiod}times {P}_{Drymatter}$$
(11)
Table 2 Localised parameters for animal manure production.Full size table

Materials dissipated by mineral fertiliser use: The mineral fertilisers used in China are four types, i.e., nitrogen (N), phosphorus (P), potash (K), and compound. Their usage (WFertiliser usage) is measured in nutrient mass (WNutrient materials), which needs to be converted into the gross mass by dividing their nutrient content (PNutrient content). Equation 12 shows the estimation. This parameter of PNutrient content is localised by the Ministry of Agriculture and Rural Affairs of China28 as 29%, 22%, 35%, and 44% for N- bearing, P- bearing, K-bearing, and compound fertilisers, respectively.$${W}_{Fertiliserusage}={W}_{Nutrientmaterials}/{P}_{Nutrientcontent}$$
(12)

Materials dissipated by sewage sludge: Sewage sludge is the residue generated by municipal wastewater treatment. As demonstrated in Eq. 13, its dissipative use (Wss, dissipation) is the untreated amount of production (Wss, production), represented by the parameter of Pss, dissipation rate. Sewage sludge production (Wss, production) statistics are only available for the years 2006–202029, and data for the remaining years can be estimated using Eq. 14 and Eq. 15. In Eq. 14, Pss, production rate represents the relationship between sewage sludge production (Wss, production, 2006–2020) and wastewater treatment (Www, treatment, 2002–2020), and in Eq. 15, Pww, treatment efficiency represents the relationship between the quantity of treated wastewater (Www, treatment, 2002–2020) and the treatment capacity (Www, treatment capacity, 1990–2020). In this estimation, three assumptions are made. The first is to estimate Www, treatment, Pww, treatment efficiency is assumed to be unchanged at 63% during 1990–2001, given it has been increasing from 63% in 2002 to ~80% in recent years. The second is that, in order to estimate Wss, production, Pss, production rate is assumed to be unchanged at 3.5 between 1990 and 2005, suggesting 3.5 tonnes of sewage sludge are generated by processing 10,000 cubic metres of wastewater. This assumption is determined by that Pss, production rate is approximately 3.5 during 2006–2010 while declines sharply and stabilises at around two during 2011–2020. The last is, to estimate the Wss,dissipation, Pss,dissipation rate is assumed to be 5% between 1990 and 2005, given it has been around 5% during 2006–2020.$${W}_{ss,dissipation}={W}_{ss,production}times {P}_{ss,dissipationrate}$$
(13)
$${W}_{ss,production}={W}_{ww,treatment}times {P}_{ss,productionrate}$$
(14)
$${W}_{ww,treatment}={W}_{ww,treatmentcapacity}times {P}_{ww,treatmentefficiency}$$
(15)

Materials dissipated by composting: Composting is a natural process that uses microbes to turn organic materials into other products, which are then used for fertilising and entering the environment. In China, composting has been used to treat two materials: feces and municipal waste, whose quantities (WComposting) were only available from 2003 to 201029. The unavailable data can be estimated using Eq. 16. The dry weight of materials treated by composting (WComposting) is proportionally related to the fresh weight of all treated materials (WTotal), the proportion treated by composting (PComposting rate), and the dry content (PDry matter). Considering that China’s composting capacity has been declining since 2001 due to the implementation of waste incineration power generation technologies30, Pcomposting rate is assumed to be the same as it was in 2003 (9.5%) between 1990 and 2002, and 1.5% in 2010 between 2011 and 2020. The parameter of PDry matter is 50%4.$${W}_{Composting}={W}_{Total}times {P}_{Compostingrate}times {P}_{Drymatter}$$
(16)

The input and output by cross-border trade. Statistics of imports and exports have been gathered since 1962 and stored in the UN Comtrade database31. However, the data quality issue of outliers, and missing values, especially in weight, is reportedly identified. In our previous work, we addressed these issues, and an improved database32 is provided. Details about our estimation methods can be found in publications33,34,35. As UN Comtrade lists 5,039 different commodity types (in 6-digit HS0 commodity code), yet only 18 material types are specified in the China EW-MFA, UN Comtrade statistics need to be aligned to the China EW-MFA framework. Therefore, we compared each commodity and each material type between them and established a correspondence table to map UN Comtrade commodity types onto our EW-MFA material types. For example, non-ferrous metal materials of China EW-MFA include commodities, such as copper ores and concentrates (260300 HS0 code), silver powder (710610), manganese, articles thereof, and waste or scrap (811100), etc., whereas biomass residues include cereal straw and husks (121300), lucerne meal and pellets (121410), and other fodder and forage products (121410). This correspondence table between HS0 and EW-MFA classification for imports and exports is provided in Supplementary File 1.

The input of balancing items

O2 required for combustion: In BI, requirements for materials can be abstracted as equalling exogenous demands minus intrinsic supplies (Eq. 17). Three parts (two demands and one supply) are considered for O2 requirements by the combustion process: (1) demanding exogenous oxygen to oxidise elements (e.g., carbon, sulphur, nitrogen, etc., except for hydrogen) released into the air, (2) demanding exogenous oxygen to oxidise the hydrogen embedded in fossil energy materials, and (3) providing intrinsic oxygen embedded in fossil energy materials. The first part can be estimated via Eq. 18 by multiplying air emissions (WEmissions) of CO2, N2O, NOx, CO, and SO2 by their oxygen content (POxygen content). For the second (Eq. 19), the oxygen demand is estimated based on the principle of mass balance by converting the hydrogen amount of domestically utilised fossil energy materials (WFossil fuel materials × PHydrogen content) via molar mass conversion factor (PMass conversion factor). PMass conversion factor equals 7.92, derived by the molar mass of one oxygen (16 g/mol) divided by that of two hydrogen atoms (2 × 1.01 g/mol). The last is the intrinsic supplies from fossil fuel materials, which is identified via Eq. 20 by multiplying the domestically utilised amount of fossil fuel materials (WFossil fuel materials) by their oxygen content (POxygen content). The parameters in this estimation are presented in Table 3. As a footnote here, the domestically utilised amount is referred to as the domestic material consumption (DMC), which equals domestic extraction (DE) plus imports (IM) and minus exports (EX).$${W}_{Requirements}={W}_{Demands}-{W}_{Supplies}$$
(17)
$${W}_{Demands}={W}_{Emissions}times {P}_{Oxygencontent}$$
(18)
$${W}_{Demands}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(19)
$${W}_{Supplies}={W}_{Fossilfuelmaterials}times {P}_{Oxygencontent}$$
(20)
Table 3 Parameters related to combustion processes4.Full size table

O2 required for respiration: O2 is required by the metabolic activities of living organisms, the majority of which are humans and livestock. Bacteria are another sort of organism, which are not included in this estimation because their O2 requirements are too small to be quantified. The respiration-required O2 is related to the total quantity (QOrganisms) and their respiration activity by organism types, as shown in Eq. 21. The respiration activity is represented by the respiration requirement coefficient (PRespiration requirement coefficient), which is the average quantity of O2 that each organism utilises to maintain the metabolic activity, as listed in Table 4.$${W}_{Demands}={Q}_{Organisms}times {P}_{Respirationrequirementcoefficient}$$
(21)
Table 4 Parameters related to respiration processes4.Full size table

Water required for the domestic production of exported beverages: The exported beverages are produced domestically using domestically extracted materials, especially a large amount of water. The weight of water is considered in the output by cross-border trade but is not included in the domestic extraction input. The resulted imbalance can be identified by specifying the water weight in beverages, i.e., multiplying the traded beverage weight (WMaterials) by a parameter of the water content (PWater content), as given in Eq. 22. Fruit and vegetable juices (2009 in HS0 code) and beverages (code 22) are covered in the improved UN Comtrade database32, with PWater content of 85% for the first and 90% for the latter4.

$${W}_{Water}={W}_{Materials}times {P}_{Watercontent}$$
(22)

The output of balancing items.

Water vapour from combustion: Water vapour emissions by domestically combusting fossil fuel materials are contributed by two paths. The direct evaporation of embedded water is the first path (Eq. 23), which can be derived by multiplying the DMC of fossil fuel materials by their moisture content (PMoisture content). The PMoisture content for each type of fossil fuel material is listed in Table 3. The other is the generation of water vapour during hydrogen oxidation, which can be calculated by converting the oxidised weight of hydrogen to the water weight using the molar mass conversion factor (PMass conversion factor), as given in Eq. 24. PMass conversion factor equals 8.92 by dividing the molar mass of water (18.02 g/mol) by that of two hydrogen atoms (2 × 1.01 g/mol).$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Moisturecontent}$$
(23)
$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
(24)

Water vapour and CO2 from respiration: Respiration activities of organisms will produce water vapour and CO2, whose estimation is similar to that of O2 requirements. As shown in Eq. 25, the respiration-caused gas emissions are related to the number of organisms (QOrganisms) and the respiration activity by organism types. The latter is represented by the parameter of respiration emission coefficient (PRespiration emission coefficient), which is specified in Table 4 for water vapour and CO2 for each type of organism.$${W}_{Emissions}={Q}_{Organisms}times {P}_{Respirationemissioncoefficient}$$
(25)

Water from imported beverages: The estimation approach is the same as water by the domestic production of exported beverages, as described in Eq. 16.

Water in biomass products: Usually, the input of biomass products by domestic extraction16 has been measured in fresh weight, but their corresponding output29 by sewage sludge, composting, etc., are in dry weight, leading to an imbalance in water weight. The water weight in biomass products is calculated by multiplying their domestic extraction amount in fresh weight (WBiomass) by a parameter of moisture content at harvest (PMoisture content), as shown in Eq. 26. The values of PMoisture content by biomass products are presented in Table 5.Table 5 The moisture content at harvest for each biomass product4.Full size table

$${W}_{Water}={W}_{Biomass}times {P}_{Moisturecontent}$$
(26)
Material flow quantificationThe above attempts have quantified material inputs and outputs by flows and presented a detailed profile of material utilisation for each material in China’s economy. In order to depict the economy in a more general way, EW-MFA indicators are assessed by aggregating flows by materials or periods as below.

Domestic extraction (DE): is referred to as natural materials that are extracted from the domestic environment and are used in the domestic economy, i.e., the total input of natural materials by extraction.

Domestic processed output (DPO): is referred to as materials that are released to the domestic environment after being processed in the domestic economy, i.e., the total output of processed materials by release.

Import (IM): is referred to as all goods (in the form of raw materials, semi-finished materials, and final products) that originated from other economies and are further used in the domestic economy. It is calculated as the sum of all imported goods.

Export (EX): is referred to as all goods that originated from the domestic economy and are transported to other economies to be used. It is calculated as the sum of all exported goods.

Domestic material input (DMI): is referred to as materials that originated from the domestic environment by extraction and other economies and are available (to be used or to be stored) for the domestic economy. It is calculated as the sum of DE plus IM, as shown in Eq. 27.$$DMI=DE+IM$$
(27)

Domestic material consumption (DMC): is referred to as materials that are directly used in the domestic economy after parts of them are exported to other economies. It is calculated as the difference between DMI and EX.

Physical trade balance (PTB): is referred to as a surplus or deficit of materials for the domestic economy. It is calculated as the difference between IM and EX.

Net additions to stock (NAS): is referred to as materials that remain in the domestic economy. It is calculated by taking BI items into account, as shown in Eq. 28.

$$NAS=DMC+B{I}_{in}-DPO-B{I}_{out}$$
(28) More

Darwin, C. On the Origin of Species. Facsimile of the First Edition (Harvard University Press, 1859).
Google Scholar 
Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 326, 119–157 (1989).ADS 
CAS 
PubMed 

Google Scholar 
Sillero, N., Reis, M., Vieira, C. P., Vieira, J. & Morales-Hojas, R. Niche evolution and thermal adaptation in the temperate species Drosophila americana. J. Evol. Biol. 27, 1549–1561 (2014).CAS 
PubMed 

Google Scholar 
Ramos, R. et al. Global spatial ecology of three closely-related gadfly petrels. Sci. Rep. 6, 23447 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, B., Cheng, J., Ge, D., Xia, L. & Yang, Q. Phylogeography and ecological niche modeling unravel the evolutionary history of the Yarkand hare, Lepus yarkandensis (Mammalia: Leporidae), through the Quaternary. BMC Evol. Biol. 19, 113 (2019).PubMed 
PubMed Central 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. 36, 519–539 (2005).
Google Scholar 
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).PubMed 

Google Scholar 
Crisp, M. D. & Cook, L. G. Phylogenetic niche conservatism: What are the underlying evolutionary and ecological causes?. New Phytol. 196, 681–694 (2012).PubMed 

Google Scholar 
Qian, H. & Ricklefs, R. E. Geographical distribution and ecological conservatism of disjunct genera of vascular plants in eastern Asia and eastern North America. J. Ecol. 92, 253–265 (2004).
Google Scholar 
Vitt, L. J., Zani, P. A. & Espósito, M. C. Historical ecology of Amazonian lizards: Implications for community ecology. Oikos 87, 286–294 (1999).
Google Scholar 
Rice, N. H., Martínez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: A phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
Google Scholar 
Jost, L. Explosive local radiation of the genus Teagueia (Orchidaceae) in the Upper Pastaza Watershed of Ecuador. Lyonia 7, 42–47 (2004).
Google Scholar 
Antonelli, A., Verola, C. F., Parisod, C. & Gustafsson, A. L. S. Climate cooling promoted the expansion and radiation of a threatened group of South American orchids (Epidendroideae: Laeliinae). Biol. J. Linn. Soc. 100, 597–607 (2010).
Google Scholar 
Johnson, S. D., Linder, H. P. & Steiner, K. E. Phylogeny and radiation of pollination systems in Disa (Orchidaceae). Am. J. Bot. 85, 402–411 (1998).CAS 
PubMed 

Google Scholar 
Kolanowska, M., Grochocka, E. & Konowalik, K. Phylogenetic climatic niche conservatism and evolution of climatic suitability in Neotropical Angraecinae (Vandeae, Orchidaceae) and their closest African relatives. PeerJ 5, e3328 (2017).PubMed 
PubMed Central 

Google Scholar 
Dressler, R. L., Blanco, M. A., Pupulin, F. & Neubig, K. M. Proposal to conserve the name Sobralia (Orchidaceae) with a conserved type. Taxon 60, 907–908 (2011).
Google Scholar 
Baranow, P., Dudek, M. & Szlachetko, D. L. Brasolia, a new genus highlighted from Sobralia (Orchidaceae). Plant Syst. Evol. 303, 853–871 (2017).CAS 

Google Scholar 
Dressler, R. L. The major sections or groups within Sobralia, with four new species from Panama and Costa Rica, S. crispissima, S. gloriana, S. mariannae and S. nutans. Lankesteriana 5, 9–15 (2002).
Google Scholar 
Pridgeon, A. M., Cribb, P. J., Chase, M. W. & Rasmussen, F. N. Genera Orchidacearum Vol. 4: Epidendroideae Part 1 (Oxford University Press, 2005).
Google Scholar 
Van der Cingel, N. A. An Atlas of Orchid Pollination: America, Africa, Asia and Australia (Balkema, 2001).
Google Scholar 
Dodson, C. H. Why are there so many orchid species. Lankesteriana 7, 99–103 (2003).
Google Scholar 
Van Der Pijl, L. & Dodson, C. H. Orchid Flowers: Their Pollination and Evolution (University of Miami Press, 1966).
Google Scholar 
Neubig, K. M. Systematics of Tribe Sobralieae (Orchidaceae): Phylogenetics, Pollination, Anatomy, and Biogeography of a Group of Neotropical Orchids (University of Florida, 2012).
Google Scholar 
Neubig, K. M. et al. Preliminary molecular phylogenetics of Sobralia and relatives (Orchidaceae; Sobralieae). Lankesteriana 11, 307–317 (2011).
Google Scholar 
Ramírez, S. R., Roubik, D. W., Skov, C. & Pierce, N. E. Phylogeny, diversification patterns and historical biogeography of euglossine orchid bees (Hymenoptera: Apidae). Biol. J. Linn. Soc. 100, 552–572 (2010).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Sundell, K. E., Saylor, J. E., Lapen, T. J. & Horton, B. K. Implications of variable late Cenozoic surface uplift across the Peruvian central Andes. Sci. Rep. 9, 4877 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mescua, J. F. et al. Middle to late miocene contractional deformation in Costa Rica triggered by plate geodynamics. Tectonics 36, 2936–2949 (2017).ADS 

Google Scholar 
Kolanowska, M., Mystkowska, K., Kras, M., Dudek, M. & Konowalik, K. Evolution of the climatic tolerance and postglacial ranges of the most primitive orchids (Apostasioideae) within Sunduland, Wallacea and Sahul. PeerJ 4, e2384 (2016).PubMed 
PubMed Central 

Google Scholar 
Arnal, P. et al. The evolution of climate tolerance in conifer-feeding aphids in relation to their host’s climatic niche. Ecol. Evol. 9, 11657–11671 (2019).PubMed 
PubMed Central 

Google Scholar 
Zangiabadi, S., Zaremaivan, H., Brotons, L., Mostafavi, H. & Ranjbar, H. Using climatic variables alone overestimate climate change impacts on predicting distribution of an endemic species. PLoS ONE 16, e0256918. https://doi.org/10.1371/journal.pone.0256918 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).Article 

Google Scholar 
Jiménez-Valverde, A., Lobo, J. & Hortal, J. Not as good as they seem: The importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x (2008).Article 

Google Scholar 
Bonetti, M. F. & Wiens, J. J. Evolution of climatic niche specialization: a phylogenetic analysis in amphibians. Proc. Biol. Sci. 281, 20133229. https://doi.org/10.1098/rspb.2013.3229 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
George, P. M., Walter, E. W. & Yeuh-Lih, Y. Realized versus fundamental niche functions in a model of chaparral response to climatic change. Ecol. Modell. 7, 261–277 (1992).
Google Scholar 
Hijmans, R. J., Schreuder, M., Cruz, J. & Guarino, L. Using GIS to check co-ordinates of genebank accessions. Genet. Resour. Crop Evol. 46, 291–296 (1999).
Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In ICML ’04. Proceedings of the Twenty-First International Conference on MACHINE LEARNing, 655–662 (ACM, New York, 2004).Phillips, S. J., Anderson, R. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Modell. 222, 1810–1819 (2011).
Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Brown, J. L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).
Google Scholar 
Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. https://doi.org/10.1002/ece3.5555 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression (Wiley, 2000).MATH 

Google Scholar 
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128, 2145–2166 (2002).ADS 

Google Scholar 
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2008).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Warren, D. L. et al. ENMTools 1.0: An R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).
Google Scholar 
Schoener, T. W. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
Google Scholar 
Heibl, C. & Calenge, C. Phyloclim: integrating phylogenetics and climatic niche modeling. R package version 0.9-4. http://CRAN.R-project.org/package=phyloclim (2013).Evans, M. E., Smith, S. A., Flynn, R. S. & Donoghue, M. J. Climate, niche evolution, and diversification of the ‘“bird-cage”’ evening primroses (Oenothera, sections Anogra and Kleinia). Am. Nat. 173, 225–240 (2009).PubMed 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 

Google Scholar 
Galtier, N., Gouy, M. & Gautier, C. SeaView and Phylo_win, two graphic tools for sequence alignment and molecular phylogeny. Comput. Appl. Biosci. 12, 543–548 (1996).CAS 
PubMed 

Google Scholar 
Edgar, R. MUSCLE: Mulitiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nylander, J. A. A. MrModeltest v2 (Uppsala University, 2004).
Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MRBAYES: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 

Google Scholar 
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Givnish, T. et al. Orchid phylogenomics and multiple drivers of their extraordinary diversification. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2015.1553 (2015).Article 
PubMed 
PubMed Central 

Google Scholar  More

DataSpatial and temporal rangeWe focused on a geographic area that is defined by a cropping window with the corner points P1 (Lon = −180, Lat = 25) and P2 (Lon = −52, Lat = 80), covering the majority of the North American continent (e.g., Fig. 3). We focused on the last 30 Myr, a time span encompassing most of our available sites with paleovegetation information (Supplementary Fig. 1). From the following data sources, we only selected those data points that fall within this spatiotemporal range.Our approach described below required discretizing the input data of past vegetation labels and fossil occurrences into time-bins. For this, we chose the age boundaries of geological stages defined in the International Chronostratigraphic Chart, v2020/0345, since these stages are expected to represent meaningful temporal units for analyzing both faunal and floral patterns. A total of 17 geological stages fell within our selected time frame of the last 30 Myr. We discretized the ages of all data points (vegetation data and fossil occurrences) that fell within a given stage by setting them to the midpoint of the respective stage.Paleovegetation dataWe reviewed a large body of peer-reviewed literature containing paleovegetation reconstructions and compiled a database of 331 sites with paleovegetation data for North America (Supplementary Data 1). These sites represent individual vegetation reconstructions based on fossil evidence (phytoliths, pollen, macrofossil assemblages) of distinct locations in time and space. We condensed the vegetation interpretation of the compiled vegetation data, which in many cases described specific vegetation ecosystem components, into the broader labels “open” versus “closed” vegetation. This resulted in 180 sites being labeled as closed and 151 as open, their dating rounded to the midpoint of the nearest geological stage (Supplementary Data 1). For several of these sites we found multiple vegetation reconstructions in the reviewed literature, for example when multiple sediment samples were taken from the same horizon of a given formation, belonging to the same geological stage. We treated these spatiotemporal duplicates as a single data point, excluding sites with mixed vegetation information (i.e., containing both open and closed vegetation reconstructions).Current vegetation dataTo supplement the limited number of paleovegetation sites, we compiled data about the current vegetation within our study area. In order to obtain current vegetation patterns, we downloaded the SYNMAP Global Potential Vegetation data29. As for the paleovegetation data, we collapsed the more detailed biome data into broader categories by coding the SYNMAP biome IDs  More

Genetic structure of the parental populationBased on the lnPD and ΔK values obtained using STRUCTURE, we identified two genetic groups within the DHS Acer population (Supplementary Fig. S1). The q value from STRUCTURE analysis represents the proportion of ancestral origin28 (Fig. 2a). Among the 70 individual trees, 72.9% were assigned a q value smaller than 0.1 or larger than 0.9, thereby signifying a typical bimodal distribution (Fig. 2b). Individuals with q value greater than 0.9 and consistent genetic origin from the NEA region were defined as the NEA lineage (hereafter “NEA-DHS”), whereas those with values less than 0.1 and with consistent genetic origin from the SEA region were defined as the SEA lineage (hereafter “SEA-DHS”). Individuals with intermediate q value between 0.1 and 0.9 were defined as hybrid genetic types (hereafter “Hybrid-DHS”). Accordingly, we identified 27 SEA-DHS (38.6%), 24 NEA-DHS (34.3%), and 19 Hybrid-DHS (27.1%) (Fig. 2b).Figure 2Genetic structure of the parental and offspring population. (a) Bar plots illustrating the genetic composition of the adult (leaf) and offspring (fruit) populations in the Daheishan National Nature Reserve (DHS). Each individual is represented by a line partitioned into color segments corresponding to its ancestral proportion. Red color represents the ancestral proportion of Southern East Asia lineage. Green color represents the ancestral proportion of Northern East Asia lineage. Black lines in bar plots of leaf population separate individuals with ancestral proportion (q value) bigger than 0.9 or smaller than 0.1 from hybrids (0.1  0.5) produced by the SEA-DHS were obtained from a single tree, which was identified as SEA-DHS based on the DHS-only dataset, although it was indicated to be Hybrid-DHS based on the whole-range dataset. The Hybrid-DHS maternal trees produced 17.6% pure SEA-DHS seeds, 57.6% pure NEA-DHS seeds, and 24.7% hybrid seeds.Flowering phenologyThe sexual system of Acer has four phenotypes: duodichogamous, protogynous, protandrous, and male31. Hence, there are three functional sex types: (1) “Male I” flowers open earlier than “Female” flowers, with mature stamens, no style, and ovary; (2) “Female” flowers have mature pistils, short filaments, and indehiscence anthers; (3) “Male II” flowers open later than “Female” flowers, with mature stamens, ovaries, and separated stigmas. Duodichogamy is characterized by “Male I,” “Female,” and “Male II” types; protandry by “Male I” and “Female” types; and protogyny by “Female” and “Male II” types31.During the flowering season, we monitored a total of 10,074 flowers produced by 29 trees (Fig. 2d), among which one tree (SEA-DHS) was protandrous, four trees (three Hybrid-DHS and one NEA-DHS) were protogynous, and the remaining 24 trees were duodichogamous. We observed that the blooming phenology of SEA-DHS and NEA-DHS differed significantly to most assessed phenological indices, with a single exception being a marginally significant difference in the peak blooming time of Male I (Table 1). Compared with NEA-DHS, SEA-DHS were characterized by significantly later flowering phenology, with Male I commencement and cessation of blooming being on average two and three days later, respectively. Similarly, the commencement, peak, and cessation of Female occurred later by averages of 4, 4, and 5 days, respectively, whereas those of Male II occurred later by 5, 4, and 5 days, respectively. Furthermore, the duration of blooming was significantly longer in the SEA-DHS group than in the NEA-DHS group by three days. In the case of Hybrid-DHS, the values obtained for all assessed phenological indices were intermediate between those of the two parental types. Among these, the values of the six indices differed significantly from one or the other parental types, with the majority (5/6) differing from those of the SEA-DHS. Thus, phenologically, Hybrid-DHS appeared to be closer to NEA-DHS.Table 1 Flowering phenology of SEA-DHS, Hybrid-DHS, and NEA-DHS.Full size tableHowever, despite the differing phenology of the SEA-DHS and NEA-DHS, we observed instances of overlap in the blooming periods of male or female flowers in one genetic type with those of flowers of the opposite sex in another genetic type. For example, the peak of Female among NEA-DHS (11.67 ± 0.67) was found to coincide with the peak of Male I (11.44 ± 1.06; p = 0.879) in SEA-DHS. Similarly, Female blooming in the SEA-DHS peaked (16.11 ± 1.09) just 1 d after the peak of Male II (15.50 ± 0.43) in the NEA-DHS (p = 0.667), which at this time still retained an abundance of male flowers in bloom. In contrast, we detected no overlapping phenology with respect to the blooming of Male I of NEA-DHS or Male II of SEA-DHS with the Female in another genetic type.Morphological variation of leaves and fruitLeaves Among the eight leaf indices, all except InfectionRatio were significantly different between lineages. Generally, the leaves of NEA-DHS were found to have seven lobes, whereas those of SEA-DHS were typically five lobed (Lobes#), thereby contributing to significantly larger leaves in NEA-DHS than in SEA-DHS (TotalArea). Furthermore, NEA-DHS leaves had shorter and wider central lobes (CentralLength and CentralWidth), as well as an earlier and narrower inflection of the central lobes (InflectionLength and InflectionWidth), compared with those of SEA-DHS (Table 2). Six indices had correlation coefficients of less than 0.7, which were used for principal component analysis (PCA) analysis (Supplementary Table S2). The first two axes of the PCA were found to explain 63.7% of the variation in leaf morphology (Fig. 3a), with InflectionLength, CentralLength, and CentralRatio contributing the most to the first axis (38.2%), whereas TotalArea contributed the most to the second axis (25.5%) (Supplementary Table S3). The leaves of SEA-DHS and NEA-DHS plants were largely clustered in separate groups (Fig. 3a). However, all indices were continuous variables with large overlaps between the lineages (Table 2). For example, NEA-DHS had a significantly larger leaf area (21.06–88.70 cm2) than SEA-DHS (11.34–70.09 cm2). The shape of the central lobe is another major leaf trait that distinguishes between the two species. NEA-DHS had a shorter and wider central lobe (CentralRatio:0.67–2.49), while SEA-DHS had a longer and narrower central lobe (CentralRatio:0.9–3.46).Table 2 Morphological variation in the leaves and fruits of Acer trees in the Daheishan National Nature Reserve.Full size tableFigure 3Morphological variation in the leaves (a) and fruits (b) of southern and northern East Asia lineages of the Acer species complex in the Daheishan National Nature Reserve based on principal component analysis. SEA-DHS: Southern East Asia lineage of the Acer species complex in the DHS; NEA-DHS: Northern East Asia lineage of the Acer species complex in the DHS; Hybrid-DHS: hybrids between SEA-DHS and NEA-DHS lineages.Full size imageWith regard to Hybrid-DHS, the leaves were morphologically intermediate between those of the two parental types (Fig. 3a), as were the values of the assessed morphological trait indices (Table 2).Fruits 11 indices of fruits were significantly different between lineages. NEA-DHS tend to be characterized by smaller fruits (FruitLength and FruitWidth), seeds (SeedLength, SeedWidth and JunctionWidth), and fruit wings (WingLength and WingWidth). Moreover, the seed wings of NEA-DHS fruits are typically oriented at an obtuse angle, whereas those of SEA-DHS fruits tend to be aligned at a right angle (FruitAngle). The length ratio of the wing and seed (Wing:Seed) was larger in NEA-DHS than in SEA-DHS (1.24 vs 1.06, respectively, Table 2). Eight indices had correlation coefficients of less than 0.7, which were retained for PCA analysis (Supplementary Table S4). The first two axes of the PCA explained 58.4% of the variation in fruit morphology (Fig. 3b), with JunctionWidth and SeedLength contributing the most to the first axis (35.1%), whereas SeedRatio and WingRatio contributed the most to the second axis (23.3%) (Supplementary Table S3). The fruits of SEA-DHS and NEA-DHS plants were largely clustered in separate groups, with most fruits of SEA-DHS having negative values in Axis 1, while most fruits of NEA-DHS having positive values (Fig. 3b). Both JunctionWidth and SeedLength in Axis 1 reflect the size of the seed. NEA-DHS had smaller seed (SeedLength: 0.63–1.21 cm, SeedWidth:0.43–0.75 cm), while larger seed in SEA-DHS (SeedLength:0.79–1.49 cm, SeedWidth:0.49–0.93 cm). All indices were continuous variables with large overlaps between the lineages (Table 2).The morphology of Hybrid-DHS fruits was generally intermediate between that of the two parental types (Fig. 3b), as reflected in the values of the different morphological traits. The exceptions in this regard were FruitLength, WingLength, as well as two ratio indices (SeedRatio and WingRatio), with hybrid trees typically producing longer fruit with longer fruit wings (Table 2).Ecological niche divergence between NEA and SEAWe found a positive correlation between q value from Structure analysis and altitude (Pearson’s r = 0.83, p  670 m), whereas SEA-DHS was clustered at the foothill ( More

Ecological networks: Linking structure to dynamics in food webs. (Oxford University Press, 2006).Adaptive food webs: Stability and transitions of real and model ecosystems. (Cambridge University Press, 2018).Pimm, S. L. Food Webs (Springer, 1982).Book 

Google Scholar 
Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. (Cambridge University Press, 2017). doi:https://doi.org/10.1017/9781316871867.da Mata, A. S. Complex Networks: A Mini-review. Braz. J. Phys. 50, 658–672 (2020).ADS 
Article 

Google Scholar 
Zhang, W. Fundamentals of Network Biology. (World Scientific (Europe), 2018). https://doi.org/10.1142/q0149.Reichman, O. J., Jones, M. B. & Schildhauer, M. P. Challenges and opportunities of open data in ecology. Science 331, 703–705 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. situating ecology as a big-data science: Current advances, challenges, and solutions. Bioscience 68, 563–576 (2018).Article 

Google Scholar 
Osawa, T. Perspectives on biodiversity informatics for ecology. Ecol. Res. 34, 446–456 (2019).Article 

Google Scholar 
Shin, N. et al. Toward more data publication of long-term ecological observations. Ecol. Res. 35, 700–707 (2020).Article 

Google Scholar 
Pringle, R. M. & Hutchinson, M. C. Resolving food-web structure. Annu. Rev. Ecol. Evol. Syst. 51, 55–80 (2020).Article 

Google Scholar 
Derocles, S. A. P. et al. Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. in Advances in Ecological Research vol. 58 1–62 (Elsevier, 2018).Vacher, C. et al. Learning ecological networks from next-generation sequencing data. in Advances in Ecological Research vol. 54, 1–39 (Elsevier, 2016).Evans, D. M., Kitson, J. J. N., Lunt, D. H., Straw, N. A. & Pocock, M. J. O. Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems. Funct. Ecol. 30, 1904–1916 (2016).Article 

Google Scholar 
Pocock, M. J. O. et al. A vision for global biodiversity monitoring with citizen science. in Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).Sultana, M. & Storch, I. Suitability of open digital species records for assessing biodiversity patterns in cities: A case study using avian records. J. Urban Ecol. 7, juab014 (2021).Article 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400 (2016).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).Article 

Google Scholar 
Fontaine, C. et al. The ecological and evolutionary implications of merging different types of networks: Merging networks with different interaction types. Ecol. Lett. 14, 1170–1181 (2011).PubMed 
Article 

Google Scholar 
Martinson, H. M. & Fagan, W. F. Trophic disruption: A meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems. Ecol. Lett. 17, 1178–1189 (2014).PubMed 
Article 

Google Scholar 
Marczak, L. B., Thompson, R. M. & Richardson, J. S. Meta-analysis: Trophic level, Habitat, and productivity shape the food web effects of resource subsidies. Ecology 88, 140–148 (2007).PubMed 
Article 

Google Scholar 
McCary, M. A., Mores, R., Farfan, M. A. & Wise, D. H. Invasive plants have different effects on trophic structure of green and brown food webs in terrestrial ecosystems: A meta-analysis. Ecol. Lett. 19, 328–335 (2016).PubMed 
Article 

Google Scholar 
Cirtwill, A. R., Stouffer, D. B. & Romanuk, T. N. Latitudinal gradients in biotic niche breadth vary across ecosystem types. Proc. R. Soc. B Biol. Sci. 282, 20151589 (2015).Article 
CAS 

Google Scholar 
Fortuna, M. A., Ortega, R. & Bascompte, J. The Web of Life. ArXiv14032575 Q-Bio (2014).Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evol. 3, 919–927 (2019).PubMed 
Article 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
Article 

Google Scholar 
Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peng, J. et al. Linking ecosystem services and circuit theory to identify ecological security patterns. Sci. Total Environ. 644, 781–790 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Su, Y. et al. Modeling the optimal ecological security pattern for guiding the urban constructed land expansions. Urban For. Urban Green. 19, 35–46 (2016).Article 

Google Scholar 
Kowarik, I. Novel urban ecosystems, biodiversity, and conservation. Environ. Pollut. 159, 1974–1983 (2011).CAS 
PubMed 
Article 

Google Scholar 
Di Marco, M., Watson, J. E. M., Venter, O. & Possingham, H. P. Global biodiversity targets require both sufficiency and efficiency. Conserv. Lett. 9, 395–397 (2016).Article 

Google Scholar 
Kim, K.-H. & Pauleit, S. Landscape character, biodiversity and land use planning: The case of Kwangju City Region, South Korea. Land Use Policy 24, 264–274 (2007).Article 

Google Scholar 
Young, J. et al. Towards sustainable land use: Identifying and managing the conflicts between human activities and biodiversity conservation in Europe. Biodivers. Conserv. 14, 1641–1661 (2005).Article 

Google Scholar 
Dardonville, M., Urruty, N., Bockstaller, C. & Therond, O. Influence of diversity and intensification level on vulnerability, resilience and robustness of agricultural systems. Agric. Syst. 184, 102913 (2020).Article 

Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J. & Ellison, A. M. Ecological network metrics: Opportunities for synthesis. Ecosphere 8, e01900 (2017).Article 

Google Scholar 
Newman, M. E. J. Networks. (Oxford University Press, 2018).Levine, S. Several measures of trophic structure applicable to complex food webs. J. Theor. Biol. 83, 195–207 (1980).ADS 
Article 

Google Scholar 
Guimarães, P. R. The structure of ecological networks across levels of organization. Annu. Rev. Ecol. Evol. Syst. 51, 433–460 (2020).Article 

Google Scholar 
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Jordán, F., Benedek, Z. & Podani, J. Quantifying positional importance in food webs: A comparison of centrality indices. Ecol. Model. 205, 270–275 (2007).Article 

Google Scholar 
Jordán, F., Liu, W. & Davis, A. J. Topological keystone species: Measures of positional importance in food webs. Oikos 112, 535–546 (2006).Article 

Google Scholar 
Jordán, F., Okey, T. A., Bauer, B. & Libralato, S. Identifying important species: Linking structure and function in ecological networks. Ecol. Model. 216, 75–80 (2008).Article 

Google Scholar 
Jiang, L. Determination of keystone species in CSM food web: A topological analysis of network structure. Netw. Biol. 5, 13 (2015).
Google Scholar 
Abarca-Arenas, L. G., Franco-Lopez, J., Peterson, M. S., Brown-Peterson, N. J. & Valero-Pacheco, E. Sociometric analysis of the role of penaeids in the continental shelf food web off Veracruz. Mexico Based By-catch Fish. Res. 87, 46–57 (2007).
Google Scholar 
Abascal-Monroy, I. M. et al. Functional and structural food web comparison of Terminos Lagoon, Mexico in Three Periods (1980, 1998, and 2011). Estuaries Coasts 39, 1282–1293 (2016).Article 

Google Scholar 
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Windsor, F. M. et al. Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks. J. Appl. Ecol. 58, 2770–2782 (2021).Article 

Google Scholar 
Klaise, J. & Johnson, S. The origin of motif families in food webs. Sci. Rep. 7, 16197 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Estrada, E. Characterization of topological keystone species. Ecol. Complex. 4, 48–57 (2007).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Impacts on stream food webs of native and exotic forest: An intercontinental comparison. Ecology 84, 145–161 (2003).Article 

Google Scholar 
Bascompte, J., Melian, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dunne, J. A. et al. The roles and impacts of human hunter-gatherers in North Pacific marine food webs. Sci. Rep. 6, 21179 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gauzens, B., Legendre, S., Lazzaro, X. & Lacroix, G. Food-web aggregation, methodological and functional issues. Oikos 122, 1606–1615 (2013).Article 

Google Scholar 
Patonai, K. & Jordán, F. Aggregation of incomplete food web data may help to suggest sampling strategies. Ecol. Model. 352, 77–89 (2017).Article 

Google Scholar 
Thompson, R. M. & Townsend, C. R. Is resolution the solution?: The effect of taxonomic resolution on the calculated properties of three stream food webs. Freshw. Biol. 44, 413–422 (2000).Article 

Google Scholar 
Abarca-Arenas, L. G. & Ulanowicz, R. E. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296 (2002).Article 

Google Scholar 
Jordán, F. & Osváth, G. The sensitivity of food web topology to temporal data aggregation. Ecol. Model. 220, 3141–3146 (2009).Article 

Google Scholar 
European Commission. Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions: EU Biodiversity Strategy for 2030 Bringing nature back into our lives. Preprint at https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0380 (2020).European Parliament. European Parliament resolution of 9 June 2021 on the EU Biodiversity Strategy for 2030: Bringing nature back into our lives (P9_TA(2021)0277). Preprint at https://www.europarl.europa.eu/doceo/document/TA-9-2021-0277_EN.html (2021).Felson, A. J. & Ellison, A. M. Designing (for) Urban Food Webs. Front. Ecol. Evol. 9, 582041 (2021).Article 

Google Scholar 
Warren, P. et al. Urban food webs: Predators, prey, and the people who feed them. Bull. Ecol. Soc. Am. 87, 387–393 (2006).Article 

Google Scholar 
De Montis, A., Ganciu, A., Cabras, M., Bardi, A. & Mulas, M. Comparative ecological network analysis: An application to Italy. Land Use Policy 81, 714–724 (2019).Article 

Google Scholar 
Poisot, T. et al. Mangal—making ecological network analysis simple. Ecography 39, 384–390 (2016).Article 

Google Scholar 
Morris, Z. B., Weissburg, M. & Bras, B. Ecological network analysis of urban–industrial ecosystems. J. Ind. Ecol. 25, 193–204 (2021).Article 

Google Scholar 
Chamberlain, S. A. & Szöcs, E. taxize: Taxonomic search and retrieval in R. F1000 Research 2, 191 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkX. in Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008).Scotti, M. & Jordán, F. Relationships between centrality indices and trophic levels in food webs. Community Ecol. 11, 59–67 (2010).Article 

Google Scholar 
Gouveia, C., Móréh, Á. & Jordán, F. Combining centrality indices: Maximizing the predictability of keystone species in food webs. Ecol. Indic. 126, 107617 (2021).Article 

Google Scholar 
Allesina, S. & Pascual, M. Googling Food Webs: Can an Eigenvector Measure Species’ Importance for Coextinctions?. PLoS Comput. Biol. 5, e1000494 (2009).ADS 
MathSciNet 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Patro, S. G. K. & Sahu, K. K. Normalization: A preprocessing stage. https://doi.org/10.48550/ARXIV.1503.06462(2015).Reback, J. et al. pandas-dev/pandas: Pandas 1.2.3. (Zenodo, 2021). 10.5281/ZENODO.4572994.Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).Article 

Google Scholar 
Waskom, M. et al. mwaskom/seaborn: v0.11.1 (December 2020). (Zenodo, 2020). 10.5281/ZENODO.4379347.Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).Article 

Google Scholar 
Gao, P. & Kupfer, J. A. Uncovering food web structure using a novel trophic similarity measure. Ecol. Inform. 30, 110–118 (2015).Article 

Google Scholar 
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: Two levels of group detection in food webs. J. R. Soc. Interface 12, 20141176 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Rudiger, P. et al. holoviz/holoviews: Version 1.14.2. (Zenodo, 2021). 10.5281/ZENODO.4581995.Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar  More

Climate models need to capture a full spectrum of data from forests such as the Brazilian Amazon.Credit: Florence Goisnard/AFP/Getty

Humanity’s understanding of how forests are responding to climate change is disconcertingly fragile. Take carbon fertilization, for example — the phenomenon by which plants absorb more carbon dioxide as its concentration in the atmosphere increases. This is one of the principal mechanisms by which nature has so far saved us from the worst of climate change, but there’s little understanding of its future trajectory. In fact, researchers don’t fully understand how climate change interacts with a multitude of forest processes. Complex, unsolved questions include how climate warming affects forest health; how it affects the performance of forests as a carbon sink; and whether it alters the ecosystem services that forests provide. Forests are our life-support system, and we should be more serious about taking their pulse.Six papers in this week’s Nature provide important insights into those questions. They also underline some of the challenges that must be overcome if we are to fully understand forests’ potential in the fight against climate change. These challenges are not only in the science itself, but also relate to how forest scientists collaborate, how they are funded (especially where data collection is concerned) and how they are trained.Forest science is an amalgam of disciplines. Ecologists and plant scientists measure tree growth, soil nutrients and other parameters in thousands of forest plots around the world. Physical scientists monitor factors such as forest height and above-ground forest biomass using remote-sensing data from drones or satellites. Experimental scientists investigate how forests might behave in a warming world by artificially altering factors such as temperature or carbon dioxide levels in experimental plots. Some of the data they generate are absorbed by yet another community: the modellers, who have created dynamic global vegetation models (DGVMs). These simulate how carbon and water cycles change with climate and, in turn, inform broader earth-system and climate models of the type that feed into policymaking.Different DGVMs make different predictions about how long forests will continue to absorb anthropogenic CO2. One reason for these differences is that models are sensitive to assumptions made about the processes in forests. There are many influences — including temperature, moisture, fire and nutrients — that are generally studied in isolation. Yet they interact with each other.Not all DGVMs account for the dampening effect that a lack of soil phosphorus can have on carbon fertilization, for example. Much of central and eastern Amazonia is poor in phosphorus, and research has shown that introducing phosphorus limitation into DGVMs can cut the carbon-fertilization effect1. This week, Hellen Fernanda Viana Cunha at the National Institute for Amazonian Research in Manaus, Brazil, and her colleagues report2 a powerful experimental demonstration of how the soil’s poor phosphorus content limits carbon absorption in an old-growth Amazonian forest.Models simulating the northward spread of boreal forest as temperatures rise are also missing key drivers3, according to Roman Dial at Alaska Pacific University in Anchorage and his colleagues. They report today that a white-spruce population has migrated surprisingly far north into the Arctic tundra. To explain this, it is necessary to take into account winter winds (which facilitate long-distance dispersal) along with the availability of deep snow and soil nutrients (which promote plant growth).Models are often based on a small number of ‘functional tree types’ — for example, ‘evergreen broadleaf’ or ‘evergreen needle leaf’. These are chosen as a proxy for the behaviour of the planet’s more than 60,000 known tree species. Yet ecologists are discovering that the biology of individual species matters when it comes to a tree’s response to climate change.David Bauman at the Environmental Change Institute at the University of Oxford, UK, and his co-workers reported in May that tree mortality on 24 moist tropical plots in northern Australia has doubled in the past 35 years (and life expectancy has halved), apparently owing to the increasing dryness of the air4. But that was an average of the 81 dominant tree species: mortality rates varied substantially between species, a variation that seemed to be related to the density of their wood.Peter Reich at the Institute for Global Change Biology at the University of Michigan in Ann Arbor and his colleagues now report that modest alterations in temperature and rainfall led to varying rates of growth and survival5 for different species in southern boreal-forest trees. The species that prospered were rare.Failure to examine multiple factors simultaneously means that scientists are making findings that challenge the assumptions in models. Spring is coming earlier for temperate forests and most models assume that, by prolonging the growing season, this increases woody-stem biomass. However, observational work carried out in temperate deciduous forests by Kristina Anderson-Teixeira at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, and her colleagues found no sign of this happening6.Modellers are all too aware of the need to incorporate more complexity into their models, and of the potential that increasing amounts of computing power have to assist them in this endeavour. But they need more data.Continuity problemTo obtain comprehensive, valuable data for the models, continuous, long-term observations need to be made, and that depends on the availability of long-term funding. Achieving such continuity is a problem for both remote-sensing and ground-based operations. The former can cost hundreds of millions of dollars, but the value of its long-term data sets is immense, as demonstrated by a team led by Giovanni Forzieri at the University of Florence in Italy. The authors used 20 years of satellite data to show that nearly one-quarter of the world’s intact forests have already reached their critical threshold for abrupt decline7. But even field-based data collection, which costs a pittance by comparison, struggles to achieve financial security.Important ground-based operations include the Forest Global Earth Observatory (ForestGEO), part of the Smithsonian Tropical Research Institute, which is headquartered in Washington DC. This monitors 7.5 million individual trees in plots around the world. The amount of work that goes into this monitoring is formidable. For example, at present, ForestGEO is conducting the eighth five-yearly census of a plot in Peninsular Malaysia. This involves determining the species for each of the 350,000 trees (there are some 800 species growing there) and measuring the circumference of each trunk. It will take 16 skilled people a year to measure all the trees. Delays in the provision of funding to ForestGEO have held up similar censuses at plots in countries including Papua New Guinea, Vietnam, Brunei and Ecuador.

A ForestGEO researcher making tree measurements at a forest plot in Barro Colorado Island, Panama.Credit: Jorge Aleman, STRI

The future of the plots in North Queensland, which supplied Bauman with a rare 49 years’ worth of continuous data, is uncertain. They have been monitored since the mid-1970s by the Australian public research-funding agency CSIRO — initially every two years, then, more recently, every five years. In 2019, monitoring of the plots was switched to every 50 years because of funding shortages at CSIRO, leaving scientists searching for new sources of funding.Without continuity of funding, organizations such as ForestGEO can’t equip researchers with the requisite skills or collect data over periods longer than an individual’s time in a specific post or a funder’s cycle. “We have trained people and then lost them due to job insecurity,” says Stuart Davies, who leads ForestGEO.Different groups of forest researchers are trying to address these problems. ForestGEO is coordinating the Alliance for Tropical Forest Science in an effort to make it easier to share data, and to bolster the morale and careers of the skilled technicians and scientists — many of whom live in low- and middle-income countries — who do the bulk of the data collection.But we also need more-imaginative funding mechanisms that lift long-term observational plots out of three- to five-year funding cycles. Space agencies that fund remote-sensing satellites could collaborate with other funding agencies, for example, so that earth-observation missions include a fully funded component for ground-based data collection — which is, after all, crucial for calibrating their results. Journals, too, could do more to value and incentivise the production of long-term data sets.And there is a need for more interdisciplinarity. The US Department of Energy is funding a project called NGEE–Tropics (Next-Generation Ecosystem Experiments–Tropics) in which modellers will work with empirical researchers, both observational and experimental, who study tropical forests to create a full, process-rich model of such forests. This is encouraging, and the idea could be pushed further. What is needed is an initiative that pulls the disciplines together towards a goal of building a better understanding of forest processes. Among other things, such an initiative would encourage researchers in different disciplines to take each other’s data needs into account when planning their projects.For this to work, we need to remember that the edifice of forest science relies on the long-term data that scientists wring from forests over decades. Our chances of overcoming climate change are small, but they will diminish further if we forget the basics of monitoring our home planet. More

Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 

Google Scholar 
Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
Grossart, H. P. Ecological consequences of bacterioplankton lifestyles: Changes in concepts are needed. Environ. Microbiol. Rep. 2, 706–714 (2010).PubMed 
Article 

Google Scholar 
Simon, M., Grossart, H. P., Schweitzer, B. & Ploug, H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211 (2002).Article 

Google Scholar 
Smith, D. C., Simon, M., Alldredge, A. L. & Azam, F. Intense hydrolytic enzyme activity on marine aggregates and implication for rapid particle dissolution. Nature 359, 139–141 (1992).ADS 
CAS 
Article 

Google Scholar 
Grossart, H. P., Tang, K. W., Kiørboe, T. & Ploug, H. Comparison of cell-specific activity between free-living and attached bacteria using isolates and natural assemblages. FEMS Microbiol. Lett. 206, 194–200 (2007).Article 
CAS 

Google Scholar 
Rieck, A., Herlemann, D. P. R., Jürgens, K. & Grossart, H. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6, 1297 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Karner, M. & Herndl, G. J. Extracellular enzymatic activity and secondary production in free-living and marine-snow-associated bacteria. Mar. Biol. 113, 341–347 (1992).CAS 
Article 

Google Scholar 
Lyons, M. M. & Dobbs, F. C. Differential utilization of carbon substrates by aggregate-associated and water-associated heterotrophic bacterial communities. Hydrobiologia 686, 181–193 (2012).CAS 
Article 

Google Scholar 
Simon, H. M., Smith, M. W. & Herfort, L. Metagenomic insights into particles and their associated microbiota in a coastal margin ecosystem. Front. Microbiol. 5, 466 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, M. W., Allen, L. Z., Allen, A. E., Herfort, L. & Simon, H. M. Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front. Microbiol. 4, 120 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mestre, M. et al. Spatial variability of marine bacterial and archaeal communities along the particulate matter continuum. Mol. Ecol. 26, 6827–6840 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bižic-Ionescu, M. et al. Comparison of bacterial communities on limnic versus coastal marine particles reveals profound differences in colonization. Environ. Microbiol. 17, 3500–3514 (2015).PubMed 
Article 
CAS 

Google Scholar 
Hollibaugh, J. T., Wong, P. S. & Murrell, M. C. Similarity of particle-associated and free-living bacterial communities in northern San Francisco Bay, California. Aquat. Microb. Ecol. 21, 103–114 (2000).Article 

Google Scholar 
Ortega-Retuerta, E., Joux, F., Jeffrey, W. H. & Ghiglione, J. F. Spatial variability of particle-attached and free-living bacterial diversity in surface waters from the Mackenzie River to the Beaufort Sea (Canadian Arctic). Biogeosciences 10, 2747–2759 (2013).ADS 
Article 

Google Scholar 
Noble, P. A., Bidle, K. D. & Fletcher, M. Natural microbial community compositions compared by a back-propagating neural network and cluster analysis of 5S rRNA. Appl. Environ. Microbiol. 63, 1762–1770 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, J. & Ning, D. Stochastic community assembly: Does it matter in microbial ecology?. Microbiol. Mol. Biol. Rev. 81, e00002-17 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Jain, A., Balmonte, J. P., Singh, R., Bhaskar, P. V. & Krishnan, K. P. Spatially resolved assembly, connectivity and structure of particle-associated and free-living bacterial communities in a high Arctic fjord. FEMS Microbiol. Ecol. 97, 1–12 (2021).Article 
CAS 

Google Scholar 
Yao, Z. et al. Bacterial community assembly in a typical estuarine marsh. Appl. Environ. Microbiol. 85, e02602-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, J. et al. Assembly processes and source tracking of planktonic and benthic bacterial communities in the Yellow River estuary. Environ. Microbiol. 23, 2578–2591 (2021).PubMed 
Article 

Google Scholar 
Balmonte, J. P. et al. Sharp contrasts between freshwater and marine microbial enzymatic capabilities, community composition, and DOM pools in a NE Greenland fjord. Limnol. Oceanogr. 65, 77–95 (2019).ADS 
Article 
CAS 

Google Scholar 
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563 (2012).CAS 
PubMed 
Article 

Google Scholar 
Yawata, Y., Carrara, F., Menolascina, F. & Stocker, R. Constrained optimal foraging by marine bacterioplankton on particulate organic matter. Proc. Natl. Acad. Sci. USA 117, 25571–25579 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hu, Y. et al. The relationships between the free-living and particle-attached bacterial communities in response to elevated eutrophication. Front. Microbiol. 11, 423 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the grobal plankton interactome. Science (80-) 348, 1262073-1–10 (2015).Article 
CAS 

Google Scholar 
Milici, M. et al. Co-occurrence analysis of microbial taxa in the Atlantic ocean reveals high connectivity in the free-living bacterioplankton. Front. Microbiol. 7, 649 (2016).PubMed 
PubMed Central 

Google Scholar 
Herren, C. M. & McMahon, K. D. Cohesion: A method for quantifying the connectivity of microbial communities. ISME J. 11, 2426–2438 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).Article 

Google Scholar 
Labry, C. et al. High alkaline phosphatase activity in phosphate replete waters: The case of two macrotidal estuaries. Limnol. Oceanogr. 61, 1513–1529 (2016).ADS 
Article 

Google Scholar 
Crump, B. C. et al. Quantity and quality of particulate organic matter controls bacterial production in the Columbia River estuary. Limnol. Oceanogr. 62, 2713–2731 (2017).ADS 
CAS 
Article 

Google Scholar 
Canuel, E. A. & Hardison, A. K. Sources, ages, and alteration of organic matter in Estuaries. Ann. Rev. Mar. Sci. 8, 409–434 (2016).PubMed 
Article 

Google Scholar 
He, W., Chen, M., Schlautman, M. A. & Hur, J. Dynamic exchanges between DOM and POM pools in coastal and inland aquatic ecosystems: A review. Sci. Total Environ. 551–552, 415–428 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Bianchi, T. S. The role of terrestrially derived organic carbon in the coastal ocean: A changing paradigm and the priming effect. Proc. Natl. Acad. Sci. 108, 19473–19481 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Auffret, G. A. Dynamique sédimentaire de la marge continentale celtique-Evolution Cénozoïque-Spécificité du Pleistocène supérieur et de l’Holocène (Université de Bordeaux I, 1983).
Google Scholar 
Delmas, R. & Tréguer, P. Évolution saisonnière des nutriments dans un écosystème eutrophe d’Europe occidentale (la rade de Brest). Interactions marines et terrestres. Oceanol. Acta 6, 345–356 (1983).CAS 

Google Scholar 
Bassoullet, P. Etude de la dynamique des sédiments en suspension dans l’estuaire de l’Aulne (rade de Brest) (Université de Bretagne Occidentale, 1979).
Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bolyen, E. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Olesen, S. W., Duvallet, C. & Alm, E. J. dbOTU3: A new implementation of distribution-based OTU calling. PLoS ONE 12, 1–13 (2017).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2013).Whickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar 
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (2011).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package (2022).Liu, C., Cui, Y., Li, X. & Yao, M. Microeco: An R package for data mining in microbial community ecology. FEMS Microbiol. Ecol. 97, fiaa255 (2021).CAS 
PubMed 
Article 

Google Scholar 
Kandlikar, G. ranacapa: Utility Functions and ‘shiny’ App for Simple Environmental DNA Visualizations and Analyses (2021).Cao, Y. microbiomeMarker: microbiome biomarker analysis toolkit (2021).Tsirogiannis, C. & Brody, S. PhyloMeasures: Fast and Exact Algorithms for Computing Phylogenetic Biodiversity Measures (2017).McKnight, D. T. et al. Methods for normalizing microbiome data: An ecological perspective. Methods Ecol. Evol. 10, 389–400 (2019).Article 

Google Scholar 
Paradis, E. & Schliep, K. Ape 50: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics https://doi.org/10.1093/bioinformatics/bty633 (2019).Article 
PubMed 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Third English Edition) (Elsevier, 2012).MATH 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Microbiol. 6, 370 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models (2017).Wu, W., Xu, Z., Dai, M., Gan, J. & Liu, H. Homogeneous selection shapes free-living and particle-associated bacterial communities in subtropical coastal waters. Divers. Distrib. 00, 1–14 (2020).
Google Scholar 
Wang, Y. et al. Patterns and processes of free-living and particle-associated bacterioplankton and archaeaplankton communities in a subtropical river-bay system in South China. Limnol. Oceanogr. 65, 161–179 (2020).
Google Scholar 
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934 (2021).CAS 
PubMed 
Article 

Google Scholar 
Graham, E. B. & Stegen, J. C. Dispersal-based microbial community assembly decreases biogeochemical function. Processes 5, 65 (2017).Article 

Google Scholar 
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 
Article 

Google Scholar 
Herlemann, D. P. R. 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 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science (80-) 348, 1261359 (2015).Article 
CAS 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: An unexpected contribution of verrucomicrobia. PLoS ONE 7, e35314 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reintjes, G., Arnosti, C., Fuchs, B. M. & Amann, R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 11, 1640–1650 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Liu, J., Meng, Z., Liu, X. & Zhang, X. H. Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 
Article 

Google Scholar 
Hernandez, D. J., David, A. S., Menges, E. S., Searcy, C. A. & Afkhami, M. E. Environmental stress destabilizes microbial networks. ISME J. 15, 1722–1734 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).PubMed 
Article 

Google Scholar 
Liénart, C. et al. Dynamics of particulate organic matter composition in coastal systems: A spatio-temporal study at multi-systems scale. Prog. Oceanogr. 156, 221–239 (2017).Article 

Google Scholar 
Fraisse, S., Bormans, M. & Lagadeuc, Y. Morphofunctional traits reflect differences in phytoplankton community between rivers of contrasting flow regime. Aquat. Ecol. 47, 315–327 (2013).Article 

Google Scholar 
Treguer, P. & Queguiner, B. Seasonal variations in conservative and nonconservative mixing of nitrogen compounds in a West European macrotidal estuary. Oceanol. Acta 12, 371–380 (1989).CAS 

Google Scholar 
Grossart, H. P. & Tang, K. W. Communicative & integrative biology. Commun. Integr. Biol. 3, 491–494 (2010).PubMed 
PubMed Central 
Article 

Google Scholar  More