Philippa Kaur

Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
MacDicken, K. G. Global forest resources assessment 2015: what, why and how? For. Ecol. Manag. 352, 3–8 (2015).
Google Scholar 
Li, M.-M. et al. An overview of the “Three-North” Shelterbelt project in China. Forestry Stud. China 14, 70–79 (2012).ADS 

Google Scholar 
Zhang, P. et al. China’s forest policy for the 21st century. Science 288, 2135–2136 (2000).CAS 
PubMed 

Google Scholar 
Chen, Y. et al. Balancing green and grain trade. Nat. Geosci. 8, 739–741 (2015).ADS 

Google Scholar 
Xu, J., Yin, R., Li, Z. & Liu, C. China’s ecological rehabilitation: unprecedented efforts, dramatic impacts, and requisite policies. Ecol. Econ. 57, 595–607 (2006).
Google Scholar 
Piao, S., Fang, J., Liu, H. & Zhu, B. NDVI-indicated decline in desertification in China in the past two decades. Geophys. Res. Lett. 32, L06402 (2005).ADS 

Google Scholar 
Wang, X., Chen, F., Hasi, E. & Li, J. Desertification in China: an assessment. Earth Sci. Rev. 88, 188–206 (2008).ADS 

Google Scholar 
Ouyang, Z. et al. Improvements in ecosystem services from investments in natural capital. Science 352, 1455–1459 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Bryan, B. A. et al. China’s response to a national land-system sustainability emergency. Nature 559, 193–204 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6, 1019–1022 (2016).ADS 

Google Scholar 
Cao, S., Zhang, J., Chen, L. & Zhao, T. Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manag. 183, 843–849 (2016).
Google Scholar 
Liu, Y. et al. Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield. Environ. Res. Lett. 11, 094010 (2016).ADS 

Google Scholar 
Yao, Y. et al. The effect of afforestation on soil moisture content in Northeastern China. PLoS ONE 11, e0160776 (2016).PubMed 
PubMed Central 

Google Scholar 
An, W. et al. Exploring the effects of the “Grain for Green” program on the differences in soil water in the semi-arid Loess Plateau of China. Ecol. Eng. 107, 144–151 (2017).
Google Scholar 
Li, Y. et al. Divergent hydrological response to large-scale afforestation and vegetation greening in China. Sci. Adv. 4, eaar4182 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).Baumgartner, R. J. Sustainable development goals and the forest sector—a complex relationship. Forests 10, 152 (2019).
Google Scholar 
15-year Comprehensive Plan for Ecological System Protection and Recovery Work (National Development and Reform Commission, 2020).Prigent, C., Jimenez, C. & Bousquet, P. Satellite-derived global surface water extent and dynamics over the last 25 years (GIEMS-2). J. Geophys. Res. Atmos. 125, e2019JD030711 (2020).ADS 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy. 19, GB1015 (2005).ADS 

Google Scholar 
Tootchi, A. Development of a global wetland map and application to describe hillslope hydrology in the ORCHIDEE land surface model. Sorbonne Université, https://www.metis.upmc.fr/~ducharne/documents/These_Tootchi_revised_11Sep2019.pdf (2019).Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. B. 24, 43–69 (1979).
Google Scholar 
Stocker, B. D., Spahni, R. & Joos, F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands. Geosci. Model Dev. 7, 3089–3110 (2014).ADS 

Google Scholar 
Xi, Y., Peng, S., Ciais, P. & Chen, Y. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Chang. 11, 45–51 (2021).ADS 

Google Scholar 
Kim, H. Global soil wetness project phase 3 atmospheric boundary conditions (Experiment 1). Data Integration and Analysis System (DIAS). (2017).Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12, 2097–2120 (2020).ADS 

Google Scholar 
Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810–813 (2016).ADS 

Google Scholar 
Zhu, Q. et al. Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades. Sci. Rep. 6, 38020 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mao, D. et al. Remote observations in China’s Ramsar Sites: wetland dynamics, anthropogenic threats, and implications for sustainable development goals. J. Remote Sens. 2021, 9849343 (2021).ADS 

Google Scholar 
Budyko, M. I. Climate and Life (Academic Press, 1974).Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37, 701–708 (2001).ADS 

Google Scholar 
Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Zhang, Z., Zimmermann, N. E., Kaplan, J. O. & Poulter, B. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties. Biogeosciences 13, 1387–1408 (2016).ADS 

Google Scholar 
Ringeval, B. et al. Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data. Geosci. Model Dev. 5, 941 (2012).ADS 

Google Scholar 
Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).ADS 

Google Scholar 
List of Protected Wetlands in China. http://www.zrbhq.cn/web/confirm.html (National Forestry and Grassland Administration, 2011).Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).ADS 

Google Scholar 
Lu, F. et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl Acad. Sci. USA 115, 4039–4044 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Levia, D. F. et al. Homogenization of the terrestrial water cycle. Nat. Geosci. 13, 656–658 (2020).ADS 
CAS 

Google Scholar 
Zhang, J., Fu, B., Stafford-Smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet sustainable development goal 15. Nat. Ecol. Evol. 5, 10–13 (2020).
Google Scholar 
Zeng, Z. et al. Impact of earth greening on the terrestrial water cycle. J. Clim. 31, 2633–2650 (2018).ADS 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Meier, R. et al. Empirical estimate of forestation-induced precipitation changes in Europe. Nat. Geosci. 14, 473–478 (2021).ADS 
CAS 

Google Scholar 
Bosch, J. M. & Hewlett, J. D. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J. Hydrol. 55, 3–23 (1982).ADS 

Google Scholar 
Teuling, A. J. & Hoek van Dijke, A. J. Forest age and water yield. Nature 578, E16–E18 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Doelman, J. C. et al. Afforestation for climate change mitigation: Potentials, risks and trade-offs. Glob. Change Biol. 26, 1576–1591 (2020).ADS 

Google Scholar 
Peng, S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Chang. 9, 84–87 (2019).ADS 

Google Scholar 
Brown, I. Challenges in delivering climate change policy through land use targets for afforestation and peatland restoration. Environ. Sci. Policy 107, 36–45 (2020).
Google Scholar 
The 2nd – 9th National Forest Resource Inventory Report (State Forestry Administration of the People’s Republic of China, 1973–2018).Fang, J. et al. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob. Change Biol. 20, 2019–2030 (2014).ADS 

Google Scholar 
Hou, X. Vegetation atlas of China. Chinese Academy of Science, the editorial board of vegetation map of China (2001).Xi, Y. et al. Contributions of climate change, CO2, land-use change, and human activities to changes in river flow across 10 Chinese Basins. J. Hydrometeorol. 19, 1899–1914 (2018).ADS 

Google Scholar 
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).ADS 

Google Scholar 
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).ADS 

Google Scholar 
Herold, M., Van Groenestijn, A., Kooistra, L., Kalogirou, V. & Arino, O. Land cover CCI, product user guide version 2.0. https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (2015).Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).ADS 
CAS 

Google Scholar 
Zhou, G. et al. Global pattern for the effect of climate and land cover on water yield. Nat. Commun. 6, 5918 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Yang, H. et al. Changing retention properties of catchments and their influence on runoff under climate change. Environ. Res. Lett. 13, 094019 (2018).ADS 

Google Scholar 
Berghuijs, W. R., Larsen, J. R., van Emmerik, T. H. M. & Woods, R. A. A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors. Water Resour. Res. 53, 8475–8486 (2017).ADS 

Google Scholar 
Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl Acad. Sci. USA 104, 15242 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guimberteau, M. et al. Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin. Geosci. Model Dev. 7, 1115–1136 (2014).ADS 

Google Scholar 
Traore, A. K. et al. Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements. J. Geophys. Res. Biogeosci. 119, 1554–1575 (2014).
Google Scholar 
de Rosnay, P. & Polcher, J. Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes. J. Geophys. Res. Atmos. 107, ACL 3-1–ACL 3-19 (2002).
Google Scholar 
Campoy, A. et al. Influence of soil bottom hydrological conditions on land surface fluxes and climate in a general circulation model. J. Geophys. Res. Atmos. 118, 10725–10739 (2013).ADS 

Google Scholar 
Guimberteau, M. et al. Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets. Hydrol. Earth Syst. Sci. 16, 11171–11232 (2012).
Google Scholar 
Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Sy. 12, e2019MS002010 (2020).ADS 

Google Scholar 
Fan, Y. et al. Hillslope hydrology in global change research and earth system modeling. Water Resour. Res. 55, 1737–1772 (2019).ADS 

Google Scholar 
Rayner, P. J. et al. Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Glob. Biogeochem. Cy. 19, GB2026 (2005).ADS 

Google Scholar 
Ducharne, A. Reducing scale dependence in TOPMODEL using a dimensionless topographic index. Hydrol. Earth Syst. Sci. 13, 2399–2412 (2009).ADS 

Google Scholar 
Niu, G., Yang, Z., Dickinson, R. E. & Gulden, L. E. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res. 110, D21106 (2005).ADS 

Google Scholar 
Xi, Y. et al. Monthly inundated fraction over China for 2000-2015 from GIEMS-2 (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.5750962 (2021).Xi, Y. et al. Code of wetland simulation for trade-off between tree planting and wetland conservation in China (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.4435082 (2021). More

Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 

Google Scholar 
Falkowski PG, Barber RT, Smetacek V. Biogeochemical controls and feedbacks on ocean primary production. Science. 1998;281:200–6.CAS 
PubMed 
Article 

Google Scholar 
Schnepf E, Kühn S. Food uptake and fine structure of Cryothecomonas longipes sp. nov., a marine nanoflagellate incertae sedis feeding phagotrophically on large diatoms. Helgol Mar Res. 2000;54:18–32.Article 

Google Scholar 
Garvetto A, Nézan E, Badis Y, Bilien G, Arce P, Bresnan E, et al. Novel widespread marine oomycetes parasitising diatoms, including the toxic genus pseudo-nitzschia: genetic, morphological, and ecological characterisation. Front Microbiol. 2018;9:2918.PubMed 
PubMed Central 
Article 

Google Scholar 
Gutiérrez MH, Jara AM, Pantoja S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ Microbiol. 2016;18:1646–53.PubMed 
Article 

Google Scholar 
Scholz B, Guillou L, Marano AV, Neuhauser S, Sullivan BK, Karsten U, et al. Zoosporic parasites infecting marine diatoms – A black box that needs to be opened. Fungal Ecol. 2016;19:59–76.PubMed 
PubMed Central 
Article 

Google Scholar 
Hedges J, Baldock J, Gélinas Y, Lee C, Peterson M, Wakeham S. The biochemical and elemental compositions of marine plankton: A NMR perspective. Mar Chem. 2002;78:47–63.CAS 
Article 

Google Scholar 
Hedges JI, Baldock JA, Gelinas Y, Lee C, Peterson M, Wakeham SG. Evidence for non-selective preservation of organic matter in sinking marine particles. Nature. 2001;409:801–4.CAS 
PubMed 
Article 

Google Scholar 
Laine RA. A calculation of all possible oligosaccharide isomers both branched and linear yields 1.05 x 10 (12) structures for a reducing hexasaccharide: the Isomer Barrier to development of single-method saccharide sequencing or synthesis systems. Glycobiology. 1994;4:759–67.CAS 
PubMed 
Article 

Google Scholar 
Chin W-C, Orellana MV, Verdugo P. Spontaneous assembly of marine dissolved organic matter into polymer gels. Nature. 1998;391:568–72.CAS 
Article 

Google Scholar 
Passow U. Transparent exopolymer particles (TEP) in aquatic environments. Prog Oceanogr. 2002;55:287–333.Article 

Google Scholar 
Fangel JU, Pedersen HL, Vidal-Melgosa S, Ahl LI, Salmean AA, Egelund J, et al. Carbohydrate microarrays in plant science. Methods Mol Biol. 2012;918:351–62.CAS 
PubMed 
Article 

Google Scholar 
Vidal-Melgosa S, Pedersen HL, Schuckel J, Arnal G, Dumon C, Amby DB, et al. A new versatile microarray-based method for high throughput screening of carbohydrate-active enzymes. J Biol Chem. 2015;290:9020–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Becker S, Scheffel A, Polz MF, Hehemann JH. Accurate quantification of laminarin in marine organic matter with enzymes from marine microbes. Appl Environ Microbiol. 2017;83:e03389-16.Krüger K, Chafee M, Francis TB, del Rio TG, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.PubMed 
PubMed Central 
Article 

Google Scholar 
Kabisch A, Otto A, König S, Becher D, Albrecht D, Schüler M, et al. Functional characterization of polysaccharide utilization loci in the marine Bacteroidetes ‘Gramella forsetii’ KT0803. ISME J. 2014;8:1492–502.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kappelmann L, Krüger K, Hehemann JH, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.CAS 
PubMed 
Article 

Google Scholar 
Unfried F, Becker S, Robb CS, Hehemann J-H, Markert S, Heiden SE, et al. Adaptive mechanisms that provide competitive advantages to marine bacteroidetes during microalgal blooms. ISME J. 2018;12:2894–906.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xing P, Hahnke RL, Unfried F, Markert S, Huang S, Barbeyron T, et al. Niches of two polysaccharide-degrading Polaribacter isolates from the North Sea during a spring diatom bloom. ISME J. 2015;9:1410–22.CAS 
PubMed 
Article 

Google Scholar 
Bjursell MK, Martens EC, Gordon JI. Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem. 2006;281:36269–79.CAS 
PubMed 
Article 

Google Scholar 
Martens EC, Chiang HC, Gordon JI. Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe. 2008;4:447–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hehemann JH, Correc G, Barbeyron T, Helbert W, Czjzek M, Michel G. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature. 2010;464:908–12.CAS 
PubMed 
Article 

Google Scholar 
Larsbrink J, Rogers TE, Hemsworth GR, McKee LS, Tauzin AS, Spadiut O. et al. A discrete genetic locus confers xyloglucan metabolism in select human gut Bacteroidetes. Nature. 2014;506:498–502.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Larsbrink J, Thompson AJ, Lundqvist M, Gardner JG, Davies GJ, Brumer H. A complex gene locus enables xyloglucan utilization in the model saprophyte Cellvibrio japonicus. Mol Microbiol. 2014;94:418–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cuskin F, Lowe EC, Temple MJ, Zhu Y, Cameron E, Pudlo NA, et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature. 2015;517:165–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ndeh D, Rogowski A, Cartmell A, Luis AS, Basle A, Gray J, et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature. 2017;544:65–70.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reisky L, Préchoux A, Zühlke MK, Bäumgen M, Robb CS, Gerlach N, et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat Chem Biol. 2019;15:803–12.CAS 
PubMed 
Article 

Google Scholar 
Hahnke RL, Harder J. Phylogenetic diversity of Flavobacteria isolated from the North Sea on solid media. Syst Appl Microbiol. 2013;36:497–504.CAS 
PubMed 
Article 

Google Scholar 
Chen J, Robb CS, Unfried F, Kappelmann L, Markert S, Song T, et al. Alpha- and beta-mannan utilization by marine Bacteroidetes. Environ Microbiol. 2018;20:4127–40.CAS 
PubMed 
Article 

Google Scholar 
Bågenholm V, Reddy SK, Bouraoui H, Morrill J, Kulcinskaja E, Bahr CM, et al. Galactomannan catabolism conferred by a polysaccharide utilization locus of Bacteroides ovatus: enzyme synergy and crystal structure of a β-mannanase. J Biol Chem. 2017;292:229–43.PubMed 
Article 
CAS 

Google Scholar 
Le Costaouëc T, Unamunzaga C, Mantecon L, Helbert W. New structural insights into the cell-wall polysaccharide of the diatom Phaeodactylum tricornutum. Algal Res. 2017;26:172–9.Article 

Google Scholar 
Matulewicz M, Cerezo A. Water-soluble sulfated polysaccharides from the red seaweed Chaetangium fastigiatum. Analysis of the system and the structures of the α-D-(1→ 3)-linked mannans. Carbohydr Polym. 1987;7:121–32.CAS 
Article 

Google Scholar 
Tabarsa M, Karnjanapratum S, Cho M, Kim JK, You S. Molecular characteristics and biological activities of anionic macromolecules from Codium fragile. Int J Biol Macromol. 2013;59:1–12.CAS 
PubMed 
Article 

Google Scholar 
Chen Y, Mao WJ, Yan MX, Liu X, Wang SY, Xia Z, et al. Purification, chemical characterization, and bioactivity of an extracellular polysaccharide produced by the marine sponge endogenous fungus Alternaria sp. SP-32. Mar Biotechnol. 2016;18:301–13.CAS 
Article 

Google Scholar 
Gimenez-Abian MI, Bernabe M, Leal JA, Jimenez-Barbero J, Prieto A. Structure of a galactomannan isolated from the cell wall of the fungus Lineolata rhizophorae. Carbohydr Res. 2007;342:2599–603.CAS 
PubMed 
Article 

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

Google Scholar 
Bennke CM, Krüger K, Kappelmann L, Huang S, Gobet A, Schüler M, et al. Polysaccharide utilisation loci of Bacteroidetes from two contrasting open ocean sites in the North Atlantic. Environ Microbiol. 2016;18:4456–70.CAS 
PubMed 
Article 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
PubMed 
Article 

Google Scholar 
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
CAS 

Google Scholar 
Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40(W1):W445–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42(D1):D490–5.CAS 
PubMed 
Article 

Google Scholar 
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gilchrist CLM, Chooi YH. Clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics. 2021;37:2473–75.CAS 
Article 

Google Scholar 
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stecher G, Tamura K, Kumar S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol Biol Evol. 2020;37:1237–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47(W1):W256–W9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu H, Naismith JH. An efficient one-step site-directed deletion, insertion, single and multiple-site plasmid mutagenesis protocol. BMC Biotechnol. 2008;8:91.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hehemann JH, Smyth L, Yadav A, Vocadlo DJ, Boraston AB. Analysis of keystone enzyme in agar hydrolysis provides insight into the degradation (of a polysaccharide from) red seaweeds. J Biol Chem. 2012;287:13985–95.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999;112:531–52.CAS 
PubMed 

Google Scholar 
Plante OJ, Palmacci ER, Seeberger PH. Automated solid-phase synthesis of oligosaccharides. Science. 2001;291:1523–7.CAS 
PubMed 
Article 

Google Scholar 
Kabsch W. Xds. Acta Crystallogr D Biol Crystallogr. 2010;66:125–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kabsch W. Integration, scaling, space-group assignment and post-refinement. Acta Crystallogr D Biol Crystallogr. 2010;66:133–44.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. Phaser crystallographic software. J Appl Crystallogr. 2007;40:658–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cohen SX, Ben Jelloul M, Long F, Vagin A, Knipscheer P, Lebbink J. et al. ARP/wARP and molecular replacement: the next generation. Acta Crystallogr D Biol Crystallogr. 2008;64:49–60.CAS 
PubMed 
Article 

Google Scholar 
Afonine PV, Grosse-Kunstleve RW, Echols N, Headd JJ, Moriarty NW, Mustyakimov M. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr D Biol Crystallogr. 2012;68:352–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Emsley P, Lohkamp B, Scott WG, Cowtan K. Features and development of Coot. Acta Crystallogr D Biol Crystallogr. 2010;66:486–501.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Battye TG, Kontogiannis L, Johnson O, Powell HR, Leslie AG. iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM. Acta Crystallogr D Biol Crystallogr. 2011;67(Pt 4):271–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Terwilliger TC, Grosse-Kunstleve RW, Afonine PV, Moriarty NW, Zwart PH, Hung LW, et al. Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard. Acta crystallogr D Biol Crystallogr. 2008;64:61–9.CAS 
PubMed 
Article 

Google Scholar 
Murshudov GN, Skubak P, Lebedev AA, Pannu NS, Steiner RA, Nicholls RA. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr D Biol Crystallogr. 2011;67:355–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Murshudov GN, Vagin AA, Dodson EJ. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr. 1997;53:240–55.CAS 
PubMed 
Article 

Google Scholar 
Mystkowska AA, Robb C, Vidal-Melgosa S, Vanni C, Fernandez-Guerra A, Hohne M, et al. Molecular recognition of the beta-glucans laminarin and pustulan by a SusD-like glycan-binding protein of a marine. Bacteroidetes FEBS J. 2018;285:4465–81.CAS 
PubMed 
Article 

Google Scholar 
Jones DR, Xing X, Tingley JP, Klassen L, King ML, Alexander TW, et al. Analysis of active site architecture and reaction product linkage chemistry reveals a conserved cleavage substrate for an endo-alpha-mannanase within diverse yeast mannans. J Mol Biol. 2020;432:1083–97.CAS 
PubMed 
Article 

Google Scholar 
Starr CM, Masada RI, Hague C, Skop E, Klock JC. Fluorophore-assisted carbohydrate electrophoresis in the separation, analysis, and sequencing of carbohydrates. J Chromatogr A. 1996;720:295–321.CAS 
PubMed 
Article 

Google Scholar 
Ivanova EP, Bowman JP, Christen R, Zhukova NV, Lysenko AM, Gorshkova NM, et al. Salegentibacter flavus sp. nov. Int J Syst Evol Microbiol. 2006;56:583–6.CAS 
PubMed 
Article 

Google Scholar 
Liang QY, Xu ZX, Zhang J, Chen GJ, Du ZJ. Salegentibacter sediminis sp. nov., a marine bacterium of the family Flavobacteriaceae isolated from coastal sediment. Int J Syst Evol Microbiol. 2018;68:2375–80.CAS 
PubMed 
Article 

Google Scholar 
Nedashkovskaya OI, Kim SB, Lysenko AM, Mikhailov VV, Bae KS, Kim IS. Salegentibacter mishustinae sp. nov., isolated from the sea urchin Strongylocentrotus intermedius. Int J Syst Evol Microbiol. 2005;55:235–8.CAS 
PubMed 
Article 

Google Scholar 
Nedashkovskaya OI, Kim SB, Vancanneyt M, Shin DS, Lysenko AM, Shevchenko LS, et al. Salegentibacter agarivorans sp. nov., a novel marine bacterium of the family Flavobacteriaceae isolated from the sponge Artemisina sp. Int J Syst Evol Microbiol. 2006;56:883–7.CAS 
PubMed 
Article 

Google Scholar 
Nedashkovskaya OI, Suzuki M, Vancanneyt M, Cleenwerck I, Zhukova NV, Vysotskii MV, et al. Salegentibacter holothuriorum sp. nov., isolated from the edible holothurian Apostichopus japonicus. Int J Syst Evol Microbiol. 2004;54:1107–10.CAS 
PubMed 
Article 

Google Scholar 
Xia HF, Li XL, Liu QQ, Miao TT, Du ZJ, Chen GJ. Salegentibacter echinorum sp. nov., isolated from the sea urchin Hemicentrotus pulcherrimus. Antonie Van Leeuwenhoek. 2013;104:315–20.CAS 
PubMed 
Article 

Google Scholar 
Yoon JH, Jung SY, Kang SJ, Jung YT, Oh TK. Salegentibacter salarius sp. nov., isolated from a marine solar saltern. Int J Syst Evol Microbiol. 2007;57:2738–42.CAS 
PubMed 
Article 

Google Scholar 
Regmi A, Boyd EF. Carbohydrate metabolic systems present on genomic islands are lost and gained in Vibrio parahaemolyticus. BMC Microbiol. 2019;19:112-.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shi H, Zhang Y, Xu B, Tu M, Wang F. Characterization of a novel GH2 family alpha-L-arabinofuranosidase from hyperthermophilic bacterium Thermotoga thermarum. Biotechnol Lett. 2014;36:1321–8.CAS 
PubMed 
Article 

Google Scholar 
Zhu Y, Suits MD, Thompson AJ, Chavan S, Dinev Z, Dumon C, et al. Mechanistic insights into a Ca2+-dependent family of alpha-mannosidases in a human gut symbiont. Nat Chem Biol. 2010;6:125–32.CAS 
PubMed 
Article 

Google Scholar 
Gregg KJ, Zandberg WF, Hehemann JH, Whitworth GE, Deng L, Vocadlo DJ, et al. Analysis of a new family of widely distributed metal-independent alpha-mannosidases provides unique insight into the processing of N-linked glycans. J Biol Chem. 2011;286:15586–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thompson AJ, Speciale G, Iglesias-Fernandez J, Hakki Z, Belz T, Cartmell A, et al. Evidence for a boat conformation at the transition state of GH76 alpha-1,6-mannanases-key enzymes in bacterial and fungal mannoprotein metabolism. Angew Chem. 2015;54:5378–82.CAS 
Article 

Google Scholar 
Thompson AJ, Cuskin F, Spears RJ, Dabin J, Turkenburg JP, Gilbert HJ, et al. Structure of the GH76 α-mannanase homolog, BT2949, from the gut symbiont Bacteroides thetaiotaomicron. Acta Crystallogr D Biol Crystallogr. 2015;71:408–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eklöf JM, Shojania S, Okon M, McIntosh LP, Brumer H. Structure-function analysis of a broad specificity Populus trichocarpa endo-β-glucanase reveals an evolutionary link between bacterial licheninases and plant XTH gene products. J Biol Chem. 2013;288:15786–99.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Venugopal V. Marine polysaccharides: food applications. Boca Raton: CRC Press; 2016.Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
Article 
CAS 

Google Scholar 
Comeau AM, Vincent WF, Bernier L, Lovejoy C. Novel chytrid lineages dominate fungal sequences in diverse marine and freshwater habitats. Sci Rep. 2016;6:30120.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hassett BT, Gradinger R. Chytrids dominate arctic marine fungal communities. Environ Microbiol. 2016;18:2001–9.CAS 
PubMed 
Article 

Google Scholar 
Duan Y, Xie N, Song Z, Ward CS, Yung C-M, Hunt DE, et al. A high-resolution time series reveals distinct seasonal patterns of planktonic fungi at a temperate coastal ocean site (Beaufort, North Carolina, USA). Appl Environ Microbiol. 2018;84:e00967–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Priest T, Fuchs B, Amann R, Reich M. Diversity and biomass dynamics of unicellular marine fungi during a spring phytoplankton bloom. Environ Microbiol. 2021;23:448–63.CAS 
PubMed 
Article 

Google Scholar 
Picard KT. Coastal marine habitats harbor novel early-diverging fungal diversity. Fungal Ecol. 2017;25:1–13.Article 

Google Scholar 
Taylor JD, Cunliffe M. Multi-year assessment of coastal planktonic fungi reveals environmental drivers of diversity and abundance. ISME J. 2016;10:2118–28.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Banos S, Gysi DM, Richter-Heitmann T, Glöckner FO, Boersma M, Wiltshire KH, et al. Seasonal dynamics of pelagic mycoplanktonic communities: interplay of taxon abundance, temporal occurrence, and biotic interactions. Front Microbiol. 2020;11:1305.Tisthammer KH, Cobian GM, Amend AS. Global biogeography of marine fungi is shaped by the environment. Fungal Ecol. 2016;19:39–46.Article 

Google Scholar 
Tian T, Merico A, Su J, Staneva J, Wiltshire K, Wirtz K. Importance of resuspended sediment dynamics for the phytoplankton spring bloom in a coastal marine ecosystem. J Sea Res. 2009;62:214–28.Article 

Google Scholar 
Gutiérrez MH, Pantoja S, Tejos E, Quiñones RA. The role of fungi in processing marine organic matter in the upwelling ecosystem off Chile. Mar Biol. 2011;158:205–19.Article 

Google Scholar 
Cunliffe M, Hollingsworth A, Bain C, Sharma V, Taylor JD. Algal polysaccharide utilisation by saprotrophic planktonic marine fungi. Fungal Ecol. 2017;30:135–8.Article 

Google Scholar 
Chambouvet A, Monier A, Maguire F, Itoïz S, del Campo J, Elies P, et al. Intracellular infection of diverse diatoms by an evolutionary distinct relative of the fungi. Curr Biol. 2019;29:4093–101.e4.CAS 
PubMed 
Article 

Google Scholar 
Buaya AT, Ploch S, Hanic L, Nam B, Nigrelli L, Kraberg A, et al. Phylogeny of Miracula helgolandica gen. et sp. nov. and Olpidiopsis drebesii sp. nov., two basal oomycete parasitoids of marine diatoms, with notes on the taxonomy of Ectrogella-like species. Mycol Prog. 2017;16:1041–50.Article 

Google Scholar 
Meyers SP, Ahearn DG, Gunkel W, Roth FJ. Yeasts from the North Sea. Mar Biol. 1967;1:118–23.Article 

Google Scholar 
Grossart H-P, Van den Wyngaert S, Kagami M, Wurzbacher C, Cunliffe M, Rojas-Jimenez K. Fungi in aquatic ecosystems. Nat Rev Microbiol. 2019;17:339–54.CAS 
PubMed 
Article 

Google Scholar  More

Animal culture and mediumFive different clonal lineages of the water flea Daphnia magna were sampled from two ponds on agricultural land in Belgium (Vleteren: 50°55′06.7″ N, 2°43′27.0″ E and De Haan 51°13′53.8″ N, 3°01′49.2″). They were cultured separately in 210 ml glass jars under optimized laboratory conditions (20 ± 1 °C, 14:10 h light:dark cycle). Seed shrimp and rotifer resting eggs were obtained from a commercial supplier (MicroBioTests Inc., H. incongruens strain MBT/1999/10, product code TB36; B. calyciflorus, product code TK21, Belgium) and represent laboratory cultured, single clonal lineages. More details on animal culture are reported in the online supplementary methods (Appendix 3).Natural pond water was used as medium both in animal cultures and the experiment. It was extracted from a Belgian region (50°59′00.92″ N, 5°19′55.85″ E, Zonhoven) with soft, poorly buffered water (Alkalinity 3–8°d; pH 6.5–8.5) which is likely to be susceptible to acidification under elevated pCO2. More information on medium and mineral composition is reported in the online supplementary information (Appendix 3; Table S3, Appendix 1).Experimental set-upOrganisms were exposed to three pCO2 treatments, an ambient control (C; 1,520 ppm ± 702 SD), an elevated (T1; 25,609 ppm ± 4,541 SD) and an extreme pCO2 level (T2; 83,201 ppm ± 15,533 SD). The control pCO2 level represents the current global mean that is measured in lentic freshwaters considering most ponds and lakes are already supersaturated10,12. The T1 level is currently only observed in more extreme cases11. However, it reflects a pCO2 level that could be encountered more commonly in the field in the future. The T2 treatment represents an extreme test of the tolerance limits of extant species. These treatments are a necessary simplification of reality since pCO2 can experience strong fluctuations in ponds and lakes. An overview of freshwater pCO2 concentrations from literature can be found in Table S1 (Appendix 1).The elevated pCO2 concentrations were manipulated in the water by injecting pure CO2 (99.998% pure, ALPHAGAZ CO2 SFC * B50-N48, Airliquide, Belgium) from gas cylinders into the water (cf.49) at a constant flowrate, using a high-pressure regulator (HBS 200–10.2,5; AirLiquide, Belgium) and a flow controller (Sho-rate model 1350G, Brooks Instruments, USA). In the control treatment, ambient air was supplied at a similar rate as the CO2 to ensure equal perturbation levels across all containers. Water of all experimental containers (including control) were also injected with ambient air to keep the water oxygenated. A relatively constant pCO2 was ensured by continuously monitoring pH and kept between a range of ~ 20,000–30,000 ppm (pH 6.9–6.7) for T1 and ~ 70,000–120,000 ppm (pH 6.4–6.1) for T2 (Figure S2, Appendix 2).Each treatment included 13 replicate 210 mL glass jars per species, resulting in a total of 117 experimental units. Per replicate, one mature water flea (8–11 days old) was inoculated in a jar containing aerated pond water. The five clonal lineages were distributed evenly over the experimental conditions so that each condition had the same number of replicates per clone. Seed shrimp replicates each contained one newly hatched ( More

Deterministic logistic modelThe following population dynamics model was applied to reconstruct the initial dugong population size in 1894 from fishery statistics between 1894 and 1914:$$N_{t + 1} = N_{t} left( {1 , + r{-}r , N_{t} /K} right) – C_{t} ,$$where r is the intrinsic rate of population increase, Nt is the population size in year t, K is the carrying capacity, and Ct is the number of individuals removed from the waters near the Ryukyu Islands in year t. The carrying capacity (K) in 1893 was sufficient to sustain the initial population of dugongs at that time (N1894). The intrinsic rate of population increase (r) was given between 1 and 5% within a range of natural one.Approximate Bayesian calculationWe conducted approximate Bayesian calculation (ABC)32 to estimate the number of individuals in 1979 based on bycatch data between 1979 and 2019, and the constraints of the numbers of individuals were 11 in 1997, three in 2007, and almost extinct in 2019. We denoted fecundity as f, the survival rate until 1 year old as s0, the annual survival rate after 1 year old as s, the age at maturity as am, and the physiological longevity as A. We assumed that the sex ratio at birth was 1:1 on average; the age at maturity am was eight years of age33, and the physiological longevity A was 73 years6. We ignored environmental stochasticity because no mass deaths caused by infectious diseases or changes in survival or mortality rates due to environmental fluctuations have not been recorded during this period. We also ignored density effects because the carrying capacity of the location was sufficiently greater than the initial population size, and our goal was to investigate the possibility of population recovery after a decrease in population using a population dynamics model and estimate the natural growth rate during this period. The detailed extinction risk depends on age structure.According to the life history parameters, except the physiological longevity compiled by (ref.33), the annual survival probability of an a year-old individual is s for a = 1, 2, …, 72; s0 for a = 0, and 0 for a = 73; the reproductive probability of an adult female  > 8 years old is 2f. As the number of years for a population to become extinct or recover depends on age composition, age-specific survival, and reproductive rates, we obtain the population growth rate by the maximum eigenvalue of the following Leslie matrix, L = {Lij} (i = 1,…73, j = 1,…,73) as:$$L_{i1} = s_{0} f/2quad {text{for}}quad i ge a_{m} ,L_{i+ 1,i} = squad {text{for}}quad i = 1, ldots ,72,quad {text{and}}quad L_{ij} = 0,{text{otherwise}}{.}$$We used the population growth rate λ, defined by the maximum eigenvalue of L, as an indicator of the population growth rate.We assumed that the sex of each individual in 1979 was randomly sampled by the 1:1 sex ratio, and its age was randomly sampled by the stable age structure that is given by the eigenvector of the Leslie matrix with the maximum eigenvalue. We assumed that the number of individuals at age 1 year in year t + 1, denoted by N1,t+1, is determined by the binomial distribution:$$Prleft[ {N_{1,t + 1} = x} right] = left( {begin{array}{*{20}c} {N_{f} } \ x \ end{array} } right)left( {s_{0} f} right)^{x} left[ {1 – left( {s_{0} f} right)} right]^{{N_{f} – x}} ,$$where Nf represents the number of adult females in year t. We assumed that no twins were born. We assumed that the probability that an individual with age x survived in the next year is s if x = 1 or s0 if x = 0. We also assumed that Ct individuals who died by bycatch were randomly chosen from any sex and age because the age of individuals caught by bycatch is rarely known. We do not know the sex of some individuals.We assumed the following prior distributions for N1997, f, and s: N1979 (in) U(11, 80), f (in) U(1/14, 1/6) if at least one adult male existed in the population, s0 (in) U(0.1, 0.85); and s (in) U(0.8, 0.97), where U(a, b) is the uniform random variable between a and b. These probabilities were constant for each simulation trial from 1997 to 2019. We selected the set of parameters with the population growth rate (λ) obtained when the maximum eigenvalue of the Leslie matrix was between 0.96 and 1.01.We rejected trials that did not satisfy the following summary statistics: N1997 ≥ 11 (intensive survey in 1997), Nt ≥ 3 during 2004–2017 (monitoring), and N2019 ≤ 1 (“local extinction”). We obtained the prior distributions of N1997, f, s0, s, and N2004, and of the  > 130,000 trials in the prior distribution with natural population growth rates λ of 96.1–98.8%, 99.3% were rejected. For 95% of the 1000 adopted trials, N1979 ranged from 14 to 58. If λ  > 98%, N1997 was ≤ 45 for the adopted trials (Extended Data Fig. 7. Even if all the stranding deaths were due to anthropogenic factors, such as the release of dugongs after bycatch or boat strike, the range of N1997 changed to  98%, with only a slight upward shift, but positive natural growth rate (or λ  > 1) was again very unlikely (0.3%) among the adopted trials.Population viability analysis to assess the impact of bycatch on the extinction riskWe re-evaluated the extinction risk with and without bycatch using the 1000 parameter sets of N1979, f, s0, and s that satisfied the summary statistics in the ABC and stochastic individual-based model, beginning from N1979 for the corresponding parameters. For each parameter set, 100 trials were conducted for each scenario to compare the extinction risks. More

Karr, J.R., & Chu, E.W. Introduction: sustaining living rivers. In Assessing the Ecological Integrity of Running Waters, Developments in Hydrobiology, vol 149 (eds. Jungwirth, M., Muhar, S., & S. Schmutz, S.) 1–14. (Springer: Dordrecht, 2000).Lu, S., Dai, W., Tang, Y. & Guo, M. A review of the impact of hydropower reservoirs on global climate change. Sci. Total Environ. 711, 134996 (2020).ADS 
CAS 
Article 

Google Scholar 
Liu, C., Ahn, C. R., An, X. & Lee, S. H. Life-cycle assessment of concrete dam construction: comparison of environmental impact of rock-filled and conventional concrete. J. Constr. Eng. Manage. 20139(12), A4013009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000752 (2013).Article 

Google Scholar 
Maavara, T. et al. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103–116 (2020).ADS 
Article 

Google Scholar 
Grigg, N. S. Global water infrastructure: state of the art review. Int. J. Water Resour. Dev. 35(2), 181–205. https://doi.org/10.1080/07900627.2017.1401919 (2019).Article 

Google Scholar 
European Environment Agency. European waters: Assessment of status and pressures 2018. https://www.eea.europa.eu/publications/state-of-water (Publications Office of the European Union (2018).Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).ADS 
CAS 
Article 

Google Scholar 
Grill, G. et al. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales. Environ. Res. Lett. 10(1), 015001 (2015).ADS 
Article 

Google Scholar 
Kim, J. & An, K. G. Integrated ecological river health assessments, based on water chemistry, physical habitat quality and biological integrity. Water 7(11), 6378–6403. https://doi.org/10.3390/w7116378 (2015).ADS 
CAS 
Article 

Google Scholar 
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561. https://doi.org/10.1038/nature09440 (2010).ADS 
CAS 
Article 

Google Scholar 
McCartney, M. Living with dams: managing the environmental impacts. Water Policy 11(S1), 121–139 (2009).MathSciNet 
Article 

Google Scholar 
Van Cappellen, P. & Maavara, T. Rivers in the Anthropocene: global scale modifications of riverine nutrient fluxes by damming. Ecohydrol. Hydrobiol. 16(2), 106–111 (2016).Article 

Google Scholar 
Drouineau, H. et al. Freshwater eels: a symbol of the effects of global change. Fish Fish 19(5), 903–930 (2018).Article 

Google Scholar 
Jones, J. et al. A comprehensive assessment of stream fragmentation in Great Britain. Sci. Total Environ. 673, 756–762. https://doi.org/10.1016/j.scitotenv.2019.04.125 (2019).ADS 
CAS 
Article 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
Hermoso, V., Clavero, M., Blanco-Garrido, F. & Prenda, J. Invasive species and habitat degradation in Iberian streams: an analysis of their role in freshwater fish diversity loss. Ecol. Appl. 21(1), 175–188 (2011).Article 

Google Scholar 
Maceda-Veiga, A. Towards the conservation of freshwater fish: Iberian Rivers as an example of threats and management practices. Rev. Fish Biol. Fish. 23(1), 1–22 (2013).Article 

Google Scholar 
Sánchez-Pérez, A. et al. Seasonal use of fish passes in a modified Mediterranean river: first insights of the LIFE+ Segura-Riverlink. FiSHMED 008, 3. https://doi.org/10.29094/FiSHMED.2016.008 (2016).Article 

Google Scholar 
Schiermeir, Q. Dam removal restores rivers. Nature 557, 290–291. https://doi.org/10.1038/d41586-018-05182-1 (2018).ADS 
CAS 
Article 

Google Scholar 
Benjankar, R. et al. Dam operations may improve aquatic habitat and offset negative effects of climate change. J. Environ. Manage. 213, 126–134. https://doi.org/10.1016/j.jenvman.2018.02.066 (2018).Article 

Google Scholar 
Tupiño Salinas, C. E., Pinto Vidal de Oliveira, V., Brito, L., Ferreira, A. V. & de Araújo, J. C. Social impacts of a large-dam construction: the case of Castanhão, Brazil. Water Int. 44(8), 871–885. https://doi.org/10.1080/02508060.2019.1677303 (2019).Article 

Google Scholar 
Opperman, J. J. et al. Valuing Rivers: How the diverse benefits of healthy rivers underpin economies. WWF Global Science (2018).Kellner, E. Social acceptance of a multi-purpose reservoir in a recently deglaciated landscape in the Swiss Alps. Sustainability 11, 3819. https://doi.org/10.3390/su11143819 (2019).Article 

Google Scholar 
Boyé, H., & de Vivo, M. The environmental and social acceptability of dams. Field Actions Sci. Rep. http://journals.openedition.org/factsreports/4055 (2016).Wiejaczka, Ł, Piróg, D. & Fidelus-Orzechowska, J. Cost-benefit analysis of dam projects: the perspectives of resettled and non-resettled communities. Water Resour. Manag. 34(1), 343–357 (2020).Article 

Google Scholar 
Rodeles, A. A., Galicia, D. & Miranda, R. Recommendations for monitoring freshwater fishes in river restoration plans: a wasted opportunity for assessing impact. Aquat. Conserv. 27(4), 880–885. https://doi.org/10.1002/aqc.2753 (2017).Article 

Google Scholar 
Birnie-Gauvin, K., Tummers, J. S., Lucas, M. C. & Aarestrup, K. Adaptive management in the context of barriers in European freshwater ecosystems. J. Environ. Manag. 204, 436–441. https://doi.org/10.1016/j.jenvman.2017.09.023 (2017).Article 

Google Scholar 
Yousefi-Sahzabi, A. et al. Turkish challenges for low-carbon society: current status, government policies and social acceptance. Renew. Sustain. Energy Rev. 68, 596–608. https://doi.org/10.1016/j.rser.2016.09.090 (2017).Article 

Google Scholar 
Jiang, H., Lin, P. & Qiang, M. Public-opinion sentiment analysis for large hydro projects. J. Construct. Eng. Manage. 142(2), 05015013. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001039 (2016).Article 

Google Scholar 
Schulz, C., Martin-Ortega, J. & Glenk, K. Understanding public views on a dam construction boom: the role of values. Water Resour. Manage. 33, 4687–4700. https://doi.org/10.1007/s11269-019-02383-9 (2019).Article 

Google Scholar 
Kirchherr, J., Pohlner, H. & Charles, K. J. Cleaning up the big muddy: A meta-synthesis of the research on the social impact of dams. Environ. Impact Assess. Rev. 60, 115–125. https://doi.org/10.1016/j.eiar.2016.02.007 (2016).Article 

Google Scholar 
Piróg, D., Fidelus-Orzechowska, J., Wiejaczka, L. & Łajczak, A. Hierarchy of factors affecting the social perception of dam reservoirs. Environ. Impact Assess. Rev. 79, 106301. https://doi.org/10.1016/j.eiar.2019.106301 (2019).Article 

Google Scholar 
Arboleya, E., Fernandez, S., Clusa, L., Dopico, E. & Garcia-Vazquez, E. River connectivity is crucial for safeguarding biodiversity but may be socially overlooked. Insights from Spanish University students. Front. Environ. Sci. 9, 643820. https://doi.org/10.3389/fenvs.2021.643820 (2021).Article 

Google Scholar 
Gilg, A., & Barr, S. Behavioural attitudes towards water saving? Evidence from a study of environmental actions. Ecol. Econ. 57(3), 400–414. doi:https://doi.org/10.1016/j.ecolecon.2005.04.010 (2006)Schapper, A., Unrau, C., & Killoh, S. Social mobilization against large hydroelectric dams: a comparison of Ethiopia, Brazil, and Panama. Sustain. Develop. 28, 413–423. doi:https://doi.org/10.1002/sd.1995 (2020)Flaminio, S., Piégay, H., & Le Lay, Y-F. To dam or not to dam in an age of anthropocene: insights from a genealogy of media discourses. Anthropocene. 36, 100312, doi:https://doi.org/10.1016/j.ancene.2021.100312 (2021)Bellmore, J. R. et al. Conceptualizing ecological responses to dam removal: If you remove it, what’s to come?. Bioscience 69(1), 26–39. https://doi.org/10.1093/biosci/biy152 (2019).Article 

Google Scholar 
Heberlein, T. A. Navigating environmental attitudes. Conserv. Biol. 26(4), 583–585. https://doi.org/10.1111/j.1523-1739.2012.01892.x (2012).Article 

Google Scholar 
Lewandowsky, S., Gignac, G. E. & Vaughan, S. The pivotal role of perceived scientific consensus in acceptance of science. Nat. Clim. Change. 3, 399–404. https://doi.org/10.1038/NCLIMATE1720 (2013).ADS 
Article 

Google Scholar 
Schuldt, J. P., Roh, S. & Schwarz, N. Questionnaire design effects in climate change surveys: Implications for the partisan divide. Ann. Am. Acad. Pol. Soc. Sci. 658(1), 67–85. https://doi.org/10.1177/0002716214555066 (2015).Article 

Google Scholar 
Bowden, V., Nyberg, D. & Wright, C. Planning for the past: local temporality and the construction of denial in climate change adaptation. Glob. Environ. Change 57, 101939. https://doi.org/10.1016/j.gloenvcha.2019.101939 (2019).Article 

Google Scholar 
Venus, T. E., Hinzmann, M., Bakken, T. H., Gerdes, H., Nunes Godinho, F., Hansen, B., Pinheiro, A., & Sauer, J. The public’s perception of run-of-the-river hydropower across Europe. Energy Policy. 140, 111422. doi:https://doi.org/10.1016/j.enpol.2020.111422 (2020)Schober, M. F. The future of face-to-face interviewing. Qual. Assur. Educ. 26(2), 290–302. https://doi.org/10.1108/QAE-06-2017-0033 (2018).MathSciNet 
Article 

Google Scholar 
Couper, M. P. The future of modes of data collection. Public Opin. Q. 75, 889–908. https://doi.org/10.1093/poq/nfr046 (2011).Article 

Google Scholar 
Zhang, X., Kuchinke, L., Woud, M. L., Velten, J. & Margraf, J. Survey method matters: Online/offline questionnaires and face-to-face or telephone interviews differ. Comput. Hum. Behav. 71, 172–180. https://doi.org/10.1016/j.chb.2017.02.006 (2017).Article 

Google Scholar 
Garcia de Leaniz, C., Berkhuysen, A., & Belletti, B. Beware small dams, they can do damage, too. Nature 570, 164–164; doi:https://doi.org/10.1038/d41586-019-01826-y (2019).Belletti, B., et al. Small isn’t beautiful: the impact of small barriers on longitudinal connectivity of European rivers. Geophys. Res. Abst. 20: EGU2018-PREVIEW (2018).Hophmayer-Tokich, S. & Krozer, Y. Public participation in rural area water management: experiences from the North Sea countries in Europe. Water Int. 33(2), 243–257. https://doi.org/10.1080/02508060802027604 (2008).Article 

Google Scholar 
San-Martín, E., Larraz, B. & Gallego, M. S. When the river does not naturally flow: a case study of unsustainable management in the Tagus River (Spain). Water Int. 45(3), 189–221. https://doi.org/10.1080/02508060.2020.1753395 (2020).Article 

Google Scholar 
Dunlap, R. E. Environmental concern. The Wiley‐Blackwell Encyclopedia of Globalization. (Wiley, Amsterdam, 2012).European Commission Ethics for researchers. Facilitating Research Excellence in FP7. https://doi.org/10.2777/7491 (Publications Office of the European Union, 2013).Jenner, B. M. & Myers, K. C. Intimacy, rapport, and exceptional disclosure: a comparison of in-person and mediated interview contexts. Int. J. Soc. Res. Methodol. 22(2), 165–177. https://doi.org/10.1080/13645579.2018.1512694 (2019).Article 

Google Scholar 
Given, L. M. 100 questions (and answers) about qualitative research (Sage, 2015).
Google Scholar 
Saris, W. E. & Gallhofer, I. N. Design, evaluation, and analysis of questionnaires for survey research (Wiley, 2014).Book 

Google Scholar 
Avella, J. R. Delphi panels: research design, procedures, advantages, and challenges. IJDS 11(1), 305–321. https://doi.org/10.28945/3561 (2016).Article 

Google Scholar 
Vandenplas, C. & Loosveldt, G. Modeling the weekly data collection efficiency of face-to-face surveys: six rounds of the European social survey. J. Surv. Stat. Methodol. 5(2), 212–232. https://doi.org/10.1093/jssam/smw034 (2017).Article 

Google Scholar 
Barbero-García, M. I., Vila-Abad, E. & Holgado-Tello, F. P. Tests adaptation in cross-cultural comparative studies. Acción Psicol. 5, 7–16. https://doi.org/10.5944/ap.5.2.454 (2008).Article 

Google Scholar 
Flick, U. Triangulation in data collection. The SAGE Handbook of Qualitative Data Collection. (Sage, London, 2018).Heesen, R., Bright, L. K. & Zucker, A. Vindicating methodological triangulation. Synthese 196(8), 3067–3081. https://doi.org/10.1007/s11229-016-1294-7 (2019).MathSciNet 
Article 

Google Scholar 
DeVellis, R. F. Scale development: Theory and applications (Sage, 2012).
Google Scholar 
Hammer, Ø., Harper, D.A.T., & Ryan, P.D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Elect. 4(1), 9. http://palaeo-electronica.org/2001_1/past/issue1_01.htm (2001). More

From the popular algorithms, we chose the Random forest model as the most suitable for our case. The data required for predictions can be divided into plant occurrence records and environmental features. Bioclimatic variables and soil properties were selected as the main environmental features. All of the data were obtained from open sources.Heracleum Sosnowskiy plant descriptionHeracleum sosnowskyi is a monocarpic perennial plant of the Apiaceae family. The height is up to 3–5 m with a straight stem up to 12 cm in diameter. HS compound steam leaves can reach 150 cm, both long and wide38. The blooming period starts in July and continues until the end of September. Plant reproduction is performed by seeds only. The seeds’ depth of germination is reported as mainly in the upper 5 cm down to 15 cm of soil. One plant can produce 10–20,000 seeds39,40. Seeds germinate in the early spring, while some have reported that a period of cold stratification for the dormancy break is obligatory for germination development. Suitable conditions for HS include a temperate climate with warm humid summers and cold winters, while it is probably not drought resistant. Plants of HS tend to neutral soils with a pH range from 6 to 7, rich in nutrients, and being reported as nitrophilous, so the eutrophication of the environment favours HS development. HS plants do not tolerate shade conditions in the first growing period.HS is mostly spread in artificial and semi-natural habitats, including grasslands, pastures, parks, roadsides, agricultural fields, riverbanks or canal sides, and other distributed habitats. Currently, the main pathways of spread include an involuntary entry with soil on vehicles, machinery, footwear or the use of soil as a commodity (as the growing medium rich in organic matter)39.Study areaThe area for modelling extends from approximately 41(^{circ }) to 70(^{circ }) N and from 27(^{circ }) to 60(^{circ }) E, and Kaliningrad region, it equals to approximately 4 mln km2 (Fig. 4).Figure 4Map of the study area: white colour represents the territory used for prediction, red points correspond to the dataset of HS occurrence, collected from the available sources.Full size imageThe European part of Russia is the most inhabited part of the country, and it is the home of approximately 80% of the total population of Russia. It includes the East European Plain, Caucasus mountains and Ural mountains, with the predominance of the East European Plain. Environmental characteristics across the territory of study vary significantly. The climate is changing from semi-arid in the south to subarctic in the north, including humid continental climate conditions. Natural vegetation is represented by almost all types of biomes with the prevalence of different types of forests: broadleaf and mixed forests, coniferous forests, and boreal forests (taiga), while the area of arable lands is reported to be approximately 650,000 km241,42. The territory is subjected to the constant land-use types and cover changes due to the urbanization and switch of the status of arable lands—i.e. reduction of croplands and development of fallows and forests, and, vice versa, returning of some of them into the cultivation process43. The soil cover is represented by the contrast by their physicochemical properties groups, in the northern part of Luvisols, Podzols, Histosols, while of the southern part—by Chernozems, Kastanozems, Solonetz44.Collection of the input dataPlant occurrence dataPlant occurrence coordinates were collected from several publicly available sources related to citizen science projects: the Global Biodiversity Information Facility database45, iNaturalist database46, and the database of the “Antiborschevik” community47. Records were documented by human observation and collected from 2000 to 2021. The overall number of initial occurrence points from combined sources is 7637.Environmental predictorsClimate data Modelling was performed for current and future climate conditions at its two scenarios, selected year ranges were 2000–2018 and 2040–2060 respectively.Climatic variables were collected from the Worldclim database48, containing the average seasonal information relevant to the physiological characteristics of species and available at different resolutions. We chose 10 arc-minutes spatial resolution taking into account the size of the studied area. Table 1 provides a short description of the used bioclimatic features, and we refer the reader to the Worldclim project for detailed information on the variables’ calculation.For the future climate scenarios, we used two Shared Socioeconomic Pathways (SSPs)49—1-2.6 and 5-8.5, corresponding to the lowest (keeping global mean temperature increase below 2 (^{circ })C) and the highest (at the increase of population without technological change) predicted future greenhouse gases emission scenarios. For these data, we took the same resolution (10 arc-minutes) as discussed above.We used the Equilibrium Climate Sensitivity to select the climate model to model future HS distribution. Equilibrium climate sensitivity (ECS) is defined as the global mean surface air temperature change due to a rapid doubling of carbon dioxide concentrations as soon as the associated ocean-atmosphere-sea ice system reaches equilibrium. As the ECS value increases, the model’s sensitivity to the CO(_2) concentration in the atmosphere increases. We have chosen CanESM5 model (ECS—5.6), CNRM-CM6-1 model (ECS—4.3) and BCC-CSM2-MR model (ECS—3.0)50.Table 1 Description of used bioclimatic variables.Full size tableFor the future climate scenarios we selected three climate models:

BCC-CSM2-MR Beijing Climate Center climate system model developed in Beijing Climate Center, China Meteorological Administration51. Model has horizontal resolution 1.125(^{circ }) by 1.125(^{circ }).

CanESM5 Canadian Earth System Model version 5 developed in Canadian Center for Climate Modelling and Analysis, Canada52. Horizontal resolution 2.81(^{circ }) by 2.81(^{circ }).

CNRM-CM6-1 Climate model developed in National Center of Meteorological Research, France53. Horizontal resolution 1.4(^{circ }) by 1.4(^{circ }).

Authors of the WorldClim project prepared historical and future climate data to a uniform spatial (10 arc-minutes) and temporal resolution.Soil data Soil data were downloaded from the SoilGrids database54—a system for global digital soil mapping. SoilGrids provides continuous data at several depths of the spatial distribution of soil properties across the globe with selected resolution. It uses a machine learning approach to reconstruct continuous data from 230,000 soil profile observations from the WoSIS (The World Soil Information Service) database and a series of environmental covariates.From the whole set of the data provided by SoilGrids several properties were chosen for the forecasting: relative percentage of silt (Silt, %), sand (Sand, %), a volumetric fraction of coarse fragments (CF, %), cation exchange capacity (CEC, ({text{cmol}}_{c}/{text{kg}})) and soil organic carbon (SOC, g/kg) at the depth 5–15 cm, where the HS seeds are assumed to be located. These variables are expected to be more stable over time than bioclimatic predictors; thus, chosen soil properties could be implemented for the future time the same as in the present.Data pre-processingAll the data were transformed to the ASCII format by R script and using software DIVA-GIS following the tutorial for the preparation of WorldClim files for use in SDM (http://www.lep-net.org/wp-content/uploads/2016/08/WorldClim_to_MaxEnt_Tutorial.pdf) with unified selected resolution 340 sq.km.Optimization of the occurrence points amountThe general problem in using the available data collected from the databases of the citizen science projects is that the points of observation are distributed non-uniformly. For instance, the frequency of the records depends on the density of the population directly. The spatial filtering of the data (reducing the number of points) can be performed to reduce the sampling bias55. We prepared three datasets with a distance between points of 4, 7 and 10 km with 2402, 1846 and 1504 occurrence points correspondingly filtering the initial dataset. For the thinning step thin() function was used within the R package spThin with 100 iterations for each of chosen thinning distances. To understand how much data we could lose, we used the analysis of feature distribution and evaluated the general fairness of the model performance.Pseudo-absence generationDue to the availability only of the presence points, it is important to generate the absence points for further implementation of the selected algorithm. Although the generation of pseudo-absence points in SDM research is a widespread solution, a closer look at the literature reveals several gaps and shortcomings. Since the raw dataset of the HS distribution demonstrates strong sampling bias, the generation of pseudo-absence points using the usual ‘random’ strategy can aggravate the sampling bias problem. Thus, the combination of the ‘disk’ and ‘random’ strategies was applied for the generation of the pseudo-absence points using the biomod R package17.

The ‘disk’ strategy is established on the geographic distance works as separation from truth presence and possible absence points. The optimal geographic distance for HS was chosen as 25 km. This distance was chosen empirically by trial-and-error. We started with 18 km (because the size of the cell is   9–18 km depending on location) and finished with 50 km. Using distances such as 30–50 km lead to a positive spatial autocorrelation. Thus, we decided to set 25 km which finally provided both optimal model performance and reduced spatial autocorrelation.

The second part of the generation was based on the ‘random’ strategy with filtration: according to the different range of climate conditions on the territory of Russia, there are several places where HS is not detected, thus not growing. The selection of unsuitable places for HS related to the north of Russia, where it is might be too cold for plant species. From all amount of randomly generated generated points we selected points with condition latitude ( > 64^{circ }), according to tundra board line.

Features selection procedureTo avoid over-fitting and to choose the most conscientious set of parameters for final modelling, two approaches were combined. We searched features that are not correlated with others by a selected threshold is equal to 0.8 in absolute values56 and estimated variable importance using the Mean Decrease Gini (MDG) and the Mean Decrease Accuracy (MDA) as the result of modelling on enumerated parameters’ combinations. MDG score is related to the homogeneity of the nodes and leaves coefficient. With the rise of the MDG score the importance of the corresponding feature is also increasing. MDA describes how much accuracy decrease by removing the feature. We selected the most important features according to the MDG and MDA scores by the highest values of both metrics using a sequential search from an initial set of variables.Modelling approachRandom forestChoosing the appropriate method for creating the tool for accurate SDM is crucial because the overall performance could vary dramatically, depending on the selected model and particular use case. There is a limited amount of acceptable machine learning methods that can be used in SDM. Several popular methods demonstrated high performance in modelling on large areas: GBM, RF, and GLM. In particular, for modelling and prediction of the potential distribution of invasive species, GLM and RF were used57. We decided to use RF because this model was successfully implemented for solving a variety of tasks such as predictions of animal and plant distributions, and also was used for making predictions on a large territory58. The other important advantage that should be noticed is the straightforward interpretability of RF, which means that it is possible to evaluate the impact of each environmental parameter on the occurrence of the invasive species.Approach to the cross-validation of the modelA unique approach for the model calibration is needed to reduce spatial autocorrelation caused by the absence of a strict sampling design. In our case, the data was split into training and testing folds using the spatial blocks technique in a scheme of 13-fold cross-validation. Random spatial splitting was performed 20 times to calibrate the model, with a distance between blocks set as 100 km. To calibrate the model we used a spatial blocks approach with random type from R package blockCV.Evaluation of the model performanceTo evaluate the performance of the model a classic approach for ecology was used—Area Under Curve (AUC) or Receiver operating characteristic (ROC), related to the independent threshold techniques16. The principle of methods lies in the standard confusion matrix, where rows and columns represent actual and predicted classes. The construction of ROC curves uses all possible thresholds to obtain different confusion matrices which leads to the reproduction of the curve with two-dimensional space: (1) on y-axis is True Positive Rate (sensitivity, recall); (2) on x-axis is False Positive Rate (equal to 1 − specificity). In our case true positive (TP, sensitivity) rate means that predicted places where HS grows correspond to actual. Similarly, true negative rate (TN, specificity) indicates correctly classified locations as absence points. In contrast, the missteps when the model predicted places as presence points for plants that are incorrect are False Positive, FP, and places where HS is absent, according to the model, while this is not true are recognised as False Negative, FN. More

Rochman CM. Microplastics research—from sink to source. Science. 2018;360:28–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Galloway TS, Cole M, Lewis C. Interactions of microplastic debris throughout the marine ecosystem. Nat Ecol Evol. 2017;1:116.PubMed 
Article 
PubMed Central 

Google Scholar 
Hale RC, Seeley ME, La Guardia MJ, Mai L, Zeng EY. A global perspective on microplastics. J Geophys Res Oceans. 2020;125:1–40.Article 

Google Scholar 
Harrison JP, Hoellein TJ, Sapp M, Tagg AS, Ju-Nam Y, Ojeda JJ. Microplastic-associated biofilms: a comparison of freshwater and marine environments. In: Freshwater microplastics. Cham: Springer; 2018. p. 181–201.Dunne WM Jr. Bacterial adhesion: seen any good biofilms lately? Clin Microbiol Rev. 2002;15:155–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dang H, Lovell CR. Microbial surface colonization and biofilm development in marine environments. Microbiol Mol Biol Rev. 2016;80:91–138.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
McCormick AR, Hoellein TJ, London MG, Hittie J, Scott JW, Kelly JJ. Microplastic in surface waters of urban rivers: concentration, sources, and associated bacterial assemblages. Ecosphere. 2016;7:e01556.Article 

Google Scholar 
Kesy K, Oberbeckmann S, Kreikemeyer B, Labrenz M. Spatial environmental heterogeneity determines young biofilm assemblages on microplastics in Baltic Sea mesocosms. Front Microbiol. 2019;10:1665.PubMed 
PubMed Central 
Article 

Google Scholar 
Oberbeckmann S, Loeder MG, Gerdts G, Osborn AM. Spatial and seasonal variation in diversity and structure of microbial biofilms on marine plastics in Northern European waters. FEMS Microbiol Ecol. 2014;90:478–92.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Masó M, Garcés E, Pagès F, Camp J. Drifting plastic debris as a potential vector for dispersing Harmful Algal Bloom (HAB) species. Sci Mar. 2003;67:107–11.Article 

Google Scholar 
Kirstein IV, Kirmizi S, Wichels A, Garin-Fernandez A, Erler R, Loder M, et al. Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles. Mar Environ Res. 2016;120:1–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Zettler ER, Mincer TJ, Amaral-Zettler LA. Life in the “plastisphere”: microbial communities on plastic marine debris. Environ Sci Technol. 2013;47:7137–46.CAS 
Article 

Google Scholar 
Oberbeckmann S, Kreikemeyer B, Labrenz M. Environmental factors support the formation of specific bacterial assemblages on microplastics. Front Microbiol. 2018;8:2709.PubMed 
PubMed Central 
Article 

Google Scholar 
Dussud C, Meistertzheim AL, Conan P, Pujo-Pay M, George M, Fabre P, et al. Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. Environ Pollut. 2018;236:807–16.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Frère L, Maignien L, Chalopin M, Huvet A, Rinnert E, Morrison H, et al. Microplastic bacterial communities in the Bay of Brest: Influence of polymer type and size. Environ Pollut. 2018;242:614–25.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Amaral-Zettler LA, Zettler ER, Slikas B, Boyd GD, Melvin DW, Morrall CE, et al. The biogeography of the Plastisphere: implications for policy. Front Ecol Environ. 2015;13:541–6.Article 

Google Scholar 
Amaral-Zettler LA, Ballerini T, Zettler ER, Asbun AA, Adame A, Casotti R, et al. Diversity and predicted inter- and intra-domain interactions in the Mediterranean Plastisphere. Environ Pollut. 2021;286.Li W, Zhang Y, Wu N, Zhao Z, Xu W, Ma Y, et al. Colonization characteristics of bacterial communities on plastic debris influenced by environmental factors and polymer types in the Haihe Estuary of Bohai Bay, China. Environ Sci Technol. 2019;53:10763–73.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Oberbeckmann S, Labrenz M. Marine microbial assemblages on microplastics: diversity, adaptation, and role in degradation. Ann Rev Mar Sci. 2020;12:209–32.PubMed 
Article 
PubMed Central 

Google Scholar 
Yang Y, Liu W, Zhang Z, Grossart HP, Gadd GM. Microplastics provide new microbial niches in aquatic environments. Appl Microbiol Biotechnol. 2020;104:6501–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lebreton LCM, van der Zwet J, Damsteeg JW, Slat B, Andrady A, Reisser J. River plastic emissions to the world’s oceans. Nat Commun. 2017;8:15611.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Song J, Jongmans-Hochschulz E, Mauder N, Imirzalioglu C, Wichels A, Gerdts G. The Travelling Particles: Investigating microplastics as possible transport vectors for multidrug resistant E. coli in the Weser estuary (Germany). Sci Total Environ. 2020;720:137603.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3:e00021-18.PubMed 
PubMed Central 
Article 

Google Scholar 
Team RC. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Beule L, Karlovsky P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ. 2020;8:e9593.PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R, et al. Package ‘vegan’. Community ecology package, version. 2013;2:1–295.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dinno A. dunn. test: Dunn’s test of multiple comparisons using rank sums. R package version. Vienna, Austria: R Foundation for Statistical Computing. 2017;1:1.Foster ZS, Sharpton TJ, Grunwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput Biol. 2017;13:e1005404.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Clarke K, Gorley R. Getting started with PRIMER v7. PRIMER-E, 20. Plymouth: Plymouth Marine Laboratory; 2015.Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20:1983–92.PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley Statsref: Statistics Reference Online; 2014. p. 1–15.Baselga A, Orme CDL. betapart: an R package for the study of beta diversity. Methods Ecol Evol. 2012;3:808–12.Article 

Google Scholar 
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.PubMed 
PubMed Central 
Article 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
PubMed Central 
Article 

Google Scholar 
Richter-Heitmann T, Hofner B, Krah FS, Sikorski J, Wust PK, Bunk B, et al. Stochastic dispersal rather than deterministic selection explains the spatio-temporal distribution of soil bacteria in a temperate grassland. Front Microbiol. 2020;11:1391.PubMed 
PubMed Central 
Article 

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

Google Scholar 
Miao L, Wang P, Hou J, Yao Y, Liu Z, Liu S, et al. Distinct community structure and microbial functions of biofilms colonizing microplastics. Sci Total Environ. 2019;650:2395–402.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Cai L, Wu D, Xia J, Shi H, Kim H. Influence of physicochemical surface properties on the adhesion of bacteria onto four types of plastics. Sci Total Environ. 2019;671:1101–7.CAS 
Article 

Google Scholar 
Wang L, Luo Z, Zhen Z, Yan Y, Yan C, Ma X, et al. Bacterial community colonization on tire microplastics in typical urban water environments and associated impacting factors. Environ Pollut. 2020;265:114922.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Vukanti R, Crissman M, Leff LG, Leff AA. Bacterial communities of tyre monofill sites: growth on tyre shreds and leachate. J Appl Microbiol. 2009;106:1957–66.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Wagner S, Huffer T, Klockner P, Wehrhahn M, Hofmann T, Reemtsma T. Tire wear particles in the aquatic environment – a review on generation, analysis, occurrence, fate and effects. Water Res. 2018;139:83–100.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Degaffe FS, Turner A. Leaching of zinc from tire wear particles under simulated estuarine conditions. Chemosphere. 2011;85:738–43.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Halsband C, Sørensen L, Booth AM, Herzke D. Car tire crumb rubber: does leaching produce a toxic chemical cocktail in coastal marine systems? Front Environ Sci. 2020;8:1–15.Article 

Google Scholar 
Thavamani P, Malik S, Beer M, Megharaj M, Naidu R. Microbial activity and diversity in long-term mixed contaminated soils with respect to polyaromatic hydrocarbons and heavy metals. J Environ Manage. 2012;99:10–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Toshchakov SV, Korzhenkov AA, Chernikova TN, Ferrer M, Golyshina OV, Yakimov MM, et al. The genome analysis of Oleiphilus messinensis ME102 (DSM 13489(T)) reveals backgrounds of its obligate alkane-devouring marine lifestyle. Mar. Genomics. 2017;36:41–7.Article 

Google Scholar 
Love CR, Arrington EC, Gosselin KM, Reddy CM, Van Mooy BAS, Nelson RK, et al. Microbial production and consumption of hydrocarbons in the global ocean. Nat Microbiol. 2021;6:489–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Ribicic D, McFarlin KM, Netzer R, Brakstad OG, Winkler A, Throne-Holst M, et al. Oil type and temperature dependent biodegradation dynamics – combining chemical and microbial community data through multivariate analysis. BMC Microbiol. 2018;18:83.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ribicic D, Netzer R, Hazen TC, Techtmann SM, Drablos F, Brakstad OG. Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Mar Pollut Bull. 2018;129:370–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Rezaei Somee M, Dastgheib SMM, Shavandi M, Ghanbari Maman L, Kavousi K, Amoozegar MA, et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Sci Rep. 2021;11:11316.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ren X, Tang J, Wang L, Sun H. Combined effects of microplastics and biochar on the removal of polycyclic aromatic hydrocarbons and phthalate esters and its potential microbial ecological mechanism. Front Microbiol. 2021;12:647766.PubMed 
PubMed Central 
Article 

Google Scholar 
Dussud C, Hudec C, George M, Fabre P, Higgs P, Bruzaud S, et al. Colonization of non-biodegradable and biodegradable plastics by marine microorganisms. Front Microbiol. 2018;9:1571.PubMed 
PubMed Central 
Article 

Google Scholar 
Vaksmaa A, Knittel K, Abdala Asbun A, Goudriaan M, Ellrott A, Witte HJ, et al. Microbial communities on plastic polymers in the Mediterranean Sea. Front Microbiol. 2021;12:673553.PubMed 
PubMed Central 
Article 

Google Scholar 
Pinto M, Langer TM, Huffer T, Hofmann T, Herndl GJ. The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession. PLoS ONE. 2019;14:e0217165.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Erni-Cassola G, Wright RJ, Gibson MI, Christie-Oleza JA. Early colonization of weathered polyethylene by distinct bacteria in Marine Coastal Seawater. Microb Ecol. 2020;79:517–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Berry D, Gutierrez T. Evaluating the detection of hydrocarbon-degrading bacteria in 16S rRNA gene sequencing surveys. Front Microbiol. 2017;8:896.PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang P, Zhao S, Zhu L, Li D. Microplastic-associated bacterial assemblages in the intertidal zone of the Yangtze Estuary. Sci Total Environ. 2018;624:48–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dang H, Li T, Chen M, Huang G. Cross-ocean distribution of Rhodobacterales bacteria as primary surface colonizers in temperate coastal marine waters. Appl Environ Microbiol. 2008;74:52–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.PubMed 
PubMed Central 
Article 

Google Scholar 
Tobias-Hunefeldt S. Community assembly drivers shift from bottom-up to top-down in a maturing in situ marine biofilm model. University of Otago; 2020.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
Article 
PubMed Central 

Google Scholar 
Rocca JD, Simonin M, Bernhardt ES, Washburne AD, Wright JP. Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental mixing. Ecology. 2020;101:e02956.PubMed 
Article 
PubMed Central 

Google Scholar 
Crump BC, Hopkinson CS, Sogin ML, Hobbie JE. Microbial biogeography along an estuarine salinity gradient: combined influences of bacterial growth and residence time. Appl Environ Microbiol. 2004;70:1494–505.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stewart PS. Diffusion in biofilms. J Bacteriol. 2003;185:1485–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Palomo A, Dechesne A, Smets BF. Genomic profiling of Nitrospira species reveals ecological success of comammox Nitrospira. 2019. https://www.biorxiv.org/content/10.1101/612226v1.Kielak AM, van Veen JA, Kowalchuk GA. Comparative analysis of acidobacterial genomic fragments from terrestrial and aquatic metagenomic libraries, with emphasis on acidobacteria subdivision 6. Appl Environ Microbiol. 2010;76:6769–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCormick A, Hoellein TJ, Mason SA, Schluep J, Kelly JJ. Microplastic is an abundant and distinct microbial habitat in an urban river. Environ Sci Technol. 2014;48:11863–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Teixeira L, Merquior V. The family moraxellaceae. The prokaryotes: Gammaproteobacteria. Berlin: Springer. 2014. p. 443–76.Stalder T, Press MO, Sullivan S, Liachko I, Top EM. Linking the resistome and plasmidome to the microbiome. ISME J. 2019;13:2437–46.PubMed 
PubMed Central 
Article 

Google Scholar 
Lu SY, Zhang YL, Geng SN, Li TY, Ye ZM, Zhang DS, et al. High diversity of extended-spectrum beta-lactamase-producing bacteria in an urban river sediment habitat. Appl Environ Microbiol. 2010;76:5972–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Glasgow Leaders’ Declaration on Forests and Land Use (UNFCCC, 2021); https://go.nature.com/3FmrE2iIPCC: Summary for Policymakers. In Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (WMO, 2019); https://go.nature.com/3itqkRWTomppo, E. et al. National Forest Inventories: Pathways for Common Reporting (Springer, 2010).Jeanjean, H. & Achard, F. Int. J. Remote Sens. 18, 2455–2461 (1997).Article 

Google Scholar 
Ceccherini, G. et al. Nature 583, 72–77 (2020).CAS 
Article 

Google Scholar 
Palahí, M. et al. Nature 592, E15–E17 (2021).Article 

Google Scholar 
Breidenbach, J. et al. Ann. For. Sci. 79, 2 (2022).Article 

Google Scholar 
ForestPlots.net Forest. et al. Biol. Conserv. 260, 108849 (2021).Article 

Google Scholar 
A Fresh Perspective: Global Forest Resources Assessment 2020 (FAO, 2020); https://go.nature.com/3uhpfBZCurtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Science 361, 1108–1111 (2018).CAS 
Article 

Google Scholar 
Chazdon, R. L. et al. Ambio 45, 538–550 (2016).Article 

Google Scholar 
Sasaki, N. & Putz, F. E. Conserv. Lett. 2, 226–232 (2009).Article 

Google Scholar 
Wulder, M. A. & Coops, N. C. Nature 513, 30–31 (2014).CAS 
Article 

Google Scholar 
Reiche, J. et al. Nat. Clim. Change 6, 120–122 (2016).Article 

Google Scholar 
Gorelick, N. et al. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
Valbuena, R. et al. Trends Ecol. Evol. 35, 656–667 (2020).CAS 
Article 

Google Scholar 
Porter-Bolland, L. et al. For. Ecol. Manage. 268, 6–17 (2012).Article 

Google Scholar 
Boissière, M. et al. PLoS ONE 12, e0176897 (2017).Article 

Google Scholar 
Armenteras, D. Nat. Ecol. Evol. 5, 1193–1194 (2021).Article 

Google Scholar 
Forest Information System for Europe (FISE) (EEA, 2022); https://go.nature.com/3D1CcUw More