Ecology

Ecosystems and Human Well-being: Synthesis (Millennium Ecosystem Assessment, 2005).Huang, J. et al. Dryland climate change: recent progress and challenges. Rev. Geophys. 55, 719–778 (2017).Article 

Google Scholar 
Fu, B. et al. The Global-DEP conceptual framework — research on dryland ecosystems to promote sustainability. Curr. Opin. Environ. Sustain. 48, 17–28 (2021).Article 

Google Scholar 
He, C. et al. Detecting global urban expansion over the last three decades using a fully convolutional network. Environ. Res. Lett. 14, 034008 (2019).Article 

Google Scholar 
Güneralp, B., Reba, M., Hales, B. U., Wentz, E. A. & Seto, K. C. Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis. Environ. Res. Lett. 15, 044015 (2020).Article 

Google Scholar 
McDonald, R. I. et al. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat. Sustain. 3, 16–24 (2019).Article 

Google Scholar 
Liu, X. et al. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nat. Sustain. 3, 564–570 (2020).Article 

Google Scholar 
Güneralp, B. & Seto, K. C. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. 8, 014025 (2013).Article 

Google Scholar 
McDonald, R. I., Kareiva, P. & Forman, R. T. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

Google Scholar 
McDonald, R. I., Marcotullio, P. J. & Güneralp, B. Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities (Springer, 2013).van Vliet, J. Direct and indirect loss of natural area from urban expansion. Nat. Sustain. 2, 755–763 (2019).Article 

Google Scholar 
Sharp, R. et al. InVEST 3.2.0 User’s Guide (The Natural Capital Project, Stanford Univ., Univ. Minnesota, The Nature Conservancy and World Wildlife Fund, 2015).Terrado, M. et al. Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning. Sci. Total Environ. 540, 63–70 (2016).CAS 
Article 

Google Scholar 
Bai, Y. et al. Developing China’s Ecological Redline Policy using ecosystem services assessments for land use planning. Nat. Commun. 9, 3034 (2018).Article 
CAS 

Google Scholar 
McDonald, R. I. et al. Urban effects, distance, and protected areas in an urbanizing world. Landsc. Urban Plan. 93, 63–75 (2009).Article 

Google Scholar 
Mirzabaev, A. et al. in Climate Change and Land (eds Shukla, P. R. et al.) 249–343 (IPCC, 2019).Friis, C. & Nielsen, J. Telecoupling. Exploring Land-use Change in a Globalised World (Palgrave Macmillan, 2019).Maestre, F. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Ecol. Evol. Syst. 47, 215–237 (2016).Article 

Google Scholar 
Leh, M. D. K., Matlock, M. D., Cummings, E. C. & Nalley, L. L. Quantifying and mapping multiple ecosystem services change in West Africa. Agric. Ecosyst. Environ. 165, 6–18 (2013).Article 

Google Scholar 
Xie, W., Huang, Q., He, C. & Zhao, X. Projecting the impacts of urban expansion on simultaneous losses of ecosystem services: a case study in Beijing, China. Ecol. Indic. 84, 183–193 (2018).Article 

Google Scholar 
Whitford, W. & Wade, E. L. Ecology of Desert Systems (Academic Press, 2002).Brito, J. C. et al. Conservation biogeography of the Sahara‐Sahel: additional protected areas are needed to secure unique biodiversity. Divers. Distrib. 22, 371–384 (2016).Article 

Google Scholar 
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).CAS 
Article 

Google Scholar 
Salafsky, N. et al. A standard lexicon for biodiversity conservation: unified classifications of threats and actions. Conserv. Biol. 22, 897–911 (2008).Article 

Google Scholar 
Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Article 
CAS 

Google Scholar 
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).Article 

Google Scholar 
Díaz, S. M. et al. The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers (IPBES, 2019).Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).CAS 
Article 

Google Scholar 
Pautasso, M. Scale dependence of the correlation between human population presence and vertebrate and plant species richness. Ecol. Lett. 10, 16–24 (2007).Article 

Google Scholar 
Luck, G. W. A review of the relationships between human population density and biodiversity. Biol. Rev. Camb. Phil. Soc. 82, 607–645 (2007).Article 

Google Scholar 
McDonald, R. I., Güneralp, B., Huang, C.-W., Seto, K. C. & You, M. Conservation priorities to protect vertebrate endemics from global urban expansion. Biol. Conserv. 224, 290–299 (2018).Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2017-3 (IUCN, 2017); https://www.iucnredlist.org/resources/spatial-data-downloadTucker, M. A. et al. Moving in the Anthropocene: global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).CAS 
Article 

Google Scholar 
Howard, C., Flather, C. H. & Stephens, P. A. A global assessment of the drivers of threatened terrestrial species richness. Nat. Commun. 11, 993 (2020).CAS 
Article 

Google Scholar 
Guidelines for Geoconservation in Protected and Conserved Areas (IUCN, 2020).Gao, J. How China will protect one-quarter of its land. Nature 569, 457 (2019).CAS 
Article 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).Article 

Google Scholar 
Gao, B., Huang, Q., He, C., Sun, Z. & Zhang, D. How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data. Landsc. Urban Plan. 148, 89–98 (2016).Article 

Google Scholar 
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
Lambin, E. A. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl Acad. Sci. USA 108, 3465–3472 (2011).CAS 
Article 

Google Scholar 
Arlidge, W. et al. A global mitigation hierarchy for nature conservation. Bioscience 68, 336–347 (2018).Article 

Google Scholar 
Moallemi, E. A., Kwakkel, J., de Haan, F. J. & Bryan, B. A. Exploratory modeling for analyzing coupled human-natural systems under uncertainty. Glob. Environ. Change 65, 102186 (2020).Article 

Google Scholar 
Luck, M. A., Jenerette, G. D., Wu, J. & Grimm, N. B. The urban funnel model and the spatially heterogeneous ecological footprint. Ecosystems 4, 782–796 (2001).Article 

Google Scholar 
Ramaswami, A. et al. A social‐ecological‐infrastructural systems framework for interdisciplinary study of sustainable city systems. J. Ind. Ecol. 16, 801–813 (2012).Article 

Google Scholar 
Boerema, A. et al. Soybean trade: balancing environmental and socio-economic impacts of an intercontinental market. PLoS ONE 11, e0155222 (2016).Article 
CAS 

Google Scholar 
Garrett, R. D., Lambin, E. F. & Naylor, R. L. Land institutions and supply chain configurations as determinants of soybean planted area and yields in Brazil. Land Use Policy 31, 385–396 (2013).Article 

Google Scholar 
Friess, D. A., Rogers, K., Lovelock, C. E., Krauss, K. W. & Shi, S. The state of the world’s mangrove forests: past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115 (2019).Article 

Google Scholar 
Ferreira, A. C. & Lacerda, L. D. Degradation and conservation of Brazilian mangroves, status and perspectives. Ocean Coast. Manage. 125, 38–46 (2016).Article 

Google Scholar 
Richards, D. R. & Friess, D. A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl Acad. Sci. USA 113, 201510272 (2016).
Google Scholar 
García-Vega, D. & Newbold, T. Assessing the effects of land use on biodiversity in the world’s drylands and Mediterranean environments. Biodivers. Conserv. 29, 393–408 (2020).Article 

Google Scholar 
Martínez-Valderrama, J., Guirado, E. & Maestre, F. Desertifying deserts. Nat. Sustain. 3, 572–575 (2020).Article 

Google Scholar 
Maestre, F. et al. Biogeography of global drylands. New Phytol. 231, 540–558 (2021).Article 

Google Scholar 
United Nations Environment World Conservation Monitoring Centre. World dryland areas according to UNCCD and CBD definitions. https://resources.unep-wcmc.org/products/789fcac8959943ab9ed7a225e5316f08 (2022).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Goldewijk, K. 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).Article 

Google Scholar 
Revision of World Urbanization Prospects (United Nations, 2018); https://esa.un.org/unpd/wupLand Cover CCI—Product User Guide Version 2.0. (European Space Agency, 2017); http://maps.elie.ucl.ac.be/CCI/viewer/index.phpGrekousis, G., Mountrakis, G. & Kavouras, M. An overview of 21 global and 43 regional land-cover mapping products. Int. J. Remote Sens. 36, 5309–5335 (2015).Article 

Google Scholar 
Xu, X., Jain, A. K. & Calvin, K. V. Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia. Glob. Change Biol. 25, 2137–2151 (2019).Article 

Google Scholar 
Gong, P. et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ. 236, 111510 (2020).Article 

Google Scholar 
Huang, Q. et al. The occupation of cropland by global urban expansion from 1992 to 2016 and its implications. Environ. Res. Lett. 15, 084037 (2020).Article 

Google Scholar 
He, C., Liu, Z., Tian, J. & Ma, Q. Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Glob. Change Biol. 20, 2886–2902 (2014).Article 

Google Scholar 
Di Febbraro, M. et al. Expert-based and correlative models to map habitat quality: which gives better support to conservation planning? Glob. Ecol. Conserv. 16, e00513 (2018).Article 

Google Scholar 
Anselin, L. Local Indicators of Spatial Association—LISA. Geogr. Anal. 27, 93–115 (2010).Article 

Google Scholar  More

Middleton, N., Stringer, L., Goudie, A., & Thomas, D. The Forgotten Billion: MDG Achievement in the Drylands (UNDP United Nations Convention to Combat Desertification, 2011).Soong, J. L., Phillips, C. L., Ledna, C., Koven, C. D. & Torn, M. S. CMIP5 models predict rapid and deep soil warming over the 21st century. J. Geophys. Res. 125, e2019JG005266 (2020).
Google Scholar 
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).Article 

Google Scholar 
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).CAS 
PubMed 
Article 

Google Scholar 
Schlaepfer, D. et al. Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nat. Commun. 8, 14196 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang, H. in The End of Desertification? (eds Behnke, R. & Mortimore, M.) 513–536 (Springer, 2016).Gadzama, N. M. Attenuation of the effects of desertification through sustainable development of Great Green Wall in the Sahel of Africa. World J. Sci. Technol. Sustain. Dev. 14, 279–289 (2017).Article 

Google Scholar 
United Nations Decade on Restoration (accessed January 2021); https://www.decadeonrestoration.org/Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).Article 

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

Google Scholar 
Megdal, S. B. Transboundary groundwater resources: sustainable management and conflict resolution. Groundwater 55, 701–702 (2017).CAS 
Article 

Google Scholar 
Jarvis, W.T. in Advances in Groundwater Governance (eds Villholth, K. G. et al.) 177–192 (CRC Press, 2017).Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).CAS 
PubMed 
Article 

Google Scholar 
Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).PubMed 
Article 
CAS 

Google Scholar 
Mbow, C. The Great Green Wall in the Sahel. Oxf. Res. Encycl. Clim. Sci. https://doi.org/10.1093/acrefore/9780190228620.013.559 (2017).Petrie, M. D. et al. Climate change may restrict dryland forest regeneration in the 21st century. Ecology 98, 1548–1559 (2017).CAS 
PubMed 
Article 

Google Scholar 
Liu, S., Jiang, D. & Lang, X. Mid-Holocene drylands: a multi-model analysis using Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) simulations. Holocene 29, 1425–1438 (2019).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Effects of climate legacies on above- and belowground community assembly. Glob. Change Biol. 24, 4330–4339 (2018).Article 

Google Scholar 
Hoelzmann, P. et al. Mid-Holocene land-surface conditions in northern Africa and the Arabian Peninsula: a data set for the analysis of biogeophysical feedbacks in the climate system. Glob. Biogeochem. Cycles 12, 35–51 (1998).CAS 
Article 

Google Scholar 
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 
PubMed 
Article 

Google Scholar 
Smettem, K. R. J., Waring, R. H., Callow, J. N., Wilson, M. & Mu, Q. Satellite-derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: implications for groundwater decline in a drying climate. Glob. Change Biol. 19, 2401–2412 (2013).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Schmidt, R. et al. GRACE observations of changes in continental water storage. Glob. Planet. Change 50, 112–126 (2006).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Friedl, M. A. et al. ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000–2001 (ORNL DAAC, Oak Ridge, TN, USA, 2010); https://doi.org/10.3334/ORNLDAAC/968Chen, M. et al. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci. Data 7, 320 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
National Centre for Earth Observation & Los, S.O. Global Vegetation Height Frequency Distributions from the ICESAT GLAS instrument produced as part of the National Centre for Earth Observation (NCEO) (NERC Earth Observation Data Centre, accessed 10 December 2020); http://catalogue.ceda.ac.uk/uuid/85e7d70a74244c73b71446940e05cde6Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 
PubMed 
Article 

Google Scholar 
Cherlet, M. et al. World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (Publications Office of the European Union, 2018).Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V. & Petoukhov, V. The influence of vegetation-atmosphere-ocean interaction on climate during the mid-holocene. Science 280, 1916–1919 (1998).CAS 
PubMed 
Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 

Google Scholar 
Scheffer, M. Tipping Points (Princeton Univ. Press, 2009).Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).CAS 
PubMed 
Article 

Google Scholar 
Runyan, C. W. & D’Odorico, P. Global Deforestation (Cambridge Univ. Press, 2016).Herzschuh, U. et al. Global taxonomically harmonized pollen data set for Late Quaternary with revised chronologies (LegacyPollen 1.0). PANGAEA https://doi.org/10.1594/PANGAEA.929773 (2021).Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Belsky, A. J. et al. The effects of trees on their physical, chemical and biological environments in a semi-arid savanna in Kenya. J. Appl. Ecol. 26, 1005–1024 (1989).Article 

Google Scholar 
Li, C. et al. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2, 858–873 (2021).Article 

Google Scholar 
Tatebe, H. et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev. 12, 2727–2765 (2019).CAS 
Article 

Google Scholar 
Trees, Forests and Land Use in Drylands: the First Global Assessment. Full Report (FAO, 2019).Diallo, H. A. in The Future of Drylands (eds Lee, C. & Schaaf, T.) 13–16 (Springer, 2008).A Spatial Analysis Approach to the Global Delineation of Dryland Areas of Relevance to the CBD Programme of Work on Dry and Subhumid Lands (UNEP-WCMC, 2014).Abatzoglou, J. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Tachikawa, T., Hato, M., Kaku, M. & Iwasaki, A. Characteristics of ASTER GDEM version 2. IEEE Int. Geosci. Remote Sens. Symp. Proc. https://doi.org/10.1109/igarss.2011.6050017 (2011).Alibakhshi, S., Crowther, T. W. & Naimi, B. Land surface black-sky albedo at a fixed solar zenith angle and its relation to forest structure during peak growing season based on remote sensing data. Data Brief. 31, 105720 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Hamazaki, T. Advanced land observation satellite (ALOS). 5 Outline of ALOS satellite system. J. Jpn Soc. Photogramm. Remote Sens. 38, 25–26 (1999).
Google Scholar 
Mu, Q., Zhao, M., & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).https://doi.org/10.1016/j.rse.2011.02.019Zlotnicki, V., Bettadpur, S., Landerer, F. W. & Watkins, M. M. in Encyclopedia of Sustainability Science and Technology (ed. Meyers, R. A.) 4563–4584 (Springer, 2012).https://doi.org/10.1007/978-1-4419-0851-3_745Schepaschenko, D. et al. Comment on ‘The extent of forest in dryland biomes’. Science 358, 6362 (2017).Article 
CAS 

Google Scholar 
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).CAS 
PubMed 
Article 

Google Scholar 
Cheng, G., Han, J. & Lu, X. Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105, 1865–1883 (2017).Article 

Google Scholar 
Xia, X., Xu, C. & Nan, B. Inception-v3 for flower classification. In Proc. 2nd International Conference on Image, Vision and Computing (ICIVC) 783–787 (IEEE, 2017).Fei-Fei, L., Deng, J. & Li, K. ImageNet: constructing a large-scale image database. J. Vis. 9, 1037 (2010).Article 

Google Scholar 
Guirado, E. et al. Tree cover estimation in global drylands from space using deep learning. Remote Sens. 12, 343 (2020).Article 

Google Scholar 
Legendre, P., Borcard, D. & Roberts, D. W. Variation partitioning involving orthogonal spatial eigenfunction submodels. Ecology 93, 1234–1240 (2012).PubMed 
Article 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Lahouar, A. & Slama, J. B. H. Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manage. 103, 1040–1051 (2015).Article 

Google Scholar 
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995).
Google Scholar 
Piñeiro, G., Perelman, S., Guerschman, J. P. & Paruelo, J. M. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol. Model. 216, 316–322 (2008).Article 

Google Scholar 
Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019); https://doi.org/10.5067/MODIS/MCD12Q1.006The CMIP6 landscape. Nat. Clim. Change 9, 727 (2019).Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109.1, 213–241 (2011).Article 
CAS 

Google Scholar 
Cao, X. et al. A taxonomically harmonized and temporally standardized fossil pollen dataset from Siberia covering the last 40 kyr. Earth Syst. Sci. Data 12, 119–135 (2020).Article 

Google Scholar 
Cao, X. et al. A late Quaternary pollen dataset from eastern continental Asia for vegetation and climate reconstructions: set up and evaluation. Rev. Palaeobot. Palynol. 194, 21–37 (2013).Article 

Google Scholar 
Li, C. et al. Harmonized chronologies of a global late Quaternary pollen dataset (LegacyAge 1.0). PANGAEA https://doi.org/10.1594/PANGAEA.933132 (2021).GlobalTreeSearch Online Database (Botanic Gardens Conservation International, UK, accessed 20 January 2022); https://tools.bgci.org/global_tree_search.php More

Hanski, I. Habitat fragmentation and species richness. J. Biogeogr. 42, 989–993. https://doi.org/10.1111/jbi.12478 (2015).Article 

Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509. https://doi.org/10.1126/science.1194442 (2010).ADS 
CAS 
Article 
PubMed 

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

Google Scholar 
Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502. https://doi.org/10.1046/j.1523-1739.2002.00386.x (2002).Article 

Google Scholar 
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: Predictions of landscape connectivity change with demography. J. Appl. Ecol. 51, 1169–1178. https://doi.org/10.1111/1365-2664.12282 (2014).Article 

Google Scholar 
Carroll, C. Interacting effects of climate change, landscape conversion, and harvest on carnivore populations at the range margin: Marten and lynx in the northern Appalachians. Conserv. Biol. 21, 1092–1104. https://doi.org/10.1111/j.1523-1739.2007.00719.x (2007).Article 
PubMed 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484. https://doi.org/10.1126/science.1241484 (2014).CAS 
Article 
PubMed 

Google Scholar 
Farris, Z. J. et al. Hunting, exotic carnivores, and habitat loss: Anthropogenic effects on a native carnivore community, Madagascar. PLOS ONE 10, e0136456. https://doi.org/10.1371/journal.pone.0136456 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Farris, Z. J. et al. Threats to a rainforest carnivore community: A multi-year assessment of occupancy and co-occurrence in Madagascar. Biol. Cons. 210, 116–124. https://doi.org/10.1016/j.biocon.2017.04.010 (2017).Article 

Google Scholar 
Swihart, R. K., Gehring, T. M., Kolozsvary, M. B. & Nupp, T. E. Responses of “resistant” vertebrates to habitat loss and fragmentation: The importance of niche breadth and range boundaries. Divers. Distrib. 9, 1–18. https://doi.org/10.1046/j.1472-4642.2003.00158.x (2003).Article 

Google Scholar 
Caryl, F. M., Quine, C. P. & Park, K. J. Martens in the matrix: The importance of nonforested habitats for forest carnivores in fragmented landscapes. J. Mammal. 93, 464–474. https://doi.org/10.1644/11-MAMM-A-149.1 (2012).Article 

Google Scholar 
Pereboom, V. et al. Movement patterns, habitat selection, and corridor use of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented landscape. Can. J. Zool. 86, 983–991. https://doi.org/10.1139/Z08-076 (2008).Article 

Google Scholar 
Fleschutz, M. M. et al. Response of a small felid of conservation concern to habitat fragmentation. Biodivers. Conserv. 25, 1447–1463. https://doi.org/10.1007/s10531-016-1118-6 (2016).Article 

Google Scholar 
Gálvez, N. et al. Forest cover outside protected areas plays an important role in the conservation of the Vulnerable guiña Leopardus guigna. Oryx 47, 251–258. https://doi.org/10.1017/S0030605312000099 (2013).Article 

Google Scholar 
Belcher, C. A. Demographics of tiger quoll (Dasyurus maculatus maculatus) populations in south-eastern Australia. Aust. J. Zool. 51, 611–626. https://doi.org/10.1071/ZO02051 (2003).Article 

Google Scholar 
Maxwell, S., Burbidge, A. & Morris, K. Spotted-tailed Quoll (SE mainland and Tas); recovery outline. (1996).Jones, M. E., Rose, R. K. & Burnett, S. Dasyurus maculatus. Mammalian Species 676, 1–9 (2001).Article 

Google Scholar 
Long, K. & Nelson, J. National recovery plan for the spotted-tailed Quoll Dasyurus maculatus. Victorian Department of Sustainability and Environment (2010).Claridge, A. W. et al. Home range of the spotted-tailed quoll (Dasyurus maculatus), a marsupial carnivore, in a rainshadow woodland. Wildl. Res. 32, 7–14. https://doi.org/10.1071/WR04031 (2005).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Home range, denning behaviour and microhabitat use of the carnivorous marsupial Dasyurus maculatus in eastern Australia. J. Zool. 268, 347–354. https://doi.org/10.1111/j.1469-7998.2006.00064.x (2006).Article 

Google Scholar 
Körtner, G. et al. Population structure, turnover and movement of spotted-tailed quolls on the New England Tablelands. Wildl. Res. 31, 475–484. https://doi.org/10.1071/WR03041 (2004).Article 

Google Scholar 
Belcher, C. The Largest Surviving Marsupial Carnivore on Mainland Australia: The Tiger or Spotted-Tailed Quoll Dasyurus maculatus, A Nationally Threatened, Forest-Dependent Species 612–623 (Royal Zoological Society of New South Wales, Sydney, 2004).
Google Scholar 
Henderson, T., Fancourt, B. A., Rajaratnam, R., Vernes, K. & Ballard, G. Spatial and temporal interactions between endangered spotted-tailed quolls and introduced red foxes in a fragmented landscape. J. Zool. https://doi.org/10.1111/jzo.12919 (2021).Article 

Google Scholar 
Troy, S. N. Spatial Ecology of the Tasmanian Spotted-Tailed Quoll. Ph.D. Thesis, University of Tasmania, (2014).Jones, M. E. et al. Research supporting restoration aiming to make a fragmented landscape ‘functional’ for native wildlife. Ecol. Manag. Restor. 22, 65–74. https://doi.org/10.1111/emr.12504 (2021).Article 

Google Scholar 
Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Use of anthropogenic linear features by two medium-sized carnivores in reserved and agricultural landscapes. Scientific Reports 7, 1–11. https://doi.org/10.1038/s41598-017-11454-z (2017).CAS 
Article 

Google Scholar 
Nichols, J. D. in Applied Ecology and Human Dimensions in Biological Conservation (eds L. M. Verdade, M.C. Lyra-Jorge, & C.I. Pina) 117–131 (Springer, 2014).Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. in Spatial Capture-Recapture (eds J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, & Beth Gardner) 3–19 (Academic Press, 2014).Sollmann, R., Gardner, B. & Belant, J. L. How does spatial study design influence density estimates from spatial capture-recapture models?. PLoS ONE 7, e34575 (2012).ADS 
CAS 
Article 

Google Scholar 
Kalle, R., Ramesh, T., Qureshi, Q. & Sankar, K. Density of tiger and leopard in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, as estimated using photographic capture–recapture sampling. Acta Theriol. 56, 335–342. https://doi.org/10.1007/s13364-011-0038-9 (2011).Article 

Google Scholar 
Vissia, S., Wadhwa, R. & van Langevelde, F. Co-occurrence of high densities of brown hyena and spotted hyena in central Tuli, Botswana. J. Zool. 314, 143–150. https://doi.org/10.1111/jzo.12873 (2021).Article 

Google Scholar 
Henderson, T., Fancourt, B. A. & Ballard, G. The importance of species-specific survey designs: Prey camera trap surveys significantly underestimate the detectability of endangered spotted-tailed quolls. Aust. Mammalogy https://doi.org/10.1071/AM21039 (2022).Gorta, S. B. Z., Alting, B., Claridge, A. & Henderson, T. Apparent piebald variants in quolls (Dasyurus): Examples of three recent cases in the spotted-tailed quoll Dasyurus maculatus. Aust. Mammalogy 43, 373–377. https://doi.org/10.1071/AM20058 (2021).Article 

Google Scholar 
Kowalksi, M. (https://exifpro.informer.com/2.1/, 2011).Efford, M. in R package version 4.5.3 (2022).R Core Team. (R Foundation for Statistical Computing, Vienna, Austria, 2022).Rovero, F. & Zimmermann, F. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd, London, 2016).
Google Scholar 
Efford, M. Density estimation in live-trapping studies. Oikos 106, 598–610. https://doi.org/10.1111/j.0030-1299.2004.13043.x (2004).Article 

Google Scholar 
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: An R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462. https://doi.org/10.1111/2041-210X.12600 (2016).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model Sel. Multimodel Inference 2, 70–71 (2002).MATH 

Google Scholar 
Hamer, R. P. et al. Differing effects of productivity on home-range size and population density of a native and an invasive mammalian carnivore. Wildlife Res. 49, 158–168. https://doi.org/10.1071/WR20134 (2021).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Complex interactions among mammalian carnivores in Australia, and their implications for wildlife management. Biol. Rev. 80, 387–401. https://doi.org/10.1017/s1464793105006718 (2005).Article 
PubMed 

Google Scholar 
Glen, A. S., Pennay, M., Dickman, C. R., Wintle, B. A. & Firestone, K. B. Diets of sympatric native and introduced carnivores in the Barrington Tops, eastern Australia. Austral Ecol. 36, 290–296. https://doi.org/10.1111/j.1442-9993.2010.02149.x (2011).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Population viability analysis shows spotted-tailed quolls may be vulnerable to competition. Aust Mammalogy 35, 180–183. https://doi.org/10.1071/AM12045 (2013).Article 

Google Scholar 
Graham, C. A., Maron, M. & McAlpine, C. A. Influence of landscape structure on invasive predators: Feral cats and red foxes in the brigalow landscapes, Queensland Australia. Wildl. Res. 39, 661–676. https://doi.org/10.1071/WR12008 (2012).Article 

Google Scholar 
Glen, A. S. Population attributes of the spotted-tailed quoll (Dasyurus maculatus) in north-eastern New South Wales. Aust. J. Zool. 56, 137–142. https://doi.org/10.1071/ZO08025 (2008).Article 

Google Scholar 
Chua, M. A., Sivasothi, N. & Meier, R. Population density, spatiotemporal use and diet of the leopard cat (Prionailurus bengalensis) in a human-modified succession forest landscape of Singapore. Mammal Res. 61, 99–108 (2016).Article 

Google Scholar 
Lorica, M. & Heaney, L. Survival of a native mammalian carnivore, the leopard cat Prionailurus bengalensis Kerr, 1792 (Carnivora: Felidae), in an agricultural landscape on an oceanic Philippine island. J. Threatened Taxa, 4451–4460 (2013).Rajaratnam, R., Sunquist, M., Rajaratnam, L. & Ambu, L. Diet and habitat selection of the leopard cat (Prionailurus bengalensis borneoensis) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209–217 (2007).Article 

Google Scholar 
Belcher, C. A. & Darrant, J. P. Den use by the spotted-tailed quoll Dasyurus maculatus in south-eastern Australia. Aust Mammalogy 28, 59–64. https://doi.org/10.1071/AM06007 (2006).Article 

Google Scholar 
Glen, A. & Dickman, C. Why are there so many spotted-tailed Quolls Dasyurus maculatus in parts of north-eastern New South Wales?. Aust Zool 35, 711–718. https://doi.org/10.7882/az.2011.023 (2011).Article 

Google Scholar 
Hanski, I. Metapopulation ecology (Oxford University Press, Oxford, 1999).
Google Scholar 
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
Belcher, C. A. Susceptibility of the tiger quoll, Dasyurus maculatus, and the eastern quoll, D. viverrinus, to 1080-poisoned baits in control programmes for vertebrate pests in eastern Australia. Wildl. Res. 25, 33–40. https://doi.org/10.1071/WR95077 (1998).Article 

Google Scholar 
Schmidt, G. M., Graves, T. A., Pederson, J. C. & Carroll, S. L. Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study. Ecological Applications 32, e2618. https://doi.org/10.1002/eap.2618 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
White, G. C. Capture-Recapture and Removal Methods for Sampling Closed Populations (Los Alamos National Laboratory, New Mexico, 1982).
Google Scholar 
Thornton, D. H. & Pekins, C. E. Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: Implications for mesocarnivore monitoring. Wildl. Res. 42, 394–404. https://doi.org/10.1071/WR15092 (2015).Article 

Google Scholar 
Sollmann, R. et al. Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil. Biol. Cons. 144, 1017–1024 (2011).Article 

Google Scholar 
Green, A. M., Chynoweth, M. W. & Şekercioğlu, Ç. H. Spatially explicit capture-recapture through camera trapping: A review of benchmark analyses for wildlife density estimation. Front. Ecol. Evol. 8, 473. https://doi.org/10.3389/fevo.2020.563477 (2020).Article 

Google Scholar 
du Preez, B. D., Loveridge, A. J. & Macdonald, D. W. To bait or not to bait: a comparison of camera-trapping methods for estimating leopard Panthera pardus density. Biol. Cons. 176, 153–161 (2014).Article 

Google Scholar 
Zimmermann, F., Breitenmoser-Würsten, C., Molinari-Jobin, A. & Breitenmoser, U. Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture. Integr. Zool. 8, 232–243 (2013).Article 

Google Scholar 
Dupont, P., Milleret, C., Gimenez, O. & Bischof, R. Population closure and the bias-precision trade-off in spatial capture–recapture. Methods Ecol. Evol. 10, 661–672. https://doi.org/10.1111/2041-210X.13158 (2019).Article 

Google Scholar 
Mergey, M., Helder, R. & Roeder, J. J. Effect of forest fragmentation on space-use patterns in the European pine marten (Martes martes). J. Mammal. 92, 328–335. https://doi.org/10.1644/09-MAMM-A-366.1 (2011).Article 

Google Scholar 
Silmi, M. et al. Activity and ranging behavior of leopard cats (Prionailurus bengalensis) in an oil palm landscape. Frontiers in Environmental Science 9, 651939. https://doi.org/10.3389/fenvs.2021.651939 (2021).Article 

Google Scholar  More

Ogle K, Barber JJ, Barron-Gafford GA, Bentley LP, Young JM, Huxman TE, et al. Quantifying ecological memory in plant and ecosystem processes. Ecol Lett. 2015;18:221–35.PubMed 
Article 

Google Scholar 
Schweiger AH, Boulangeat I, Conradi T, Davis M, Svenning JC. The importance of ecological memory for trophic rewilding as an ecosystem restoration approach. Biol Rev. 2019;94:1–15.Article 

Google Scholar 
Webster CR, Dickinson YL, Burton JI, Frelich LE, Jenkins MA, Kern CC, et al. Promoting and maintaining diversity in contemporary hardwood forests: confronting contemporary drivers of change and the loss of ecological memory. Ecol Manag. 2018;421:98–108.Article 

Google Scholar 
Hughes TP, Kerry JT, Connolly SR, Baird AH, Eakin CM, Heron SF, et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat Clim Change. 2019;9:40–43.Article 

Google Scholar 
Stockwell SR, Landry CR, Rifkin SA. The yeast galactose network as a quantitative model for cellular memory. Mol Biosyst. 2015;11:28–37.PubMed 
Article 
CAS 

Google Scholar 
Wolf DM, Fontaine-Bodin L, Bischofs I, Price G, Keasling J, Arkin AP. Memory in microbes: quantifying history-dependent behavior in a bacterium. PLoS ONE. 2008;3:e1700.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lyon P. The cognitive cell: bacterial behavior reconsidered. Front Microbiol. 2015;6:264.PubMed 
PubMed Central 
Article 

Google Scholar 
Smith MB, Rocha AM, Smillie CS, Olesen SW, Paradis C, Wu L, et al. Natural bacterial communities serve as quantitative geochemical biosensors. mBio. 2015;6:e00326–15.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cordeiro MC, Garcia GD, Rocha AM, Tschoeke DA, Campeão ME, Appolinario LR, et al. Insights on the freshwater microbiomes metabolic changes associated with the world’s largest mining disaster. Sci Total Environ. 2019;654:1209–17.PubMed 
Article 
CAS 

Google Scholar 
Kuster SP, Rudnick W, Shigayeva A, Green K, Baqi M, Gold WL, et al. Previous antibiotic exposure and antimicrobial resistance in invasive pneumococcal disease: results from prospective surveillance. Clin Infect Dis. 2014;59:944–52.PubMed 
Article 
CAS 

Google Scholar 
Carmody RN, Gerber GK, Luevano JM, Gatti DM, Somes L, Svenson KL, et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe. 2015;17:72–84.PubMed 
Article 
CAS 

Google Scholar 
David LA, Weil A, Ryan ET, Calderwood SB, Harris JB, Chowdhury F, et al. Gut microbial succession follows acute secretory diarrhea in humans. mBio. 2015;6:e00381–15.PubMed 
PubMed Central 

Google Scholar 
Stacy A, Andrade-Oliveira V, McCulloch JA, Hild B, Oh JH, Perez-Chaparro PJ, et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell. 2021;184:615–27.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thaiss CA, Itav S, Rothschild D, Meijer MT, Levy M, Moresi C, et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature. 2016;540:544–51.PubMed 
Article 
CAS 

Google Scholar 
Coyte KZ, Rakoff-Nahoum S. Understanding competition and cooperation within the mammalian gut microbiome. Curr Biol. 2019;29:R538–R544.Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL, Shields-Cutler RR, et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe. 2019;25:789–802.PubMed 
Article 
CAS 

Google Scholar 
Tarini J, Wolever TMS. The fermentable fibre inulin increases postprandial serum short-chain fatty acids and reduces free-fatty acids and ghrelin in healthy subjects. Appl Physiol Nutr Metab. 2010;35:9–16.PubMed 
Article 
CAS 

Google Scholar 
van Loo J, Coussement P, de Leenheer L, Hoebreg H, Smits G. On the presence of inulin and oligofructose as natural ingredients in the western diet. Crit Rev Food Sci Nutr. 1995;35:525–52.PubMed 
Article 

Google Scholar 
Holmes ZC, Silverman JD, Dressman HK, Wei Z, Dallow EP, Armstrong SC, et al. Short-chain fatty acid production by gut microbiota from children with obesity differs according to prebiotic choice and bacterial community composition. mBio. 2020;11:e00914–20.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shafquat A, Joice R, Simmons SL, Huttenhower C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol. 2014;22:261–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Silverman JD, Durand HK, Bloom RJ, Mukherjee S, David LA. Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. Microbiome. 2018;6:1–20.Article 

Google Scholar 
Pompei A, Cordisco L, Raimondi S, Amaretti A, Pagnoni UM, Matteuzzi D, et al. In vitro comparison of the prebiotic effects of two inulin-type fructans. Anaerobe. 2008;14:280–86.den Besten G, van Eunen K, Groen AK, Venema K, Reijngoud D-J, Bakker BM. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. 2013;54:2325–40.Article 
CAS 

Google Scholar 
Reichardt N, Vollmer M, Holtrop G, Farquharson FM, Wefers D, Bunzel M, et al. Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production. ISME J. 2018;12:610–22.PubMed 
Article 
CAS 

Google Scholar 
Sonnenburg ED, Zheng H, Joglekar P, Higginbottom SK, Firbank SJ, Bolam DN, et al. Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations. Cell. 2010;141:1241–52.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rakoff-Nahoum S, Foster KR, Comstock LE. The evolution of cooperation within the gut microbiota. Nature. 2016;533:255–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wong JMW, de Souza R, Kendall CWC, Emam A, Jenkins DJA. Colonic health: fermentation and short chain fatty acids. J Clin Gastroenterol. 2006;40:235–43.PubMed 
Article 
CAS 

Google Scholar 
van de Wiele T, Boon N, Possemiers S, Jacobs H, Verstraete W. Inulin-type fructans of longer degree of polymerization exert more pronounced in vitro prebiotic effects. J Appl Microbiol. 2007;102:452–60.PubMed 

Google Scholar 
Aguirre M, Eck A, Koenen ME, Savelkoul PHM, Budding AE, Venema K. Diet drives quick changes in the metabolic activity and composition of human gut microbiota in a validated in vitro gut model. Res Microbiol. 2016;167:114–25.PubMed 
Article 
CAS 

Google Scholar 
Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA 2014;111:7427–32.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N, Preciat G, et al. The Virtual Metabolic Human database: Integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 2019;47:D614–D624.PubMed 
Article 
CAS 

Google Scholar 
Li H, Liu F, Lu J, Shi J, Guan J, Yan F, et al. Probiotic mixture of Lactobacillus plantarum strains improves lipid metabolism and gut microbiota structure in high fat diet-fed mice. Front Microbiol. 2020;11:512.PubMed 
PubMed Central 
Article 

Google Scholar 
Terrapon N, Lombard V, Drula É, Lapébie P, Al-Masaudi S, Gilbert HJ, et al. PULDB: the expanded database of polysaccharide utilization loci. Nucleic Acids Res. 2018;46:D677–D683.PubMed 
Article 
CAS 

Google Scholar 
Bolam DN, van den Berg B. TonB-dependent transport by the gut microbiota: novel aspects of an old problem. Curr Opin Struct Biol. 2018;51:35–43.PubMed 
Article 
CAS 

Google Scholar 
Duncan SH, Holtrop G, Lobley GE, Calder AG, Stewart CS, Flint HJ. Contribution of acetate to butyrate formation by human faecal bacteria. Br J Nutr. 2004;91:915–23.PubMed 
Article 
CAS 

Google Scholar 
Holmes ZC, Villa MM, Durand HK, Jiang S, Dallow EP, Petrone BL, et al. Microbiota responses to different prebiotics are conserved within individuals and associated with habitual fiber intake. bioRxiv. 2021. https://doi.org/10.1101/2021.06.26.450023.Holscher HD, Gregory Caporaso J, Hooda S, Brulc JM, Fahey GC, Swanson KS. Fiber supplementation influences phylogenetic structure and functional capacity of the human intestinal microbiome: follow-up of a randomized controlled trial. Am J Clin Nutr. 2015;101:55–64.Liu H, Liao C, Wu L, Tang J, Chen J, Lei C, et al. Ecological dynamics of the gut microbiome in response to dietary fiber. ISME J. 2022;16:2040–55.David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.PubMed 
Article 
CAS 

Google Scholar 
Kaczmarek JL, Musaad SMA, Holscher HD. Time of day and eating behaviors are associated with the composition and function of the human gastrointestinal microbiota. Am J Clin Nutr. 2017;106:1220–31.Basan M, Honda T, Christodoulou D, Hörl M, Chang YF, Leoncini E, et al. A universal trade-off between growth and lag in fluctuating environments. Nature. 2020;584:470–4.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Matenchuk BA, Mandhane PJ, Kozyrskyj AL. Sleep, circadian rhythm, and gut microbiota. Sleep Med Rev. 2020;53:101340.PubMed 
Article 

Google Scholar 
Costello EK, Stagaman K, Dethlefsen L, Bohannan BJM, Relman DA. The application of ecological theory toward an understanding of the human microbiome. Science. 2012;336:1255–62.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.PubMed 
Article 

Google Scholar 
Vich Vila A, Collij V, Sanna S, Sinha T, Imhann F, Bourgonje AR, et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat Commun. 2020;11:1–11.Article 
CAS 

Google Scholar 
Wilson ID, Nicholson JK. Gut microbiome interactions with drug metabolism, efficacy, and toxicity. Transl Res. 2017;179:204–22.PubMed 
Article 
CAS 

Google Scholar 
Salonen A, Lahti L, Salojärvi J, Holtrop G, Korpela K, Duncan SH, et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J. 2014;8:2218–30.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wissel EF, Smith LK. Inter-individual variation shapes the human microbiome. Behav Brain Sci. 2019;42:E79.Wurster JI, Peterson RL, Brown CE, Penumutchu S, Guzior DV, Neugebauer K, et al. Streptozotocin-induced hyperglycemia alters the cecal metabolome and exacerbates antibiotic-induced dysbiosis. Cell Rep. 2021;37:110113.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kerimi A, Kraut NU, da Encarnacao JA, Williamson G. The gut microbiome drives inter- and intra-individual differences in metabolism of bioactive small molecules. Sci Rep. 2020;10:1–12.Article 
CAS 

Google Scholar 
di Bartolomeo F, van den Ende W. Fructose and fructans: opposite effects on health? Plant Foods Hum Nutr. 2015;70:227–37.Pereira FC, Berry D. Microbial nutrient niches in the gut. Environ Microbiol. 2017;19:1366–78.PubMed 
PubMed Central 
Article 

Google Scholar 
Rettedal EA, Gumpert H, Sommer MOA. Cultivation-based multiplex phenotyping of human gut microbiota allows targeted recovery of previously uncultured bacteria. Nat Commun. 2014;5:1–9.Article 
CAS 

Google Scholar 
Oliphant K, Parreira VR, Cochrane K, Allen-Vercoe E. Drivers of human gut microbial community assembly: coadaptation, determinism and stochasticity. ISME J. 2019;13:3080–92.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Possemiers S, Verthé K, Uyttendaele S, Verstraete W. PCR-DGGE-based quantification of stability of the microbial community in a simulator of the human intestinal microbial ecosystem. FEMS Microbiol Ecol. 2004;49:495–507.PubMed 
Article 
CAS 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108(supplement_1):4516–22.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–24.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Markowitz VM, Chen IMA, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 2012;40:D115–D122.Bioinformatics B, Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003;4:41.Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34.Webb EC. Enzyme nomenclature 1992: Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of Enzymes. Cambridge, MA, USA: Academic Press; 1992.Enriquez-Hesles E, Smith DL, Maqani N, Wierman MB, Sutcliffe MD, Fine RD, et al. A cell-nonautonomous mechanism of yeast chronological aging regulated by caloric restriction and one-carbon metabolism. J Biol Chem. 2021;296:100125.Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S, et al. FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem. 2009;81:10038–48.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS ONE. 2013;8:e67019.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sakamoto M, Ohkuma M. Identification and classification of the genus Bacteroides by multilocus sequence analysis. Microbiology. 2011;157:3388–97.PubMed 
Article 

Google Scholar 
Silverman JD, Roche K, Holmes ZC, David LA, Mukherjee S. Bayesian multinomial logistic normal models through marginally latent matrix-T processes. J Mach Learn Res. 2022;23:1–42.
Google Scholar  More

Hand, S.C. Metabolic dormancy in aquatic invertebrates. In Advances in Comparative and Environmental Physiology, Vol. 8 (ed. Gilles, R.) 1–50. https://doi.org/10.1007/978-3-642-75900-0_1 (1991).Cáceres, C. E. Dormancy in Invertebrates. Invertebr. Biol. 116(4), 371–383. https://doi.org/10.2307/3226870 (1997).Article 

Google Scholar 
Wilsterman, K., Ballinger, M. A. & Williams, C. M. A unifying, eco-physiological framework for animal dormancy. Funct. Ecol. 35, 11–31. https://doi.org/10.1111/1365-2435.13718 (2021).Article 

Google Scholar 
Bertolani, R., Guidetti, R., Altiero, T., Nelson, D. R. & Rebecchi, L. Dormancy in Freshwater Tardigrades. In Dormancy in Aquatic Organisms. Theory, Human Use and Modeling. Monographiae Biologicae Vol. 92 (eds Alekseev, V. & Pinel-Alloul, B.) (Springer, Cham, 2019). https://doi.org/10.1007/978-3-030-21213-1_3.Chapter 

Google Scholar 
Guidetti, R., Altiero, T. & Rebecchi, L. On dormancy strategies in tardigrades. J. Insect Physiol. 57(5), 567–576. https://doi.org/10.1016/j.jinsphys.2011.03.003 (2011).CAS 
Article 
PubMed 

Google Scholar 
Hahn, D. A. & Denlinger, D. L. Energetics of insect diapause. Annu. Rev. Entomol. 56, 103–121. https://doi.org/10.1146/annurev-ento-112408-085436 (2011).CAS 
Article 
PubMed 

Google Scholar 
Ragland, G. J. & Keep, E. Comparative transcriptomics support evolutionary convergence of diapause responses across Insecta. Physiol. Entomol. 42(3), 246–256. https://doi.org/10.1111/phen.12193 (2017).CAS 
Article 

Google Scholar 
Wang, Y., Ezemaduka, A. N., Tang, Y. & Chang, Z. Understanding the mechanism of the dormant dauer formation of C. elegans: From genetics to biochemistry. IUBMB Life 61(6), 607–12. https://doi.org/10.1002/iub.211 (2009).CAS 
Article 
PubMed 

Google Scholar 
Dias, I. B., Bouma, H. R. & Henning, R. H. Unraveling the big sleep: Molecular aspects of stem cell dormancy and hibernation. Front. Physiol. 12, 624950. https://doi.org/10.3389/fphys.2021.624950 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Storey, K. B. & Storey, J. M. Metabolic regulation and gene expression during aestivation. Prog. Mol. Subcell. Biol. 49, 25–45. https://doi.org/10.1007/978-3-642-02421-4_2 (2010).CAS 
Article 
PubMed 

Google Scholar 
Hand, S. C., Denlinger, D. L., Podrabsky, J. E. & Roy, R. Mechanisms of animal diapause: Recent developments from nematodes, crustaceans, insects, and fish. Am. J. Physiol. Regul. Integr. Comp. Physiol. 310(11), R1193–R1211. https://doi.org/10.1152/ajpregu.00250.2015 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ikeda, H., Ohtsu, K. & Uye, S. I. Fine structure, histochemistry, and morphogenesis during excystment of the podocysts of the giant jellyfish Nemopilema nomurai (Scyphozoa, Rhizostomeae). Biol. Bull. 221(3), 248–260 (2011).PubMed 
Article 

Google Scholar 
Bushnell, J. H. & Rao, K. S. Dormant or quiescent stages and structures among the Ectoprocta: Physical and chemical factors affecting viability and germination of statoblasts. Trans. Am. Microsc. Soc. 93, 524–543. https://doi.org/10.2307/3225156 (1974).Article 

Google Scholar 
Hyman, L. H. The Invertebrates: Acanthocephala, Aschelminthes and Entoprocta Vol. III (McGraw-Hill, 1951).
Google Scholar 
Mukai, H. & Toshiki, M. Studies on the regeneration of an entoproct, Barentsia discreta. J. Exp. Zool. 205(2), 261–276. https://doi.org/10.1002/jez.1402050210 (1978).Article 

Google Scholar 
Nakauchi, M. Asexual development of ascidians: Its biological significance, diversity, and morphogenesis. Am. Zool. 22(4), 753–763. https://doi.org/10.1093/icb/22.4.753 (1982).Article 

Google Scholar 
Hyams, Y., Paz, G., Rabinowitz, C. & Rinkevich, B. Insights into the unique torpor of Botrylloides leachi, a colonial urochordate. Dev. Biol. 428(1), 101–117. https://doi.org/10.1016/j.ydbio.2017.05.020 (2017).CAS 
Article 
PubMed 

Google Scholar 
Brown, C. J. D. A limnological study of certain fresh-water Polyzoa with special reference to their statoblasts. Trans. Am. Microsc. Soc. 52, 271–313 (1933).CAS 
Article 

Google Scholar 
Mukai, H. Development of freshwater bryozoans (Phylactolaemata). In Developmental Biology of Freshwater Invertebrates (eds Harrison, R. W. & Cowden, R. R.) 535–576 (Alan R. Liss Inc., 1982).
Google Scholar 
Wood, T. S. Phyla ectoprocta and entoprocta (Bryozoans). In Freshwater Invertebrates (eds Thorp, J. H. & Covich, A. P.) 327–345 (Academic Press, 2015). https://doi.org/10.1016/B978-0-12-385026-3.00016-4.Chapter 

Google Scholar 
Simpson, T. L. The Cell Biology of Sponges (Springer, New York, 1984). https://doi.org/10.1007/978-1-4612-5214-6.Book 

Google Scholar 
Alié, A., Hiebert, L. S., Scelzo, M. & Tiozzo, S. The eventful history of nonembryonic development in tunicates. J. Exp. Zool. Part B Mol. Dev. Evol. 33(3), 181–217. https://doi.org/10.1002/jez.b.22940 (2020).Article 

Google Scholar 
Brown, F. D. & Swalla, B. J. Evolution and development of budding by stem cells: Ascidian coloniality as a case study. Dev. Biol. 3692, 151–162 (2012).Article 
CAS 

Google Scholar 
Kawamura, K. & Fujiwara, S. Cellular and molecular characterization of transdifferentiation in the process of morphallaxis of budding tunicates. Semin. Cell Biol. 6, 117–126 (1995).CAS 
PubMed 
Article 

Google Scholar 
Kassmer, S. H., Langenbacher, A. D. & De Tomaso, A. W. Integrin-alpha-6+ candidate stem cells are responsible for whole body regeneration in the invertebrate chordate Botrylloides diegensis. Nat. Commun. 11(1), 4435–4511. https://doi.org/10.1038/s41467-020-18288-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Freeman, G. The role of blood cells in the process of asexual reproduction in the tunicate Perophora viridis. J. Exp. Zool. 156, 157–183 (1964).CAS 
PubMed 
Article 

Google Scholar 
Kürn, U., Rendulic, S., Tiozzo, S. & Lauzon, R. J. Asexual propagation and regeneration in colonial ascidians. Biol. Bull. 221(1), 43–61. https://doi.org/10.1086/BBLv221n1p43 (2011).Article 
PubMed 

Google Scholar 
Sköld, H. N., Obst, M., Sköld, M. & Åkesson, B. Stem cells in asexual reproduction of marine invertebrates. In Stem Cells in Marine Organisms (eds Rinkevich, B. & Matranga, V.) 105–137 (Springer, Dordrecht, 2009).Chapter 

Google Scholar 
Tiozzo, S., Brown, F. D. & De Tomaso, A. W. Regeneration and stem cells in ascidians. In Stem Cells (ed. Bosch, T. C. G.) (Springer, Dordrecht, 2008). https://doi.org/10.1007/978-1-4020-8274-0_6.Chapter 

Google Scholar 
Mukai, H., Koyama, H. & Watanabe, H. Studies on the reproduction of three species of Perophora (Ascidiacea). Biol. Bull. 164(2), 251–266 (1983).Article 

Google Scholar 
Huxley, J. Memoirs: studies in dedifferentiation: II. Dedifferentiation and resorption in Perophora. Q. J. Microsc. Sci. s2-65(260), 643–697 (1921).
Google Scholar 
Huxley, J. Studies in dedifferentiation. VI. Reduction phenomena in Clavelina lepadiformis. Pubb. Staz. Zool. Napoli. 7, 1–34 (1926).
Google Scholar 
Turon, X. Periods of nonfeeding in Polysyncraton-lacazei (Ascidiacea, Didemnidae)—A process. Mar. Biol. 112, 647–655 (1992).Article 

Google Scholar 
Delsuc, F. et al. A phylogenomic framework and timescale for comparative studies of tunicates. BMC Biol. 16, 39 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Giard, M. A. & Caullery, M. On the hibernation of Clavelina lepadiformis, Müller. Ann. Mag. Nat. Hist. 18(108), 485–486. https://doi.org/10.1080/00222939608680499 (1896).Article 

Google Scholar 
Orton, J. H. The production of living Clavellina Zooids in winter by experiment. Nature 107, 75. https://doi.org/10.1038/107075a0 (1921).ADS 
Article 

Google Scholar 
Della, Valle P. Studii sui rapporti fra differenziazione e rigenerazione. 4. Bollettino Della Società Dei Naturalisti in Napoli 7, 1–37 (1915).
Google Scholar 
Scelzo, M. et al. Novel budding mode in Polyandrocarpa zorritensis: a model for comparative studies on asexual development and whole body regeneration. EvoDevo https://doi.org/10.1186/s13227-019-0121-x (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Berrill, N. J. Regeneration and budding in tunicates. Biol. Rev. 26, 456–475. https://doi.org/10.1111/j.1469-185X.1951.tb01207.x/full (1951).Article 

Google Scholar 
Kilpatrick, K. A., Podestá, G. P. & Evans, R. Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res. 106(C5), 9179–9197. https://doi.org/10.1029/1999JC000065 (2001).ADS 
Article 

Google Scholar 
Berrill, N. J. & Cohen, A. Regeneration in Clavelina lepadiformis. J. Exp. Biol. 13(3), 352–362. https://doi.org/10.1242/jeb.13.3.352 (1936).Article 

Google Scholar 
Jiménez-Merino, J. et al. Putative stem cells in the hemolymph and in the intestinal submucosa of the solitary ascidian Styela plicata. EvoDevo https://doi.org/10.1186/s13227-019-0144-3 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Du, Q., Luu, P.-L., Stirzaker, C. & Clark, S. J. Methyl-CpG-binding domain proteins: Readers of the epigenome. Epigenomics UK 7, 1051–1073 (2015).CAS 
Article 

Google Scholar 
Rea, S. & Akhtar, A. MSL proteins and the regulation of gene expression. In DNA Methylation: Development, Genetic Disease and Cancer: Current Topics in Microbiology and Immunology Vol. 310 (eds Doerfler, W. & Böhm, P.) (Springer, 2006). https://doi.org/10.1007/3-540-31181-5_7.Chapter 

Google Scholar 
Orton, J. H. Preliminary account of a contribution to an evaluation of the sea. J. Mar. Biol. Assoc. UK 10(2), 312–326. https://doi.org/10.1017/S0025315400007815 (1914).Article 

Google Scholar 
Mukai, H. Histological and histochemical studies of two compound ascidians, Clavelina lepadiformis and Diazona violacea, with special reference to the trophocytes, ovary and pyloric gland. Sci. Rep. Fac. Educ. Gunma Univ. 26, 37–77 (1977).
Google Scholar 
de Caralt, S., López-Legentil, S., Tarjuelo, I., Uriz, M. J. & Turon, X. Contrasting biological traits of Clavelina lepadiformis (Ascidiacea) populations from inside and outside harbours in the western Mediterranean. Mar. Ecol. Prog. Ser. 244, 125–137 (2002).ADS 
Article 

Google Scholar 
Turon, X. A new mode of colony multiplication by modified budding in the ascidian Clavelina gemmae n. sp. (Clavelinidae). Invertebr. Biol. 124(3), 273–283. https://doi.org/10.1111/j.1744-7410.2005.00025.x (2005).Article 

Google Scholar 
Pyo, J. & Shin, S. A new record of invasive alien colonial tunicate Clavelina lepadiformis (Ascidiacea: Aplousobranchia: Clavelinidae) in Korea. Anim. Syst. Evol. Divers. 27, 197–200 (2011).Article 

Google Scholar 
Reinhardt, J. et al. First record of the non-native light bulb tunicate Clavelina lepadiformis (Müller, 1776) in the northwest Atlantic. Aquat. Invasions 5(2), 185–190. https://doi.org/10.3391/ai.2010.5.2.09 (2010).Article 

Google Scholar 
Turon, X., Tarjuelo, I., Duran, S. & Pascual, M. Characterising invasion processes with genetic data: An Atlantic clade of Clavelina lepadiformis (Ascidiacea) introduced into Mediterranean harbours. Hydrobiologia 503(1–3), 29–35. https://doi.org/10.1023/b:hydr.0000008481.10705.c2 (2003).Article 

Google Scholar 
Van Name, W. G. The North and South American ascidians. Bull. Am. Mus. Nat. Hist. 84, 1–476 (1945).
Google Scholar 
Carman, M. et al. Ascidians at the Pacific and Atlantic entrances to the Panama Canal. Aquat. Invasions 6(4), 371–380. https://doi.org/10.3391/ai.2011.6.4.02 (2011).Article 

Google Scholar 
Holman, L. E. et al. Managing human-mediated range shifts: Understanding spatial, temporal and genetic variation in marine non-native species. Philos. Trans. R. Soc. B 377, 20210025 (2022).CAS 
Article 

Google Scholar 
Lambert, C. C. & Lambert, G. Persistence and differential distribution of nonindigenous ascidians in harbors of the Southern California Bight. Marine Ecology Progress Series 259, 145–161. https://doi.org/10.3354/meps259145 (2003).ADS 
Article 

Google Scholar 
Brunetti, R. Polyandrocarpa zorritensis (Van Name, 1931). A colonial ascidian new to the Mediterranean record. Vie et Milieu 28–29, 647–652 (1978).
Google Scholar 
Brunetti, R. & Mastrototaro, F. The non-indigenous stolidobranch ascidian Polyandrocarpa zorritensis in the Mediterranean: Description, larval morphology and pattern of vascular budding. Zootaxa 528, 1–8 (2004).Article 

Google Scholar 
Mastrototaro, F., D’Onghia, G. & Tursi, A. Spatial and seasonal distribution of ascidians in a semi-enclosed basin of the Mediterranean Sea. J. Mar. Biol. Assoc. UK 88, 1053–1061 (2008).Article 

Google Scholar 
Stabili, L., Licciano, M., Longo, C., Lezzi, M. & Giangrande, A. The Mediterranean non- indigenous ascidian Polyandrocarpa zorritensis: Microbiological accumulation capability and environmental implications. Mar. Pollut. Bull. 101, 146–152 (2015).CAS 
PubMed 
Article 

Google Scholar 
Turon, X. & Becerro, M. A. Growth and survival of several ascidian species from the northwestern Mediterranean. Mar. Ecol. Prog. Ser. 82, 235–247 (1992).ADS 
Article 

Google Scholar 
Sumida, P. Y. G. et al. Pressure tolerance of tadpole larvae of the Atlantic ascidian Polyandrocarpa zorritensis: Potential for deep-sea invasion. Braz. J. Oceanogr. 63, 515–520 (2015).Article 

Google Scholar 
Vázquez, E. & Young, C. M. Responses of compound ascidian larvae to haloclines. Mar. Ecol. Prog. Ser. 133, 179–190 (1996).ADS 
Article 

Google Scholar 
Vázquez, E. & Young, C. M. Ontogenetic changes in phototaxis during larval life of the Ascidian Polyandrocarpa zorritensis (Van Name, 1931). J. Exp. Mar. Biol. Ecol. 231, 267–277 (1998).Article 

Google Scholar 
Brien, P. & Brien-Gavage, E. Contribution à l’étude de la Blastogénèse des Tuniciers: III: Bourgeonnement de Clavelina Lepadiformis Müller. Recueil de L’Institut Zoologique Torley-Rousseau 1–56 (1927).Fujimoto, H. & Watanabe, H. The characterization of granular amoebocytes and their possible roles in the asexual reproduction of the polystyelid ascidian, Polyzoa vesiculiphora. J. Morphol. 150(3), 623–637. https://doi.org/10.1002/jmor.1051500303 (1976).Article 
PubMed 

Google Scholar 
Cima, F., Franchi, N. & Ballarin, L. Origin and functions of tunicate hemocytes. In The Evolution of the Immune System (ed. Malagoli, D.) 29–49 (Elsevier, 2016). https://doi.org/10.1016/B978-0-12-801975-7/00002-5.Chapter 

Google Scholar 
Kerb, H. Biologische Beiträge zur Frage der Überwinterung der Ascidien. Arch. Mikrosk. Anat. 72(1), 386–414 (1908).Article 

Google Scholar 
Driesch, H. Studien über das Regulationsvermögen de Organismen. 6. Die Restitutionen der Clavellina lepadiformis. Arch. F. Entw.-Mech. 14, 247–287 (1902).Article 

Google Scholar 
Schultz, E. Über Reductionen. III. Die Reduction und Regeneration des abgeschnitten Kiemenkorbes von Clavellina lepadiformis. Arch. Entw. Mech. Org. 24, 503–523 (1907).
Google Scholar 
Spek, J. Über die Winterknospenentwicklung, Regeneration und Reduktion bei Clavellina lepadiformis und die Bedeutung besonderer “omnipotenter” Zellelemente für diese Vorgänge. Wilhelm Roux’Archiv Entwicklungsmechanik der Org 111(119), 172 (1927).
Google Scholar 
Brien, P. Contribution à l’étude de la régéneration naturelle et expérimentale chez les Clavelinidae. Soc. R. Zool. Belg. Ann LXI, 19–112 (1930).
Google Scholar 
Ries, E. Die Tropfenzellen und ihre Bedeutung für die Tunicabildung bei Clavelina. Wilhelm Roux Arch. Entwickl. Mech. Org. 137(3), 363–371. https://doi.org/10.1007/BF00593066 (1937).Article 
PubMed 

Google Scholar 
Fischer, I. Über das Verhalten des stolonialen Gewebes der Ascidie Clavelina lepadiformis in vitro. Wilhelm Roux Arch. Entwickl. Mech. Org. 137(3), 383–403. https://doi.org/10.1007/BF00593068 (1937).Article 
PubMed 

Google Scholar 
Seelinger, O. Eibildung und Knospung von Clavelina lepadiformis. Sitzungsber. d. Kais. Kgl. Acad. d. Wiss 1–56 (1882).Van Beneden, E. & Julin, C. Recherches sur la morphologie des tuniciers. Arch. Biol. 6, 237–476 (1886).
Google Scholar 
Garstang, W. Memoirs: The morphology of the Tunicata, and its bearings on the phylogeny of the Chordata. J. Cell Sci. 1928(2), 51–187 (1928).Article 

Google Scholar 
Kimura, K. D., Tissenbaum, H. A., Liu, Y. X. & Ruvkun, G. daf-2, an insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science 277, 942–946 (1997).CAS 
PubMed 
Article 

Google Scholar 
Ogawa, A. & Brown, F. Dauer formation and dauer-specific behaviours in Pristionchus pacificus. In Pristionchus pacificus—A nematode model for comparative and evolutionary biology (ed. Sommer, R. J.) (Brill, 2015). https://doi.org/10.1163/9789004260306_011.Chapter 

Google Scholar 
Wisdom, R. AP-1: One switch for many signals. Exp. Cell Res. 253(1), 180–185. https://doi.org/10.1006/excr.1999.4685 (1999).CAS 
Article 
PubMed 

Google Scholar 
Karin, M., Liu, Z. & Zandi, E. AP-1 function and regulation. Curr. Opin. Cell Biol. 9, 240–246 (1997).CAS 
PubMed 
Article 

Google Scholar 
Srivastava, M. Beyond casual resemblances: rigorous frameworks for comparing regeneration across species. Annu. Rev. Cell Dev. Biol. 37, 1–26 (2021).Article 
CAS 

Google Scholar 
Alié, A. et al. Convergent acquisition of nonembryonic development in styelid ascidians. Mol. Biol. Evol. 35, 1728–1743. https://doi.org/10.1093/molbev/msy068 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, W., Razy-Krajka, F., Siu, E., Ketcham, A. & Christiaen, L. NK4 antagonizes Tbx1/10 to promote cardiac versus pharyngeal muscle fate in the ascidian second heart field. PLoS Biol. 11, 1. https://doi.org/10.1371/journal.pbio.1001725 (2013).CAS 
Article 

Google Scholar 
Prünster, M. M., Ricci, L., Brown, F. D. & Tiozzo, S. Modular co-option of cardiopharyngeal genes during non-embryonic myogenesis. EvoDevo https://doi.org/10.1186/s13227-019-0116-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kawamura, K., Shiohara, M., Kanda, M. & Fujiwara, S. Retinoid X receptor-mediated transdifferentiation cascade in budding tunicates. Dev. Biol. 384, 343–355 (2013).CAS 
PubMed 
Article 

Google Scholar 
Rinkevich, Y., Paz, G., Rinkevich, B. & Reshef, R. Systemic bud induction and retinoic acid signaling underlie whole body regeneration in the urochordate Botrylloides leachi. PLoS Biol. 5, e71. https://doi.org/10.1371/journal.pbio.0050071 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. GigaScience. 4(1), 48. https://doi.org/10.1186/s13742-015-0089-y (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krueger, F. Trim Galore!: A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files. http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (2015).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17(1), 10–12. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9(4), 357–359. https://doi.org/10.1038/nmeth.1923 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. FastQC: A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652. https://doi.org/10.1038/nbt.1883 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Laetsch, D. R. & Blaxter, M. L. BlobTools: Interrogation of genome assemblies. F1000Research 6, 1287. https://doi.org/10.12688/f1000research.12232.1 (2017).Article 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13), 1658–1659. https://doi.org/10.1093/bioinformatics/btl158 (2006).CAS 
Article 
PubMed 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Gene prediction (ed. Kollmar, M.) 227–245 (Humana, New York, 2019). https://doi.org/10.1007/978-1-4939-9173-0_14.Chapter 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368. https://doi.org/10.1038/s41592-021-01101-x (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
The UniProt Consortium. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 49(D1), D480–D489. https://doi.org/10.1093/nar/gkaa1100 (2021).CAS 
Article 

Google Scholar 
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34(5), 525–527. https://doi.org/10.1038/nbt.3519 (2016).CAS 
Article 
PubMed 

Google Scholar 
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15(2), 1–17. https://doi.org/10.1186/gb-2014-15-2-r29 (2014).CAS 
Article 

Google Scholar 
Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12(1), 35. https://doi.org/10.1186/1471-2105-12-35 (2011).Article 

Google Scholar 
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. https://doi.org/10.1002/pro.3715 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res 49(D1), D545–D551. https://doi.org/10.1093/nar/gkaa970 (2021).CAS 
Article 
PubMed 

Google Scholar 
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2009).CAS 
Article 

Google Scholar 
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37(1), 1–13 (2009).Article 
CAS 

Google Scholar  More

Chen, L. et al. Geographic variation in traits of fruit stones and seeds of Melia azedarach. J. Beijing For. Univ. 36, 15–20 (2014).CAS 

Google Scholar 
Angamuthu, D., Purushothaman, I., Kothandan, S. & Swaminathan, R. Antiviral study on Punica granatum L., Momordica charantia L., Andrographis paniculata Nees, and Melia azedarach L., to human herpes virus-3. Eur. J. Integr. Med. 28, 98–108. https://doi.org/10.1016/j.eujim.2019.04.008 (2019).Article 

Google Scholar 
Wang, N. et al. Selective ERK1/2 agonists isolated from Melia azedarach with potent anti-leukemic activity. BMC Cancer 19, 1–9. https://doi.org/10.1186/s12885-019-5914-8 (2019).CAS 
Article 

Google Scholar 
Khoshraftar, Z., Safekordi, A., Shamel, A. & Zaefizadeh, M. Evaluation of insecticidal activity of nanoformulation of Melia azedarach (leaf) extract as a safe environmental insecticide. Int. J. Environ. Sci. Technol. 17, 1159–1170. https://doi.org/10.1007/s13762-019-02448-7 (2020).CAS 
Article 

Google Scholar 
Sivaraj, I., Nithaniyal, S., Bhooma, V., Senthilkumar, U. & Parani, M. Species delimitation of Melia dubia Cav. from Melia azedarach L. complex based on DNA barcoding. Botany 96, 329–336. https://doi.org/10.1139/cjb-2017-0148 (2018).CAS 
Article 

Google Scholar 
Liao, B. et al. Population structure and genetic relationships of Melia Taxa in China assayed with sequence-related amplified polymorphism (SRAP) markers. Forests 7, 81. https://doi.org/10.3390/f7040081 (2016).Article 

Google Scholar 
Wu, L., Kaewmano, A., Fu, P., Wang, W. & Fan, Z. Intra-annual radial growth of Melia azedarach in a tropical moist seasonal forest and its response to environmental factors in Xishuangbanna Southwest China. Acta Ecol. Sin. 40, 6831–6840. https://doi.org/10.5846/stxb202003120508 (2020).Article 

Google Scholar 
Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 C. Science 365, eaaw6974. https://doi.org/10.1126/science.aaw6974 (2019).CAS 
Article 
PubMed 

Google Scholar 
López-Tirado, J., Vessella, F., Schirone, B. & Hidalgo, P. J. Trends in evergreen oak suitability from assembled species distribution models: Assessing climate change in south-western Europe. New For. 49, 471–487. https://doi.org/10.1007/s11056-018-9629-5 (2018).Article 

Google Scholar 
Xu, Y. et al. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 12, 429. https://doi.org/10.3390/f12040429 (2021).Article 

Google Scholar 
Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manag. 430, 196–203. https://doi.org/10.1016/j.foreco.2018.08.019 (2018).Article 

Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163. https://doi.org/10.1111/gcb.13925 (2018).ADS 
Article 

Google Scholar 
Zhong, Y. et al. A generalized linear mixed model approach to assess emerald ash Borer diffusion. ISPRS Int. J. Geo Inf. 9, 414. https://doi.org/10.3390/ijgi9070414 (2020).Article 

Google Scholar 
Chang, Z., Meng, J., Shi, Y. & Mo, F. Lnc RNA recognition by fusing multiple features and its function prediction. CAAI Trans. Intell. Syst. 13, 928–934. https://doi.org/10.11992/tis.201806008 (2018).Article 

Google Scholar 
Shiferaw, H., Bewket, W. & Eckert, S. Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem. Ecol. Evol. 9, 2562–2574. https://doi.org/10.1002/ece3.4919 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Tang, X., Yuan, Y., Li, X. & Zhang, J. Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China. Front. Plant Sci. 12, 764. https://doi.org/10.3389/fpls.2021.652500 (2021).Article 

Google Scholar 
Chhogyel, N., Kumar, L., Bajgai, Y. & Jayasinghe, L. S. Prediction of Bhutan’s ecological distribution of rice (Oryza sativa L.) under the impact of climate change through maximum entropy modelling. J. Agric. Sci. 158, 25–37. https://doi.org/10.1017/S0021859620000350 (2020).Article 

Google Scholar 
Ahmad, Z. et al. Melia Azedarach impregnated Co and Ni zero-valent metal nanoparticles for organic pollutants degradation: Validation of experiments through statistical analysis. J. Mater. Sci. Mater. Electron. 31, 16938–16950. https://doi.org/10.1007/s10854-020-04250-5 (2020).CAS 
Article 

Google Scholar 
Hijmans, R. J., Huaccho, L. & Zhang, D. In I International Conference on Sweetpotato. Food and Health for the Future 583, 41–49.Luo, M., Wang, H. & Lyu, Z. Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data. J. Appl. Ecol. 28, 4001–4006. https://doi.org/10.13287/j.1001-9332.201712.011 (2017).Article 

Google Scholar 
Wang, T., Wang, G., Innes, J. L., Seely, B. & Chen, B. ClimateAP: An application for dynamic local downscaling of historical and future climate data in Asia Pacific. Front. Agric. Sci. Eng. 4, 448–458. https://doi.org/10.15302/J-FASE-2017172 (2017).CAS 
Article 

Google Scholar 
Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L Lesser Himalayan foothills. Ecol. Eng. 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 (2013).CAS 
Article 

Google Scholar 
Pepe, M. S., Cai, T. & Longton, G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62, 221–229. https://doi.org/10.1111/j.1541-0420.2005.00420.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
McHugh, M. L. Interrater reliability: The kappa statistic. Biochem. Med. 22, 276–282. https://hrcak.srce.hr/89395 (2012).Article 

Google Scholar 
Lu, C. Y., Gu, W., Dai, A. H. & Wei, H. Y. Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China. Ecol. Model. 242, 105–115. https://doi.org/10.1016/j.ecolmodel.2012.06.002 (2012).Article 

Google Scholar 
Zhang, L. et al. The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis. Acta Ecol. Sin. 34, 650–659. https://doi.org/10.5846/stxb201306031292 (2014).Article 

Google Scholar 
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x (2009).Article 

Google Scholar 
Akpoti, K., Kabo-Bah, A. T., Dossou-Yovo, E. R., Groen, T. A. & Zwart, S. J. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Sci. Total Environ. 709, 136165. https://doi.org/10.1016/j.scitotenv.2019.136165 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dutra Silva, L., de Brito, A. E., Vieira Reis, F., Bento Elias, R. & Silva, L. Limitations of species distribution models based on available climate change data: a case study in the Azorean forest. Forests 10, 575. https://doi.org/10.3390/f10070575 (2019).Article 

Google Scholar 
Lin, H. Y. et al. Climate-based approach for modeling the distribution of montane forest vegetation in Taiwan. Appl. Veg. Sci. 23, 239–253. https://doi.org/10.1111/avsc.12485 (2020).Article 

Google Scholar 
Zhang, L. et al. Consensus forecasting of species distributions: The effects of niche model performance and niche properties. PLoS ONE 10, e0120056. https://doi.org/10.1371/journal.pone.0120056 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, H. The optimality of naive Bayes. Am. Assoc. Artif. Intell. 1, 3 (2004).
Google Scholar 
Wang, Q., Nguyen, T.-T., Huang, J. Z. & Nguyen, T. T. An efficient random forests algorithm for high dimensional data classification. Adv. Data Anal. Classif. 12, 953–972. https://doi.org/10.1007/s11634-018-0318-1 (2018).MathSciNet 
Article 
MATH 

Google Scholar 
Zheng-tao, Y., Bin, D., Bo, H., Lu, H. & Jian-yi, G. Word sense disambiguation based on bayes model and information gain. Proc. Int. J. Adv. Sci. Technol. 2, 153–157. https://doi.org/10.1109/FGCN.2008.188 (2009).Article 

Google Scholar 
Yu, B. et al. SubMito-XGBoost: Predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 36, 1074–1081. https://doi.org/10.1093/bioinformatics/btz734 (2020).CAS 
Article 
PubMed 

Google Scholar 
Hailu, B. T., Siljander, M., Maeda, E. E. & Pellikka, P. Assessing spatial distribution of Coffea arabica L. in Ethiopia’s highlands using species distribution models and geospatial analysis methods. Ecol. Inf. 42, 79–89. https://doi.org/10.1016/j.ecoinf.2017.10.001 (2017).Article 

Google Scholar 
Ramirez-Reyes, C. et al. Embracing ensemble species distribution models to inform at-risk species status assessments. J. Fish Wildl. Manag. 12, 98–111. https://doi.org/10.3996/JFWM-20-072 (2021).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).Article 

Google Scholar 
Feng, L., Sun, J., Shi, Y., Wang, G. & Wang, T. Predicting suitable habitats of camptotheca acuminata considering both climatic and soil variables. Forests 11, 891. https://doi.org/10.3390/f11080891 (2020).Article 

Google Scholar 
Wang, T., Campbell, E. M., O’Neill, G. A. & Aitken, S. N. Projecting future distributions of ecosystem climate niches: Uncertainties and management applications. For. Ecol. Manag. 279, 128–140. https://doi.org/10.1016/j.foreco.2012.05.034 (2012).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. L. & Murdock, T. Q. ClimateWNA—high-resolution spatial climate data for western North America. J. Appl. Meteorol. Climatol. 51, 16–29. https://doi.org/10.1175/JAMC-D-11-043.1 (2012).ADS 
Article 

Google Scholar 
Feng, L. et al. Predicting suitable habitats of ginkgo biloba L. fruit forests in China. Clim. Risk Manag. 34, 100364. https://doi.org/10.1016/j.crm.2021.100364 (2021).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720. https://doi.org/10.1371/journal.pone.0156720 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Y. et al. Spatial prediction and delineation of Ginkgo biloba production areas under current and future climatic conditions. Ind. Crops Prod. 166, 113444. https://doi.org/10.1016/j.indcrop.2021.113444 (2021).Article 

Google Scholar 
Jiao, C., Lan, G., Sun, Y., Wang, G. & Sun, Y. Dopamine alleviates chilling stress in watermelon seedlings via modulation of proline content, antioxidant enzyme activity, and polyamine metabolism. J. Plant Growth Regul. 40, 2. https://doi.org/10.1007/s00344-020-10096-2 (2021).CAS 
Article 

Google Scholar 
Thakur, S., Thakur, I. & Sankanur, M. Assessment of genetic diversity in drek (Melia azedarach) using molecular markers. J. Tree Sci. 36, 78–85. https://doi.org/10.5958/2455-7129.2017.00011.5 (2017).Article 

Google Scholar 
Sivasubramaniam, K. et al. Seed priming: Triumphs and tribulations. The Madras Agricultural Journal 98, 197–209. https://www.researchgate.net/publication/267298497 (2011).
Google Scholar 
Xu, L. et al. Effect of salt stress on growth and physiology in Melia azedarach seedlings of six provenances. Int. J. Agric. Biol. 20, 471–480. https://doi.org/10.17957/IJAB/15.0618 (2018).CAS 
Article 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science. 320, 1768–1771. https://doi.org/10.1126/science.1156831 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ou-Yang, C.-F. et al. Impact of equatorial and continental airflow on primary greenhouse gases in the northern South China Sea. Environ. Res. Lett. 10, 065005. https://doi.org/10.1088/1748-9326/10/6/065005 (2015).ADS 
CAS 
Article 

Google Scholar 
Liu, B., Zhu, C., Su, J., Ma, S. & Xu, K. Record-breaking northward shift of the western North Pacific subtropical high in July 2018. J. Meteorol. Soc. Japan. 97, 913–925. https://doi.org/10.2151/jmsj.2019-047 (2019).ADS 
Article 

Google Scholar 
Huang, J. et al. Dryland climate change: Recent progress and challenges. Rev. Geophys. 55, 719–778. https://doi.org/10.1002/2016RG000550 (2017).ADS 
Article 

Google Scholar 
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250. https://doi.org/10.1073/pnas.0409902102 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Waldvogel, A. M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18. https://doi.org/10.1002/evl3.154 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vilà-Cabrera, A., Coll, L., Martínez-Vilalta, J. & Retana, J. Forest management for adaptation to climate change in the Mediterranean basin: A synthesis of evidence. For. Ecol. Manag. 407, 16–22. https://doi.org/10.1016/j.foreco.2017.10.021 (2018).Article 

Google Scholar 
He, X., Li, J., Wang, F., Zhang, J. & Chen, X. Variation and selection of Melia azedarach provenances and families. J. Northeast For. Univ. 47, 1–7. https://doi.org/10.13332/j.1000-1522.20170321 (2019).CAS 
Article 

Google Scholar 
Smith, A. B., Alsdurf, J., Knapp, M., Baer, S. G. & Johnson, L. C. Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change. Glob. Change Biol. 23, 4365–4375. https://doi.org/10.1111/gcb.13666 (2017).Article 

Google Scholar 
Bellon, M. R., Dulloo, E., Sardos, J., Thormann, I. & Burdon, J. J. In situ conservation—harnessing natural and human-derived evolutionary forces to ensure future crop adaptation. Evol. Appl. 10, 965–977. https://doi.org/10.1111/eva.12521 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bidak, L. M., Heneidy, S. Z., Halmy, M. W. A. & El-Kenany, E. T. Sustainability potential for Ginkgo biloba L. plantations under climate change uncertainty: An ex-situ conservation perspective. Acta Ecol. Sin. 42, 101–114. https://doi.org/10.1016/j.chnaes.2021.09.012 (2021).Article 

Google Scholar 
Qin, F., Liu, S. & Yu, S. Effects of allelopathy and competition for water and nutrients on survival and growth of tree species in Eucalyptus urophylla plantations. For. Ecol. Manag. 424, 387–395. https://doi.org/10.1016/j.foreco.2018.05.017 (2018).Article 

Google Scholar 
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 1–10. https://doi.org/10.1038/s41467-019-10775-z (2019).ADS 
CAS 
Article 

Google Scholar  More

Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2(10), 720–735. https://doi.org/10.1038/s43017-021-00207-2 (2021).ADS 
Article 

Google Scholar 
O’Mara, F. P. The role of grasslands in food security and climate change. Ann. Bot. 110, 1263–1270. https://doi.org/10.1093/aob/mcs209 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Eze, S., Palmer, S. M. & Chapman, P. J. Soil organic carbon stock in grasslands: Effects of inorganic fertilizers, liming and grazing in different climate settings. J. Environ. Manage. 223, 74–84. https://doi.org/10.1016/j.jenvman.2018.06.013 (2018).CAS 
Article 
PubMed 

Google Scholar 
Makoudi, B. et al. Phosphorus deficiency increases nodule phytase activity of faba bean rhizobia symbiosis. Acta Physiol. Plant 40, 63. https://doi.org/10.1007/s11738-018-2619-6 (2018).CAS 
Article 

Google Scholar 
Stecca, J. D. L. et al. Inoculation of soybean seeds coated with osmoprotector in differentssoil pH’s. Acta Sci. Agron. 41, 9. https://doi.org/10.4025/actasciagron.v41i1.39482 (2019).Article 

Google Scholar 
Afonso, S., Arrobas, M. & Rodrigues, M. Â. Soil and plant analyses to diagnose hop fields irregular growth. J. Soil Sci. Plant Nutr. 20, 1999–2013. https://doi.org/10.1007/s42729-020-00270-6 (2020).CAS 
Article 

Google Scholar 
Crews, T. E. & Peoples, M. B. Legume versus fertilizer sources of nitrogen: ecological tradeoffs and human needs. Agric. Ecosyst. Environ 102(3), 279–297. https://doi.org/10.1016/j.agee.2003.09.018 (2004).Article 

Google Scholar 
Ossler, J. N., Zielinski, C. A. & Heath, K. D. Tripartite mutualism: Facilitation or trade-offs between rhizobial and mycorrhizal symbionts of legume hosts. Am. J. Bot. 102, 1332–1341. https://doi.org/10.3732/ajb.1500007 (2015).CAS 
Article 
PubMed 

Google Scholar 
Backer, R. et al. Plant growth-promoting rhizobacteria: context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 9, 1473. https://doi.org/10.3389/fpls.2018.01473 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Keet, J. H., Ellis, A. G., Hui, C. & Le Roux, J. J. Strong spatial and temporal turnover of soil bacterial communities in South Africa’s hyper diverse fynbos biome. Soil Biol. Biochem. 136, 107541. https://doi.org/10.1016/j.soilbio.2019.107541 (2019).CAS 
Article 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103(3), 626–631. https://doi.org/10.1073/pnas.0507535103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kracmarova, M. et al. Response of soil microbes and soil enzymatic activity to 20 years of fertilization. Agronomy 10, 1542. https://doi.org/10.3390/agronomy10101542 (2020).CAS 
Article 

Google Scholar 
Wang, C., Liu, D. H. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120, 126–133. https://doi.org/10.1016/j.soilbio.2018.02.003 (2018).CAS 
Article 

Google Scholar 
Lucas, R. W. et al. A meta-analysis of the effects of nitrogen additions on base cations: Implications for plants, soils, and streams. For. Ecol. Manage. 262, 95–104. https://doi.org/10.1016/j.foreco.2011.03.018 (2011).Article 

Google Scholar 
Wang, Y. et al. Soil pH is a major driver of soil diazotrophic community assembly in Qinghai-Tibet alpine meadows. Soil Biol. Biochem. 115, 547–555. https://doi.org/10.1016/j.soilbio.2017.09.024 (2017).CAS 
Article 

Google Scholar 
Wan, S. et al. Effects of lime application and understory removal on soil microbial communities in subtropical eucalyptus L’Hér. plantations. Forests 10, 338 (2019).Article 

Google Scholar 
Yin, C., Schlatter, D. C., Kroese, D. R., Paulitz, T. C. & Hagerty, C. H. Impacts of lime application on soil bacterial microbiome in dryland wheat soil in the Pacific Northwest. Appl. Soil Ecol. 168, 104113 (2021).Article 

Google Scholar 
Schroeder, K. L., Schlatter, D. C. & Paulitz, T. C. Location-dependent impacts of liming and crop rotation on bacterial communities in acid soils of the Pacific Northwest. Appl. Soil. Ecol. 130, 59–68 (2018).Article 

Google Scholar 
Sudhakaran, M. & Ravanachandar, A. Role of soil enzymes in agroecosystem. Biotica Res. Today 2(6), 443–444 (2020).
Google Scholar 
Lacava, P. T., Machado, P. C. & de Andrade, P. H. M. Phosphate solubilization by endophytes from the tropical plants. Endophytes 3, 207–226 (2021).
Google Scholar 
Nannipieri, P., Giagnoni, L., Landi, L. & Renella, G. Role of Phosphatase Enzymes in Soil. Phosphorus in Action 215–243 (Springer, 2011).Book 

Google Scholar 
Zhang, L. et al. Soil labile organic carbon fractions and soil enzyme activities after 10 years of continuous fertilization and wheat residue incorporation. Sci. Rep. 10(1), 11318. https://doi.org/10.1038/s41598-020-68163-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Turner, B. L. Variation in pH optima of hydrolytic enzyme activities in tropical rain forest soils. Appl. Environ. Microbiol. 76, 6485–6493 (2010).ADS 
CAS 
Article 

Google Scholar 
Acosta-Martínez, V., Pérez-Guzmán, L. & Johnson, J. M. Simultaneous determination of β-glucosidase, β-glucosaminidase, acid phosphomonoesterase, and arylsulfatase activities in a soil sample for a biogeochemical cycling index. Appl. Soil Ecol. 142, 72–80. https://doi.org/10.12691/aees-8-6-26 (2019).CAS 
Article 

Google Scholar 
Parham, J. A. & Deng, S. P. Detection, quantification and characterization of β-glucosaminidase activity in soil. Soil Biol. Biochem. 32(8–9), 1183–1190. https://doi.org/10.1016/S0038-0717(00)00034-1 (2000).CAS 
Article 

Google Scholar 
Olajuyigbe, F. M. & Fatokun, C. O. Biochemical characterization of an extremely stable pH-versatile laccase from Sporothrix carnis CPF-05. Int. J. Biol. Macromol. 94, 535–543. https://doi.org/10.1016/j.ijbiomac.2016.10.037 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bhuyan, M. B. et al. Explicating physiological and biochemical responses of wheat cultivars under acidity stress: insight into the antioxidant defense and glyoxalase systems. Physiol. Mol. Biol. Plants 25, 865–879. https://doi.org/10.1007/s12298-019-00678-0 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M., Grinyer, J., Reich, P. B. & Singh, B. K. Relative importance of soil properties and microbial community for soil functionality: Insights from a microbial swap experiment. Funct. Ecol. 30, 1862–1873 (2016).Article 

Google Scholar 
Zhao, L. et al. Mercury methylation in rice paddies and its possible controlling factors in the Hg mining area, Guizhou province, Southwest China. Environ. Pollut. 215, 1–9. https://doi.org/10.1016/j.envpol.2016.05.001 (2016).CAS 
Article 
PubMed 

Google Scholar 
Ward, D., Kirkman, K., Hagenah, N. & Tsvuura, Z. Soil respiration declines with increasing nitrogen fertilization and is not related to productivity in long-term grassland experiments. Soil Biol. Biochem. 115, 415–422. https://doi.org/10.1016/j.soilbio.2017.08.035 (2017).CAS 
Article 

Google Scholar 
Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4(10), 1321–1326. https://doi.org/10.1038/s41559-020-1251-1 (2020).Article 
PubMed 

Google Scholar 
Fynn, R. W. & O’Connor, T. G. Determinants of community organization of a South African mesic grassland. J. Veg. Sci. 16(1), 93–102 (2005).Article 

Google Scholar 
Morris, C. & Fynn, R. The Ukulinga long-term grassland trials: Reaping the fruits of meticulous, patient research. Bull. Grassl. Soc. S. Afr. 11(1), 7–22 (2001).
Google Scholar 
Le Roux, N. P. & Mentis, M. Veld compositional response to fertilization in the tall grassveld of Natal. S. Afr. J. Plant Soil 3(1), 1–10. https://doi.org/10.1080/02571862.1986.10634177 (1986).Article 

Google Scholar 
Tsvuura, Z. & Kirkman, K. P. Yield and species composition of a mesic grassland savannah in South Africa are influenced by long-term nutrient addition. Austral Ecol. 38, 959–970 (2013).Article 

Google Scholar 
Goldman, E. & Green, L. H. Practical Handbook of Microbiology 2nd edn, 864 (CRC Press Taylor and Francis Group, 2008).Book 

Google Scholar 
Akinbowale, O. L., Peng, H. & Barton, M. D. Diversity of tetracycline resistance genes in bacteria from aquaculture sources in Australia. J. Appl. Microbiol. 103(5), 2016–2025 (2007).CAS 
Article 

Google Scholar 
Jackson, C. R., Tyler, H. L. & Millar, J. J. Determination of microbial extracellular enzyme activity in waters, soils, and sediments using high throughput microplate assays. Preparation of substrate and buffer solutions for colorimetric analyses of enzyme. J. Vis. Exp. 80, 1–9. https://doi.org/10.3791/50399 (2013).CAS 
Article 

Google Scholar 
Goyal, M. & Kaur, R. Interactive effect of nitrogen nutrition, nitrate reduction and seasonal variation on oxalate synthesis in leaves of Napier-bajar hybrid (Pennisetum purpureum P. glaucum). Crop Pasture Sci 70, 669–675 (2019).CAS 
Article 

Google Scholar 
Pavlovic, J., Kostic, L., Bosnic, P., Kirkby, E. A. & Nikolic, M. Interactions of silicon with essential and beneficial elements in plants. Front. Plant Sci. 12, 1224. https://doi.org/10.3389/fpls.2021.697592 (2021).Article 

Google Scholar 
Li, Y., Tremblay, J., Bainard, L. D., Cade-Menun, B. & Hamel, C. Long-term effects of nitrogen and phosphorus fertilization on soil microbial community structure and function under continuous wheat production. Environ. Microbiol. 22, 1066–1088 (2020).CAS 
Article 

Google Scholar 
Guo, Z., Han, J., Li, J., Xu, Y. & Wang, X. Effects of long-term fertilization on soil organic carbon mineralization and microbial community structure. PLoS ONE 14, e0211163 (2019).CAS 
Article 

Google Scholar 
Shang, L., Wan, L. I., Zhou, X., Li, S. & Li, X. Effects of organic fertilizer on soil nutrient status, enzyme activity, and bacterial community diversity in Leymus chinensis steppe in Inner Mongolia, China. PLoS ONE https://doi.org/10.1371/journal.pone.0240559 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gautam, A. et al. Responses of soil microbial community structure and enzymatic activities to long-term application of mineral fertilizer and beef manure. Environ. Sustain. Indic. 8, 10007S. https://doi.org/10.1016/j.indic.2020.100073 (2020).Article 

Google Scholar 
Wang, J., Lu, X., Zhang, J., Wei, G. & Xiong, Y. Regulating soil bacterial diversity, community structure and enzyme activity using residues from golden apple snails. Sci. Rep. 10(1), 1–11 (2020).CAS 
Article 

Google Scholar 
Xu, D., Carswell, A., Zhu, Q., Zhang, F. & de Vries, W. Modelling long-term impacts of fertilization and liming on soil acidification at Rothamsted experimental station. Sci. Total Environ. 713, 136249 (2020).ADS 
CAS 
Article 

Google Scholar 
von Tucher, S., Hörndl, D. & Schmidhalter, U. Interaction of soil pH and phosphorus efficacy: Long-term effects of P fertilizer and lime applications on wheat, barley, and sugar beet. Ambio 47, 41–49 (2018).Article 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl. Acad. Sci. USA 112, 10967–10972 (2015).ADS 
CAS 
Article 

Google Scholar 
Pan, J. et al. Dynamics of soil nutrients, microbial community structure, enzymatic activity, and their relationships along a chronosequence of Pinus massoniana plantations. Forests 12, 376 (2021).Article 

Google Scholar 
Andrés, J. A., Rovera, M., Guiñazú, L. B., Pastor, N. A. & Rosas, S. B. Role of in crop improvement. In Bacteria in Agrobiology: Plant Growth Responses 107–122 (Springer, 2011).Chapter 

Google Scholar 
Jeong, H., Choi, S. K., Ryu, C. M. & Park, S. H. Chronicle of a soil bacterium: Paenibacillus polymyxa E681 as a tiny guardian of plant and human health. Front. Microbiol. 10, 467 (2019).Article 

Google Scholar 
Garbeva, P. V., van Veen, J. A. & van Elsas, J. D. Microbial diversity in soil: Selection of microbial populations by plant and soil type and implications for disease suppressiveness. Annu. Rev. Phytopathol. 42, 243–270. https://doi.org/10.1146/annurev.phyto.42.012604.135455 (2004).CAS 
Article 
PubMed 

Google Scholar 
Sinsabaugh, R. L. & Moorhead, D. L. Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition. Soil Biol. Biochem. 26(10), 1305–1311. https://doi.org/10.1016/0038-0717(94)90211-9 (1994).Article 

Google Scholar 
Xiao, W., Chen, X., Jing, X. & Zhu, B. A meta-analysis of soil extracellular enzyme activities in response to global change. Soil Biol. Biochem. 123, 21–32. https://doi.org/10.1016/j.soilbio.2018.05.001 (2018).CAS 
Article 

Google Scholar 
Billah, M. et al. Phosphorus & phosphate solubilizing bacteria: Keys for sustainable agriculture. Geomicrobiol. J. 36(10), 904–916. https://doi.org/10.1080/01490451.2019.1654043 (2019).CAS 
Article 

Google Scholar 
Turner, B. L., McKelvie, I. D. & Haygarth, P. M. Characterisation of water-extractable soil organic phosphorus by phosphatase hydrolysis. Soil Biol Biochem. 34, 27–35. https://doi.org/10.1016/S0038-0717(01)00144-4 (2002).CAS 
Article 

Google Scholar 
van Aarle, I. M. & Plassard, C. Spatial distribution of phosphatase activity associated with ectomycorrhizal plants related to soil type. Soil Biol. Biochem. 42(2), 324–330. https://doi.org/10.1016/j.soilbio.2009.11.011 (2020).CAS 
Article 

Google Scholar  More

Experimental designThis experiment was set up containing two levels of soil biodiversity (high and low soil biodiversity) and seven treatments considering the number of global change factors (GCFs) (0, 1, 2, 4, 6, 8, 10) (Table 1, Supplementary Fig. 1 and Supplementary Data 1). We used the dilution-to-extinction approach to create the high and low soil biodiversity treatments (Supplementary Methods). Soil dilution can lead to a gradual loss of rare soil microbes, which can simulate a realistic loss of soil biodiversity, because rare soil microbes are more sensitive to anthropogenic pressures, e.g., warming, nitrogen addition and drought15. We note that the low soil biodiversity treatment is a subset of the high biodiversity, as many rare species have been eliminated through the dilution; this approach will likely lead to relatively more tolerant microbes in the resulting communities.An increasing number of GCFs was created inspired by the experimental design of the studies on biodiversity-ecosystem function relationships, based on random sampling from a species pool5,6,14. The combination of multiple GCFs was replicated 15 times at each level by randomly selecting GCFs from a pool with 10 GCFs for each replicate (Table 1 and Supplementary Data 1). For each replicate of combined GCFs, there were identical GCF combinations between the high and low soil biodiversity treatments to avoid a confounding effect of GCF combination and soil biodiversity treatments. The pool of 10 GCFs included: warming, nitrogen deposition, drought, heavy metal pollution, plastic mulching film residues, salinity, agricultural fungicide, bactericide application, surfactant contaminant and soil compaction (Supplementary Methods). These GCFs frequently occur in intensively managed agroecosystems and are treated as anthropogenic pressures10,13,14,15.MicrocosmsThis experiment was conducted using 50 ml conical Mini Bioreactors (Product Number 431720, Corning Inc., NY) as experimental units (Supplementary Fig. 2). Each Mini Bioreactor has four vents in the cap, where a hydrophobic membrane avoids microbial contamination but allows gas exchange. We filled each Mini Bioreactor with 40.0 g (dry weight, d.w.) of soil in total, which received the appropriate treatments.Soil sterilization and inoculum preparationWe collected the field soil from the top 10 cm of an intensive farming system in Berlin (52.466°N, 13.303°E). Field soil was passed through a 2 mm mesh to remove large roots and stones. We sterilized 20 kg of soil for 90 min at 121 °C, and stored 2 kg of fresh soil at 4 °C. The dilution-to-extinction approach38,39,40,41,42 was used to create high and low soil biodiversity (Supplementary Fig. 1). A parent inoculum suspension was prepared by mixing 100 g of fresh soil with 200 ml of sterilized VE water. The sediment settled for 1 min. The upper 200 ml of soil suspension was treated as parent inoculum suspension. 50 ml of parent inoculum suspension was added to 500 g of sterilized soil in a plastic bag, and homogenized by turning the bag up and down 30 times to obtain the inoculum of high soil biodiversity. Another 5 ml of parent inoculum suspension was mixed with 45 ml of sterilized parent inoculum suspension to create the 10-1 dilution. This procedure was repeated five times to reach the 10-6 dilution. 50 ml of the 10-6 dilution was mixed and homogenized with 500 g of sterilized soil in a plastic bag to obtain the inoculum of low soil biodiversity. This whole dilution procedure was repeated five times to obtain 10 bags of soil inoculum (five bags for each soil biodiversity inoculum).Sterile water was added to each plastic bag to reach the water content of the fresh soil in the field. All bags were closed with a sterilized cotton plug and a rubber band to avoid microbial contamination but allow gas exchange42. All bags were incubated in a dark room at 20 °C until similar microbial abundance was observed between the high and low soil biodiversity inoculum. Soil inoculum was homogenized by shaking and turning the bags once a week. After incubation, 2.0 g of soil in each bag was collected and stored at −80 °C for DNA extraction. Quantitative real-time PCR (qPCR) was used to determine fungal and bacterial abundance. In the present study, it took two months to recover soil microbial biomass (Supplementary Fig. 3).The implementation of GCFs and harvestAgroecosystems, some of the most intensively managed ecosystems, are affected by the co-occurrence of multiple GCFs13,14,15. This study focused on GCFs that frequently occur in agroecosystems, including warming, nitrogen deposition, drought, heavy metal pollution, plastic film residues, salinity, agricultural fungicide and bactericide application, surfactant contaminant and soil compaction. We present the rationale for the 10 tested GCFs in the Supplementary Methods.Loading soils were used to achieve an effective mixing of chemical agents into 40.0 g soil in each Mini Bioreactor. We created separate ‘loading soil’ for each GCF with chemical addition by mixing an appropriate dose of a chemical agent with sterilized soil through careful homogenization. This was done to avoid exaggerated effects of more concentrated chemicals when mixed with soil. For each chemical, 1.0 g (d.w.) of loading soil contained an appropriate dose for 40.0 g soil in a Mini Bioreactor. For instance, 1 634 mg of NH4NO3 was mixed with 100 g (d.w.) of sterilized soil, to ensure that there was about 16.34 mg of NH4NO3 in 1.0 g of sterilized loading soil. We weighed 40.0 g (d.w.) of soils, including 1.0 g (d.w.) of each loading soil, 5.0 g (d.w.) of soil inoculum (high or low soil biodiversity), an appropriate amount of film (0 or 0.16 g plastic film) and sterilized soil, according to GCF combination for each experimental unit. We put 40.0 g of mixed soils into a clean and sterilized cup (200 ml) with a cap, and then homogenized it by turning the cup up and down for 5 min using a shaking machine (Heidolph Reax 2, Heidolph Instruments GmbH & CO. KG, Schwabach, Germany). After homogenization, 40.0 g of mixed soils was transferred to a Mini Bioreactor, and a mesh bag containing about 100 mg of dry Medicago lupulina leaves (65 °C for 72 h) was buried 1 cm below the soil surface. We used a stick to press soils in each Mini Bioreactor to simulate an ambient condition (1.3 g cm−3) or mechanical compaction (1.7 g cm−3) in farmland.For the warming treatment with an increment of 5.0 °C over the ambient temperature (20 °C), we wrapped heating cables (Exo Terra PT-2012; Hagen Deutschland GmbH & Co. KG, Holm, Germany) around the outside of the bioreactors. A set temperature was maintained by temperature controllers (Voltcraft ETC-902; Conrad Electronic SE, Hirschau, Germany), which can switch off and on heating cables depending on the real-time temperature of the outside surface of Mini Bioreactors. Mini Bioreactors were placed in beakers filled with sand to reduce thermal radiation from neighboring units. At the start of the experiment, we added suitable amounts of sterilized water to each experimental unit to reach 60% of water holding capacity for the non-drought treatment and 30% water holding capacity for the drought treatment.All Mini Bioreactors were incubated at 20.0 °C in the dark for six weeks before the final harvest. Because there was 2.0 g of weight loss on average in each Mini Bioreactor in the first three weeks, we added 2 ml of sterilized water to each Mini Bioreactor on the first day of the fourth week. During the final harvest, soil in each Mini Bioreactor was gently homogenized using a spoon, and then 2.0 g of fresh soil was collected and stored at −80 °C for DNA extraction; 5.0 g was stored at 4 °C for the determination of soil enzyme activity; the leftover was oven-dried at 40 °C for other measurements. DNA of each soil sample was extracted from 250 mg soil, using DNeasy PowerSoil Pro Kit (QIAGEN GmbH, Hilden, Germany), following manufacturer’s instructions. Soil DNA extraction was stored at −80 °C for further analysis.The measurement of response variablesWe measured the following response variables: microbial activity (soil respiration), microbial abundance (bacterial and fungal abundance), nutrient cycling (litter decomposition rate and soil enzyme activity), physical properties (water-stable soil aggregates and soil water repellency), bacterial and fungal biodiversity (richness and microbial network features) (See details in the Supplementary Methods). We measured soil respiration as CO2 concentration (ppm h−1 g−1 soil) in the third and sixth week as an indicator for soil microbial activity. Bacterial and fungal abundance was estimated by qPCR. The proportional loss of litter (Medicago lupulina leaves) dry weight during soil incubation was used as an indication of decomposition rate. We measured the activity of β-glucosidase (cellulose degradation), β-D-celluliosidase (cellulose degradation), N-acetyl-β-glucosaminidase (chitin degradation) and phosphatase (organic phosphorus mineralization) using high throughput microplate assay43,44. A modified protocol by Kemper and Rosenau was used to measure water-stable soil aggregates45. Soil water repellency was measured using the water drop penetration time method46. High throughput sequencing (Illumina MiSeq) was used to measure the taxonomic composition of soil fungal and bacterial communities with the primers fITS7 and ITS4 for fungi and 515F-Y and 806 R for bacteria47,48 (Supplementary Methods).Statistical analysesFor diversity and community composition analysis, we excluded the samples with less than 1% of the observations of the largest sample in the ASV table. For network analysis, we then removed ASVs with low prevalence, which presented less than 20% of samples across all experimental units to reduce the high percentage of zero counts. A co-occurrence network was constructed based on both fungal and bacterial ASV tables. The PLNnetwork function in the R package PLNmodels was employed to infer the network, using a sparse multivariate Poisson log-normal (PLN) model49. According to the Extended Bayesian Information Criterion (EBIC), the best model was extracted with the function getBestModel. The network was compartmentalized into different modules using the cluster_fast_greedy function in the igraph package and visualized with partial correlations with |ρ| > 0.05. We focused on the response of the relative abundance of modules, also known as clusters, which represent the closely associated microbes, e.g., groups of coexisting or co-evolving microbes27. The relative abundance of modules was calculated by summing relative abundances for individual ASVs in modules. We used the package FUNGuildR50 to taxonomically parse fungal trait information, using the FUNGuild database51.For each single GCF treatment, we took the average response from the 10 replicates before analysis. To confirm how the effect of soil biodiversity treatment can change along with the increasing number of GCFs, we quantified the effect size of soil biodiversity treatment for each response variable using Hedges’ g (mean and 95% CIs) at each level of the number of GCFs, using the R package effsize52. Hedges’ g is calculated as the mean difference between the high and low soil biodiversity treatments in units of the pooled standard deviation as a paired-samples because there were identical GCFs and GCF combinations for both high and low biodiversity conditions, with the exception of the zero and 10 GCF treatments.To evaluate how each of the response variables changes along with the number of GCFs, we applied a generalized additive model (GAM)53. GAM is a penalized generalized linear model that fits a nonparametric, nonlinear smooth curve54. The degree of smoothness of model terms is estimated as part of fitting, using the generalized cross validation. We reasonably assume that the curve shapes are different between high and low soil biodiversity treatments. Therefore, we included biodiversity conditions (low/high) as the model intercept and as the “factor smooth” smoothing class, where a smooth function is created for each factor level independently55. For GAM modeling, we used the mgcv package55. The dimension of the basis used to represent the smooth term was set as k = 5 so that the model does not overfit to the data. For this, we compared some other values (from 3 to 8) and confirmed that the results are essentially the same within the tested range. The other parameters were set as default.The relationships between soil microbial indices and other soil indices were tested using Spearman correlation in the package microbiome, and the adjustment method “fdr” was employed to control the false discovery rate for multiple testing correction56. For the further multivariate integration of soil functions/properties and composition of modules, the DIABLO (Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies) was employed to detect correlation (Pearson’s correlation |r| > 0.5) among variables using the package mixOmics57.The Z-scores for each of the eight soil functions (as shown in Fig. 1, with the exception of soil water repellency) were evaluated, and then we computed an improved weighted multifunctionality metric to represent soil multifunctionality (Supplementary Methods)58. Structural equation models (SEMs) were used to reveal the direct and indirect effects of an increasing number of GCFs on soil multifunctionality within each soil biodiversity treatment using the package lavaan59. We assumed that an increasing number of GCFs influences soil multifunctionality by regulating the bacterial and fungal abundance and the relative abundance of modules. All response variables were standardized to the same comparison scale using the z-score transformation before constructing SEMs. Models with optimal fitting indices were reported (Supplementary Fig. 11).The permutational multivariate analysis (ADONIS) and non-metric multidimensional scaling (NMDS) ordination based on the Bray-Curtis distance were conducted to test the effect of soil biodiversity and GCF treatments on the community composition of bacteria and fungi using the R package vegan60. For the data handling, processing, and visualization, we used the packages tidyverse61, reshaping62, cowplot63, RColorBrewer64, qgraph65, igraph66, factoextra67, phyloseq68 and itsadug69. These data manipulation and analyses were conducted using R version 4.1.370. The R script and data are available in a publicly accessible database71.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More