Philippa Kaur

Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).ADS 
CAS 

Google Scholar 
Previdi, M., Smith, K. L. & Polvani, L. M. Arctic amplification of climate change: a review of underlying mechanisms. Environ. Res. Lett. 16, 093003 (2021).ADS 
CAS 

Google Scholar 
Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3, 1–10 (2022).ADS 

Google Scholar 
Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).ADS 

Google Scholar 
Kopec, B. G., Feng, X., Michel, F. A. & Posmentier, E. S. Influence of sea ice on Arctic precipitation. Proc. Natl. Acad. Sci. 113, 46–51 (2016).ADS 
CAS 

Google Scholar 
Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3, 10–23 (2022).ADS 

Google Scholar 
Overland, J. et al. The urgency of Arctic change. Polar Sci. 21, 6–13 (2019).ADS 

Google Scholar 
Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).ADS 
CAS 

Google Scholar 
Ciavarella, A. et al. Prolonged Siberian heat of 2020 almost impossible without human influence. Clim. Change 166, 9 (2021).ADS 

Google Scholar 
Dobricic, S., Russo, S., Pozzoli, L., Wilson, J. & Vignati, E. Increasing occurrence of heat waves in the terrestrial Arctic. Environ. Res. Lett. 15, 024022 (2020).ADS 

Google Scholar 
Graham, R. M. et al. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 44, 6974–6983 (2017).ADS 

Google Scholar 
Knight, J. & Harrison, S. The impacts of climate change on terrestrial Earth surface systems. Nat. Clim. Change 3, 24–29 (2013).ADS 

Google Scholar 
Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).ADS 

Google Scholar 
Beck, P. S. A. et al. Changes in forest productivity across Alaska consistent with biome shift. Ecol. Lett. 14, 373–379 (2011).
Google Scholar 
Reichle, L. M., Epstein, H. E., Bhatt, U. S., Raynolds, M. K. & Walker, D. A. Spatial Heterogeneity of the Temporal Dynamics of Arctic Tundra Vegetation. Geophys. Res. Lett. 45, 9206–9215 (2018).ADS 

Google Scholar 
Sturm, M., Racine, C. & Tape, K. Increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).ADS 
CAS 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).ADS 

Google Scholar 
Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).ADS 

Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).ADS 
CAS 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Google Scholar 
Virkkala, A.-M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Change Biol. 27, 4040–4059 (2021).CAS 

Google Scholar 
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).ADS 

Google Scholar 
Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).ADS 

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).
Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Google Scholar 
Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Sci. Data 7, 248 (2020).
Google Scholar 
Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).
Google Scholar 
Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).ADS 

Google Scholar 
Slatyer, R. A., Umbers, K. D. L. & Arnold, P. A. Ecological responses to variation in seasonal snow cover. Conserv. Biol. 36, e13727 (2022).
Google Scholar 
Serreze, M. C. et al. Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts. Environ. Res. Lett. 16, 105009 (2021).ADS 

Google Scholar 
López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Change Biol. 27, 1704–1720 (2021).ADS 

Google Scholar 
Ennos, A. R. Wind as an ecological factor. Trends Ecol. Evol. 12, 108–111 (1997).CAS 

Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).ADS 

Google Scholar 
Boussetta, S. et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere 12, 723 (2021).ADS 
CAS 

Google Scholar 
Munõz-Sabater, J. ERA5-Land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2019). Munõz-Sabater, J. ERA5-Land hourly data from 1950 to 1980. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).Hoyer, S. & Hamman, J. xarray: N-D labeled Arrays and Datasets in Python. J. Open Res. Softw. 5, 10 (2017).
Google Scholar 
Sen, P. K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).MATH 

Google Scholar 
Theil, H. A rank-invariant method of linear and polynomial regression analysis I, II and III. Indag. Math. 173 (1950).Hussain, M. M. & Mahmud, I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019).ADS 

Google Scholar 
Aalto, J. et al. High-resolution analysis of observed thermal growing season variability over northern Europe. Clim. Dyn. 58, 1477–1493 (2022).
Google Scholar 
Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 035004 (2018).ADS 

Google Scholar 
Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26 (2012).Wu, F., Jiang, Y., Wen, Y., Zhao, S. & Xu, H. Spatial synchrony in the start and end of the thermal growing season has different trends in the mid-high latitudes of the Northern Hemisphere. Environ. Res. Lett. 16, 124017 (2021).ADS 

Google Scholar 
Ruosteenoja, K., Räisänen, J., Venäläinen, A. & Kämäräinen, M. Projections for the duration and degree days of the thermal growing season in Europe derived from CMIP5 model output. Int. J. Climatol. 36, 3039–3055 (2016).
Google Scholar 
Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018).
Google Scholar 
McMaster, G. S. & Wilhelm, W. W. Growing degree-days: one equation, two interpretations. Agric. For. Meteorol. 87, 291–300 (1997).ADS 

Google Scholar 
Körner, C. Plant adaptation to cold climates. F1000Research 5, F1000 Faculty Rev-2769 (2016).Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Change 10, 1143–U134 (2020).ADS 

Google Scholar 
Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).ADS 

Google Scholar 
Mooney, P. A. & Li, L. Near future changes to rain-on-snow events in Norway. Environ. Res. Lett. 16, 064039 (2021).ADS 

Google Scholar 
Preece, C., Callaghan, T. V. & Phoenix, G. K. Impacts of winter icing events on the growth, phenology and physiology of sub-arctic dwarf shrubs. Physiol. Plant. 146, 460–472 (2012).CAS 

Google Scholar 
Putkonen, J. & Roe, G. Rain-on-snow events impact soil temperatures and affect ungulate survival. Geophys. Res. Lett. 30, (2003).Treharne, R., Bjerke, J. W. & Tømmervik, H. & Phoenix, G. K. Development of new metrics to assess and quantify climatic drivers of extreme event driven Arctic browning. Remote Sens. Environ. 243, 111749 (2020).ADS 

Google Scholar 
Bokhorst, S. et al. Impacts of extreme winter warming events on plant physiology in a sub-Arctic heath community. Physiol. Plant. 140, 128–140 (2010).CAS 

Google Scholar 
Russo, S., Sillmann, J. & Fischer, E. M. Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ. Res. Lett. 10, 124003 (2015).ADS 

Google Scholar 
Alduchov, O. A. & Eskridge, R. E. Improved Magnus Form Approximation of Saturation Vapor Pressure. J. Appl. Meteorol. Climatol. 35, 601–609 (1996).ADS 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 

Google Scholar 
De Frenne, P. et al. Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27, 2279–2297 (2021).ADS 

Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).ADS 
CAS 

Google Scholar 
Berner, L. T., Jantz, P., Tape, K. D. & Goetz, S. J. Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska. Environ. Res. Lett. 13, 035002 (2018).ADS 

Google Scholar 
Walker, D. A. et al. Phytomass, LAI, and NDVI in northern Alaska: Relationships to summer warmth, soil pH, plant functional types, and extrapolation to the circumpolar Arctic. J. Geophys. Res. Atmospheres 108, (2003).Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).
Google Scholar 
Peng, S. et al. Change in snow phenology and its potential feedback to temperature in the Northern Hemisphere over the last three decades. Environ. Res. Lett. 8, 014008 (2013).ADS 

Google Scholar 
Wheeler, J. A. et al. Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175, 219–229 (2014).ADS 
CAS 

Google Scholar 
Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).ADS 

Google Scholar 
Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int. J. Biometeorol. 61, 349–361 (2017).ADS 

Google Scholar 
Kling, M. M. & Ackerly, D. D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Change 10, 868–875 (2020).ADS 

Google Scholar 
Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608, 546–551 (2022).ADS 
CAS 

Google Scholar 
Nathan, R. et al. Mechanisms of long-distance dispersal of seeds by wind. Nature 418, 409–413 (2002).ADS 
CAS 

Google Scholar 
Sakai, A. Mechanism of Desiccation Damage of Conifers Wintering in Soil-Frozen Areas. Ecology 51, 657–664 (1970).
Google Scholar 
Wilson, J. W. Notes on Wind and its Effects in Arctic-Alpine Vegetation. J. Ecol. 47, 415–427 (1959).
Google Scholar 
Rantanen, M. et al. Bioclimatic atlas of the terrestrial Arctic, figshare, https://doi.org/10.6084/m9.figshare.c.6216368 (2023).Räisänen, J. Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change. The Cryosphere 15, 1677–1696 (2021).ADS 

Google Scholar 
Xu, J., Ma, Z., Yan, S. & Peng, J. Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. J. Hydrol. 605, 127353 (2022).
Google Scholar 
Behrangi, A., Singh, A., Song, Y. & Panahi, M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations. Geophys. Res. Lett. 46, 11358–11366 (2019).ADS 

Google Scholar 
Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J. & Lawrimore, J. H. The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. J. Clim. 31, 9835–9854 (2018).ADS 

Google Scholar 
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An Overview of the Global Historical Climatology Network-Daily Database. J. Atmospheric Ocean. Technol. 29, 897–910 (2012).ADS 

Google Scholar 
Atlaskin, E. & Vihma, T. Evaluation of NWP results for wintertime nocturnal boundary-layer temperatures over Europe and Finland. Q. J. R. Meteorol. Soc. 138, 1440–1451 (2012).ADS 

Google Scholar 
Lindsay, R., Wensnahan, M., Schweiger, A. & Zhang, J. Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic. J. Clim. 27, 2588–2606 (2014).ADS 

Google Scholar 
Wang, C., Graham, R. M., Wang, K., Gerland, S. & Granskog, M. A. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution. The Cryosphere 13, 1661–1679 (2019).ADS 

Google Scholar 
Wesslén, C. et al. The Arctic summer atmosphere: an evaluation of reanalyses using ASCOS data. Atmospheric Chem. Phys. 14, 2605–2624 (2014).ADS 

Google Scholar  More

Chakraborty, A. & Jones, T. E. in Natural Heritage of Japan Geoheritage, Geoparks and Geotourism (Conservation and Management Series) (eds Chakraborty, A. et al.) Ch. 16 (Springer, 2018).Nakamura, K. Possible nascent trench along the eastern Japan Sea as the convergent boundary between Eurasian and North American plates (in Japanese). Bull. Earthq. Res. Inst. 58, 711–722 (1983).
Google Scholar 
Seno, T. Is northern Honshu a microplate? Tectonophysics 115, 177–196 (1985).Article 

Google Scholar 
Ogawa, Y., Takami, Y. & Takazawa, S. in Formation and Applications of the Sedimentary Record in Arc Collision Zones Vol. 436 (eds Draut, A. E. at al.) 155–170 (Geological Society of America, 2008).Tsuya, H. & Morimoto, R. Types of volcanic eruptions in Japan (in Japanese). Bull. Volcanol. 26, 209–222 (1963).Article 
CAS 

Google Scholar 
Aoki, Y., Tsunematsu, K. & Yoshimoto, M. Recent progress of geophysical and geological studies of Mt. Fuji Volcano, Japan. Earth Sci. Rev. 194, 264–282 (2019).Article 

Google Scholar 
Tsuchi, R. Geology and groundwater of Mt. Fuji, Japan (in Japanese). J. Geogr. 126, 33–42 (2017).Article 

Google Scholar 
Vittecoq, B., Reninger, P.-A., Lacquement, F., Martelet, G. & Violette, S. Hydrogeological conceptual model of andesitic watersheds revealed by high-resolution heliborne geophysics. Hydrol. Earth Sys. Sci. 23, 2321–2338 (2019).Article 
CAS 

Google Scholar 
Yamamoto, S. Hydrologic study of volcano Fuji and its adjacent areas (in Japanese). Geogr. Rev. Jpn 43, 267–184 (1970).Article 

Google Scholar 
Yamamoto, T. & Nakada, S. in Volcanic Hazards, Risks, and Disasters (eds Shroder, J. F. & Papale, P.) 355–376 (Elsevier, 2015).Hasegawa, A. et al. Plate subduction, and generation of earthquakes and magmas in Japan as inferred from seismic observations: an overview. Gondwana Res. 16, 370–400 (2009).Article 

Google Scholar 
Kashiwagi, H. & Nakajima, J. Three‐dimensional seismic attenuation structure of central Japan and deep sources of arc magmatism. Geophys. Res. Lett. 46, 13746–13755 (2019).Article 

Google Scholar 
Obrochta, S. P. et al. Mt. Fuji Holocene eruption history reconstructed from proximal lake sediments and high-density radiocarbon dating. Quat. Sci. Rev. 200, 395–405 (2018).Article 

Google Scholar 
Tosaki, Y. & Asai, K. Groundwater ages in Mt. Fuji (in Japanese). J. Geogr. 126, 89–104 (2017).Article 

Google Scholar 
Imtiaz, M. et al. Vanadium, recent advancements and research prospects: a review. Environ. Int. 80, 79–88 (2015).Article 
CAS 

Google Scholar 
Koshimizu, S., & Tomura, K. (2000). Geochemical behavior of trace vanadium in the spring, groundwater and lake water at the foot of Mt. Fuji, Central Japan. In K. Sato & Y. Iwasa (Eds.), Groundwater Updates. Springer, Tokyo. 171-176. https://doi.org/10.1007/978-4-431-68442-8_29Ono, M. et al. Regional groundwater flow system in a stratovolcano adjacent to a coastal area: a case study of Mt. Fuji and Suruga Bay, Japan. Hydrogeol. J. 27, 717–730 (2019).Article 

Google Scholar 
UNESCO Fujisan, Sacred Place and Source of Artistic Inspiration (World Heritage Convention, 2013); https://whc.unesco.org/en/list/1418Nationally Designated Cultural Properties Database (in Japanese) (Agency of Cultural Affairs Japan, 2020); https://kunishitei.bunka.go.jp/bsys/indexShowa’s 100 Famous Waters of Japan (Ministry of the Environment Japan (MOEJ), 1985); https://www.env.go.jp/water/meisui/Heisei’s 100 Famous Waters of Japan (MOEJ, 2009): https://www.env.go.jp/water/meisui/An Overview of the Bottled Water Market in Japan (Frost & Sullivan, 2016).Fujiyoshida Mineral Water Conservation Association FMWCA Regulations (in Japanese) (Mt. Fuji Springs Inc., 2016); http://fujiyoshida-hozen.org/aboutwater/Adachi, Y. et al. The physiological effects of the undercurrent water from Mt. Fuji on type 2 diabetic KK-Ay mice. Biomed. Res. Trace Elem. 15, 76–78 (2004).CAS 

Google Scholar 
Isogai, A., Kanada, R., Iawata, H. & Sudo, S. The influence of vanadium on the components of hineka (in Japanese). J. Brew. Soc. Jpn 107, 443–450 (2012).Article 

Google Scholar 
Tamada, Y., Tokui, M., Yamashita, N., Kubodera, T. & Akashi, T. Analyzing the relationship between the inorganic element profile of sake dilution water and dimethyl trisulfide formation using multi-element profiling. J. Biosci. Bioeng. 127, 710–713 (2019).Article 
CAS 

Google Scholar 
London Sake Challenge 2018: Awarded Sake (Sake Somelier Association (SSA), 2018); https://londonsakechallenge.com/awarded-sake-2019/London Sake Challenge 2019: Awarded Sake (SSA, 2019); https://londonsakechallenge.com/awarded-sake-2019/Yasuhara, M., Hayashi, T. & Asai, K. Overview of the special issue “Groundwater in Mt. Fuji”. J. Geogr. 126, 25–27 (2017).Article 

Google Scholar 
Yasuhara, M., Hayashi, T., Asai, K., Uchiyama, M. & Nakamura, T. Overview of the special issue “Groundwater in Mt. Fuji (Part 2)”. J. Geogr. 129, 657–660 (2020).Article 

Google Scholar 
Gmati, S., Tase, N., Tsujimura, M. & Tosaki, Y. Aquifers interaction in the southwestern foot of Mt. Fuji, Japan, examined through hydrochemistry and statistical analyses. Hydrol. Res. Lett. 5, 58–63 (2011).Article 

Google Scholar 
Ikeda, K. Water-sediments interaction of salinized groundwater, and its chemical compositions in coastal areas (in Japanese). Jpn. J. Limnol. 46, 303–314 (1985).Article 
CAS 

Google Scholar 
Kato, K. et al. Unveiled groundwater flushing from the deep seafloor in Suruga Bay. Limnology https://doi.org/10.1007/s10201-014-0445-0 (2015).Segawa, T. et al. Microbes in groundwater of a volcanic mountain, Mt. Fuji; 16S rDNA phylogenetic analysis as a possible indicator for the transport routes of groundwater. Geomicrobiol. J. 32, 677–688 (2015).Article 

Google Scholar 
Sugiyama, A., Masuda, S., Nagaosa, K., Tsujimura, M. & Kato, K. Tracking the direct impact of rainfall on groundwater at Mt. Fuji by multiple analyses including microbial DNA. Biogeosciences 15, 721–732 (2018).Article 
CAS 

Google Scholar 
Yasuhara, M., Kazahaya, K. & Marui, A. in Fuji Volcano (eds Aramaki, S. et al.) 389–405 (Yamanashi Institute of Environmental Sciences, 2007).Tsuchi, R. in Fuji Volcano (eds Aramaki, S. et al.) 375–387 (Yamanashi Institute of Environmental Sciences, 2007).Takada, A., Yamamoto, T., Ishizuka, Y. & Nakano, S. in Miscellaneous Map Series No. 12, 56 (Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST), 2016).Uchiyama, T. Hydrogeological structure and hydrological characterization in the northern foot area of Fuji volcano, central Japan (in Japanese). J. Geogr. 129, 697–724 (2020).Article 

Google Scholar 
Ikawa, R. et al. in S-5: Seamless Geoinformation of Coastal Zone “Northern Coastal Zone of Suruga Bay” (GSJ, AIST, 2016).AIST 2014 Marine Geological and Environmental Survey Confirmation Technology Development Results Report (in Japanese) (AIST, 2015).AIST 2015 Marine Geological and Environmental Survey Confirmation Technology Development Results Report (in Japanese) (AIST, 2016).Lin, A., Iida, K. & Tanaka, H. On-land active thrust faults of the Nankai–Suruga subduction zone: the Fujikawa-kako Fault Zone, central Japan. Tectonophysics 601, 1–19 (2013).Article 

Google Scholar 
Fujita, E. et al. Stress field change around the Mount Fuji volcano magma system caused by the Tohoku megathrust earthquake, Japan. Bull. Volcanol. 75, 679 (2013).Article 

Google Scholar 
Kano, K.-I., Odawara, K., Yamamoto, G. & Ito, T. Tectonics of the Fujikawa-kako Fault Zone around the Hoshiyama Hills, central Japan, since 1 Ma. Geosci. Rep. Shizuoka Univ. 46, 19–49 (2019).
Google Scholar 
Schilling, O. S., Cook, P. G. & Brunner, P. Beyond classical observations in hydrogeology: the advantages of including exchange flux, temperature, tracer concentration, residence time and soil moisture observations in groundwater model calibration. Rev. Geophys. 57, 146–182 (2019).Article 

Google Scholar 
Schilling, O. S. et al. Quantifying groundwater recharge dynamics and unsaturated zone processes in snow-dominated catchments via on-site dissolved gas analysis. Water Resour. Res. 57, e2020WR028479 (2021).Article 

Google Scholar 
National Hydrological Environment Database of Japan (GSJ, AIST, 2020).Hayashi, T. Understanding the groundwater flow system at the northern part of Mt. Fuji: current issues and prospects (in Japanese). J. Geogr. 129, 677–695 (2020).Article 

Google Scholar 
Yasuhara, M., Marui, A., & Kazahaya, K. (1997). Stable isotopic composition of groundwater from Mt. Yatsugatake and Mt. Fuji, Japan. Proceedings of the Rabat Symposium. Rabat Symposium, April 1997, Wallingford, UK.Jasechko, S. Global isotope hydrogeology—review. Rev. Geophys. https://doi.org/10.1029/2018RG000627 (2019).Yaguchi, M., Muramatsu, Y., Chiba, H., Okumura, F. & Ohba, T. The origin and hydrochemistry of deep well waters from the northern foot of Mt. Fuji, central Japan. Geochem. J. 50, 227–239 (2016).Article 
CAS 

Google Scholar 
Aizawa, K. et al. Gas pathways and remotely triggered earthquakes beneath Mount Fuji, Japan. Geology 44, 127–130 (2016).Article 
CAS 

Google Scholar 
Kipfer, R. et al. Injection of mantle type helium into Lake Van (Turkey): the clue for quantifying deep water renewal. Earth Planet. Sci. Lett. 125, 357–370 (1994).Article 
CAS 

Google Scholar 
Kipfer, R., Aeschbach-Hertig, W., Peeters, F. & Stute, M. in Noble Gases in Geochemistry and Cosmochemistry Reviews in Mineralogy and Geochemistry Vol. 47 (eds Porcelli, D. et al.) Ch. 14 (De Gruyter, 2002).Sano, Y. & Fischer, T. P. in The Noble Gases as Geochemical Tracers: Advances in isotope geochemistry (ed. Burnard, O.) Ch. 10 (Springer, 2013).Sano, Y. & Wakita, H. Distribution of 3He/4He ratios and its implications for geotectonic structure of the Japanese Islands. J. Geophys. Res. 90, 8729–8741 (1985).Article 
CAS 

Google Scholar 
Tomonaga, Y. et al. Fluid dynamics along the Nankai Trough: He isotopes reveal direct seafloor mantle-fluid emission in the Kumano Basin (Southwest Japan). ACS Earth Space Chem. 4, 2015–2112 (2020).Article 

Google Scholar 
Chen, A. et al. Mantle fluids associated with crustal-scale faulting in a continental subduction setting, Taiwan. Sci Rep. 9, 10805 (2019).Article 

Google Scholar 
Crossey, L. J. et al. Continental smokers couple mantle degassing and distinctive microbiology within continents. Earth Planet. Sci. Lett. 435, 22–30 (2016).Article 
CAS 

Google Scholar 
Crossey, L. J. et al. Degassing of mantle-derived CO2 and He from springs in the southern Colorado Plateau region—neotectonic connections and implications for groundwater systems. Geol. Soc. Am. Bull. 121, 1034–1053 (2009).Article 
CAS 

Google Scholar 
Kusuda, C., Iwamori, H., Nakamura, H., Kazahaya, K. & Morikawa, N. Arima hot spring waters as a deep-seated brine from subducting slab. Earth Planets Space 66, 119 (2014).Article 

Google Scholar 
Sano, Y., Kameda, A., Takahata, N., Yamamoto, J. & Nakajima, J. Tracing extinct spreading center in SW Japan by helium-3 emanation. Chem. Geol. 266, 50–56 (2009).Article 
CAS 

Google Scholar 
Sano, Y. et al. Groundwater helium anomaly reflects strain change during the 2016 Kumamoto earthquake in Southwest Japan. Sci. Rep. 6, 37939 (2016).Article 
CAS 

Google Scholar 
Peeters, F. et al. Improving noble gas based paleoclimate reconstruction and groundwater dating using 20Ne/22Ne ratios. Geochim. Cosmochim. Acta 67, 587–600 (2002).Article 

Google Scholar 
Reimann, C. & de Caritat, P. Chemical Elements in the Environment 398 (Springer, 1998).Hamada, T. in Vanadium in the Environment. Part 1: Chemistry and Biochemistry Advances in Environmental Sciences and Technology Vol. 10 (ed. Nriagu, J. O.) 97–123 (Wiley & Sons, 1998).Koshimizu, S. & Kyotani, T. Geochemical behaviors of multi-elements in water samples from the Fuji and Sagami Rivers, Central Japan, using vanadium as an effective indicator. Jpn J. Limnol. 63, 113–124 (2002).Article 
CAS 

Google Scholar 
Sohrin, R. in Green Science and Technology (eds Park, E. Y. et al.) Ch. 7 (CRC, 2019).Wehrli, B. & Stumm, W. Oxygenation of vanadyl(IV). Effect of coordinated surface hydroxyl groups and hydroxide ion. Langmuir 4, 753–758 (1988).Article 
CAS 

Google Scholar 
Wright, M. T. & Belitz, K. Factors controlling the regional distribution of vanadium in groundwater. Ground Water 48, 515–525 (2010).Article 
CAS 

Google Scholar 
Deverel, S. J., Goldberg, S. & Fujii, R. in Agricultural salinity assessment and management (eds W.W. Wallender & K.K. Tanji) 89–137 (American Society of Civil Engineers, 2012).Wehrli, B. & Stumm, W. Vanadyl in natural waters: adsorption and hydrolysis promote oxygenation. Geochim. Cosmochim. Acta 53, 69–77 (1989).Article 
CAS 

Google Scholar 
Chen, G. & Liu, H. Understanding the reduction kinetics of aqueous vanadium(V) and transformation products using rotating ring-disk electrodes. Environ. Sci. Technol. 51, 11643–11651 (2017).Article 
CAS 

Google Scholar 
Telfeyan, K., Johannesson, K. H., Mohajerin, T. J. & Palmore, C. D. Vanadium geochemistry along groundwater flow paths in contrasting aquifers of the United States: Carrizo Sand (Texas) and Oasis Valley (Nevada) aquifers. Chem. Geol. 410, 63–78 (2015).Article 
CAS 

Google Scholar 
Kan, K. et al. Archaea in Yellowstone Lake. ISME J. 5, 1784–1795 (2011).Article 
CAS 

Google Scholar 
Wong, H. L. et al. Dynamics of archaea at fine spatial scales in Shark Bay mat microbiomes. Sci. Rep. 7, 46160 (2017).Article 
CAS 

Google Scholar 
Ikeda, K. A study on chemical characteristics of ground water in Fuji area (in Japanese). J. Groundw. Hydrol. 24, 77–93 (1982).
Google Scholar 
Aizawa, K. et al. Hydrothermal system beneath Mt. Fuji volcano inferred from magnetotellurics and electric self-potential. Earth Planet. Sci. Lett. 235, 343–355 (2005).Article 
CAS 

Google Scholar 
Yamamoto, T., Takada, A., Ishizuka, Y., Miyaji, N. & Tajima, Y. Basaltic pyroclastic flows of Fuji volcano, Japan: characteristics of the deposits and their origin. Bull. Volcanol. 67, 622–633 (2005).Article 

Google Scholar 
Yamamoto, T., Takada, A., Ishizuka, Y. & Nakano, S. Chronology of the products of Fuji volcano based on new radiometoric carbon ages (in Japanese). Bull. Volcanol. 50, 53–70 (2005).CAS 

Google Scholar 
Aizawa, K., Yoshimura, R. & Oshiman, N. Splitting of the Philippine Sea Plate and a magma chamber beneath Mt. Fuji. Geophys. Res. Lett. 31, L09603 (2004).Article 

Google Scholar 
Nakamura, H., Iwamori, H. & Kimura, J.-I. Geochemical evidence for enhanced fluid flux due to overlapping subducting plates. Nat. Geosci. 1, 380–384 (2008).Article 
CAS 

Google Scholar 
Kaneko, T., Yasuda, A., Fujii, T. & Yoshimoto, M. Crypto-magma chambers beneath Mt. Fuji. J. Volcanol. Geotherm. Res. 193, 161–170 (2010).Article 
CAS 

Google Scholar 
Tsuya, H., Machida, H., & Shimozuru, D. (1988). Geology of volcano Mt. Fuji. Explanatory text of the geologic map of Mt. Fuji (scale 1:50,000; second printing). Geological Survey of Japan (GSJ), Tsukuba, Japan.Yoshimoto, M. et al. Evolution of Mount Fuji, Japan: inference from drilling into the subaerial oldest volcano, pre-Komitake. Isl. Arc. 19, 470–488 (2010).Article 

Google Scholar 
Shikazono, N., Arakawa, T. & Nakano, T. Groundwater quality, flow, and nitrogen pollution at the southern foot of Mt. Fuji (in Japanese). J. Geogr. 123, 323–342 (2014).Article 

Google Scholar 
Tosaki, Y., Tase, N., Sasa, K., Takahashi, T. & Nagashima, Y. Estimation of groundwater residence time using the 36Cl bomb pulse. Groundwater 49, 891–902 (2011).Article 
CAS 

Google Scholar 
Yamamoto, T. Geology of the Southwestern Part of Fuji Volcano (in Japanese) 27 (GSJ, AIST, 2014).Tsuya, H. Geology of volcano Mt. Fuji. Explanatory text of the geologic map of Mt. Fuji (scale 1:50,000). Geological Survey of Japan, Tsukuba, Japan. (1968).Tomiyama, S., Ii, H., Miyaike, S., Hattori, R. & Ito, Y. Estimation of the sources and flow system of groundwater in Fuji-Gotenba area by stable isotopic analysis and groundwater flow simulation (in Japanese). Bunseki Kagaku 58, 865–872 (2009).Article 
CAS 

Google Scholar 
Oguchi, T. & Oguchi, C. T. in Geomorphological Landscapes of the World (ed. Migoń, P.) Ch. 31 (Springer, 2010).Mean Annual Precipitation from 1981-2010 Recorded at the Four Mt. Fuji Observatories (Mishima, Fuji, Furuseki, Yamanaka) (Japan Meteorological Agency, 2015).Schilling, O. S., Park, Y.-J., Therrien, R. & Nagare, R. M. Integrated surface and subsurface hydrological modeling with snowmelt and pore water freeze-thaw. Groundwater 57, 63–74 (2018).Article 

Google Scholar 
Sakio, H. & Masuzawa, T. Advancing timberline on Mt. Fuji between 1978 and 2018. Plants 9, 1537 (2020).Article 

Google Scholar 
Asai, K. & Koshimizu, S. 3H/3He-based groundwater ages for springs located at the foot of Mt. Fuji (in Japanese). J. Groundw. Hydrol. 61, 291–298 (2019).Article 

Google Scholar 
Sakai, Y., Shita, K., Koshimizu, S. & Tomura, K. Geochemical study of trace vanadium in water by preconcentrational neutron activation analysis. J. Radioanal. Nucl. Chem. 216, 203–212 (1997).Article 
CAS 

Google Scholar 
Nahar, S. & Zhang, J. Concentration and distribution of organic and inorganic water pollutants in eastern Shizuoka, Japan. Toxicol. Environ. Chem. https://doi.org/10.1080/02772248.2011.610498 (2011).Kamitani, T., Watanabe, M., Muranaka, Y., Shin, K.-C. & Nakano, T. Geographical characteristics and sources of dissolved ions in groundwater at the southern part of Mt. Fuji (in Japanese). J. Geogr. 126, 43–71 (2017).Article 

Google Scholar 
Kawagucci, S. et al. Disturbance of deep-sea environments induced by the M9.0 Tohoku earthquake. Sci Rep. 2, 270 (2012).Article 

Google Scholar 
Uchida, N. & Bürgmann, R. A decade of lessons learned from the 2011 Tohoku-Oki earthquake. Rev. Geophys. 59, e2020RG000713 (2021).Article 

Google Scholar 
Mahara, Y., Igarashi, T. & Tanaka, Y. Groundwater ages of confined aquifer in Mishima lava flow, Shizuoka (in Japanese). J. Groundw. Hydrol. 35, 201–215 (1993).Article 

Google Scholar 
Nakamura, T. et al. Sources of water and nitrate in springs at the northern foot of Mt. Fuji and nitrate loading in the Katsuragawa River (in Japanese). J. Geogr. 126, 73–88 (2017).Article 

Google Scholar 
Notsu, K., Mori, T., Sumino, H. & Ohno, M. in Fuji Volcano (eds Aramaki, S. et al.) 173–182 (Yamanashi Institute of Environmental Sciences, 2007).Ogata, M. & Kobayashi, H. Hydrologic Science Research for the Management and Utilization of Ground Water Resources in the Northern Piedmont Area of Mt. Fuji: Fluorine Ion and Vanadium Contained in Ground Water at the Northern Foot of Mt. Fuji (Yamanashi Industrial Technology Center, 2015).Ogata, M., Kobayashi, H. & Koshimizu, S. Concentration of fluorine in groundwater and groundwater table at the northern foot of Mt. Fuji (in Japanese). J. Groundw. Hydrol. 56, 35–51 (2014).Article 

Google Scholar 
Ohno, M., Sumino, H., Hernandez, P. A., Sato, T. & Nagao, K. Helium isotopes in the Izu Peninsula, Japan: relation of magma and crustal activity. J. Volcanol. Geotherm. Res. 199, 118–126 (2011).Article 
CAS 

Google Scholar 
Okabe, S., Shibasaki, M., Oikawa, T., Kawaguchi, Y. & Nihongi, H. Geochemical studies of spring and lake waters on and around Mt. Fuji (in Japanese). J. Sch. Mar. Sci. Technol. Tokai Univ. 14, 81–105 (1981).CAS 

Google Scholar 
Ono, M., Ikawa, R., Machida, H. & Marui, A. Distribution of radon concentration in groundwater at the southwestern foot of Mt. Fuji (in Japanese). Radioisotopes 65, 431–439 (2016).Article 
CAS 

Google Scholar 
Tosaki, Y. Estimation of Groundwater Residence Time Using Bomb-Produced Chlorine-36. PhD thesis, Univ. Tsukuba (2008).Umeda, K., Asamori, K. & Kusano, T. Release of mantle and crustal helium from a fault following an inland earthquake. Appl. Geochem. 37, 134–141 (2013).Article 
CAS 

Google Scholar 
Yamamoto, C. Estimation of Groundwater Flow System Using Multi-tracer Techniques in Mt. Fuji, Japan. (in Japanese) PhD thesis, Univ. Tsukuba (2016).Yamamoto, S. & Nakamura, T. Visit to valuable water springs (129) valuable water at the northern foot of Mount Fuji (Fuji-Kawaguchiko Town) (in Japanese). J. Groundw. Hydrol. 62, 329–336 (2020).Article 

Google Scholar 
Yamamoto, S. et al. Water sources of lake bottom springs in Lake Kawaguchi, northern foot of Mount Fuji, Japan (in Japanese). J. Geogr. 129, 665–676 (2020).Article 

Google Scholar 
Yamamoto, S., Nakamura, T. & Uchiyama, T. Newly discovered lake bottom springs from Lake Kawaguchi, the northern foot of Mount Fuji, Japan (in Japanese). J. Jpn Assoc. Hydrol. Sci. 47, 49–59 (2017).
Google Scholar 
Yamamoto, S., Nakamura, T., Koishikawa, H. & Uchiyama, T. Water quality of shallow groundwater in the southern coast area of Lake Kawaguchi at the northern foot of Mt. Fuji, Yamanashi, Japan (in Japanese). Mt Fuji Res. 11, 1–9 (2017).
Google Scholar 
Coplen, T. B. Reporting of stable hydrogen, carbon, and oxygen isotopic abundances. Geothermics 66, 273–276 (1994).CAS 

Google Scholar 
Nimz, G. J. in Isotope Tracers in Catchment Hydrology (eds Kendall, C. & McDonnell, J. J.) Ch. 8 (Elsevier, 1998).Bullen, T. D. & Kendall, C. in Isotope Tracers in Catchment Hydrology (eds Kendall, C. & McDonnell, J. J.) Ch. 18 (Elsevier, 1998).Vanadium Pentoxide and Other Inorganic Vanadium Compounds Vol. 29 (WHO, 2001).Nagai, T., Takahashi, M., Hirahara, Y. & Shuto, K. Sr-Nd isotopic compositions of volcanic rocks from Fuji, Komitake and Ashitaka Volcanoes, Central Japan (in Japanese). Proc. Inst. Nat. Sci. Nihon Univ. 39, 205–215 (2004).CAS 

Google Scholar 
Hogan, J. F. & Blum, J. D. Tracing hydrologic flow paths in a small forested watershed using variations in 87Sr/86Sr, [Ca]/[Sr], [Ba]/[Sr] and δ18O. Water Resour. Res. 39, 1282 (2003).Article 

Google Scholar 
Koshikawa, M. K. et al. Using isotopes to determine the contribution of volcanic ash to Sr and Ca in stream waters and plants in a granite watershed, Mt. Tsukuba, central Japan. Environ. Earth Sci. 75, 501 (2016).Article 

Google Scholar 
Graustein, W. C. in Stable Isotopes in Ecological Research Ecological Studies (Analysis and Synthesis) (eds Rundel, JP.W. et al.) Ch. 28 (Springer, 1989).Cook, P. G. & Böhlke, J.-K. in Environmental Tracers in Subsurface Hydrology (eds Cook, P. G. & Herczeg, A. L.) Ch. 1 (Springer, 2000).Aeschbach-Hertig, W. & Solomon, D. K. in The Noble Gases as Geochemical Tracers (ed. Burnard, P.) Ch. 5 (Springer, 2013).Popp, A. L. et al. A framework for untangling transient groundwater mixing and travel times. Water Resour. Res. 57, e2020WR028362 (2021).Article 

Google Scholar 
Schilling, O. S. et al. Advancing physically-based flow simulations of alluvial systems through observations of 222Rn, 3H/3He, atmospheric noble gases and the novel 37Ar tracer method. Water Resour. Res. 53, 10465–10490 (2017).Article 

Google Scholar 
Tomonaga, Y. et al. Using noble-gas and stable-isotope data to determine groundwater origin and flow regimes: applicatoin to the Ceneri Base Tunnel (Switzerland). J. Hydrol. 545, 395–409 (2017).Article 
CAS 

Google Scholar 
Niu, Y. et al. Noble gas signatures in the island of Maui, Hawaii – characterizing groundwater sources in fractured systems. Water Resour. Res. 53, 3599–3614 (2017).Article 

Google Scholar 
Warrier, R. B., Castro, M. C. & Hall, C. M. Recharge and source-water insights from the Galapagos Islands using noble gases and stable isotopes. Water Resour. Res. https://doi.org/10.1029/2011WR010954 (2012).Schilling, O. S. et al. Buried paleo-channel detection with a groundwater model, tracer-based observations, and spatially varying, preferred anisotropy pilot point calibration. Geophys. Res. Lett. 49, e2022GL098944 (2022).Article 

Google Scholar 
Brennwald, M. S., Schmidt, M., Oser, J. & Kipfer, R. A portable and autonomous mass spectrometric system for on-site environmental gas analysis. Environ. Sci. Technol. 50, 13455–12463 (2016).Article 
CAS 

Google Scholar 
Tomonaga, Y. et al. On-line monitoring of the gas composition in the full-scale emplacement experiment at Mont Terri (Switzerland). Appl. Geochem. 100, 234–243 (2019).Article 
CAS 

Google Scholar 
Brennwald, M. S., Tomonaga, Y. & Kipfer, R. Deconvolution and compensation of mass spectrometric overlap interferences with the miniRUEDI portable mass spectrometer. MethodsX 7, 101038 (2020).Article 
CAS 

Google Scholar 
Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).Beyerle, U. et al. A mass spectrometric system for the analysis of noble gases and tritium from water samples. Environ. Sci. Technol. 34, 2042–2050 (2000).Article 
CAS 

Google Scholar 
Clarke, W. B., Jenkins, W. J. & Top, Z. Determination of tritium by mass spectrometric measurement of 3He. Int. J. Appl. Radiat. Isotopes 27, 515–522 (1976).Article 
CAS 

Google Scholar 
Bucci, A., Petrella, E., Celivo, F. & Naclerio, G. Use of molecular approaches in hydrogeological studies: the case of carbonate aquifers in southern Italy. Hydrogeol. J. 25, 1017–1031 (2017).Article 
CAS 

Google Scholar 
Proctor, C. R. et al. Phylogenetic clustering of small low nucleic acid-content bacteria across diverse freshwater ecosystems. ISME J. 12, 1344–1359 (2018).Article 
CAS 

Google Scholar 
Pronk, M., Goldscheider, N. & Zopfi, J. Microbial communities in karst groundwater and their potential use for biomonitoring. Hydrogeol. J. 17, 37–48 (2009).Article 

Google Scholar 
Miller, J. B., Frisbee, M. D., Hamilton, T. L. & Murugapiran, S. K. Recharge from glacial meltwater is critical for alpine springs and their microbiomes. Environ. Res. Lett. 16, 064012 (2021).Article 
CAS 

Google Scholar 
Ginn, T. R. et al. in Encyclopedia of Hydrological Sciences (ed. Anderson, M.G.) Ch. 105 (John Wiley & Sons, 2005).Tufenkji, N. & Emelko, M. B. in Encyclopedia of Environmental Health (ed. Nriagu, J.O.) Vol. 2, 715–726 (Elsevier, 2011).Nevecherya, I. K., Shestakov, V. M., Mazaev, V. T. & Shlepnina, T. G. Survival rate of pathogenic bacteria and viruses in groundwater. Water Res. 32, 209–214 (2005).Article 
CAS 

Google Scholar 
Franzosa, E. A. et al. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nature Rev. Microbiol. 13, 360–372 (2015).Article 
CAS 

Google Scholar 
Kimura, H., Ishibashi, J. I., Masuda, H., Kato, K. & Hanada, S. Selective phylogenetic analysis targeting 16S rRNA genes of hyperthermophilic archaea in the deep-subsurface hot biosphere. Appl. Environ. Microbiol. 73, 2110–2117 (2007).Article 
CAS 

Google Scholar 
Somerville, C. C., Knight, I. T., Straube, W. L. & Colwell, R. R. Simple, rapid method for direct isolation of nucleic-acids from aquatic environments. Appl. Environ. Microbiol. 55, 548–554 (1989).Article 
CAS 

Google Scholar 
Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE https://doi.org/10.1371/journal.pone.0105592 (2014).Wasimuddin et al. Evaluation of primer pairs for microbiome profiling from soils to humans within the One Health framework. Mol. Ecol. Resour. 20, 1558–1571 (2020).Article 
CAS 

Google Scholar 
Suzuki, Y., Shimizu, H., Kuroda, T., Takada, Y. & Nukazawa, K. Plant debris are hotbeds for pathogenic bacteria on recreational sandy beaches. Sci Rep. 11, 11496 (2021).Article 
CAS 

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

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high- throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
CAS 

Google Scholar 
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).Article 
CAS 

Google Scholar 
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).Article 
CAS 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 
CAS 

Google Scholar 
R: A Language and Environment for Statistical Computing v.3.6.2 (R Foundation for Statistical Computing, 2019).Porter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora. Limnol. Oceanogr. 25, 943–948 (1980).Article 

Google Scholar 
Schilling, O. S. et al. Mt. Fuji hydrogeochemical and microbiological dataset. HydroShare https://doi.org/10.4211/hs.4eac370d12e142b5aa718e5deb57da39 (2022).Gotelli, N. J. & Chao, A. in Encyclopedia of Biodiversity Vol. 5 (ed. Levin, S. A.) 195–211 (Academic, 2013).World Imagery (Esri, 2021); https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9Elevation Tile Map of Japan (DEM5A; Resolution: 5m) (Geospatial Information Authority of Japan (GSI), 2021).Chiba, T., Kaneta, S. & Suzuki, Y. in The International Archives of the Photogrammetry Vol. XXXVII Ch. B2 (Remote Sensing and Spatial Information Sciences, 2008).Air Asia Survey Co. Ltd Red Relief Image Map of Japan (RRIM 10_2016) (GSI, 2016).Active Fault Database of Japan April 26 2019 edn Disclosure database DB095 (AIST, 2019).Bird, P. An updated digital model of plate boundaries. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2001GC000252 (2003).Van Horne, A., Sato, H. & Ishiyama, T. Evolution of the Sea of Japan back-arc and some unsolved issues. Tectonophysics 710–711, 6–20 (2017).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).Article 

Google Scholar 
2019 Coastal Disposal System Evaluation Confirmation Technology Results Report (in Japanese) (AIST, 2019). More

Simberloff, D. et al. (eds) Invasive Species in a Globalized World (University of Chicago Press, 2015).
Google Scholar 
Gippet, J. M., Liebhold, A. M., Fenn-Moltu, G. & Bertelsmeier, C. Human-mediated dispersal in insects. Curr. Opin. Insect Sci. 35, 96–102 (2019).Article 

Google Scholar 
Hall, C. M. Biological invasion, biosecurity, tourism, and globalisation. In Handbook of Globalisation and Tourism (Edward Elgar Publishing, 2019).
Google Scholar 
Bertelsmeier, C. Globalization and the anthropogenic spread of invasive social insects. Curr. Opin. Insect Sci. https://doi.org/10.1016/j.cois.2021.01.006 (2021).Article 

Google Scholar 
Simberloff, D. How common are invasion-induced ecosystem impacts?. Biol. Invasions 13, 1255–1268 (2011).Article 

Google Scholar 
Hayes, K. R. & Barry, S. C. Are there any consistent predictors of invasion success?. Biol. Invasions 10, 483–506 (2008).Article 

Google Scholar 
Catford, J. A., Vesk, P. A., Richardson, D. M. & Pyšek, P. Quantifying levels of biological invasion: Towards the objective classification of invaded and invasible ecosystems. Glob. Change Biol. 18, 44–62 (2012).Article 
ADS 

Google Scholar 
Arim, M., Abades, S. R., Neill, P. E., Lima, M. & Marquet, P. A. Spread dynamics of invasive species. Proc. Natl. Acad. Sci. USA 103, 374–378 (2006).Article 
ADS 
CAS 

Google Scholar 
Kamenova, S. et al. Invasions toolkit: Current methods for tracking the spread and impact of invasive species. Adv. Ecol. Res. 56, 85–182 (2017).Article 

Google Scholar 
Hulme, P. E. Trade, transport and trouble: Managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18 (2009).Article 

Google Scholar 
Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).Article 

Google Scholar 
Crooks, J. A. & Rilov, G. The establishment of invasive species. In Biological Invasions in Marine Ecosystems 173–175 (Springer, 2009).Chapter 

Google Scholar 
Lockwood, J. L., Cassey, P. & Blackburn, T. M. The more you introduce the more you get: The role of colonization pressure and propagule pressure in invasion ecology. Divers. Distrib. 15, 904–910 (2009).Article 

Google Scholar 
Sakai, A. K. et al. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).Article 

Google Scholar 
O’Reilly-Nugent, A. et al. Landscape effects on the spread of invasive species. Curr. Landsc. Ecol. Rep. 1, 107–114 (2016).Article 

Google Scholar 
Simberloff, D. We can eliminate invasions or live with them. Successful management projects. In Ecological Impacts of Non-native Invertebrates and Fungi on Terrestrial Ecosystems 149–157 (Springer, 2008).
Google Scholar 
Gutierrez, A. P. & Ponti, L. Eradication of invasive species: Why the biology matters. Environ. Entomol. 42, 395–411 (2013).Article 

Google Scholar 
McLaughlin, G. M. & Dearden, P. K. Invasive insects: Management methods explored. J. Insect Sci. 19, 17 (2019).Article 

Google Scholar 
Han, J. M. et al. Lycorma delicatula (hemiptera: Auchenorrhyncha: Fulgoridae: Aphaeninae) finally, but suddenly arrived in Korea. Entomol. Res. 38, 281–286 (2008).Article 

Google Scholar 
Park, J.-D. et al. Biological characteristics of lycorma delicatula and the control effects of some insecticides. Korean J. Appl. Entomol. 48, 53–57 (2009).Article 

Google Scholar 
Shin, Y.-H., Moon, S.-R., Yoon, C.-M., Ahn, K.-S. & Kim, G.-H. Insecticidal activity of 26 insectcides against eggs and nymphs of Lycorma delicatula (hemiptera: Fulgoridae). Korean J. Pestic. Sci. 14, 157–163 (2010).
Google Scholar 
Dara, S. K., Barringer, L. & Arthurs, S. P. Lycorma delicatula (hemiptera: Fulgoridae): A new invasive pest in the United States. J. Integr. Pest Manag. 6, 20 (2015).Article 

Google Scholar 
Urban, J. M. Perspective: Shedding light on spotted lanternfly impacts in the USA. Pest Manag. Sci. 76, 10–17 (2020).Article 
CAS 

Google Scholar 
Liu, G. Some extracts from the history of entomology in china. Psyche 46, 23–28 (1939).Article 

Google Scholar 
Barringer, L. E., Donovall, L. R., Spichiger, S.-E., Lynch, D. & Henry, D. The first new world record of Lycorma delicatula (insecta: Hemiptera: Fulgoridae). Entomol. News 125, 20–23 (2015).Article 

Google Scholar 
Parra, G., Moylett, H. & Bulluck, R. Technical Working Group Summary Report: Spotted Lanternfly, Lycorma Delicatula (White, 1845). (2018).Harper, J. K., Stone, W., Kelsey, T. W. & Kime, L. F. Potential Economic Impact of the Spotted Lanternfly on Agriculture and Forestry in Pennsylvania 1–84 (The Center for Rural Pennsylvania, 2019).
Google Scholar 
Kim, J. G., Lee, E.-H., Seo, Y.-M. & Kim, N.-Y. Cyclic behavior of Lycorma delicatula (insecta: Hemiptera: Fulgoridae) on host plants. J. Insect Behav. 24, 423–435 (2011).Article 

Google Scholar 
Albright, T. A. et al. Pennsylvania forests 2014. Resour. Bull. 111, 1–140 (2017).
Google Scholar 
Liu, H. Oviposition substrate selection, egg mass characteristics, host preference, and life history of the spotted lanternfly (hemiptera: Fulgoridae) in North America. Environ. Entomol. 48, 1452–1468 (2019).
Google Scholar 
Barringer, L. & Ciafré, C. M. Worldwide feeding host plants of spotted lanternfly, with significant additions from North America. Environ. Entomol. 49, 999–1011 (2020).Article 

Google Scholar 
Murman, K. et al. Distribution, survival, and development of spotted lanternfly on host plants found in North America. Environ. Entomol. 49, 1270–1281 (2020).Article 

Google Scholar 
Huron, N. A., Behm, J. E. & Helmus, M. R. Paninvasion severity assessment of a us grape pest to disrupt the global wine market. bioRxiv (2021).Dara, S. K. Update on the Spotted Lanternfly.Jung, J.-M., Jung, S., Byeon, D.-H. & Lee, W.-H. Model-based prediction of potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (hemiptera: Fulgoridae), by using climex. J. Asia-Pac. Biodivers. 10, 532–538 (2017).Article 

Google Scholar 
Namgung, H., Kim, M.-J., Baek, S., Lee, J.-H. & Kim, H. Predicting potential current distribution of Lycorma delicatula (hemiptera: Fulgoridae) using maxent model in south korea. J. Asia-Pac. Entomol. 23, 291–297 (2020).Article 

Google Scholar 
Wakie, T. T., Neven, L. G., Yee, W. L. & Lu, Z. The establishment risk of Lycorma delicatula (hemiptera: Fulgoridae) in the United States and globally. J. Econ. Entomol. 113, 306–314 (2020).
Google Scholar 
Grimm, V. et al. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198, 115–126 (2006).Article 

Google Scholar 
DeAngelis, D. L. Individual-Based Models and Approaches in Ecology: Populations, Communities and Ecosystems (CRC Press, 2018).Book 

Google Scholar 
Łomnicki, A. Individual-based models and the individual-based approach to population ecology. Ecol. Model. 115, 191–198 (1999).Article 

Google Scholar 
Grimm, V. & Railsback, S. F. A conceptual framework for designing individual-based models. In Individual-Based Modeling and Ecology 71–121 (Princeton University Press, 2005).Chapter 
MATH 

Google Scholar 
Smith, N. R. et al. Agent-based models of malaria transmission: A systematic review. Malar. J. 17, 1–16 (2018).Article 
CAS 

Google Scholar 
Venkatramanan, S. et al. Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics 22, 43–49 (2018).Article 

Google Scholar 
Harris, C. M., Park, K. J., Atkinson, R., Edwards, C. & Travis, J. Invasive species control: Incorporating demographic data and seed dispersal into a management model for rhododendron ponticum. Ecol. Inform. 4, 226–233 (2009).Article 

Google Scholar 
Gallien, L., Münkemüller, T., Albert, C. H., Boulangeat, I. & Thuiller, W. Predicting potential distributions of invasive species: Where to go from here?. Divers. Distrib. 16, 331–342 (2010).Article 

Google Scholar 
Rebaudo, F., Crespo-Pérez, V., Silvain, J.-F. & Dangles, O. Agent-based modeling of human-induced spread of invasive species in agricultural landscapes: Insights from the potato moth in ecuador. J. Artif. Soc. Soc. Simul. 14, 7 (2011).Article 

Google Scholar 
Day, C. C., Landguth, E. L., Bearlin, A., Holden, Z. A. & Whiteley, A. R. Using simulation modeling to inform management of invasive species: A case study of eastern brook trout suppression and eradication. Biol. Conserv. 221, 10–22 (2018).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Phillips, S. J., Dudı’k, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-first International Conference on Machine Learning 83 (2004).Phillips, S. J. et al. A brief tutorial on maxent. AT&T Res. 190, 231–259 (2005).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Urbanek, S. RJava: Low-Level R to Java Interface. (2020).Hijmans, R. J., Phillips, S., Leathwick, J., Elith, J. & Hijmans, M. R. J. Package ‘dismo’. Circles 9, 1–68 (2017).
Google Scholar 
Elith, J. et al. A statistical explanation of maxent for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
Lane, M. A. & Edwards, J. L. The global biodiversity information facility (gbif). Syst. Assoc. Spec. 73, 1 (2007).
Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous united states. US Geol. Surv. Data Ser. 691, 4–9 (2012).
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 
Hijmans, R. J. Raster: Geographic Data Analysis and Modeling. (2020).Venter, O. et al. Last of the wild project, version 3 (lwp-3): 2009 human footprint, 2018 release. NASA Socioeconomic Data and Applications Center (SEDAC) 10, H46T40JQ44 (2018).Park, M. Overwintering ecology and population genetics of Lycorma delicatula (hemiptera: Fulgoridae) in Korea. Seoul National University, Seoul, Korea Doctoral Thesis (2015).Pearson, K. I. Mathematical contributions to the theory of evolution. VII. On the correlation of characters not quantitatively measurable. Philos. Trans. R. Soc. Lond. Ser. A 195, 1–47 (1900).ADS 
MATH 

Google Scholar 
Warmerdam, F. The geospatial data abstraction library. In Open Source Approaches in Spatial Data Handling 87–104 (Springer, 2008).Chapter 

Google Scholar 
Greenberg, J. A., Mattiuzzi, M. & SystemRequirements, G. Package ‘gdalUtils’. (2020).Domingue, M. J. & Baker, T. C. Orientation of flight for physically disturbed spotted lanternflies, Lycorma delicatula, (Hemiptera, fulgoridae). J. Asia-Pac. Entomol. 22, 117–120 (2019).Article 

Google Scholar 
Myrick, A. J. & Baker, T. C. Analysis of anemotactic flight tendencies of the spotted lanternfly (Lycorma delicatula) during the 2017 mass dispersal flights in pennsylvania. J. Insect Behav. 32, 11–23 (2019).Article 

Google Scholar 
Wolfin, M. S., Myrick, A. J. & Baker, T. C. Flight duration capabilities of dispersing adult spotted lanternflies, Lycorma delicatula. J. Insect Behav. 33, 125–137 (2020).Article 

Google Scholar 
Strömbom, D. & Pandey, S. Modeling the life cycle of the spotted lanternfly (Lycorma delicatula) with management implications. Math. Biosci. 340, 108670 (2021).Article 
MATH 

Google Scholar 
Wellington, W. G. Conditions governing the distribution of insects in the free atmosphere. Can. Entomol. 77, 7–15 (1945).Article 

Google Scholar 
DeLong, D. M. The bionomics of leafhoppers. Annu. Rev. Entomol. 16, 179–210 (1971).Article 

Google Scholar 
Baker, T. et al. Progression of seasonal activities of adults of the spotted lanternfly, Lycorma delicatula, during the 2017 season of mass flight dispersal behavior in eastern Pennsylvania. J. Asia-Pac. Entomol. 22, 705–713 (2019).Article 

Google Scholar 
Leach, H. & Leach, A. Seasonal phenology and activity of spotted lanternfly (Lycorma delicatula) in eastern us vineyards. J. Pest Sci. 93, 1215–1224 (2020).Article 

Google Scholar 
Goutte, C. & Gaussier, E. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In European Conference on Information Retrieval 345–359 (Springer, 2005).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Tukey, J. Multiple comparisons. J. Am. Stat. Assoc. 48, 624–625 (1953).
Google Scholar 
Mendiburu, F. de & Mendiburu, M. F. de. Package ‘agricolae’. R Package, Version 1-2 (2019).McAvoy, T. J., Snyder, A. L., Johnson, N., Salom, S. M. & Kok, L. T. Road survey of the invasive tree-of-heaven (Ailanthus altissima) in Virginia. Invasive Plant Sci. Manag. 5, 506–512 (2012).Article 

Google Scholar 
Casella, F. & Vurro, M. Ailanthus altissima (tree of heaven): Spread and harmfulness in a case-study urban area. Arboricult. J. 35, 172–181 (2013).Article 

Google Scholar 
Takahashi, D. & Park, Y.-S. Spatial heterogeneities of human-mediated dispersal vectors accelerate the range expansion of invaders with source–destination-mediated dispersal. Sci. Rep. 10, 1–9 (2020).Article 

Google Scholar 
Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).Article 
ADS 

Google Scholar 
Turner, R. M. et al. Worldwide border interceptions provide a window into human-mediated global insect movement. Ecol. Appl. 31, e02412 (2021).Article 

Google Scholar 
Ricciardi, A. Are modern biological invasions an unprecedented form of global change?. Conserv. Biol. 21, 329–336 (2007).Article 

Google Scholar 
Wilson, J. R., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: Dispersal pathways affect invasion success. Trends Ecol. Evol. 24, 136–144 (2009).Article 

Google Scholar 
Auffret, A. G., Berg, J. & Cousins, S. A. The geography of human-mediated dispersal. Divers. Distrib. 20, 1450–1456 (2014).Article 

Google Scholar 
Koch, F. H., Yemshanov, D., Magarey, R. D. & Smith, W. D. Dispersal of invasive forest insects via recreational firewood: A quantitative analysis. J. Econ. Entomol. 105, 438–450 (2012).Article 

Google Scholar 
Eyer, P.-A. et al. Extensive human-mediated jump dispersal within and across the native and introduced ranges of the invasive termite Reticulitermes flavipes. Mol. Ecol. 30, 3948–3964 (2020).Article 

Google Scholar 
Petrice, T. R. & Haack, R. A. Effects of cutting date, outdoor storage conditions, and splitting on survival of Agrilus planipennis (coleoptera: Buprestidae) in firewood logs. J. Econ. Entomol. 99, 790–796 (2006).Article 

Google Scholar 
Petrice, T. R. & Haack, R. A. Can emerald ash borer, Agrilus planipennis (coleoptera: Buprestidae), emerge from logs two summers after infested trees are cut?. Great Lakes Entomol. 40, 92–95 (2007).
Google Scholar 
Muirhead, J. R. et al. Modelling local and long-distance dispersal of invasive emerald ash borer Agrilus planipennis (coleoptera) in North America. Divers. Distrib. 12, 71–79 (2006).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 
ADS 

Google Scholar 
Hulme, P. E. Unwelcome exchange: International trade as a direct and indirect driver of biological invasions worldwide. One Earth 4, 666–679 (2021).Article 
ADS 

Google Scholar  More

Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).Article 
CAS 

Google Scholar 
DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).Article 
CAS 

Google Scholar 
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).Article 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Gazol, A. et al. Forest resilience to drought varies across biomes. Glob. Change Biol. 24, 2143–2158 (2018).Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Change Biol. 24, 504–516 (2018).Article 

Google Scholar 
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).Article 
CAS 

Google Scholar 
Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

Google Scholar 
Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2019).Article 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).Article 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).Article 
CAS 

Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).Article 
CAS 

Google Scholar 
Lian, X. et al. Seasonal biological carryover dominates northern vegetation growth. Nat. Commun. 12, 983 (2021).Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 
CAS 

Google Scholar 
Jeong, S. J. et al. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sens. Environ. 190, 178–187 (2017).Article 

Google Scholar 
Zeng, Z. et al. Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere. Agric. Meteorol. 310, 108630 (2021).Article 

Google Scholar 
Kelsey, K. C. et al. Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities. Glob. Change Biol. 27, 1572–1586 (2021).Article 

Google Scholar 
Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

Google Scholar 
Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).Article 
CAS 

Google Scholar 
Zhang, Y. et al. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).Article 
CAS 

Google Scholar 
Liu, Y. Y. et al. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).Article 

Google Scholar 
Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

Google Scholar 
Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).Article 
CAS 

Google Scholar 
D’Andrea, E. et al. Unravelling resilience mechanisms in forests: role of non-structural carbohydrates in responding to extreme weather events. Tree Physiol. 41, 1808–1818 (2021).Article 

Google Scholar 
Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 24, 5176–5187 (2018).Article 

Google Scholar 
Xie, J. et al. Spring temperature and snow cover climatology drive the advanced springtime phenology (1991–2014) in the European Alps. J. Geophys. Res. Biogeosci. 126, e2020JG006150 (2021).Xie, J. et al. Altitude-dependent influence of snow cover on alpine land surface phenology. J. Geophys. Res. Biogeosci. 122, 1107–1122 (2017).Article 

Google Scholar 
Peng, S. et al. Change in winter snow depth and its impacts on vegetation in China. Glob. Change Biol. 16, 3004–3013 (2010).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).Article 

Google Scholar 
Angert, A. et al. Drier summers cancel out the CO2 uptake enhancement induced by warmer springs. Proc. Natl Acad. Sci. USA 102, 10823–10827 (2005).Article 
CAS 

Google Scholar 
Musselman, K. N. et al. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Change 11, 418–424 (2021).Article 

Google Scholar 
Kreyling, J. Winter climate change: a critical factor for temperate vegetation performance. Ecology 91, 1939–1948 (2010).Article 

Google Scholar 
Bose, A. K. et al. Growth and resilience responses of Scots pine to extreme droughts across Europe depend on predrought growth conditions. Glob. Change Biol. 26, 4521–4537 (2020).Article 

Google Scholar 
Martinez-Vilalta, J. et al. Hydraulic adjustment of Scots pine across Europe. New Phytol. 184, 353–364 (2009).Article 
CAS 

Google Scholar 
Klein, T. et al. Drought stress, growth and nonstructural carbohydrate dynamics of pine trees in a semi-arid forest. Tree Physiol. 34, 981–992 (2014).Article 
CAS 

Google Scholar 
Kannenberg, S. A. & Phillips, R. P. Non-structural carbohydrate pools not linked to hydraulic strategies or carbon supply in tree saplings during severe drought and subsequent recovery. Tree Physiol. 40, 259–271 (2020).Article 
CAS 

Google Scholar 
Karst, J. et al. Stress differentially causes roots of tree seedlings to exude carbon. Tree Physiol. 37, 154–164 (2017).CAS 

Google Scholar 
Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).Article 

Google Scholar 
Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).Wu, X. et al. Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere. Geophys. Res. Lett. 44, 6173–6181 (2017).Article 

Google Scholar 
Anderegg, W. R. L. et al. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).Article 

Google Scholar 
Martin-Benito, D. & Pederson, N. Convergence in drought stress, but a divergence of climatic drivers across a latitudinal gradient in a temperate broadleaf forest. J. Biogeogr. 42, 925–937 (2015).Article 

Google Scholar 
Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).Article 

Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).Article 
CAS 

Google Scholar 
Zhang, W. et al. Divergent response of vegetation growth to soil water availability in dry and wet periods over Central Asia. J. Geophys. Res. Biogeosci. 126, e2020JG005912 (2021).Article 

Google Scholar 
Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Liang, W. et al. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric. For. Meteorol. 204, 22–36 (2015).Article 

Google Scholar 
Zhang, Y. et al. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).Article 
CAS 

Google Scholar 
Jones, M. O. et al. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011).Article 

Google Scholar 
Konings, A. G. et al. Interannual variations of vegetation optical depth are due to both water stress and biomass changes. Geophys. Res. Lett. 48, e2021GL095267 (2021).Article 

Google Scholar 
Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data 9, 791–808 (2017).Article 

Google Scholar 
Harris, I. et al. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Barichivich, J. et al. Temperature and snow-mediated moisture controls of summer photosynthetic activity in northern terrestrial ecosystems between 1982 and 2011. Remote Sens. 6, 1390–1431 (2014).Article 

Google Scholar 
Vicente-Serrano, S. M., Begueria, S. & Lopez-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Wieder, W. R. et al. Regridded Harmonized World Soil Database v1.2 (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1247Kottek, M. et al. World map of the Koppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).Article 

Google Scholar 
Jakubauskas, M. E., Legates, D. R. & Kastens, J. H. Harmonic analysis of time-series AVHRR NDVI data. Photogramm. Eng. Remote Sens. 67, 461–470 (2001).
Google Scholar 
Liu, Q. et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol. 22, 644–655 (2016).Article 
CAS 

Google Scholar 
Fu, Y. H. et al. Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob. Ecol. Biogeogr. 23, 1255–1263 (2014).Article 

Google Scholar 
Jiang, P. et al. Enhanced growth after extreme wetness compensates for post-drought carbon loss in dry forests. Nat. Commun. 10, 195 (2019).Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Pham, L. T. H. & Brabyn, L. Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J. Photogramm. Remote Sens. 128, 86–97 (2017).Article 

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

Google Scholar 
Li, Y. Code for ‘Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems’. GitHub https://github.com/leeyang1991/phenology-effects-on-drought-recovery (2022). More

Study site descriptionThe study was carried out at the edge of an arid region in mature plantations dominated by P. halepensis, of age of 40-50 years (Pinus halepensis), and their adjacent non-forested ecosystems. The sites were distributed across a climatic gradient from arid and semi-arid to dry sub-humid (Fig. 1, Supplementary Table S1). Three selected paired sites included: (1) An arid site at Yatir forest (annual precipitation, P: 280 mm; aridity index, AI: 0.18; elevation: 650 m; light brown Rendzina soil, and forest density: 300 trees ha−1), where a permanent flux tower has been operating since the year 2000 (http://fluxnet.ornl.gov). Note that an AI of 0.2 marks the boundary between arid and semi-arid regions. Yatir, with AI = 0.18, is formally within an arid zone, but on the edge of a semi-arid zone. (2) An intermediate semi-arid site in Eshtaol forest (P: 480 mm; AI: 0.37; elevation: 350 m; light brown Rendzina soil, and forest density: 450 trees ha−1). (3) A dry-subhumid site in northern Israel at the Birya forest (P: 770 mm; AI: 0.64; elevation: 755 m; dark brown Terra-Rossa and Rendzina soil, and forest density: 600 trees ha−1). Non-forest ecosystems were sparse dwarf shrublands, dominated by Sarcopoterium spinosum in a patchy distribution with a wide variety of herbaceous species, mostly annuals, that grew in between the shrubs during winter to early spring, and then dried out. All non-forested sites had been subjected to livestock grazing (exposing soils). Finally, an additional site that was characterized as Oak-forest vegetation was added. The site was dominated by two oak species, Quercus calliprinos and Quercus ithaburensis (P: 540 mm; AI: 0.4; see previous publication for more details on the oak site53). All sites were under high solar radiation load, with an annual average of approximately 240 Wm−2 in the arid region and only 3% lower in the northern site in the dry-subhumid region (Table 1).Mobile laboratoryMeasurements were conducted on a campaign basis using a mobile lab with a flux measurement system at all sites except the Yatir forest, where the permanent flux tower was used (http://fluxnet.ornl.gov;54). Repeated campaigns of approximately two weeks at each site, along the seasonal cycle, were undertaken during 4 years of measurements, 2012–2015 (a total of 6-7 campaigns per site, evenly distributed between the seasons) the 4 years of measurements were found to be representative of previous 70 years of precipitation record (Supplementary Figure S1 and Table S2). The mobile lab was housed on a 12-ton 4 × 4 truck with a pneumatic mast with an eddy-covariance system and provided the facility for any auxiliary and related measurements. Non-radiative flux measurements were undertaken using an eddy-covariance system to quantify CO2, sensible heat (H), and latent heat (LE) fluxes using a 3D sonic anemometer (R3, Gill Instruments, Hampshire, UK) and an enclosed-path CO2/H2O infrared gas analyzer (IRGA; LI-7200, LI-COR). Non-radiative flux measurements were accompanied by meteorological sensors, including air temperature (Ta), relative humidity (RH), and pressure (Campbell Scientific Inc., Logan, UT, USA), radiation fluxes of net solar- and net long-wave radiation (SWnet and LWnet, respectively), and photosynthetic radiation sensors (Kipp & Zonen, Delft, Holland). Raw EC data and the data from the meteorological sensors were collected using a computer and a CR3000 logger (Campbell Sci., Logan, UT, USA), respectively. The EC system was positioned at the center of each field site with the location and height aimed at providing sufficient ‘fetch’ of relatively homogeneous terrains. For detailed information on the use of the mobile lab and the following data processing of short and long-term fluxes see previous publications29,55,56.Data processingMean 30-min fluxes (CO2, LE, and H) were computed using Eddy-pro 5.1.1 software (LiCor, Lincoln, Nebraska, USA). Quality control of the data included a spike removal procedure. A linear fit was used for filling short gaps (below three hours) of missing values due to technical failure. Information about background meteorological parameters, including P, Ta, RH, and global radiation (Rg), was collected from meteorological stations (standard met stations maintained by the Israel Meteorological Service, https://ims.gov.il/en). The data were obtained at half-hourly time resolution and for a continuous period of 15 years since 2000.Estimating continuous fluxes using the flux meteorological algorithmEstimation of the flux-based annual carbon and radiation budgets was undertaken using the short campaign measurements as a basis to produce a continuous, seasonal, annual, and inter-annual scale dataset of ecosystem fluxes (flux meteorological algorithm). The flux meteorological algorithm method was undertaken based on the relationships between measured fluxes (CO2, LE, H, SWnet, and LWnet) and meteorological parameters (Ta, RH, Rg, VPD, and transpiration deficit, a parameter that correlated well with soil moisture, see main text and supplementary material of previous publication29. A two-step multiple stepwise regression was established, first between the measured fluxes (H, LE, and the ecosystem net carbon exchange) and the meteorological parameters measured by the mobile lab devices, and then between the two meteorological datasets (i.e., the variables measured by the Israel meteorological stations) for the same measurement times. Annual fluxes were calculated for the combined dataset of all campaigns at each site using the following generic linear equation:$$y={{{{{rm{a}}}}}}+{Sigma }_{i}{b}_{i}{x}_{i}$$
(1)
where, y is the ecosystem flux of interest, the daily average for radiative fluxes (LWnet and SWnet), non-radiative fluxes (H and LE), and daily sum for net ecosystem exchange (NEE), a and ({b}_{i}) are parameters, and ({x}_{i}) is Ta, RH, Rg, vapor pressure deficit, or transpiration deficit. The meteorological variables (({x}_{i})) were selected by stepwise regression, with ({b}_{i}=0) when a specific ({x}_{i}) was excluded.Based on this methodology, ecosystem flux data were extrapolated to the previous 7–15 years (since 2000 in the dry-subhumid and arid sites, since 2004 in the semi-arid sites, and since 2008 in the Oak-forest site) using all the available continuous meteorological parameters from the meteorological stations associated with our field sites. The long-term annual sums and means of extrapolated ecosystem fluxes were averaged for multi-year means of each site for the period of available extrapolated data. In the first, previously published phase of this study29, the extrapolation method was extensively tested, including simulation experiments at the arid forest site, where continuous flux data from the 20 years old permanent flux tower were available. Five percent of the daily data were selected by bootstrap (with 20 repetitions), a stepwise regression was performed for this sample, and then, the prediction of fluxes using Eq. 1 above was performed for the entire observation period. In the current phase of the study additional flux measurements are included with the R2 coefficients for the additional measurements ranging between 0.4-0.9, see Supplementary Table S3.The aridity index of the Oak-forest was in between those of the semi-arid and dry-subhumid Pine-forests (0.4 compared to 0.37 and 0.67, respectively). Therefore, to compare the Oak-forest with Pine-forest and non-forest sites, the average results from the semi-arid and dry-subhumid paired sites were used.Radiative forcing and carbon equivalence equationsTo compare the changes in the carbon and radiation budgets caused by forestation, we adopted the approach of Myhre et al. 30, and used Eq. 2:$${{RF}}_{triangle C}=5.35{{{{mathrm{ln}}}}}left(1+frac{triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(2)
where land-use changes in radiative forcing (RFΔC) are calculated based on the CO2 reference concentration, C0 (400 ppm for the measured period of study), and ΔC, which is the change in atmospheric CO2 in ppm, with a constant radiative efficiency (RE) value of 5.35. Here, ΔC is calculated based on the annual net ecosystem productivity (NEP; positive carbon gain by the forest, which is identical to net ecosystem exchange (NEE), the negative carbon removal from the atmosphere) as the difference between forested and non-forested ecosystems (ΔNEP) multiplied by a unit conversion constant:$$triangle C={left[{overline{{NEP}}}_{F}-{overline{{NEP}}}_{{NF}}right]}_{[gC{m}^{-2}{y}^{-1}]}cdot k ,[{ppm}]$$
(3)
where, k is a unit conversion factor, from ppm to g C (k = 2.13 × 109), calculated as the ratio between the air molar mass (Ma = 28.95; g mol−1), to carbon molar mass (Mc = 12.0107; g mol−1), and total air mass (ma = 5.15 × 109; g).Etminan et al. 57 introduced an updated approach to calculate the RE as a co-dependent of the change in CO2 concentration and atmospheric N2O:$${RE}={a}_{1}{left(triangle Cright)}^{2}+{b}_{1}left|triangle Cright|+{c}_{1}bar{N}+5.36,left[W,{m}^{-2}right]$$
(4)
where, (triangle {{{{{rm{C}}}}}}) is the change in atmospheric CO2 in ppm resulting from the forestation, as calculated in Eq. 3, (bar{{{{{{rm{N}}}}}}}) is the atmospheric N2O concentration in ppb (323), and the coefficients a1, b1, and c1 are −2.4 × 10−7 Wm−2ppm−1, 7.2 × 10−4 Wm−2 ppm−1, and −2.1 × 10−4 Wm−2ppb−1, respectively.Combining Eqs. 2 and 4 with an airborne fraction of (zeta =0.44)58, we obtain Eq. 5:$${{RF}}_{triangle C}={RE}cdot {{{{mathrm{ln}}}}}left(1+frac{zeta cdot triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(5)
Next, the annual average radiative forcing due to differences in radiation flux was calculated as follows:$${{RF}}_{triangle R}=frac{triangle Rcdot {A}_{F}}{{A}_{E}},left[W,{m}^{-2}right]$$
(6)
where, ΔR is the difference between forest and non-forest reflected short-wave or emitted long-wave radiation (ΔSWnet and ΔLWnet, respectively), assuming that the atmospheric incoming solar and thermal radiation fluxes are identical for the two, normalized by the ratio of the forest area (({A}_{F})) to the Earth’s area (({A}_{E}=5.1times {10}^{14},{m}^{2})).As forest conversion mostly has a lower albedo, the number of years needed to balance (‘Break even time’) the warming effect of changes in radiation budget by the cooling effect of carbon sequestration is calculated by combining Eqs. 5 and 6:$$^prime{Break},{even},{time}^prime=frac{{{RF}}_{triangle alpha }}{{{RF}}_{triangle C}},[{{{{{rm{years}}}}}}]$$
(7)
The multiyear averages of NEP for each of the three paired sites (forest and non-forest) were then modeled over a forest life span of 80 years. This was done based on a logarithmic model, modified for dryland, which takes as an input the long-term averages of NEP ((overline{{NEP}})) as in Eq. 3:$${{NEP}}_{t}=overline{{NEP}}(1-{exp }^{bcdot t}),left[g{{{{{rm{C}}}}}},{{{{{{rm{m}}}}}}}^{-2}{{yr}}^{-1}right]$$
(8)
where annual carbon gain at time t (NEPt) is a function of the multiyear average carbon gain ((overline{{{{{{rm{NEP}}}}}}})), forest age (t), and growth rate (b). Parameter b is a constant (b = −0.17) and is calculated based on the global analysis of Besnard et al. 59, limiting the data to only dryland flux sites60. Note that this analysis indicates NEP reaching a steady state and was used here to describe the initial forest growth phase, while growth analyses indicate that carbon sequestration peaks after about 80 years, followed by a steep decline50. This is consistent with the time scale for forest carbon sequestration considered here.In contrast to the one-year differences presented in Table 1 (ΔNEP), the net sequestration potential ((triangle)SP) for each of the paired sites was calculated as the accumulated ecosystem ΔNEPt along with forest age ((triangle) is the difference between forest and non-forest sites):$$triangle {SP}=mathop{sum }limits_{t=0}^{{age}}{triangle {NEP}}_{t}/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(9)
The ΔSP growth model was compared with previously published data of long-term carbon stock changes in arid forests (i.e., cumulative NEP over 50 years since forest establishment, t = 50), demonstrating agreement within ± 10%27.For comparison with previous studies, the carbon emission equivalent of shortwave forcing (EESF) was calculated using an inverse version of Eqs. 5 and 6 based on Betts1:$${EESF}={C}_{0}left({e}^{frac{{{RF}}_{triangle R}}{zeta cdot {RE}}}-1right)cdot k/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(10)
where, C0 is the reference atmospheric CO2 concentration (400 ppm, the average atmospheric concentration for the past decade), RFΔR is the multiyear average change in radiative forcing as a result of the change in surface albedo (Eq. 6 W m−2), RE is the radiative efficiency (Eq. 4, W m−2), ζ is the airborne fraction (0.44 as in Eq. 5), and k is a conversion factor, from ppm to g C (2.13 × 109 as in Eq. 3). Equation 10 was also used to calculate the emission equivalent of longwave forcing (EELF) with the RFΔR as the multiyear average change in radiative forcing as a result of the change in net long-wave radiation (ΔLWnet).Finally, the net equivalent change in carbon stock due to both the cooling effect of carbon sequestration and the warming effect due to albedo change (net equivalent stock change; NESC), was calculated by a simple subtraction:$${NESC}=triangle {SP}-{EESF},left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(11)
A comparison of the ΔSP (Eq. 9), the EESF (Eq. 10), and NESC (Eq. 11) with the same metrics as those used in other studies1,14,61 was done when appropriate. An exception was made for Arora & Montenegro (2011), where only carbon stock changes (ΔSP) were available in carbon units, and biogeophysical (BGP) and biogeochemical (BGC) effects were expressed as temperature changes. To overcome this metric difference, we converted the biogeophysical to carbon equivalent units (EESF + EELF) by multiplying the carbon stock changes (ΔSP) by the ratio between the BGP and BGC effects on temperature (EESF + EELF = ΔSP × BGP/BGC).Statistical and data analysesThe paired t-test was used to compare multi-annual averages of all variables between forested and adjacent non-forested sites and between sites across the climatic gradient. The variables of interest that were detected for their significant differences were albedo, net radiation and its longwave and shortwave components, latent heat fluxes, sensible heat fluxes, and net ecosystem productivity. All statistical and data analyses were performed using R 3.6.0 (R Core Team, 2020)62.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. https://doi.org/10.1126/sciadv.1600821 (2017).Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 
CAS 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).Article 
CAS 

Google Scholar 
Grantham, H. S. et al. The emerging threat of extractives sector to intact forest landscapes. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2021.692338 (2021).IPBES: Summary for Policymakers. In The Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES, 2019).Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01026-5 (2021).Article 

Google Scholar 
Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019).Article 
CAS 

Google Scholar 
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).Article 
CAS 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 

Google Scholar 
Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).Article 
CAS 

Google Scholar 
Coad, L. et al. Measuring impact of protected area management interventions: current and future use of the global database of protected area management effectiveness. Phil. Trans. R. Soc. B 370, 20140281 (2015).Article 

Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
CAS 

Google Scholar 
Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).Article 
CAS 

Google Scholar 
Zhang, J., Nielsen, S. E., Mao, L., Chen, S. & Svenning, J. C. Regional and historical factors supplement current climate in shaping global forest canopy height. J. Ecol. 104, 469–478 (2016).Article 

Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).Article 
CAS 

Google Scholar 
Knight, C. A. et al. Land management explains major trends in forest structure and composition over the last millennium in California’s Klamath Mountains. Proc. Natl Acad. Sci. USA 119, e2116264119 (2022).Article 
CAS 

Google Scholar 
Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).Article 
CAS 

Google Scholar 
Asner, G. P., Llactayo, W., Tupayachi, R. & Luna, E. R. Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring. Proc. Natl Acad. Sci. USA 110, 18454–18459 (2013).Article 
CAS 

Google Scholar 
Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).Article 

Google Scholar 
Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).Article 

Google Scholar 
Sandel, B. & Svenning, J. C. Human impacts drive a global topographic signature in tree cover. Nat Commun. https://doi.org/10.1038/ncomms3474 (2013).Potapov, P. et al. Mapping the world’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 51 (2008).Article 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).Article 
CAS 

Google Scholar 
Yang, H. et al. A global assessment of the impact of individual protected areas on preventing forest loss. Sci. Total Environ. 777, 145995 (2021).Article 
CAS 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
CAS 

Google Scholar 
Clerici, N. et al. Deforestation in Colombian protected areas increased during post-conflict periods. Sci. Rep. 10, 4971 (2020).Article 
CAS 

Google Scholar 
Heino, M. et al. Forest loss in protected areas and intact forest landscapes: a global analysis. PLoS ONE 10, e0138918 (2015).Article 

Google Scholar 
Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F. & Pereira, H. M. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv. 241, 108299 (2020).Article 

Google Scholar 
Wade, C. M. et al. What is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018. Forests 11, 539 (2020).Article 

Google Scholar 
Transforming Our World: The 2030 Agenda for Sustainable Development (UN DESA, 2016).Burleson, E. Paris Agreement and consensus to address climate challenge. ASIL Insight 20, 8 (2016).
Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 
CAS 

Google Scholar 
Quegan, S. et al. The European Space Agency BIOMASS mission: measuring forest above-ground biomass from space. Remote Sens. Environ. 227, 44–60 (2019).Article 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2011JG001708 (2011).Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

Google Scholar 
Atkins, J. W., Fahey, R. T., Hardiman, B. S. & Gough, C. M. Forest canopy structural complexity and light absorption relationships at the subcontinental scale. J. Geophys. Res. Biogeosci. 123, 1387–1405 (2018).Article 

Google Scholar 
Scarth, P., Armston, J., Lucas, R. & Bunting, P. A structural classification of Australian vegetation using ICESat/GLAS, ALOS PALSAR, and Landsat sensor data. Remote Sens. 11, 147 (2019).Article 

Google Scholar 
Dubayah, R. et al. The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).Article 

Google Scholar 
Lang, N. et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760 (2022).Article 

Google Scholar 
Marselis, S. M., Keil, P., Chase, J. M. & Dubayah, R. The use of GEDI canopy structure for explaining variation in tree species richness in natural forests. Environ. Res. Lett. 17, 045003 (2022).Article 

Google Scholar 
MacArthur, R. H. & MacArthur, J. W. On bird species diversity. Ecology 42, 594–598 (1961).Article 

Google Scholar 
Walter, J. A., Stovall, A. E. L. & Atkins, J. W. Vegetation structural complexity and biodiversity in the Great Smoky Mountains. Ecosphere 12, e03390 (2021).Article 

Google Scholar 
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).Article 
CAS 

Google Scholar 
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Change Biol. 25, 811–826 (2019).Article 

Google Scholar 
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).Article 
CAS 

Google Scholar 
Chazdon, R. L. et al. A policy‐driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).Article 

Google Scholar 
Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).Article 

Google Scholar 
Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).Article 

Google Scholar 
Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).Article 
CAS 

Google Scholar 
Ponta, N. et al. Drivers of transgression: what pushes people to enter protected areas. Biol. Conserv. 257, 109121 (2021).Article 

Google Scholar 
Pack, S. M. et al. Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biol. Conserv. 197, 32–39 (2016).Article 

Google Scholar 
Tollefson, J. Illegal mining in the Amazon hits record high amid Indigenous protests. Nature 598, 15–16 (2021).Article 
CAS 

Google Scholar 
Thies, C., Rosoman, G., Cotter, J. & Meaden, S. Intact Forest Landscapes. Why It Is Crucial to Protect Them from Industrial Exploitation Technical Note Bd 5 (Greenpeace, 2011).Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008).Article 
CAS 

Google Scholar 
Lindenmayer, D. B. et al. New policies for old trees: averting a global crisis in a keystone ecological structure. Conserv. Lett. 7, 61–69 (2014).Article 

Google Scholar 
Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2018).Tang, H. & Armston, J. Algorithm Theoretical Basis Document (ATBD) for GEDI L2B Footprint Canopy Cover and Vertical Profile Metrics (Goddard Space Flight Center, 2019).Adam, M., Urbazaev, M., Dubois, C. & Schmullius, C. Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters. Remote Sens. 12, 3948 (2020).Article 

Google Scholar 
Dorado-Roda, I. et al. Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in Mediterranean forests. Remote Sens. 13, 2279 (2021).Article 

Google Scholar 
Duncanson, L. et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sens. Environ. 270, 112845 (2022).Article 

Google Scholar 
Hofton, M., Blair, J. B., Story, S. & Yi, D. Algorithm Theoretical Basis Document (ATBD) (NASA, 2020).Dubayah, R. et al. GEDI L3 Gridded Land Surface Metrics v.2 (ORNL DAAC, 2021).Roy, D. P., Kashongwe, H. B. & Armston, J. The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring. Sci. Remote Sens. 4, 100024 (2021).Article 

Google Scholar 
Potapov, P., Hansen, M. C., Stehman, S. V., Loveland, T. R. & Pittman, K. Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sens. Environ. 112, 3708–3719 (2008).Article 

Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

Google Scholar 
Silva, C. A. et al. rGEDI: NASA’s global ecosystem ynamics investigation (GEDI) data visualization and processing. R package version 0.1.2. (2020).The R Project for Statistical Computing (The R Foundation, 2014); https://www.R-project.org/Fischer, B., Smith, M., Pau, G., Morgan, M. & van Twisk, D. rhdf5: R interface to HDF5. R package version 2.40.0 (2022).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).Article 

Google Scholar 
Giglio, L., Loboda, T., Roy, D. P., Quayle, B. & Justice, C. O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 113, 408–420 (2009).Article 

Google Scholar 
Hengl, T. & Wheeler, I. Soil organic carbon content in x 5 g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Zenodo https://doi.org/10.5281/zenodo.1475458 (2018).Farr, T. The shuttle radar topography mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Bivand, R. et al. Package ‘spdep’: spatial dependence: weighting schemes, statistics version 1.2-7 (The Comprehensive R Archive Network, 2015).Bivand, R., Yu, D., Nakaya, T., Garcia-Lopez, M.-A. & Bivand, M. R. Package ‘spgwr’: geographically eighted regression. R package version 0.6-35 (2020).Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003). More

Our main objective was to test the SJH (positing that adolescents living in “traditional”, non-industrial environments will more closely fulfil their “biological/natural” sleep requirements25,26) by comparing sleep deprivation among adolescents in rural and urban societies. The SJH argues that adolescent “biological/natural” sleep quotas and circadian cycles can be ascertained from free days, when sleep patterns are minimally shaped by social commitments5,37. Therefore, we predicted that sleep deprivation would be rare in the more rural agricultural settings of Puebla and Campeche but more frequent among participants in Mexico City. Likewise, we predicted that we would not see sleep deprivation on free days among any of the rural participants.Our predictions were not supported, instead, we found that short sleep quotas during school nights are common in both rural agricultural settings, with over 75% of adolescents in each group sleeping  More

Sampling and incubationFour rock samples were collected from the 3.7 km-deep Auka vent field in the Southern Pescadero Basin (23.956094N, 108.86192W)20,23. Sample NA091.008 was collected in 2017 on cruise NA091 with the Eexploration vessle Nautilus and incubated as described previously34. Samples 12,019 (S0200-R1), 11,719 (S0193-R2) and 11,868 (S0197-PC1), the latter representing a lithified nodule recovered from a sediment push core, were collected with Remotely operated vehicle SuBastian and Research vessel Falkor on cruise FK181031 in November 2018. These samples were processed shipboard and stored under anoxic conditions at 4 °C for subsequent incubation in the laboratory. In the laboratory, rock samples 12,019 and 11,719 were broken into smaller pieces under sterile conditions, immersed in N2-sparged sterilized artificial sea water and incubated under anoxic conditions with methane, as described previously for NA091.008 (ref. 34). Additional sampling information can be found in Supplementary Table 1. Mineralogical analysis by X-ray Powder Diffraction (XRD) identified barite in several of these samples, collected from two locations in the Auka vent field, including on the western side of the Matterhorn vent (11,719, NA091.008), and one oil-saturated sample (12,019) recovered from the sedimented flanks from the southern side of Z vent. Our analysis also includes metagenomic data from two sediment cores from the Auka vent field (DR750-PC67 and DR750-PC80) collected in April 2015 with the ROV Doc Ricketts and R/V Western Flyer (MBARI2015), previously published (ref. 23).Fluorescence in situ hybridizationSamples were fixed shipboard using freshly prepared paraformaldehyde (2 vol% in 3× Phosphate Buffer Solution (PBS), EMS15713) at 4 °C overnight, rinsed twice using 3× PBS, and stored in ethanol (50% in 1× PBS) at −20 °C until processing. Small pieces ( More