Philippa Kaur

Mishra, A. K. & Singh, V. P. J. Hydrol. 391, 202–216 (2010).Article 

Google Scholar 
Jiao, W. et al. Nat. Commun. 12, 3777 (2021).Article 
CAS 

Google Scholar 
Gampe, D. et al. Nat. Clim. Change 11, 772–779 (2021).Article 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Schwalm, C. R. et al. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
Li, Y. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01584-2 (2023).Fourth National Climate Assessment: Volume II—Impacts, Risks, and Adaptation in the United States (US Global Change Research Program, 2018).Daryanto, S., Wang, L. & Jacinthe, P. A. PLoS ONE 11, e0156362 (2016).Article 

Google Scholar 
Jiao, W. et al. J. Geophys. Res. Biogeosci. 127, e2021JG006431 (2022).Augspurger, C. K. Oecologia 156, 281–286 (2008).Article 

Google Scholar 
Lian, X. et al. Nat. Commun. 12, 983 (2021).Article 
CAS 

Google Scholar 
Buermann, W. et al. Nature 562, 110–114 (2018).Article 
CAS 

Google Scholar 
Lian, X. et al. Sci. Adv. 6, eaax0255 (2020).Article 

Google Scholar 
Jiao, W., Wang, L. & McCabe, M. F. Rem. Sens. Environ. 256, 112313 (2021).Article 

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

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

Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. 11, 306–312 (2021).Article 
ADS 

Google Scholar 
Garajeh, M. K. & Feizizadeh, B. A comparative approach of data-driven split-window algorithms and MODIS products for land surface temperature retrieval. Appl. Geomat. 13, 715–733 (2021).Article 

Google Scholar 
Alizadeh-Choobari, O., Ahmadi-Givi, F., Mirzaei, N. & Owlad, E. Climate change and anthropogenic impacts on the rapid shrinkage of Lake Urmia. Int. J. Climatol. 36, 4276–4286 (2016).Article 

Google Scholar 
Rembold, F., Kerdiles, H., Lemoine, G. & Perez-Hoyos, A. Impact of El Niño on agriculture in Southern Africa for the 2015/2016 main season. Joint Research Centre (JRC) MARS Bulletin–Global Outlook Series. European Commission, Brussels (2016).Zampieri, M., Ceglar, A., Dentener, F. & Toreti, A. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett. 12, 064008 (2017).Article 
ADS 

Google Scholar 
Toté, C. et al. Evaluation of the SPOT/VEGETATION Collection 3 reprocessed dataset: Surface reflectances and NDVI. Remote Sens. Environ. 201, 219–233 (2017).Article 
ADS 

Google Scholar 
Solomon, N. et al. Environmental impacts and causes of conflict in the Horn of Africa: A review. Earth Sci. Rev. 177, 284–290 (2018).Article 
ADS 

Google Scholar 
Dresse, A., Fischhendler, I., Nielsen, J. Ø. & Zikos, D. Environmental peacebuilding: Towards a theoretical framework. Coop. Confl. 54, 99–119 (2019).Article 

Google Scholar 
Vos, R., Jackson, J., James, S. & Sánchez, M. V. Refugees and Conflict-Affected People: Integrating Displaced Communities into Food Systems. 2020 Global Food Policy Report, 46–53 (2020).Zulfiqar, F., Navarro, M., Ashraf, M., Akram, N. A. & Munné-Bosch, S. Nanofertilizer use for sustainable agriculture: Advantages and limitations. Plant Sci. 289, 110270 (2019).Article 
CAS 

Google Scholar 
Viana, C. M. & Rocha, J. Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method. Sustainability 12, 4332 (2020).Article 

Google Scholar 
Vasile, A. J., Popescu, C., Ion, R. A. & Dobre, I. From conventional to organic in Romanian agriculture—Impact assessment of a land use changing paradigm. Land Use Policy 46, 258–266 (2015).Article 

Google Scholar 
Veloso, A. et al. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 199, 415–426 (2017).Article 
ADS 

Google Scholar 
Samasse, K., Hanan, N. P., Tappan, G. & Diallo, Y. Assessing cropland area in West Africa for agricultural yield analysis. Remote Sens. 10, 1785 (2018).Article 
ADS 

Google Scholar 
Van Esse, H. P., Reuber, T. L. & van der Does, D. Genetic modification to improve disease resistance in crops. New Phytol. 225, 70–86 (2020).Article 

Google Scholar 
FAO. The Future of Food and Agriculture—Trends and Challenges (FAO, 2017).
Google Scholar 
Müller, B. et al. Modelling food security: Bridging the gap between the micro and the macro scale. Glob. Environ. Chang. 63, 102085 (2020).Article 

Google Scholar 
Food and Agriculture Organization of the United Nations. Forest Management and Conservation Agriculture: Experiences of Smallholder Farmers in the Eastern Region of Paraguay (FAO, 2013).
Google Scholar 
FAO Food Price Index. World Food Situation (FAO, 2021).
Google Scholar 
Sishodia, R. P., Ray, R. L. & Singh, S. K. Applications of remote sensing in precision agriculture: A review. Remote Sens. 12, 3136 (2020).Article 
ADS 

Google Scholar 
Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 236, 111402 (2020).Article 
ADS 

Google Scholar 
Feizizadeh, B., Garajeh, M. K., Blaschke, T. & Lakes, T. An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. CATENA 198, 105073 (2021).Article 

Google Scholar 
Wen, W., Timmermans, J., Chen, Q. & van Bodegom, P. M. A review of remote sensing challenges for food security with respect to salinity and drought threats. Remote Sens. 13, 6 (2020).Article 
ADS 

Google Scholar 
Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T. & Blaschke, T. Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. J. Environ. Plan. Manag. https://doi.org/10.1080/09640568.2021.2001317 (2021).Article 

Google Scholar 
Westerveld, J. J. et al. Forecasting transitions in the state of food security with machine learning using transferable features. Sci. Total Environ. 786, 147366 (2021).Article 
ADS 
CAS 

Google Scholar 
Anderson, R., Bayer, P. E. & Edwards, D. Climate change and the need for agricultural adaptation. Curr. Opin. Plant Biol. 56, 197–202 (2020).Article 

Google Scholar 
Baniya, B., Tang, Q., Xu, X., Haile, G. G. & Chhipi-Shrestha, G. Spatial and temporal variation of drought based on satellite derived vegetation condition index in Nepal from 1982–2015. Sensors 19, 430 (2019).Article 
ADS 

Google Scholar 
Kubitza, C., Krishna, V. V., Schulthess, U. & Jain, M. Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review. Agron. Sustain. Dev. 40, 1–21 (2020).Article 

Google Scholar 
Lees, T., Tseng, G., Atzberger, C., Reece, S. & Dadson, S. Deep learning for vegetation health forecasting: a case study in Kenya. Remote Sens. 14, 698 (2022).Article 
ADS 

Google Scholar 
Khanian, M., Serpoush, B. & Gheitarani, N. Balance between place attachment and migration based on subjective adaptive capacity in response to climate change: The case of Famenin County in Western Iran. Clim. Dev. 11, 69–82 (2019).Article 

Google Scholar 
Khanian, M., Marshall, N., Zakerhaghighi, K., Salimi, M. & Naghdi, A. Transforming agriculture to climate change in Famenin County, West Iran through a focus on environmental, economic and social factors. Weather Clim. Extremes 21, 52–64 (2018).Article 

Google Scholar 
Leroux, L. et al. Crop monitoring using vegetation and thermal indices for yield estimates: Case study of a rainfed cereal in semi-arid West Africa. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 347–362 (2015).Article 
ADS 

Google Scholar 
Sun, J. et al. Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 5048–5060 (2020).Article 
ADS 

Google Scholar 
Tian, H. et al. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China. Agric. Forest Meteorol. 310, 108629 (2021).Article 
ADS 

Google Scholar 
Weng, Y., Chang, S., Cai, W. & Wang, C. Exploring the impacts of biofuel expansion on land use change and food security based on a land explicit CGE model: A case study of China. Appl. Energy 236, 514–525 (2019).Article 
ADS 

Google Scholar 
Rojas, O., Rembold, F., Royer, A. & Negre, T. Real-time agrometeorological crop yield monitoring in Eastern Africa. Agron. Sustain. Dev. 25, 63–77 (2005).Article 

Google Scholar 
Rembold, F. et al. ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis. Agric. Syst. 168, 247–257 (2019).Article 

Google Scholar 
Gohar, A. A., Cashman, A. & El-bardisy, H. A. H. Modeling the impacts of water-land allocation alternatives on food security and agricultural livelihoods in Egypt: Welfare analysis approach. Environ. Dev. 39, 100650 (2021).Article 

Google Scholar 
Mekonnen, A., Tessema, A., Ganewo, Z. & Haile, A. Climate change impacts on household food security and farmers adaptation strategies. J. Agric. Food Res. 6, 100197 (2021).Article 

Google Scholar 
Hervas, A. Mapping oil palm-related land use change in Guatemala, 2003–2019: Implications for food security. Land Use Policy 109, 105657 (2021).Article 

Google Scholar 
Viana, C. M., Freire, D., Abrantes, P., Rocha, J. & Pereira, P. Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Sci. Total Environ. 806, 150718 (2022).Article 
ADS 
CAS 

Google Scholar 
Bazzana, D., Foltz, J. & Zhang, Y. Impact of climate smart agriculture on food security: An agent-based analysis. Food Policy 111, 102304 (2022).Article 

Google Scholar 
Parven, A. et al. Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh. Int. J. Disaster Risk Reduct. 78, 103119 (2022).Article 

Google Scholar 
Mohajane, M. et al. Land use/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5, 131 (2018).Article 

Google Scholar 
Duarte, L., Teodoro, A. C., Sousa, J. J. & Pádua, L. QVigourMap: A GIS open source application for the creation of canopy vigour maps. Agronomy 11, 952 (2021).Article 

Google Scholar 
Tavares, P. A., Beltrão, N. E. S., Guimarães, U. S. & Teodoro, A. C. Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors 19, 1140 (2019).Article 
ADS 

Google Scholar 
Atuoye, K. N., Luginaah, I., Hambati, H. & Campbell, G. Who are the losers? Gendered-migration, climate change, and the impact of large scale land acquisitions on food security in coastal Tanzania. Land Use Policy 101, 105154 (2021).Article 

Google Scholar 
Yang, S., Gu, L., Li, X., Jiang, T. & Ren, R. Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery. Remote Sens. 12, 3119 (2020).Article 
ADS 

Google Scholar 
Milojevic-Dupont, N. & Creutzig, F. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustain. Cities Soc. 64, 102526 (2021).Article 

Google Scholar 
Santos, D. et al. Spectral analysis to improve inputs to random forest and other boosted ensemble tree-based algorithms for detecting NYF Pegmatites in Tysfjord, Norway. Remote Sens. 14, 3532 (2022).Article 
ADS 

Google Scholar 
Hitouri, S. et al. Hybrid machine learning approach for gully erosion mapping susceptibility at a watershed scale. ISPRS Int. J. Geo Inf. 11, 401 (2022).Article 

Google Scholar 
Alvarez-Mendoza, C. I., Teodoro, A., Freitas, A. & Fonseca, J. Spatial estimation of chronic respiratory diseases based on machine learning procedures—An approach using remote sensing data and environmental variables in quito, Ecuador. Appl. Geogr. 123, 102273 (2020).Article 

Google Scholar 
Teodoro, A., Pais-Barbosa, J., Gonçalves, H., Veloso-Gomes, F. & Taveira-Pinto, F. Identification of beach features/patterns through image classification techniques applied to remotely sensed data. Int. J. Remote Sens. 32, 7399–7422 (2011).Article 

Google Scholar 
Saleem, M. H., Potgieter, J. & Arif, K. M. Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precis. Agric. 22, 2053–2091 (2021).Article 

Google Scholar 
Carrasco, L., O’Neil, A. W., Morton, R. D. & Rowland, C. S. Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sens. 11, 288 (2019).Article 
ADS 

Google Scholar 
Kumar, L. & Mutanga, O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 10, 1509 (2018).Article 
ADS 

Google Scholar 
Kakooei, M., Nascetti, A. & Ban, Y. in IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium 6836–6839 (IEEE).Castillo, E., Iglesias, A. & Ruiz-Cobo, R. Functional Equations in Applied Sciences (Elsevier, 2004).MATH 

Google Scholar 
Zhao, G., Gao, H. & Cai, X. Estimating lake temperature profile and evaporation losses by leveraging MODIS LST data. Remote Sens. Environ. 251, 112104 (2020).Article 
ADS 

Google Scholar 
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article 
ADS 

Google Scholar 
Monteith, J. L. in Symposia of the society for experimental biology 205–234 (Cambridge University Press (CUP) Cambridge).Kidd, C. et al. So, how much of the Earth’s surface is covered by rain gauges?. Bull. Am. Meteorol. Soc. 98, 69–78 (2017).Article 
ADS 

Google Scholar 
Huffman, G. J. et al. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). In Algorithm Theoretical Basis Document (ATBD) Version 4 (2015).Zhang, W., Cao, H. & Liang, Y. Plant uptake and soil fractionation of five ether-PFAS in plant-soil systems. Sci. Total Environ. 771, 144805 (2021).Article 
ADS 
CAS 

Google Scholar 
Jiang, S. et al. Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard. Sci. Total Environ. 811, 152377 (2022).Article 
ADS 
CAS 

Google Scholar 
Ghimire, C., Bruijnzeel, L., Lubczynski, M. & Bonell, M. Negative trade-off between changes in vegetation water use and infiltration recovery after reforesting degraded pasture land in the Nepalese Lesser Himalaya. Hydrol. Earth Syst. Sci. 18, 4933–4949 (2014).Article 
ADS 

Google Scholar 
Zhang, J., Chen, H., Fu, Z. & Wang, K. Effects of vegetation restoration on soil properties along an elevation gradient in the karst region of southwest China. Agric. Ecosyst. Environ. 320, 107572 (2021).Article 
CAS 

Google Scholar 
Yan, W. Y., Shaker, A. & El-Ashmawy, N. Urban land cover classification using airborne LiDAR data: A review. Remote Sens. Environ. 158, 295–310 (2015).Article 
ADS 

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

Google Scholar 
Zhang, C. et al. Joint deep learning for land cover and land use classification. Remote Sens. Environ. 221, 173–187 (2019).Article 
ADS 

Google Scholar 
Interdonato, R., Ienco, D., Gaetano, R. & Ose, K. DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn. ISPRS J. Photogramm. Remote. Sens. 149, 91–104 (2019).Article 
ADS 

Google Scholar 
Nyamekye, C., Ghansah, B., Agyapong, E. & Kwofie, S. Mapping changes in artisanal and small-scale mining (ASM) landscape using machine and deep learning algorithms—a proxy evaluation of the 2017 ban on ASM in Ghana. Environ. Chall. 3, 100053 (2021).Article 

Google Scholar 
Rahmati, O. et al. Land subsidence modelling using tree-based machine learning algorithms. Sci. Total Environ. 672, 239–252 (2019).Article 
ADS 
CAS 

Google Scholar 
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014).Gupta, A. A comprehensive guide on deep learning optimizers. Analytics Vidhya. Dostopno na: https://www.analyticsvidhya.com/blog/2021/10/acomprehensive-guide-on-deep-learningoptimizers/#:~:text=An%20optimizer%20is%20a%20function,loss%20and%20improve%20the%20accuracy [22 May 2022] (2021).Reddy, V. K. & AV, R. K. Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network. Biomed. Signal Process. Control 77, 103774 (2022).Article 

Google Scholar 
Pulatov, B., Linderson, M.-L., Hall, K. & Jönsson, A. M. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292 (2015).Article 
ADS 

Google Scholar 
Parker, L., Pathak, T. & Ostoja, S. Climate change reduces frost exposure for high-value California orchard crops. Sci. Total Environ. 762, 143971 (2021).Article 
ADS 
CAS 

Google Scholar 
Svystun, T., Lundströmer, J., Berlin, M., Westin, J. & Jönsson, A. M. Model analysis of temperature impact on the Norway spruce provenance specific bud burst and associated risk of frost damage. For. Ecol. Manage. 493, 119252 (2021).Article 

Google Scholar 
Kheybari, S., Rezaie, F. M. & Farazmand, H. Analytic network process: An overview of applications. Appl. Math. Comput. 367, 124780 (2020).MATH 

Google Scholar 
Saaty, T. The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation (McGraw-Hill, 1980).MATH 

Google Scholar 
Saaty, T. L. & Ozdemir, M. S. The Encyclicon-Volume 1: A Dictionary of Decisions with Dependence and Feedback Based on the Analytic Network Process (RWS Publications, 2021).
Google Scholar 
Saaty, T. L. Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network. J. Syst. Sci. Syst. Eng. 13, 129–157 (2004).Article 
ADS 

Google Scholar 
Chung, K. L. Markov Chains with Stationary Transition Probabilities 5–11 (Springer, 1960).
Google Scholar 
Mokarram, M., Pourghasemi, H. R., Hu, M. & Zhang, H. Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA–Markov model. Sci. Total Environ. 781, 146703 (2021).Article 
ADS 
CAS 

Google Scholar 
Maleki, T., Koohestani, H. & Keshavarz, M. Can climate-smart agriculture mitigate the Urmia Lake tragedy in its eastern basin?. Agric. Water Manag. 260, 107256 (2022).Article 

Google Scholar 
Rahmani, J. & Danesh-Yazdi, M. Quantifying the impacts of agricultural alteration and climate change on the water cycle dynamics in a headwater catchment of Lake Urmia Basin. Agric. Water Manag. 270, 107749 (2022).Article 

Google Scholar 
Schmidt, M., Gonda, R. & Transiskus, S. Environmental degradation at Lake Urmia (Iran): Exploring the causes and their impacts on rural livelihoods. GeoJournal 86, 2149–2163 (2021).Article 

Google Scholar 
Eimanifar, A. & Mohebbi, F. Urmia Lake (northwest Iran): A brief review. Saline Syst. 3, 1–8 (2007).Article 

Google Scholar 
Shadkam, S., Ludwig, F., van Oel, P., Kirmit, Ç. & Kabat, P. Impacts of climate change and water resources development on the declining inflow into Iran’s Urmia Lake. J. Great Lakes Res. 42, 942–952 (2016).Article 

Google Scholar 
Chaudhari, S., Felfelani, F., Shin, S. & Pokhrel, Y. Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century. J. Hydrol. 560, 342–353 (2018).Article 
ADS 

Google Scholar 
Khazaei, B. et al. Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. J. Hydrol. 569, 203–217 (2019).Article 
ADS 

Google Scholar 
Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M. & Schüth, C. Climate change or irrigated agriculture—What drives the water level decline of Lake Urmia. Sci. Rep. 10, 1–10 (2020).Article 

Google Scholar 
Azarnivand, A., Hashemi-Madani, F. S. & Banihabib, M. E. Extended fuzzy analytic hierarchy process approach in water and environmental management (case study: Lake Urmia Basin, Iran). Environ. Earth Sci. 73, 13–26 (2015).Article 
ADS 

Google Scholar 
Bonham-Carter, G. F. & Bonham-Carter, G. Geographic Information Systems for Geoscientists: Modelling with GIS (Elsevier, 1994).
Google Scholar 
Garajeh, M. K. et al. An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran. Sci. Total Environ. 778, 146253 (2021).Article 
ADS 
CAS 

Google Scholar  More

Authors and AffiliationsStatistics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyFrancesco N. Tubiello, Giulia Conchedda, Leon Casse & Giorgia De SantisDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyHao Pengyu & Chen ZhongxinInternational Institute for Applied Systems Analysis, Laxenburg, AustriaSteffen FritzGeospatial Unit, Land and Water Division, Food and Agriculture Organization of the United Nations, Rome, ItalyDouglas MuchoneyAuthorsFrancesco N. TubielloGiulia ConcheddaLeon CasseHao PengyuChen ZhongxinGiorgia De SantisSteffen FritzDouglas MuchoneyCorresponding authorCorrespondence to
Francesco N. Tubiello. 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

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