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    Evolutionary diversification of methanotrophic ANME-1 archaea and their expansive virome

    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

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    An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

    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

  • in

    Spring phenology alters vegetation drought recovery

    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

  • in

    A new technique to study nutrient flow in host-parasite systems by carbon stable isotope analysis of amino acids and glucose

    Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Lafferty, K. D., Dobson, A. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. 103, 11211–11216 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Amundsen, P. A. et al. Food web topology and parasites in the pelagic zone of a subarctic lake. J. Anim. Ecol. 78, 563–572. https://doi.org/10.1111/j.1365-2656.2008.01518.x (2009).Article 

    Google Scholar 
    Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).Article 

    Google Scholar 
    Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482. https://doi.org/10.1111/j.1600-0706.2013.00243.x (2013).Article 

    Google Scholar 
    Sato, T. et al. Nematomorph parasites drive energy flow through a riparian ecosystem. Ecology 92, 201–207 (2011).Article 

    Google Scholar 
    Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).Article 

    Google Scholar 
    Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2018).Article 
    CAS 

    Google Scholar 
    Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50, 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 

    Google Scholar 
    Barber, I. & Svensson, P. A. Effects of experimental Schistocephalus solidus infections on growth, morphology and sexual development of female three-spined sticklebacks Gasterosteus aculeatus. Parasitology 126, 359–367. https://doi.org/10.1017/s0031182002002925 (2003).Article 
    CAS 

    Google Scholar 
    Scharsack, J. P., Koch, K. & Hammerschmidt, K. Who is in control of the stickleback immune system: Interactions between Schistocephalus solidus and its specific vertebrate host. Proc. Biol. Sci. 274, 3151–3158. https://doi.org/10.1098/rspb.2007.1148 (2007).Article 

    Google Scholar 
    Hopkins, C. A. Studies on cestode metabolism. I. glycogen metabolism in Schistocephalus solidus In vivo. J. Parasitol. 36, 384–390 (1950).Article 
    CAS 

    Google Scholar 
    Körting, W. & Barrett, J. Carbohydrate catabolism in the plerocercoids of Schistocephalus solidus (Cestoda: Pseudophyllidea). Int. J. Parasitol. 7, 411–417 (1977).Article 

    Google Scholar 
    Hebert, F. O., Grambauer, S., Barber, I., Landry, C. R. & Aubin-Horth, N. Major host transitions are modulated through transcriptome-wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite. Mol. Ecol. 26, 1118–1130. https://doi.org/10.1111/mec.13970 (2017).Article 
    CAS 

    Google Scholar 
    Berger, C. S. et al. The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions. Parasites Vectors 14, 436. https://doi.org/10.1186/s13071-021-04933-w (2021).Article 
    CAS 

    Google Scholar 
    Jolles, J. W., Mazue, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282. https://doi.org/10.1038/s41598-020-69057-0 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Scharsack, J. P. et al. Climate change facilitates a parasite’s host exploitation via temperature-mediated immunometabolic processes. Glob. Change Biol. 27, 94–107. https://doi.org/10.1111/gcb.15402 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Kochneva, A., Borvinskaya, E. & Smirnov, L. Zone of interaction between the parasite and the host: Protein profile of the body cavity fluid of Gasterosteus aculeatus L. infected with the Cestode Schistocephalus solidus (Muller, 1776). Acta Parasitol. 66, 569–583. https://doi.org/10.1007/s11686-020-00318-8 (2021).Article 
    CAS 

    Google Scholar 
    Barber, I. & Scharsack, J. P. The three-spined stickleback-Schistocephalus solidus system: An experimental model for investigating host-parasite interactions in fish. Parasitology 137, 411–424. https://doi.org/10.1017/S0031182009991466 (2010).Article 
    CAS 

    Google Scholar 
    Weber, J. N., Steinel, N. C., Shim, K. C. & Bolnick, D. I. Recent evolution of extreme cestode growth suppression by a vertebrate host. Proc. Natl. Acad. Sci. U. S. A. 114, 6575–6580. https://doi.org/10.1073/pnas.1620095114 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: A neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).Article 
    CAS 

    Google Scholar 
    Hayes, J. M. Factors controlling 13C contents of sedimentary organic compounds: Principles and evidence. Mar. Geol. 113, 111–125 (1993).Article 
    ADS 
    CAS 

    Google Scholar 
    France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313 (1995).Article 
    ADS 

    Google Scholar 
    Post, D. M. Using stable isotopes to estimate trophic position: Models, methods and assumptions. Ecology 83, 703–718 (2002).Article 

    Google Scholar 
    O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326. https://doi.org/10.1007/s00442-017-3881-9 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 

    Google Scholar 
    Liu, H.-z, Luo, L. & Cai, D.-l. Stable carbon isotopic analysis of amino acids in a simplified food chain consisting of the green alga Chlorella spp., the calanoid copepod Calanus sinicus, and the Japanese anchovy (Engraulis japonicus). Can. J. Zool. 96, 23–30. https://doi.org/10.1139/cjz-2016-0170 (2018).Article 
    CAS 

    Google Scholar 
    Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem. 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).Article 
    CAS 

    Google Scholar 
    Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).Article 
    ADS 

    Google Scholar 
    Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fish. Res. 219, 105303. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

    Google Scholar 
    Choy, K., Smith, C. I., Fuller, B. T. & Richards, M. P. Investigation of amino acid δ13C signatures in bone collagen to reconstruct human palaeodiets using liquid chromatography–isotope ratio mass spectrometry. Geochim. Cosmochim. Acta 74, 6093–6111. https://doi.org/10.1016/j.gca.2010.07.025 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).Article 
    CAS 

    Google Scholar 
    Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid delta13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Honch, N. V., McCullagh, J. S. & Hedges, R. E. Variation of bone collagen amino acid delta13C values in archaeological humans and fauna with different dietary regimes: Developing frameworks of dietary discrimination. Am. J. Phys. Anthropol. 148, 495–511. https://doi.org/10.1002/ajpa.22065 (2012).Article 

    Google Scholar 
    Mora, A. et al. High-resolution palaeodietary reconstruction: Amino acid δ 13 C analysis of keratin from single hairs of mummified human individuals. Quatern. Int. 436, 96–113. https://doi.org/10.1016/j.quaint.2016.10.018 (2017).Article 

    Google Scholar 
    Matos, M. P. V., Konstantynova, K. I., Mohr, R. M. & Jackson, G. P. Analysis of the (13)C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources. Anal. Bioanal. Chem. 410, 7943–7954. https://doi.org/10.1007/s00216-018-1416-9 (2018).Article 
    CAS 

    Google Scholar 
    Bontempo, L. et al. Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes. Food Chem. 318, 126426. https://doi.org/10.1016/j.foodchem.2020.126426 (2020).Article 
    CAS 

    Google Scholar 
    Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
    CAS 

    Google Scholar 
    Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

    Google Scholar 
    Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).Article 

    Google Scholar 
    Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 

    Google Scholar 
    Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 

    Google Scholar 
    Hesse, T. et al. Insights into amino acid fractionation and incorporation by compound-specific carbon isotope analysis of three-spined sticklebacks. Sci. Rep. 12, 11690. https://doi.org/10.1038/s41598-022-15704-7 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Riekenberg, P. M. et al. Stable nitrogen isotope analysis of amino acids as a new tool to clarify complex parasite–host interactions within food webs. Oikos 130, 1650–1664. https://doi.org/10.1111/oik.08450 (2021).Article 
    CAS 

    Google Scholar 
    Carleton, S. A. & Del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812. https://doi.org/10.1111/j.1365-2435.2010.01700.x (2010).Article 

    Google Scholar 
    Perga, M. E. & Gerdeaux, D. ‘Are fish what they eat’ all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Grey, J. Trophic fractionation and the effects of diet switch on the carbon stable isotopic ‘signatures’ of pelagic consumers. SIL Proc. 1922–2010(27), 3187–3191. https://doi.org/10.1080/03680770.1998.11898266 (2000).Article 

    Google Scholar 
    Danfaer, A. Nutrient metabolism and utilization in the liver. Livest. Prod. Sci. 39, 115–127 (1994).Article 

    Google Scholar 
    Read, C. P. & Simmons, J. E. Biochemistry and physiology of tapeworms. Physiol. Rev. 43, 263–305 (1963).Article 
    CAS 

    Google Scholar 
    Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 1–9. https://doi.org/10.1186/s13071-017-2030-y (2017).Article 
    CAS 

    Google Scholar 
    Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).Article 
    CAS 

    Google Scholar 
    Gilbert, B. M. et al. You are how you eat: differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 119, 1393–1400. https://doi.org/10.1007/s00436-020-06619-1 (2020).Article 

    Google Scholar 
    Gilbert, B. M. et al. Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host—Parasite system from the Vaal River System, South Africa. Int. J. Parasitol. Parasites Wildl. 12, 134–141. https://doi.org/10.1016/j.ijppaw.2020.05.011 (2020).Article 

    Google Scholar 
    Felig, P. The glucose-alanine cycle. Metabolism 22, 179–207 (1973).Article 
    CAS 

    Google Scholar 
    Dale, R. A. Catabolism of threonine in mammals by coupling of L-threonine 3-dehydrogenase with 2-amino-3-oxobutyrate-CoA ligase. Biochem. Biophys. Acta. 544, 496–503 (1978).Article 
    CAS 

    Google Scholar 
    Jordan, P. M. & Akhtar, M. The mechanism of action of serine Transhydroxymethylase. Biochem. J. 116, 277–286 (1970).Article 
    CAS 

    Google Scholar 
    Linstead, D. J., Klein, R. A. & Cross, G. A. M. Threonine catabolism in Trypanosoma brucei. J. Gen. Microbiol. 101, 243–251 (1977).Article 
    CAS 

    Google Scholar 
    Hare, P. E., Fogel, M. L., Stafford, T. W. Jr., Mitchell, A. D. & Hoering, T. C. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292 (1991).Article 

    Google Scholar 
    Petzke, K. J., Boeing, H., Klaus, S. & Metges, C. C. Carbon and nitrogen stable isotopic composition of hair protein and amino acids can be used as biomarkers for animal-derived dietary protein intake in humans. J. Nutr. 135, 1515–1520 (2005).Article 
    CAS 

    Google Scholar 
    McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 

    Google Scholar 
    Fuller, B. T. & Petzke, K. J. The dietary protein paradox and threonine (15) N-depletion: Pyridoxal-5’-phosphate enzyme activity as a mechanism for the delta (15) N trophic level effect. Rapid Commun. Mass Spectrom. 31, 705–718. https://doi.org/10.1002/rcm.7835 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Bowyer, A. et al. Structure and function of the l-threonine dehydrogenase (TkTDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis. J. Struct. Biol. 168, 294–304. https://doi.org/10.1016/j.jsb.2009.07.011 (2009).Article 
    CAS 

    Google Scholar 
    Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: Reaction mechanism, physiological significance and hyperglycinemia. Proc. Jpn. Acad. https://doi.org/10.2183/pjab/84.246 (2008).Article 

    Google Scholar 
    Locasale, J. W. Serine, glycine and one-carbon units: Cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583. https://doi.org/10.1038/nrc3557 (2013).Article 
    CAS 

    Google Scholar 
    Kalhan, S. C. & Hanson, R. W. Resurgence of serine: An often neglected but indispensable amino Acid. J. Biol. Chem. 287, 19786–19791. https://doi.org/10.1074/jbc.R112.357194 (2012).Article 
    CAS 

    Google Scholar 
    Larsen, T., Wang, Y. V. & Wan, A. H. L. Tracing the Trophic fate of aquafeed macronutrients with carbon isotope ratios of amino acids. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.813961 (2022).Article 

    Google Scholar 
    Sweeting, C. J., Polunin, N. V. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601. https://doi.org/10.1002/rcm.2347 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Tarallo, A., Bailey, C., Agnisola, C. & D’Onofrio, G. A theoretical evaluation of the respiration rate partition in the Gasterosteus aculeatus-Schistocephalus solidus host-parasite system. Int. Aquat. Res. 13, 185. https://doi.org/10.22034/IAR.2021.1924974.1142 (2021).Article 

    Google Scholar 
    Takizawa, Y. et al. A new insight into isotopic fractionation associated with decarboxylation in organisms: Implications for amino acid isotope approaches in biogeoscience. Progress Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00364-w (2020).Article 

    Google Scholar 
    Ron-Harel, N. et al. T cell activation depends on extracellular alanine. Cell Rep. 28, 3011-3021.e4. https://doi.org/10.1016/j.celrep.2019.08.034 (2019).Article 
    CAS 

    Google Scholar 
    Wang, W. et al. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45, 463–477. https://doi.org/10.1007/s00726-013-1493-1 (2013).Article 
    CAS 

    Google Scholar 
    Mathis, D. & Shoelson, S. E. Immunometabolism: An emerging frontier. Nat. Rev. Immunol. 11, 81. https://doi.org/10.1038/nri2922 (2011).Article 
    CAS 

    Google Scholar 
    Guo, C. et al. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47, 664–673. https://doi.org/10.1016/j.fsi.2015.09.034 (2015).Article 
    CAS 

    Google Scholar 
    Peuss, R. et al. Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish. Nat. Ecol. Evol. 4, 1416–1430. https://doi.org/10.1038/s41559-020-1234-2 (2020).Article 

    Google Scholar 
    Smyth, J. D. Fertilization of Schistocephalus solidus in vitro. Exp. Parasitol. 3, 64–71 (1954).Article 
    CAS 

    Google Scholar 
    Schärer, L. & Wedekind, C. Lifetime reproductive output in a hermaphrodite cestode when reproducing alone or in pairs. Evol. Ecol. 13, 381–394 (1999).Article 

    Google Scholar 
    McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid delta13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Fry, B., Carter, J. F., Yamada, K., Yoshida, N. & Juchelka, D. Position-specific (13) C/(12) C analysis of amino acid carboxyl groups—Automated flow-injection-analysis based on reaction with ninhydrin. Rapid Commun. Mass Spectrom. https://doi.org/10.1002/rcm.8126 (2018).Article 

    Google Scholar 
    Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─A proof-of-concept study. Anal Chem 94, 2981–2987 (2022).Article 
    CAS 

    Google Scholar 
    Sun, Y. et al. A method for stable carbon isotope measurement of underivatized individual amino acids by multi-dimensional high-performance liquid chromatography and elemental analyzer/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 34, e8885. https://doi.org/10.1002/rcm.8885 (2020).Article 
    CAS 

    Google Scholar 
    Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Köster, D., Villalobos, I. M. S., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. New concepts for the determination of oxidation efficiencies in liquid chromatography-isotope ratio mass spectrometry. Anal. Chem. 91, 5067–5073. https://doi.org/10.1021/acs.analchem.8b05315 (2019).Article 
    CAS 

    Google Scholar 
    Boschker, H. T., Moerdijk-Poortvliet, T. C., van Breugel, P., Houtekamer, M. & Middelburg, J. J. A versatile method for stable carbon isotope analysis of carbohydrates by high-performance liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 22, 3902–3908. https://doi.org/10.1002/rcm.3804 (2008).Article 
    ADS 
    CAS 

    Google Scholar  More

  • in

    Bioclimatic atlas of the terrestrial Arctic

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
    Google Scholar 
    Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Sci. Data 7, 248 (2020).
    Google Scholar 
    Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).
    Google Scholar 
    Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).ADS 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Google Scholar  More

  • in

    Revisiting Mt Fuji’s groundwater origins with helium, vanadium and environmental DNA tracers

    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

  • in

    Human fingerprint on structural density of forests globally

    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

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    Genetic structure and relatedness of juvenile sicklefin lemon shark (Negaprion acutidens) at Dongsha Island

    Dulvy, N. K., Sadovy, Y. & Reynolds, J. D. Extinction vulnerability in marine populations. Fish Fish. 4, 25–64 (2003).Article 

    Google Scholar 
    Fowler S. L. et al. Sharks, Rays and Chimaeras: The Status of the Chondrichthyan Fishes. IUCN/SSC Shark Specialist Group, Gland, Switzerland and Cambridge, UK (2005).Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, e00590 (2014).Article 

    Google Scholar 
    Lack M. & Sant G. Illegal, Unreported and Unregulated Shark Catch: A review of current knowledge and action. Department of the Environment, Water, Heritage and the Arts and TRAFFIC, Canberra http://www.traffic.org/fish/ (2008).Rose D.A. An Overview of World Trade in Sharks and Other Cartilaginous Fishes. TRAFFIC International, Cambridge, UK (1996).Lam, V. Y. & Sadovy, M. Y. The sharks of South East Asia–unknown, unmonitored and unmanaged. Fish Fish 12, 51–74 (2011).Article 

    Google Scholar 
    Kessel S.T. Investigation into the behaviour and population dynamics of the lemon shark (Negaprion brevirostris). Cardiff University (United Kingdom) (2010).Morrissey, J. F. & Gruber, S. H. Habitat selection by juvenile lemon sharks Negaprion brevirostris. Environ. Biol. Fishes 38, 311–319 (1993).Article 

    Google Scholar 
    Filmalter, J. D., Dagorn, L. & Cowley, P. D. Spatial behaviour and site fidelity of the sicklefin lemon shark Negaprion acutidens in a remote Indian Ocean atoll. Mari. Biol. 160, 2425–2436 (2013).Article 

    Google Scholar 
    DiBattista, J. D. et al. A genetic assessment of polyandry and breeding site fidelity in lemon sharks. Mol. Ecol. 17, 3337–3351 (2008).Article 

    Google Scholar 
    Wetherbee, B. M., Gruber, S. H. & Rosa, R. S. Movement patterns of juvenile lemon sharks Negaprion brevirostris within Atol das Rocas, Brazil: A nursery characterized by tidal extremes. Mar. Ecol. Prog. Seri. 343, 283–293 (2007).Article 
    ADS 

    Google Scholar 
    Feldheim, K. A. et al. Two decades of genetic profiling yields first evidence of natal philopatry and long-term fidelity to parturition sites in sharks. Mol. Ecol. 23, 110–117 (2014).Article 

    Google Scholar 
    Stevens J. D. et al. Diversity, abundance and habitat utilisation of sharks and rays: Final report to West Australian Marine Science Institute. CSIRO, editor. Hobart (2009).Schultz, J. K. et al. Global phylogeography and seascape genetics of the lemon sharks (genus Negaprion). Mol. Ecol. 17, 5336–5348 (2008).Article 
    CAS 

    Google Scholar 
    Mourier, J., Buray, N., Schultz, J. K., Clua, E. & Planes, S. Genetic network and breeding patterns of a sicklefin lemon shark (Negaprion acutidens) population in the Society Islands, French Polynesia. PLoS ONE 8, e73899 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Speed, C. W. et al. Reef shark movements relative to a coastal marine protected area. Reg. Stud. Mar. Sci. 3, 58–66 (2016).
    Google Scholar 
    Huang, Z. Marine Species and Their Distribution in China’s Seas (Krieger Publishing Company, 2001).
    Google Scholar 
    Chang, C. W., Huang, C. S. & Wang, S. I. Species composition and sizes of fish in the lagoon of dongsha island (Pratas Island), Dongsha Atoll of the South China sea. Platax 2012, 25–32 (2012).
    Google Scholar 
    Pillans, R. D. et al. Long-term acoustic monitoring reveals site fidelity, reproductive migrations, and sex specific differences in habitat use and migratory timing in a large coastal shark (Negaprion acutidens). Front. Mar. Sci. 8, 616633 (2021).Article 

    Google Scholar 
    Daly-Engel, T. S. et al. Global phylogeography with mixed-marker analysis reveals male-mediated dispersal in the endangered scalloped hammerhead shark (Sphyrna lewini). PLoS ONE 7, e29986 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Félix-López, D. G. et al. Possible female philopatry of the smooth hammerhead shark Sphyrna zygaena revealed by genetic structure patterns. J. Fish Biol. 94, 671–679 (2019).Article 

    Google Scholar 
    Nosal, A. P., Caillat, A., Kisfaludy, E. K., Royer, M. A. & Wegner, N. C. Aggregation behavior and seasonal philopatry in male and female leopard sharks Triakis semifasciata along the open coast of southern California, USA. Mar. Ecol. Prog. Ser. 499, 157–175 (2014).Article 
    ADS 

    Google Scholar 
    Jirik, K. E. & Lowe, C. G. An elasmobranch maternity ward: Female round stingrays Urobatis halleri use warm, restored estuarine habitat during gestation. J. Fish. Biol. 80(5), 1227–1245 (2012).Article 
    CAS 

    Google Scholar 
    Jacoby, D. M., Croft, D. P. & Sims, D. W. Social behaviour in sharks and rays: Analysis, patterns and implications for conservation. Fish Fish 13(4), 399–417 (2012).Article 

    Google Scholar 
    Su, S. H., Liu, S. Y. V., Liu, K. M. & Tsai, W. P. Development and characterization of novel microsatellite loci for an endangered hammerhead shark Sphyrna lewini by using shotgun sequencing. Taiwania 65(2), 261–263 (2020).
    Google Scholar 
    Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3, 167–169 (2003).Article 
    CAS 

    Google Scholar 
    Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. Micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 

    Google Scholar 
    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).Article 
    CAS 

    Google Scholar 
    Earl, D. A. & VonHoldt, B. M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14), 1801–1806 (2007).Article 
    CAS 

    Google Scholar 
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in excel population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539 (2012).Article 
    CAS 

    Google Scholar 
    Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. POPPR: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).Article 

    Google Scholar 
    Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: A computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Resour. 6, 576–579 (2006).Article 
    CAS 

    Google Scholar 
    Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
    CAS 

    Google Scholar 
    Oh, B. Z. et al. Contrasting patterns of residency and space use of coastal sharks within a communal shark nursery. Mar. Freshw. Res. 68, 1501–1517 (2017).Article 

    Google Scholar 
    McClelland J. Genetic Assessment of Breeding Patterns and Population Size of the Sicklefin Lemon Shark Negaprion acutidens in a Tropical Marine Protected Area: Implications for Conservation and Management (Doctoral dissertation, University of York) (2020).Compagno L. J .V. FAO species catalogue Sharks of the world: An annotated and illustrated catalogue of shark species known to date. FAO Fish. Synop. No. 125 Rome 4, 1–655 (1984).Stevens, J. D. Life-history and ecology of sharks at aldabra Atoll. Indian Ocean. Proc R Soc. B 222, 79–106 (1984).ADS 

    Google Scholar 
    Kool, J. T., Moilanen, A. & Treml, E. A. Population connectivity: Recent advances and new perspectives. Landsc. Ecol. 28, 165–185 (2013).Article 

    Google Scholar 
    Ruzzante, D. E. et al. Effective number of breeders, effective population size and their relationship with census size in an iteroparous species Salvelinus fontinalis. Proc. R Soc. B 283, 20152601 (2016).Article 

    Google Scholar 
    Van Wyngaarden, M. et al. Identifying patterns of dispersal, connectivity and selection in the sea scallop, Placopecten magellanicus, using RADseq-derived SNPs. Evol. Appl. 10, 102–117 (2017).Article 

    Google Scholar 
    Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red list criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).Article 

    Google Scholar 
    Pazmiño, D. A., Maes, G. E., Simpfendorfer, C. A., Salinas-de-León, P. & van Herwerden, L. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark (Carcharhinus galapagensis). Conserv. Genet. 18, 1151–1163 (2017).Article 

    Google Scholar 
    Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).Article 

    Google Scholar 
    Dudgeon, C. L. & Ovenden, J. R. The relationship between abundance and genetic effective population size in elasmobranchs: An example from the globally threatened zebra shark Stegostoma fasciatum within its protected range. Conserv. Genet. 16, 1443–1454 (2015).Article 

    Google Scholar 
    Feldheim, K. A., Gruber, S. H. & Ashley, M. V. Population genetic structure of the lemon shark (Negaprion brevirostris) in the western Atlantic: DNA microsatellite variation. Mol. Ecol. 10, 295–303 (2001).Article 
    CAS 

    Google Scholar 
    Feldheim, K. A., Gruber, S. H. & Ashley, M. V. The breeding biology of lemon sharks at a tropical nursery lagoon. Proc. R. Soc. Lond. B 269, 1471–2954 (2002).Article 

    Google Scholar 
    Portnoy, D., McDowell, J. R., Thompson, K., Musick, J. A. & Graves, J. E. Isolation and characterization of five dinucleotide microsatellite loci in the sandbar shark, Carcharhinus plumbeus. Mol. Ecol. Notes 6, 431–433 (2006).Article 
    CAS 

    Google Scholar  More