Philippa Kaur

Here at the Germplasm Bank of Wild Species of China at the Kunming Institute of Botany, we want to preserve the seeds of as many wild plants as possible from across China’s vast land area. I work on developing the best techniques to freeze plant seeds and tissues at ultracold temperatures, to maintain their viability for years. The idea is that if we plant these seeds again in hundreds of years, a plant will grow.The picture shows me taking a sample of embryos from the seeds of a magnolia tree out of a liquid-nitrogen cryopreservation tank to test whether they’ll regrow when thawed. I dress in protective equipment from head to toe to protect me from the nitrogen, which has a temperature of −196 °C.When I came to the institute in 2009 as a PhD student, it had just purchased its first liquid-nitrogen cryopreservation system, but no one knew how to operate it. I was the one to work it out.Over the years, human activities and climate change have had a negative impact on plant biodiversity. The ultimate goal of the plant seed bank is to collect and preserve all wild plant species in China that are endangered, rare or valuable. We want to save these species before they go extinct. We’ve collected seeds from nearly 11,000 plant species, but that’s only one-third of what grows in China.Many wild plants have genes that help them to survive in harsh environments and make them disease- or drought-resistant. In the future, we might need these genetic materials to breed new crops that can better adapt to the changing climate.I am constantly amazed by how diverse and beautiful seeds are. Some of them are brightly coloured and others are star-shaped. I feel proud when I see the unfrozen seeds germinate in test tubes and gradually grow into large plants. We have three plants in the seed-bank lobby that we cultivated from preserved tissues, and they are all now taller than me. More

Ellis, E. C. & Ramankutty, N. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6, 439–447 (2008).Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, (2017).Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. Changes in human footprint drive changes in species extinction risk. Nature communications 9, 1–9 (2018).ADS 
Article 
CAS 

Google Scholar 
Kreidenweis, U. et al. Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets. Global change biology 24, 3199–3213 (2018).ADS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
Mu, H. et al. Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018. Remote Sensing 13, 1849 (2021).ADS 
Article 

Google Scholar 
Wang, L. et al. Mapping population density in China between 1990 and 2010 using remote sensing. Remote sensing of environment 210, 269–281 (2018).ADS 
Article 

Google Scholar 
Raiter, K. G., Possingham, H. P., Prober, S. M. & Hobbs, R. J. Under the radar: mitigating enigmatic ecological impacts. Trends in ecology & evolution 29, 635–644 (2014).Article 

Google Scholar 
Nikhil, S. et al. Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis 5, 1–14 (2021).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Steffen W, et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, (2015).Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature communications 7, 1–11 (2016).Article 
CAS 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Scientific data 3, 1–10 (2016).Article 

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

Google Scholar 
Mu, H. et al. Evaluation of the policy-driven ecological network in the Three-North Shelterbelt region of China. Landscape and Urban Planning 218, 104305 (2022).Article 

Google Scholar 
Hoffmann, S., Irl, S. D. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nature communications 10, 1–10 (2019).Article 
CAS 

Google Scholar 
Sanderson, E. W. et al. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. Bioscience 52, 891–904 (2002).Article 

Google Scholar 
Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).ADS 
Article 

Google Scholar 
Allan, J. R., Venter, O. & Watson, J. E. Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild. Scientific data 4, 1–8 (2017).Article 

Google Scholar 
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Yang, R. et al. Cost-effective priorities for the expansion of global terrestrial protected areas: Setting post-2020 global and national targets. Science Advances 6, eabc3436 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Theobald, D. M. et al. Earth transformed: detailed mapping of global human modification from 1990 to 2017. Earth System Science Data 12, 1953–1972 (2020).ADS 
Article 

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. Global Change Biology 25, 811–826 (2019).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nature ecology & evolution 2, 599–610 (2018).Article 

Google Scholar 
Wolkovich, E., Cook, B., McLauchlan, K. & Davies, T. Temporal ecology in the Anthropocene. Ecology letters 17, 1365–1379 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Li, X., Zhou, Y., Zhao, M. & Zhao, X. A harmonized global nighttime light dataset 1992–2018. Scientific data 7, 1–9 (2020).Article 
CAS 

Google Scholar 
Luck, G. W., Ricketts, T. H., Daily, G. C. & Imhoff, M. Alleviating spatial conflict between people and biodiversity. Proceedings of the National Academy of Sciences 101, 182–186 (2004).ADS 
CAS 
Article 

Google Scholar 
Gong, P., Li, X. & Zhang, W. 40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin 64, 756–763 (2019).ADS 
Article 

Google Scholar 
Hu, T., Yang, J., Li, X. & Gong, P. Mapping urban land use by using landsat images and open social data. Remote Sensing 8, 151 (2016).ADS 
CAS 
Article 

Google Scholar 
Li, X. et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environmental Research Letters 15, 094044 (2020).ADS 
Article 

Google Scholar 
Li, X., Zhou, Y., Zhu, Z. & Cao, W. A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth System Science Data 12, 357–371 (2020).ADS 
Article 

Google Scholar 
Zhang, X. et al. Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data 12, 1625–1648 (2020).ADS 
Article 

Google Scholar 
Li, X., Gong, P. & Liang, L. A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment 166, 78–90 (2015).ADS 
Article 

Google Scholar 
Butchart, S. H. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Tratalos, J., Fuller, R. A., Warren, P. H., Davies, R. G. & Gaston, K. J. Urban form, biodiversity potential and ecosystem services. Landscape and urban planning 83, 308–317 (2007).Article 

Google Scholar 
Fry, J. A. et al. Completion of the 2006 national land cover database for the conterminous United States. PE&RS. Photogrammetric Engineering & Remote Sensing 77, 858–864 (2011).
Google Scholar 
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C. & Ghosh, T. VIIRS night-time lights. International Journal of Remote Sensing 38, 5860–5879 (2017).ADS 
Article 

Google Scholar 
Zhou, Y., Li, X., Asrar, G. R., Smith, S. J. & Imhoff, M. A global record of annual urban dynamics (1992–2013) from nighttime lights. Remote Sensing of Environment 219, 206–220 (2018).ADS 
Article 

Google Scholar 
Li, X. & Zhou, Y. A stepwise calibration of global DMSP/OLS stable nighttime light data (1992–2013). Remote Sensing 9, 637 (2017).ADS 
Article 

Google Scholar 
Li, X. & Zhou, Y. Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing 38, 6030–6046 (2017).ADS 
Article 

Google Scholar 
Cincotta, R. P., Wisnewski, J. & Engelman, R. Human population in the biodiversity hotspots. Nature 404, 990–992 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
McKee, J. K., Sciulli, P. W., Fooce, C. D. & Waite, T. A. Forecasting global biodiversity threats associated with human population growth. Biological Conservation 115, 161–164 (2004).Article 

Google Scholar 
Lloyd, C. T. et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3, 108–139 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Scientific data 4, 1–17 (2017).Article 

Google Scholar 
Gaston, K. J., Bennie, J., Davies, T. W. & Hopkins, J. The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biological reviews 88, 912–927 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
Zhao, M. et al. Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives. Remote Sensing 11, 1971 (2019).ADS 
Article 

Google Scholar 
Folberth, C. et al. The global cropland-sparing potential of high-yield farming. Nature Sustainability 3, 281–289 (2020).Article 

Google Scholar 
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nature communications 10, 1–10 (2019).ADS 
CAS 
Article 

Google Scholar 
Plummer, S., Lecomte, P. & Doherty, M. The ESA climate change initiative (CCI): A European contribution to the generation of the global climate observing system. Remote Sensing of Environment 203, 2–8 (2017).ADS 
Article 

Google Scholar 
Ramankutty N, Evan AT, Monfreda C, Foley JA. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global biogeochemical cycles 22, (2008).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conservation biology 14, 18–30 (2000).Article 

Google Scholar 
Paton, D. G., Ciuti, S., Quinn, M. & Boyce, M. S. Hunting exacerbates the response to human disturbance in large herbivores while migrating through a road network. Ecosphere 8, e01841 (2017).Article 

Google Scholar 
Center For International Earth Science Information Network –Columbia University, Georgia ITOSUO. Global roads open access data set, version 1 (gROADSv1). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), (2013).Wolter, C. & Arlinghaus, R. Navigation impacts on freshwater fish assemblages: the ecological relevance of swimming performance. Reviews in Fish Biology and Fisheries 13, 63–89 (2003).Article 

Google Scholar 
Wolter, C. Conservation of fish species diversity in navigable waterways. Landscape and Urban Planning 53, 135–144 (2001).Article 

Google Scholar 
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union 89, 93–94 (2008).ADS 
Article 

Google Scholar 
Mu, H. et al. An annual global terrestrial Human Footprint dataset from 2000 to 2018. figshare https://doi.org/10.6084/m9.figshare.16571064.v5 (2021).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Arino, O. et al. Global land cover map for 2009 (GlobCover 2009). European Space Agency (ESA) & Université catholique de Louvain (UCL), PANGAEA https://doi.org/10.1594/PANGAEA.787668 (2012).Watson, J. E. et al. Catastrophic declines in wilderness areas undermine global environment targets. Current Biology 26, 2929–2934 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
Oakleaf, J. R. et al. Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors. Scientific data 6, 1–17 (2019).Article 

Google Scholar 
Rehbein, J. A. et al. Renewable energy development threatens many globally important biodiversity areas. Global change biology 26, 3040–3051 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

AffiliationsDepartment of Earth and Environmental Sciences, Macquarie University, Sydney, New South Wales, AustraliaErnest F. Asamoah & Joseph M. MainaDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaLinda J. BeaumontAuthorsErnest F. AsamoahLinda J. BeaumontJoseph M. MainaCorresponding authorCorrespondence to
Ernest F. Asamoah. More

As well as the humanitarian catastrophe it is inflicting, Russia’s invasion of Ukraine in February is disrupting global flows of vital commodities such as fuel, food and fertilizer. This will affect biodiversity and the environment far beyond the war zones, with implications for sustainability and well-being worldwide.
Competing Interests
The authors declare no competing interests. More

Woollings, T., Hannachi, A. & Hoskins, B. Variability of the North Atlantic eddy-driven jet stream. Q J. R. Meteorol. Soc. 136, 856–868 (2010).ADS 
Article 

Google Scholar 
Coumou, D., Capua, D. I., Vavrus, G., Wang, L. S. & Wang, S. The influence of Arctic amplification on mid-latitude summer circulation. Nat. Commun. 9, 2959 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Belmecheri, S., Babst, F., Hudson, A. R., Betancourt, J. & Trouet, V. Northern Hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interact. 21, 1–23 (2017).Article 

Google Scholar 
Trouet, V., Babst, F. & Meko, M. Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nat. Commun. 9, 180 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lehmann, J. & Coumou, D. The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes. Sci. Rep. 5, 17491 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mahlstein, I., Martius, O., Chevalier, C. & Ginsbourger, D. Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet. Geophys. Res. Lett. 39, 1–6 (2012).
Google Scholar 
Röthlisberger, M., Pfahl, S. & Martius, O. Regional-scale jet waviness modulates the occurrence of midlatitude weather extremes. Geophys. Res. Lett. 43, 10,910–989,997 (2016).Article 

Google Scholar 
Brunner, L., Schaller, N., Anstey, J., Sillmann, J. & Steiner, A. K. Dependence of present and future European temperature extremes on the location of atmospheric blocking. Geophys. Res. Lett. 45, 6311–6320 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, B., Sutton, R. T., Woollings, T. & Hodges, K. Variability of the North Atlantic summer storm track: mechanisms and impacts on European climate. Environ. Res. Lett. 8, 34037 (2013).Article 

Google Scholar 
Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 45242 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 
Article 

Google Scholar 
Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).ADS 
CAS 
Article 

Google Scholar 
Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).ADS 
Article 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Buras, A., Rammig, A. & Zang, C. S. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672 (2020).ADS 
Article 

Google Scholar 
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).ADS 
Article 

Google Scholar 
Sillmann, J. et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extrem. 18, 65–74 (2017).Article 

Google Scholar 
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bastos, A., Gouveia, C. M., Trigo, R. M. & Running, S. W. Analysing the spatio-temporal impacts of the 2003 and 2010 extreme heatwaves on plant productivity in Europe. Biogeosciences 11, 3421–3435 (2014).ADS 
Article 

Google Scholar 
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D. & Schär, C. Soil moisture–atmosphere interactions during the 2003 european summer heat wave. J. Clim. 20, 5081–5099 (2007).ADS 
Article 

Google Scholar 
Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
Article 

Google Scholar 
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).ADS 
Article 

Google Scholar 
Rammig, A. et al. Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity. Biogeosciences 12, 373–385 (2015).ADS 
Article 

Google Scholar 
Spinoni, J., Naumann, G., Vogt, J. V. & Barbosa, P. The biggest drought events in Europe from 1950 to 2012. J. Hydrol. Reg. Stud. 3, 509–524 (2015).Article 

Google Scholar 
Madonna, E., Li, C., Grams, C. M. & Woollings, T. The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector. Q J. R. Meteorol. Soc. 143, 2960–2972 (2017).ADS 
Article 

Google Scholar 
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7, 557–562 (2017).Article 

Google Scholar 
Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).Article 

Google Scholar 
Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. N. Phytol. 201, 1289–1303 (2014).CAS 
Article 

Google Scholar 
Zweifel, R. & Sterck, F. A conceptual tree model explaining legacy effects on stem growth. Front. Glob. Change 1, 9 (2018).Article 

Google Scholar 
Fatichi, S., Pappas, C., Zscheischler, J. & Leuzinger, S. Modelling carbon sources and sinks in terrestrial vegetation. N. Phytol. 221, 652–668 (2019).CAS 
Article 

Google Scholar 
Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Chang. Biol. 24, 504–516 (2018).ADS 
PubMed 
Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Davini, P. & Cagnazzo, C. On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Clim. Dyn. 43, 1497–1511 (2014).Article 

Google Scholar 
Pfahl, S. & Wernli, H. Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-)daily time scales. Geophys. Res. Lett. 39 (2012).Drouard, M. & Woollings, T. Contrasting mechanisms of summer blocking over western Eurasia. Geophys. Res. Lett. 45, 12,040–12,048 (2018).Article 

Google Scholar 
Bastos, A. et al. European land CO2 sink influenced by NAO and East-Atlantic pattern coupling. Nat. Commun. 7, 10315 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ascoli, D. et al. Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction. Nat. Commun. 8, 1–9 (2017).CAS 
Article 

Google Scholar 
Sousa, P. M. et al. Responses of European precipitation distributions and regimes to different blocking locations. Clim. Dyn. 48, 1141–1160 (2017).Article 

Google Scholar 
Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation of growth by high temperature and low precipitation from range core to southern edge of European beech indicates widespread vulnerability to changing climate. Eur. J. Res. 135, 897–909 (2016).Article 

Google Scholar 
Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest resistance to growth suppression are found in the range core of the tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23, 362–379 (2017).ADS 
PubMed 
Article 

Google Scholar 
Leuschner, C. Drought response of European beech (Fagus sylvatica L.): A review. Perspect. Plant Ecol. Evol. Syst. 47, 125576 (2020).Article 

Google Scholar 
Muffler, L. et al. Lowest drought sensitivity and decreasing growth synchrony towards the dry distribution margin of European beech. J. Biogeogr. 47, 1910–1921 (2020).Article 

Google Scholar 
Wang, F. et al. Seedlings from marginal and core populations of European beech (Fagus sylvatica L.) respond differently to imposed drought and shade. Trees 35, 53–67 (2021).CAS 
Article 

Google Scholar 
Hall, R. J., Jones, J. M., Hanna, E., Scaife, A. A. & Erdélyi, R. Drivers and potential predictability of summertime North Atlantic polar front jet variability. Clim. Dyn. 48, 3869–3887 (2017).Article 

Google Scholar 
Screen, J. A. & Simmonds, I. Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Clim. Change 4, 704–709 (2014).ADS 
Article 

Google Scholar 
Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 54002 (2019).Article 

Google Scholar 
Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).ADS 
CAS 
Article 

Google Scholar 
Peings, Y., Cattiaux, J., Vavrus, S. J. & Magnusdottir, G. Projected squeezing of the wintertime North-Atlantic jet. Environ. Res. Lett. 13, 74016 (2018).Article 

Google Scholar 
Matsueda, M. & Endo, H. The robustness of future changes in Northern Hemisphere blocking: a large ensemble projection with multiple sea surface temperature patterns. Geophys. Res. Lett. 44, 5158–5166 (2017).ADS 
Article 

Google Scholar 
Kwon, Y. O., Camacho, A., Martinez, C. & Seo, H. North Atlantic winter eddy-driven jet and atmospheric blocking variability in the Community Earth System Model version 1 Large Ensemble simulations. Clim. Dyn. 51, 3275–3289 (2018).Article 

Google Scholar 
Cohen, J. et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Change 10, 20–29 (2020).ADS 
Article 

Google Scholar 
de Vries, H., Woollings, T., Anstey, J., Haarsma, R. J. & Hazeleger, W. Atmospheric blocking and its relation to jet changes in a future climate. Clim. Dyn. 41, 2643–2654 (2013).Article 

Google Scholar 
Woollings, T. et al. Blocking and its response to climate change. Curr. Clim. Chang. Rep. 4, 287–300 (2018).Article 

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

Google Scholar 
Sousa-Silva, R. et al. Tree diversity mitigates defoliation after a drought-induced tipping point. Glob. Chang. Biol. 24, 4304–4315 (2018).ADS 
PubMed 
Article 

Google Scholar 
Magri, D. Patterns of post-glacial spread and the extent of glacial refugia of European beech (Fagus sylvatica). J. Biogeogr. 35, 450–463 (2008).Article 

Google Scholar 
Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).ADS 
PubMed 
Article 

Google Scholar 
Dorado-Liñán, I. et al. Geographical adaptation prevails over species-specific determinism in trees’ vulnerability to climate change at Mediterranean rear-edge forests. Glob. Chan. Biol. 25, 1296–1314 (2019).ADS 
Article 

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

Google Scholar 
Hacket-Pain, A. J., Friend, A. D., Lageard, J. G. A. & Thomas, P. A. The influence of masting phenomenon on growth–climate relationships in trees: explaining the influence of previous summers’ climate on ring width. Tree Physiol. 35, 319–330 (2015).PubMed 
Article 

Google Scholar 
Bréda, N., Huc, R., Granier, A. & Dreyer, E. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann. Sci. 63, 625–644 (2006).Article 

Google Scholar 
Hacket-Pain, A. J. et al. Climatically controlled reproduction drives interannual growth variability in a temperate tree species. Ecol. Lett. 21, 1833–1844 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Popkin, G. How much can forests fight climate change? Nature 565, 280–282 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Davini, P. & D’Andrea, F. Northern Hemisphere atmospheric blocking representation in global climate models: twenty years of improvements? J. Clim. 29, 8823–8840 (2016).ADS 
Article 

Google Scholar 
Barton, N. P. & Ellis, A. W. Variability in wintertime position and strength of the North Pacific jet stream as represented by re-analysis data. Int. J. Climatol. 29, 851–862 (2009).Article 

Google Scholar 
Doblas-Reyes, F. J., Casado, M. J. & Pastor, M. A. Sensitivity of the Northern Hemisphere blocking frequency to the detection index. J. Geophys. Res. Atmos. 107, D2 (2002).Article 

Google Scholar 
Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
Article 

Google Scholar 
Team, R. Core (2020). R A Lang. Environ. Stat. Comput. R Found. Stat. Comput. Vienna, Austria. URL https://www.R-project.org (2020).Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Barton, K. Mu-MIn: Multi-model inference. R Package Version 0.12.2/r18, (2009) http://R-Forge.R-project.org/projects/mumin/ More

Biogeographic pattern of LOSSThe original forest plot data aggregated at 0.25 degree show large spatial variations (Fig. 1a) across the continents, with the greatest LOSS in Asia & Australia (mean ± 1 SE; 6.5 ± 0.5 Mg ha−1 y−1) > South America (4.9 ± 0.2 Mg ha−1 y−1) and Africa (4.6 ± 0.2 Mg ha−1 y−1) > North America (2.3 ± 0.1 Mg ha−1 y−1 in boreal and 2 ± 0.1 Mg ha−1 y−1 in temperate)36 (Fig. 1b; Supplementary Fig. 5a). This pattern was robust to bootstrapping (1000 iterations) to randomly select 90% of plots for estimating the probability distribution of the mean continental values (Supplementary Fig. 5b). The upscaled gridded LOSS maps generated by our random forest algorithm (see Methods) over the spatial domain of our full datasets shows hotspots of high LOSS in Southern Asia & Australia ( > 6 Mg ha−1 y−1), Northwestern South America (Amazon) ( > 5 Mg ha−1 y−1), and the western coast of North America ( >3 Mg ha−1 y−1)10,36,37,38 (Supplementary Fig. 6a). These patterns were robust to two bootstrapping approaches – based on the sampled biomes of each point feature and also randomly sampling 90% data with replacement (see Methods) (Fig. 2a; Supplementary Fig. 6b). The uncertainty (coefficient of variance – CV; %mean) was generally low ( 10%), despite the larger sample size (n  > 500 at 0.25 degree) (Fig. 2b; Supplementary Fig. 6c), likely because of potential effects of forest recovery or regrowth following past disturbance16 as well as the small plot size (i.e., 0.067 ha in each plot)39.Fig. 1: Map of sample locations and biomass loss to mortality (LOSS) data.a Sampling sites. A total of 2676 samples were collected and aggregated into 814 grids at 0.25 degree that were used for geospatial modeling. b The median and interquartile range of LOSS across continents—North America, South America, Africa, and Asia & Australia.Full size imageFig. 2: Map of biomass loss to mortality (LOSS) and its uncertainty across continents.a, b Ensemble mean of LOSS a and its uncertainty (coefficient of variation, b across continents at 0.25 degree derived from forest plot data using the bootstrapped (10 iterations) approach by randomly sampling 90% plots with replacement. c, d Ensemble mean of LOSS c and its uncertainty (coefficient of variation, d across continents at 0.5 degree derived from six dynamic vegetation models (DGVMs, ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL, and SEIB-DGVM). Coefficient of variation was quantified as the standard deviation divided by the mean predicted value as a measure of prediction accuracy. e The difference of LOSS between ensemble mean of DGVMs and ensemble mean of LOSS derived from forest plots data across continents at 0.5 degree, quantified as difference between c and a, whereby LOSS in Fig. 2a is resampled at 0.5 degree.Full size imageDrivers of LOSSMean annual temperature (MAT), aridity index (the ratio of precipitation to potential evapotranspiration), and precipitation seasonality were identified as the dominant predictors of LOSS across continents (Supplementary Fig. 7a), with positive relationships with LOSS (Fig. 3a)10,36. In contrast to local-scale studies40,41, wood density, forest stand density, and soil conditions were poor predictors of LOSS when all data were used. These relationships were largely driven by the spatial pattern of LOSS and climate gradients, whereby LOSS and MAT, aridity index, and precipitation seasonality were high in tropical forests (Supplementary Fig. 8). This motivated us to examine the drivers of LOSS in tropical vs non-tropical biomes (Supplementary Fig. 7b, c; Fig. 3b–d). With a smaller gradient in climate within wet tropical forests, soil properties such as nutrient content and cation exchange capacity (CEC) were significant predictors of LOSS (Supplementary Fig. 7b; Fig. 3b)42. In wet tropical forests, the relationships between soil nutrient content and CEC and LOSS were positive (Fig. 3b) and thus appeared to support the pattern of higher mortality in more productive tropical forests growing over nutrient rich soils42,43. In non-tropical regions, basal area or a competition index based on the degree of crowding within stocked areas44 (see Methods) were the dominant predictors of LOSS, especially in extra-tropical North America (Supplementary Fig. 7c; Fig. 3c, d). This result highlights the role of stand competition in driving the spatial patterns of LOSS44,45. This pattern also supports the existence of a spatial tradeoff between faster growth and higher mortality because of resource limitations or younger death, whereby competition plays the fundamental role13,45. In contrast to other studies15,46, forest age (available in boreal and temperate forests in North America) was not a good predictor of LOSS (Supplementary Fig. 9), likely because of our focus on mature and old-growth forests (i.e., age > 80 and 100 years in boreal and temperate forests, respectively).Fig. 3: Standardized response coefficients (mean ± 95% CIs) between LOSS and dominant environmental drivers.The scales analyzed were at continents a, tropical regions b vs non-tropical regions c, d. The response coefficients were quantified by linear mixed model which account for each plot as a random effect. Panels c and d used basal area and stand density index (SDI) as competition index, respectively. SDI was defined as the degree of crowding within stocked areas and quantified as a function of tree density and the quadratic mean diameter in centimeters. Basal area is strongly correlated with total biomass and higher LOSS in higher basal area may be merely because of its correlations. Thus, we used another competition metrics – SDI to further confirm the role of competition in LOSS. The error bars denote the 95% confidence interval. *P  130%) in western boreal forests in North America (Fig. 2d).Conventional emergent constraintWe first used the conventional emergent constraint approach27 to constrain the projected (2015–2099) NPP and HR across continents. This approach was conducted by building the statistic (linear) relationship between the historical LOSS averaged at forest-plot scale (derived from original plot data of LOSS) or continental scale (derived from the map of LOSS) and projected NPP and HR summed across continents (see Methods and Supplementary Fig. 4 for details). We found that the emergent constraint approach worked well in North America, where the relationship between historical LOSS and projected NPP and HR was significant (the scenario of using original plot data of LOSS: R2 = 0.68 and P = 0.04 for grid-level NPP; R2 = 0.97 and P = 0.0001 for grid-level HR; the scenario of using map of LOSS at continent scale: R2 = 0.7 and P = 0.04 for grid-level NPP; R2 = 0.95 and P = 0.0008 for grid-level HR) (Supplementary Fig. 11a; Supplementary Fig. 12a). This emergent constraint approach was less effective, however, for other continents, where tropical forests are predominant (all P  > 0.05; Supplementary Fig. 11b, c, d; Supplementary Fig. 12b, c, d). These results suggest a weak linear relationships when observations are lumped or averaged at broad continental scales for tropical continents, thus highlighting the importance of spatial scale and non-linear relationships in emergent constraint25. We interpret the result that this LOSS emergent constraint works better in North America than in the tropical forests, by a better representation of forest plot distribution and couplings of LOSS and NPP and HR across space in North America.Machine learning constraintTo overcome this limitation, we trained a machine learning algorithm34 to reproduce the emerging relationship between historical LOSS and projected NPP and HR at grid level in each DGVM by incorporating all grid values without or with climate predictors, expressed as NPPpro or HRpro = f(LOSShis) or f(LOSShis, MATpro, MAPpro), respectively, where pro refers to projected variables, his refers to historical variables, and MAT and MAP is mean annual temperature precipitation, respectively (see Methods). The results show consistently positive non-linear relationships between LOSShis and NPPpro or HRpro across DGVMs (Supplementary Fig. 3). Our machine learning algorithms can surrogate well the results of process-based models between the historical LOSS and the projected NPP and HR (R  > 0.65 and R  > 0.9 in both scenarios without climate effects and with climate effects, respectively; see Methods) (Supplementary Fig. 13). After including the observed LOSShis (derived from LOSS) in the machine learning algorithm, we were able to generate spatially explicit constrained estimates34 of projected NPP and HR, and then compare them with the scenario without the constraint (Supplementary Fig. 14; Supplementary Fig. 15). These patterns essentially show a lower NPPpro or HRpro in locations of overestimated LOSShis in DGVMs, consistent with the positive relationship between LOSShis and NPPpro or HRpro (Supplementary Fig. 3).Our results show that most DGVMs overestimate tree mortality, particularly in tropical regions (Fig. 2c, e). Thus, if modeled mortality is over-estimated, we expect that NPP is over-estimated as well. Ultimately, we used a bootstrap approach to generate 100 maps of mean value of LOSS with its distribution following the values of the average and 2 times of standard deviation of LOSS maps as a conservative constraint (see Methods). Then the 100 maps of mean value of LOSS were used to constrain the projected NPP or HR as ensemble means in our ML constraint and the uncertainty of the constraint was assessed. Our bootstrapping constraint approach by LOSS reduces this common bias of models and decreases projected NPP down to 7.9, 2.3, 2 Pg C y−1 in South America, Africa and Asia & Australia, compared to original NPP values of 9, 2.4, 2.3 Pg C y−1 (Fig. 4a). The reason for this is that NPP or growth is strongly positively correlated with LOSS across space in both inventory data and DGVMs (Supplementary Figs. 2 and 3; Supplementary Fig. 16). The constant mortality parameter used in most models may be too large if modelers have tuned this parameter to obtain reasonable biomass stocks, thus compensating for an overestimate of NPP in absence of modeled competition between individuals and nutrients (e.g. phosphorus) limitations in tropical forests13. Likewise, HRpro showed similar patterns with NPPpro because of coupling of HR and NPP and LOSS at broad spatial and long term scales20,21, despite the likely decoupling of the instantaneous rate of HR and NPP and LOSS at local and short-term scales22,23. Thus, we also constrained a decrease in projected grid-level HR with values of 6.5, 1.9, 1.7 Pg C y−1 in South America, Africa and Asia & Australia compared to 7, 1.9, 1.8 Pg C y−1 in the original model ensemble (Fig. 4b). Taken together, our results constrain a weaker future tropical forest carbon sink from observation-based LOSS estimates down to 1.4, 0.4, 0.3 Pg C y−1 in South America, Africa and Asia & Australia as compared to 2, 0.5, 0.5 Pg C y−1 in the original models. The projected sink is reduced in particular over the Amazon basin, while North America showed an enhanced future carbon sink (1.1 and 0.8 Pg C y−1 after and before constraint, respectively). The constraint by the machine learning approach significantly reduced the model spread in grid-level NPPpro and HRpro generally in tropical regions and particularly in South America (Fig. 4; Table 1). This was in contrast to the case of constraint at the whole North America scale (Fig. 4; Table 1), presumably because of spatial trade-off or compensation from regions of mortality overestimation (i.e., eastern North America—temperate zones) vs underestimation (i.e., boreal zones). To this end, we further divided the whole North America into temperate and boreal forests and found the significant effects of the ML constraint (Supplementary Fig. 17). These results highlight the importance of spatial scale in the ML constraint approach. We thus recommend accounting for the role of spatial trade-off in our ML constraint approach or using our ML constraint approach at broad spatial scales whereby the effect of spatial trade-off is minimal. We also caution that the bootstrapping (100 times) approach used in our ML constraint increases the sample size and could have increased the significant difference with and without LOSS constraint. Overall, the uncertainty of the ML constraint was low in the bootstrapping approach (Supplementary Fig. 18).Fig. 4: Projected grid-level NPP and grid heterotrophic respiration (HR) across continents.a, b Projected (2015–2099) grid-level NPP a and grid-level HR b across continents quantified by six dynamic vegetation models—DGVMs (ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL, and SEIB-DGVM). The y axes are the minimum, mean, and maximum values in six DGVMs. ‘DGVMs’ refers to the scenario before constraint and ‘DGVMs + Observation’ refers to the scenario after constraint without climate predictors. The constraint was achieved by using the observational maps (n = 100; through a bootstrapping approach; see Methods for details) of LOSS derived from forest plots data to feed into the trained ML (random forest) model. Reported are ensemble means of constraint. The constraint effect was significant when North America were divided into temperate and boreal forests (see results of Supplementary Fig. 17). *P  More

Ministry of the Environment Government of Japan. Designation of Evacuation Zone (accessed 07 April 2021); https://www.env.go.jp/chemi/rhm/h29kisoshiryo/h29kiso-09-04-01.html. (in Japanese).Fukushima Prefectural Government, Japan. About the Transition of Evacuation Zone (accessed 07 April 2021); https://www.pref.fukushima.lg.jp/site/portal/cat01-more.html. (in Japanese).Chino, M. et al. Preliminary estimation of release amounts of 131I and 137Cs accidentally discharged from the Fukushima Daiichi Nuclear Power Plant into the atmosphere. J. Nucl. Sci. Technol. 48, 1129–1134 (2011).CAS 
Article 

Google Scholar 
Koarashi, J., Atarashi-Andoh, M., Takeuchi, E. & Nishimura, S. Topographic heterogeneity effect on the accumulation of Fukushima-derived radiocaesium on forest floor driven by biologically mediated processes. Sci. Rep. 4, 6853 (2014).ADS 
CAS 
Article 

Google Scholar 
Saito, R., Nemoto, Y. & Tsukada, H. Relationship between radiocaesium in muscle and physicochemical fractions of radiocaesium in the stomach of wild boar. Sci. Rep. 10, 6796. https://doi.org/10.1038/s41598-020-63507-5 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tsukada, H. From soil to agricultural-plants-transfer and distribution of radiocaesium. Kagaku (Chemistry). 67, 20–23 (2012) (in Japanese).CAS 

Google Scholar 
Saito, R. & Tsukada, H. Chapter 23: Physicochemical fractions of radiocaesium in the stomach contents of wild boar and its transfer to muscle tissue. In Behavior of Radionuclides in the Environment III (eds Nanba, K. et al.) 495–505 (Springer, 2022).Chapter 

Google Scholar 
Ishii, Y., Hayashi, S. & Takamura, T. Radiocaesium transfer in forest insect communities after the Fukushima Dai-ichi Nuclear Power Plant accident. PLoS ONE 12, e0171133 (2017).Article 

Google Scholar 
Matsushima, N., Ihara, S., Takase, M. & Horiguchi, T. Assessment of radiocaesium contamination in frogs 18 months after the Fukushima Daiichi nuclear disaster. Sci. Rep. 5, 9712 (2015).ADS 
CAS 
Article 

Google Scholar 
Ishii, Y., Matsuzaki, S. S. & Hayashi, S. Different factors determine 137Cs concentration factors of freshwater fish and aquatic organisms in lake and river ecosystems. J. Environ. Radioact. 213, 106102 (2020).CAS 
Article 

Google Scholar 
Wada, T. et al. Strong contrast of cesium radioactivity between marine and freshwater fish in Fukushima. J. Environ. Radioact. 204, 132–142 (2019).CAS 
Article 

Google Scholar 
Morishita, D. et al. Spatial and seasonal variations of radiocaesium concentrations in an algae-grazing annual fish, ayu Plecoglossus altivelis collected from Fukushima Prefecture in 2014. Fish. Sci. 85, 561–569 (2019).CAS 
Article 

Google Scholar 
Saito, R., Kabeya, M., Nemoto, Y. & Oomachi, H. Monitoring 137Cs concentrations in bird species occupying different ecological niches; game birds and raptors in Fukushima Prefecture. J. Environ. Radioact. 197, 67–73 (2019).CAS 
Article 

Google Scholar 
Merz, S., Shozugawa, K. & Steinhauser, G. Analysis of Japanese radionuclide monitoring data of food before and after the Fukushima nuclear accident. Environ. Sci. Technol. 49, 2875–2885 (2015).ADS 
CAS 
Article 

Google Scholar 
Steinhauser, G. & Saey, P. R. J. 137Cs in the meat of wild boars: A comparison of the impacts of Chernobyl and Fukushima. J. Radioanal. Nucl. Chem. 307, 1801–1806 (2016).CAS 
Article 

Google Scholar 
Nemoto, Y., Saito, R. & Oomachi, H. Seasonal variation of caesium-137 concentration in Asian black bear (Ursus thibetanus) and wild boar (Sus scrofa) in Fukushima Prefecture, Japan. PLoS ONE 13, e0200797. https://doi.org/10.1371/journal.pone.0200797 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Nemoto, Y. et al. Effects of 137Cs contamination after the TEPCO Fukushima Dai-ichi Nuclear Power Station accident on food and habitat of wild boar in Fukushima Prefecture. J. Environ. Radioact. 225, 106342 (2020).CAS 
Article 

Google Scholar 
Saito, R., Oomachi, H., Nemoto, Y. & Osako, M. Estimation of the total amount of the radiocaesium in the wild boar in their body – each organs survey and incineration residue survey. J. Soc. Rem. Radioact. Contam. Environ. 7, 165–173 (2019) (in Japanese).
Google Scholar 
Cui, L. et al. Radiocaesium concentrations in wild boars captured within 20 km of the Fukushima Daiichi Nuclear Power Plant. Sci. Rep. 10, 9272. https://doi.org/10.1038/s41598-020-66362-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tagami, K., Howard, B. J. & Uchida, S. The time-dependent transfer factor of radiocaesium from soil to game animals in Japan after the Fukushima Dai-ichi nuclear accident. Environ. Sci. Technol. 50, 9424–9431. https://doi.org/10.1021/acs.est.6b03011 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Fuma, S. et al. Radiocaesium contamination of wild boars in Fukushima and surrounding regions after the Fukushima nuclear accident. Environ. Radioact. 164, 60–64 (2016).CAS 
Article 

Google Scholar 
Fukushima Prefectural Government, Japan. Monitoring of Wild Animals. Accessed 7 Apr 2021. https://www.pref.fukushima.lg.jp/site/portal/wildlife-radiationmonitoring1.html. (in Japanese).Strebl, F. & Tataruch, F. Time trends (1986–2003) of radiocaesium transfer to roe deer and wild boar in two Austrian forest regions. J. Environ. Radioactiv. 98, 137–152 (2007).CAS 
Article 

Google Scholar 
Ohtsuka-Ito, E. & Kanzaki, N. Population trends of the Japanese wild boar during the Showa era. Wildl. Cons. Jpn. 3, 95–105 (1998).Article 

Google Scholar 
Ueda, H. & Jiang, Z. The use of Orchards and Abandoned Orchard by wild boars in Yamanashi. Mamm. Sci. 44, 23–33 (2004) (in Japanese).
Google Scholar 
Fukushima Prefectural Government, Japan. Fukushima Prefecture Wild Boar Management Plan (Phase 3) (accessed 07 April 2021); https://www.pref.fukushima.lg.jp/uploaded/life/497785_1296285_misc.pdf (in Japanese).Anderson, D. et al. A comparison of methods to derive aggregated transfer factors using wild boar data from the Fukushima Prefecture. J. Environ. Radioact. 197, 101–108 (2019).CAS 
Article 

Google Scholar 
Pröhl, G. et al. Ecological half-lives of 90Sr and 137Cs in terrestrial and aquatic ecosystems. J. Environ. Radioactiv. 91, 41–72 (2006).Article 

Google Scholar 
Palo, R. T., White, N. & Danell, K. Spatial and temporal variations of 137Cs in moose Alces alces and transfer to man in northern Sweden. Wildlife Biol. 9, 207–212 (2003).Article 

Google Scholar 
Kodera, Y., Kanzaki, N., Ishikawa, N. & Minagawa, A. Food habits of wild boar (Sus scrofa) inhabiting Iwami District, Shimane Prefecture, western Japan. J. Mammal. Soc. Jpn. 53, 279–287 (2013) (in Japanese).
Google Scholar 
Kodera, Y. & Kanzaki, N. Food habits and nutritional condition of Japanese wild boar in Iwami district, Shimane Prefecture, western Japan. Wildl. Cons. Jpn. 6, 109–117 (2001) (in Japanese).
Google Scholar 
Arita, S. et al. Radioactive cesium accumulation during developmental stages of Largemouth Bass, Micropterus salmoides. Proc. JSCE. G. (Environment) 71, 267–276 (2015).Article 

Google Scholar 
Kodera, Y. C. S. F. prevention of epidemics from a point of view of the ecology of wild boar. J. Vet. Epidemiol. 23, 4–8 (2019) (in Japanese).Article 

Google Scholar 
Calenge, C., Maillard, D., Vassant, J. & Brandt, S. Summer and hunting season home ranges of wild boar (Sus scrofa) in two habitats in France. Game Wildl. Sci. 19, 281–301 (2002).
Google Scholar 
Massei, G., Genov, P. V., Staines, B. W. & Gorman, M. L. Factors influencing home range and activity of wild boar (Sus scrofa) in a Mediterranean coastal area. J. Zool. 242, 411–423 (1997).Article 

Google Scholar 
Morelle, K. et al. Towards understanding wild boar Sus scrofa movement: A synthetic movement ecology approach. Mammal Rev. 45, 15–29 (2015).Article 

Google Scholar 
Kapata, J., Mnich, K., Mnich, S., Karpińska, M. & Bielawska, A. Time-dependence of 137Cs activity concentration in wild game meat in Knyszyn Primeval Forest (Poland). J. Environ. Radioactiv. 141, 76–81 (2015).Article 

Google Scholar 
Gulakov, A. V. Accumulation and distribution of 137Cs and 90Sr in the body of the wild boar (Sus scrofa) found on the territory with radioactive. J. Environ. Radioactiv. 127, 171–175 (2014).CAS 
Article 

Google Scholar 
Hohmann, U. & Huckschlag, D. Investigations on the radiocaesium contamination of wild boar (Sus scrofa) meat in Rhineland-Palatinate: A stomach content analysis. Eur. J. Wildl. Res. 51, 263–270 (2005).Article 

Google Scholar 
Škrkal, J., Rulík, P., Fantínová, K., Mihalík, J. & Timková, J. Radiocaesium levels in game in the Czech Republic. J. Environ. Radioactiv. 139, 18–23 (2015).Article 

Google Scholar 
Japan Atomic Energy Agency (JAEA). 5th airborne monitoring survey (accessed 07 April 2021); https://emdb.jaea.go.jp/emdb/en/portals/b1020201/Steinhauser, G. Monitoring and radioecological characteristics of radiocaesium in Japanese beef after the Fukushima nuclear accident. J. Radioanal. Nucl. Chem. 311, 1367–1373 (2017).CAS 
Article 

Google Scholar 
Merz, S., Shozugawa, K. & Steinhauser, G. Effective and ecological half-lives of 90Sr and 137Cs observed in wheat and rice in Japan. J. Radioanal. Nucl. Chem. 307, 1807–1810 (2016).CAS 
Article 

Google Scholar  More

Fatty acid profilePlukenetia volubilisThe fatty acid composition of P. volubilis is the most well studied in the genus, and the results from the two P. volubilis accessions from Ecuador and Peru in the current study are similar to previous results. The most abundant fatty acid in the seed oil of P. volubilis from Ecuador and Peru, respectively, is α-linolenic acid (C18:3 n-3, ω-3, ALA; 51.5 ± 3.3 and 46.6 ± 1.2%), followed by linoleic acid (C18:2 n-6, ω-6, LA; 32.5 ± 3.9 and 36.5 ± 0.8%), oleic acid (C18:1, OA; 8.5 ± 1,2 and 8.3 ± 0,4%) and smaller amounts ( More