Philippa Kaur

International Energy Agency. Offshore Wind Outlook 2019. https://iea.blob.core.windows.net/assets/495ab264-4ddf-4b68-b9c0-514295ff40a7/Offshore_Wind_Outlook_2019.pdf (2019).United Nations. Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. (E/CN.3/2016/2/Rev.1). 49. (New York: United Nations Economic and Social Council, 2016).Copping, A. et al. Annex IV State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World. https://tethys.pnnl.gov/sites/default/files/publications/Annex-IV-2016-State-of-the-Science-Report_MR.pdf. Accessed 27 Feb 2020. (2016).Dean, N. Performance factors. Nature Energy 5, 5–5 (2020).Article 

Google Scholar 
Global Wind Energy Council. Globarl offshore wind report 2020. https://gwec.net/wp-content/uploads/dlm_uploads/2020/08/GWEC-offshore-wind-2020-5.pdf (2020).Jansen, M. et al. Offshore wind competitiveness in mature markets without subsidy. Nat. Energy 5, 614–622 (2020).Article 

Google Scholar 
IRENA. Global Renewables Outlook: Energy transformation 2050 (Edition: 2020), International Renewable Energy Agency, Abu Dhabi. ISBN 978-92-9260-238-3. www.irena.org/publications (2020).Wiser, R. et al. Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050. Nat. Energy 6, 555–565 (2021).Article 

Google Scholar 
IRENA. Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf (2019).European Commission. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. The European Green Deal. Brussels, 11.12.2019 COM(2019) 640 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2019%3A640%3AFIN (2019).European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. An EU Strategy to harness the potential of offshore renewable energy for a climate neutral future. Brussels, 19.11.2020 COM(2020) 741 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2020%3A741%3AFIN (2020).European Parliament. European Parliament resolution of 14 March 2019 on climate change – a European strategic long-term vision for a prosperous, modern, competitive and climate neutral economy in accordance with the Paris Agreement (2019/2582(RSP)). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52019IP0217 (2019).Arneth, A. et al. Post-2020 biodiversity targets need to embrace climate change. Proc. Natl. Acad. Sci. 117, 30882–30891 (2020).CAS 
Article 

Google Scholar 
Copping, A. E., Freeman, M. C., Gorton, A. M. & Hemery, L. G. Risk Retirement—Decreasing Uncertainty and Informing Consenting Processes for Marine Renewable Energy Development. J. Marine Sci. Eng. 8, 172 (2020).Article 

Google Scholar 
WWF. Environmental Impacts of Offshore Wind Power Production in the North Sea. A Literature Overview. https://tethys.pnnl.gov/sites/default/files/publications/WWF-OSW-Environmental-Impacts.pdf (2014).Cook, A. S. C. P., Humphreys, E. M., Bennet, F., Masden, E. A. & Burton, N. H. K. Quantifying avian avoidance of offshore wind turbines: Current evidence and key knowledge gaps. Marine Environ. Res. 140, 278–288 (2018).CAS 
Article 

Google Scholar 
Willsteed, E. A., Jude, S., Gill, A. B. & Birchenough, S. N. R. Obligations and aspirations: A critical evaluation of offshore wind farm cumulative impact assessments. Renew. Sustain. Energy Rev. 82, 2332–2345 (2018).Article 

Google Scholar 
Stelzenmüller, V. et al. Operationalizing risk-based cumulative effect assessments in the marine environment. Sci. Total Environ. 724, 138118 (2020).Article 
CAS 

Google Scholar 
Ehler, C. & Douvere, F. in Intergovernmental Oceanographic Commission and Man and the Biosphere Programme. IOC Manual and Guides No. 53, ICAM Dossier No. 6. Paris: UNESCO. 99pp. (2009).Borja, A. et al. Good Environmental Status of marine ecosystems: What is it and how do we know when we have attained it? Marine Pollut. Bull. 76, 16–27 (2013).CAS 
Article 

Google Scholar 
Peters, J. L., Remmers, T., Wheeler, A. J., Murphy, J. & Cummins, V. A systematic review and meta-analysis of GIS use to reveal trends in offshore wind energy research and offer insights on best practices. Renew. Sustain. Energy Rev. 128, 109916 (2020).Article 

Google Scholar 
Gasparatos, A., Doll, C. N. H., Esteban, M., Ahmed, A. & Olang, T. A. Renewable energy and biodiversity: Implications for transitioning to a Green Economy. Renew. Sustain. Energy Rev. 70, 161–184 (2017).Article 

Google Scholar 
Xiao, Y. & Watson, M. Guidance on Conducting a Systematic Literature Review. J. Plan. Education Res. 39, 93–112 (2017).Article 

Google Scholar 
Mengist, W., Soromessa, T. & Legese, G. Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX 7, 100777 (2020).Article 

Google Scholar 
Pullin, A. & Stewart, G. Guidelines for Systematic Review in Environmental Management. Conserv. Biol. 20, 1647–1656 (2007).Article 

Google Scholar 
van der Molen, J., Smith, H. C. M., Lepper, P., Limpenny, S. & Rees, J. Predicting the large-scale consequences of offshore wind turbine array development on a North Sea ecosystem. Continental Shelf Res. 85, 60–72 (2014).Article 

Google Scholar 
De Backer, A., Van Hoey, G., Coates, D., Vanaverbeke, J. & Hostens, K. Similar diversity-disturbance responses to different physical impacts: Three cases of small-scale biodiversity increase in the Belgian part of the North Sea. Marine Pollut. Bull. 84, 251–262 (2014).Article 
CAS 

Google Scholar 
Floeter, J. et al. Pelagic effects of offshore wind farm foundations in the stratified North Sea. Prog. Oceanograph. 156, 154–173 (2017).Article 

Google Scholar 
Lindeboom, H. J. et al. Short-term ecological effects of an offshore wind farm in the Dutch coastal zone; A compilation. Environ. Res. Lett. 6, 035101 (2011).Article 

Google Scholar 
Bray, L. et al. Expected effects of offshore wind farms on Mediterranean Marine Life. J. Marine Sci. Eng. 4, 18 (2016).Article 

Google Scholar 
Dannheim, J. et al. Benthic effects of offshore renewables: identification of knowledge gaps and urgently needed research. ICES J. Marine Sci. 77, 1092–1108 (2019).Article 

Google Scholar 
Wilson, J. C. & Elliott, M. The habitat-creation potential of offshore wind farms. Wind Energy 12, 203–212 (2009).Article 

Google Scholar 
Hall, R., João, E. & Knapp, C. W. Environmental impacts of decommissioning: Onshore versus offshore wind farms. Environ. Impact Assess. Rev. 83, 106404 (2020).Article 

Google Scholar 
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar 
Korpinen, S. & Andersen, J. H. A Global Review of Cumulative Pressure and Impact Assessments in Marine Environments. Front. Marine Sci. 3, 00153 (2016).Article 

Google Scholar 
Nõges, P. et al. Quantified biotic and abiotic responses to multiple stress in freshwater, marine and ground waters. Sci. Total Environ. 540, 43–52 (2016).Article 
CAS 

Google Scholar 
Gissi, E. et al. A review of the combined effects of climate change and other local human stressors on the marine environment. Sci. Total Environ. 755, 142564 (2021).CAS 
Article 

Google Scholar 
Gușatu, L. F. et al. Spatial and temporal analysis of cumulative environmental effects of offshore wind farms in the North Sea basin. Sci. Rep. 11, 10125 (2021).Article 
CAS 

Google Scholar 
Gissi, E. et al. Addressing uncertainty in modelling cumulative impacts within maritime spatial planning in the Adriatic and Ionian region. PLoS ONE 12, e0180501 (2017).Article 
CAS 

Google Scholar 
Vaissière, A. C., Levrel, H., Pioch, S. & Carlier, A. Biodiversity offsets for offshore wind farm projects: The current situation in Europe. Marine Policy 48, 172–183 (2014).Article 

Google Scholar 
Iglesias, G., Tercero, J. A., Simas, T., Machado, I. & Cruz, E. Environmental Effects. In Wave and Tidal Energy (eds Greaves, D. & Iglesias, G.). https://doi.org/10.1002/9781119014492.ch9 (2018).Causon, P. D. & Gill, A. B. Linking ecosystem services with epibenthic biodiversity change following installation of offshore wind farms. Environ. Sci. Policy 89, 340–347 (2018).Article 

Google Scholar 
Copping, A. E. & Hemery, L. G. OES-Environmental 2020 State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World. Report for Ocean Energy Systems (OES). 323 pp., (2020).Gill, A. B. Offshore renewable energy: ecological implications of generating electricity in the coastal zone. J. Appl. Ecol. 42, 605–615 (2005).Article 

Google Scholar 
Scheidat, M. et al. Harbour porpoises (Phocoena phocoena) and wind farms: A case study in the Dutch North Sea. Environ. Res. Lett. 6, 025102 (2011).Article 

Google Scholar 
Skov, H. et al. Patterns of migrating soaring migrants indicate attraction to marine wind farms. Biol. Lett. 12, 20160804 (2016).Article 

Google Scholar 
Vanermen, N. et al. Attracted to the outside: a meso-scale response pattern of lesser black-backed gulls at an offshore wind farm revealed by GPS telemetry. ICES J. Marine Sci. 77, 701–710 (2020).Article 

Google Scholar 
Frank, B. Research on marine mammals summary and discussion of research results. In Offshore Wind Energy: Research on Environmental Impacts. 77–86 https://doi.org/10.1007/978-3-540-34677-7_8 (2006).Thaxter, C. B. et al. Bird and bat species’ global vulnerability to collision mortality at wind farms revealed through a trait-based assessment. Proc. Royal Soc. B.: Biol Sci. 284, 20170829 (2017).Article 

Google Scholar 
Wilson, J. C. et al. Coastal and Offshore Wind Energy Generation: Is It Environmentally Benign? Energies 3, 1383–1422 (2010).Article 

Google Scholar 
Busch, M., Kannen, A., Garthe, S. & Jessopp, M. Consequences of a cumulative perspective on marine environmental impacts: Offshore wind farming and seabirds at North Sea scale in context of the EU Marine Strategy Framework Directive. Ocean Coastal Manag. 71, 213–224 (2013).Article 

Google Scholar 
Garthe, S., Markones, N. & Corman, A.-M. Possible impacts of offshore wind farms on seabirds: a pilot study in Northern Gannets in the southern North Sea. J. Ornithol. 158, 345–349 (2017).Article 

Google Scholar 
Brandt, M. J., Diederichs, A., Betke, K. & Nehls, G. Responses of harbour porpoises to pile driving at the Horns Rev II offshore wind farm in the Danish North Sea. Marine Ecol. Prog. Ser. 421, 205–216 (2011).Article 

Google Scholar 
Wilhelmsson, D., Malm, T. & Öhman, M. C. The influence of offshore windpower on demersal fish. ICES J. Marine Sci. 63, 775–784 (2006).Article 

Google Scholar 
Bergström, L., Sundqvist, F. & Bergström, U. Effects of an offshore wind farm on temporal and spatial patterns in the demersal fish community. Marine Ecol. Progr. Ser. 485, 199–210 (2013).Article 

Google Scholar 
van Hal, R., Griffioen, A. B. & van Keeken, O. A. Changes in fish communities on a small spatial scale, an effect of increased habitat complexity by an offshore wind farm. Marine Environ. Res. 126, 26–36 (2017).Article 
CAS 

Google Scholar 
Degraer, S. et al. Offshore wind farm artificial reefs affect ecosystem structure and functioning: A synthesis. Oceanography 33, 48–57 (2020).Article 

Google Scholar 
Zettler, M. L. & Pollehne, F. The Impact of Wind Engine Constructions on Benthic Growth Patterns in the Western Baltic. In Offshore Wind Energy: Research on Environmental Impacts (eds Köller, J., Köppel, J. & Peters, W.). 201–222 (Springer Berlin Heidelberg, 2006).Wilhelmsson, D. Marine environmental aspects of offshore wind power development. (Nova Science Publishers, Inc, 2010).Teilmann, J. & Carstensen, J. Negative long term effects on harbour porpoises from a large scale offshore wind farm in the Baltic – Evidence of slow recovery. Environ. Res. Lett. 7, 045101 (2012).Article 

Google Scholar 
Halouani, G. et al. A spatial food web model to investigate potential spillover effects of a fishery closure in an offshore wind farm. J. Marine Syst. 212, 103434 (2020).Article 

Google Scholar 
Reubens, J. T., Degraer, S. & Vincx, M. The ecology of benthopelagic fishes at offshore wind farms: a synthesis of 4 years of research. Hydrobiologia 727, 121–136 (2014).CAS 
Article 

Google Scholar 
Wilber, D. H., Carey, D. A. & Griffin, M. Flatfish habitat use near North America’s first offshore wind farm. J. Sea Res. 139, 24–32 (2018).Article 

Google Scholar 
Welcker, J. & Nehls, G. Displacement of seabirds by an offshore wind farm in the North Sea. Marine Ecol. Prog. Ser. 554, 173–182 (2016).Article 

Google Scholar 
Vallejo, G. C. et al. Responses of two marine top predators to an offshore wind farm. Ecol. Evol. 7, 8698–8708 (2017).Article 

Google Scholar 
Tougaard, J., Henriksen, O. D. & Miller, L. A. Underwater noise from three types of offshore wind turbines: Estimation of impact zones for harbor porpoises and harbor seals. J. Acoustical Soc. Am. 125, 3766–3773 (2009).Article 

Google Scholar 
Kastelein, R. A., Jennings, N., Kommeren, A., Helder-Hoek, L. & Schop, J. Acoustic dose-behavioral response relationship in sea bass (Dicentrarchus labrax) exposed to playbacks of pile driving sounds. Marine Environ. Res. 130, 315–324 (2017).CAS 
Article 

Google Scholar 
Vanermen, N. et al. Assessing seabird displacement at offshore wind farms: power ranges of a monitoring and data handling protocol. Hydrobiologia 756, 155–167 (2015).Article 

Google Scholar 
Wahlberg, M. & Westerberg., H. Hearing in fish and their reactions to sounds from offshore wind farms. Marine Ecol. Prog. Ser. 288, 295–309 (2005).Article 

Google Scholar 
Desholm, M. Avian sensitivity to mortality: Prioritising migratory bird species for assessment at proposed wind farms. J. Environ. Manag. 90, 2672–2679 (2009).Article 

Google Scholar 
Vanermen, N. et al. Seabird avoidance and attraction at an offshore wind farm in the Belgian part of the North Sea. Hydrobiologia 756, 51–61 (2015).Article 

Google Scholar 
Brandt, M. J. et al. Disturbance of harbour porpoises during construction of the first seven offshore wind farms in Germany. Marine Ecol. Prog. Ser. 596, 213–232 (2018).Article 

Google Scholar 
Masden, E. A., Haydon, D. T., Fox, A. D. & Furness, R. W. Barriers to movement: Modelling energetic costs of avoiding marine wind farms amongst breeding seabirds. Marine Pollut. Bull. 60, 1085–1091 (2010).CAS 
Article 

Google Scholar 
Lloret, J. et al. Unravelling the ecological impacts of large-scale offshore wind farms in the Mediterranean Sea. Sci. Total Environ. 824, 153803 (2022).CAS 
Article 

Google Scholar 
Everaert, J. Collision risk and micro-avoidance rates of birds with wind turbines in Flanders. Bird Study 61, 220–230 (2014).Article 

Google Scholar 
Rice, J. et al. Indicators for Sea-floor Integrity under the European Marine Strategy Framework Directive. Ecol. Indicators 12, 174–184 (2012).Article 

Google Scholar 
Teixeira, H. et al. A Catalogue of Marine Biodiversity Indicators. Front. Marine Sci. 3, 00207 (2016).Article 

Google Scholar 
Brabant, R., Vanermen, N., Stienen, E. & Degraer, S. Towards a cumulative collision risk assessment of local and migrating birds in North Sea offshore wind farms. Hydrobiologia 756, 63–74 (2015).Article 

Google Scholar 
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).Article 

Google Scholar 
Kelsey, E. C., Felis, J. J., Czapanskiy, M., Pereksta, D. M. & Adams, J. Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf. J. Environ. Manag. 227, 229–247 (2018).Article 

Google Scholar 
Graham, I. et al. Harbour porpoise responses to pile-driving diminish over time. R. Soc. Open Sci. 6, 190335 (2019).Article 

Google Scholar 
Lindeboom, H. J. & Degraer, S. In Long-term Research Challenges in Wind Energy—A Research Agenda by the European Academy of Wind Energy (eds Gijs van Kuik & Joachim Peinke) 77–81 (Springer International Publishing, 2016).Stenberg, C. et al. Long-term effects of an offshore wind farm in the North Sea on fish communities. Marine Ecol. Prog. Ser. 528, 257–265 (2015).Article 

Google Scholar 
Salvador, S., Gimeno, L. & Sanz Larruga, F. J. The influence of regulatory framework on environmental impact assessment in the development of offshore wind farms in Spain: Issues, challenges and solutions. Ocean Coastal Manag. 161, 165–176 (2018).Article 

Google Scholar 
Bailey, H., Brookes, K. L. & Thompson, P. M. Assessing environmental impacts of offshore wind farms: lessons learned and recommendations for the future. Aquatic Biosyst. 10, 8 (2014).Article 

Google Scholar 
Apolonia, M., Fofack-Garcia, R., Noble, D. R., Hodges, J. & Correia da Fonseca, F. X. Legal and Political Barriers and Enablers to the Deployment of Marine Renewable Energy. Energies 14, 4896 (2021).Article 

Google Scholar 
Borja, A. et al. Moving Toward an Agenda on Ocean Health and Human Health in Europe. Front. Marine Sci. 7, 00037 (2020).Article 

Google Scholar 
European Commission, Directorate-General for Environment, Guidance document on wind energy developments and EU nature legislation, Publications Office of the European Union https://data.europa.eu/doi/10.2779/095188 (2021).O’Hagan, A. M. & Lewis, A. W. The existing law and policy framework for ocean energy development in Ireland. Marine Policy 35, 772–783 (2011).Article 

Google Scholar 
Long, R. D., Charles, A. & Stephenson, R. L. Key principles of marine ecosystem-based management. Marine Policy 57, 53–60 (2015).Article 

Google Scholar 
Borgwardt, F. et al. Exploring variability in environmental impact risk from human activities across aquatic ecosystems. Sci. Total Environ. 652, 1396–1408 (2019).Article 
CAS 

Google Scholar 
Copping, A., Hanna, L., Van Cleve, B., Blake, K. & Anderson, R. M. Environmental Risk Evaluation System-an Approach to Ranking Risk of Ocean Energy Development on Coastal and Estuarine Environments. Estuaries Coasts 38, S287–S302 (2015).Article 

Google Scholar 
Lüdeke, J. Offshore Wind Energy: Good Practice in Impact Assessment, Mitigation and Compensation. J. Environ. Assess. Policy Manag. 19, 1750005 (2017).Article 

Google Scholar 
Boehlert, G. W. & Gill, A. B. Environmental and ecological effects of ocean renewable energy development: a current synthesis. J. Oceanograph. 23, 68–81 (2010).Article 

Google Scholar 
Hammar, L., Wikström, A. & Molander, S. Assessing ecological risks of offshore wind power on Kattegat cod. Renew. Energy 66, 414–424 (2014).Article 

Google Scholar 
Nunneri, C., Lenhart, H. J., Burkhard, B. & Windhorst, W. Ecological risk as a tool for evaluating the effects of offshore wind farm construction in the North Sea. Reg Environ. Change 8, 31–43 (2008).Article 

Google Scholar 
Hutchison, Z. L. et al. Offshore Wind Energy and Benthic Habitat Changes: Lessons from Block Island Wind Farm. Oceanography 33, 58–69 (2020).Article 

Google Scholar 
Pirttimaa, P. & Cruz, E. Ocean energy and the environment: Research and strategic actions. European Technology and Innovation Platform for Ocean Energy (ETIP Ocean), pp.36. https://www.etipocean.eu/assets/Uploads/ETIP-Ocean-Ocean-energy-and-the-environment.pdf (2020).Hooper, T., Beaumont, N. & Hattam, C. The implications of energy systems for ecosystem services: A detailed case study of offshore wind. Renew. Sustain. Energy Rev. 70, 230–241 (2017).Article 

Google Scholar 
Mangi, S. C. The Impact of Offshore Wind Farms on Marine Ecosystems: A Review Taking an Ecosystem Services Perspective. Proceedings of the IEEE 101, 999–1009, (2013).Pınarbaşı, K. et al. A modelling approach for offshore wind farm feasibility with respect to ecosystem-based marine spatial planning. Sci. Total Environ. 667, 306–317 (2019).Article 
CAS 

Google Scholar 
Maldonado, A. D. et al. A Bayesian Network model to identify suitable areas for offshore wave energy farms, in the framework of ecosystem approach to marine spatial planning. Sci. Total Environ. 838, 156037 (2022).CAS 
Article 

Google Scholar 
Stelzenmüller, V., Gimpel, A., Letschert, J., Kraan, C. & DÖRING, R. Research for PECH Committee – Impact of the use of offshore wind and other marine renewables on European fisheries. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/652212/IPOL_STU(2020)652212_EN.pdf (2020).Galparsoro, I. et al. A new framework and tool for ecological risk assessment of wave energy converters projects. Renew. Sustain. Energy Rev. 151, 111539 (2021).Article 

Google Scholar 
Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L. & Lehikoinen, A. Bayesian Networks in Environmental Risk Assessment: A Review. Integr. Environ. Assess. Manag. 17, 62–78 (2020).Article 

Google Scholar 
González, D. A., Gleeson, J. & McCarthy, E. Designing and developing a web tool to support Strategic Environmental Assessment. Environ. Modell. Softw. 111, 472–482 (2019).Article 

Google Scholar 
Pınarbaşı, K. et al. Decision support tools in marine spatial planning: Present applications, gaps and future perspectives. Marine Policy 83, 83–91 (2017).Article 

Google Scholar 
Pınarbaşı, K., Galparsoro, I. & Borja, Á. End users’ perspective on decision support tools in marine spatial planning. Marine Policy 108, 103658 (2019).Article 

Google Scholar  More

Authors and AffiliationsDepartment of Agronomy, Universidade Federal do Espírito Santo, Alegre, BrazilAléxia Gonçalves Pereira, Marcia Flores da Silva Ferreira, Thamyres Cardoso da Silveira, José Henrique Soler-Guilhen, Guilherme Bravim Canal, Luziane Brandão Alves, Francine Alves Nogueira de Almeida & Adésio FerreiraDepartment of Biological Sciences, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, BrazilFernanda Amato GaiottoAuthorsAléxia Gonçalves PereiraMarcia Flores da Silva FerreiraThamyres Cardoso da SilveiraJosé Henrique Soler-GuilhenGuilherme Bravim CanalLuziane Brandão AlvesFrancine Alves Nogueira de AlmeidaFernanda Amato GaiottoAdésio FerreiraCorresponding authorCorrespondence to
Marcia Flores da Silva Ferreira. More

Short-term experimentsPotential toxic and cryoprotection effects of different CPA combinationsFocusing on toxicity bioassays (Figs. 1A, 2A), although there were certain CPA combinations that yielded significant abnormality percentages compared to controls, in general the CPA combinations did not yield any significant toxic effect. The use of Milli-Q Water instead of FSW did not enhance normal larval development after CPA exposure, neither did the addition of PVP at the concentrations tested, even in combination with trehalose (TRE) (p  > 0.05). In fact, the highest concentrations of PVP used in this experiment (9 and 12%) yielded significant abnormal development on exposed trochophores (Fig. 1A) (p  More

White and black spruce are the dominant conifers at Arctic treelines and the boreal forest–tundra ecotone more generally in North America, with white spruce dominating on better drained sites. White spruce reaches its northwestern-most limit in Alaska, USA, at 68.1º N, 163.2º W. For comparison, the northeastern range extent of the species26 is Labrador, Canada, at 57.9º N, 62.5º W (ref. 12), giving an east–west range of >100º in longitude. Of the approximately 6,500-km-long northern boundary of white spruce in North America, 10–15% is located in Alaska’s Brooks Range, where white spruce is the dominant treeline tree.Study areaThe 1,000-km Brooks Range is a high-latitude mountain range dividing Arctic tundra from boreal forest in Alaska. The mountains and nearby lowlands are notable for their wilderness character, protected as a near-contiguous conservation area of >150,000 km2. In the east between the Arctic Ocean’s Beaufort Sea and the uppermost Yukon River basin, the range is cold and dry, reaching 2,736 m above sea level. The south slope of the eastern Brooks Range is included in Alaska’s Northeast Interior climate division, where precipitation is among the lowest in the state51. Descending to the Chukchi Sea in the west, the range is included in Alaska’s West Coast climate division, where precipitation is the highest in northern Alaska51.The Noatak and Kobuk rivers flow in their entirety above the Arctic Circle, draining the western Brooks Range. Both rivers empty into the Chukchi Sea near Kotzebue, Alaska (Fig. 1a). The Baird Mountains of the southwestern Brooks Range separate the Kobuk from the Noatak basin, and the De Long Mountains of the northwestern Brooks Range separate the Noatak from the river basins of the North Slope and from the Wulik basin, located northwest of the Noatak basin. The lower basins of the Noatak and Kobuk rivers are included in the West Coast climate division, with greater precipitation, warmer winters and cooler summers than in the Central Interior climate division and greater precipitation and warmer temperatures than in the North Slope climate division51. The upper basin of the 700-km Noatak River lies at the intersection of all three climate divisions, which warmed from 1949 to 2012; December–January precipitation increased from 1949 to 2012 in the West Coast climate division, as did North Slope winter precipitation from 1980 to 2012 (ref. 52).The Noatak River basin is entirely protected within federal conservation units. Its vegetation includes dwarf, low and tall shrub tundra communities that cover about 60% of the 33,000 km2 basin53. Tussock sedge tundra covers another 30%, and wetlands and barrens cover most of the remainder. The main valley and tributaries along the lowest 200 km of the Noatak River support stands of white spruce, typically associated with a deeper active layer or an absence of permafrost. The treelines bounding these forests have long been identified as the northwest range extent of white spruce26.The upper Noatak basin, a 500-km reach, is underlain by extensive continuous permafrost54. It has been considered empty of spruce since US Geological Survey (USGS) geologist Philip Smith explored the Kobuk, Alatna and Noatak rivers by canoe in 1911 (ref. 55). The adjacent Kobuk and Alatna river basins support boreal forests of black and white spruce, paper birch and aspen along much of their lengths. By surveying transects at and beyond hydrological divides separating the Noatak, Wulik, Kobuk and Alatna river basins, as well as further east in the Brooks Range (Fig. 1a), and informed by very high-resolution satellite scenes (Fig. 1b and Supplementary Figs. 1–13), we documented the locations of over 7,000 individual spruce colonists (Extended Data Fig. 1b–d and Supplementary Figs. 1–3). Overall, we traversed 22° of longitude (141–163° W) in the field, mostly along the treeline from Canada to the Chukchi Sea, locating dozens of populations of colonizing spruce (Fig. 1a) above alpine and beyond Arctic treelines (see ‘Regional extent of colonization’).The primary AOI (Fig. 1a) included the USGS Hydrological Unit Code (HUC) 10 watersheds Kaluich, Cutler, Amakomanak and Imelyak located in the HUC 8 Upper Noatak Subbasin. However, we also documented (longitude, latitude, distance from established treeline) fast-growing, healthy spruce well beyond established treelines within six additional western Arctic watersheds, each separated by over 30 km in the western Brooks Range and 80–200 km distant from the AOI. These populations are within the far upper reaches of the Noatak basin (Lucky Six Creek, 67.594° N, 154.858° W; Kugrak River, 67.428° N, 155.723° W; Ipnelivuk River, 67.552° N, 156.293° W; upper Wrench Creek, 68.251° N, 162.617° W); 25 km northwest of the nearest established treeline and outside the Noatak basin in the Wulik River valley (68.120° N, 163.219° W); and along the Chukchi Sea coast (67.041° N, 163.114° W). We also note that, in the central Brooks Range, humans have actively or inadvertently disseminated spruce seeds and juveniles on the North Slope, with individual white spruce germinating and surviving there for at least 20 years37,56.Patterns of expansionDigitizing spruce shadowsWe used cloud-free Maxar Digital Globe WorldView-1 and WorldView-2 satellite scenes (WV; https://evwhs.digitalglobe.com/myDigitalGlobe/login) of snow-covered landscapes from three missions in early spring 2018, a near-record year for snow depth in northwest Alaska (Fig. 1b, Extended Data Table 1 and Supplementary Figs. 1–13). Ground sample distances of 0.47–0.5 m, a root-mean-squared error of 3.91–3.94 m and off-nadir angles of 5–25º with low sun-elevation angles of 18–27º provided clear images from which to digitize the lengths of individual spruce shadows and identify their locations (Supplementary Information sections 1.2 and 1.3). One technician (S. Taylor), supervised in quality assurance and quality control (QAQC) by R.J.D., digitized 5,986 shadows (densities in Extended Data Fig. 1b, locations in Supplementary Fig. 1) on GEP using WV images as super-overlays. The technician identified all spruce shadows across the imported image tiles and then digitized them as line segments from base to shadow tip.The super-overlays degraded the imagery somewhat, making small tree shadows more difficult to distinguish from snowdrift, rock or shrub shadows (Supplementary Figs. 5 and 6). We suspect that many trees in the height class of 2–3 m were missed. These line segments, saved as .kml files, were imported into R (v.4.1.1)57 using the sf package58, where the length of each line segment was calculated and the coordinates of the shadow’s base were identified. The line segment lengths were used to estimate tree heights, and the coordinates were used in nearest-neighbour calculations and extractions of gridded data values. We estimated snow depth at 2.5–3 m because geolocated trees measured as ≤2.5 m in the field (see below) did not appear on imagery. We observed some trees taller than 2.5 m with no visible shadows on imagery, possibly buried in deeper snow or growing in shadows cast by terrain at the time of image capture. Thus, our estimates of adult populations may be underestimates, although there were also errors of commission where shrub shadows were mistakenly classified as spruce (see following).Digitizing and field validationTo estimate identification accuracy (Supplementary Information sections 1.2 and 1.3) among the 1,971 digitized shadows used for population reconstruction (enclosed by red rectangles in Supplementary Figs. 1–4), we visited 157 shadow locations first identified on imagery (8% of the 1,971) and located in the field with the built-in GNSS of late-model Apple iPhones (models 12 Pro Max, 12 Pro and second-generation SE) with positional accuracy in the open landscapes estimated at 3 m. At these 157 locations, 11 shadows were cast by very tall willows (7%). Of the 146 shadows confirmed as trees, 2 were dead (1%) and 1 had a recently broken top with green foliage on the ground. We added the length of the broken top to the standing height measured with a laser range-finder. Trees that were collinear in the solar azimuth at image capture contributed to errors of omission. The tree standing to solar azimuth obscured others as overlapping shadows fell in line, generating both errors of omission and an overestimate of the height of the first tree in the series. Six trees shadowed in three instances by what we identified on imagery as single shadows fell in this category. An additional three trees were missed during digitizing, also going unnoticed during QAQC, and were discovered in the field when matching shadows with trees. Supplementary Information section 1.3 provides details and a confusion matrix.In summary, 157 trees were expected from digitized shadows and 155 were found in the field. Applying the accuracy of the count overall suggests that 1,945 trees would better estimate the reconstructed population. Across the AOI, the total adult count of 5,988 shadows may represent 5,910 trees. Moreover, in so far as our estimates of ages based on tree heights are predictive, perhaps 2% of the ‘trees’ in our reconstruction are not a single tree casting a long shadow, but 2–3 younger, collinear trees. Thus, our estimate of past populations may be slightly biased to older trees, implying that the population growth rate may be slightly higher than estimated. However, the slightly fewer trees than shadows would suggest that the growth rate is lower. The relative size of these errors appears minor, and we did not incorporate them into the analysis, which seems to us robust and perhaps conservative in adult abundance estimates owing to image degradation with GEP super-overlays and other errors of omission. This study would have benefited from less image degradation using dedicated geographic information system (GIS) or image software. However, the low cost, simplicity and convenience of GEP was appealing for the large-scale digitizing.Returning from the field with individual tree data, R.J.D. displayed digitized shadow points together with field points on GEP, visually matching each field point to the nearest shadow, conditional on relative congruence between shadow size and tree height. This required care in clumps of trees with varying heights (example in Supplementary Information sections 1.2–1.3). The relative patterning of field points compared with shadows and the lengths of shadows compared with tree heights in these cases provided some measure of confidence in attribution.We made field expeditions to six study areas within the extent of the WV imagery we used for digitizing, three within the ‘simulated population area’ rectangle in Extended Data Fig. 1a (red rectangle in Supplementary Figs. 1–4) and three study areas further east (Extended Data Fig. 1c and Supplementary Fig. 2). Among-area variability was apparent in snow depth, terrain slope relative to the solar azimuth at the time of image capture and the solar-elevation angle itself because of the timing of image capture. The variability was identified, calculated and applied on the basis of geographic variability in the heights of trees casting shadows and from the slope and intercept of a mixed-model linear regression of field-measured height on digitized shadow length (see below).Field surveysWe validated species and heights of spruce casting shadows within the AOI along 403 km of ground transects. Our sampling did not appear spatially biased when compared with imagery as measured by proximity to a remote fixed-wing-aircraft landing site. Four field campaigns focused on three objectives in watersheds that were within or adjacent to the Noatak basin but did not have established treelines visible on WV growing season scenes: (1) to locate and document colonists at the geographic range boundary of white spruce; (2) to verify the locations of a sample of trees suggested by imagery in the AOI; and (3) to collect ecological measurements germane to white spruce range expansion. For adults (trees ≥2.5 m), datasets included height above ground (n = 340), diameter at breast height (DBH (~1.4 m); n = 296), CAG (n = 17), foliar nutrient content (n = 17), basal increment cores taken ≤20 cm above the ground (n = 140), tall shrub abundance within 5 m of sampled adults (n = 246), counts of juveniles within 5 m of sampled adults (n = 250), abundance class of cones (n = 339) and status of adults (live, n = 340; dead, n = 8). Of the dead adults, seven of eight were standing and largely without bark, with a median height of 4.1 m. The fallen dead tree was 6.2 m long with a DBH of 13.4 cm; all bark and limbs to fine branches remained. Only one dead adult, 4.1 m tall with a DBH of 4 cm, showed signs of decomposition with shelf fungus on the stem and decomposed limbs on the ground. Five juveniles ≥1.5 m tall had been stripped of their bark and all but their uppermost branches by apparently either porcupine (Erethizon dorsatum) or snowshoe hare (Lepus americanus). Anecdotally, we recorded other signs and possible causes of damage such as wind, bear (Ursus arctos), caribou (Rangifer tarandus) or struggling growth such as layering, stunted krummholz or clonal reproduction, although these growth forms were nearly totally absent.Field measurements for n = 770 juveniles located in the AOI and presented here included overall height, height above ground of bud scars representing 2015–2020 height (n = 302), damage and status. We used these measures to estimate age to increment core of adults (Supplementary Information section 2) and the RGR of juveniles (Supplementary Information section 3).Range expansion analysesDigitized established treelines (DETs) used here were downloaded as CTM_Treeline.kml from https://arcticdata.io/catalog/view/doi:10.18739/A2280506H. Ref. 34 describes drawing DETs on very high-resolution satellite imagery such as WV and Quick Bird. We clipped DETs to the four USGS HUC 10 watersheds within the HUC 8 Middle Kobuk subbasin and adjacent to the AOI (see ‘Environmental conditions’ below). The coordinates of the vertices for the clipped DETs provided the 3,366 locations of established treelines.We used the rdist.earth() function in the R package fields59 to identify the nearest neighbouring mapped adult and juvenile colonists in the AOI and DET vertices in adjacent Kobuk watersheds (Supplementary Information sections 1.8 and 1.9). Using the coordinates of nearest neighbours, we calculated differences in latitude as latitudinal displacement. Displacement north equalled the product of latitudinal displacement and 111.32 km, the distance between 67º and 68º N along 157.6891º W, which splits the AOI. Displacement in elevation was found by extracting from Interferometric Synthetic Aperture Radar (IFSAR) Alaska 5-m digital elevation models (DEMs) the elevation of DET vertices, mapped adults and mapped juveniles using the extract() function in the raster R package60 and then subtracting the elevation of the nearest neighbours from focal adults and juveniles. When geolocated adults or juveniles had estimated establishment years (see ‘Individual growth’ below), we calculated movement rates as the difference between the establishment year of an aged tree and the establishment year of the oldest tree sampled (1901, year of founding) as the denominator and displacement (difference in metres above sea level, kilometres or degrees of latitude) as the numerator (Supplementary Information sections 1.19–1.21). To time the progression of spruce away from DETs, we also binned establishment year by decade as decadal class, identifying within each decadal class the maximum displacement in kilometres north of and elevation in metres above (or below) nearest neighbours.Population growthFrom the 5,986 spruce shadow lengths within the AOI (Extended Data Fig. 1b and Supplementary Fig. 1) that we digitized from snow-covered scenes of DigitalGlobe WV imagery (Extended Data Table 1), we identified a sample of shadows stratified by length and cast by spruce that we located with GNSS-equipped late-model iPhones. We measured the height of n = 260 trees using a laser range-finder (LTI TruPulse 200) and/or a smartphone app (Arboreal Tree on iPhone 12 Pro and Pro Max with laser scanners) and collected n = 122 basal cores from individuals ≥2.5 m in height, then matched to shadows on imagery as described above (see ‘Digitizing and field validation’). Using the relationship between height and shadow length and the probability distribution of establishment year for the 122 cored trees identified within five height classes (Extended Data Fig. 2b), we simulated population growth within two contiguous sub-watersheds (the 135 km2 ‘simulated population area’in Extended Data Fig. 1a; western portion in Extended Data Fig. 2a; red rectangles in Supplementary Figs. 1–4; details in Supplementary Information section 4). These sub-watersheds contained n = 1,971 shadows cast on 26 March 2018. We treated these shadows as single spruce but recognize that they include as many as 138 willows (7%) and calculate an additional 118 (6%) spruce missed either by digitizing omission or by collinearity (Supplementary Information sections 1.2 and 1.3). Incorporating these errors together would not change the outcome of the simulations enough to change the doubling time of the population by more than a few percent.Estimates of tree height from shadow lengthOn a flat landscape covered uniformly in snow, the total height H of a tree equals snow depth S added to the product of shadow length L on the snow surface and the tangent of solar-elevation angle 𝛼, as H = S + Ltan(𝛼). However, because both the relative solar elevation and snow depth vary with terrain, we used a linear mixed-effects model (lmer() in the lme4 R package61) of height on shadow length (random factor of sample area with six levels), interpreting the fixed-effects intercept as the average snow depth (mean ± s.e. = 2.84 ± 0.14 m, t = 20.29) and the regression coefficient as the average tangent of solar elevation relative to the terrain slope (0.27 ± 0.04 m m−1, t = 6.96; details in Supplementary Information sections 4.1 and 4.2).Using these fixed-effects estimates and the random-effects covariance matrix, we applied Monte Carlo sampling to estimate the 1,971 heights with each run of the simulation, thereby propagating the error in height estimates. These 1,971 heights were then binned into five height classes with 0.5-m intervals from 4–5.5 m and with ≥1-m intervals from 3–4 m and 5.5–7 m (details in Supplementary Information sections 4.3 and 4.4). Height classes deduced from the shadow measurements were in some cases only 0.5 m in width. Because the mean snow depth (the intercept in the mixed-effects model) differed by more than this from one part of the study area to another (BobWoods, GaiaHill and BuffaloDrifts in Supplementary Information sections 4.1 and 4.2), this approach may have introduced systematic misclassification between locations. While applying a Monte Carlo model with coefficients drawn randomly using the mvrnorm() function from the MASS package in R with the random-effects covariance matrix was meant to alleviate this, we also ran the simulation with three uniform height classes with a wider interval (1.3-m width, for classes of 3–4.3 m, 4.3–5.6 m and 5.6–7 m).Estimating population-scale establishment yearWe estimated establishment years for each of the 1,971 trees (Supplementary Information sections 4.3 and 4.4). We did so by using the establishment yeardistributions by height class as Gaussian kernel densities for the 122 aged adults binned into the five height classes defined above (Extended Data Fig. 2b). Kernel density estimates were constructed using the function density() in R with options bw = “SJ” as the smoothing bandwidth, n = 107 as the number of consecutive establishment years, from = 1897 as the earliest year and to = 2004 as the latest year. For each of the 1,971 estimated heights binned into height classes, an establishment year was drawn (with replacement) from the corresponding kernel density distribution. We interpreted the total number of individuals in each establishment year as ‘recruitment by year’ into the population of survivors that we had digitized on the 2018 imagery. Sorting and cumulatively summing recruitment by year gave what we interpreted as population size (N) for each year (t) for trees that survived to 2018. Resampling in this manner for 1,000 runs, each time fitting exponential growth equation N(t) = N0ek(t – 1900) using nls() in R and then averaging the population RGR, provided population doubling time as ln(2) divided by mean k. The simulation was run again using three height classes, each of 1.3 m in width. The resulting mean doubling time was unchanged, but variability increased (Supplementary Information section 4.6).Individual growthCurrent annual growth and foliar chemistryIn autumn 2019, we collected current-year lateral branch tips on the west and east sides of each sampled spruce (n1 = 17 adult colonists and n2 = 457 adults at established treelines) at 1.4 m above the ground. Current annual branch growth was measured on 2–6 branches per spruce from the previous year’s bud scar to the tip of the branch. The number of samples varied, ensuring sufficient mass for foliar chemical analysis. Established treelines were sampled for adult foliage in 12 watersheds of the Noatak, Kobuk and Koyukuk river basins where we have ongoing experiments. At these sites, we used a replicated nested plot-based design (Extended Data Table 3). Colonist foliage sample locations (n = 8) in the upper Noatak basin were widespread across three watersheds. At each location, except the upper Noatak where 1–3 spruce per location were sampled, we sampled n = 5 white spruce separated by ≥10  m at a DBH of 8–12 cm. Needles from each branch tip were pooled by individual, dried for 48 h at 60 °C and weighed. Needles of individuals were pooled by treeline location after grinding to powder using a steel ball mill grinder (Mini-Beadbeater, Biospec Products) and subsampled for chemical analysis. Foliar N and 15N isotope were analysed for one subsample run on an Elemental Combustion Analyzer (Costech, 4010) coupled to an isotope ratio mass spectrometer (Delta Plus XP, Thermo Fisher Scientific) at the University of Alaska Anchorage Environment and Natural Resources Institute Stable Isotope Laboratory. Foliar P was measured for another subsample by the Pennsylvania State College Analytical Services Lab using the acid digestion method and analysed by inductively coupled plasma emission spectroscopy62.Juvenile RGRSeveral results presented here depend on juvenile vertical height growth during 2015–2020, which we assumed followed h(t) = h2015e(RGR t), where h(t) is height above ground for year t after 2015, h2015 is the height above ground in 2015 and RGR is the relative growth rate (Supplementary Information section 3). We used juvenile RGR in three contexts: (1) as a means of estimating establishment year in juveniles (Supplementary Information section 3.3); (2) as a metric of growth for comparison between colonist and established treeline juveniles (Supplementary Information section 6); and (3) to estimate the establishment year of cored trees (see second paragraph in ‘Dendrochronology’ below and Supplementary Information section 2).To estimate the RGR for each of 505 juveniles (n1 = 300 juveniles from m1 = 4 colonist populations and n2 = 205 juveniles from m2 = 14 established treelines; Extended Data Table 2), we measured the heights above ground (h) of the six uppermost bud scars in 2020, representing height increments in 2016–2020, the five consecutive years with the warmest mean daily July air temperature on record for Kotzebue. RGR in each juvenile was calculated as the regression slope of ln(h(t)) against t (mean R2 = 0.99 for 300 colonist regressions and 0.98 for 271 established treeline regressions; Supplementary Information section 3.4).To estimate the establishment year of juveniles, we used RGR to back-calculate T, the years required for an individual colonist to grow from 2 cm to h2015, as T = ln(h2015/2)/RGR. By subtracting T from 2020, we estimated the establishment year of each juvenile (Supplementary Information section 3.3).RGR values for colonist and established treeline juveniles (Extended Data Table 2) were compared using a linear mixed-effects model with field site (m = 24) as a random intercept, ln(RGR) as the dependent variable, ln(h2015) as a covariate to capture allometric growth and population (colonist or established treeline) as the fixed factor of interest (Supplementary Information section 6). Using the lmer() function of the lme4 package61 in R with REML = F, we found that the Akaike information criterion (AIC) for the interaction model was lower than that for the corresponding additive one (∆AIC = 22, likelihood ratio test χ2 = 24, degrees of freedom = 1, P 1 km beyond the established treeline, we recorded the location, age classes and presence of cones when possible. In watersheds of the uppermost Noatak basin and the Wulik basin, we also recorded both the total height of juveniles and the height above ground of the sixth bud scar from the tip to estimate RGR and so estimate age. We encountered three watersheds with tree island krummholz >1 km beyond the treeline but do not include these as colonist populations because clonal growth can be very old9,10,11,12,13,14,15,16,17,18,19. Of the 34 watersheds in which we encountered colonist populations >1 km beyond established treelines, 4 watersheds were located between 141° and 149.7° W (eastern Brooks Range), 21 watersheds were located between 149.7° and 156.3° W (central Brooks Range) and 9 watersheds were located between 156.3° and 163.3° W (western Brooks Range). Watersheds west of 150.5° W with colonists are shown in Fig. 1a.In 2021, R.J.D. led a field expedition to a small watershed in the Koyukuk basin (Arrigetch Creek, 67.439° N, 154.090° W). The watershed had been purposefully surveyed for juvenile white spruce above and beyond the treeline during 1978–1980 when seven juveniles 11–112 cm tall (six seedlings More

Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40. https://doi.org/10.1126/science.281.5374.237CAS 
Article 
PubMed 

Google Scholar 
Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9. https://doi.org/10.1126/science.1153213CAS 
Article 
PubMed 

Google Scholar 
Gattuso J-P, Magnan A, Billé R, Cheung WWL, Howes EL, Joos F, et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science. 2015;349:aac4722. https://doi.org/10.1126/science.aac4722Steinacher M, Joos F, Frölicher TL, Bopp L, Cadule P, Cocco V, et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences. 2010;7:979–1005. https://doi.org/10.5194/bg-7-979-2010CAS 
Article 

Google Scholar 
Henson SA, Cael BB, Allen SR, Dutkiewicz S. Future phytoplankton diversity in a changing climate. Nat Commun. 2021;12:5372. https://doi.org/10.1038/s41467-021-25699-wCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thomas MK, Kremer CT, Klausmeier CA, Litchman E. A global pattern of thermal adaptation in marine phytoplankton. Science. 2012;338:1085–8. https://doi.org/10.1126/science.1224836CAS 
Article 
PubMed 

Google Scholar 
Collins S, Boyd PW, Doblin MA. Evolution, microbes, and changing ocean conditions. Annu Rev Mar Sci. 2020;12:181–208. https://doi.org/10.1146/annurev-marine-010318-095311Article 

Google Scholar 
Schaum CE, Buckling A, Smirnoff N, Studholme DJ, Yvon-Durocher G. Environmental fluctuations accelerate molecular evolution of thermal tolerance in a marine diatom. Nat Commun. 2018;9:1719. https://doi.org/10.1038/s41467-018-03906-5CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lohbeck KT, Riebesell U, Reusch TBH. Adaptive evolution of a key phytoplankton species to ocean acidification. Nat Geosci. 2012;5:346–51. https://doi.org/10.1038/ngeo1441CAS 
Article 

Google Scholar 
Jin P, Gao K, Beardall J. Evolutionary responses of a coccolithophorid Gephyrocapsa oceanica to ocean acidification. Evolution. 2013;67:1869–78. https://doi.org/10.1111/evo.12112CAS 
Article 
PubMed 

Google Scholar 
Schlüter L, Lohbeck KT, Gutowska MA, Gröger JP, Riebesell U, Reusch TBH. Adaptation of a globally important coccolithophore to ocean warming and acidification. Nat Clim Change. 2014;4:1024–30. https://doi.org/10.1038/nclimate2379CAS 
Article 

Google Scholar 
Listmann L, LeRoch M, Schlüter L, Thomas MK, Reusch TBH. Swift thermal reaction norm evolution in a key marine phytoplankton species. Evol Appl. 2016;9:1156–64. https://doi.org/10.1111/eva.12362Article 
PubMed 
PubMed Central 

Google Scholar 
Zhong J, Guo Y, Liang Z, Huang Q, Lu H, Pan J, et al. Adaptation of a marine diatom to ocean acidification and warming reveals constraints and trade-offs. Sci Total Environ. 2021;771:145167. https://doi.org/10.1016/j.scitotenv.2021.145167CAS 
Article 
PubMed 

Google Scholar 
Brennan GL, Colegrave N, Collins S. Evolutionary consequences of multidriver environmental change in an aquatic primary producer. Proc Natl Acad Sci USA. 2017;114:9930–5. https://doi.org/10.1073/pnas.1703375114CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang S, Wu Y, Lin L, Wang D. Molecular insights into the circadian clock in marine diatoms. Acta Oceano Sin. 2022;41:1–12. https://doi.org/10.1007/s13131-021-1962-4Article 

Google Scholar 
Nagelkerken I, Connell SD. Global alteration of ocean ecosystem functioning due to increasing human CO2 emissions. Proc Natl Acad Sci USA. 2015;112:13272–7. https://doi.org/10.1073/pnas.1510856112CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boyd PW, Collins S, Dupont S, Fabricius K, Gattuso JP, Havenhand J, et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-a review. Glob Change Biol. 2018;24:2239–61. https://doi.org/10.1111/gcb.14102Article 

Google Scholar 
Matsuda Y, Nakajima K, Tachibana M. Recent progresses on the genetic basis of the regulation of CO2 acquisition systems in response to CO2 concentration. Photosynth Res. 2011;109:191–203. https://doi.org/10.1007/s11120-011-9623-7CAS 
Article 
PubMed 

Google Scholar 
Ohno N, Inoue T, Yamashiki R, Nakajima K, Kitahara Y, Ishibashi M, et al. CO2-cAMP-responsive cis-elements targeted by a transcription factor with CREB/ATF-like basic zipper domain in the marine diatom Phaeodactylum tricornutum. Plant Physiol. 2012;158:499–513. https://doi.org/10.1104/pp.111.190249CAS 
Article 
PubMed 

Google Scholar 
Hennon GMM, Ashworth J, Groussman RD, Berthiaume C, Morales RL, Baliga NS, et al. Diatom acclimation to elevated CO2 via cAMP signalling and coordinated gene expression. Nat Clim Change. 2015;5:761–5. https://doi.org/10.1038/nclimate2683CAS 
Article 

Google Scholar 
Toseland A, Daines SJ, Clark JR, Kirkham A, Strauss J, Uhlig C, et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat Clim Change. 2013;3:979–84. https://doi.org/10.1038/nclimate1989CAS 
Article 

Google Scholar 
Gao K, Beardall J, Häder DP, Hall-Spencer JM, Gao G, Hutchins DA. Effects of ocean acidification on marine photosynthetic organisms under the concurrent influences of warming, UV radiation, and deoxygenation. Front Mar Sci. 2019;6:322. https://doi.org/10.3389/fmars.2019.00322Article 

Google Scholar 
Tu L, Su P, Zhang Z, Gao L, Wang J, Hu T, et al. Genome of Tripterygium wilfordii and identification of cytochrome P450 involved in triptolide biosynthesis. Nat Commun. 2020;11:971. https://doi.org/10.1038/s41467-020-14776-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Treves H, Siemiatkowska B, Luzarowska U, Murik O, Fernandez-Pozo N, Moraes TA, et al. Multi-omics reveals mechanisms of total resistance to extreme illumination of a desert alga. Nat Plants. 2020;6:1031–43. https://doi.org/10.1038/s41477-020-0729-9CAS 
Article 
PubMed 

Google Scholar 
Van den Bergh B, Swings T, Fauvart M, Michels J. Experimental design, population dynamics, and diversity in microbial experimental evolution. Microbiol Mol Biol Rev. 2018;82:e00008–18.PubMed 
PubMed Central 

Google Scholar 
Elena SF, Lenski RE. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet. 2003;4:457–69. https://doi.org/10.1038/nrg1088CAS 
Article 
PubMed 

Google Scholar 
Colegrave N, Collins S. Experimental evolution: experimental evolution and evolvability. Heredity. 2008;100:464–70. https://doi.org/10.1038/sj.hdy.6801095CAS 
Article 
PubMed 

Google Scholar 
Jin P, Ji Y, Huang Q, Li P, Pan J, Lu H, et al. A reduction in metabolism explains the trade‐offs associated with the long‐term adaptation of phytoplankton to high CO2 concentrations. N Phytol. 2022;233:2155–67. https://doi.org/10.1111/nph.17917CAS 
Article 

Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA. 2013;110:9824–9. https://doi.org/10.1073/pnas.1307701110CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hutchins DA, Walworth NG, Webb EA, Saito MA, Moran D, Mcllvin MR, et al. Irreversibly increased nitrogen fixation in Trichodesmium experimentally adapted to elevated carbon dioxide. Nat Commun. 2015;6:8155. https://doi.org/10.1038/ncomms9155Article 
PubMed 

Google Scholar 
Padfield D, Yvon-Durocher G, Buckling A, Jennings S, Yvon-Durocher G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol Lett. 2016;19:133–42.Article 

Google Scholar 
Coles VJ, Stukel MR, Brooks MT, Burd A, Crump BC, Moran MA, et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science. 2017;358:1149–54. https://doi.org/10.1126/science.aan5712CAS 
Article 
PubMed 

Google Scholar 
Linnen CR, Kingsley EP, Jensen JD, Hoekstra HE. On the origin and spread of an adaptive allele in deer mice. Science. 2009;325:1095–8. https://doi.org/10.1126/science.1175826CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van’t Hof AE, Campagne P, Rigden DJ, Yung CJ, Lingley J, Quail MA, et al. The industrial melanism mutation in British peppered moths is a transposable element. Nature. 2016;534:102–5. https://doi.org/10.1038/nature17951CAS 
Article 
PubMed 

Google Scholar 
Bitter MC, Kapsenberg L, Gattuso JP, Pfister CA. Standing genetic variation fuels rapid adaptation to ocean acidification. Nat Commun. 2019;10:5821. https://doi.org/10.1038/s41467-019-13767-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lai YT, Yeung CK, Omland KE, Pang EL, Hao Y, Liao BY, et al. Standing genetic variation as the predominant source for adaptation of a songbird. Proc Natl Acad Sci USA. 2019;116:2152–7. https://doi.org/10.1073/pnas.1813597116Armbrust EV. The life of diatoms in the world’s oceans. Nature. 2009;459:185–92. https://doi.org/10.1038/nature08057CAS 
Article 
PubMed 

Google Scholar 
Rastogi A, Vieira FRJ, Deton-Cabanillas AF, Veluchamy A, Cantrel C, Wang G, et al. A genomics approach reveals the global genetic polymorphism, structure, and functional diversity of ten accessions of the marine model diatom Phaeodactylum tricornutum. ISME J. 2020;14:347–63. https://doi.org/10.1038/s41396-019-0528-3Article 
PubMed 

Google Scholar 
Jin P, Agustí S. Fast adaptation of tropical diatoms to increased warming with trade-offs. Sci Rep. 2018;8:17771. https://doi.org/10.1038/s41598-018-36091-yCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barton S, Jenkins J, Buckling A, Schaum CE, Smirnoff N, Raven JA, et al. Evolutionary temperature compensation of carbon fixation in marine phytoplankton. Ecol Lett. 2020;23:722–33.Article 

Google Scholar 
Guillard RR, Ryther JH. Studies of marine planktonic diatoms: I. Cyclotella nana Hustedt, and Detonula confervacea (Cleve) Gran. Can J Microbiol. 1962;8:229–39. https://doi.org/10.1139/m62-029CAS 
Article 
PubMed 

Google Scholar 
Huysman MJ, Martens C, Vandepoele K, Gillard J, Rayko E, Heijde M, et al. Genome-wide analysis of the diatom cell cycle unveils a novel type of cyclins involved in environmental signaling. Genome Biol. 2010;11:R17. https://doi.org/10.1186/gb-2010-11-2-r17CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
IPCC. Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, et al. editors. Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Switzerland: IPCC; 2021.Jiang H, Gao K. Effects of lowering temperature during culture on the production of polyunsaturated fatty acids in the marine diatom Phaeodactylum tricornutum (Bacillariophyceae). J Phycol. 2004;40:651–4. https://doi.org/10.1111/j.1529-8817.2004.03112.xCAS 
Article 

Google Scholar 
Pérez EB, Pina IC, Rodríguez LP. Kinetic model for growth of Phaeodactylum tricornutum in intensive culture photobioreactor. Biochem Eng J. 2008;40:520–5. https://doi.org/10.1016/j.bej.2008.02.007CAS 
Article 

Google Scholar 
Boyd PW, Rynearson TA, Armstrong EA, Fu F, Hayashi K, Hu Z, et al. Marine phytoplankton temperature versus growth responses from polar to tropical waters-outcome of a scientific community-wide study. PLoS One. 2013;8:e63091 https://doi.org/10.1371/journal.pone.0063091CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zeng X, Jin P, Jiang Y, Yang H, Zhong J, Liang Z, et al. Light alters the responses of two marine diatoms to increased warming. Mar Environ Res. 2020;154:104871. https://doi.org/10.1016/j.marenvres.2019.104871CAS 
Article 
PubMed 

Google Scholar 
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–i890. https://doi.org/10.1093/bioinformatics/bty560CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008;456:239–44.CAS 
Article 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60. https://doi.org/10.1093/bioinformatics/btp324CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164. https://doi.org/10.1093/nar/gkq603CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. https://doi.org/10.1038/nmeth.1923CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60. https://doi.org/10.1038/nmeth.3317CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5. https://doi.org/10.1038/nbt.3122CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11:1650–67. https://doi.org/10.1038/nprot.2016.095CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gifford RM. Plant respiration in productivity models: conceptualisation, representation and issues for global terrestrial carbon-cycle research. Funct Plant Biol. 2003;30:171–86. https://doi.org/10.1071/FP02083Article 
PubMed 

Google Scholar 
Jassby AD, Platt T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol Oceanogr. 1976;21:540–7. https://doi.org/10.4319/lo.1976.21.4.0540CAS 
Article 

Google Scholar  More

Arias PA, Bellouin N, Coppola E, Jones RG, Krinner G, Marotzke J, et al. Technical Summary. In Climate Change 2021: The Physical Science Basis, the Working Group I contribution to the Sixth Assessment Report. Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021. 42–4.Donhauser J, Frey B. Alpine soil microbial ecology in a changing world. FEMS Microbiol Ecol. 2018;94:1–31.Article 
CAS 

Google Scholar 
Bradley JA, Singarayer JS, Anesio AM. Microbial community dynamics in the forefield of glaciers. Proc R Soc B. 2014; 281.Hood E, Battin TJ, Fellman J, O’neel S, Spencer RGM. Storage and release of organic carbon from glaciers and ice sheets. Nat Geosci. 2015;8:91–96.CAS 
Article 

Google Scholar 
Harden JW, Mark RK, Sundquist ET, Stallard RF. Dynamics of Soil Carbon During Deglaciation of the Laurentide Ice Sheet. Science. 1992;258:1921–4.CAS 
PubMed 
Article 

Google Scholar 
Egli M, Favilli F, Krebs R, Pichler B, Dahms D. Soil organic carbon and nitrogen accumulation rates in cold and alpine environments over 1 Ma. Geoderma. 2012;183-4:109–23.Article 
CAS 

Google Scholar 
Khedim N, Cécillon L, Poulenard J, Barré P, Baudin F, Marta S, et al. Topsoil organic matter build-up in glacier forelands around the world. Glob Chang. Biol. 2021;27:1662–77.
Google Scholar 
Amico MED, Freppaz M, Filippa G, Zanini E. Vegetation in fluence on soil formation rate in a proglacial chronosequence (Lys Glacier, NW Italian Alps). Catena. 2014;113:122–37.Article 
CAS 

Google Scholar 
Mateos-Rivera A, Yde JC, Wilson B, Finster KW, Reigstad LJ, Øvreås L The effect of temperature change on the microbial diversity and community structure along the chronosequence of the sub-arctic glacier forefield of Styggedalsbreen (Norway). FEMS Microbiol Ecol. 2016; 92. https://doi.org/10.1093/femsec/fiw038.Vilmundardóttir OK, Gísladóttir G, Lal R. Soil carbon accretion along an age chronosequence formed by the retreat of the Skaftafellsjökull glacier. SE-Iceland. Geomorphology. 2015;228:124–33.Article 

Google Scholar 
Strauss SL, Ruhland CT, Day TA. Trends in soil characteristics along a recently deglaciated foreland on Anvers Island, Antarctic Peninsula. Polar Biol. 2009;32:1779–88.Article 

Google Scholar 
Kabala C, Zapart J. Initial soil development and carbon accumulation on moraines of the rapidly retreating Werenskiold Glacier, SW Spitsbergen, Svalbard archipelago. Geoderma. 2012;175-6:9–20.Article 
CAS 

Google Scholar 
Fernández-martínez MA, Pointing SB, Pérez-ortega S, Arróniz-crespo M, Green TGA, Rozzi R, et al. Functional ecology of soil microbial communities along a glacier forefield in Tierra del Fuego (Chile). Int Microbiol. 2016;19:161–73.PubMed 

Google Scholar 
Kazemi S, Hatam I, Lanoil B. Bacterial community succession in a high-altitude subarctic glacier foreland is a three-stage process. Mol Ecol. 2016;25:5557–67.CAS 
PubMed 
Article 

Google Scholar 
He L, Tang Y. Soil development along primary succession sequences on moraines of Hailuogou Glacier, Gongga Mountain, Sichuan, China. Catena. 2008;72:259–69.Article 

Google Scholar 
Zhou J, Bing HJ, Wu YH, Yang ZJ, Wang JP, Sun HY, et al. Rapid weathering processes of a 120-year-old chronosequence in the Hailuogou Glacier foreland, Mt. Gongga, SW China Jun. Geoderma. 2016;267:78–91.CAS 
Article 

Google Scholar 
Zeng J, Lou K, Zhang CJ, Wang JT, Hu HW, Shen JP, et al. Primary succession of nitrogen cycling microbial communities along the deglaciated forelands of Tianshan Mountain, China. Front Microbiol. 2016; 7. https://doi.org/10.3389/fmicb.2016.01353.Wei TF, Shangguan DH, Yi SH, Ding YJ. Characteristics and controls of vegetation and diversity changes monitored with an unmanned aerial vehicle (UAV) in the foreland of the Urumqi Glacier No. 1, Tianshan, China. Sci Total Environ. 2021;771:145433.CAS 
PubMed 
Article 

Google Scholar 
Huang MH, Shi YF. Progress in the study on basic features of glaciers in China in the last thirty years. J Glaciol Geocryol. 1988;10:228–37.
Google Scholar 
Xu XK, Pan BL, Hu E, Li YJ, Liang YH. Responses of two branches of Glacier No. 1 to climate change from 1993 to 2005, Tianshan, China. Quat Int. 2011;236:143–50.Article 

Google Scholar 
Liu YS, Qin X, Chen JZ, Li ZL, Wang J, Du WT, et al. Variations of Laohugou Glacier No. 12 in the western Qilian Mountains, China, from 1957 to 2015. J Mt Sci. 2018;15:25–32.Article 

Google Scholar 
Schulz S, Brankatschk R, Dümig A, Kögel-Knabner I, Schloter M, Zeyer J. The role of microorganisms at different stages of ecosystem development for soil formation. Biogeosciences. 2013;10:3983–96.Article 

Google Scholar 
Odum EP. The strategy of ecosystem development. Science. 1969;164:262–70.CAS 
PubMed 
Article 

Google Scholar 
Schmidt SK, Reed SC, Nemergut DR, Grandy AS, Cleveland CC, Weintraub MN, et al. The earliest stages of ecosystem succession in high-elevation (5000 metres above sea level), recently deglaciated soils. Proc R Soc B. 2008;275:2793–802.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Knelman JE, Legg TM, O’Neill SP, Washenberger CL, González A, Cleveland CC, et al. Bacterial community structure and function change in association with colonizer plants during early primary succession in a glacier forefield. Soil Biol Biochem. 2012;46:172–80.CAS 
Article 

Google Scholar 
Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 
Article 

Google Scholar 
Chen H, Wang F, Kong WD, Jia HZ, Zhou TQ, Xu R, et al. Soil microbial CO2 fixation plays a significant role in terrestrial carbon sink in a dryland ecosystem: A four-year small-scale field-plot observation on the Tibetan Plateau. Sci Total Environ. 2021; 761. https://doi.org/10.1016/j.scitotenv.2020.143282.Bond-lamberty B, Wang CK, Gower ST. A global relationship between the heterotrophic and autotrophic components of soil respiration? Gloal Chang. Biol. 2004;10:1756–66.
Google Scholar 
Barnett SE, Youngblut ND, Koechli CN, Buckley DH. Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling. Proc Natl Acad Sci USA. 2021; 118. https://doi.org/10.1073/pnas.2115292118/-/DCSupplemental.Published.Margesin R, Jud M, Tscherko D, Schinner F. Microbial communities and activities in alpine and subalpine soils. FEMS Microbiol Ecol. 2009;67:208–18.CAS 
PubMed 
Article 

Google Scholar 
Zhou ZH, Wang CK, Luo YQ. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat Commun. 2020;11:3072.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guelland K, Hagedorn F, Smittenberg RH, Göransson H, Bernasconi SM, Hajdas I, et al. Evolution of carbon fluxes during initial soil formation along the forefield of Damma glacier, Switzerland. Biogeochemistry. 2013;113:545–61.CAS 
Article 

Google Scholar 
Chen QL, Ding J, Li CY, Yan ZZ, He JZ, Hu HW. Microbial functional attributes, rather than taxonomic attributes, drive top soil respiration, nitrification and denitrification processes. Sci Total Environ. 2020;734:139479.CAS 
PubMed 
Article 

Google Scholar 
Zheng BX, Zhu YG, Sardans J, Peñuelas J, Su JQ. QMEC: a tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling. Sci China (Life Sciences). 2018;61:1451–62.CAS 
Article 

Google Scholar 
Fan KK, Delgado-Baquerizo M, Guo XS, Wang DZ, Zhu YG, Chu HY. Biodiversity of key-stone phylotypes determines crop production in a 4-decade fertilization experiment. ISME J. 2021;15:550–61.CAS 
PubMed 
Article 

Google Scholar 
Zhou JZ, Xue K, Xie JP, Deng Y, Wu LY, Cheng XH, et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Chang. 2012;2:106–10.CAS 
Article 

Google Scholar 
Chen JZ, Qin X, Kang SC, Du WT, Sun WJ, Liu YS. Potential effect of black carbon on glacier mass balance during the past 55 years of Laohugou Glacier No. 12, western Qilian Mountains. J Earth Sci. 2020;31:410–8.CAS 
Article 

Google Scholar 
Zhang LN, Jiang Y, Zhao SD, Jiao L, Wen Y. Relationships between tree age and climate sensitivity of radial growth in different drought conditions of Qilian Mountains, northwestern China. Forests. 2018; 9. https://doi.org/10.3390/f9030135.Sun WJ, Qin X, Ren JW, Yang XG, Zhang T, Liu YS, et al. The surface energy budget in the accumulation zone of the laohugou glacier No. 12 in the western Qilian mountains, China, in summer 2009. Arctic, Antarct Alp Res. 2012;44:296–305.Article 

Google Scholar 
Wang YW, Ma AZ, Liu GH, Ma JP, Wei J, Zhou HC, et al. Potential feedback mediated by soil microbiome response to warming in a glacier forefield. Glob Chang Biol. 2020;26:697–708.PubMed 
Article 

Google Scholar 
Harris D, Horwa WR, Van Kessel C. Acid fumigation of soils to remove carbonates prior to total organic carbon or carbon-13 isotopic analysis. Soil Sci Soc Am J.2001;65:1853–6.CAS 
Article 

Google Scholar 
Zhou HC, Ma AZ, Liu GH, Zhou XR, Yin J, Liang Y, et al. Reduced interactivity during microbial community degradation leads to the extinction of Tricholomas matsutake. L Degrad Dev. 2021;32:5118–28.Article 

Google Scholar 
Frey B, Rime T, Phillips M, Stierli B, Hajdas I, Widmer F, et al. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiol Ecol. 2016;92:fiw018.PubMed 
Article 
CAS 

Google Scholar 
Feng K, Zhang ZJ, Cai WW, Liu WZ, Xu MY, Yin HQ, et al. Biodiversity and species competition regulate the resilience of microbial biofilm community. Mol Ecol. 2017;26:6170–82.PubMed 
Article 

Google Scholar 
McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
PubMed 
Article 

Google Scholar 
Mackelprang R, Burkert A, Haw M, Mahendrarajah T, Conaway CH, Douglas TA, et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 2017;11:2305–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang YW, Ma AZ, Zhong GS, Xie F, Zhou HC, Liu GH, et al. Effect of Simulated Warming on Microbial Community in Glacier Forefield. Environ Sci. 2020;41:2918–23.
Google Scholar 
Lei YB, Zhou J, Xiao HF, Duan BL, Wu YH, Korpelainen H, et al. Soil nematode assemblages as bioindicators of primary succession along a 120-year-old chronosequence on the Hailuogou Glacier forefield, SW China. Soil Biol Biochem. 2015;88:362–71.CAS 
Article 

Google Scholar 
Sigler WV, Crivii S, Zeyer J. Bacterial succession in glacial forefield soils characterized by community structure, activity and opportunistic growth dynamics. Microb Ecol. 2002;44:306–16.CAS 
PubMed 
Article 

Google Scholar 
Hu WM, Schmidt SK, Sommers P, Darcy JL, Porazinska DL. Multiple-trophic patterns of primary succession following retreat of a high-elevation glacier. Ecosphere. 2021; 12. https://doi.org/10.1002/ecs2.3400.Nemergut DR, Anderson SP, Cleveland CC, Martin AP, Miller AE, Seimon A, et al. Microbial community succession in an unvegetated, recently deglaciated soil. Microb Ecol. 2007;53:110–22.PubMed 
Article 

Google Scholar 
Whelan P, Bach AJ. Retreating glaciers, incipient soils, emerging forests: 100 years of landscape change on Mount Baker, Washington, USA. Ann Am Assoc Geogr. 2017;107:336–49.
Google Scholar 
Cleveland CC, Liptzin ÆD. C:N:P stoichiometry in soil: is there a ‘Redfield ratio’ for the microbial biomass? Biogeochemistry. 2007;85:235–52.Article 

Google Scholar 
Manzoni S, Taylor P, Richter A, Porporato A, Ågren GI. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 2012;196:79–91.CAS 
PubMed 
Article 

Google Scholar 
Tian J, Zong N, Hartley IP, He NP, Zhang JJ, Powlson D, et al. Microbial metabolic response to winter warming stabilizes soil carbon. Gloal Chang Biol. 2021;27:2011–28.CAS 
Article 

Google Scholar 
Zhu XF, Liang C, Masters MD, Kantola IB, DeLucia EH. The impacts of four potential bioenergy crops on soil carbon dynamics as shown by biomarker analyses and DRIFT spectroscopy. Glob Chang Biol Bioenergy. 2018;10:489–500.CAS 
Article 

Google Scholar 
Evans MCW, Buchanan BB, Arnon DI. A new ferredoxin-dependent carbon reduction cycle in a photosynthetic bacterium. Biochemistry. 1966;55:928–34.CAS 

Google Scholar 
Menendez C, Bauer Z, Huber H, Gad’on N, Stetter K, Fuchs G. Presence of acetyl coenzyme A (CoA) carboxylase and propionyl-CoA carboxylase in autotrophic crenarchaeota and indication for operation of a 3-hydroxypropionate cycle in autotrophic carbon fixation. J Bacteriol. 1999;181:1088–98.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li Y, Cha QQ, Dang YR, Chen XL, Wang M, Mcminn A, et al. Reconstruction of the functional ecosystem in the high light, low temperature union glacier region, Antarctica. Front Microbiol. 2019;10:1–14.Article 

Google Scholar 
Lazzaro A, Hilfiker D, Zeyer J. Structures of microbial communities in alpine soils: Seasonal and elevational effects. Front Microbiol. 2015; 6. https://doi.org/10.3389/fmicb.2015.01330.Aylward FO, McDonald BR, Adams SM, Valenzuela A, Schmidt RA, Goodwin LA, et al. Comparison of 26 sphingomonad genomes reveals diverse environmental adaptations and biodegradative capabilities. Appl Environ Microbiol. 2013;79:3724–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bardgett RD, Freeman C, Ostle NJ. Microbial contributions to climate change through carbon cycle feedbacks. ISME J. 2008;2:805–14.CAS 
PubMed 
Article 

Google Scholar 
Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.CAS 
PubMed 
Article 

Google Scholar 
Schäfer A, Konrad R, Kuhnigk T, Kämpfer P, Hertel H, König H. Hemicellulose-degrading bacteria and yeasts from the termite gut. J Appl Bacteriol. 1996;80:471–8.PubMed 
Article 

Google Scholar 
Lange M, Roth V-N, Nico E, Roscher C, Thorsten D, Fischer-bedtke C, et al. Plant diversity enhances production and downward transport of biodegradable dissolved organic matter. J Ecol. 2021;109:1284–97.CAS 
Article 

Google Scholar 
Ho A, Di Lonardo DP, Bodelier PLE. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol. 2017;93:1–14.CAS 

Google Scholar 
Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
Article 

Google Scholar 
Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol. 2020;18:35–46.CAS 
PubMed 
Article 

Google Scholar 
Yan BS, Sun LP, Li JJ, Liang CQ, Wei FR, Xue S, et al. Change in composition and potential functional genes of soil bacterial and fungal communities with secondary succession in Quercus liaotungensis forests of the Loess Plateau, western China. Geoderma. 2020;364:114199.CAS 
Article 

Google Scholar 
Wu MH, Chen SY, Chen JW, Xue K, Chen SL, Wang XM, et al. Reduced microbial stability in the active layer is associated with carbon loss under alpine permafrost degradation. Proc Natl Acad Sci USA. 2021;118:1–9.
Google Scholar  More

van Elsas JD, Chiurazzi M, Mallon CA, Elhottovā D, Krištůfek V, Salles JF. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA 2012;109:1159–64.PubMed 
PubMed Central 
Article 

Google Scholar 
Bardgett RD, Van Der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505–11.CAS 
PubMed 
Article 

Google Scholar 
George PB, Lallias D, Creer S, Seaton FM, Kenny JG, Eccles RM, et al. Divergent national-scale trends of microbial and animal biodiversity revealed across diverse temperate soil ecosystems. Nat Commun. 2019;10:1–11.Article 
CAS 

Google Scholar 
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.PubMed 
Article 

Google Scholar 
Xiao E, Ning Z, Xiao T, Sun W, Jiang S. Soil bacterial community functions and distribution after mining disturbance. Soil Biol Biochem. 2021;157:108232.CAS 
Article 

Google Scholar 
Jiao S, Zhang Z, Yang F, Lin Y, Chen W, Wei G. Temporal dynamics of microbial communities in microcosms in response to pollutants. Mol Ecol. 2017;26:923–36.CAS 
PubMed 
Article 

Google Scholar 
Fajardo C, Costa G, Nande M, Botías P, García-Cantalejo J, Martín M. Pb, Cd, and Zn soil contamination: monitoring functional and structural impacts on the microbiome. Appl Soil Ecol. 2019;135:56–64.Article 

Google Scholar 
Krabbenhoft DP, Sunderland EM. Global change and mercury. Science. 2013;341:1457–8.CAS 
PubMed 
Article 

Google Scholar 
Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio. 2018;47:116–40.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Amos HM, Jacob DJ, Streets DG, Sunderland EM. Legacy impacts of all-time anthropogenic emissions on the global mercury cycle. Global Biogeochem Cycles. 2013;27:410–21.CAS 
Article 

Google Scholar 
Zhang L, Wong MH. Environmental mercury contamination in China: sources and impacts. Environ Int. 2007;33:108–21.CAS 
PubMed 
Article 

Google Scholar 
Müller AK, Westergaard K, Christensen S, Sørensen SJ. The effect of long-term mercury pollution on the soil microbial community. FEMS Microbiol Ecol. 2001;36:11–9.PubMed 
Article 

Google Scholar 
Liu YR, Wang JJ, Zheng YM, Zhang LM, He JZ. Patterns of bacterial diversity along a long-term mercury-contaminated gradient in the paddy soils. Microb Ecol. 2014;68:575–83.CAS 
PubMed 
Article 

Google Scholar 
Liu YR, Delgado-Baquerizo M, Bi L, Zhu J, He JZ. Consistent responses of soil microbial taxonomic and functional attributes to mercury pollution across China. Microbiome. 2018;6:183.PubMed 
PubMed Central 
Article 

Google Scholar 
Li D, Li X, Tao Y, Yan Z, Ao Y. Deciphering the bacterial microbiome in response to long-term mercury contaminated soil. Ecotoxicol Environ Saf. 2022;229:113062.CAS 
PubMed 
Article 

Google Scholar 
Zappelini C, Karimi B, Foulon J, Lacercat-Didier L, Maillard F, Valot B, et al. Diversity and complexity of microbial communities from a chlor-alkali tailings dump. Soil Biol Biochem. 2015;90:101–10.CAS 
Article 

Google Scholar 
Baldrian P, in der Wiesche C, Gabriel J, Nerud F, Zadražil F. Influence of cadmium and mercury on activities of ligninolytic enzymes and degradation of polycyclic aromatic hydrocarbons by Pleurotus ostreatus in soil. Appl Environ Microbiol. 2000;66:2471–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crane S, Dighton J, Barkay T. Growth responses to and accumulation of mercury by ectomycorrhizal fungi. Fungal Biol. 2010;114:873–80.CAS 
PubMed 
Article 

Google Scholar 
Johansen JL, Rønn R, Ekelund F. Toxicity of cadmium and zinc to small soil protists. Environ Pollut. 2018;242:1510–7.CAS 
PubMed 
Article 

Google Scholar 
Wanner M, Birkhofer K, Fischer T, Shimizu M, Shimano S, Puppe D. Soil testate amoebae and diatoms as bioindicators of an old heavy metal contaminated floodplain in Japan. Microb Ecol. 2020;79:123–33.CAS 
PubMed 
Article 

Google Scholar 
Zhou Y, Sun B, Xie B, Feng K, Zhang Z, Zhang Z, et al. Warming reshaped the microbial hierarchical interactions. Glob Chang Biol. 2021;27:6331–47.CAS 
PubMed 
Article 

Google Scholar 
Zhao ZB, He JZ, Geisen S, Han LL, Wang JT, Shen JP, et al. Protist communities are more sensitive to nitrogen fertilization than other microorganisms in diverse agricultural soils. Microbiome. 2019;7:33.PubMed 
PubMed Central 
Article 

Google Scholar 
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;42:293–323.CAS 
PubMed 
Article 

Google Scholar 
Jiang Y, Luan L, Hu K, Liu M, Chen Z, Geisen S, et al. Trophic interactions as determinants of the arbuscular mycorrhizal fungal community with cascading plant-promoting consequences. Microbiome. 2020;8:1–14.CAS 
Article 

Google Scholar 
Huang X, Wang J, Dumack K, Liu W, Zhang Q, He Y, et al. Protists modulate fungal community assembly in paddy soils across climatic zones at the continental scale. Soil Biol Biochem. 2021;160:108358.CAS 
Article 

Google Scholar 
Grossmann L, Jensen M, Heider D, Jost S, Glücksman E, Hartikainen H, et al. Protistan community analysis: key findings of a large-scale molecular sampling. ISME J. 2016;10:2269–79.PubMed 
PubMed Central 
Article 

Google Scholar 
Jassey VE, Signarbieux C, Hättenschwiler S, Bragazza L, Buttler A, Delarue F, et al. An unexpected role for mixotrophs in the response of peatland carbon cycling to climate warming. Sci Rep. 2015;5:1–10.Article 
CAS 

Google Scholar 
Thakur MP, Geisen S. Trophic regulations of the soil microbiome. Trends Microbiol. 2019;27:771–80.CAS 
PubMed 
Article 

Google Scholar 
Geisen S, Hu S, Dela Cruz TEE, Veen GFC. Protists as catalyzers of microbial litter breakdown and carbon cycling at different temperature regimes. ISME J. 2021;15:618–21.CAS 
PubMed 
Article 

Google Scholar 
Guo S, Xiong W, Hang X, Gao Z, Jiao Z, Liu H, et al. Protists as main indicators and determinants of plant performance. Microbiome. 2021;9:64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feng X, Li P, Qiu G, Wang S, Li G, Shang L, et al. Human exposure to methylmercury through rice intake in mercury mining areas, Guizhou Province, China. Environ Sci Technol. 2008;42:326–32.CAS 
PubMed 
Article 

Google Scholar 
Meng M, Li B, Shao JJ, Wang T, He B, Shi JB, et al. Accumulation of total mercury and methylmercury in rice plants collected from different mining areas in China. Environ Pollut. 2014;184:179–86.CAS 
PubMed 
Article 

Google Scholar 
Liu YR, Dong JX, Zhang QG, Wang JT, Han LL, Zeng J, et al. Longitudinal occurrence of methylmercury in terrestrial ecosystems of the Tibetan Plateau. Environ Pollut. 2016;218:1342–9.CAS 
PubMed 
Article 

Google Scholar 
Walkley A, Black IA. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 1934;37:29–38.CAS 
Article 

Google Scholar 
Jones D, Willett V. Experimental evaluation of methods to quantify dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) in soil. Soil Biol Biochem. 2006;38:991–9.CAS 
Article 

Google Scholar 
Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:1–8.Article 
CAS 

Google Scholar 
Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
PubMed 
Article 

Google Scholar 
Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
PubMed 
Article 

Google Scholar 
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D6.CAS 
PubMed 
Article 

Google Scholar 
Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D64.CAS 
PubMed 
Article 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–604.CAS 
PubMed 
Article 

Google Scholar 
Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finland MotE: Government decree on the assessment of soil contamination and remediation needs (214/2007). In.: Ministry of the Environment Helsinki (FI); 2007.Carlon C. Derivation methods of soil screening values in europe: A review of national procedures towards harmonisation: A report of the ENSURE action. EUR-OP. 2007.Toth G, Hermann T, Da Silva MR, Montanarella L. Heavy metals in agricultural soils of the European Union with implications for food safety. Environ Int. 2016;88:299–309.CAS 
PubMed 
Article 

Google Scholar 
De Caceres M, Jansen F. Relationship between species and groups of sites. Package ‘indicspecies’, version 1.7.6. 2016.Frossard A, Donhauser J, Mestrot A, Gygax S, Bååth E, Frey B. Long-and short-term effects of mercury pollution on the soil microbiome. Soil Biol Biochem. 2018;120:191–9.CAS 
Article 

Google Scholar 
Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46:1–17.Article 

Google Scholar 
Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J. Application of random matrix theory to biological networks. Phys Lett A. 2006;357:420–3.CAS 
Article 

Google Scholar 
Deng Y, Jiang YH, Yang YF, He ZL, Luo F, Zhou JZ. Molecular ecological network analyses. BMC Bioinform. 2012;13:1–20.Article 

Google Scholar 
Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.Article 

Google Scholar 
Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media. 2009;3:361–2.Csardi G, Nepusz T. The igraph software package for complex network research. InterJ Complex Syst. 2006;1695:1–9.
Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara R, et al. Vegan: community ecology package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists. R Package Ver. 2015;2:3–1.
Google Scholar 
Chen B, Xiong W, Qi J, Pan H, Chen S, Peng Z, et al. Trophic interrelationships drive the biogeography of protistan community in agricultural ecosystems. Soil Biol Biochem. 2021;163:108445.CAS 
Article 

Google Scholar 
Jiao S, Lu Y, Wei G. Soil multitrophic network complexity enhances the link between biodiversity and multifunctionality in agricultural systems. Glob Chang Biol. 2022;28:140–53.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 
Article 
CAS 

Google Scholar 
Revelle WR. psych: Procedures for personality and psychological research. 2017.Archer E. rfPermute: estimate permutation p-values for random forest importance metrics. R package version. 2016;1(2).Wang JT, Zheng YM, Hu HW, Li J, Zhang LM, Chen BD, et al. Coupling of soil prokaryotic diversity and plant diversity across latitudinal forest ecosystems. Sci Rep. 2016;6:1–7.Article 
CAS 

Google Scholar 
Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods Psychol Res Online. 2003;8:23–74.
Google Scholar 
Zinger L, Taberlet P, Schimann H, Bonin A, Boyer F, De Barba M, et al. Body size determines soil community assembly in a tropical forest. Mol Ecol. 2019;28:528–43.CAS 
PubMed 
Article 

Google Scholar 
Stefan G, Cornelia B, Jörg R, Michael B. Soil water availability strongly alters the community composition of soil protists. Pedobiologia. 2014;57:205–13.Article 

Google Scholar 
Luan L, Jiang Y, Cheng M, Dini-Andreote F, Sui Y, Xu Q, et al. Organism body size structures the soil microbial and nematode community assembly at a continental and global scale. Nat Commun. 2020;11:6406.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qi Q, Hu C, Lin J, Wang X, Tang C, Dai Z, et al. Contamination with multiple heavy metals decreases microbial diversity and favors generalists as the keystones in microbial occurrence networks. Environ Pollut. 2022;306:119406.CAS 
PubMed 
Article 

Google Scholar 
Wu W, Lu HP, Sastri A, Yeh YC, Gong GC, Chou WC, et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 2018;12:485–94.PubMed 
Article 

Google Scholar 
Villarino E, Watson JR, Jönsson B, Gasol JM, Salazar G, Acinas SG, et al. Large-scale ocean connectivity and planktonic body size. Nat Commun. 2018;9:1–13.CAS 
Article 

Google Scholar 
Mitsch WJ, Gosselink JG Wetlands. John Wiley & Sons; 2015.Margesin R, Feller G, Gerday C, Russell N. The Encyclopedia of Environmental Microbiology. 2002;2.Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.CAS 
PubMed 
Article 

Google Scholar 
Hall B, St Louis V, Rolfhus K, Bodaly R, Beaty K, Paterson M, et al. Impacts of reservoir creation on the biogeochemical cycling of methyl mercury and total mercury in boreal upland forests. Ecosystems. 2005;8:248–66.CAS 
Article 

Google Scholar 
Clarholm M. Protozoan grazing of bacteria in soil-impact and importance. Microb Ecol. 1981;7:343–50.CAS 
PubMed 
Article 

Google Scholar 
Asiloglu R, Shiroishi K, Suzuki K, Turgay OC, Harada N. Soil properties have more significant effects on the community composition of protists than the rhizosphere effect of rice plants in alkaline paddy field soils. Soil Biol Biochem. 2021;161:108397.CAS 
Article 

Google Scholar 
Asiloglu R, Kenya K, Samuel SO, Sevilir B, Murase J, Suzuki K, et al. Top-down effects of protists are greater than bottom-up effects of fertilisers on the formation of bacterial communities in a paddy field soil. Soil Biol Biochem. 2021;156:108186.CAS 
Article 

Google Scholar 
Nguyen BAT, Chen QL, He JZ, Hu HW. Livestock manure spiked with the antibiotic tylosin significantly altered soil protist functional groups. J Hazard Mater. 2021;427:127867.Nguyen BAT, Chen QL, He JZ, Hu HW. Oxytetracycline and ciprofloxacin exposure altered the composition of protistan consumers in an agricultural soil. Environ Sci Technol. 2020;54:9556–63.CAS 
PubMed 
Article 

Google Scholar 
Nguyen BAT, Chen QL, Yan ZZ, Li CY, He JZ, Hu HW. Distinct factors drive the diversity and composition of protistan consumers and phototrophs in natural soil ecosystems. Soil Biol Biochem. 2021;160:108317.CAS 
Article 

Google Scholar 
Wu S, Dong Y, Deng Y, Cui L, Zhuang X. Protistan consumers and phototrophs are more sensitive than bacteria and fungi to pyrene exposure in soil. Sci Total Environ. 2022;822:153539.CAS 
PubMed 
Article 

Google Scholar 
Potts LD, Douglas A, Perez Calderon LJ, Anderson JA, Witte U, Prosser JI, et al. Chronic environmental perturbation influences microbial community assembly patterns. Environ Sci Technol. 2022;56:2300–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ge AH, Liang ZH, Xiao JL, Zhang Y, Zeng Q, Xiong C, et al. Microbial assembly and association network in watermelon rhizosphere after soil fumigation for Fusarium wilt control. Agric Ecosyst Environ. 2021;312:107336.CAS 
Article 

Google Scholar 
Pernthaler J, Sattler B, Simek K, Schwarzenbacher A, Psenner R. Top-down effects on the size-biomass distribution of a freshwater bacterioplankton community. Aquat Microb Ecol. 1996;10:255–63.Article 

Google Scholar 
Holtze MS, Ekelund F, Rasmussen LD, Jacobsen CS, Johnsen K. Prey-predator dynamics in communities of culturable soil bacteria and protozoa: differential effects of mercury. Soil Biol Biochem. 2003;35:1175–81.CAS 
Article 

Google Scholar 
Fuhrman JA. Microbial community structure and its functional implications. Nature. 2009;459:193–9.CAS 
PubMed 
Article 

Google Scholar 
Meisner A, Wepner B, Kostic T, van Overbeek LS, Bunthof CJ, de Souza RSC, et al. Calling for a systems approach in microbiome research and innovation. Curr Opin Biotechnol. 2022;73:171–8.CAS 
PubMed 
Article 

Google Scholar  More

NPFC. 8th Meeting of the Small Scientific Committee on Pacific Saury Report. NPFC-2021-SSC PS08-Final Report. Preprint at https://www.npfc.int/meetings/8th-ssc-ps-meeting (2021).Hubbs, C. L. & Wisner, R. L. Revision of the sauries (Pisces, Scomberesocidae) with descriptions of two new genera and one new species. Fish. Bull. 77, 521–566 (1980).
Google Scholar 
Tian, Y., Akamine, T. & Suda, M. Variations in the abundance of Pacific saury (Cololabis saira) from the northwestern Pacific in relation to oceanic-climate changes. Fish. Res. 60, 439–454 (2003).Article 

Google Scholar 
Huang, W. B. Comparisons of monthly and geographical variations in abundance and size composition of Pacific saury between the high-seas and coastal fishing grounds in the northwestern Pacific. Fish. Sci. 76, 21–31 (2010).CAS 
Article 

Google Scholar 
Watanabe, Y., Builer, J. L. & Mori, T. Growth of Pacific saury, Cololabis saira, in the northeastern and northwestern Pacific Ocean. Fish. Bull. 86, 489–498 (1988).
Google Scholar 
Nakaya, M. et al. Growth and maturation of Pacific saury Cololabis saira under laboratory conditions. Fish. Sci. 76, 45–53 (2010).CAS 
Article 

Google Scholar 
Kosaka, S. Life history of Pacific saury Cololabis saira in the Northwest Pacific and consideration of resource fluctuation based on it. Bull. Tohoku Natl. Fish. Res. Inst. 63, 1–96 (2000).
Google Scholar 
Suyama, S. Study on the age, growth, and maturation process of Pacific saury Cololabis saira (Brevoort) in the north Pacific. Bull. Fish. Res. Agen. 5, 68–113 (2002).
Google Scholar 
Huang, W. B., Lo, N. C. H., Chiu, T. S. & Chen, C. S. Geographical distribution and abundance of Pacific saury fishing stock in the Northwestern Pacific in relation to sea temperature. Zool. Stud. 46, 705–716 (2007).
Google Scholar 
Liu, S. et al. Using novel spawning ground indices to analyze the effects of climate change on Pacifc saury abundance. J. Mar. Syst. 191, 13–23 (2019).Article 

Google Scholar 
Tian, Y., Akamine, T. & Suda, M. Long-term variability in the abundance of Pacific Saury in the Northwestern Pacific Ocean and climate changes during the last century. Bull. Jpn. Soc. Fish. Oceanogr. 66, 16–25 (2002).
Google Scholar 
Tian, Y., Ueno, Y., Suda, M. & Akamine, T. Decadal variability in the abundance of Pacific saury and its response to climatic/oceanic regime shifts in the northwestern subtropical Pacific during the last half century. J. Mar. Syst. 52, 235–257 (2004).Article 

Google Scholar 
Yasuda, I. & Watanabe, T. Chlorophyll a variation in the Kuroshio Extension revealed with a mixed-layer tracking float: Implication on the long-term change of Pacific saury (Cololabis saira). Fish. Oceanogr. 16, 482–488 (2007).Article 

Google Scholar 
Fuji, T., Kurita, Y., Suyama, S. & Ambe, D. Estimating the spawning ground of Pacific saury Cololabis saira by using the distribution and geographical variation in maturation status of adult fish during the main spawning season. Fish. Oceanogr. 30, 382–396 (2020).Article 

Google Scholar 
Yasuda, I. & Watanabe, Y. On the relationship between the Oyashio front and saury fishing grounds in the northewestern Pacific: A forecasting method for fishing ground locations. Fish. Oceanogr. 3, 172–181 (1994).Article 

Google Scholar 
Kuroda, H. & Yokouchi, K. Interdecadal decrease in potential fishing areas for Pacific saury off the southeastern coast of Hokkaido, Japan. Fish. Oceanogr. 26, 439–454 (2017).Article 

Google Scholar 
Fukushima, S. Synoptic analysis of migration and fishing conditions of saury in the northwestern Pacific Ocean. Bull. Tohoku. Reg. Fish. Res. Lab 41, 1–70 (1979).
Google Scholar 
Sugisaki, H. & Kurita, Y. Daily rhythm and seasonal variation of feeding habit of Pacific saury (Cololabis saira) in relation to their migration and oceanographic conditions off Japan. Fish. Oceanogr. 13, 63–73 (2004).Article 

Google Scholar 
Huang, W. B. & Huang, Y. C. Maturity characteristics of Pacific saury during fishing season in the Northwest pacific. J. Mar. Sci. Tech. 23, 819–826 (2015).
Google Scholar 
Tseng, C. T. et al. Influence of climate-driven sea surface temperature increase on potential habitats of the Pacific saury (Cololabis saira). ICES J. Mar. Sci. 68, 1105–1113 (2011).Article 

Google Scholar 
Tseng, C. T. et al. Sea surface temperature fronts affect distribution of Pacific saury (Cololabis saira) in the Northwestern Pacific Ocean. Deep Sea Res II Top. Stud. Oceanogr. 107, 15–21 (2014).ADS 
Article 

Google Scholar 
Hua, C., Li, F., Zhu, Q., Zhu, G. & Meng, L. Habitat suitability of Pacific saury (Cololabis saira) based on a yield-density model and weighted analysis. Fish. Res. 221, 105408. https://doi.org/10.1016/j.fishres.2019.105408 (2020).Article 

Google Scholar 
Mugo, R., Saitoh, S. I., Nihira, A. & Kuroyama, T. Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: A remote sensing perspective. Fish. Oceanogr. 19, 382–396 (2010).Article 

Google Scholar 
Yu, W., Chen, X., Chen, Y., Yi, Q. & Zhang, Y. Effects of environmental variations on the abundance of western winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Acta Oceanol. Sin. 34, 43–51 (2015).CAS 
Article 

Google Scholar 
Kakehi, S. et al. Forecasting Pacific saury (Cololabis saira) fishing grounds off Japan using a migration model driven by an ocean circulation model. Ecol. Model. 431, 109150. https://doi.org/10.1016/j.ecolmodel.2020.109150 (2020).Article 

Google Scholar 
Swain, D. P. & Wade, E. J. Spatial distribution of catch and effort in a fishery for snow crab (Chionoecetes opilio): Tests of predictions of the ideal free distribution. Can. J. Fish. Aquat. Sci. 60, 897–909 (2003).Article 

Google Scholar 
Chang, Y. J. et al. Modelling the impacts of environmental variation on habitat suitability for Pacific saury in the Northwestern Pacific Ocean. Fish. Oceanogr. 28, 291–304 (2018).Article 

Google Scholar 
Bakun, A. Fronts and eddies as key structures in the habitat of marine fish larvae: Opportunity, adaptive response and competitive advantage. Sci. Mar. 70, 105–122 (2006).Article 

Google Scholar 
Oozeki, Y., Watanabe, Y. & Kitagawa, D. Environmental factors affecting larval growth of Pacific saury, Cololabis saira, in the northwestern Pacific Ocean. Fish. Oceanogr. 13, 44–53 (2004).Article 

Google Scholar 
Ito, S. I. et al. Initial design for a fish bioenergetics model of Pacific saury coupled to a lower trophic ecosystem model. Fish. Oceanogr. 13, 111–124 (2004).Article 

Google Scholar 
Miyamoto, H. et al. Geographic variation in feeding of Pacific saury Cololabis saira in June and July in the North Pacific Ocean. Fish. Oceanogr. 29, 558–571 (2020).CAS 
Article 

Google Scholar 
Tseng, C. T. et al. Spatial and temporal variability of the Pacific saury (Cololabis saira) distribution in the northwestern Pacific Ocean. ICES J. Mar. Sci. 70, 991–999 (2013).Article 

Google Scholar 
Ichii, T. et al. Oceanographic factors affecting interannual recruitment variability of Pacific saury (Cololabis saira) in the central and western North Pacific. Fish. Oceanogr. 27, 445–457 (2018).Article 

Google Scholar 
Coletto, J. L., Pinho, M. P. & Madureira, L. S. P. Operational oceanography applied to skipjack tuna (Katsuwonus pelamis) habitat monitoring and fishing in south-western Atlantic. Fish. Oceanogr. 28, 82–93 (2018).Article 

Google Scholar 
Shi, Y., Zhu, Q., Hua, C. & Zhang, Y. Evaluation of saury stick-held net performance between model test and on-sea measurements. Haiyang Xuebao 41, 123–133 (2019).CAS 

Google Scholar 
Semedi, B., Saitoh, S., Saitoh, K. & Yoneta, K. Application of multi-sensor satellite remote sensing for determining distribution and movement of Pacific saury, Cololabis saira. Fish. Sci. 68, 1781–1784 (2002).Article 

Google Scholar 
Syah, A. F., Saitoh, S. I., Alabia, I. D. & Hirawake, T. Detection of potential fishing zone for Pacific saury (Cololabis saira) using generalized additive model and remotely sensed data. IOP Conf. Ser. Earth Env. Sci. 54, 012074. https://doi.org/10.1088/1755-1315/54/1/012074 (2017).Article 

Google Scholar 
Xing, Q. et al. Application of a fish habitat model considering mesoscale oceanographic features in evaluating climatic impact on distribution and abundance of Pacific saury (Cololabis saira). Prog. Oceanogr. 201, 102743. https://doi.org/10.1016/j.pocean.2022.102743 (2022).Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Prants, S. V., Budyansky, M. V. & Uleysky, M. Y. Identifying Lagrangian fronts with favourable fishery conditions. Deep Sea Res. Part I Oceanogr. Res. Pap. 90, 27–35 (2014).ADS 
Article 

Google Scholar 
Saito, H., Tsuda, A. & Kasai, H. Nutrient and plankton dynamics in the Oyashio region of the western subarctic Pacific Ocean. Deep Sea Res. II Top. Stud. Oceanogr. 49, 5463–5486 (2002).ADS 
CAS 
Article 

Google Scholar 
Watanabe, Y., Kurita, Y., Noto, M., Oozeki, Y. & Kitagawa, D. Growth and survival of Pacific Saury Cololabis saira in the Kuroshio-Oyashio transitional waters. J. Oceanogr. 59, 403–414 (2003).Article 

Google Scholar 
Bakun, A. Ocean eddies, predator pits and bluefin tuna: Implications of an inferred ‘low risk-limited payoff’ reproductive scheme of a (former) archetypical top predator. Fish Fish. 14, 424–438 (2013).Article 

Google Scholar 
Iwahashi, M., Isoda, Y., Ito, S. I., Oozeki, Y. & Suyama, S. Estimation of seasonal spawning ground locations and ambient sea surface temperatures for eggs and larvae of Pacific saury (Cololabis saira) in the western North Pacific. Fish. Oceanogr. 15, 128–138 (2006).Article 

Google Scholar 
Oozeki, Y., Okunishi, T., Takasuka, A. & Ambe, D. Variability in transport processes of Pacific saury Cololabis saira larvae leading to their broad dispersal: Implications for their ecological role in the western North Pacific. Prog. Oceanogr. 138, 448–458 (2015).ADS 
Article 

Google Scholar 
Polovina, J. J., Kleiber, P. & Kobayashi, D. R. Application of TOPEX-Poseidon satellite altimetry to simulate transport dynamics of larvae of spiny lobster, Panulirus marginatus, in the Northwestern Hawaiian Islands, 1993–1996. Fish. Bull. 97, 132–143 (1999).
Google Scholar 
Kawai, H. Hydrography of the Kuroshio extension. In Kuroshio—Its Physical Aspects (eds Stommel, H. & Yoshida, K.) 235–352 (University of Tokyo, 1972).
Google Scholar 
Yamada, F. & Sekine, Y. Variations in sea surface temperature and 500 hPa height over the north Pacific with reference to the occurrence of anomalous southward Oyashio intrusion east of Japan. J. Meteorol. Soc Jpn. Ser. II 75, 995–1000 (1997).Article 

Google Scholar 
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: Calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 

Google Scholar 
Hastie, T. J. & Tibshirani, R. J. Generalized additive models. Stat. Sci. 1, 297–310 (1986).MathSciNet 
MATH 

Google Scholar 
Litzow, M. A., Hobday, A. J., Frusher, S. D., Dann, P. & Tuck, G. N. Detecting regime shifts in marine systems with limited biological data: An example from southeast Australia. Prog. Oceanogr. 141, 96–108 (2016).ADS 
Article 

Google Scholar 
Pang, Y. et al. Variability of coastal cephalopods in overexploited China Seas under climate change with implications on fisheries management. Fish. Res. 208, 22–33 (2018).Article 

Google Scholar  More