Ecology

Kejun, J. et al. Transition of the Chinese economy in the face of deep greenhouse gas emissions cuts in the future. Asian Econ. Policy Rev. 16(1), 142–162 (2021).
Google Scholar 
COP26, United nations climate change. https://unfccc.int/news/cop26-facts-and-figures, (2020).Dong, Y., Coleman, M. and Miller, S. A. Greenhouse gas emissions from air conditioning and refrigeration service expansion in developing countries. Annual Rev. Environ. Resour. 46 (2021).Azam, M. & Khan, A. Q. Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries. Renew. Sustain. Energy Rev. 63, 556–567 (2016).CAS 

Google Scholar 
Li, Z. et al. An economic analysis software for evaluating best management practices to mitigate greenhouse gas emissions from cropland. Agric. Syst. 186, 102950 (2021).
Google Scholar 
Dinda, S. Environmental Kuznets curve hypothesis: A survey. Ecol. Econ. 49(4), 431–455 (2004).
Google Scholar 
Xia, Q. et al. Drivers of global and national CO2 emissions changes 2000–2017. Climate Policy 21(5), 604–615 (2021).
Google Scholar 
Fatima, T., Shahzad, U. & Cui, L. Renewable and nonrenewable energy consumption, trade and CO2 emissions in high emitter countries: Does the income level matter?. J. Environ. Planning Manage. 64(7), 1227–1251 (2021).
Google Scholar 
Kılavuz, E. & Doğan, İ. Economic growth, openness, industry and CO2 modelling: Are regulatory policies important in Turkish economies?. Int. J. Low-Carbon Technol. 16(2), 476–487 (2021).
Google Scholar 
Setyari, N. P. W. & Kusuma, W. G. A. Economics and environmental development: Testing the environmental Kuznets Curve hypothesis. Int. J. Energy Econ. Policy 11(4), 51 (2021).
Google Scholar 
Gołasa, P. et al. Sources of greenhouse gas emissions in agriculture, with particular emphasis on emissions from energy used. Energies 14(13), 3784 (2021).
Google Scholar 
Liobikienė, G. & Butkus, M. The challenges and opportunities of climate change policy under different stages of economic development. Sci. Total Environ. 642, 999–1007 (2018).ADS 
PubMed 

Google Scholar 
Koondhar, M. A. et al. A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on co-citation analysis theory and pathfinder network scaling algorithms. Environ. Sci. Pollut. Res. 28(13), 16690–16706 (2021).CAS 

Google Scholar 
Bilgili, F., Koçak, E. & Bulut, Ü. The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach. Renew. Sustain. Energy Rev. 54, 838–845 (2016).
Google Scholar 
Gorus, M. S. & Aydin, M. The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: Causality analysis in the frequency domain. Energy 168, 815–822 (2019).
Google Scholar 
Kirikkaleli, D. & Adebayo, T. S. Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustain. Develop. 29(4), 583–594 (2021).
Google Scholar 
Godil, D. I. et al. Investigate the role of technology innovation and renewable energy in reducing transport sector CO2 emission in China: A path toward sustainable development. Sustain. Develop. (2021).An, T., Xu, C. & Liao, X. The impact of FDI on environmental pollution in China: Evidence from spatial panel data. Environ. Sci. Pollut. Res. 1–13 (2021).Halliru, A. M., Loganathan, N. and Golam Hassan, A. A. Does FDI and economic growth harm environment? Evidence from selected West African countries. Trans. Corp. Rev., 13(2), 237–251 (2021.).Al-Mulali, U., Ozturk, I. & Solarin, S. A. Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecol. Ind. 67, 267–282 (2016).
Google Scholar 
Zhang, D. et al. Public spending and green economic growth in BRI region: Mediating role of green finance. Energy Policy 153, 112256 (2021).
Google Scholar 
Usman, M. et al. How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy, 122515 (2021).Rehman, A. et al. The impact of globalization, energy use, and trade on ecological footprint in Pakistan: does environmental sustainability exist?. Energies 14(17), 5234 (2021).CAS 

Google Scholar 
Bremond, U. et al. A vision of European biogas sector development towards 2030: Trends and challenges. J. Clean. Prod. 287, 125065 (2021).
Google Scholar 
Abdul Latif, S. N. et al. The trend and status of energy resources and greenhouse gas emissions in the malaysia power generation mix. Energies 14(8), 2200 (2021).CAS 

Google Scholar 
Chen, P.-Y. et al. Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renew. Sustain. Energy Rev. 65, 420–431 (2016).CAS 

Google Scholar 
Kais, S. & Sami, H. An econometric study of the impact of economic growth and energy use on carbon emissions: Panel data evidence from fifty eight countries. Renew. Sustain. Energy Rev. 59, 1101–1110 (2016).
Google Scholar 
Rüstemoğlu, H. & Andrés, A. R. Determinants of CO2 emissions in Brazil and Russia between 1992 and 2011: A decomposition analysis. Environ. Sci. Policy 58, 95–106 (2016).
Google Scholar 
Yao, C., Feng, K. & Hubacek, K. Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010. Eco. Inform. 26, 93–100 (2015).
Google Scholar 
González, P. F., Landajo, M. & Presno, M. The driving forces behind changes in CO2 emission levels in EU-27. Differences between member states. Environ. Sci. Policy 38, 11–16 (2014).
Google Scholar 
Nathaniel, S. P. Environmental degradation in ASEAN: assessing the criticality of natural resources abundance, economic growth and human capital. Environ. Sci. Pollut. Res. 28(17), 21766–21778 (2021).
Google Scholar 
Baloch, M. A., Mahmood, N. & Zhang, J. W. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries. Sci. Total Environ. 678, 632–638 (2019).ADS 
PubMed 

Google Scholar 
Balsalobre-Lorente, D. et al. How economic growth, renewable electricity and natural resources contribute to CO2 emissions?. Energy Policy 113, 356–367 (2018).
Google Scholar 
Bekun, F. V., Alola, A. A. & Sarkodie, S. A. Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ. 657, 1023–1029 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Baloch, M. A. & Meng, F. Modeling the non-linear relationship between financial development and energy consumption: Statistical experience from OECD countries. Environ. Sci. Pollut. Res. 26(9), 8838–8846 (2019).
Google Scholar 
Dong, K., Sun, R. & Hochman, G. Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy 141, 1466–1478 (2017).
Google Scholar 
Omri, A. et al. Determinants of environmental sustainability: Evidence from Saudi Arabia. Sci. Total Environ. 657, 1592–1601 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Zhu, H. et al. The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Econ. Model. 58, 237–248 (2016).
Google Scholar 
Cheng, C. et al. Heterogeneous impacts of renewable energy and environmental patents on CO2 emission-evidence from the BRIICS. Sci. Total Environ. 668, 1328–1338 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Zhang, C. & Zhou, X. Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China. Renew. Sustain. Energy Rev. 58, 943–951 (2016).
Google Scholar 
Phung, T. Q., Rasoulinezhad, E. and Luong Thi Thu, H. How are FDI and green recovery related in Southeast Asian economies? Econ. Change Restruct. 1–21 (2022).Quang, P.T. and Thao, D. P. Analyzing the green financing and energy efficiency relationship in ASEAN. J. Risk Financ. (2022)(ahead-of-print).Ahmad, M. et al. Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: Does financial globalization matter?. Sustain. Cities Soc. 70, 102881 (2021).
Google Scholar 
Murshed, M. An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ. Sci. Pollut. Res. 27(29), 36254–36281 (2020).CAS 

Google Scholar 
Díaz-García, C., González-Moreno, Á. & Sáez-Martínez, F. J. Eco-innovation: Insights from a literature review. Innovation 17(1), 6–23 (2015).
Google Scholar 
Wang, L. et al. Are eco-innovation and export diversification mutually exclusive to control carbon emissions in G-7 countries?. J. Environ. Manage. 270, 110829 (2020).PubMed 

Google Scholar 
Su, H.-N. & Moaniba, I. M. Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technol. Forecast. Soc. Chang. 122, 49–62 (2017).
Google Scholar 
Ding, Q., Khattak, S. I. & Ahmad, M. Towards sustainable production and consumption: assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustain. Prod. Consum. 27, 254–268 (2021).
Google Scholar 
Zhang, Y.-J. et al. Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100, 18–28 (2017).
Google Scholar 
Solarin, S. A. & Bello, M. O. Energy innovations and environmental sustainability in the US: the roles of immigration and economic expansion using a maximum likelihood method. Sci. Total Environ. 712, 135594 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Hashmi, R. & Alam, K. Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation. J. Clean. Prod. 231, 1100–1109 (2019).
Google Scholar 
Sinha, A., Sengupta, T. & Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 242, 118549 (2020).
Google Scholar 
Gormus, S. & Aydin, M. Revisiting the environmental Kuznets curve hypothesis using innovation: New evidence from the top 10 innovative economies. Environ. Sci. Pollut. Res. 27(22), 27904–27913 (2020).
Google Scholar 
Usman, M. & Hammar, N. Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: Fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries. Environ. Sci. Pollut. Res. 28(12), 15519–15536 (2021).
Google Scholar 
Shahbaz, M., Mutascu, M. & Azim, P. Environmental Kuznets curve in Romania and the role of energy consumption. Renew. Sustain. Energy Rev. 18, 165–173 (2013).
Google Scholar 
Kong, Q. et al. Trade openness and economic growth quality of China: Empirical analysis using ARDL model. Financ. Res. Lett. 38, 101488 (2021).
Google Scholar 
Kasman, A. & Duman, Y. S. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Econ. Model. 44, 97–103 (2015).
Google Scholar 
Ali, S. et al. Impact of trade openness, human capital, public expenditure and institutional performance on unemployment: Evidence from OIC countries. Int. J. Manpower, (2021).Chen, F., Jiang, G. & Kitila, G. M. Trade openness and CO2 emissions: The heterogeneous and mediating effects for the belt and road countries. Sustainability 13(4), 1958 (2021).
Google Scholar 
Sun, H. et al. Nexus between environmental infrastructure and transnational cluster in one belt one road countries: Role of governance. Bus. Strategy Develop. 1(1), 17–30 (2018).
Google Scholar 
Jebli, M. B. & Youssef, S. B. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew. Sustain. Energy Rev. 47, 173–185 (2015).
Google Scholar 
Jebli, M. B., Youssef, S. B. & Ozturk, I. Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecol. Ind. 60, 824–831 (2016).
Google Scholar 
Shahbaz, M. et al. Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates. Ecol. Ind. 45, 622–631 (2014).CAS 

Google Scholar 
Xu, B. & Lin, B. How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models. Energy Econ. 48, 188–202 (2015).
Google Scholar 
Ertugrul, H. M. et al. The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries. Ecol. Ind. 67, 543–555 (2016).
Google Scholar 
Najarzadeh, R. et al. Kyoto Protocol and global value chains: Trade effects of an international environmental policy. Environ. Develop. 40, 100659 (2021).
Google Scholar 
Liobikienė, G. & Butkus, M. Environmental Kuznets Curve of greenhouse gas emissions including technological progress and substitution effects. Energy 135, 237–248 (2017).
Google Scholar 
Liobikienė, G. The revised approaches to income inequality impact on production-based and consumption-based carbon dioxide emissions: Literature review. Environ. Sci. Pollut. Res. 27(9), 8980–8990 (2020).
Google Scholar 
Li, G., Zakari, A. & Tawiah, V. Energy resource melioration and CO2 emissions in China and Nigeria: Efficiency and trade perspectives. Resour. Policy 68, 101769 (2020).
Google Scholar 
Ali, M. U. et al. Fossil energy consumption, economic development, inward FDI impact on CO2 emissions in Pakistan: Testing EKC hypothesis through ARDL model. Int. J. Financ. Econ. 26(3), 3210–3221 (2021).
Google Scholar 
Özbuğday, F. C. & Erbas, B. C. How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis. Energy 82, 734–745 (2015).
Google Scholar 
Wang, Q., Chiu, Y.-H. & Chiu, C.-R. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energy Econ. 51, 252–260 (2015).
Google Scholar 
Dong, K. et al. Energy intensity and energy conservation potential in China: A regional comparison perspective. Energy 155, 782–795 (2018).
Google Scholar 
Tan, R. & Lin, B. What factors lead to the decline of energy intensity in China’s energy intensive industries?. Energy Econ. 71, 213–221 (2018).
Google Scholar 
Tariq, G. et al. Energy consumption and economic growth: Evidence from four developing countries. Am. J. Multidiscip. Res. 7(1), (2018).Sharif, A. et al. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from quantile ARDL approach. Sustain. Cities Soc. 57, 102138 (2020).
Google Scholar 
Khan, I., Hou, F. & Le, H. P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ. 754, 142222 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Bölük, G. & Mert, M. Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: Evidence from a panel of EU (European Union) countries. Energy 74, 439–446 (2014).
Google Scholar 
Sugiawan, Y. & Managi, S. The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy 98, 187–198 (2016).
Google Scholar 
Bölük, G. & Mert, M. The renewable energy, growth and environmental Kuznets curve in Turkey: An ARDL approach. Renew. Sustain. Energy Rev. 52, 587–595 (2015).
Google Scholar 
Sebri, M. & Ben-Salha, O. On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries. Renew. Sustain. Energy Rev. 39, 14–23 (2014).
Google Scholar 
Tiwari, A. K. A structural VAR analysis of renewable energy consumption, real GDP and CO2 emissions: Evidence from India. Econ. Bull. 31(2), 1793–1806 (2011).
Google Scholar 
Apergis, N. & Payne, J. E. Renewable energy consumption and growth in Eurasia. Energy Econ. 32(6), 1392–1397 (2010).
Google Scholar 
Menyah, K. & Wolde-Rufael, Y. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6), 2911–2915 (2010).CAS 

Google Scholar 
Fareed, Z. et al. Financial inclusion and the environmental deterioration in Eurozone: The moderating role of innovation activity. Technol. Soc. 69, 101961 (2022).
Google Scholar 
Adebayo, T. S. Renewable energy consumption and environmental sustainability in Canada: does political stability make a difference? Environ. Sci. Pollut. Res., 1–16 (2022).Shahbaz, M. et al. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries?. Energy Econ. 51, 275–287 (2015).
Google Scholar 
Tariq, G. et al. Trade liberalization, FDI inflows economic growth and environmental sustanaibility in Pakistan and India. J. Agric. Environ. Int. Develop. (JAEID) 112(2), 253–269 (2018).
Google Scholar 
Lee, J. W. The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth. Energy Policy 55, 483–489 (2013).
Google Scholar 
Sun, H.-P. et al. Evaluating the environmental effects of economic openness: Evidence from SAARC countries. Environ. Sci. Pollut. Res. 26(24), 24542–24551 (2019).CAS 

Google Scholar 
Adebayo, T. S. Environmental consequences of fossil fuel in Spain amidst renewable energy consumption: a new insights from the wavelet-based Granger causality approach. Int. J. Sustain. Develop. World Ecol. 1–14 (2022).Adebayo, T. S. et al. Impact of tourist arrivals on environmental quality: A way towards environmental sustainability targets. Current Issues Tourism, 1–19 (2022).Akadiri, S.S. et al. Testing the role of economic complexity on the ecological footprint in China: A nonparametric causality-in-quantiles approach. Energy Environ. 0958305X221094573 (2022).Xie, Q. et al. Race to environmental sustainability: Can renewable energy consumption and technological innovation sustain the strides for China? Renew. Energy (2022).Du, L. et al. Asymmetric effects of high-tech industry and renewable energy on consumption-based carbon emissions in MINT countries. Renew. Energy 196, 1269–1280 (2022).CAS 

Google Scholar 
Al-Mulali, U. & Tang, C. F. Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy 60, 813–819 (2013).
Google Scholar 
Jiang, Y. Foreign direct investment, pollution, and the environmental quality: A model with empirical evidence from the Chinese regions. Int. Trade J. 29(3), 212–227 (2015).
Google Scholar 
Ren, S. et al. International trade, FDI (foreign direct investment) and embodied CO2 emissions: A case study of Chinas industrial sectors. China Econ. Rev. 28, 123–134 (2014).
Google Scholar 
Tang, C. F. & Tan, B. W. The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy 79, 447–454 (2015).
Google Scholar 
Omri, A. & Kahouli, B. Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models. Energy Policy 67, 913–922 (2014).
Google Scholar 
Dong, K.-Y. et al. A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool. Pet. Sci. 14(1), 214–227 (2017).
Google Scholar 
WDI, World Development Indicator. https://data.worldbank.org/, (2022).OECD, Organisation for Economic Co-operation and Development. https://data.oecd.org/, (2021).Levin, A., Lin, C.-F. & Chu, C.-S.J. Unit root tests in panel data: Asymptotic and finite-sample properties. J. Econom. 108(1), 1–24 (2002).MathSciNet 
MATH 

Google Scholar 
Breitung, J. The local power of some unit root tests for panel data. (Emerald Group Publishing Limited, 2001).Im, K. S., Pesaran, M. H. & Shin, Y. Testing for unit roots in heterogeneous panels. J. Econom. 115(1), 53–74 (2003).MathSciNet 
MATH 

Google Scholar 
Hlouskova, J. & Wagner, M. The performance of panel unit root and stationarity tests: Results from a large scale simulation study. Economet. Rev. 25(1), 85–116 (2006).MathSciNet 
MATH 

Google Scholar 
Narayan, P. K. & Narayan, S. Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy 38(1), 661–666 (2010).
Google Scholar 
Pedroni, P. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull. Econ. Stat. 61(S1), 653–670 (1999).
Google Scholar 
Pedroni, P. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Economet. Theor. 20(3), 597–625 (2004).MathSciNet 
MATH 

Google Scholar 
Kao, C. Spurious regression and residual-based tests for cointegration in panel data. J. Econom. 90(1), 1–44 (1999).MathSciNet 
MATH 

Google Scholar 
Breusch, T. S. & Pagan, A. R. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47(1), 239–253 (1980).MathSciNet 
MATH 

Google Scholar 
Baltagi, B. H. and Hashem Pesaran, M. Heterogeneity and cross section dependence in panel data models: Theory and applications introduction. 229–232 (Wiley Online Library, 2007).Levine, S. & Kendall, K. Energy efficiency and conservation: Opportunities, obstacles, and experiences. Vt. J. Envtl. L. 8, 101 (2006).
Google Scholar 
Stock, J. H. and Watson, M. W. A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: J. Econom. Soc. 783–820 (1993).Phillips, P.C. and Hansen, B.E. Estimation and inference in models of cointegration: A simulation study. (Cowles Foundation for Research in Economics, Yale University, 1988).Pedroni, P. Fully modified OLS for heterogeneous cointegrated panels, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Kao, C. and Chiang, M.-H. On the estimation and inference of a cointegrated regression in panel data, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Liobikienė, G. & Butkus, M. Scale, composition, and technique effects through which the economic growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions. Renew. Energy 132, 1310–1322 (2019).
Google Scholar 
Balsalobre-Lorente, D. et al. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew. Energy 185, 1441–1455 (2022).CAS 

Google Scholar 
Sarkodie, S. A. & Adams, S. Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci. Total Environ. 643, 1590–1601 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Mohamued, E. A. et al. Global oil price and innovation for sustainability: The impact of R&D spending, oil price and oil price volatility on GHG emissions. Energies 14(6), 1757 (2021).
Google Scholar 
Iqbal, N. et al. Does exports diversification and environmental innovation achieve carbon neutrality target of OECD economies?. J. Environ. Manage. 291, 112648 (2021).PubMed 

Google Scholar 
Edenhofer, O. et al. Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change. (Cambridge University Press, 2011).Owusu, P. A. & Asumadu-Sarkodie, S. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. 3(1), 1167990 (2016).
Google Scholar  More

Comparative characteristics of rations for feeding cattle from different regions of the Republic of Kazakhstan and the impact of animal feeding types on the faecal microbiotaDue to the huge differences in the natural and climatic conditions of Kazakhstan, animals from different regions of Kazakhstan were enrolled for this study. The difference in soil and climatic conditions of different zones has a significant impact on the type of feeding (Table 1) and the composition of diets, which has a certain effect on the microbiota of intestinal contents and methanogenic archaea in particular.Table 1 Animal diets in different regions of Kazakhstan.Full size tableIn the course of the research work, regions and specific agricultural formations were identified in the context of these regions.In North Kazakhstan, the fodder base is represented by such fodders as alfalfa hay, herb hay, alfalfa haylage, wheat straw, fodder wheat and sunflower cake. The feed is mainly of 2 quality classes. The live weight of cattle ranged from 375 to 480 kg. Feeding type: hay-concentrate and haylage-hay-concentrate.In the Western region, the animals were on the pasture, represented by the green mass of feather grass, hair, sage and tansy. Beef cattle are represented by the following breeds: Kazakh white-headed, Aberdeen-Angus and Hereford. Average live weight is 350–550 kg.In the Southeast region, the fodder base consists of wheat hay, sainfoin + alfalfa hay, mountain hay, herb haylage, corn silage and crushed corn. The feed is mainly of 2 and 3 classes. Hay-concentrate type of feeding is used, as well as pastures. Livestock of Angus, Kazakh white-headed breeds and animals of the local population are kept. Live weight of young animals is in the range of 360–380 kg.The diets of the Southern Region include natural grass hay, alfalfa hay, wheat straw, alfalfa haylage and concentrates. Hay-concentrate type of livestock feeding is widespread in the region. The average live weight of bulls for fattening of the Kazakh white-headed and Angus breeds—360–420 kg with a daily increase in live weight of 870–920 g.The composition of the fecal microbiota depending on the type of feeding is presented in Table 2.Table 2 The content of methanogenic archaea in feces.Full size tableFrom the data of Table 2 it follows that the largest amount of Bacteria was found in the faeces of animals with silage-concentrated feeding (98.59 ± 13.0%), and the smallest—with pasture-concentrated (93.24 ± 3.73%) and haylage—concentrated (93.8 ± 12.41%) types of feeding. The differences amounted to 5.35 and 4.79 absolute percent, respectively. However, the differences were not significant at P  More

Systematic palaeontologyOrder Hemiptera Linnaeus, 1758Suborder Sternorrhyncha Amyot et Audinet-Serville, 1843Superfamily Protopsyllidioidea Carpenter, 1931Family Postopsyllidiidae Hakim, Azar et Huang, 2019Genus Megalophthallidion Drohojowska et Szwedo, gen. nov.LSID urn:lsid:zoobank.org:act:A6F71390-9B8E-4A19-8F30-C2A024B6EFB1Type speciesMegalophthallidion burmapateron Drohojowska et Szwedo, sp. nov.; by present designation and monotypy.EtymologyGeneric name is derived from Classic Greek megas (μέγας)—large, ophthalmos (ὀφθαλμός)—an eye and Greek form of generic name Psyllidium. Gender: masculine.Type localityNorthern Myanmar: state of Kachin, Noije bum 2001 Summit Site amber mine in the Hukawng Valley, SW of Maingkhwan.Type stratumLowermost Cenomanian, Upper Cretaceous (‘mid-Cretaceous’).DiagnosisHead capsule with 12 stiff setae on tubercles (18 setae in Postopsyllidium); fore wing without pterostigma (tiny pterostigma, widening of ScP + RA present in Postopsyllidium); vein CuP not thickened distally (distinctly thickened distally in Postopsyllidium); profemur with a row of ventral (ventrolateral) setae (two rows in Postopsyllidium).Megalophthallidion burmapateron Drohojowska et Szwedo, sp. nov.LSID urn:lsid:zoobank.org:act:F3F971F4-AE04-4F41-98B0-9A0A04470625.(Figs. 1A–F, 2A–I).Figure 1Megalophthallidion burmapteron gen. et sp. nov., holotype (MAIG 6687), imago. (A) Photo of body, ventral side; (B) photo of right antennae and (C) drawing of antenna; (D) drawing of body, dorsal side; (E) drawing of thorax structure with sclerites marked: red—pronotum; orange—mesopraescutum; yellow—mesoscutum; light green—mesoscutellum, dark green—mesopostnotum; light blue—metascutum; dark blue—metascutellum; violet—metapostnotum; (F) photo of thorax dorsal side. Scale bars: 0.5 mm (A), 0.2 mm (B–D), 0.1 mm (F).Full size imageFigure 2Megalophthallidion burmapteron gen. et sp. nov., holotype (MAIG 6687), imago. (A) Photo of right fore wing; (B) photo of right wings; (C) photo of antenna and proleg; (D) photo of proleg and mesoleg, and (E) photo of femur of proleg, and (F) photo of right metatarsus and left mesotarsus in the background, and (G) photo of right mesotarsus of mesoleg, and (H) Photo of tarsi; (I) photo of male genital block. Scale bars: 0.5 mm (A–D), 0.2 mm (B,E,F,H), 0.1 mm (G,I).Full size imageMaterialHolotype, number MAIG 6687 (BUB 96), deposited in Museum of Amber Inclusions (MAIG), University of Gdańsk, Poland. Imago, a complete and well-preserved male. Piece of amber 8 × 6 × 3 mm, cut from larger lump, polished flat on both sides.Type localityNorthern Myanmar: state of Kachin, Noije bum 2001 Summit Site amber mine in the Hukawng Valley, SW of Maingkhwan.Type stratumLowermost Cenomanian, Upper Cretaceous (‘mid-Cretaceous’).DiagnosisAs for the genus with the following additions: three ocelli distinct, antennomere IX the longest, about as long as pedicel, antennomeres III–VII and XI of similar length, antennomere XII the shortest, subconically tapered in apical portion. Paramere lobate, ventral margin with acute, small process, apical and dorsal margins rounded. Aedeagus geniculately bent at base, directed dorsally, tapered apicad.DescriptionMale (Figs. 1A–F, 2A–I). Head with compound eyes distinctly wider than pronotum (Fig. 1D–F). Compound eyes subglobular, protruding laterally. Vertex short in midline, about 2.5 times as wide as posterior margin and as long in middle; trapezoidal, anterior margin slightly arched, lateral margins diverging posteriad, posterior margin shallowly arched, disc of vertex with distinct setae on large tubercles: four setae at posterior margin, two at anterior angles of compound eyes, two medial, over the median ocellus. Three ocelli present, median ocellus distinct, visible from above, lateral ocelli near anterior angles of compound eyes. Frons about as wide as long in midline, two rows of setae on tubercles, upper row at level of median ocellus, lower one, below half of compound eye height. Clypeus, elongate, triangular, in lower portion roof-like; two setae on tubercles near upper margin. Genae very narrow. Rostrum reaching slightly beyond mesocoxae, apical segment slightly shorter than subapical one, darker. Antennae bases placed at lower margin of compound eyes; antennal fovea elevated; scapus shorter than pedicel, cylindrical; pedicel cylindrical; antennomeres IIIrd–VIIth and XIth of similar length, VIIIth slightly longer than VIIth, as long as Xth antennomere, IXth the longest, XIIth the shortest, tapered apically; rhinaria absent.Thorax (Fig. 1D–F): pronotum quadrangular, about as long as mesothorax; pronotum with anterior and posterior margins parallel, merely arcuate, disc with transverse groove in the median portion, lateral margins slightly arcuate, two distinct setae on tubercles in anterolateral angle, two setae on tubercles anterior margin at distance1/3 to median line, three distinct setae on tubercles in posterolateral angles. Mesopraescutum subtriangular, with apex widely rounded, about 0.4 times as wide as pronotum, about 0.4 times as long as wide, delicately separated from mesoscutum. Mesoscutum as wide as pronotum at widest point, distinctly narrowed medially, anterior angles rounded, anterolateral margin sigmoid, lateral angle acute, posterior angles wide, posterior margin V-shape incised, posterolateral areas of mesoscutum disc declivent posteriorly; disc with two setae on tubercles, at 1/3 of mesoscutum width. Mesoscutellum about as long as wide, diamond-shape, anterior and lateral angles acute, posterior angle rounded. Mesopostnotum in form of transverse band, slightly widened in median portion. Metascutum narrower than mesoscutum, anterior angles widely rounded, lateral angles acute, anterolateral margin concave, posterior margin arcuate, with deep median arcuate incision. The suture between metascutum and metascutellum weakly visible, metascutellum subtriangular, longer than wide at base.Parapteron with three distinct setae.Fore wing (Fig. 2A,B) membranous, narrow, elongate, about 3.5 times as long as wide, widest at 2/3 of length. Anterior margin merely arcuate, slightly bent at very base, anteroapical angle widely arcuate, apex rounded, posteroapical angle widely arcuate, tornus arcuate, claval margin straight, with incision between terminals of Pcu (claval apex) and A1. Stem ScP + R + MP + CuA slightly arcuate, very short stalk ScP + R + MP + CuA leaving basal cell, stem ScP + R oblique, straight, forked in basal half of fore wing length, branch ScP + RA, oblique, reaching anterior margin slightly distally of half of fore wing length, slightly distally of ending of CuA2 branch; branch RP slightly arcuate, a little more curved in basal section, reaching margin at anteroapical angle; stalk MP + CuA slightly shorter than basal cell; stem MP almost straight, forked in apical half of fore wing, at about 2/3 of fore wing length, with three terminals reaching margin between apex and posteroapical angle; stem CuA shorter than branches CuA1 and CuA2, about half as long as branch CuA1; claval vein CuP weak at base, not thickened distally; claval vein Pcu straight, claval vein A1 straight. Basal cell present, subtriangular, about twice as long as wide, basal veinlet cua-cup oblique, no other veinlets present; cell r (radial) very long, longer than half of fore wing length; cell m (medial) the shortest, shorter than cell cu (areola postica). Margins of fore wing with fringe of long setae, starting on costal margin near base of fore wing, ending at level of middle of cell cu; longitudinal veins with distinct, scarcely but evenly dispersed, movable setae; terminal section of CuP with two setae; costal margin with row of short, densely distributed setae, apical margin, tornus and claval margin with rows of scaly setae.Hind wing (Fig. 2B) membranous, shorter than fore wing, 3.23 times as long as wide. Costal margin bent at base, then almost straight up to the level of ScP + RA end and wing coupling lobe, then straight to anteroapical angle, anteroapical angle widely arcuate, apex arcuate, posteroapical angle arcuate, tornus straight, claval margin merely arcuate, posteroclaval angle angulate; stem ScP + R + MP bent at base, then straight, stem ScP + R short, branch ScP + RA short, about as long as stem ScP + R, branch RP arcuate basally than straight, reaching apex; stem MP arcuate, forked slightly distad CuA1 terminus level, branch MP1+2 slightly arcuate, reaching margin at posteroapical angle, branch MP3+4 straight, reaching tornus; stem CuA slightly bent at base, then straight, forked slightly distad ScP + R forking, branch CuA1 arcuate, branch CuA2 short, straight, slightly oblique, reaching tornus; claval vein CuP weak, visible only at base, claval vein Pcu slightly arcuate; wing coupling apparatus (fold) with a few short setae.Legs slender, relatively long, profemora armed (Fig. 2C–H). Procoxa as long as profemur, narrow, flattened. Protrochanter scaphoid, elongate, with long apical and subapical setae. Profemur flattened laterally, about as long as protibia, ventrally armed with four large setae on elevated plinths; dorsal margin with row of short, decumbent setae. Protibia narrow, rounded in cross section, covered with short setae, a few longer setae in distal portion. Protarsus—single, long tarsomere, plantar surface with row of semi-erect setae; tarsal claws long, straight, directed ventrally, no arolium nor empodium.Mesocoxa elongate, narrow, slightly flattened. Mesotrochanter scaphoid. Mesofemur slender, flattened laterally, dorsal margin with short setae. Mesotibia subequal to mesofemur, slender, covered with setae, two apical setae slightly thicker and longer. Mesotarsus with three tarsomeres, basimesotarsomere the longest, shorter than cumulative length of mid- and apical mesotarsomere, plantar margins with setae, two apical setae slightly longer and thicker; midmesotarsomere the shortest, 1/3 of basimesotarsomere length, a few setae on plantar surface; apical tarsomere shorter than basimesotarsomere, twice as long as midmesotarsomere, plantar surface with a few, scarcely dispersed setae, tarsal claws long, narrow, directed ventrally, no arolium nor empodium.Metacoxa conical, narrow. Metatrochanter scaphoid, elongate. Metafemur slender, laterally flattened, longer than mesofemur, dorsal margin with row of short setae. Metatibia, long, slender, 1.6 times as long as metafemur, with suberect setae of different size, two larger and longer and two shorter setae subapical setae. Metatarsus slightly less than half of metatibia length, with three tarsomeres, basimetatarsomere the longest, more than twice as long as apical metatarsomere, 1.5 times as long as combined length of mid- and apical metatarsomere, plantar surface with scarce decumbent setae; mid metatarsomere the shortest, 1/4 of basimetatarsomere length, plantar surface with a few setae, two apical ones slightly thicker; apical metatarsomere about 0.4 of basimetatarsomere length, with scarcely dispersed setae on along plantar surface; tarsal claws, long, slender, other pretarsal structures absent.Abdomen (Fig. 1F) narrowly attached to thorax, tergite segment shorter, 2nd tergite distinctly longer, 3rd to 8th tergites of similar length; pygofer narrowing apicad, ventral margin strongly elongated posteriorly; anal tube short, directed posterodorsad, anal style shorter than anal tube. Paramere lobate, ventral margin with acute, small process, apical and dorsal margins rounded. Aedeagus (Fig. 2I) geniculately bent at base, directed dorsad, tapered apicad.Female. Unknown.Megalophthallidion sp. (5th instar nymph)(Figs. 3A–D, 4A–F)Figure 3Megalophthallidion sp. (MAIG 6688), nymph. (A) Photo of body, dorsal side and (B) drawing of body dorsal side; (C) photo of body dorsal side and (D) drawing of body ventral side. Scale bars: 0.5 mm (A–D).Full size imageFigure 4Megalophthallidion sp. (MAIG 6688), nymph. Photo of clypeus and (B) drawing of clypeus; (C) photo of proleg, and (D) photo of mesoleg, and (E) photo of metaleg; (F) photo of posterior part of abdomen ventral side. Scale bars: 0.1 mm (A–F).Full size imageMaterialNymph, 5th instar, MAIG 6688 (BUB 1799), deposited in Museum of Amber Inclusions (MAIG), University of Gdańsk, Poland. Piece of amber 13 × 6 × 2 mm, cut from larger lump, polished flat on one side, more convex on the other.Diagnostic charactersThe nymph of Megalophthallidion gen. nov. is similar in general body shape to the only known fossil protopsyllidioidean nymph described from Lower Cretaceous Lebanese amber—Talaya batraba Drohojowska et Szwedo, 2013. The nymph of Talaya batraba is 2nd or 3rd instar, therefore some features are difficult to compare with this last instar nymph of Megalophthallidion gen. nov. The morphological states observed in those two specimens are: head covered with strongly expanded disc and expanded disc of pronotum, however shapes and ratios of these structures differ; compound eyes on ventral side of head, shifted laterad (ommatidia on cones in T. batraba, while ventroposterior expansions are present in Megalophthallidion gen. nov.); compound eyes visible from above as short, stout cones in fissure between posterior margin of disc (hypertrophied vertex) and anterior margin of pronotum (compound eyes (?) are visible on dorsal side of Permian Aleuronympha bibulla Riek, 1974); in Megalophthallidion gen. nov. rostrum reached mesocoxa, while in Talaya batraba distinctly exceeds length of the body; abdomen with 9 segments; tergites of abdominal segments 5th–9th expanded posterolaterad in form of fan-like expansion; 9th abdominal segment short, placed ventral; anal tube short, cylindrical, epiproct (?) globular.DescriptionNymph, 5th instar (Figs. 3A–D, 4A–F). Body oval shaped, dorso-ventrally flattened, 1.5 times longer than wide with segmentation visible; on the ventral side slightly concave. Length of body c. 1.56 mm long, outline, in dorsal view, maximum width of body 0.94 mm; length of head and pronotum (cephaloprothorax) c. 0.46 mm in midline, width c. 0.83 mm; cumulative length of mesonotum + metanotum c. 0.25 mm; abdomen c. 0.8 mm long. Dorsal side (Fig. 3A,B) with distinct median line (ecdysial line), not reaching anterior or posterior margin of the body, the line distinctly roof-like in abdominal portion. Anterior margin of head (cephaloprothorax) disc arcuate, lateral angles rounded; anterior margin of pronotum arcuate, lateral margins arcuately diverging posteriad, posterior margin distinctly arcuate, anterior angles widely rounded, posterior angles acutely rounded, disc elevated, convex, lateral portions declivitous; the fissure between posterior margin of head disc and anterior margin of pronotum narrow, widened medially, with stalked compound eyes popping out.Head partly separated from prothorax, wide in ventral view. Bases of antennae protruding anterolaterally, wide, anterior margin arcuate, with a small lump extending anteriorly connecting margin with vertex expansion. Suture separating anteclypeus and postclypeus visible in ventral aspect (Fig. 4A,B). Postclypeus about three times as long as wide, oval, slightly swollen, without any setae; weak traces of salivary pump muscle attachments visible. Anteclypeus about as long as postclypeus, widened in upper section below clypeal suture, convex, carinately elevated in lower section, with sides distinctly declivitous, clypellus long, carinately elevated. Lora (mandibulary plates) distinct, separated from anteclypeus by shallow suture, with upper angles at half of postclypeus length, lower angles at half of anteclypeus length, about as wide as half of postclypeus width. Maxillary plates narrow. Genal portion of head enlarged, medial portion arcuately convex; lateral sections narrowing laterally, terminally encircling bases of compound eyes. Antennae short (Fig. 3C,D), placed in front of genal portion. Antennal flagellum indistinctly subdivided into four segments. Rostrum (Fig. 4A,B) three-segmented, 0.2 mm long, with apex reaching apex of mesocoxae; apical segment about 2.5 times as long as subapical one.No lateral sclerites on meso- and metathorax, only one plus one large medial sclerite on both meso- and metathorax. Mesothoracic and metathoracic wing pads distinct, wide, subtriangular, with posterior apices directed posteriorly; lateral portions of mesothoracic wing pads arcuate. Fore wing pad 0.6 mm long, with small, straight humeral lobe, forming a right angle, not protruding anteriorly. Mesothoracic tergites slightly larger than metathoracic segments (respectively c. 0.14 mm and c. 0.12 mm long in midline, 0.26 mm and 0.27 mm in lateral lines); mesothoracic tergum with distinct median elevation (low double crest with ecdysial line in between), slightly wider than long in midline, anterior margin arcuate, lateral margins straight, subparallel, posterior margin concave. Metathoracic wing pad apex slightly exceeding mesothoracic wing pad. Metathoracic tergum wider than long, slightly shorter than mesothoracic tergum, with distinct elevation in the middle.Legs relatively long (Figs. 3C,D, 4C–E). Coxae of legs placed near the median axis of the body. Prolegs: procoxal pit with margins elevated, procoxa conical (c. 0.1 mm long), protrochanter scaphoid, about as long as procoxa, profemur c. 0.13 mm long, slightly flattened laterally, merely thickened, protibia longer than profemur, c. 0.23 mm long; tarsus shorter than protibia, basiprotarsomere about as long as apical protarsomere, the latter with distinct tarsal claws, and wide arolium. Mesoleg similar to proleg, mesocoxa conical (c. 0.1 mm long), mesotrochanter scaphoid, mesofemur (c. 0.13 mm) slightly flattened laterally, mesotibia slightly longer than mesofemur (c. 0.18 mm), mesotarsus slightly shorter than mesotibia, three-segmented, basimesotarsomere the longest (c. 0.07 mm), about as long as combined length of mid- and apical mesotarsomeres (c. 0.04 mm respectively), arolium wide, tarsal claws distinct. Metaleg: metacoxa conical (c. 0.1 mm), metatrochanter scaphoid, about as long as metacoxa (c. 0.12 mm). Metafemur (c. 0.17 mm) slightly more thickened than pro- and mesofemur, metatibia slightly longer (0.19 mm) than pro- and mesotibiae. Metatarsus three-segmented: basimetatarsomere about as long (0.08 mm) as combined length of mid- and apical metatarsomeres (0.04 mm respectively), arolium lobate, wide, tarsal claws distinct, widely spread.Abdomen (Fig. 3A–D) 9-segmented, narrow at base, widening fan-shape posteriorly, 1st segment visible from above, segmentation visible, abdominal terga 5th–9th expanded posterolaterally. Tergites carinately elevated in the middle, separated by ecdysial line. 1st sternite visible in ventral view, sternites 2nd–4th fused medially, sternites 5th–9th separated; 9th abdominal segment short (Fig. 4F), placed ventrally, under tergal expansion; anal tube short, cylindrical, epiproct (?) globular. More

DeVault, T. L., Rhodes, O. E. & Shivik, J. A. Scavenging by vertebrates: Behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).
Google Scholar 
Selva, N., Jedrzejewska, B., Jedrzejewski, W. & Wajrak, A. Scavenging on European bison carcasses in Bialowieza Primeval Forest (eastern Poland). Ecoscience 10, 303–311 (2003).
Google Scholar 
Wilson, E. E. & Wolkovich, E. M. Scavenging: How carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).PubMed 

Google Scholar 
Inger, R., Cox, D. T. C., Per, E., Norton, B. A. & Gaston, K. J. Ecological role of vertebrate scavengers in urban ecosystems in the UK. Ecol. Evol. 6, 7015–7023 (2016).PubMed 
PubMed Central 

Google Scholar 
Moleón, M. et al. Humans and scavengers: The evolution of interactions and ecosystem services. Bioscience 64, 394–403 (2014).
Google Scholar 
Moleón, M., Sánchez-Zapata, J. A., Selva, N., Donázar, J. A. & Owen-Smith, N. Inter-specific interactions linking predation and scavenging in terrestrial vertebrate assemblages. Biol. Rev. 89, 1042–1054 (2014).PubMed 

Google Scholar 
Mateo-Tomás, P., Olea, P. P., Moleón, M., Selva, N. & Sánchez-Zapata, J. A. Both rare and common species support ecosystem services in scavenger communities. Glob. Ecol. Biogeogr. 26, 1459–1470 (2017).
Google Scholar 
Houston, D. C. Scavenging efficiency of turkey vultures in tropical forest. Condor 88, 318–323 (1986).
Google Scholar 
Morales-Reyes, Z. et al. Scavenging efficiency and red fox abundance in Mediterranean mountains with and without vultures. Acta Oecol. 79, 81–88 (2017).ADS 

Google Scholar 
Kane, A. & Kendall, C. J. Understanding how mammalian scavengers use information from avian scavengers: Cue from above. J. Anim. Ecol. 86, 837–846 (2017).PubMed 

Google Scholar 
Sebastián-González, E. et al. Functional traits driving species role in the structure of terrestrial vertebrate scavenger networks. Ecology. https://doi.org/10.1002/ecy.3519 (2021).PubMed 

Google Scholar 
Beasley, J. C., Olson, Z. H. & DeVault, T. L. Ecological role of vertebrate scavengers. In Carrion Ecology, Evolution and Their Applications (eds Benbow, M. E. et al.) 107–127 (CRC Press, 2015).
Google Scholar 
Bassi, E., Battocchio, D., Marcon, A., Stahlberg, S. & Apollonio, M. Scavenging on ungulate carcasses in a mountain forest area in Northern Italy. Mamm. Study 43, 1–11 (2018).
Google Scholar 
Enari, H. & Enari, H. S. Not avian but mammalian scavengers efficiently consume carcasses under heavy snowfall conditions: A case from northern Japan. Mamm. Biol. 101, 419–428 (2021).
Google Scholar 
Peers, M. J. L. et al. Prey availability and ambient temperature influence carrion persistence in the boreal forest. J. Anim. Ecol. 89, 2156–2167 (2020).PubMed 

Google Scholar 
Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. R. Soc. B Biol. Sci. 274, 1101–1108 (2007).
Google Scholar 
Inagaki, A. et al. Vertebrate scavenger guild composition and utilization of carrion in an East Asian temperate forest. Ecol. Evol. 10, 1223–1232 (2020).PubMed 
PubMed Central 

Google Scholar 
Sebastián-González, E. et al. Network structure of vertebrate scavenger assemblages at the global scale: Drivers and ecosystem functioning implications. Ecography (Cop.) 43, 1143–1155 (2020).
Google Scholar 
Cortés-Avizanda, A., Selva, N., Carrete, M. & Donázar, J. A. Effects of carrion resources on herbivore spatial distribution are mediated by facultative scavengers. Basic Appl. Ecol. 10, 265–272 (2009).
Google Scholar 
Sebastián-González, E. et al. Nested species-rich networks of scavenging vertebrates support high levels of interspecific competition. Ecology 97, 95–105 (2016).PubMed 

Google Scholar 
Beasley, J. C., Olson, Z. H. & Devault, T. L. Carrion cycling in food webs: Comparisons among terrestrial and marine ecosystems. Oikos 121, 1021–1026 (2012).
Google Scholar 
Ray, R. R., Seibold, H. & Heurich, M. Invertebrates outcompete vertebrate facultative scavengers in simulated lynx kills in the Bavarian Forest National Park, Germany. Anim. Biodivers. Conserv. 37, 77–88 (2014).
Google Scholar 
Sugiura, S. & Hayashi, M. Functional compensation by insular scavengers: The relative contributions of vertebrates and invertebrates vary among islands. Ecography (Cop.) 41, 1173–1183 (2018).
Google Scholar 
Wilmers, C. C., Stahler, D. R., Crabtree, R. L., Smith, D. W. & Getz, W. M. Resource dispersion and consumer dominance: Scavenging at wolf- and hunter-killed carcasses in Greater Yellowstone, USA. Ecol. Lett. 6, 996–1003 (2003).
Google Scholar 
Putman, A. R. J. Patterns of carbon dioxide evolution from decaying carrion: Decomposition of small mammal carrion in temperate systems, Part 1. Oikos 31, 47–57 (1978).CAS 

Google Scholar 
DeVault, T. L. & Rhodes, O. E. Identification of vertebrate scavengers of small mammal carcasses in a forested landscape. Acta Theriol. (Warsz.) 47, 185–192 (2002).
Google Scholar 
Selva, N., Jȩdrzejewska, B., Jȩdrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).
Google Scholar 
Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).CAS 
PubMed 

Google Scholar 
Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 

Google Scholar 
Arrondo, E. et al. Rewilding traditional grazing areas affects scavenger assemblages and carcass consumption patterns. Basic Appl. Ecol. 41, 56–66 (2019).
Google Scholar 
Moleón, M. et al. Carrion availability in space and time. In Carrion Ecology and Management (eds Pedro, P. O. et al.) 23–44 (Springer, 2019).
Google Scholar 
Pereira, L. M., Owen-Smith, N. & Moleón, M. Facultative predation and scavenging by mammalian carnivores: Seasonal, regional and intra-guild comparisons. Mamm. Rev. 44, 44–55 (2014).
Google Scholar 
Animal Care and Use Committee. Guidelines for the capture, handling, and care of mammals as approved by the American Society of Mammalogists. J. Mamm. 79, 1416–1431 (1998).
Google Scholar 
Committee of Reviewing Taxon Names and Specimen Collections. Guidelines for the Procedure of Obtaining Mammal Specimens as Approved by the Mammal Society of Japan (Revised in 2009) (Mammal Society of Japan, 2009).
Google Scholar 
Yoshino, M. Microclimate: New Edition (Chijin Shokan, 1986).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2019).Sokal, R. R. & Rohlf, F. J. Biometry 4th edn. (WH Freeman and Company, 2012).MATH 

Google Scholar 
Fisher, R. A. Statistical Methods for Research Workers (Oliver and Boyd, 1934).MATH 

Google Scholar 
Therneau, T. A Package for Survival Analysis in S. Version 2.38 (2015).Pardo-Barquín, E., Mateo-Tomás, P. & Olea, P. P. Habitat characteristics from local to landscape scales combine to shape vertebrate scavenging communities. Basic Appl. Ecol. 34, 126–139 (2019).
Google Scholar 
Moleón, M., Sánchez-Zapata, J. A., Sebastián-González, E. & Owen-Smith, N. Carcass size shapes the structure and functioning of an African scavenging assemblage. Oikos 124, 1391–1403 (2015).
Google Scholar 
DeVault, T. L., Brisbin, I. L. & Rhodes, O. E. Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).
Google Scholar  More

McCallum, M. L. Vertebrate biodiversity losses point to a sixth mass extinction. Biodivers. Conserv. 24, 2497–2519 (2015).
Google Scholar 
Wake, D. B. & Vredenburg, V. T. Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proc. Natl. Acad. Sci. 105, 11466–11473. https://doi.org/10.1073/pnas.0801921105 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
Article 
PubMed 

Google Scholar 
Pautasso, M., Aas, G., Queloz, V. & Holdenrieder, O. European ash (Fraxinus excelsior) dieback—A conservation biology challenge. Biol. Cons. 158, 37–49 (2013).
Google Scholar 
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Infectious disease and amphibian population declines. Divers. Distrib. 9, 141–150 (2003).
Google Scholar 
Fisher, M. C., Gow, N. A. R. & Gurr, S. J. Tackling emerging fungal threats to animal health, food security and ecosystem resilience. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2016.0332 (2016).Article 

Google Scholar 
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).CAS 
PubMed 

Google Scholar 
Lips, K. R., Reeve, J. D. & Witters, L. R. Ecological traits predicting amphibian population declines in Central America. Conserv. Biol. 17, 1078–1088 (2003).
Google Scholar 
Zipkin, E. F., DiRenzo, G. V., Ray, J. M., Rossman, S. & Lips, K. R. Tropical snake diversity collapses after widespread amphibian loss. Science 367, 814–816. https://doi.org/10.1126/science.aay5733 (2020).CAS 
Article 
PubMed 

Google Scholar 
Berger, L. et al. Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America. Proc. Natl. Acad. Sci. 95, 9031–9036 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martel, A. et al. Recent introduction of a chytrid fungus endangers Western Palearctic salamanders. Science 346, 630–631. https://doi.org/10.1126/science.1258268 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yap, T. A., Koo, M. S., Ambrose, R. F., Wake, D. B. & Vredenburg, V. T. Averting a North American biodiversity crisis. Science 349, 481–482 (2015).CAS 
PubMed 

Google Scholar 
Weldon, C., du Preez, L. H., Hyatt, A. D., Muller, R. & Speare, R. Origin of the amphibian chytrid fungus. Emerg. Infect. Dis. 10, 2100–2105 (2004).PubMed 
PubMed Central 

Google Scholar 
Talley, B. L., Muletz, C. R., Vredenburg, V. T., Fleischer, R. C. & Lips, K. R. A century of Batrachochytrium dendrobatidis in Illinois amphibians (1888–1989). Biol. Cons. 182, 254–261 (2015).
Google Scholar 
Rodriguez, D., Becker, C., Pupin, N., Haddad, C. & Zamudio, K. Long-term endemism of two highly divergent lineages of the amphibian-killing fungus in the Atlantic Forest of Brazil. Mol. Ecol. 23, 774–787 (2014).CAS 
PubMed 

Google Scholar 
Goka, K. et al. Amphibian chytridiomycosis in Japan: Distribution, haplotypes and possible route of entry into Japan. Mol. Ecol. 18, 4757–4774 (2009).CAS 
PubMed 

Google Scholar 
Bataille, A. et al. Genetic evidence for a high diversity and wide distribution of endemic strains of the pathogenic chytrid fungus Batrachochytrium dendrobatidis in wild Asian amphibians. Mol. Ecol. 23, 4196–4209. https://doi.org/10.1111/mec.12385 (2013).CAS 
Article 

Google Scholar 
O’Hanlon, S. J. et al. Recent Asian origin of chytrid fungi causing global amphibian declines. Science 360, 621–627. https://doi.org/10.1126/science.aar1965 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Swei, A. et al. Is chytridiomycosis an emerging infectious disease in Asia?. PLoS ONE 6, e23179 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bai, C. M., Garner, T. W. J. & Li, Y. M. First evidence of Batrachochytrium dendrobatidis in China: Discovery of chytridiomycosis in introduced American bullfrogs and native amphibians in the Yunnan Province, China. EcoHealth 7, 127–134. https://doi.org/10.1007/s10393-010-0307-0 (2010).Article 
PubMed 

Google Scholar 
Yang, H. et al. First detection of the amphibian chytrid fungus Batrachochytrium dendrobatidis in free-ranging populations of amphibians on mainland Asia: Survey in South Korea. Dis. Aquat. Org. 86, 9–13 (2009).
Google Scholar 
Fong, J. J. et al. Early 1900s detection of Batrachochytrium dendrobatidis in Korean amphibians. PLoS ONE 10, e0115656 (2015).PubMed 
PubMed Central 

Google Scholar 
Kusrini, M., Skerratt, L., Garland, S., Berger, L. & Endarwin, W. Chytridiomycosis in frogs of Mount Gede Pangrango, Indonesia. Diseases Aquat. Organ. 82, 187–194 (2008).CAS 

Google Scholar 
Laking, A. E., Ngo, H. N., Pasmans, F., Martel, A. & Nguyen, T. T. Batrachochytrium salamandrivorans is the predominant chytrid fungus in Vietnamese salamanders. Sci. Rep. 7, 44443. https://doi.org/10.1038/srep44443 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhu, W. et al. A survey for Batrachochytrium salamandrivorans in Chinese amphibians. Curr. Zool. 60, 729–735 (2014).
Google Scholar 
Beukema, W. et al. Environmental context and differences between native and invasive observed niches of Batrachochytrium salamandrivorans affect invasion risk assessments in the Western Palaearctic. Divers. Distrib. 24, 1788–1801. https://doi.org/10.1111/ddi.12795 (2018).Article 

Google Scholar 
Auliya, M. et al. The global amphibian trade flows through Europe: The need for enforcing and improving legislation. Biodivers. Conserv. https://doi.org/10.1007/s10531-016-1193-8 (2016).Article 

Google Scholar 
Scheffers, B. R., Edwards, D. P., Diesmos, A., Williams, S. E. & Evans, T. A. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).
Google Scholar 
Schmeller, D. S. et al. People, pollution and pathogens—Global change impacts in mountain freshwater ecosystems. Sci. Total Environ. 622–623, 756–763. https://doi.org/10.1016/j.scitotenv.2017.12.006 (2018).CAS 
Article 
PubMed 

Google Scholar 
Bernardo-Cravo, A., Schmeller, D. S., Chatzinotas, A., Vredenburg, V. T. & Loyau, A. Environmental factors and host microbiomes shape host-pathogen dynamics. Trends Parasitol. 36, 29–36 (2020).
Google Scholar 
Harris, R. N. et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 3, 818–824. https://doi.org/10.1038/ismej.2009.27 (2009).CAS 
Article 
PubMed 

Google Scholar 
Harris, R. N., James, T. Y., Lauer, A., Simon, M. A. & Patel, A. Amphibian pathogen Batrachochytrium dendrobatidis is inhibited by the cutaneous bacteria of amphibian species. EcoHealth 3, 53–56. https://doi.org/10.1007/s10393-10005-10009-10391 (2006).Article 

Google Scholar 
Piovia-Scott, J. et al. Greater species richness of bacterial skin symbionts better suppresses the amphibian fungal pathogen Batrachochytrium dendrobatidis. Microb. Ecol. 74, 217–226 (2017).PubMed 

Google Scholar 
Ellison, S., Knapp, R. A., Sparagon, W., Swei, A. & Vredenburg, V. T. Reduced skin bacterial diversity correlates with increased pathogen infection intensity in an endangered amphibian host. Mol. Ecol. 28, 127–140 (2019).PubMed 

Google Scholar 
Jani, A. J. & Briggs, C. J. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc. Natl. Acad. Sci. USA 111, E5049-5058. https://doi.org/10.1073/pnas.1412752111 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kueneman, J. G. et al. The amphibian skin-associated microbiome across species, space and life history stages. Mol. Ecol. 23, 1238–1250 (2014).PubMed 

Google Scholar 
Kueneman, J. G. Ecology of the Amphibian Skin-Associated Microbiome and Its Role in Pathogen Defense (University of Colorado at Boulder, 2015).
Google Scholar 
Kueneman, J. G. et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat. Ecol. Evolut. 3, 381–389. https://doi.org/10.1038/s41559-019-0798-1 (2019).Article 

Google Scholar 
Jiménez, R. R. & Sommer, S. The amphibian microbiome: Natural range of variation, pathogenic dysbiosis, and role in conservation. Biodivers. Conserv. 26, 763–786. https://doi.org/10.1007/s10531-016-1272-x (2017).Article 

Google Scholar 
Walke, J. B. et al. Amphibian skin may select for rare environmental microbes. ISME J 8, 2207–2217. https://doi.org/10.1038/ismej.2014.77 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKenzie, V. J., Bowers, R. M., Fierer, N., Knight, R. & Lauber, C. L. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J 6, 588–596. https://doi.org/10.1038/ismej.2011.129 (2012).CAS 
Article 
PubMed 

Google Scholar 
Bates, K. A. et al. Amphibian chytridiomycosis outbreak dynamics are linked with host skin bacterial community structure. Nat. Commun. 9, 693. https://doi.org/10.1038/s41467-018-02967-w (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ellison, S. et al. The influence of habitat and phylogeny on the skin microbiome of amphibians in Guatemala and Mexico. Microb. Ecol. 78, 257–267 (2019).PubMed 

Google Scholar 
Fisher, M. C., Pasmans, F. & Martel, A. Virulence and pathogenicity of chytrid fungi causing amphibian extinctions. Annu. Rev. Microbiol. https://doi.org/10.1146/annurev-micro-052621-124212 (2021).Article 
PubMed 

Google Scholar 
Haver, M. et al. The role of abiotic variables in an emerging global amphibian fungal disease in mountains. Sci. Total Environ. 815, 152735 (2021).PubMed 

Google Scholar 
Turner, A., Wassens, S., Heard, G. & Peters, A. Temperature as a driver of the pathogenicity and virulence of amphibian chytrid fungus Batrachochytrium dendrobatidis: A systematic review. J. Wildl. Dis. 57, 477–494 (2021).PubMed 

Google Scholar 
Woodhams, D., Alford, R., Briggs, C., Johnson, M. & Rollins-Smith, L. Life history trade-offs influence disease in changing climates: Strategies of an amphibian pathogen. Ecology 89, 1627–1639 (2008).PubMed 

Google Scholar 
Sonn, J. M., Berman, S. & Richards-Zawacki, C. L. The influence of temperature on chytridiomycosis in vivo. EcoHealth 14, 762–770. https://doi.org/10.1007/s10393-017-1269-2 (2017).Article 
PubMed 

Google Scholar 
Schmidt, B., Küpfer, E., Geiger, C., Wolf, S. & Schär, S. Elevated temperature clears chytrid fungus infections from tadpoles of the midwife toad, Alytes obstetricans. Amphibia-Reptilia 32, 276–280 (2011).
Google Scholar 
Bielby, J., Cooper, N., Cunningham, A. A., Garner, T. W. J. & Purvis, A. Predicting susceptibility to future declines in the world’s frogs. Conserv. Lett. 1, 82–90 (2008).
Google Scholar 
Gray, M. J., Miller, D. L. & Hoverman, J. T. Ecology and pathology of amphibian ranaviruses. Dis. Aquat. Org. 87, 243–266 (2009).
Google Scholar 
Murray, K., Skerratt, L., Speare, R. & McCallum, H. Impact and dynamics of disease in species threatened by the amphibian chytrid fungus, Batrachochytrium dendrobatidis. Conserv. Biol. 23, 1242–1252 (2009).PubMed 

Google Scholar 
Schmeller, D. S. et al. Microscopic aquatic predators strongly affect infection dynamics of a globally emerged pathogen. Curr. Biol. 24, 176–180. https://doi.org/10.1016/j.cub.2013.11.032 (2014).CAS 
Article 
PubMed 

Google Scholar 
Metzger, M. J. et al. Environmental stratifications as the basis for national, European and global ecological monitoring. Ecol. Ind. 33, 26–35. https://doi.org/10.1016/j.ecolind.2012.11.009 (2013).Article 

Google Scholar 
Metzger, M. J. et al. A high-resolution bioclimate map of the world: A unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638. https://doi.org/10.1111/geb.12022 (2013).Article 

Google Scholar 
Clare, F., Daniel, O., Garner, T. & Fisher, M. Assessing the ability of swab data to determine the true burden of infection for the amphibian pathogen Batrachochytrium dendrobatidis. EcoHealth 13, 360–367. https://doi.org/10.1007/s10393-016-1114-z (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Cheng, T. L., Rovito, S. M., Wake, D. B. & Vredenburg, V. T. Coincident mass extirpation of neotropical amphibians with the emergence of the infectious fungal pathogen Batrachochytrium dendrobatidis. Proc. Natl. Acad. Sci. 108, 9502–9507 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vredenburg, V. T. et al. Pathogen invasion history elucidates contemporary host pathogen dynamics. PLoS ONE 14, e0219981. https://doi.org/10.1371/journal.pone.0219981 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hyatt, A. D. et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis. Aquat. Org. 73, 175–192 (2007).CAS 

Google Scholar 
Blooi, M. et al. Duplex real-time PCR for rapid simultaneous detection of Batrachochytrium dendrobatidis and B. salamandrivorans in amphibian samples. J. Clin. Microbiol. 51, 4173–4177 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boyle, D. G., Boyle, D. B., Olsen, V., Morgan, J. A. T. & Hyatt, A. D. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis. Aquat. Org. 60, 141–148 (2004).CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. & Mills, D. A. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl. Environ. Microbiol. https://doi.org/10.1128/aem.03870-12 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191. https://doi.org/10.1038/sdata.2017.191 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wells, N., Goddard, S. & Hayes, M. J. A self-calibrating Palmer Drought Severity Index. J. Clim. 17, 2335–2351 (2004).
Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Fisher, M. C. et al. RACE: Risk assessment of chytridiomycosis to European Amphibian Biodiversity. Froglog 101, 45–47 (2012).
Google Scholar  More

Analysis of contents of heavy metals in wasteland soilThe test results show (Table 5) that the contents of Hg, Cd, As, Pb, Cr, Zn, Ni and Cu in the surface soil within Shigetai Coal Mine vary from 0.043 to 0.255, 0.44 to 2.23, 2.66 to 18.40, 11.80 to 42.80, 40.50 to 118.60, 18.90 to 70.10, 4.31 to 28.10, 4.96 to 46.25 mg/kg, respectively; the average contents of Hg, Cd, As, Pb, Cr, Zn, Ni and Cu are 0.128, 1.03, 4.73, 23.08, 76.22, 46.94, 16.11 and 12.10 mg/kg, respectively. The average contents of Hg, Cd, Pb and Cr in soil within the research area are 2.03, 1.36, 1.11 and 1.23 times of the soil background values in Shaanxi Province, respectively. The average contents of As, Zn and Cu are lower than the soil background value in Shaanxi Province, but the maximum contents of these three elements are 1.65, 1.01 and 2.16 times of the soil background values in Shaanxi Province, respectively. It is reported that the average concentration of lead in agricultural soil affected by coal mines is relatively high (433 mg kg−1)38. Lead is usually related to minerals in coal and occurs mainly in the form of sulfide such as PbS and PbSe39. In addition, aluminosilicate and carbonate also contain lead40. Chromium is a non-volatile element, which is related to aluminosilicate minerals41. In the mining process, chromium may be accumulated in coal, gangue or other tailings, and then enter the soil or water body through rain leaching42.Table 5 Statistics of contents of heavy metals in wasteland soil (n = 79).Full size tableThe coefficient of variation (CV) of Hg and Cd contents in soil within the research area is 0.050 and 0.37, respectively, with moderate variation, indicating that the content of these two heavy metals is less affected by the external factors; the coefficient of variation (CV) of As, Pb, Cr, Zn, Ni and Cu contents is 2.81, 7.46, 18.00, 13.51, 5.44 and 5.64, respectively, with strong variation (CV  > 0.50)43, indicating that the content of these eight heavy metals may be affected by some local pollution sources. The skewness coefficient (SK) ranges from − 3 to 3, and the larger its absolute value, the greater its skewness. When SK  > 0, it is positive skewness; when SK  More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

Google Scholar 
Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P. & Polovina, J. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9–25 (2016).
Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).CAS 

Google Scholar 
Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).CAS 

Google Scholar 
Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).CAS 

Google Scholar 
Angel, M. V. & de C. Baker, A. Vertical distribution of the standing crop of plankton and micronekton at three stations in the northeast Atlantic. Biol. Oceanogr. 2, 1–30 (1982).
Google Scholar 
Cook, A. B., Sutton, T. T., Galbraith, J. K. & Vecchione, M. Deep-pelagic (0–3000 m) fish assemblage structure over the Mid-Atlantic Ridge in the area of the Charlie-Gibbs Fracture Zone. Deep Sea Res. 2 98, 279–291 (2013).
Google Scholar 
Hidaka, K., Kawaguchi, K., Murakami, M. & Takahashi, M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep Sea Res. 1 48, 1923–1939 (2001).Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342 (2015).
Google Scholar 
Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1639–1664 (2021).CAS 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 

Google Scholar 
Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).
Google Scholar 
Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).
Google Scholar 
Kwiatkowski, L., Aumont, O. & Bopp, L. Consistent trophic amplification of marine biomass declines under climate change. Glob. Change Biol. 25, 218–229 (2019).
Google Scholar 
Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).CAS 

Google Scholar 
Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
Google Scholar 
Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).
Google Scholar 
Reid, S. B., Hirota, J., Young, R. E. & Hallacher, L. E. Mesopelagic-boundary community in Hawaii: micronekton at the interface between neritic and oceanic ecosystems. Mar. Biol. 109, 427–440 (1991).
Google Scholar 
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).
Google Scholar 
Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. PICES Scientific Report 38 (North Pacific Marine Science Organization, 2010).Kaartvedt, S., Staby, A. & Aksnes, D. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6 (2012).
Google Scholar 
Gjøsaeter, J. & Kawaguchi, K. A Review of the World Resources of Mesopelagic Fish Fisheries Technical Paper 193 (FAO, 1980).Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).
Google Scholar 
Benoit-Bird, K. J. & Lawson, G. L. Ecological insights from pelagic habitats acquired using active acoustic techniques. Annu. Rev. Mar. Sci. 8, 463–490 (2016).
Google Scholar 
Annasawmy, P. et al. Micronekton diel migration, community composition and trophic position within two biogeochemical provinces of the south west Indian Ocean: insight from acoustics and stable isotopes. Deep Sea Res. 1 138, 85–97 (2018).CAS 

Google Scholar 
Haris, K. et al. Sounding out life in the deep using acoustic data from ships of opportunity. Sci. Data 8, 23 (2021).CAS 

Google Scholar 
Irigoien, X. et al. The Simrad EK60 echosounder dataset from the Malaspina circumnavigation. Sci. Data 8, 259 (2021).
Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
Google Scholar 
Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).CAS 

Google Scholar 
Proud, R., Cox, M., Le Guen, C. & Brierley, A. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Mar. Ecol. Prog. Ser. 598, 35–48 (2018).
Google Scholar 
Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119 (2017).CAS 

Google Scholar 
Ramsay, J. O. & Silverman, B. W. Functional Data Analysis (Springer, 2005).Moriarty, R. & O’Brien, T. D. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).
Google Scholar 
Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, e1602468 (2017).
Google Scholar 
Bertrand, A., Ballón, M. & Chaigneau, A. Acoustic observation of living organisms reveals the upper limit of the oxygen minimum zone. PLoS ONE 5, e10330 (2010).
Google Scholar 
Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).CAS 

Google Scholar 
Godø, O. R., Patel, R. & Pedersen, G. Diel migration and swimbladder resonance of small fish: some implications for analyses of multifrequency echo data. ICES J. Mar. Sci. 66, 1143–1148 (2009).
Google Scholar 
Agersted, M. D. et al. Mass estimates of individual gas-bearing mesopelagic fish from in situ wideband acoustic measurements ground-truthed by biological net sampling. ICES J. Mar. Sci. 78, 3658–3673 (2021).
Google Scholar 
Backus, R. & Craddock, J. in Oceanic Sound Scattering Prediction (eds Anderson, N. R. & Zahuranec, B. J.) 529–547 (Springer, 1977).Longhurst, A. Ecological Geography of the Sea (Elsevier, 2010).Spalding, M. D., Agostini, V. N., Rice, J. & Grant, S. M. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean Coast. Manage. 60, 19–30 (2012).
Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).
Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge Univ. Press, 2010).Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 

Google Scholar 
Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).
Google Scholar 
Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J. & Brierley, A. S. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES J. Mar. Sci. 76, 718–733 (2019).
Google Scholar 
Chapman, R. P., Bluy, O. Z., Adlington, R. H. & Robison, A. E. Deep scattering layer spectra in the Atlantic and Pacific oceans and adjacent seas. J. Acoust. Soc. Am. 56, 1722–1734 (1974).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proc. R. Soc. B 286, 20190353 (2019).
Google Scholar 
Escobar-Flores, P. C., O’Driscoll, R. L., Montgomery, J. C., Ladroit, Y. & Jendersie, S. Estimates of density of mesopelagic fish in the Southern Ocean derived from bulk acoustic data collected by ships of opportunity. Polar Biol. 43, 43–61 (2020).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. Proc. R. Soc. B 289, 20211781 (2022).
Google Scholar 
Reygondeau, G. et al. Climate change-induced emergence of novel biogeochemical provinces. Front. Mar. Sci. 7, 657 (2020).
Google Scholar 
Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).
Google Scholar 
Bianchi, D., Carozza, D. A., Galbraith, E. D., Guiet, J. & DeVries, T. Estimating global biomass and biogeochemical cycling of marine fish with and without fishing. Sci. Adv. 7, eabd7554 (2021).
Google Scholar 
Grimaldo, E. et al. Investigating the potential for a commercial fishery in the northeast Atlantic utilizing mesopelagic species. ICES J. Mar. Sci. 77, 2541–2556 (2020).
Google Scholar 
Olsen, R. E. et al. Can mesopelagic mixed layers be used as feed sources for salmon aquaculture? Deep Sea Res. 2 180, 104722 (2020).CAS 

Google Scholar 
De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291 (2007).
Google Scholar 
Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493 (2015).
Google Scholar 
Perrot, Y. et al. Matecho: an open-source tool for processing fisheries acoustics data. Acoust. Aust. 46, 241–248 (2018).
Google Scholar 
Stanton, T. Review and recommendations for the modelling of acoustic scattering by fluid-like elongated zooplankton: euphausiids and copepods. ICES J. Mar. Sci. 57, 793–807 (2000).
Google Scholar 
GEBCO: A Continuous Terrain Model of the Global Oceans and Land (British Oceanographic Data Centre, 2019).EchoPY v.1.1: Fisheries Acoustic Data Processing in Python (Python, 2020); https://pypi.org/project/echopyde Boor, C. A Practical Guide to Splines (Springer, 1978).Clustering (SciKit Learn, 2021); https://scikit-learn.org/stable/modules/clusteringEyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 
Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, eaay4740 (2020).
Google Scholar 
Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. J. Adv. Model. Earth Syst. 13, e2021MS002496 (2021).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Locarnini, R. et al. World Ocean Atlas 2018, Volume 1: Temperature NOAA Atlas NESDIS 81 (NOAA, 2018).García, H. et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation NOAA Atlas NESDIS 83 (NOAA, 2018).Sathyendranath, S. et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 19 May 2021; http://www.esa-oceancolour-cci.org More

The overall magnitude of changes in SOC, TN, and C:N in response to chemical nitrogen fertilizers reductionThe results showed that chemical nitrogen fertilizers reduction significantly decreased SOC and TN by 2.76% and 4.19% respectively, while increased C:N by 6.11% across all database (Fig. 1). SOC mainly derives from crop residues and secretions which closely related to crops growths, and crops growths were affected by fertilization, especially nitrogen fertilization20,21. The reduction of chemical nitrogen fertilizer led to poor crop growth, which reduced the amount of crop residues return, and then decreased SOC. Similarly, TN from crops was reduced due to poor crop growth. In addition, the reduction of chemical nitrogen fertilizers directly reduced the input of soil nitrogen. The increase of C:N was the result of the decrease of TN being greater than that of SOC. The responses of C:N to chemical nitrogen fertilizers reduction enhanced the comprehension of the couple relationship between SOC and TN, which was beneficial to the evolution of the C-N coupling models. Moreover, the accuracy of C-N coupling models depends on the precise quantification of the responses of SOC and TN to nitrogen fertilization. And our results accurately quantified the difference responses of SOC and TN to different nitrogen fertilization regimes, thus optimizing the C-N coupling models.Figure 1The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The value represents independent sample size.Full size imageResponses of SOC, TN and C:N to chemical nitrogen fertilizers reduction magnitudeWhen grouped by chemical nitrogen fertilizers reduction magnitude, SOC showed a significant increase by 6.9% in medium magnitude, while SOC was significantly decreased by 3.10% and 7.26% in high and total magnitude respectively (Fig. 1a). Liu and Greaver22 also stated the reduction of medium nitrogen fertilizer increased the average microbial biomass from 15 to 20%, thereby increasing the SOC content. Previous studies had reported that there were strong positive correlations between soil organic matter and soil microbial biomass in both the agricultural ecosystem and natural ecosystem23,24. Numerous researchers have demonstrated the significance of nitrogen availability in soil for the plant biomass across most ecosystems25,26. Moreover, nitrogen deficient would inhibit the activity of extracellular enzymes and root activities27. Generally, soil degradation caused by continuous rising chemical nitrogen fertilizers application may inhibit the growth of crops and ultimately reduce the SOC28.TN showed no significant difference in low and medium chemical nitrogen fertilizers reduction magnitude (p  > 0.05), while TN in high magnitude and total chemical nitrogen fertilizers reduction magnitude exhibited a decrease with 3.10% and 9.37% respectively (Fig. 1b). Numerous studies described that the amount of nitrogen fertilizers used in China was higher than the demand of N for crop, which caused serious N leaching and runoff29,30. Chemical nitrogen fertilizers in low and medium magnitude would not decrease the TN of soil by reducing N leaching and runoff. However, the residual nitrogen in soil cannot meet the requirement for the sustainable growth of plant with litter or without exogenous nitrogen supplement, which resulted in the decrease of TN in high and total chemical nitrogen fertilizers magnitude. Consequently, optimal nitrogen fertilizers application rates will take into account crops yield and environment friendliness.Additionally, C:N had a significant increase with ranging from 1.82% to 8.98% under the four chemical nitrogen fertilizers reduction magnitude (Fig. 1c), suggesting the relative increase of SOC compared to TN. Previous studies have revealed that C:N had significantly influence on the soil bacterial community structures31. And there were also considerable studies indicated that chemical nitrogen fertilizers have impact on the soil bacterial communities32,33. We may speculate that the change of C:N would bring about the variations of soil bacteria communities under the chemical nitrogen fertilizers regimes.Responses of SOC, TN, and C:N to chemical nitrogen fertilizers reduction durationNegative response of SOC to short-term chemical nitrogen fertilizers reduction was observed in our study, which was consistent with the study of Gong, et al.34 that chemical nitrogen fertilizers reduction decreased SOC by reducing crop-derived carbon by one year. However, SOC was significantly increased by 1.06% and 4.65% at mid-term and long-term chemical nitrogen fertilizers reduction respectively, which was similar with the findings of Ning, et al.11 that SOC was significantly increased under more than 5 years of chemical nitrogen fertilizers reduction treatment. TN was significantly decreased by 1.96% at short-term chemical nitrogen fertilizers reduction duration, while the results converted at mid-term chemical nitrogen fertilizers reduction duration. The effect of long-term chemical nitrogen fertilizers reduction on TN was not significant (p  > 0.05). The divergent response of TN to different chemical nitrogen fertilizers duration was mainly caused by the various treatments. In terms of C:N, a greater positive response was observed at short-term chemical nitrogen fertilizers duration (9.06%) than mid-term and long-term duration (1.99%). Moreover, with the prolongation of the chemical reduction time of nitrogen, the response ratio tends to zero, suggesting that the effect of chemical fertilizers gradually decrease. This may be ascribed to the buffer capacity of soil to resist the changes from external environment, including nutrients, pollutants, and redox substances35.Responses of SOC, TN, and C:N to different chemical nitrogen fertilizers reduction patternsUnder the pattern of chemical nitrogen fertilizers reduction without organic fertilizers supplement, SOC and TN significantly decreased by 3.83% and 11.46% respectively, however, chemical nitrogen fertilizers reduction with organic fertilizers supplement significantly increased SOC and TN by 4.92% and 8.33% respectively. Moreover, C:N significantly increased under the two chemical nitrogen fertilizers patterns (p  0.05), but there was a negative effect on SOC in high and total magnitude (p  0.05). The no significant decrease at mid-term duration might result from the limited information reported in original studies of this meta-analysis36. TN showed no significant response to chemical nitrogen fertilizers without organic fertilizers supplement in the low and medium magnitude (p  > 0.05). However, TN was significantly decreased by 8.62% and 16.7% respectively in the high and total magnitude. When regarding to chemical nitrogen fertilizers reduction duration, TN was significantly reduced at all of the categories, ranging from 3.13% to 13.4% (Fig. 2c). In the pattern of chemical nitrogen fertilizers reduction with organic fertilizers supplement, chemical nitrogen fertilizers reduction at medium, high, and total magnitudes significantly increased SOC by 13.85%, 13.03%, and 5.46%respectively, however, the response of SOC in the low chemical nitrogen fertilizers magnitude was not significant. Chemical nitrogen fertilizers reduction duration significantly increased SOC by 7.01%, 1.71%, and 22.02% in the short-term, mid-term, and long-term respectively. Comparatively, TN showed a significantly increase in most chemical nitrogen fertilizers categories expect for the long-term chemical nitrogen fertilizers duration, with an increasing from 4.90% to 14.69% (Fig. 2d).Figure 2The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c) under the two patterns (with organic fertilizers ; without organic fertilizers). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The values represent independent sample size.Full size imageOrganic fertilizers were mainly derived from animal manure or crops straws, which contained large amount of organic matter and nitrogen elements37,38. The application of organic fertilizers increased the input of SOC and TN directly. Moreover, organic fertilizer could promote the growth of crops by releasing phenols, vitamins, enzymes, auxins and other substances during the decomposition process, thus the SOC derived from crops would be increased37,39. In addition, organic fertilizers provide various nutrients for microbial reproduction, which increase the microbial population and organic carbon and total nitrogen content37. More importantly, the application of organic fertilizers could improve organic carbon sequestration and maintain its stability in aggregates, thereby reducing losses of SOC and TN40.C:N showed an increase under all of the chemical nitrogen fertilizers reduction with organic fertilizer supplement. The positive response of C:N to organic fertilizer supplement may be related to the higher C:N of organic fertilizer than soil. The average values of C:N of the commonly used organic fertilizers including animal manure, crop straws and biochar were 14, 60 and 30 respectively, while the C:N of soil was lower than 10 in average according to extensive literature researches41. Therefore, organic fertilizers would be a favorable alternative of chemical fertilizers for the sustainable development of agriculture.The correlation between the response of SOC, TN, and C:N and environmental variablesThe analysis of linear regression was conducted to analyze the environmental variables including mean annual temperature (MAT), mean annual precipitation (MAP), accumulated temperature above 10 °C (MATA), which may exert influence on SOC, TN and C:N. No significant correlation among the lnRR of SOC, TN, C:N and environmental variables were observed among the whole database (p  > 0.05; Fig. S1). Rule out the interference of organic fertilizers supplement, we analyzed the relationship between lnRR of SOC, TN, C:N and environmental variables as the Figures showed in Figs. 3 and 4 respectively. Under chemical nitrogen fertilizers without organic fertilizers supplement, there was a significant negative correlation between lnRR of SOC and MAT (p  More