Philippa Kaur

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

Physico-chemical characterization of the Sado EstuaryThe seasonal cycle of water temperature in the Sado Estuary in 2018 and 2019, showed the expected pattern, with maxima temperature observed in summer and minima in winter (Fig. 2A). During summer, warmer temperatures were found in the inner regions of the estuary (AC and MC) and lower temperatures near the mouth of the estuary (EM). During winter, there was an inversion of the pattern, with the coolest waters recorded inside the estuary (Fig. 2A). Near the estuary mouth (EM), salinities recorded were always between 35 and 36 (Fig. 2B). In the inner stations, higher salinities ( > 30) were found during summer/early-autumn of 2018 and late-spring/summer of 2019. Maxima salinities ( > 36) were recorded in the summer of 2019 (Fig. 2B). The lowest salinities were always found in the upper region (AC), reaching a minimum of 12 in March 2018 (Fig. 2B).Figure 2Discrete time series of physico-chemical variables obtained in the Sado Estuary during sampling surveys. (A)—Water temperature (°C); (B)—Salinity; (C)—Turbidity (NTU); (D)—Coloured dissolved organic matter at 443 nm (CDOM, m−1); (E)—pH; (F)—Dissolved oxygen (DO, mg L−1); (G)—Dissolved inorganic nitrogen (DIN, µmol L−1); (H)—Phosphate (PO43−, µmol L−1); and (I) –Silicate (Si(OH)4, µmol L−1).Full size imageThe water turbidity was substantially higher in AC, reaching values above 10 NTU in the summer of 2018 and since spring of 2019, with a maximum of 40 NTU recorded in March 2018 (Fig. 2C). The turbidity values and seasonal pattern for stations MC and MR were similar, with a maximum of 10 NTU recorded in MR during spring of 2018 (Fig. 2C). Lower turbidity was observed during winter in stations AC, MC, and MR (Fig. 2C). Water turbidity was always lower than 1.5 NTU at EM (Fig. 2C). The CDOM was higher in the upper region and lower in the downstream area (Fig. 2D). At AC, a CDOM value  10 µmol L−1) (Fig. 2G). Phosphate concentrations were below 1.5 µmol L−1 in the entire estuary, with higher values in the inner stations, and lower ( More

Díez B, Ininbergs K. Ecological importance of cyanobacteria. In Cyanobacteria (pp. 41–63). John Wiley & Sons, Ltd. (2013) https://doi.org/10.1002/9781118402238.ch3Fristachi A, Sinclair JL, Hall S, Berkman JAH, Boyer G, Burkholder J, et al. Occurrence of cyanobacterial harmful algal blooms workgroup report. Adv Experimental Med Biol. 2008;619:45–103. https://doi.org/10.1007/978-0-387-75865-7_3CAS 
Article 

Google Scholar 
Huisman J, Codd GA, Paerl HW, Ibelings BW, Verspagen JMH, Visser PM. Cyanobacterial blooms. Nat Rev Microbiol. 2018;16:471–83. https://doi.org/10.1038/s41579-018-0040-1CAS 
Article 
PubMed 

Google Scholar 
Plaas HE, Paerl HW. Toxic Cyanobacteria: A Growing Threat to Water and Air Quality. In Environmental Science and Technology (Vol. 55, Issue 1, pp. 44–64). American Chem Soc. 2021. https://doi.org/10.1021/acs.est.0c06653Kurmayer R, Deng L, Entfellner E. Role of toxic and bioactive secondary metabolites in colonization and bloom formation by filamentous cyanobacteria Planktothrix. Harmful Algae. 2016;54:69–86. https://doi.org/10.1016/j.hal.2016.01.004CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rohrlack T, Christiansen G, Kurmayer R. Putative antiparasite defensive system involving ribosomal and nonribosomal oligopeptides in cyanobacteria of the genus planktothrix. Appl Environ Microbiol. 2013;79:2642–7. https://doi.org/10.1128/AEM.03499-12CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Legnani E, Copetti D, Oggioni A, Tartari G, Palumbo MT, Morabito G. Planktothrix rubescens’ seasonal dynamics and vertical distribution. J Limnol. 2005;64:61–73.Article 

Google Scholar 
Walsby A, Ng G, Dunn C, Davis PA. Comparison of the depth where Planktothrix rubescens stratifies and the depth where the daily insolation supports its neutral buoyancy. New Phytologist. 2004;162:133–45. https://doi.org/10.1111/j.1469-8137.2004.01020.xArticle 

Google Scholar 
Bruning K. Effects of temperature and light on the population dynamics of the Asterionella-Rhizophydium association. J Plankton Res. 1991a;13:707–19. https://doi.org/10.1093/plankt/13.4.707Article 

Google Scholar 
Rohrlack T, Haande S, Molversmyr Å, Kyle M. Environmental Conditions Determine the Course and Outcome of Phytoplankton Chytridiomycosis. 2015;1–17. https://doi.org/10.1371/journal.pone.0145559Tao Y, Wolinska J, Hölker F, Agha R. Light intensity and spectral distribution affect chytrid infection of cyanobacteria via modulation of host fitness. Parasitology. 2020;147:1206–15. https://doi.org/10.1017/S0031182020000931CAS 
Article 
PubMed 

Google Scholar 
Davis PA, Walsby A. Comparison of measured growth rates with those calculated from rates of photosynthesis in Planktothrix spp. isolated from Blelham Tarn, English Lake District. New Phytologist. 2002;156:225–39. https://doi.org/10.1046/j.1469-8137.2002.00495.xCAS 
Article 
PubMed 

Google Scholar 
Oberhaus L, Briand JF, Leboulanger C, Jacquet S, Humbert JF. Comparative effects of the quality and quantity of light and temperature on the growth of Planktothrix agardhii and P. rubescens 1. J Phycol. 2007;43:1191–9. https://doi.org/10.1111/j.1529-8817.2007.00414.xCAS 
Article 

Google Scholar 
Reynolds CS Growth and replication of phytoplankton. In The Ecology of Phytoplankton (pp. 145–77). Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511542145.005Litchman E, Klausmeier CA . Trait-based community ecology of phytoplankton. Ann Rev Ecol, Evol, Syst. 2008;39:615–39.Edwards KF, Thomas MK, Klausmeier CA, Litchman E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol Oceanography. 2016;61:1232–44.Article 

Google Scholar 
Thomas MK, Kremer CT, Litchman E. Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Global Ecol Biogeog. 2016;25:75–86. https://doi.org/10.1111/geb.12387Article 

Google Scholar 
Bright DI, Walsby A. The daily integral of growth by Planktothrix rubescens calculated from growth rate in culture and irradiance in Lake Zürich. New Phytologist. 2000;146:301–16. https://doi.org/10.1046/j.1469-8137.2000.00640.xArticle 
PubMed 

Google Scholar 
Jann-Para G, Schwob I, Feuillade M. Occurrence of toxic Planktothrix rubescens blooms in lake Nantua, France. Toxicon. 2004;43:279–85.CAS 
Article 

Google Scholar 
Jacquet S, Briand JF, Leboulanger C, Avois-Jacquet C, Oberhaus L, Tassin B, et al. The proliferation of the toxic cyanobacterium Planktothrix rubescens following restoration of the largest natural French lake (Lac du Bourget). Harmful Algae. 2005;4:651–72.Article 

Google Scholar 
Lenard T. Metalimnetic bloom of Planktothrix rubescens in relation to environmental conditions. Oceanological Hydrobiological Studies. 2009;38:45–53.
Google Scholar 
Van den Wyngaert S, Salcher MM, Pernthaler J, Zeder M, Posch T. Quantitative dominance of seasonally persistent filamentous cyanobacteria (Planktothrix rubescens) in the microbial assemblages of a temperate lake. Limnol Oceanogr. 2011;56:97–109.Article 

Google Scholar 
Walsby A. Stratification by cyanobacteria in lakes: A dynamic buoyancy model indicates size limitations met by Planktothrix rubescens filaments. New Phytologist. 2005;168:365–76. https://doi.org/10.1111/j.1469-8137.2005.01508.xArticle 
PubMed 

Google Scholar 
Conroy JD, Kane DD, Quinlan EL, Edwards WJ, Culver DA. Abiotic and biotic controls of phytoplankton biomass dynamics in a freshwater tributary, estuary, and large lake ecosystem: Sandusky bay (lake erie) chemostat. Inland Waters. 2017;7:473–92. https://doi.org/10.1080/20442041.2017.1395142CAS 
Article 

Google Scholar 
Sommer U, Maciej Gliwics Z, Lampert W, Duncan A. The PEG-model of seasonal succession of planktonic events in fresh waters. Archiv Für Hydrobiologie. 1986;106:433–71.
Google Scholar 
Sommer U, Adrian R, De Senerpont Domis L, Elser JJ, Gaedke U, Ibelings B, et al. Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession. Ann Rev Ecol, Evol, Syst. 2012;43:429–48. https://doi.org/10.1146/annurev-ecolsys-110411-160251Article 

Google Scholar 
Hatcher MJ, Dunn AM Parasites in ecological communities: from interactions to ecosystems. Cambridge University Press (2011).Marcogliese DJ. Parasites: Small Players with Crucial Roles in the Ecological Theater. EcoHealth. 2004;1:151–64. https://doi.org/10.1007/s10393-004-0028-3Article 

Google Scholar 
Sime-Ngando T, Lafferty KD, Biron DG. Roles and Mechanisms of Parasitism in Aquatic Microbial Communities. 2007. https://doi.org/10.3389/978-2-88919-588-6Frenken T, Alacid E, Berger SA, Bourne EC, Gerphagnon M, Grossart HP, et al. Integrating chytrid fungal parasites into plankton ecology: research gaps and needs. Environmental Microbiology. 2017;19:3802–22. https://doi.org/10.1111/1462-2920.13827Article 
PubMed 

Google Scholar 
Brussaard CPD, Kuipers B, Veldhuis MJW. A mesocosm study of Phaeocystis globosa population dynamics: I. Regulatory role of viruses in bloom control. Harmful Algae. 2005;4:859–74. https://doi.org/10.1016/j.hal.2004.12.015Article 

Google Scholar 
Gerphagnon M, Macarthur DJ, Gachon C, Van Ogtrop F, Latour D, et al. The biological factors affecting the dynamics of cyanobacterial blooms. 2009.Gleason FH, Jephcott TG, Küpper FC, Gerphagnon M, Sime-Ngando T, Karpov SA, et al. Potential roles for recently discovered chytrid parasites in the dynamics of harmful algal blooms. Fungal Biol Rev. 2015;29:20–33. https://doi.org/10.1016/j.fbr.2015.03.002Article 

Google Scholar 
Ibelings BW, Gsell AS, Mooij WM, Van Donk E, Van Den Wyngaert S, De Senerpont Domis LN. Chytrid infections and diatom spring blooms: Paradoxical effects of climate warming on fungal epidemics in lakes. Freshwater Biol. 2011;56:754–66. https://doi.org/10.1111/j.1365-2427.2010.02565.xArticle 

Google Scholar 
Kagami M, De Bruin A, Ibelings BW, Van Donk E. Parasitic chytrids: Their effects on phytoplankton communities and food-web dynamics. Hydrobiologia. 2007;578:113–29. https://doi.org/10.1007/s10750-006-0438-zArticle 

Google Scholar 
Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. PNAS. 2005;103:3165–70.Article 

Google Scholar 
McKenzie VJ, Peterson AC. Pathogen pollution and the emergence of a deadly amphibian pathogen. Molecular Ecol. 2012;21:5151–4. https://doi.org/10.1111/mec.12013Article 

Google Scholar 
Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth. 2007;4:125–34. https://doi.org/10.1007/s10393-007-0093-5Article 

Google Scholar 
Ibelings BW, De Bruin A, Kagami M, Rijkeboer M, Brehm M, Van Donk E. Host parasite interactions between freshwater phytoplankton and chytrid fungi (Chytridiomycota). J Phycol. 2004;40:437–53.Article 

Google Scholar 
Bosch J, Martínez-Solano I, García-París. Evidence of a chytrid fungus infection involved in the decline of the common midwife toad (Alytes obstetricans) in protected areas of central Spain. Biological Conserv. 2001;97:331–7. https://doi.org/10.1016/S0006-3207(00)00132-4Article 

Google Scholar 
Bruning K, Lingeman R, Ringelberg J. Estimating the impact of fungal parasites on phytoplankton populations. Limnol Oceanogr. 1992;37:252–60. https://doi.org/10.4319/lo.1992.37.2.0252Article 

Google Scholar 
Paterson RA. Infestation of Chytridiaceous Fungi on Phytoplankton in Relation to Certain Environmental Factors. Ecology. 1960;41:416–24. https://doi.org/10.2307/1933316Article 

Google Scholar 
Ṣen B. Fungal parasitism of planktonic algae in Shearwater. IV: Parasitic occurrence of a new chytrid species on the blue-green alga Microcystis aeruginosa Kuetz. emend. Elenkin. 1998.van Donk E, Ringelberg J. The effect of fungal parasitism on the succession of diatoms in Lake Maarsseveen I. Netherlands Freshwater Biol. 1983;13:241–51. https://doi.org/10.1111/j.1365-2427.1983.tb00674.xArticle 

Google Scholar 
Agha R, Saebelfeld M, Manthey C, Rohrlack T, Wolinska J. Chytrid parasitism facilitates trophic transfer between bloom-forming cyanobacteria and zooplankton (Daphnia). Scientific Rep. 2016;6. https://doi.org/10.1038/srep35039Frenken T, Wierenga J, van Donk E, Declerck SAJ, de Senerpont Domis LN, Rohrlack T, et al. Fungal parasites of a toxic inedible cyanobacterium provide food to zooplankton. Limnol Oceanogr. 2018;63:2384–93. https://doi.org/10.1002/lno.10945Article 

Google Scholar 
Kagami M, von Elert E, Ibelings BW, de Bruin A, van Donk E. The parasitic chytrid, Zygorhizidium, facilitates the growth of the cladoceran zooplankter, Daphnia, in cultures of the inedible alga, Asterionella. Proc Biological Sci/ Royal Soc. 2007;274:1561–6. https://doi.org/10.1098/rspb.2007.0425Article 

Google Scholar 
Gsell AS, de Senerpont Domis LN, van Donk E, Ibelings BW. Temperature alters host genotype-specific susceptibility to chytrid infection. PLoS One. 2013;8:e71737. https://doi.org/10.1371/journal.pone.0071737CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKindles KM, Manes MA, McKay RM, Davis TW, Bullerjahn GS. Environmental factors affecting chytrid (Chytridiomycota) infection rates on Planktothrix agardhii. J Plankton Res. 2021a;43:658–72.Article 

Google Scholar 
Fallowfield HJ, Daft MJ. The extracellular release of dissolved organic carbon by freshwater cyanobacteria and algae and the interaction with Lysobacter CP-1. Br Phycol J. 1988;1617:317–26. https://doi.org/10.1080/00071618800650351Article 

Google Scholar 
Mueller B, den Haan J, Visser PM, Vermeij MJA, van Duyl FC. Effect of light and nutrient availability on the release of dissolved organic carbon (DOC) by Caribbean turf algae. Scientific Rep. 2016;6:1–9. https://doi.org/10.1038/srep23248CAS 
Article 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. II. Effects of light on survival and epidemic development of the parasite. J Plankton Res. 1991c;13:119–29. https://doi.org/10.1093/plankt/13.1.119Article 

Google Scholar 
Van den Wyngaert S, Gsell AS, Spaak P, Ibelings BW. Herbicides in the environment alter infection dynamics in a microbial host-parasite system. Environ Microbiol. 2013;15:837–47. https://doi.org/10.1111/j.1462-2920.2012.02874.xCAS 
Article 
PubMed 

Google Scholar 
Almocera AES, Hsu SB, Sy PW. Extinction and uniform persistence in a microbial food web with mycoloop: Limiting behavior of a population model with parasitic fungi. Mathematical Biosci Eng. 2019;16:516–37.Article 

Google Scholar 
Frenken T, Miki T, Kagami M, Van de Waal DB, Van Donk E, Rohrlack T, et al. The potential of zooplankton in constraining chytrid epidemics in phytoplankton hosts. Ecology. 2020;101. https://doi.org/10.1002/ecy.2900Gerla DJ, Gsell AS, Kooi BW, Ibelings BW, Van Donk E, Mooij WM. Alternative states and population crashes in a resource-susceptible-infected model for planktonic parasites and hosts. FMeier, M. H. et al. (2015) Neuropsychological Decline in Schizophrenia from the Premorbid to Post-Onset Period: Evidence from a Population-Representative Longitudinal Study. American J Psychiatry. 2013;58:538–51. https://doi.org/10.1111/fwb.12010Article 

Google Scholar 
Miki T, Takimoto G, Kagami M. Roles of parasitic fungi in aquatic food webs: A theoretical approach. Freshwater Biol. 2011;56:1173–83. https://doi.org/10.1111/j.1365-2427.2010.02562.xArticle 

Google Scholar 
Guillard RRL, Lorenzen CJ. Yellow-green algae with chlorophyllid C. In Phycology. 1972;8:10–14.CAS 

Google Scholar 
McKindles KM, Jorge AN, McKay RM, Davis TW, Bullerjahn GS. Isolation and characterization of Rhizophydiales (Chytridiomycota), obligate parasites of Planktothrix agardhii in a Laurentian Great Lakes embayment. Appl Environ Microbiol. 2021b;87:e02308–20.CAS 
Article 

Google Scholar 
R Core Team. (2021). R: A Language and Environment for Statistical Computing.RStudio Team. (2021). RStudio: Integrated Development Environment for R (1.4.1106).Wickham, H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the {tidyverse}. J Open Source Software. 2019;4:1686. https://doi.org/10.21105/joss.01686Article 

Google Scholar 
Champely, S (2018). PairedData (1.1.1).Soetaert K, Petzoldt T, Setzer RW. Solving Differential Equations in {R}: Package deSolve. J Statistical Software. 2010;33:1–25. https://doi.org/10.18637/jss.v033.i09Article 

Google Scholar 
Frenken T, Velthuis M, de Senerpont Domis LN, Stephan S, Aben R, Kosten S, et al. Warming accelerates termination of a phytoplankton spring bloom by fungal parasites. Global Change Biol. 2016;22:299–309. https://doi.org/10.1111/gcb.13095Article 

Google Scholar 
Scholz B, Vyverman W, Küpper FC, Ólafsson HG, Karsten U. Effects of environmental parameters on chytrid infection prevalence of four marine diatoms: A laboratory case study. Botanica Marina. 2017;60:419–31. https://doi.org/10.1515/bot-2016-0105CAS 
Article 

Google Scholar 
Sønstebø JH, Rohrlack T. Possible implications of Chytrid parasitism for population subdivision in freshwater cyanobacteria of the genus Planktothrix. Appl Environ Microbiol. 2011;77:1344–51. https://doi.org/10.1128/AEM.02153-10CAS 
Article 
PubMed 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. I. Effects of light on reproduction and infectivity of the parasite. J Plankton Res. 1991b;13:103–17. https://doi.org/10.1093/plankt/13.1.103Article 

Google Scholar 
Muehlstein LK, Amon JP, Leffler DL. Chemotaxis in the Marine Fungus Rhizophydium littoreum. Appl Environ Microbiol. 1988;54:1668–72. https://doi.org/10.1128/aem.54.7.1668-1672.1988CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Esch GW, Fernández JC. Introduction. In A Functional Biology of Parasitism (pp. 1–25). Springer Netherlands (1993). https://doi.org/10.1007/978-94-011-2352-5_1Gerphagnon M, Colombet J, Latour D, Sime-Ngando T. Spatial and temporal changes of parasitic chytrids of cyanobacteria. Scientific Rep. 2017;7:6056. https://doi.org/10.1038/s41598-017-06273-1CAS 
Article 

Google Scholar 
Maier MA, Peterson TD. Prevalence of chytrid parasitism among diatom populations in the lower Columbia River (2009–2013). Freshwater Biol. 2017;62:414–28. https://doi.org/10.1111/fwb.12876CAS 
Article 

Google Scholar 
Sime-Ngando T. Phytoplankton chytridiomycosis: Fungal parasites of phytoplankton and their imprints on the food web dynamics. Front Microbiol. 2012;3:361. https://doi.org/10.3389/fmicb.2012.00361Article 
PubMed 
PubMed Central 

Google Scholar 
Kagami M, Urabe J. Mortality of the planktonic desmid, Staurastrum dorsidentiferum, due to interplay of fungal parasitism and low light conditions. SIL Proceed. 2002;28:1001–5. https://doi.org/10.1080/03680770.2001.11901868Article 

Google Scholar  More

Pax, F. Grundzüge der Pflanzenverbreitung in den Karpathen. 1–342 (W. Engelmann, 1898). https://doi.org/10.5962/bhl.title.20419.Popov [Попов], M. G. [М. Г.]. Ocherk rastitel’nosti i flory Karpat [Очерк растительности и флоры Карпат]. vol. 5 (XIII) (Izdatel’stvo Moskovskogo Obshchestva Ispytateley Prirody [Издательство Московского Общества Испытателей Природы], 1949).Mráz, P. & Ronikier, M. Biogeography of the Carpathians: Evolutionary and spatial facets of biodiversity. Biol. J. Linn. Soc. 119, 528–559 (2016).Article 

Google Scholar 
Breman, E. et al. Conserving the endemic flora of the Carpathian Region: An international project to increase and share knowledge of the distribution, evolution and taxonomy of Carpathian endemics and to conserve endangered species. Plant Syst. Evol. 306, 59 (2020).Article 

Google Scholar 
Bálint, M. et al. The Carpathians as a Major Diversity Hotspot in Europe. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds. Zachos, F. E. & Habel, J. C.) 189–205 (Springer, 2011). https://doi.org/10.1007/978-3-642-20992-5_11.Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hurdu, B. et al. Patterns of plant endemism in the Romanian Carpathians (South-Eastern Carpathians). Contrib. Bot. 47, 25–38 (2012).
Google Scholar 
Pawłowski, B. Remarques sur l’endemisme dans la flore des Alpes et des Carpates. Plant Ecol. 21, 181–243 (1970).Article 

Google Scholar 
Ronikier, M. Biogeography of high-mountain plants in the Carpathians: An emerging phylogeographical perspective. Taxon 373–389 (2011).Hendrych, R. Primula vulgaris in der Slowakei und in den umliegenden Gebieten. Preslia Praha 68, 135–156 (1996).
Google Scholar 
Hendrych, R. & Hendrychová, H. Preliminary report on the Dacian migroelement in the flora of Slovakia. Preslia Praha 51, 313–332 (1979).
Google Scholar 
Sramkó, G. „Dunántúli” közép-dunai flóraválasztós fajok a Matricum flórájában. KITAIBELIA 9, 31–56 (2004).
Google Scholar 
Juřičková, L. et al. Early postglacial recolonisation, refugial dynamics and the origin of a major biodiversity hotspot. A case study from the Malá Fatra mountains, Western Carpathians, Slovakia. The Holocene 28, 583–594 (2018).Kliment, J., Turis, P. & Janišová, M. Taxa of vascular plants endemic to the Carpathian Mts. Preslia -Praha- 88, 19–76 (2016).
Google Scholar 
Konowalik, K. Reconstructing reticulate relationships in the polyploid complex of Leucanthemum Mill. (Compositae, Anthemideae). (Fakultät für Biologie und Vorklinische Medizin, Universität Regensburg, 2014).Konowalik, K., Wagner, F., Tomasello, S., Vogt, R. & Oberprieler, C. Detecting reticulate relationships among diploid Leucanthemum Mill. (Compositae, Anthemideae) taxa using multilocus species tree reconstruction methods and AFLP fingerprinting. Mol. Phylogenet. Evol. 92, 308–328 (2015).Wagner, F. et al. ‘At the crossroads towards polyploidy’: Genomic divergence and extent of homoploid hybridization are drivers for the formation of the ox-eye daisy polyploid complex (Leucanthemum, Compositae-Anthemideae). New Phytol. 223, 2039–2053 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wagner, F., Härtl, S., Vogt, R. & Oberprieler, C. “Fix Me Another Marguerite!”: Species delimitation in a group of intensively hybridizing lineages of ox-eye daisies (Leucanthemum Mill., Compositae-Anthemideae). Mol. Ecol. 26, 4260–4283 (2017).Piękoś-Mirkowa, H., Mirek, Z. & Miechowka, A. Endemic vascular plants in the Polish Tatra Mts. – distribution and ecology. Pol. Bot. Stud. 12, (1996).Zelený, V. Taxonomisch-chorologische Studie über die Art Leucanthemum rotundifolium (W. K.) DC. Folia Geobot. 5, 369–400 (1970).Piękoś, H. Nowy mieszaniec między Leucanthemum rotundifolium (W. et K.) DC. a L. vulgare Lam. var. alpicolum Gremli – Hybrida nova inter Leucanthemum rotundifolium (W. et K.) DC. et L. vulgare Lam. var. alpicolum Gremli. Fragm. Florist. Geobot. 16, 319–326 (1970).Rogalski, M., do Nascimento Vieira, L., Fraga, H. P. & Guerra, M. P. Plastid genomics in horticultural species: importance and applications for plant population genetics, evolution, and biotechnology. Front. Plant Sci. 6, (2015).Greiner, R., Vogt, R. & Oberprieler, C. Evolution of the polyploid north-west Iberian Leucanthemum pluriflorum clan (Compositae, Anthemideae) based on plastid DNA sequence variation and AFLP fingerprinting. Ann. Bot. 111, 1109–1123 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oberprieler, C., Konowalik, K., Fackelmann, A. & Vogt, R. Polyploid speciation across a suture zone: phylogeography and species delimitation in S French Leucanthemum Mill. representatives (Compositae–Anthemideae). Plant Syst. Evol. 304, 1141–1155 (2018).Oberprieler, C., Greiner, R., Konowalik, K. & Vogt, R. The reticulate evolutionary history of the polyploid NW Iberian Leucanthemum pluriflorum clan (Compositae, Anthemideae) as inferred from nrDNA ETS sequence diversity and eco-climatological niche-modelling. Mol. Phylogenet. Evol. 70, 478–491 (2014).CAS 
PubMed 
Article 

Google Scholar 
Alexander, P. J., Rajanikanth, G., Bacon, C. D. & Bailey, C. D. Recovery of plant DNA using a reciprocating saw and silica-based columns. Mol. Ecol. Notes 7, 5–9 (2007).CAS 
Article 

Google Scholar 
Sang, T., Crawford, D. & Stuessy, T. Chloroplast DNA phylogeny, reticulate evolution, and biogeography of Paeonia (Paeoniaceae). Am. J. Bot. 84, 1120 (1997).CAS 
PubMed 
Article 

Google Scholar 
Scheunert, A., Dorfner, M., Lingl, T. & Oberprieler, C. Can we use it? On the utility of de novo and reference-based assembly of Nanopore data for plant plastome sequencing. PLoS ONE 15, e0226234 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Timme, R. E., Kuehl, J. V., Boore, J. L. & Jansen, R. K. A comparative analysis of the Lactuca and Helianthus (Asteraceae) plastid genomes: Identification of divergent regions and categorization of shared repeats. Am. J. Bot. 94, 302–312 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hall, T. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser 41, 95–98 (1999).CAS 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381 (2000).CAS 
PubMed 
Article 

Google Scholar 
Müller, K. SeqState: Primer design and sequence statistics for phylogenetic DNA datasets. Appl. Bioinformatics 4, 65–69 (2005).PubMed 
Article 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Meth. 9, 772 (2012).CAS 
Article 

Google Scholar 
Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 
Article 

Google Scholar 
Jukes, T. H. & Cantor, C. R. Evolution of Protein Molecules. in Mammalian Protein Metabolism 21–132 (Elsevier, 1969). https://doi.org/10.1016/B978-1-4832-3211-9.50009-7.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamura, K., Tao, Q. & Kumar, S. Theoretical foundation of the reltime method for estimating divergence times from variable evolutionary rates. Mol. Biol. Evol. 35, 1770–1782 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamura, K. et al. Estimating divergence times in large molecular phylogenies. Proc. Natl. Acad. Sci. 109, 19333–19338 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tao, Q., Tamura, K., Mello, B. & Kumar, S. Reliable confidence intervals for reltime estimates of evolutionary divergence times. Mol. Biol. Evol. 37, 280–290 (2020).CAS 
PubMed 
Article 

Google Scholar 
Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 15, e1006650 (2019).Mello, B., Tao, Q., Barba-Montoya, J. & Kumar, S. Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches. Mol. Ecol. Resour. 21, 122–136 (2021).CAS 
PubMed 
Article 

Google Scholar 
Wang, wei-M. On the origin and development of Artemisia (Asteraceae) in the geological past. Bot. J. Linn. Soc. 145, 331–336 (2004).Clement, M., Snell, Q., Walker, P., Posada, D. & Crandall, K. TCS: Estimating Gene Genealogies. in Proceedings of the 16th International Parallel and Distributed Processing Symposium 311 (IEEE Computer Society, 2002).Leigh, J. W. & Bryant, D. popart: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. & Corander, J. RhierBAPS: An R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 3, 93 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, Y., Blair, C. & He, X. RASP 4: Ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).CAS 
PubMed 
Article 

Google Scholar 
Ali, S. S., Yu, Y., Pfosser, M. & Wetschnig, W. Inferences of biogeographical histories within subfamily Hyacinthoideae using S-DIVA and Bayesian binary MCMC analysis implemented in RASP (Reconstruct Ancestral State in Phylogenies). Ann. Bot. 109, 95–107 (2012).PubMed 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).Konowalik, K. & Nosol, A. Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci. Rep. 11, 1482 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamner, B., Frasco, M. & LeDell, E. Metrics: Evaluation metrics for machine learning (2018).Ripley, B. & Venables, W. nnet: Feed-forward neural networks and multinomial log-linear models. (2020).Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. (2020).Therneau, T., Atkinson, B., port, B. R. (producer of the initial R. & maintainer 1999–2017). rpart: Recursive Partitioning and Regression Trees. (2019).Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
MATH 
Article 

Google Scholar 
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. (2017).Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).Article 

Google Scholar 
Jasiewicz, A. Rośliny naczyniowe Bieszczadów Zachodnich [The Vascular Plants of the Western Bieszczady Mts. (East Carpathians)]. Monogr. Bot. 20, 1–340 (1965).Kornaś, J. Charakterystyka geobotaniczna Gorców [Caractéristique géobotanique des Gorces (Karpathes Occidentales Polonaises)]. Monogr. Bot. 3, 3–230 (1955).Article 

Google Scholar 
de Oliveira, G., Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C. & Diniz-Filho, J. A. F. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography 37, 637–647 (2014).Article 

Google Scholar 
Sobral-Souza, T., Lima-Ribeiro, M. S. & Solferini, V. N. Biogeography of Neotropical Rainforests: past connections between Amazon and Atlantic Forest detected by ecological niche modeling. Evol. Ecol. 29, 643–655 (2015).Article 

Google Scholar 
Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37, 1084–1091 (2014).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. 7266827510 bytes (2018) 10.5061/DRYAD.KD1D4.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Wing, M. K. C. from J. et al. caret: Classification and regression training. (2019).Smith, A. B. & Santos, M. J. Testing the ability of species distribution models to infer variable importance. Ecography 43, 1801–1813 (2020).Article 

Google Scholar 
Evans, J. S., Murphy, M. A. & Ram, K. spatialEco: Spatial analysis and modelling utilities. (2021).Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Araújo, M. B., Whittaker, R. J., Ladle, R. J. & Erhard, M. Reducing uncertainty in projections of extinction risk from climate change. Glob. Ecol. Biogeogr. 14, 529–538 (2005).Article 

Google Scholar 
Zhu, G., Fan, J. & Peterson, A. T. Cautions in weighting individual ecological niche models in ensemble forecasting. Ecol. Model. 448, 109502 (2021).Article 

Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. (2021).R Core Team. R: A language and environment for statistical computing. (2019).QGIS Development Team. QGIS geographic information system. (2019).Frajman, B. & Oxelman, B. Reticulate phylogenetics and phytogeographical structure of Heliosperma (Sileneae, Caryophyllaceae) inferred from chloroplast and nuclear DNA sequences. Mol. Phylogenet. Evol. 43, 140–155 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ronikier, M., Cieślak, E. & Korbecka, G. High genetic differentiation in the alpine plant Campanula alpina Jacq. (Campanulaceae): evidence for glacial survival in several Carpathian regions and long-term isolation between the Carpathians and the Alps. Mol. Ecol. 17, 1763–1775 (2008).Ehrich, D. et al. Genetic consequences of Pleistocene range shifts: contrast between the Arctic, the Alps and the East African mountains. Mol. Ecol. 16, 2542–2559 (2007).CAS 
PubMed 
Article 

Google Scholar 
Šrámková, G. et al. Phylogeography and taxonomic reassessment of Arabidopsis halleri—a montane species from Central Europe. Plant Syst. Evol. 305, 885–898 (2019).Article 

Google Scholar 
Birks & Willis, K. J. Alpines, trees, and refugia in Europe. Plant Ecol. Divers. 1, 147–160 (2008).Jarčuška, B., Kaňuch, P., Naďo, L. & Krištín, A. Quantitative biogeography of Orthoptera does not support classical qualitative regionalization of the Carpathian Mountains. Biol. J. Linn. Soc. 128, 887–900 (2019).Article 

Google Scholar 
Tadono, T. et al. Precise global DEM generation by ALOS PRISM. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, 71–76 (2014).Article 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, 1 (2005).
Google Scholar  More

Berghauser Pont, M. Y., Perg, P. G., Haupt, P. A. & Heyman, A. A systematic review of the scientifically demonstrated effects of densification. IOP Conf. Ser. Earth Environ. Sci. 588, 052031 (2020).
Google Scholar 
Cimburova, Z. & Berghauser Pont, M. Location matters: A systematic review of spatial contextual factors mediating ecosystem services of urban trees. Ecosyst. Serv. 50, 101296 (2021).
Google Scholar 
De Valck, J. et al. Valuing urban ecosystem services in sustainable brownfield redevelopment. Ecosyst. Serv. 35, 139–149 (2019).
Google Scholar 
Zuzolo, D. et al. Divide et disperda: Thirty years of fragmentation and impacts on the eco-mosaic in the case study of the metropolitan city of Naples. Land 10, 485 (2021).
Google Scholar 
Nelson, E. The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations , edited by Pushpam Kumar, London, Earthscan Publications, United Nations Environment Programme, 2010, xxxix + 410 pp., US$76.95 (hardback), ISBN 978-1-84971-212-5. J. Nat. Resour. Policy Res. 5, 68–70 (2013).
Google Scholar 
Duraiappah, A. K. et al. Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: Synthesis. World Resources Institute vol. 5 http://www.who.int/entity/globalchange/ecosystems/ecosys.pdf (2005).Cariñanos, P., Casares-Porcel, M. & Quesada-Rubio, J. M. Estimating the allergenic potential of urban green spaces: A case-study in Granada, Spain. Landsc. Urban Plan. 123, 134–144 (2014).
Google Scholar 
Haase, D. et al. A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio 43, 413–433 (2014).PubMed 
PubMed Central 

Google Scholar 
Mexia, T. et al. Ecosystem services: Urban parks under a magnifying glass. Environ. Res. 160, 469–478 (2018).CAS 
PubMed 

Google Scholar 
Brzoska, P., Grunewald, K. & Bastian, O. A multi-criteria analytical method to assess ecosystem services at urban site level, exemplified by two German city districts. Ecosyst. Serv. 49, 101268 (2021).
Google Scholar 
Zulian, G. et al. Practical application of spatial ecosystem service models to aid decision support. Ecosyst. Serv. 29, 465–480 (2018).PubMed 
PubMed Central 

Google Scholar 
Balmford, A. et al. Ecology: Economic reasons for conserving wild nature. Science (80-). 297, 950–953 (2002).ADS 
CAS 

Google Scholar 
Koulov, B., Ivanova, E., Borisova, B., Assenov, A. & Ravnachka, A. GIS-based valuation of ecosystem services in mountain regions: A case study of the Karlovo municipality in Bulgaria. One Ecosyst. 2, e14062 (2017).
Google Scholar 
Robertson, G. P. & Swinton, S. M. Reconciling agricultural productivity and environmental integrity: A grand challenge for agriculture. Front. Ecol. Environ. 3, 38–46 (2005).
Google Scholar 
Sandhu, H. S., Wratten, S. D., Cullen, R. & Case, B. The future of farming: The value of ecosystem services in conventional and organic arable land. An experimental approach. Ecol. Econ. 64, 835–848 (2008).
Google Scholar 
Berglihn, E. C. & Gómez-Baggethun, E. Ecosystem services from urban forests: The case of Oslomarka, Norway. Ecosyst. Serv. 51, 101358 (2021).
Google Scholar 
Nowak, D. J. Understanding i-Tree. (2020). https://doi.org/10.2737/NRS-GTR-200.Selvakumaran, S., Plank, S., Geiß, C., Rossi, C. & Middleton, C. Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques. Int. J. Appl. Earth Obs. Geoinf. 73, 463–470 (2018).ADS 

Google Scholar 
Gómez-Baggethun, E. & Barton, D. N. Classifying and valuing ecosystem services for urban planning. Ecol. Econ. 86, 235–245 (2013).
Google Scholar 
Gren, Å. & Andersson, E. Being efficient and green by rethinking the urban-rural divide—Combining urban expansion and food production by integrating an ecosystem service perspective into urban planning. Sustain. Cities Soc. 40, 75–82 (2018).
Google Scholar 
Grêt-Regamey, A., Celio, E., Klein, T. M. & Wissen Hayek, U. Understanding ecosystem services trade-offs with interactive procedural modeling for sustainable urban planning. Landsc. Urban Plan. 109, 107–116 (2013).
Google Scholar 
Bennett, E. M., Peterson, G. D. & Gordon, L. J. Understanding relationships among multiple ecosystem services. Ecol. Lett. 12, 1394–1404 (2009).PubMed 

Google Scholar 
Bradford, J. B. & D’Amato, A. W. Recognizing trade-offs in multi-objective land management. Front. Ecol. Environ. 10, 210–216 (2012).
Google Scholar 
Cueva, J. et al. Synergies and trade-offs in ecosystem services from urban and peri-urban forests and their implication to sustainable city design and planning. Sustain. Cities Soc. 82, 103903 (2022).
Google Scholar 
Allocca, V., Coda, S., Calcaterra, D. & De Vita, P. Groundwater rebound and flooding in the Naples’ periurban area (Italy). J. Flood Risk Manag. 15, e12775 (2022).
Google Scholar 
Padulano, R. et al. Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis. Hydrol. Process. 35, e14436 (2021).
Google Scholar 
D’Amato, G. et al. Allergenic pollen and pollen allergy in Europe. Allergy 62, 976–990 (2007).PubMed 

Google Scholar 
Prigioniero, A., Zuzolo, D., Sciarrillo, R. & Guarino, C. Assessing pollinosis risk in the Vesuvius National Park: A novel approach for Index of Urban Green Zones Allergenicity. Environ. Res. 197, 111063 (2021).CAS 
PubMed 

Google Scholar 
AgCult 2020 Classifica visitatori 2019: Capodimonte rientra nella classifica dei primi 30 musei d’Italia.La Valva, V., Guarino, C., De Natale, A., Cuozzo, V., Menale, B. La flora del Parco di Capodimonte di Napoli. in 33–34: 143–177. (Delpinoa, 1992).Stevens, P. F. Angiosperm Phylogeny Website. 2001. http://www.mobot.org/MOBOT/research/APweb/. (2017).James Barth, B., Ian FitzGibbon, S. & Stuart Wilson, R. New urban developments that retain more remnant trees have greater bird diversity. Landsc. Urban Plan. 136, 122–129 (2015).
Google Scholar 
Heckmann, K. E., Manley, P. N. & Schlesinger, M. D. Ecological integrity of remnant montane forests along an urban gradient in the Sierra Nevada. For. Ecol. Manage. 255, 2453–2466 (2008).
Google Scholar 
Prigioniero, A. et al. Role of historic gardens in biodiversity-conservation strategy: the example of the Giardino Inglese of Reggia di Caserta (UNESCO) (Italy). Plant Biosyst. 155, 983–993 (2021).
Google Scholar 
Song, Q., Wang, B., Wang, J. & Niu, X. Endangered and endemic species increase forest conservation values of species diversity based on the Shannon-Wiener index. IForest 9, 469–474 (2016).
Google Scholar 
Hess, M. C. M., Mesléard, F. & Buisson, E. Priority effects: Emerging principles for invasive plant species management. Ecol. Eng. 127, 48–57 (2019).
Google Scholar 
Carli, E. et al. Using vegetation dynamics to face the challenge of the conservation status assessment in semi-natural habitats. Rend. Lincei. Sci. Fis. e Nat. 29, 363–374 (2018).
Google Scholar 
Canedoli, C. et al. Evaluation of ecosystem services in a protected mountain area: Soil organic carbon stock and biodiversity in alpine forests and grasslands. Ecosyst. Serv. 44, 101135 (2020).
Google Scholar 
FAO. Global Forest Resources Assessment 2010. Main report. (2010).Lindén, L., Riikonen, A., Setälä, H. & Yli-Pelkonen, V. Quantifying carbon stocks in urban parks under cold climate conditions. Urban For. Urban Green. 49, 126633 (2020).
Google Scholar 
Nowak, D. J., Hirabayashi, S., Bodine, A. & Greenfield, E. Tree and forest effects on air quality and human health in the United States. Environ. Pollut. 193, 119–129 (2014).CAS 
PubMed 

Google Scholar 
Nowak, D. J., Crane, D. E. & Stevens, J. C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4, 115–123 (2006).
Google Scholar 
Nowak, D. J. & Crane, D. E. Carbon storage and sequestration by urban trees in the USA. Environ. Pollut. 116, 381–389 (2002).CAS 
PubMed 

Google Scholar 
Kocić, K., Spasić, T., Urošević, M. A. & Tomašević, M. Trees as natural barriers against heavy metal pollution and their role in the protection of cultural heritage. J. Cult. Herit. 15, 227–233 (2014).
Google Scholar 
Yang, J., McBride, J., Zhou, J. & Sun, Z. The urban forest in Beijing and its role in air pollution reduction. Urban For. Urban Green. 3, 65–78 (2005).
Google Scholar 
Zupancic, T., Westmacott, C., Bulthuis, M. The impact of green space on heat and air pollution in urban communities: A meta-narrative systematic review (2015).Cariñanos, P., Adinolfi, C., Díaz de la Guardia, C., De Linares, C. & Casares-Porcel, M. Characterization of Allergen Emission Sources in Urban Areas. J. Environ. Qual. 45, 244–252 (2016).PubMed 

Google Scholar 
D’Auria, A., De Toro, P., Fierro, N. & Montone, E. Integration between GIS and multi-criteria analysis for ecosystem services assessment: A methodological proposal for the National Park of Cilento, Vallo di Diano and Alburni (Italy). Sustain 10, 3329 (2018).
Google Scholar 
Prigioniero, A., Zuzolo, D., Niinemets, Ü. & Guarino, C. Nature-based solutions as tools for air phytoremediation: A review of the current knowledge and gaps. Environ. Pollut. 277, 116817 (2021).CAS 
PubMed 

Google Scholar 
Szkop, Z. Evaluating the sensitivity of the i-Tree Eco pollution model to different pollution data inputs: A case study from Warsaw, Poland. Urban For. Urban Green. 55, 126859 (2020).
Google Scholar 
Tao, J. et al. Elevation-dependent effects of growing season length on carbon sequestration in Xizang Plateau grassland. Ecol. Indic. 110, 105880 (2020).CAS 

Google Scholar 
Chen, Y. et al. Grassland carbon sequestration ability in China: A new perspective from Terrestrial Aridity Zones. Rangel. Ecol. Manag. 69, 84–94 (2016).
Google Scholar 
Gopalakrishnan, V., Hirabayashi, S., Ziv, G. & Bakshi, B. R. Air quality and human health impacts of grasslands and shrublands in the United States. Atmos. Environ. 182, 193–199 (2018).ADS 
CAS 

Google Scholar 
Pace, R. et al. Comparing i-Tree eco estimates of particulate matter deposition with leaf and canopy measurements in an urban mediterranean Holm Oak Forest. Environ. Sci. Technol. 55, 6613–6622 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Losos, J. B., Walton, B. M. & Bennett, A. F. Trade-offs between sprinting and clinging ability in Kenyan Chameleons. Funct. Ecol. 7, 281 (1993).
Google Scholar 
Pretzsch, H., Moser-Reischl, A., Rahman, M. A., Pauleit, S. & Rötzer, T. Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application. Trees – Struct. Funct. (2021). https://doi.org/10.1007/s00468-021-02100-3.Grunewald, K. et al. Lessons learned from implementing the ecosystem services concept in urban planning. Ecosyst. Serv. 49, 101273 (2021).
Google Scholar 
Baldacchini, C., Sgrigna, G., Clarke, W., Tallis, M. & Calfapietra, C. An ultra-spatially resolved method to quali-quantitative monitor particulate matter in urban environment. Environ. Sci. Pollut. Res. 26, 18719–18729 (2019).CAS 

Google Scholar 
De Luca, P., Guarino, C., Gullo, G., La Valva V., 1992. Il Parco di Capodimonte di Napoli: storia ed attualità. in 33–34: 143–177. (Delpinoa, 1992).Pignatti, S. Flora d’Italia vol.2. (2017).Braun-Blanquet, J. Plant Sociology (McGraw-Hill Book Company, 1932).
Google Scholar 
Catorci, A. et al. Reproductive traits variation in the herb layer of a submediterranean deciduous forest landscape. Plant Ecol. 214, 737–749 (2013).
Google Scholar 
Šumrada, T. et al. Are result-based schemes a superior approach to the conservation of High Nature Value grasslands? Evidence from Slovenia. Land Use Policy 111, 105749 (2021).
Google Scholar 
POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Board of Trustees of the Royal Botanic Gardens, Kew http://www.plantsoftheworldonline.org/ (2022).Bímová, K., Mandák, B. & Kašparová, I. How does Reynoutria invasion fit the various theories of invasibility?. J. Veg. Sci. 15, 495–504 (2004).
Google Scholar 
Wild, J., Neuhäuslová, Z. & Sofron, J. Changes of plant species composition in the Šumava spruce forests, SW Bohemia, since the 1970s. For. Ecol. Manag. 187, 117–132 (2004).
Google Scholar 
Damato, G. & Lobefalo, G. Allergenic pollens in the southern Mediterranean area. J. Allergy Clin. Immunol. 83, 116–122 (1989).CAS 

Google Scholar 
Cariñanos, P. et al. Assessing allergenicity in urban parks: A nature-based solution to reduce the impact on public health. Environ. Res. 155, 219–227 (2017).PubMed 

Google Scholar 
Cariñanos, P. et al. Estimation of the allergenic potential of urban trees and urban parks: Towards the healthy design of urban green spaces of the future. Int. J. Environ. Res. Public Health 16, 1357 (2019).PubMed Central 

Google Scholar  More

Barnett, T. P. et al. Penetration of human-induced warming into the world’s oceans. Science 309, 284–287 (2005).CAS 
Article 

Google Scholar 
Levitus, S. et al. Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett. 36, L07608 (2009).
Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 

Google Scholar 
García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
Article 

Google Scholar 
Hughes, N. F. & Grand, T. C. Physiological ecology meets the ideal-free distribution: predicting the distribution of size-structured fish populations across temperature gradients. Environ. Biol. Fishes 59, 285–298 (2000).Article 

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

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 

Google Scholar 
Waldock, C., Stuart‐Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).Article 

Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
Article 

Google Scholar 
Beaugrand, G., Edwards, M., Raybaud, V., Goberville, E. & Kirby, R. R. Future vulnerability of marine biodiversity compared with contemporary and past changes. Nat. Clim. Change 5, 695–701 (2015).Article 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
Article 

Google Scholar 
Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).CAS 
Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).Article 

Google Scholar 
Lotterhos, K. E., Láruson, Á. J. & Jiang, L.-Q. Novel and disappearing climates in the global surface ocean from 1800 to 2100. Sci. Rep. 11, 15535 (2021).CAS 
Article 

Google Scholar 
Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).CAS 
Article 

Google Scholar 
Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).Article 

Google Scholar 
Séférian, R. et al. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: role of Earth system processes in present‐day and future climate. J. Adv. Model. Earth Syst. 11, 4182–4227 (2019).Article 

Google Scholar 
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).CAS 
Article 

Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

Google Scholar 
Beszczynska-Möller, A., Fahrbach, E., Schauer, U. & Hansen, E. Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997–2010. ICES J. Mar. Sci. 69, 852–863 (2012).Article 

Google Scholar 
Sutton, T. T. Vertical ecology of the pelagic ocean: classical patterns and new perspectives. J. Fish. Biol. 83, 1508–1527 (2013).CAS 
Article 

Google Scholar 
Richter, I. Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. WIREs Clim. Change 6, 345–358 (2015).Article 

Google Scholar 
Pozo Buil, M. et al. A dynamically downscaled ensemble of future projections for the California Current System. Front. Mar. Sci. 8, 612874 (2021).Article 

Google Scholar 
Leonard, M. et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014).Article 

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 
Article 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).Article 

Google Scholar 
Cheng, L., Abraham, J., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).CAS 
Article 

Google Scholar 
Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).CAS 
Article 

Google Scholar 
Filbee-Dexter, K. et al. Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci. Rep. 10, 13388 (2020).CAS 
Article 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
CAS 

Google Scholar 
Silvy, Y., Guilyardi, E., Sallée, J.-B. & Durack, P. J. Human-induced changes to the global ocean water masses and their time of emergence. Nat. Clim. Change 10, 1030–1036 (2020).CAS 
Article 

Google Scholar 
Cheng, L., Zheng, F. & Zhu, J. Distinctive ocean interior changes during the recent warming slowdown. Sci. Rep. 5, 14346 (2015).CAS 
Article 

Google Scholar 
Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Change 10, 576–581 (2020).CAS 
Article 

Google Scholar 
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).Article 
CAS 

Google Scholar 
Oliver, E. C. J. et al. Marine Heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).Article 

Google Scholar 
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).CAS 
Article 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 
Article 

Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B280, 20121890 (2013).Article 

Google Scholar 
Hastings, R. A. et al. Climate change drives poleward increases and equatorward declines in marine species. Curr. Biol. 30, 1572–1577.e2 (2020).CAS 
Article 

Google Scholar 
Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

Google Scholar 
Dulvy, N. K. et al. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J. Appl. Ecol. 45, 1029–1039 (2008).Article 

Google Scholar 
Thatje, S. Climate warming affects the depth distribution of marine ectotherms. Mar. Ecol. Prog. Ser. 660, 233–240 (2021).Article 

Google Scholar 
Manuel, S. A., Coates, K. A., Kenworthy, W. J. & Fourqurean, J. W. Tropical species at the northern limit of their range: composition and distribution in Bermuda’s benthic habitats in relation to depth and light availability. Mar. Environ. Res. 89, 63–75 (2013).CAS 
Article 

Google Scholar 
Peck, L. S., Webb, K. E. & Bailey, D. M. Extreme sensitivity of biological function to temperature in Antarctic marine species. Funct. Ecol. 18, 625–630 (2004).Article 

Google Scholar 
Peck, L. S., Morley, S. A., Richard, J. & Clark, M. S. Acclimation and thermal tolerance in Antarctic marine ectotherms. J. Exp. Biol. 217, 16–22 (2014).Article 

Google Scholar 
Walsh, J. E. Climate of the Arctic marine environment. Ecol. Appl. 18, S3–S22 (2008).Article 

Google Scholar 
Storch, D., Menzel, L., Frickenhaus, S. & Pörtner, H.-O. Climate sensitivity across marine domains of life: limits to evolutionary adaptation shape species interactions. Glob. Change Biol. 20, 3059–3067 (2014).Article 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 

Google Scholar 
Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: an integrative view. Philos. Trans. R. Soc. B 362, 2233–2258 (2007).Article 
CAS 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Cohen, D.M., Inada, T., Iwamoto, T. and Scialabba, N. FAO Species Catalogue, Vol. 10. Gadiform Fishes of the World (Order Gadiformes) (FAO, 1990).Strand, E. & Huse, G. Vertical migration in adult Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 64, 1747–1760 (2007).Article 

Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).CAS 
Article 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

Google Scholar 
Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 6678 (2020).CAS 
Article 

Google Scholar 
Brierley, A. S. & Kingsford, M. J. Impacts of climate change on marine organisms and ecosystems. Curr. Biol. 19, R602–R614 (2009).CAS 
Article 

Google Scholar 
Bijma, J., Pörtner, H.-O., Yesson, C. & Rogers, A. D. Climate change and the oceans—what does the future hold? Mar. Pollut. Bull. 74, 495–505 (2013).CAS 
Article 

Google Scholar 
Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
Article 

Google Scholar 
Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371, eaba4658 (2021).CAS 
Article 

Google Scholar 
Rochman, C. M. & Hoellein, T. The global odyssey of plastic pollution. Science 368, 1184–1185 (2020).CAS 
Article 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).CAS 
Article 

Google Scholar 
Madec, G. et al. NEMO ocean engine. Zenodo https://www.earth-prints.org/handle/2122/13309 (2017).Mathiot, P., Jenkins, A., Harris, C. & Madec, G. Explicit representation and parametrised impacts of under ice shelf seas in the z∗- coordinate ocean model NEMO 3.6. Geosci. Model Dev. 10, 2849–2874 (2017).Article 

Google Scholar 
Dai, A. & Bloecker, C. E. Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Clim. Dyn. 52, 289–306 (2019).Article 

Google Scholar 
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).Article 

Google Scholar 
Middag, R. et al. Intercomparison of dissolved trace elements at the Bermuda Atlantic Time Series station. Mar. Chem. 177, 476–489 (2015).CAS 
Article 

Google Scholar 
Welch, B. L. The generalization of Student’s’ problem when several different population variances are involved. Biometrika 34, 28 (1947).CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Janzen, D. H. Why mountain passes are higher in the Tropics. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
Article 

Google Scholar 
Sandblom, E. et al. Physiological constraints to climate warming in fish follow principles of plastic floors and concrete ceilings. Nat. Commun. 7, 11447 (2016).CAS 
Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).CAS 
Article 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
Article 

Google Scholar  More

Fauna, culture and chronology datasetsA geo-referenced dataset of chronometric dates covering the late MIS 3 (55–30 kyr cal bp) was compiled from the literature (dataset 1). The dataset included 363 radiocarbon, thermoluminescence, optically stimulated luminescence and uranium series dates obtained from 62 archaeological sites and seven palaeontological sites. These chronological determinations were obtained from ten palaeontological levels and 138 archaeological levels. The archaeological levels were culturally attributed to the Mousterian (n = 75), Châtelperronian (n = 6) and Aurignacian (n = 57) technocomplexes. A number of issues can potentially hamper the chronological assessment of Palaeolithic technocomplexes from radiocarbon dates, such as pretreatment protocols that do not remove sufficient contaminants or the quality of the bone collagen extracted. Moreover, discrepancies in cultural attributions or stratigraphic inconsistencies are commonly detected in Palaeolithic archaeology. Information regarding the quality of date determinations and cultural attribution or stratigraphic issues is provided in the Supplementary Information.Our dataset also included the presence of herbivore species recovered from each archaeo-palaeontological site (hereafter referred to as local faunal assemblages (LFAs)), their body masses and their chronology. The mean body mass of both sexes, for each species, was obtained from the PHYLACINE database53 and used in the macroecological modelling approach described below (see ‘Carrying capacity of herbivores’). For visual representation purposes, the herbivore species were grouped into four weight categories: small (500 kg). The chronology of the occurrence of each herbivore species was assumed to be the same as the dated archaeo-palaeontological layer where the species remains were recovered. Thus, to estimate the chronological range of each species in each region, all radiocarbon determinations were calibrated with the IntCal20 calibration curve54 and OxCAL4.2 software55. The BAMs were run to compute the upper and lower chronological boundaries at a CI of 95.4% of each LFA (see ‘Chronological assessment’ for more details). One of the purposes of the current study was to estimate the potential fluctuations in herbivore biomass during the stadial and interstadial periods of the late MIS 3. Accordingly, the time spans of the LFAs were classified into the discrete GS and GI phases provided by Rasmussen et al.51.Geographic settingsThe Iberian Peninsula locates at the southwestern edge of Europe (Fig. 1). It constitutes a large geographic area that exhibits a remarkable diversity of ecosystems, climates and landscapes. Both now and in the past, altitudinal, latitudinal and oceanic gradients affected the conformation of two biogeographical macroregions with different flora and fauna species pools: the Eurosiberian and Mediterranean regions13,46. In the north, along the Pyrenees and Cantabrian strip, the Eurosiberian region is characterized by oceanic influence and mild temperatures in the present day, whereas the Mediterranean region features drier summers and milder winters (Fig. 1). Between the Eurosiberian and Mediterranean regions, there is a transitional area termed Submediterranean or Supramediterranean. Lastly, the Mediterranean region is divided into two distinctive bioclimatic belts: (1) the Thermomediterranean region, located at lower latitudes, with high evapotranspiration rates and affected by its proximity to the coast; and (2) the Mesomediterranean region, with lower temperatures and wetter conditions (Fig. 1).Previous studies have shown that zoocoenosis and phytocenosis differed between these macroregions in the Pleistocene13,46. However, flora and fauna distributions changed during the stadial–interstadial cycles in the Iberian Peninsula, which suggests potential alterations in the boundaries of these biogeographical regions. The modelling approach used in this study to estimate the biomass of primary consumers is dependent on the reconstructed NPP and the herbivore guild structure in each biogeographical region. To test the suitability of the present-day biogeographical demarcations of the Iberian Peninsula during MIS 3, we assessed whether the temporal trends of NPP and the composition of each herbivore palaeocommunity differed between these biogeographical regions during the MUPT.Chouakria and Nagabhusan56 proposed a dissimilarity index to compare time series data by taking into consideration the proximity of values and the temporal correlation of the time series:$${rm{CORT}}(S_1,S_2) = frac{{mathop {sum}nolimits_{i = 1}^{p – 1} {left( {u_{left( {i + 1} right)} – u_i} right)} (v_{(i + 1)} – v_i)}}{{sqrt {mathop {sum}nolimits_{i = 1}^{p – 1} {(u_{(i + 1)} – u_i)^2} } sqrt {mathop {sum}nolimits_{i = 1}^{p – 1} {(v_{(i + 1)} – v)^2} } }}$$
(1)
where S1 and S2 are the time series of data, u and v represent the values of S1 and S2, respectively, and p is the length of values of each time series. CORT(S1, S2) belongs to the interval (−1,1). The value CORT(S1, S2) = 1 indicates that in any observed period (ti, ti+1), the values of the sequence S1 and those of S2 increase or decrease at the same rate, whereas CORT = −1 indicates that when S1 increases, S2 decreases or vice versa. Lastly, CORT(S1, S2) = 0 indicates that the observed trends in S1 are independent of those observed in S2. To complement this approach by considering not only the temporal correlation between each pair of time series but also the proximity between the raw values, these authors proposed an adaptive tuning function defined as follows:$$d{rm{CORT}}left( {S_1,S_2} right) = fleft({{rm{CORT}}left( {S_1,S_2} right)} right)times dleft( {S_1,S_2} right)$$
(2)
where$$fleft( x right) = frac{2}{{1 + exp left( {k,x} right)}},k ge 0$$
(3)
In this study, k was 2, meaning that the behaviour contribution was 76% and the contribution of the proximity between values was 24%57. Hence, f(x) modulates a conventional pairwise raw data distance (d(S1,S2)) according to the observed temporal correlation56. Consequently, dCORT adjusts the degree of similarity between each pair of observations according to the temporal correlation and the proximity between values. This function was used to compare the reconstructed NPP between biogeographical regions during MIS 3 in the Iberian Peninsula. However, two different biogeographical regions could have experienced similar evolutionary trends in their NPP, even though their biota composition was different. Therefore, this analysis was complemented with a JSI to assess whether the reconstructed herbivore species composition in each palaeocommunity differed among biogeographical regions during the late MIS 3. The JSI was based on presence–absence data and was calculated as follows:$${rm{JSI}} = frac{c}{{(a + b + c)}}$$
(4)
where c is the number of shared species in both regions and a and b are the numbers of species that were only present in one of the biogeographical regions. Therefore, the higher the value the more similar the palaeocommunities of both regions were.Chronological assessmentPivotal to any hypothesis of Neanderthal replacement patterns by AMHs is the chronology of that population turnover. To this end, we used three different approaches to provide greater confidence in the results: BAMs, the OLE model and SPD of archaeological assemblages. As detailed below, each of these approaches provides complementary information about the MUPT.First, we built a set of BAMs for the Mousterian, Châtelperronian and Aurignacian technocomplexes in each region during the MIS 3. As stated above, we compiled the available radiocarbon dates for Iberia between 55 and 30 kyr cal bp. However, not all dates or levels were included in the Bayesian chronology models. Radiocarbon determinations obtained from shell remains were incorporated in the dataset (dataset 1); however, the local variation of the reservoir age was unknown from 55 to 30 kyr bp. Because of uncertainties related to marine reservoir offsets, all BAMs that incorporated dates from marine shells were run twice: including and excluding these dates. All of the archaeological levels with cultural attribution issues or stratigraphic inconsistencies were excluded. The Supplementary Note provides a detailed description of the sites, levels and dates excluded and their justification. All BAMs were built for each technocomplex using the OxCAL4.2 software55 and IntCal20 calibration curve54.Bayesian chronology models were built for each archaeological and palaeontological level. Then, the dates associated with each technocomplex were grouped within a single phase to determine each culture’s regional appearance or disappearance. Our interest was not focused on the chronological duration of the Mousterian, Châtelperronian and Aurignacian cultures, but on the probability distribution function of the temporal boundaries of these cultures in each region. Thus, this chronological assessment aims to provide an updated chronological frame for Neanderthal replacement by AMHs in Iberia. For this reason, we did not differentiate between proto- and early Aurignacian cultures, since both are attributed to AMHs.In each BAM, we inserted into the same sequence the radiocarbon dates associated with a given technocomplex within a start and end boundary to bracket each culture, which allowed us to determine the probability distribution function for the beginning and end moment of each cultural phase6. The resolution of all models was set at 20 years. We used a t-type outlier model with an initial 5% probability for each determination, but when more than one radiocarbon date was obtained from the same bone remain, we used an s-type outlier model and the combine function. The thermoluminescence dating likelihoods were included in the models, together with their associated 1σ uncertainty ranges. When dates with low agreement ( More