Ecology

Chen, L. et al. Geographic variation in traits of fruit stones and seeds of Melia azedarach. J. Beijing For. Univ. 36, 15–20 (2014).CAS 

Google Scholar 
Angamuthu, D., Purushothaman, I., Kothandan, S. & Swaminathan, R. Antiviral study on Punica granatum L., Momordica charantia L., Andrographis paniculata Nees, and Melia azedarach L., to human herpes virus-3. Eur. J. Integr. Med. 28, 98–108. https://doi.org/10.1016/j.eujim.2019.04.008 (2019).Article 

Google Scholar 
Wang, N. et al. Selective ERK1/2 agonists isolated from Melia azedarach with potent anti-leukemic activity. BMC Cancer 19, 1–9. https://doi.org/10.1186/s12885-019-5914-8 (2019).CAS 
Article 

Google Scholar 
Khoshraftar, Z., Safekordi, A., Shamel, A. & Zaefizadeh, M. Evaluation of insecticidal activity of nanoformulation of Melia azedarach (leaf) extract as a safe environmental insecticide. Int. J. Environ. Sci. Technol. 17, 1159–1170. https://doi.org/10.1007/s13762-019-02448-7 (2020).CAS 
Article 

Google Scholar 
Sivaraj, I., Nithaniyal, S., Bhooma, V., Senthilkumar, U. & Parani, M. Species delimitation of Melia dubia Cav. from Melia azedarach L. complex based on DNA barcoding. Botany 96, 329–336. https://doi.org/10.1139/cjb-2017-0148 (2018).CAS 
Article 

Google Scholar 
Liao, B. et al. Population structure and genetic relationships of Melia Taxa in China assayed with sequence-related amplified polymorphism (SRAP) markers. Forests 7, 81. https://doi.org/10.3390/f7040081 (2016).Article 

Google Scholar 
Wu, L., Kaewmano, A., Fu, P., Wang, W. & Fan, Z. Intra-annual radial growth of Melia azedarach in a tropical moist seasonal forest and its response to environmental factors in Xishuangbanna Southwest China. Acta Ecol. Sin. 40, 6831–6840. https://doi.org/10.5846/stxb202003120508 (2020).Article 

Google Scholar 
Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 C. Science 365, eaaw6974. https://doi.org/10.1126/science.aaw6974 (2019).CAS 
Article 
PubMed 

Google Scholar 
López-Tirado, J., Vessella, F., Schirone, B. & Hidalgo, P. J. Trends in evergreen oak suitability from assembled species distribution models: Assessing climate change in south-western Europe. New For. 49, 471–487. https://doi.org/10.1007/s11056-018-9629-5 (2018).Article 

Google Scholar 
Xu, Y. et al. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 12, 429. https://doi.org/10.3390/f12040429 (2021).Article 

Google Scholar 
Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manag. 430, 196–203. https://doi.org/10.1016/j.foreco.2018.08.019 (2018).Article 

Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163. https://doi.org/10.1111/gcb.13925 (2018).ADS 
Article 

Google Scholar 
Zhong, Y. et al. A generalized linear mixed model approach to assess emerald ash Borer diffusion. ISPRS Int. J. Geo Inf. 9, 414. https://doi.org/10.3390/ijgi9070414 (2020).Article 

Google Scholar 
Chang, Z., Meng, J., Shi, Y. & Mo, F. Lnc RNA recognition by fusing multiple features and its function prediction. CAAI Trans. Intell. Syst. 13, 928–934. https://doi.org/10.11992/tis.201806008 (2018).Article 

Google Scholar 
Shiferaw, H., Bewket, W. & Eckert, S. Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem. Ecol. Evol. 9, 2562–2574. https://doi.org/10.1002/ece3.4919 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Tang, X., Yuan, Y., Li, X. & Zhang, J. Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China. Front. Plant Sci. 12, 764. https://doi.org/10.3389/fpls.2021.652500 (2021).Article 

Google Scholar 
Chhogyel, N., Kumar, L., Bajgai, Y. & Jayasinghe, L. S. Prediction of Bhutan’s ecological distribution of rice (Oryza sativa L.) under the impact of climate change through maximum entropy modelling. J. Agric. Sci. 158, 25–37. https://doi.org/10.1017/S0021859620000350 (2020).Article 

Google Scholar 
Ahmad, Z. et al. Melia Azedarach impregnated Co and Ni zero-valent metal nanoparticles for organic pollutants degradation: Validation of experiments through statistical analysis. J. Mater. Sci. Mater. Electron. 31, 16938–16950. https://doi.org/10.1007/s10854-020-04250-5 (2020).CAS 
Article 

Google Scholar 
Hijmans, R. J., Huaccho, L. & Zhang, D. In I International Conference on Sweetpotato. Food and Health for the Future 583, 41–49.Luo, M., Wang, H. & Lyu, Z. Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data. J. Appl. Ecol. 28, 4001–4006. https://doi.org/10.13287/j.1001-9332.201712.011 (2017).Article 

Google Scholar 
Wang, T., Wang, G., Innes, J. L., Seely, B. & Chen, B. ClimateAP: An application for dynamic local downscaling of historical and future climate data in Asia Pacific. Front. Agric. Sci. Eng. 4, 448–458. https://doi.org/10.15302/J-FASE-2017172 (2017).CAS 
Article 

Google Scholar 
Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L Lesser Himalayan foothills. Ecol. Eng. 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 (2013).CAS 
Article 

Google Scholar 
Pepe, M. S., Cai, T. & Longton, G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62, 221–229. https://doi.org/10.1111/j.1541-0420.2005.00420.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
McHugh, M. L. Interrater reliability: The kappa statistic. Biochem. Med. 22, 276–282. https://hrcak.srce.hr/89395 (2012).Article 

Google Scholar 
Lu, C. Y., Gu, W., Dai, A. H. & Wei, H. Y. Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China. Ecol. Model. 242, 105–115. https://doi.org/10.1016/j.ecolmodel.2012.06.002 (2012).Article 

Google Scholar 
Zhang, L. et al. The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis. Acta Ecol. Sin. 34, 650–659. https://doi.org/10.5846/stxb201306031292 (2014).Article 

Google Scholar 
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x (2009).Article 

Google Scholar 
Akpoti, K., Kabo-Bah, A. T., Dossou-Yovo, E. R., Groen, T. A. & Zwart, S. J. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Sci. Total Environ. 709, 136165. https://doi.org/10.1016/j.scitotenv.2019.136165 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dutra Silva, L., de Brito, A. E., Vieira Reis, F., Bento Elias, R. & Silva, L. Limitations of species distribution models based on available climate change data: a case study in the Azorean forest. Forests 10, 575. https://doi.org/10.3390/f10070575 (2019).Article 

Google Scholar 
Lin, H. Y. et al. Climate-based approach for modeling the distribution of montane forest vegetation in Taiwan. Appl. Veg. Sci. 23, 239–253. https://doi.org/10.1111/avsc.12485 (2020).Article 

Google Scholar 
Zhang, L. et al. Consensus forecasting of species distributions: The effects of niche model performance and niche properties. PLoS ONE 10, e0120056. https://doi.org/10.1371/journal.pone.0120056 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, H. The optimality of naive Bayes. Am. Assoc. Artif. Intell. 1, 3 (2004).
Google Scholar 
Wang, Q., Nguyen, T.-T., Huang, J. Z. & Nguyen, T. T. An efficient random forests algorithm for high dimensional data classification. Adv. Data Anal. Classif. 12, 953–972. https://doi.org/10.1007/s11634-018-0318-1 (2018).MathSciNet 
Article 
MATH 

Google Scholar 
Zheng-tao, Y., Bin, D., Bo, H., Lu, H. & Jian-yi, G. Word sense disambiguation based on bayes model and information gain. Proc. Int. J. Adv. Sci. Technol. 2, 153–157. https://doi.org/10.1109/FGCN.2008.188 (2009).Article 

Google Scholar 
Yu, B. et al. SubMito-XGBoost: Predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 36, 1074–1081. https://doi.org/10.1093/bioinformatics/btz734 (2020).CAS 
Article 
PubMed 

Google Scholar 
Hailu, B. T., Siljander, M., Maeda, E. E. & Pellikka, P. Assessing spatial distribution of Coffea arabica L. in Ethiopia’s highlands using species distribution models and geospatial analysis methods. Ecol. Inf. 42, 79–89. https://doi.org/10.1016/j.ecoinf.2017.10.001 (2017).Article 

Google Scholar 
Ramirez-Reyes, C. et al. Embracing ensemble species distribution models to inform at-risk species status assessments. J. Fish Wildl. Manag. 12, 98–111. https://doi.org/10.3996/JFWM-20-072 (2021).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).Article 

Google Scholar 
Feng, L., Sun, J., Shi, Y., Wang, G. & Wang, T. Predicting suitable habitats of camptotheca acuminata considering both climatic and soil variables. Forests 11, 891. https://doi.org/10.3390/f11080891 (2020).Article 

Google Scholar 
Wang, T., Campbell, E. M., O’Neill, G. A. & Aitken, S. N. Projecting future distributions of ecosystem climate niches: Uncertainties and management applications. For. Ecol. Manag. 279, 128–140. https://doi.org/10.1016/j.foreco.2012.05.034 (2012).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. L. & Murdock, T. Q. ClimateWNA—high-resolution spatial climate data for western North America. J. Appl. Meteorol. Climatol. 51, 16–29. https://doi.org/10.1175/JAMC-D-11-043.1 (2012).ADS 
Article 

Google Scholar 
Feng, L. et al. Predicting suitable habitats of ginkgo biloba L. fruit forests in China. Clim. Risk Manag. 34, 100364. https://doi.org/10.1016/j.crm.2021.100364 (2021).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720. https://doi.org/10.1371/journal.pone.0156720 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Y. et al. Spatial prediction and delineation of Ginkgo biloba production areas under current and future climatic conditions. Ind. Crops Prod. 166, 113444. https://doi.org/10.1016/j.indcrop.2021.113444 (2021).Article 

Google Scholar 
Jiao, C., Lan, G., Sun, Y., Wang, G. & Sun, Y. Dopamine alleviates chilling stress in watermelon seedlings via modulation of proline content, antioxidant enzyme activity, and polyamine metabolism. J. Plant Growth Regul. 40, 2. https://doi.org/10.1007/s00344-020-10096-2 (2021).CAS 
Article 

Google Scholar 
Thakur, S., Thakur, I. & Sankanur, M. Assessment of genetic diversity in drek (Melia azedarach) using molecular markers. J. Tree Sci. 36, 78–85. https://doi.org/10.5958/2455-7129.2017.00011.5 (2017).Article 

Google Scholar 
Sivasubramaniam, K. et al. Seed priming: Triumphs and tribulations. The Madras Agricultural Journal 98, 197–209. https://www.researchgate.net/publication/267298497 (2011).
Google Scholar 
Xu, L. et al. Effect of salt stress on growth and physiology in Melia azedarach seedlings of six provenances. Int. J. Agric. Biol. 20, 471–480. https://doi.org/10.17957/IJAB/15.0618 (2018).CAS 
Article 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science. 320, 1768–1771. https://doi.org/10.1126/science.1156831 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ou-Yang, C.-F. et al. Impact of equatorial and continental airflow on primary greenhouse gases in the northern South China Sea. Environ. Res. Lett. 10, 065005. https://doi.org/10.1088/1748-9326/10/6/065005 (2015).ADS 
CAS 
Article 

Google Scholar 
Liu, B., Zhu, C., Su, J., Ma, S. & Xu, K. Record-breaking northward shift of the western North Pacific subtropical high in July 2018. J. Meteorol. Soc. Japan. 97, 913–925. https://doi.org/10.2151/jmsj.2019-047 (2019).ADS 
Article 

Google Scholar 
Huang, J. et al. Dryland climate change: Recent progress and challenges. Rev. Geophys. 55, 719–778. https://doi.org/10.1002/2016RG000550 (2017).ADS 
Article 

Google Scholar 
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250. https://doi.org/10.1073/pnas.0409902102 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Waldvogel, A. M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18. https://doi.org/10.1002/evl3.154 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vilà-Cabrera, A., Coll, L., Martínez-Vilalta, J. & Retana, J. Forest management for adaptation to climate change in the Mediterranean basin: A synthesis of evidence. For. Ecol. Manag. 407, 16–22. https://doi.org/10.1016/j.foreco.2017.10.021 (2018).Article 

Google Scholar 
He, X., Li, J., Wang, F., Zhang, J. & Chen, X. Variation and selection of Melia azedarach provenances and families. J. Northeast For. Univ. 47, 1–7. https://doi.org/10.13332/j.1000-1522.20170321 (2019).CAS 
Article 

Google Scholar 
Smith, A. B., Alsdurf, J., Knapp, M., Baer, S. G. & Johnson, L. C. Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change. Glob. Change Biol. 23, 4365–4375. https://doi.org/10.1111/gcb.13666 (2017).Article 

Google Scholar 
Bellon, M. R., Dulloo, E., Sardos, J., Thormann, I. & Burdon, J. J. In situ conservation—harnessing natural and human-derived evolutionary forces to ensure future crop adaptation. Evol. Appl. 10, 965–977. https://doi.org/10.1111/eva.12521 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bidak, L. M., Heneidy, S. Z., Halmy, M. W. A. & El-Kenany, E. T. Sustainability potential for Ginkgo biloba L. plantations under climate change uncertainty: An ex-situ conservation perspective. Acta Ecol. Sin. 42, 101–114. https://doi.org/10.1016/j.chnaes.2021.09.012 (2021).Article 

Google Scholar 
Qin, F., Liu, S. & Yu, S. Effects of allelopathy and competition for water and nutrients on survival and growth of tree species in Eucalyptus urophylla plantations. For. Ecol. Manag. 424, 387–395. https://doi.org/10.1016/j.foreco.2018.05.017 (2018).Article 

Google Scholar 
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 1–10. https://doi.org/10.1038/s41467-019-10775-z (2019).ADS 
CAS 
Article 

Google Scholar  More

Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2(10), 720–735. https://doi.org/10.1038/s43017-021-00207-2 (2021).ADS 
Article 

Google Scholar 
O’Mara, F. P. The role of grasslands in food security and climate change. Ann. Bot. 110, 1263–1270. https://doi.org/10.1093/aob/mcs209 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Eze, S., Palmer, S. M. & Chapman, P. J. Soil organic carbon stock in grasslands: Effects of inorganic fertilizers, liming and grazing in different climate settings. J. Environ. Manage. 223, 74–84. https://doi.org/10.1016/j.jenvman.2018.06.013 (2018).CAS 
Article 
PubMed 

Google Scholar 
Makoudi, B. et al. Phosphorus deficiency increases nodule phytase activity of faba bean rhizobia symbiosis. Acta Physiol. Plant 40, 63. https://doi.org/10.1007/s11738-018-2619-6 (2018).CAS 
Article 

Google Scholar 
Stecca, J. D. L. et al. Inoculation of soybean seeds coated with osmoprotector in differentssoil pH’s. Acta Sci. Agron. 41, 9. https://doi.org/10.4025/actasciagron.v41i1.39482 (2019).Article 

Google Scholar 
Afonso, S., Arrobas, M. & Rodrigues, M. Â. Soil and plant analyses to diagnose hop fields irregular growth. J. Soil Sci. Plant Nutr. 20, 1999–2013. https://doi.org/10.1007/s42729-020-00270-6 (2020).CAS 
Article 

Google Scholar 
Crews, T. E. & Peoples, M. B. Legume versus fertilizer sources of nitrogen: ecological tradeoffs and human needs. Agric. Ecosyst. Environ 102(3), 279–297. https://doi.org/10.1016/j.agee.2003.09.018 (2004).Article 

Google Scholar 
Ossler, J. N., Zielinski, C. A. & Heath, K. D. Tripartite mutualism: Facilitation or trade-offs between rhizobial and mycorrhizal symbionts of legume hosts. Am. J. Bot. 102, 1332–1341. https://doi.org/10.3732/ajb.1500007 (2015).CAS 
Article 
PubMed 

Google Scholar 
Backer, R. et al. Plant growth-promoting rhizobacteria: context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 9, 1473. https://doi.org/10.3389/fpls.2018.01473 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Keet, J. H., Ellis, A. G., Hui, C. & Le Roux, J. J. Strong spatial and temporal turnover of soil bacterial communities in South Africa’s hyper diverse fynbos biome. Soil Biol. Biochem. 136, 107541. https://doi.org/10.1016/j.soilbio.2019.107541 (2019).CAS 
Article 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103(3), 626–631. https://doi.org/10.1073/pnas.0507535103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kracmarova, M. et al. Response of soil microbes and soil enzymatic activity to 20 years of fertilization. Agronomy 10, 1542. https://doi.org/10.3390/agronomy10101542 (2020).CAS 
Article 

Google Scholar 
Wang, C., Liu, D. H. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120, 126–133. https://doi.org/10.1016/j.soilbio.2018.02.003 (2018).CAS 
Article 

Google Scholar 
Lucas, R. W. et al. A meta-analysis of the effects of nitrogen additions on base cations: Implications for plants, soils, and streams. For. Ecol. Manage. 262, 95–104. https://doi.org/10.1016/j.foreco.2011.03.018 (2011).Article 

Google Scholar 
Wang, Y. et al. Soil pH is a major driver of soil diazotrophic community assembly in Qinghai-Tibet alpine meadows. Soil Biol. Biochem. 115, 547–555. https://doi.org/10.1016/j.soilbio.2017.09.024 (2017).CAS 
Article 

Google Scholar 
Wan, S. et al. Effects of lime application and understory removal on soil microbial communities in subtropical eucalyptus L’Hér. plantations. Forests 10, 338 (2019).Article 

Google Scholar 
Yin, C., Schlatter, D. C., Kroese, D. R., Paulitz, T. C. & Hagerty, C. H. Impacts of lime application on soil bacterial microbiome in dryland wheat soil in the Pacific Northwest. Appl. Soil Ecol. 168, 104113 (2021).Article 

Google Scholar 
Schroeder, K. L., Schlatter, D. C. & Paulitz, T. C. Location-dependent impacts of liming and crop rotation on bacterial communities in acid soils of the Pacific Northwest. Appl. Soil. Ecol. 130, 59–68 (2018).Article 

Google Scholar 
Sudhakaran, M. & Ravanachandar, A. Role of soil enzymes in agroecosystem. Biotica Res. Today 2(6), 443–444 (2020).
Google Scholar 
Lacava, P. T., Machado, P. C. & de Andrade, P. H. M. Phosphate solubilization by endophytes from the tropical plants. Endophytes 3, 207–226 (2021).
Google Scholar 
Nannipieri, P., Giagnoni, L., Landi, L. & Renella, G. Role of Phosphatase Enzymes in Soil. Phosphorus in Action 215–243 (Springer, 2011).Book 

Google Scholar 
Zhang, L. et al. Soil labile organic carbon fractions and soil enzyme activities after 10 years of continuous fertilization and wheat residue incorporation. Sci. Rep. 10(1), 11318. https://doi.org/10.1038/s41598-020-68163-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Turner, B. L. Variation in pH optima of hydrolytic enzyme activities in tropical rain forest soils. Appl. Environ. Microbiol. 76, 6485–6493 (2010).ADS 
CAS 
Article 

Google Scholar 
Acosta-Martínez, V., Pérez-Guzmán, L. & Johnson, J. M. Simultaneous determination of β-glucosidase, β-glucosaminidase, acid phosphomonoesterase, and arylsulfatase activities in a soil sample for a biogeochemical cycling index. Appl. Soil Ecol. 142, 72–80. https://doi.org/10.12691/aees-8-6-26 (2019).CAS 
Article 

Google Scholar 
Parham, J. A. & Deng, S. P. Detection, quantification and characterization of β-glucosaminidase activity in soil. Soil Biol. Biochem. 32(8–9), 1183–1190. https://doi.org/10.1016/S0038-0717(00)00034-1 (2000).CAS 
Article 

Google Scholar 
Olajuyigbe, F. M. & Fatokun, C. O. Biochemical characterization of an extremely stable pH-versatile laccase from Sporothrix carnis CPF-05. Int. J. Biol. Macromol. 94, 535–543. https://doi.org/10.1016/j.ijbiomac.2016.10.037 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bhuyan, M. B. et al. Explicating physiological and biochemical responses of wheat cultivars under acidity stress: insight into the antioxidant defense and glyoxalase systems. Physiol. Mol. Biol. Plants 25, 865–879. https://doi.org/10.1007/s12298-019-00678-0 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M., Grinyer, J., Reich, P. B. & Singh, B. K. Relative importance of soil properties and microbial community for soil functionality: Insights from a microbial swap experiment. Funct. Ecol. 30, 1862–1873 (2016).Article 

Google Scholar 
Zhao, L. et al. Mercury methylation in rice paddies and its possible controlling factors in the Hg mining area, Guizhou province, Southwest China. Environ. Pollut. 215, 1–9. https://doi.org/10.1016/j.envpol.2016.05.001 (2016).CAS 
Article 
PubMed 

Google Scholar 
Ward, D., Kirkman, K., Hagenah, N. & Tsvuura, Z. Soil respiration declines with increasing nitrogen fertilization and is not related to productivity in long-term grassland experiments. Soil Biol. Biochem. 115, 415–422. https://doi.org/10.1016/j.soilbio.2017.08.035 (2017).CAS 
Article 

Google Scholar 
Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4(10), 1321–1326. https://doi.org/10.1038/s41559-020-1251-1 (2020).Article 
PubMed 

Google Scholar 
Fynn, R. W. & O’Connor, T. G. Determinants of community organization of a South African mesic grassland. J. Veg. Sci. 16(1), 93–102 (2005).Article 

Google Scholar 
Morris, C. & Fynn, R. The Ukulinga long-term grassland trials: Reaping the fruits of meticulous, patient research. Bull. Grassl. Soc. S. Afr. 11(1), 7–22 (2001).
Google Scholar 
Le Roux, N. P. & Mentis, M. Veld compositional response to fertilization in the tall grassveld of Natal. S. Afr. J. Plant Soil 3(1), 1–10. https://doi.org/10.1080/02571862.1986.10634177 (1986).Article 

Google Scholar 
Tsvuura, Z. & Kirkman, K. P. Yield and species composition of a mesic grassland savannah in South Africa are influenced by long-term nutrient addition. Austral Ecol. 38, 959–970 (2013).Article 

Google Scholar 
Goldman, E. & Green, L. H. Practical Handbook of Microbiology 2nd edn, 864 (CRC Press Taylor and Francis Group, 2008).Book 

Google Scholar 
Akinbowale, O. L., Peng, H. & Barton, M. D. Diversity of tetracycline resistance genes in bacteria from aquaculture sources in Australia. J. Appl. Microbiol. 103(5), 2016–2025 (2007).CAS 
Article 

Google Scholar 
Jackson, C. R., Tyler, H. L. & Millar, J. J. Determination of microbial extracellular enzyme activity in waters, soils, and sediments using high throughput microplate assays. Preparation of substrate and buffer solutions for colorimetric analyses of enzyme. J. Vis. Exp. 80, 1–9. https://doi.org/10.3791/50399 (2013).CAS 
Article 

Google Scholar 
Goyal, M. & Kaur, R. Interactive effect of nitrogen nutrition, nitrate reduction and seasonal variation on oxalate synthesis in leaves of Napier-bajar hybrid (Pennisetum purpureum P. glaucum). Crop Pasture Sci 70, 669–675 (2019).CAS 
Article 

Google Scholar 
Pavlovic, J., Kostic, L., Bosnic, P., Kirkby, E. A. & Nikolic, M. Interactions of silicon with essential and beneficial elements in plants. Front. Plant Sci. 12, 1224. https://doi.org/10.3389/fpls.2021.697592 (2021).Article 

Google Scholar 
Li, Y., Tremblay, J., Bainard, L. D., Cade-Menun, B. & Hamel, C. Long-term effects of nitrogen and phosphorus fertilization on soil microbial community structure and function under continuous wheat production. Environ. Microbiol. 22, 1066–1088 (2020).CAS 
Article 

Google Scholar 
Guo, Z., Han, J., Li, J., Xu, Y. & Wang, X. Effects of long-term fertilization on soil organic carbon mineralization and microbial community structure. PLoS ONE 14, e0211163 (2019).CAS 
Article 

Google Scholar 
Shang, L., Wan, L. I., Zhou, X., Li, S. & Li, X. Effects of organic fertilizer on soil nutrient status, enzyme activity, and bacterial community diversity in Leymus chinensis steppe in Inner Mongolia, China. PLoS ONE https://doi.org/10.1371/journal.pone.0240559 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gautam, A. et al. Responses of soil microbial community structure and enzymatic activities to long-term application of mineral fertilizer and beef manure. Environ. Sustain. Indic. 8, 10007S. https://doi.org/10.1016/j.indic.2020.100073 (2020).Article 

Google Scholar 
Wang, J., Lu, X., Zhang, J., Wei, G. & Xiong, Y. Regulating soil bacterial diversity, community structure and enzyme activity using residues from golden apple snails. Sci. Rep. 10(1), 1–11 (2020).CAS 
Article 

Google Scholar 
Xu, D., Carswell, A., Zhu, Q., Zhang, F. & de Vries, W. Modelling long-term impacts of fertilization and liming on soil acidification at Rothamsted experimental station. Sci. Total Environ. 713, 136249 (2020).ADS 
CAS 
Article 

Google Scholar 
von Tucher, S., Hörndl, D. & Schmidhalter, U. Interaction of soil pH and phosphorus efficacy: Long-term effects of P fertilizer and lime applications on wheat, barley, and sugar beet. Ambio 47, 41–49 (2018).Article 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl. Acad. Sci. USA 112, 10967–10972 (2015).ADS 
CAS 
Article 

Google Scholar 
Pan, J. et al. Dynamics of soil nutrients, microbial community structure, enzymatic activity, and their relationships along a chronosequence of Pinus massoniana plantations. Forests 12, 376 (2021).Article 

Google Scholar 
Andrés, J. A., Rovera, M., Guiñazú, L. B., Pastor, N. A. & Rosas, S. B. Role of in crop improvement. In Bacteria in Agrobiology: Plant Growth Responses 107–122 (Springer, 2011).Chapter 

Google Scholar 
Jeong, H., Choi, S. K., Ryu, C. M. & Park, S. H. Chronicle of a soil bacterium: Paenibacillus polymyxa E681 as a tiny guardian of plant and human health. Front. Microbiol. 10, 467 (2019).Article 

Google Scholar 
Garbeva, P. V., van Veen, J. A. & van Elsas, J. D. Microbial diversity in soil: Selection of microbial populations by plant and soil type and implications for disease suppressiveness. Annu. Rev. Phytopathol. 42, 243–270. https://doi.org/10.1146/annurev.phyto.42.012604.135455 (2004).CAS 
Article 
PubMed 

Google Scholar 
Sinsabaugh, R. L. & Moorhead, D. L. Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition. Soil Biol. Biochem. 26(10), 1305–1311. https://doi.org/10.1016/0038-0717(94)90211-9 (1994).Article 

Google Scholar 
Xiao, W., Chen, X., Jing, X. & Zhu, B. A meta-analysis of soil extracellular enzyme activities in response to global change. Soil Biol. Biochem. 123, 21–32. https://doi.org/10.1016/j.soilbio.2018.05.001 (2018).CAS 
Article 

Google Scholar 
Billah, M. et al. Phosphorus & phosphate solubilizing bacteria: Keys for sustainable agriculture. Geomicrobiol. J. 36(10), 904–916. https://doi.org/10.1080/01490451.2019.1654043 (2019).CAS 
Article 

Google Scholar 
Turner, B. L., McKelvie, I. D. & Haygarth, P. M. Characterisation of water-extractable soil organic phosphorus by phosphatase hydrolysis. Soil Biol Biochem. 34, 27–35. https://doi.org/10.1016/S0038-0717(01)00144-4 (2002).CAS 
Article 

Google Scholar 
van Aarle, I. M. & Plassard, C. Spatial distribution of phosphatase activity associated with ectomycorrhizal plants related to soil type. Soil Biol. Biochem. 42(2), 324–330. https://doi.org/10.1016/j.soilbio.2009.11.011 (2020).CAS 
Article 

Google Scholar  More

Experimental designThis experiment was set up containing two levels of soil biodiversity (high and low soil biodiversity) and seven treatments considering the number of global change factors (GCFs) (0, 1, 2, 4, 6, 8, 10) (Table 1, Supplementary Fig. 1 and Supplementary Data 1). We used the dilution-to-extinction approach to create the high and low soil biodiversity treatments (Supplementary Methods). Soil dilution can lead to a gradual loss of rare soil microbes, which can simulate a realistic loss of soil biodiversity, because rare soil microbes are more sensitive to anthropogenic pressures, e.g., warming, nitrogen addition and drought15. We note that the low soil biodiversity treatment is a subset of the high biodiversity, as many rare species have been eliminated through the dilution; this approach will likely lead to relatively more tolerant microbes in the resulting communities.An increasing number of GCFs was created inspired by the experimental design of the studies on biodiversity-ecosystem function relationships, based on random sampling from a species pool5,6,14. The combination of multiple GCFs was replicated 15 times at each level by randomly selecting GCFs from a pool with 10 GCFs for each replicate (Table 1 and Supplementary Data 1). For each replicate of combined GCFs, there were identical GCF combinations between the high and low soil biodiversity treatments to avoid a confounding effect of GCF combination and soil biodiversity treatments. The pool of 10 GCFs included: warming, nitrogen deposition, drought, heavy metal pollution, plastic mulching film residues, salinity, agricultural fungicide, bactericide application, surfactant contaminant and soil compaction (Supplementary Methods). These GCFs frequently occur in intensively managed agroecosystems and are treated as anthropogenic pressures10,13,14,15.MicrocosmsThis experiment was conducted using 50 ml conical Mini Bioreactors (Product Number 431720, Corning Inc., NY) as experimental units (Supplementary Fig. 2). Each Mini Bioreactor has four vents in the cap, where a hydrophobic membrane avoids microbial contamination but allows gas exchange. We filled each Mini Bioreactor with 40.0 g (dry weight, d.w.) of soil in total, which received the appropriate treatments.Soil sterilization and inoculum preparationWe collected the field soil from the top 10 cm of an intensive farming system in Berlin (52.466°N, 13.303°E). Field soil was passed through a 2 mm mesh to remove large roots and stones. We sterilized 20 kg of soil for 90 min at 121 °C, and stored 2 kg of fresh soil at 4 °C. The dilution-to-extinction approach38,39,40,41,42 was used to create high and low soil biodiversity (Supplementary Fig. 1). A parent inoculum suspension was prepared by mixing 100 g of fresh soil with 200 ml of sterilized VE water. The sediment settled for 1 min. The upper 200 ml of soil suspension was treated as parent inoculum suspension. 50 ml of parent inoculum suspension was added to 500 g of sterilized soil in a plastic bag, and homogenized by turning the bag up and down 30 times to obtain the inoculum of high soil biodiversity. Another 5 ml of parent inoculum suspension was mixed with 45 ml of sterilized parent inoculum suspension to create the 10-1 dilution. This procedure was repeated five times to reach the 10-6 dilution. 50 ml of the 10-6 dilution was mixed and homogenized with 500 g of sterilized soil in a plastic bag to obtain the inoculum of low soil biodiversity. This whole dilution procedure was repeated five times to obtain 10 bags of soil inoculum (five bags for each soil biodiversity inoculum).Sterile water was added to each plastic bag to reach the water content of the fresh soil in the field. All bags were closed with a sterilized cotton plug and a rubber band to avoid microbial contamination but allow gas exchange42. All bags were incubated in a dark room at 20 °C until similar microbial abundance was observed between the high and low soil biodiversity inoculum. Soil inoculum was homogenized by shaking and turning the bags once a week. After incubation, 2.0 g of soil in each bag was collected and stored at −80 °C for DNA extraction. Quantitative real-time PCR (qPCR) was used to determine fungal and bacterial abundance. In the present study, it took two months to recover soil microbial biomass (Supplementary Fig. 3).The implementation of GCFs and harvestAgroecosystems, some of the most intensively managed ecosystems, are affected by the co-occurrence of multiple GCFs13,14,15. This study focused on GCFs that frequently occur in agroecosystems, including warming, nitrogen deposition, drought, heavy metal pollution, plastic film residues, salinity, agricultural fungicide and bactericide application, surfactant contaminant and soil compaction. We present the rationale for the 10 tested GCFs in the Supplementary Methods.Loading soils were used to achieve an effective mixing of chemical agents into 40.0 g soil in each Mini Bioreactor. We created separate ‘loading soil’ for each GCF with chemical addition by mixing an appropriate dose of a chemical agent with sterilized soil through careful homogenization. This was done to avoid exaggerated effects of more concentrated chemicals when mixed with soil. For each chemical, 1.0 g (d.w.) of loading soil contained an appropriate dose for 40.0 g soil in a Mini Bioreactor. For instance, 1 634 mg of NH4NO3 was mixed with 100 g (d.w.) of sterilized soil, to ensure that there was about 16.34 mg of NH4NO3 in 1.0 g of sterilized loading soil. We weighed 40.0 g (d.w.) of soils, including 1.0 g (d.w.) of each loading soil, 5.0 g (d.w.) of soil inoculum (high or low soil biodiversity), an appropriate amount of film (0 or 0.16 g plastic film) and sterilized soil, according to GCF combination for each experimental unit. We put 40.0 g of mixed soils into a clean and sterilized cup (200 ml) with a cap, and then homogenized it by turning the cup up and down for 5 min using a shaking machine (Heidolph Reax 2, Heidolph Instruments GmbH & CO. KG, Schwabach, Germany). After homogenization, 40.0 g of mixed soils was transferred to a Mini Bioreactor, and a mesh bag containing about 100 mg of dry Medicago lupulina leaves (65 °C for 72 h) was buried 1 cm below the soil surface. We used a stick to press soils in each Mini Bioreactor to simulate an ambient condition (1.3 g cm−3) or mechanical compaction (1.7 g cm−3) in farmland.For the warming treatment with an increment of 5.0 °C over the ambient temperature (20 °C), we wrapped heating cables (Exo Terra PT-2012; Hagen Deutschland GmbH & Co. KG, Holm, Germany) around the outside of the bioreactors. A set temperature was maintained by temperature controllers (Voltcraft ETC-902; Conrad Electronic SE, Hirschau, Germany), which can switch off and on heating cables depending on the real-time temperature of the outside surface of Mini Bioreactors. Mini Bioreactors were placed in beakers filled with sand to reduce thermal radiation from neighboring units. At the start of the experiment, we added suitable amounts of sterilized water to each experimental unit to reach 60% of water holding capacity for the non-drought treatment and 30% water holding capacity for the drought treatment.All Mini Bioreactors were incubated at 20.0 °C in the dark for six weeks before the final harvest. Because there was 2.0 g of weight loss on average in each Mini Bioreactor in the first three weeks, we added 2 ml of sterilized water to each Mini Bioreactor on the first day of the fourth week. During the final harvest, soil in each Mini Bioreactor was gently homogenized using a spoon, and then 2.0 g of fresh soil was collected and stored at −80 °C for DNA extraction; 5.0 g was stored at 4 °C for the determination of soil enzyme activity; the leftover was oven-dried at 40 °C for other measurements. DNA of each soil sample was extracted from 250 mg soil, using DNeasy PowerSoil Pro Kit (QIAGEN GmbH, Hilden, Germany), following manufacturer’s instructions. Soil DNA extraction was stored at −80 °C for further analysis.The measurement of response variablesWe measured the following response variables: microbial activity (soil respiration), microbial abundance (bacterial and fungal abundance), nutrient cycling (litter decomposition rate and soil enzyme activity), physical properties (water-stable soil aggregates and soil water repellency), bacterial and fungal biodiversity (richness and microbial network features) (See details in the Supplementary Methods). We measured soil respiration as CO2 concentration (ppm h−1 g−1 soil) in the third and sixth week as an indicator for soil microbial activity. Bacterial and fungal abundance was estimated by qPCR. The proportional loss of litter (Medicago lupulina leaves) dry weight during soil incubation was used as an indication of decomposition rate. We measured the activity of β-glucosidase (cellulose degradation), β-D-celluliosidase (cellulose degradation), N-acetyl-β-glucosaminidase (chitin degradation) and phosphatase (organic phosphorus mineralization) using high throughput microplate assay43,44. A modified protocol by Kemper and Rosenau was used to measure water-stable soil aggregates45. Soil water repellency was measured using the water drop penetration time method46. High throughput sequencing (Illumina MiSeq) was used to measure the taxonomic composition of soil fungal and bacterial communities with the primers fITS7 and ITS4 for fungi and 515F-Y and 806 R for bacteria47,48 (Supplementary Methods).Statistical analysesFor diversity and community composition analysis, we excluded the samples with less than 1% of the observations of the largest sample in the ASV table. For network analysis, we then removed ASVs with low prevalence, which presented less than 20% of samples across all experimental units to reduce the high percentage of zero counts. A co-occurrence network was constructed based on both fungal and bacterial ASV tables. The PLNnetwork function in the R package PLNmodels was employed to infer the network, using a sparse multivariate Poisson log-normal (PLN) model49. According to the Extended Bayesian Information Criterion (EBIC), the best model was extracted with the function getBestModel. The network was compartmentalized into different modules using the cluster_fast_greedy function in the igraph package and visualized with partial correlations with |ρ| > 0.05. We focused on the response of the relative abundance of modules, also known as clusters, which represent the closely associated microbes, e.g., groups of coexisting or co-evolving microbes27. The relative abundance of modules was calculated by summing relative abundances for individual ASVs in modules. We used the package FUNGuildR50 to taxonomically parse fungal trait information, using the FUNGuild database51.For each single GCF treatment, we took the average response from the 10 replicates before analysis. To confirm how the effect of soil biodiversity treatment can change along with the increasing number of GCFs, we quantified the effect size of soil biodiversity treatment for each response variable using Hedges’ g (mean and 95% CIs) at each level of the number of GCFs, using the R package effsize52. Hedges’ g is calculated as the mean difference between the high and low soil biodiversity treatments in units of the pooled standard deviation as a paired-samples because there were identical GCFs and GCF combinations for both high and low biodiversity conditions, with the exception of the zero and 10 GCF treatments.To evaluate how each of the response variables changes along with the number of GCFs, we applied a generalized additive model (GAM)53. GAM is a penalized generalized linear model that fits a nonparametric, nonlinear smooth curve54. The degree of smoothness of model terms is estimated as part of fitting, using the generalized cross validation. We reasonably assume that the curve shapes are different between high and low soil biodiversity treatments. Therefore, we included biodiversity conditions (low/high) as the model intercept and as the “factor smooth” smoothing class, where a smooth function is created for each factor level independently55. For GAM modeling, we used the mgcv package55. The dimension of the basis used to represent the smooth term was set as k = 5 so that the model does not overfit to the data. For this, we compared some other values (from 3 to 8) and confirmed that the results are essentially the same within the tested range. The other parameters were set as default.The relationships between soil microbial indices and other soil indices were tested using Spearman correlation in the package microbiome, and the adjustment method “fdr” was employed to control the false discovery rate for multiple testing correction56. For the further multivariate integration of soil functions/properties and composition of modules, the DIABLO (Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies) was employed to detect correlation (Pearson’s correlation |r| > 0.5) among variables using the package mixOmics57.The Z-scores for each of the eight soil functions (as shown in Fig. 1, with the exception of soil water repellency) were evaluated, and then we computed an improved weighted multifunctionality metric to represent soil multifunctionality (Supplementary Methods)58. Structural equation models (SEMs) were used to reveal the direct and indirect effects of an increasing number of GCFs on soil multifunctionality within each soil biodiversity treatment using the package lavaan59. We assumed that an increasing number of GCFs influences soil multifunctionality by regulating the bacterial and fungal abundance and the relative abundance of modules. All response variables were standardized to the same comparison scale using the z-score transformation before constructing SEMs. Models with optimal fitting indices were reported (Supplementary Fig. 11).The permutational multivariate analysis (ADONIS) and non-metric multidimensional scaling (NMDS) ordination based on the Bray-Curtis distance were conducted to test the effect of soil biodiversity and GCF treatments on the community composition of bacteria and fungi using the R package vegan60. For the data handling, processing, and visualization, we used the packages tidyverse61, reshaping62, cowplot63, RColorBrewer64, qgraph65, igraph66, factoextra67, phyloseq68 and itsadug69. These data manipulation and analyses were conducted using R version 4.1.370. The R script and data are available in a publicly accessible database71.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Pimentel D, McNair S, Janecka J, Wightman J, Simmonds C, O’Connell C, et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric Ecosyst Environ. 2001;84:1–20.Article 

Google Scholar 
Diagne C, Leroy B, Vaissière AC, Gozlan RE, Roiz D, Jarić I, et al. High and rising economic costs of biological invasions worldwide. Nature. 2021;592:571–6.Article 
CAS 

Google Scholar 
Catford JA, Jansson R, Nilsson C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers Distrib. 2009;15:22–40.Article 

Google Scholar 
Pearson DE, Ortega YK, Eren Ö, Hierro JL. Community assembly theory as a framework for biological invasions. Trends Ecol Evol. 2018;33:313–25.PubMed 
Article 

Google Scholar 
Inderjit, van der Putten WH. Impacts of soil microbial communities on exotic plant invasions. Trends Ecol Evol. 2010;25:512–9.PubMed 
Article 
CAS 

Google Scholar 
Keane RM, Crawley MJ. Exotic plant invasions and the enemy release hypothesis. Trends Ecol Evol. 2002;17:164–70.Article 

Google Scholar 
Stinson KA, Campbell SA, Powell JR, Wolfe BE, Callaway RM, Thelen GC, et al. Invasive plant suppresses the growth of native tree seedlings by disrupting belowground mutualisms. PLoS Biol. 2006;4:e140.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hierro JL, Callaway RM. Allelopathy and exotic plant invasion. Plant Soil. 2003;256:29–39.Article 
CAS 

Google Scholar 
Reinhart KO, Callaway RM. Soil biota and invasive plants. N Phytol. 2006;170:445–57.Article 

Google Scholar 
Waller LP, Allen WJ, Barratt BIP, Condron LM, França FM, Hunt JE, et al. Biotic interactions drive ecosystem responses to exotic plant invaders. Science. 2020;368:967–72.PubMed 
Article 
CAS 

Google Scholar 
McLeod ML, Cleveland CC, Lekberg Y, Maron JL, Philippot L, Bru D, et al. Exotic invasive plants increase productivity, abundance of ammonia-oxidizing bacteria and nitrogen availability in intermountain grasslands. J Ecol. 2016;104:994–1002.Article 
CAS 

Google Scholar 
Saul WC, Jeschke JM. Eco-evolutionary experience in novel species interactions. Ecol Lett. 2015;18:236–45.PubMed 
Article 

Google Scholar 
Desprez-Loustau M, Robin C, Buee M, Courtecuisse R, Garbaye J, Suffert F, et al. The fungal dimension of biological invasions. Trends Ecol Evol. 2007;22:472–80.PubMed 
Article 

Google Scholar 
Hierro JL, Maron JL, Callaway RM. A biogeographical approach to plant invasions: the importance of studying exotics in their introduced and native range. J Ecol. 2005;93:5–15.Article 

Google Scholar 
Callaway RM, Thelen GC, Rodriguez A, Holben WE. Soil biota and exotic plant invasion. Nature. 2004;427:731–3.PubMed 
Article 
CAS 

Google Scholar 
Maron JL, Klironomos J, Waller L, Callaway RM. Invasive plants escape from suppressive soil biota at regional scales. J Ecol. 2014;102:19–27.Article 

Google Scholar 
Brundrett MC. Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant Soil. 2009;320:37–77.Article 
CAS 

Google Scholar 
Smith SE, Read DJ. Mycorrhizal symbiosis. London: Academic Press; 2008.O’Neill EG, O’Neill RV, Norby RJ. Hierarchy theory as a guide to mycorrhizal research on large-scale problems. Environ Pollut. 1991;73:271–84.PubMed 
Article 

Google Scholar 
Johnson NC, Wilson GWTT, Wilson JA, Miller RM, Bowker MA. Mycorrhizal phenotypes and the Law of the Minimum. N Phytol. 2015;205:1473–84.Article 
CAS 

Google Scholar 
Lekberg Y, Arnillas CA, Borer ET, Bullington LS, Fierer N, Kennedy PG, et al. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils. Nat Commun. 2021;12:3484.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Richardson DM, Allsopp N, D’Antonio CM, Milton S, Rejmanek M. Plant invasions – the role of mutualisms. Biol Rev. 2000;75:65–93.PubMed 
Article 
CAS 

Google Scholar 
Marler MJ, Zabinski CA, Callaway RM. Mycorrhizae indirectly enhance competitive effects of an invasive forb on a native bunchgrass. Ecology. 1999;80:1180–6.Article 

Google Scholar 
Soti PG, Jayachandran K, Purcell M, Volin JC, Kitajima K. Mycorrhizal symbiosis and Lygodium microphyllum invasion in South Florida—a biogeographic comparison. Symbiosis. 2014;62:81–90.Article 

Google Scholar 
Fumanal B, Plenchette C, Chauvel B, Bretagnolle F. Which role can arbuscular mycorrhizal fungi play in the facilitation of Ambrosia artemisiifolia L. invasion in France? Mycorrhiza. 2006;17:25–35.PubMed 
Article 
CAS 

Google Scholar 
Hart MM, Reader RJ. Taxonomic basis for variation in the colonization strategy of arbuscular mycorrhizal fungi. N Phytol. 2002;153:335–44.Article 

Google Scholar 
Maherali H, Klironomos JN. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science. 2007;316:1746–8.PubMed 
Article 
CAS 

Google Scholar 
Kivlin SN, Hawkes CV, Treseder KK. Global diversity and distribution of arbuscular mycorrhizal fungi. Soil Biol Biochem. 2011;43:2294–303.Article 
CAS 

Google Scholar 
Davison J, Moora M, Öpik M, Adholeya A, Ainsaar L, Bâ A, et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science. 2015;349:970–3.PubMed 
Article 
CAS 

Google Scholar 
Mitchell CE, Agrawal AA, Bever JD, Gilbert GS, Hufbauer RA, Klironomos JN, et al. Biotic interactions and plant invasions. Ecol Lett. 2006;9:726–40.PubMed 
Article 

Google Scholar 
Ehrenfeld JG. Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems. 2003;6:503–23.Article 
CAS 

Google Scholar 
Rout ME, Chrzanowski TH. The invasive Sorghum halepense harbors endophytic N2-fixing bacteria and alters soil biogeochemistry. Plant Soil. 2009;315:163–72.Article 
CAS 

Google Scholar 
Sardans J, Bartrons M, Margalef O, Gargallo-Garriga A, Janssens IA, Ciais P, et al. Plant invasion is associated with higher plant-soil nutrient concentrations in nutrient-poor environments. Glob Change Biol. 2017;23:1282–91.Article 

Google Scholar 
Bossdorf O, Auge H, Lafuma L, Rogers WE, Siemann E, Prati D. Phenotypic and genetic differentiation between native and introduced plant populations. Oecologia. 2005;144:1–11.PubMed 
Article 

Google Scholar 
Lankau RA. Resistance and recovery of soil microbial communities in the face of Alliaria petiolata invasions. N Phytol. 2011;189:536–48.Article 

Google Scholar 
Blossey B, Nötzold R. Evolution of increased competitive ability in invasive nonindigenous plants: a hypothesis. J Ecol. 1995;83:887–9.Article 

Google Scholar 
van Kleunen M, Bossdorf O, Dawson W. The ecology and evolution of alien plants. Annu Rev Ecol Evol Syst. 2018;49:25–47.Article 

Google Scholar 
Rosche C, Hensen I, Schaar A, Zehra U, Jasieniuk M, Callaway RM, et al. Climate outweighs native vs. nonnative range‐effects for genetics and common garden performance of a cosmopolitan weed. Ecol Monogr. 2019;89:e01386.Article 

Google Scholar 
Weaver SE. The biology of Canadian weeds. 115. Conyza canadensis. Can J Plant Sci. 2001;81:867–75.Article 

Google Scholar 
Gange AC, Ayres RL. On the relation between arbuscular mycorrhizal colonization and plant ’ benefit. Oikos. 1999;87:615–21.Article 

Google Scholar 
Řezáčová V, Konvalinková T, Řezáč M. Decreased mycorrhizal colonization of Conyza canadensis (L.) Cronquist in invaded range does not affect fungal abundance in native plants. Biologia. 2020;75:693–9.Article 

Google Scholar 
Zhang Q, Sun Q, Koide RT, Peng Z, Zhou J, Gu X, et al. Arbuscular mycorrhizal fungal mediation of plant-plant onteractions in a marshland plant community. Sci World J. 2014;2014:1–10.
Google Scholar 
Zhang HY, Goncalves P, Copeland E, Qi SS, Dai ZC, Li GL, et al. Invasion by the weed Conyza canadensis alters soil nutrient supply and shifts microbiota structure. Soil Biol Biochem. 2020;143:107739.Article 
CAS 

Google Scholar 
Shah MA, Callaway RM, Shah T, Houseman GR, Pal RW, Xiao S, et al. Conyza canadensis suppresses plant diversity in its nonnative ranges but not at home: a transcontinental comparison. N Phytol. 2014;202:1286–96.Article 

Google Scholar 
Colautti RI, Lau JA. Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Mol Ecol. 2015;24:1999–2017.PubMed 
Article 

Google Scholar 
Rosche C, Hensen I, Lachmuth S. Local pre-adaptation to disturbance and inbreeding-environment interactions affect colonisation abilities of diploid and tetraploid Centaurea stoebe. Plant Biol. 2018;20:75–84.PubMed 
Article 
CAS 

Google Scholar 
Hart SC, Start JM, Davidson EA, Firestone MK. Nitrogen mineralization, immobilization, and nitrification. In: Weaver RW, Angle J., Bottomley P., editors. Methods of soil analysis, part 2 microbiological and biochemical properties. Madison, WI: Soil Science Society of America; 1994. p. 985–1018.Brundrett M, Bougher N, Dell B, Grove T, Malajczuk N. Working with mycorrhizas in forestry and agriculture. ACIAR Monogr. 1996;32:1–374.
Google Scholar 
McGonigle TP, Miller MH, Evans DG, Fairchild GL, Swan JA. A new method which gives an objective measure of colonization of roots by vesicular—arbuscular mycorrhizal fungi. N Phytol. 1990;115:495–501.Article 
CAS 

Google Scholar 
Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;4315:4302–15.Article 

Google Scholar 
Hijmans RJ. raster: Geographic data analysis and modeling. R package version 3.3-13. 2020. https://cran.r-project.org/package=raster.R Core Team. R: A language and environment for statistical computing [https://www.r-project.org/]. Vienna, Austria: R Foundation for Statistical Computing; 2019.Oksanen J, Guillaume BF, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package. 2019. https://cran.r-project.org/package=vegan.Dumbrell AJ, Ashton PD, Aziz N, Feng G, Nelson M, Dytham C, et al. Distinct seasonal assemblages of arbuscular mycorrhizal fungi revealed by massively parallel pyrosequencing. N Phytol. 2011;190:794–804.Article 
CAS 

Google Scholar 
Lee J, Lee S, Young JPW. Improved PCR primers for the detection and identification of arbuscular mycorrhizal fungi. FEMS Microbiol Ecol. 2008;65:339–49.PubMed 
Article 
CAS 

Google Scholar 
Bullington LS, Lekberg Y, Larkin BG. Insufficient sampling constrains our characterization of plant microbiomes. Sci Rep. 2021;11:3645.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.PubMed 
PubMed Central 
CAS 

Google Scholar 
Öpik M, Vanatoa A, Vanatoa E, Moora M, Davison J, Kalwij JM, et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). N Phytol. 2010;188:223–41.Article 
CAS 

Google Scholar 
Chen J. GUniFrac: generalized UniFrac distances. R package version 1.1. 2018. https://cran.r-project.org/package=GUniFrac.Webb CO. Exploring the phylogenetic structure of ecological communities: an example for rain forest trees. Am Nat. 2000;156:145–55.PubMed 
Article 

Google Scholar 
Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.PubMed 
Article 
CAS 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Yu G, Smith DK, Zhu H, Guan Y, Lam TT. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8:28–36.Article 

Google Scholar 
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2014;67.Borcard D, Gillet F, Legendre P. Numerical ecology with R. New York: Springer; 2011.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.
Google Scholar 
Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecol Monogr. 2013;83:557–74.Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Felker-Quinn E, Schweitzer JA, Bailey JK. Meta-analysis reveals evolution in invasive plant species but little support for Evolution of Increased Competitive Ability (EICA). Ecol Evol. 2013;3:739–51.PubMed 
PubMed Central 
Article 

Google Scholar 
Pal RW, Maron JL, Nagy DU, Waller LP, Tosto A, Liao H, et al. What happens in Europe stays in Europe: apparent evolution by an invader does not help at home. Ecology 2020;101:e03072.PubMed 
Article 

Google Scholar 
Matesanz S, Sultan SE. High-performance genotypes in an introduced plant: insights to future invasiveness. Ecology. 2013;94:2464–74.PubMed 
Article 

Google Scholar 
Hart M, Reader R. Host plant benefit from association with arbuscular mycorrhizal fungi: variation due to differences in size of mycelium. Biol Fertil Soils. 2002;36:357–66.Article 

Google Scholar 
Yang H, Zhang Q, Koide RT, Hoeksema JD, Tang J, Bian X, et al. Taxonomic resolution is a determinant of biodiversity effects in arbuscular mycorrhizal fungal communities. J Ecol. 2017;105:219–28.Article 
CAS 

Google Scholar 
Moora M, Berger S, Davison J, Öpik M, Bommarco R, Bruelheide H, et al. Alien plants associate with widespread generalist arbuscular mycorrhizal fungal taxa: evidence from a continental-scale study using massively parallel 454 sequencing. J Biogeogr. 2011;38:1305–17.Article 

Google Scholar 
Policelli N, Bruns TD, Vilgalys R, Nuñez MA. Suilloid fungi as global drivers of pine invasions. N Phytol. 2019;222:714–25.Article 

Google Scholar 
Jia Y, Heijden MGA, Wagg C, Feng G, Walder F. Symbiotic soil fungi enhance resistance and resilience of an experimental grassland to drought and nitrogen deposition. J Ecol. 2021;109:3171–81.Article 
CAS 

Google Scholar 
Van Der Heijden MGAA, Klironomos JN, Ursic M, Moutoglis P, Streitwolf-Engel R, Boller T, et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature. 1998;396:69–72.Article 
CAS 

Google Scholar 
Zhang Q, Yang R, Tang J, Yang H, Hu S, Chen X. Positive feedback between mycorrhizal fungi and plants influences plant invasion success and resistance to invasion. PLoS ONE. 2010;5:e12380.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shah MA, Reshi ZA, Khasa DP. Arbuscular mycorrhizas: drivers or passengers of alien plant invasion. Bot Rev. 2009;75:397–417.Article 

Google Scholar 
Valverde-Barrantes OJ, Horning AL, Smemo KA, Blackwood CB. Phylogenetically structured traits in root systems influence arbuscular mycorrhizal colonization in woody angiosperms. Plant Soil. 2016;404:1–12.Article 
CAS 

Google Scholar 
Wilson GWT, Hartnett DC. Interspecific variation in plant responses to mycorrhizal colonization in tallgrass prairie. Am J Bot. 1998;85:1732–8.PubMed 
Article 
CAS 

Google Scholar 
Seifert EK, Bever JD, Maron JL. Evidence for the evolution of reduced mycorrhizal dependence during plant invasion. Ecology 2009;90:1055–62.PubMed 
Article 

Google Scholar 
Deveautour C, Donn S, Power SA, Bennett AE, Powell JR. Experimentally altered rainfall regimes and host root traits affect grassland arbuscular mycorrhizal fungal communities. Mol Ecol. 2018;27:2152–63.PubMed 
Article 

Google Scholar 
Osborne OG, De-Kayne R, Bidartondo MI, Hutton I, Baker WJ, Turnbull CGN, et al. Arbuscular mycorrhizal fungi promote coexistence and niche divergence of sympatric palm species on a remote oceanic island. N Phytol. 2018;217:1254–66.Article 
CAS 

Google Scholar 
Tian B, Pei Y, Huang W, Ding J, Siemann E. Increasing flavonoid concentrations in root exudates enhance associations between arbuscular mycorrhizal fungi and an invasive plant. ISME J. 2021;15:1919–30.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pimprikar P, Gutjahr C. Transcriptional regulation of arbuscular mycorrhiza development. Plant Cell Physiol. 2018;59:673–90.PubMed 
Article 
CAS 

Google Scholar 
Wendlandt CE, Helliwell E, Roberts M, Nguyen KT, Friesen ML, Wettberg E, et al. Decreased coevolutionary potential and increased symbiont fecundity during the biological invasion of a legume‐rhizobium mutualism. Evolution. 2021;75:731–47.PubMed 
Article 

Google Scholar 
Callaway RM, Bedmar EJ, Reinhart KO, Silvan CG, Klironomos J. Effects of soil biota from different ranges on Robinia invasion: acquiring mutualists and escaping pathogens. Ecology. 2011;92:1027–35.PubMed 
Article 

Google Scholar 
Shelby N, Duncan RP, Putten WH, McGinn KJ, Weser C, Hulme PE. Plant mutualisms with rhizosphere microbiota in introduced versus native ranges. J Ecol. 2016;104:1259–70.Article 
CAS 

Google Scholar 
Yang Q, Carrillo J, Jin H, Shang L, Hovick SM, Nijjer S, et al. Plant–soil biota interactions of an invasive species in its native and introduced ranges: Implications for invasion success. Soil Biol Biochem. 2013;65:78–85.Article 
CAS 

Google Scholar 
Bronstein JL. The exploitation of mutualisms. Ecol Lett. 2001;4:277–87.Article 

Google Scholar 
Kiers ET, Duhamel M, Beesetty Y, Mensah JA, Franken O, Verbruggen E, et al. Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. Science. 2011;333:880–2.PubMed 
Article 
CAS 

Google Scholar 
Koziol L, Bever JD. Mycorrhizal feedbacks generate positive frequency dependence accelerating grassland succession. J Ecol. 2019;107:622–32.Article 

Google Scholar 
Yang H, Yuan Y, Zhang Q, Tang J, Liu Y, Chen X. Changes in soil organic carbon, total nitrogen, and abundance of arbuscular mycorrhizal fungi along a large-scale aridity gradient. Catena. 2011;87:70–7.Article 
CAS 

Google Scholar 
Zhang J, Wang F, Che R, Wang P, Liu H, Ji B, et al. Precipitation shapes communities of arbuscular mycorrhizal fungi in Tibetan alpine steppe. Sci Rep. 2016;6:23488.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Read DJ. Mycorrhizas in ecosystems. Experientia. 1991;47:376–91.Article 

Google Scholar 
Delavaux CS, Smith-Ramesh LM, Kuebbing SE. Beyond nutrients: a meta-analysis of the diverse effects of arbuscular mycorrhizal fungi on plants and soils. Ecology. 2017;98:2111–9.PubMed 
Article 

Google Scholar  More

The pre-evaporitic, evaporitic, and post-evaporitic phases are recognized for the late Aptian. These phases are recorded within the K40–K50 sequences (Fig. 2A), and show an average maximum thickness of approximately 650 m in the studied basins. The pre-evaporitic phase is represented by carbonate and siliciclastic deposits formed in fluvial and lacustrine deltaic environments within a large proto-oceanic gulf28 (Fig. 2A). The peak of the evaporitic deposition is recorded in the K50 sequence, with widespread occurrences in the Brazilian equatorial margin. The origin of these deposits is the heat intensification associated with the widening of the Atlantic Ocean. These conditions caused strong evaporation leading to a wide distribution of evaporites (mainly halite and anhydrite gypsum) in the South Atlantic basins. The eastern continental margin of Brazil contains a restricted marine section characterized by evaporites, which are particularly prominent in thickness and occurrence in the Espírito Santo Basin (Itaúnas Member of the Mariricu Formation) and the Sergipe Basin (the Ibura Member of the Muribeca Formation)28. Evaporites form the most prominent evidence of dry climates in the South Atlantic basins11, with evaporation exceeding precipitation. The post-evaporitic phase is characterized by fully marine conditions evidenced by rich assemblages of marine fossils. During this phase, carbonates were deposited, followed by muddy and sandy sediments in shallow-marine and slope environments.Figure 2Paleoclimatic phases scheme and principal component analysis for paleoclimatic phases. (A) Paleoclimatic phases scheme for the late Aptian and the main depositional environments. (B) Principal component plot of bioclimatic groups. (C) Principal component for the pre-evaporitic phase (N = 92), evaporitic phase (N = 78), and post-evaporitic phase (N = 385); see Supplementary Fig. 9 for individual basins.Full size imagePaleovegetationWe identified a rich plant community with 139 spore and pollen genera/morphotypes representing all plant groups: bryophytes (five genera), ferns (58 genera), lycophytes (18 genera), pteridosperms (one genus), gymnosperms (27 genera), and angiosperms (30 genera) (Supplementary Table 2). The inferred systematic affinities at the family level reached 100% in bryophytes, 56.9% in ferns, 100% in lycophytes, 100% in pteridosperms, 92.6% in gymnosperms, and 40.0% in angiosperms, totaling 67.6% of the recorded genera (Supplementary Table 2). Marine elements (e.g., dinoflagellate cysts and microforaminiferal linings) were identified, in particular from the Sergipe and Araripe basins (Fig. 1). Pollen grains from gymnosperms were most abundant, represented mainly by the conifer families Cheirolepidiaceae, Araucariaceae, and Podocarpaceae, although representing different climatic settings. Classopollis (Cheirolepidiaceae) is the most abundant genus in all sections studied, followed by Araucariacites (Araucariaceae). Gymnosperms showed low diversity. Spore-producing plants are the most diverse in the assemblages of all basins (82 genera) and represented by several families of bryophytes, ferns, and lycophytes (e.g., Sphagnaceae, Anemiaceae, Cyatheaceae, Marsileaceae, Selaginellaceae, and Lycopodiaceae). These plant groups depend on water to reproduce and are therefore associated with humid settings.Cicatricosisporites (Anemiaceae) is the third most abundant palynomorph in all the basins, but especially in the northeastern basins (e.g., Sergipe Basin). Angiosperms are among the least abundant; however, they are diverse and include the most abundant and controversial genus Afropollis, herein attributed to angiosperms. In the most recent publication that addressed this question, ref.29 suggest that Afropollis should be treated as an angiosperm genus, although without more precise systematic assignment. The 30 genera/morphotypes of angiosperms are assigned to 8 families, viz., Arecaceae, Chloranthaceae, Euphorbiaceae, Flacourtiaceae, Illiciaceae, Liliaceae, Solanaceae and Trimeniaceae. The second most abundant genus is Stellatopollis also without precise systematic assignment.Spatio-temporal distribution of bioclimatic groupsOn the basis of their botanical affinities, most taxa were classified into five bioclimatic groups [see “Methods” section and Supplementary information], viz., hydrophytes, hygrophytes, tropical lowland flora, upland flora, and xerophytes (Supplementary Table 2) (Fig. 3).Figure 3Relevant palynomorphs of bioclimatic groups: (1) Aequitriradites sp.; (2) Crybelosporites sp.; (3) Perotriletes sp.; (4) Cicatricosisporites sp.; (5) Echinatisporis sp.; (6) Verrucosisporites sp.; (7) Bennettitaepollenites sp.; (8) Stellatopollis sp.; (9) Afropollis sp.; (10) Dejaxpollenites microfoveolatus; (11) Classopollis classoides; (12) Equisetosporites ovatus; (13) Gnetaceaepollenites jansonii; (14) Regalipollenites sp.; (15) Araucariacites sp.; (16) Callialasporites dampieri; (17) Complicatissacus cearensis; (18) Cyathidites sp.. Scale bar 20 µm.Full size imageOverall, the vegetation is dominated by the xerophytic bioclimatic group on account of the very high abundance of Classopollis (Cheirolepidiaceae) (general mean of 60.5%). However, the stratigraphic distribution of the bioclimatic groups in the sections studied (Supplementary Figs. 1–6) indicates wet phases confirmed by the curves of the other bioclimatic groups (hygrophytes, hydrophytes, tropical lowland flora, and upland flora). We used Pearson correlation analysis (Supplementary Fig. 7) to assess the correlation between the bioclimatic groups. The analysis revealed positive correlations between the bioclimatic groups of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, and a negative correlation between these groups and the xerophyte group (Supplementary Fig. 7). The positive correlation between upland flora and hygrophytes confirms previous studies for the Sergipe Basin6,7, suggesting a relation between these groups and the hot and humid climate. The weak negative correlation between tropical lowland flora and upland flora is presumably related to elevation.The upland flora forms the second most abundant bioclimatic group, with an average of 18.9%. The large number of specimens of Araucariacites (Araucariaceae) in this group is notable. The hydrophytes are the least abundant group, with an average of only 1.4%. In this group, the highest values are attributed to the genus Crybelosporites (Marsileaceae).Principal component analyses (PCA) were used to reduce the multidimensional dataset, based on the percent abundance of the bioclimatic groups to a smaller number of dimensions for interpretive analysis. For all sections, two components or axes explain 97.6% of the observed variability (Fig. 2B). Hygrophytes, hydrophytes, tropical lowland flora, and upland flora show positive correlation (positive loading, 0.320, 0.029, 0.006, and 0.468, respectively), whereas xerophytes show a negative relationship (negative loading, − 0.823) on the first axis, which alone explains 83.0% of the variability. In summary, the first axis of the PCA reveals a separation of two major climatic conditions (wet and dry) along the axis (Fig. 2B). The wet conditions include the associations of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, with dry conditions associated with taxa from the xerophyte group. The second axis explains 14.6%, in which hygrophytes, hydrophytes, and tropical lowland flora show a positive correlation relationship (positive loading, 0.719, 0.037, 0.036, respectively), whereas upland flora and xerophytes show a negative relationship (negative loading, − 0.684 and − 0.108, respectively). With respect to the second axis, a polarization between the hygrophytes (positive loading, 0.719) and the upland flora (negative loading, − 0.684) can be interpreted as a lowland–upland trend. The same pattern was recorded for all paleoclimatic phases (Fig. 2C) and sections (Supplementary Fig. 8), that is, the first axis is related to humidity vs. aridity, and the second axis to elevation (lowland vs. upland). This suggests that these two factors, particularly the first one, controlled the vegetation distribution in the late Aptian of the region. As all bioclimatic groups occurred in the three evaporitic phases, these trends in abundance reflect expansion and contraction of the recorded vegetation.Parallel increasing trends of bioclimatic groups mark the pre-evaporitic phase: hygrophytes and upland flora in the Bragança-Viseu, São Luís, Parnaíba, Ceará, Potiguar, and Araripe basins (Supplementary Figs. 1–3 and 5), suggesting that there was a certain amount of moisture in these areas. The xerophytes show the lowest average of this phase (44.1%) (Table 1), whereas hygrophytes show the highest average (27.0%). These humid conditions are confirmed by the highest mean of the Fs/X ratio (Fs/X = 0.4), representing the predominance of spore-producing plants [see Methods section and Supplementary information]. Despite the low abundance of hydrophytes in the sections, a prominent feature is the highest average (2.5%) of this group (Table 1), which is assigned to aquatic environments, confirming relatively wet conditions in this phase. There are no pre-evaporitic samples available from the Sergipe and Espírito Santo basins.Table 1 Average abundance of bioclimatic groups, diversity, Fs/X and marine elements for the paleoclimatic phases.Full size tableThe evaporitic phase is characterized by the highest abundance of the xerophyte bioclimatic group (76.4%) (Table 1), represented mainly by Classopollis (Supplementary Figs. 1–6). A high abundance of xerophytes occurred widely distributed in all basins studied. In this phase, tropical lowland flora is notable, showing an average higher than the overall average (3.3%), particularly in the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins (Supplementary Figs. 1 and 2). This result is related to the moderate to high abundance of the genus Afropollis in these basins. The evaporitic phase is also characterized by the lowest average Fs/X ratio (Fs/X = 0.1) (Table 1), confirming the dominance of xerophytes.The post-evaporitic phase is characterized by the upland flora bioclimatic group (mean = 24.4%) (Table 1). The moderate to high abundance of upland flora in this phase is represented, in particular, by pollen grains of Araucariacites, which represent the high-relief family Araucariaceae. This bioclimatic group is associated with more humid conditions, as confirmed by an Fs/X ratio higher than the overall average (Fs/X = 0.2). The upland flora is significant in all basins, except the Espírito Santo Basin, where xerophytes predominate in both studied phases in this basin.Latitudinal biome distributionsBiome change is a fundamental biological response to climate change. In the study area, the predominance of a specific biome is mainly related to humidity, since all five recorded bioclimatic groups are related to a warm climate (Supplementary Table 2) representing two biomes: tropical xerophytic shrubland and tropical rainforest. In the rainforest biome two phytophysiognomies are recognized: lowland and montane rainforest. The tropical xerophytic shrubland biome predominates in the three paleoclimatic phases, with a wide latitudinal range from the Bragança-Viseu, São Luís, and Parnaíba basins (1° S) to the Espírito Santo Basin (20° S). This wide distribution is compatible with a predominantly arid climate in South America in the late Aptian, as indicated by paleoclimatic maps8,9,15 (Fig. 4A). Most arid and semi-arid ecosystems are mainly controlled by precipitation. Other climate parameters are less important, a condition that simplifies cause-effect interpretations. The PCA (Fig. 2B) demonstrated that the wet–dry trend, which reflects high–low precipitation, was the main determinant in the distribution of the biomes. However, considering all phases, an increasing trend in humidity was observed from the southeast (Espírito Santo Basin) to the northeast (e.g., Potiguar Basin) (Fig. 4B), coinciding with the hot and wet belt attributed to the ITCZ (Fig. 4A)15. The latitudinal distribution of diversity also follows this trend. Diversity increased significantly towards in the basins near the equator. Diversity indices (Shannon – H’) peaked in the Sergipe Basin (H’ = 3.5, CL-47 section) at 11° S. Conversely, the lowest average diversity is recorded in the Espírito Santo Basin (H’ = 1.1) at 20° S. Additionally, there is a clear correlation between high diversity (H’) and humidity (Fs/X ratio) (r = 0.691), regardless of paleoclimatic phase, as evidenced by the synchronicity of the H’ and Fs/X curves (Fig. 5). After data normalization between humidity (Fs/X) and marine elements (dinoflagellate cysts and microforaminifer linings), we performed linear correlation analyses, which showed a weak but positive correlation (r = 0.137). This is due to the fact that pre- evaporitic deposits contain only 19 occurrences of dinoflagellate cysts in 90 samples. Despite this, the curves of Fs/X, marine elements and diversity are synchronous (Fig. 5), suggesting a relation between humidity, diversity, and marine incursions.Figure 4Latitudinal changes in late Aptian biomes from southeast to center-north. (A) Paleoclimatic belts of the late Aptian in South America (climatic belts modified from refer.14). Reconstruction map at 116 Ma modified from ODSN Plate Tectonic Reconstruction Service. The Reconstruction map at 116 Ma was generated by ODSN Plate Tectonic Reconstruction Service (https://www.odsn.de/odsn/services/paleomap/paleomap.html). (B) Late Aptian latitudinal distribution of the tropical xerophytic biome in Brazil. (C) Stratigraphic distribution of biomes for individual basins. (D) Relative Importance of biomes for paleoclimatic phases.Full size imageFigure 5Biome trends in relation to paleoclimatic phases. Change in biomes, diversity, Fs/X ratio and marine elements shown by changepoint analysis plotted against paleoclimatic phases.Full size imageThe pre-evaporitic phase is marked by a certain balance between the biomes (Fig. 4C,D). In the lowlands, the tropical xerophytic shrubland biome predominated in the Bragança- Viseu, São Luís, Parnaíba, and Ceará basins, but in the Potiguar Basin it is co-dominant with the lowland rainforest. The montane rainforest was relatively extensive in this phase, although with several areal changes, and reached its widest extent in the Araripe (7° S) and Potiguar (5° S) basins in response to the deterioration of the tropical xerophytic shrubland biome. These conditions demonstrate that humidity was relatively high at this stage. The pre-evaporitic deposits were characterized by the highest diversity average (H’ = 1.8).The method of indicator species analysis (IndVal) was used to identify the key species of each paleoclimatic phase (Supplementary Table 15). The species identified for the pre-evaporitic phase, Deltoidospora spp. (Cyatheaceae-Dicksoniaceae) related to the montane rainforest, are indicator species for the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins. The Gnetaceaepollenites spp. (Gnetaceae) of the Potiguar Basin and Equisetosporites spp. (Ephedraceae) of the Araripe Basin are related to the tropical xerophytic shrubland biome (Supplementary Table 15). Even for the pre-evaporitic phase, a progressive increase in the tropical xerophytic shrubland biome was observed and interpreted as the start of a climatic deterioration stage (Fig. 4C), which culminated in the evaporitic phase. Shifts in vegetation types may occur when precipitation reaches a threshold value, which means that a regionally synchronous gradual climate change can cause abrupt vegetation shifts. The change from humid to warm and arid conditions (evaporitic phase) is directly related to a decrease in precipitation. This aridization process coincides with the appearance of marine elements (e.g., dinoflagellate cysts). The threshold effect (intense evaporation) is reflected in an abrupt decrease in the abundance of lowland and montane rainforest and a sharp increase to a very high abundance of the tropical xerophytic shrubland biome (Supplementary Figs. 4C and 5). The threshold effect was not detected in the Espírito Santo Basin, where the arid conditions remained stable with minimal shift (expansion and contraction) of the biome. The main representatives of this biome are conifers of the family Cheirolepidiaceae (Classopollis), which were most abundant in lagoons and coastal environments and are often associated with evaporates30,31,32,33,34,35. Even under xeric or water-stressed conditions there was a slight increase in biomes related to a humid climate (lowland and montane rainforest phytophysiognomies) towards the equatorial region, suggesting influence of the ITCZ (Fig. 4A,B).The evaporitic phase was characterized by the lowest diversity average (H’ = 1.2). With modest rainfall, arid regions are generally characterized by fewer species than moister biomes36. However, diversity indices peaked in the Bragança-Viseu, São Luís, and Parnaíba basins (H’ = 2.6, RL-01 section) and along the equatorial margin (2° S) (Supplementary Fig. 1).IndVal emphasizes the xeric conditions in the evaporitic phase by association with the species from the tropical xerophytic shrubland biome: Classopollis spp. (Ceará and Potiguar basins), Classopollis classoides (Sergipe Basin), Classopollis intrareticulatus (Araripe Basin), and Gnetaceaepollenites spp. (Espírito Santo Basin). For the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins, where xeric restrictions are milder, the indicator taxon is Afropollis spp. from the lowland rainforest. This genus shows the weakest negative correlation with xerophytes.After the end of evaporite deposition, all sections indicate climatic stability, which kept the climate hot and arid even in the post-evaporitic phase, although the response was not linear.The shift in the biomes, especially the tropical xerophytic shrubland in the Bragança-Viseu, São Luís, Parnaíba, Ceará, and Araripe basins, occurred in the transition between the evaporitic and post-evaporitic phases, whereas in the Potiguar and Sergipe basins it occurred within the post-evaporitic phase. As indicated in the dendrograms of each section (Supplementary Figs. 1–6), the shift occurred abruptly in all basins, except the Espírito Santo Basin. The tropical rainforest biome (lowland and montane rainforests) replaced the tropical xerophytic shrubland in almost all basins (Fig. 4C). Even the Espírito Santo Basin, far from the influence of the ITCZ, shows a slight increase in lowland rainforest. The changes in the biomes are attributable to threshold effects caused by gradual climate change related to the ITCZ intensification shift and progressive increase in marine influence, indicated by an increase in marine microplankton from an average of 3.9% in the evaporitic phase to 44.1%. The increase in marine influence is reflected in the first major flooding surface observed in the Cretaceous succession27. Thus, a climate amelioration stage was established in the post- evaporitic phase (Fig. 5). In combination with published paleotopographic information25, the bioclimatic groups associated to the humid conditions (hygrophytes, hydrophytes, tropical lowland flora, and upland flora) were combined and visualized to create Fig. 6.Figure 6Reconstruction of the transitional gradient between marine to terrestrial environment (uplands) under ITCZ influence. The illustration is based on paleoflora and environmental information from palynological data from studied sections. Original size illustration: 18 × 24 cm, by Julio Lacerda.Full size imageAccording to refs.7,37, arid conditions are characterized by sea-level lowstands, whereas warm and humid conditions are correlated with sea levels rise, which explains the increase in the tropical rainforest biome (lowland and montane rainforests). The more intense humidity is supported by the results of IndVal for the post-evaporitic phase, with all species related to humid climate: Deltoidospora spp. (Bragança-Viseu, São Luís and Parnaíba basins), Araucariacites limbatus (Ceará Basin), Cicatricosisporites spp. (Potiguar Basin), Cicatricosisporites spp. and Araucariacites australis (Sergipe Basin), Inaperturopollenites spp. (Araripe Basin) and Inaperturopollenites simplex (Espírito Santo Basin).Our results show that the ITCZ combined with the opening of the South Atlantic Ocean during the late Aptian altered vegetation dynamics. As today, the ITCZ influence is stronger in the northeastern and north-central regions of South America. It is notable that the late Aptian climate evolution in the South Atlantic, culminating in higher humidity, was accompanied by an intrinsic relation between plant diversity, humidity, and marine influence. More

Charlesworth, D., Barton, N. H. & Charlesworth, B. The sources of adaptive variation. Proc. R. Soc. B Biol. Sci. 284(1855), 20162864 (1855).Article 
CAS 

Google Scholar 
Whitlock, M. C. Fixation of new alleles and the extinction of small populations: Drift load, beneficial alleles, and sexual selection. Evolution 54(6), 1855–1861 (2000).CAS 
PubMed 
Article 

Google Scholar 
Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: a role for parasites?. Science 218(4570), 384 (1982).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hadany, L. & Beker, T. Sexual selection and the evolution of obligatory sex. BMC Evol. Biol. 7(1), 245 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Clutton-Brock, T. Reproductive competition and sexual selection. Philos. Trans. R. Soc. B Biol. Sci. 372(1729), 20160310 (2017).Article 

Google Scholar 
Taddei, F. et al. Role of mutator alleles in adaptive evolution. Nature 387(6634), 700–702 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. F. & Wang, A. D. Increased transmission of mutations by low-condition females: Evidence for condition-dependent DNA repair. PLoS Biol. 6(2), e30 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Petrie, M. & Roberts, G. Sexual selection and the evolution of evolvability. Heredity 98(4), 198–205 (2007).CAS 
PubMed 
Article 

Google Scholar 
Dugand, R. J., Kennington, W. J. & Tomkins, J. L. Evolutionary divergence in competitive mating success through female mating bias for good genes. Sci. Adv. 4(5), eaaq0369 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Siller, S. Sexual selection and the maintenance of sex. Nature 411(6838), 689–692 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. F. Sexual selection and the maintenance of sexual reproduction. Nature 411(6838), 692–695 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lehtonen, J., Jennions, M. D. & Kokko, H. The many costs of sex. Trends Ecol. Evol. 27(3), 172–178 (2012).PubMed 
Article 

Google Scholar 
Maynard Smith, J. What use is sex?. J. Theor. Biol. 30(2), 319–335 (1971).ADS 
MathSciNet 
Article 

Google Scholar 
Trivers, R. L. Parental investment and sexual selection. In Sexual Selection and the Descent of Man 1871–1971 (ed. Campbell, B.) 136–179 (Aldone, 1972).
Google Scholar 
Petrie, M. & Lipsitch, M. Avian polygyny is most likely in populations with high variability in heritable male fitness. Proc. R. Soc. Lond. Ser. B Biol. Sci. 256(1347), 275–280 (1994).ADS 
Article 

Google Scholar 
Lumley, A. J. et al. Sexual selection protects against extinction. Nature 522(7557), 470–473 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Andersson, M. Sexual Selection (Princeton University Press, 1994).Book 

Google Scholar 
Petrie, M. Improved growth and survival of offspring of peacocks with more elaborate trains. Nature 371(6498), 598–599 (1994).ADS 
CAS 
Article 

Google Scholar 
Møller, A. P. & Alatalo, R. V. Good-genes effects in sexual selection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 266(1414), 85–91 (1999).Article 

Google Scholar 
David, P. et al. Condition-dependent signalling of genetic variation in stalk-eyed flies. Nature 406(6792), 186–188 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hale, M. L. et al. Is the peacock’s train an honest signal of genetic quality at the major histocompatibility complex?. J. Evol. Biol. 22(6), 1284–1294 (2009).CAS 
PubMed 
Article 

Google Scholar 
Prokop, Z. M. et al. Meta-analysis suggests choosy females get sexy sons more than “good genes”. Evolution 66(9), 2665–2673 (2012).PubMed 
Article 

Google Scholar 
Kokko, H. et al. The sexual selection continuum. Proc. R. Soc. Lond. Ser. B Biol. Sci. 269(1498), 1331–1340 (2002).Article 

Google Scholar 
Drake, J. W. et al. Rates of Spontaneous Mutation. Genetics 148(4), 1667 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Keightley, P. D. Rates and fitness consequences of new mutations in humans. Genetics 190(2), 295–304 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Haag-Liautard, C. et al. Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila. Nature 445(7123), 82–85 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Metzgar, D. & Wills, C. Evidence for the adaptive evolution of mutation rates. Cell 101(6), 581–584 (2000).CAS 
PubMed 
Article 

Google Scholar 
Janetos, A. C. Strategies of female mate choice: A theoretical analysis. Behav. Ecol. Sociobiol. 7(2), 107–112 (1980).Article 

Google Scholar 
Johnstone, R. A. & Earn, D. J. D. Imperfect female choice and male mating skew on leks of different sizes. Behav. Ecol. Sociobiol. 45(3), 277–281 (1999).Article 

Google Scholar 
Petrie, M., Halliday, T. & Sanders, C. Peahens prefer peacocks with elaborate trains. Anim. Behav. 41(2), 323–331 (1991).Article 

Google Scholar 
Cally, J. G., Stuart-Fox, D. & Holman, L. Meta-analytic evidence that sexual selection improves population fitness. Nat. Commun. 10(1), 2017 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kotiaho, J. S. et al. On the resolution of the lek paradox. Trends Ecol. Evol. 23(1), 1–3 (2008).PubMed 
Article 

Google Scholar 
Parker, G. A., Baker, R. R. & Smith, V. G. F. The origin and evolution of gamete dimorphism and the male-female phenomenon. J. Theor. Biol. 36(3), 529–553 (1972).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Parker, G. A. The sexual cascade and the rise of pre-ejaculatory (Darwinian) sexual selection, sex roles, and sexual conflict. Cold Spring Harb. Perspect. Biol. 6(10), a017509–a017509 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Rowe, L. & Houle, D. The lek paradox and the capture of genetic variance by condition dependent traits. Proc. R. Soc. Lond. Ser. B Biol. Sci. 263(1375), 1415–1421 (1996).ADS 
Article 

Google Scholar 
Petrie, M. Evolution by sexual selection. Front. Ecol. Evol. 9, 950 (2021).Article 

Google Scholar 
Petrie, M. & Kempenaers, B. Extra-pair paternity in birds: Explaining variation between species and populations. Trends Ecol. Evol. 13(2), 52–58 (1998).CAS 
PubMed 
Article 

Google Scholar 
Møller, A. P. & Cuervo, J. J. Minisatellite mutation rates increase with extra-pair paternity among birds. BMC Evol. Biol. 9(1), 100 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Anmarkrud, J. A. et al. Factors affecting germline mutations in a hypervariable microsatellite: A comparative analysis of six species of swallows (Aves: Hirundinidae). Mutat. Res. Fundam. Mol. Mech. Mutagen. 708(1), 37–43 (2011).CAS 
Article 

Google Scholar 
Ellegren, H. Characteristics, causes and evolutionary consequences of male-biased mutation. Proc. R. Soc. B Biol. Sci. 274(1606), 1–10 (2007).CAS 
Article 

Google Scholar 
Baur, J. & Berger, D. Experimental evidence for effects of sexual selection on condition-dependent mutation rates. Nat. Ecol. Evol. 4, 737–744 (2020).PubMed 
Article 

Google Scholar 
Vrijenhoek, R. C. & Parker, E. D. Geographical parthenogenesis: General purpose genotypes and frozen niche variation. In Lost Sex (eds Schön, I. et al.) (Springer, Dordrecht, 2009).
Google Scholar 
Reudink, M. W. et al. Evolution of song and color in island birds. Wilson J. Ornithol. 133(1), 1–10 (2021).Article 

Google Scholar 
Iglesias-Carrasco, M. et al. Sexual selection, body mass and molecular evolution interact to predict diversification in birds. Proc. R. Soc. B Biol. Sci. 2019(286), 20190172 (1899).
Google Scholar 
Earl, D. J. & Deem, M. W. Evolvability is a selectable trait. Proc. Natl. Acad. Sci. U. S. A. 101(32), 11531 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Explaining the heterogeneous organization of vegetation across landscapes has proved both a puzzling and an inspiring concept as patterns have formed naturally across the world. One such pattern is the existence of treelines, i.e., the demarcation zone between forestland and vegetation without trees1,2. There is a large body of work with developed and competing theories for understanding the specific limits and drivers for the non-existence of trees beyond a treeline. Yet after decades of study, there is still debate among ecologists and biologists over the mechanisms that limit the presence of trees beyond treelines. Current explanations are rooted in, but not limited to, consideration of factors such as excessive light and wind, limited CO(_2), and low temperatures1,3,4,5,6,7.
With this in mind, we ask: Is there another perspective that could provide insights complementary to and beyond what has been developed through the prevailing mechanistic approach? While the existing explanations are based on ideas of structural stability (e.g., high winds above the treeline) and limited resources pertaining to water, energy, and nutrients1,3,4,5,6,7, we instead examine the question of what determines the existence of a treeline from the perspective of thermodynamic feasibility. Our premise is that the existence or non-existence of certain vegetation first and foremost has to be ascertained through thermodynamic feasibility or infeasibility, respectively. Therefore, we approach the question of the existence of treelines by asking: If certain vegetation does not exist at a given location, is there a role that the thermodynamic perspective can play in telling us that its existence is infeasible? By approaching the topic from the thermodynamic perspective, we seek to provide important complementary insight to the broad base of scientific understanding ecologists and biologists have developed to explain the existence of treelines. Further, this work lays out additional context for the discussion around the advance of treelines (e.g., why some treelines advance and others do not).For example, several theories assert that the stature of vegetation is limited by CO(_2) balance and photosynthetic requirements under harsh winter conditions1,6. Other hypotheses argue that plant life is instead limited by the atmospheric temperature and the local environments that the plants experience7,8. Is there a perspective that could unify both of these findings? Through this work, we demonstrate how thermodynamic infeasibility inferred from model simulations pertaining to counterfactual scenarios manifests through both of these physiological limits. This means that either of these limits, individually or together, could lead to the nonexistence of trees—which limiting factor is expressed first varies by location. Thus, the commonality among locations that have different limiting mechanisms can be found in the unifying concept of thermodynamic infeasibility. While CO(_2) limitation may prevail in one location and temperature-related constraints may be limiting in another, both lead to thermodynamic infeasibility, meaning that the thermal environment results in a mechanistic infeasibility, such as net CO(_2) loss. In the examples presented in this paper, thermodynamic infeasibility manifests through negative work associated with constraints arising from temperature gradients and net CO(_2) loss, demonstrating that both limitations can be encapsulated using the thermodynamic perspective.Ecosystem thermodynamicsIt is now generally accepted that observed patterns of vegetation composition and its organization are a result of self-organization, or the spontaneous emergence of pattern without external predetermination9,10. By framing ecosystems as open thermodynamic systems, we explore further the concept of thermodynamic feasibility and its role in the self-organization of vegetation structure. Vegetation structure consists of composition (i.e., the number and type of functional groups11) and organizational patterns on the landscape12. We focus on composition rather than the spatial pattern of vegetation organization. We utilize a one-dimensional ecohydrological model that incorporates representative functional groups with no lateral transport of energy or matter under the assumption that the vegetation composition and pattern remain spatially uniform at a given site. Thus, we are able to compare the vertical thermodynamic regimes of proximal ecosystems with varying vegetation composition. We present the case that observed organization reflected in the demarcation of differing vegetation structures on either side of a treeline is established in tandem with vertical thermodynamic gradients at a given location, driven by the incoming solar energy into an ecosystem. In other words, we hypothesize that beyond a treeline, the existence of trees is prevented by conditions of thermodynamic infeasibility.The application of thermodynamic theory to ecology has been studied for the better part of the last century through the introduction of theoretical thermodynamic properties, such as entropy and exergy, into environmental systems. This work asserts that open thermodynamic systems will evolve based on the strength of applied concentration gradients on the system and will undergo irreversible processes to dissipate energy and destroy these gradients through all means available13,14. In the context of ecosystems, fluxes of mass or energy from the external environment (i.e., above the canopy) result in concentration gradients within the system itself. State variables will transition along these gradients according to the second law of thermodynamics. When the magnitude of incoming energy and consequent spatial imbalance of energy becomes great enough, dissipative structures spontaneously emerge, or self-organize, and establish temperature gradients consistent with the dissipative need of the ecosystem13,15. In this paper we conceptualize the work performed by an ecosystem as its ability to dissipate these applied concentration gradients. Consequently, work is highly dependent upon the existence and composition of self-organized vegetation.In classical thermodynamics, work is performed due to a transfer, or physical movement, of heat15. In the context of ecosystems, work performed by an ecosystem is represented by the exchange of heat with the external environment outside the ecosystem control volume12. Work performed by an ecosystem is, therefore, estimated as the vertical transport of heat in the form of latent and sensible heat, driven by the vertical gradient in temperature within the control volume structured by both the incoming downward shortwave and longwave radiation and the vegetation structure. The bottom boundary of the ecosystem control volumes studied are significantly deep such that heat exchanges due to water infiltration at this interface are insignificant in magnitude relative to latent and sensible heat flux out of the top of the control volume above the canopy. Further, we ignore the substantially slower thermodynamic processes associated with geochemistry in the soil.The vertical temperature gradient creates a directionality of dissipation of incident radiation as heat leaves out of the ecosystem from higher surface temperatures to lower air temperatures. Throughout this paper, we measure work through the net sum of heat leaving the ecosystem as latent and sensible heat—which can either be positive or negative depending on the direction of the resultant temperature gradient (see “Thermodynamic Calculations” in the “Methods” section). This temperature gradient (Eq. 1) emerges as a result of self-organization through feedback between the incoming shortwave and longwave radiation, local environmental conditions, and the heat dissipation and work performed by the vegetation. The presence of ground cover, such as snow, is impacted by aboveground vegetation structure, which provides a physical buffer between the atmosphere and the ground, further influencing the thermal environment and temperature gradient.Although significant research has been conducted by studying plant response to snowpack7,16,17, including the physiological requirements for life under prolonged snowpack and alpine climatic conditions, the thermodynamic perspective provides further insight. In addition to the physiological/mechanistic response of plants to snowpack and other environmental conditions, the thermal regime of a column of land experiencing snowpack is fundamentally different when an ecosystem does or does not have plants with stature taller than the height of snowpack (e.g., trees). Presence of trees results in shading from solar radiation and a physical buffer between the earth/snow surface and the atmosphere. Thus, the thermal profile of an ecosystem reveals valuable information about ecosystem behavior, and there is a need to explore the thermodynamic relationship between solar radiation and vegetation composition under varying environmental conditions. Thus, through this paper we define the circumstances under which multiple functional groups that include trees are no longer feasible for the available solar radiation leading to demarcated zones identifiable as treelines.Work by Körner argues that the “climate [that] plants experience” is different than the ambient temperature7. By modeling the layers within the canopy of plants with differing stand heights and leaf distributions, we are able to characterize the thermal regime and the “climate [that] plants experience” throughout the course of a given year. This characterization helps us understand the fundamental changes in behavior under varying environmental conditions with and without trees.An ecosystem’s ability to perform work manifests into four distinct cases depending on the sign of the resultant temperature gradient and the net loss or gain of heat driven by the thermal environment derived from present ground cover, such as vegetation or snow: (1) First and most common during the day when photosynthesis is occurring, the temperature of the earth surface, which receives the solar radiation, is typically warmer than the air above the canopy, and heat leaves the ecosystem upward along the negative temperature gradient, corresponding to a positive work (Fig. 1a). (2) Even when the temperature of the earth surface is warmer than the air above the canopy, there can be situations when there is a net heat gain within the ecosystem, meaning that heat moves into the ecosystem against the direction of the temperature gradient. This case is rare and counterproductive to heat dissipation, corresponding to negative work. (3) Common during the night, temperature inversion emerges. In this case, the temperature gradient from the earth surface to the atmosphere can become positive, meaning that the temperature of the air above the canopy is greater than the temperature of the earth surface. As heat enters the ecosystem to warm the surface, positive work is performed since the heat is still moving along a negative temperature gradient into the ecosystem (Fig. 1b). (4) During snowmelt conditions during the day, particularly for Arctic and alpine ecosystems, temperature inversions also emerge18,19. When this occurs and the ecosystem experiences a net heat loss through latent and sensible heat from the canopy, the heat leaving the ecosystem travels opposite of the direction dictated by the temperature gradient. Thus, in this case, ecosystems perform negative work. Our findings demonstrate how extended periods of time in this last case of work lead to thermodynamic infeasibility for the alpine/Arctic ecosystem counterfactual vegetation scenarios; i.e., ecosystems with vegetation properties from below the treelines cannot be sustained under the environmental conditions above the treelines, and, hence, they do not occur in nature.A recent study concluded that at sites where multiple functional groups exist (e.g., forests), the vegetation structure in which all groups co-exist and interact is thermodynamically more advantageous and, thus, more likely to occur than any one of the individual functional groups that the forest comprises12. Thermodynamic advantage is defined by the production of larger fluxes of entropy, more work performed, and higher work efficiency – a quantity that captures how much of the incoming energy is converted into forms useful for actively dissipating heat. It is possible to envision that under certain environmental conditions, the thermodynamic advantage offered by the existence of multiple functional groups is not tenable, indicating a thermodynamic infeasibility. Thermodynamic infeasibility occurs when a particular vegetation structure is not supported by the thermal environment at a given location. The demarcation exhibited by treelines presents an ideal case to explore this scenario, in that there is a distinct transition from multiple functional groups below the treeline to a single functional group above.Research questionIn this paper, we examine vegetation above and below Arctic and alpine treelines to determine whether the absence of trees in ecosystems above treelines are a result of thermodynamic infeasibility. Simply speaking, we seek to answer the following research question: Is the non-existence of trees beyond the transition zone demarcated as a treeline a reflection of thermodynamic infeasibility associated with the presence of trees, and if so, how is this infeasibility exhibited?Figure 1Conceptual diagram of temperature gradients. The W+ arrow indicates the positive direction of work performed through heat transport. Although in different directions, in both cases (a) and (b), the work performed is positive because heat moves from high to low temperatures. (a) Typical summertime temperature gradients from the earth surface to the air above the canopy are negative for the two real scenarios: subalpine/sub-Arctic forest (left) and alpine tundra/Arctic meadow (right). (b) A conceptual temperature inversion, or positive temperature gradient, which arise when alpine/Arctic forest are simulated as counterfactuals.Full size imageTo address this question, we use an extensively validated multi-layer 1-D physics-based ecohydrological model, MLCan12,20,21,22,23,24,25,26, consisting of 20 above-ground layers, 1 ground surface layer, and 12 below-ground layers (see Supplementary Material). This model is chosen because of its ability to capture interactions among functional groups, such as the impact of shading on understory vegetation and the resulting thermal environment within the canopy23. To balance model performance and accuracy, standing plant species are aggregated into functional groups (i.e., evergreen needleleaf trees, shrubs, grasses; see Table 1) based on literature27,28,29,30. The model output is used to compare the thermodynamic work performed at paired sites above and below the respective treelines for three different locations: the Italian Alps (IT), the United States Rocky Mountains (US), and the Western Canadian Taiga-Tundra (CA) (Fig. 2; see Site Descriptions). For each site pair, four scenarios are performed (Table 1): (1) The subalpine/sub-Arctic forest ecosystems are modeled as they exist with multiple functional groups (Fig. 1a, left). (2) The alpine/Arctic ecosystems are modeled as they exist with one functional group (i.e., shrubs or grasses; Fig. 1a, right). (3) We construct counterfactual scenarios above the treeline in which the vegetation of the subalpine or sub-Arctic forest is simulated with the environmental conditions and parameters of the alpine meadow or Arctic tundra (i.e., adding hypothetical trees where none exist; Fig. 1b). (4) As a control, a final counterfactual scenario is constructed below the treeline in which we model the understory of the subalpine/sub-Arctic forest individually (i.e., removing trees from the existing ecosystem).Table 1 Simulation scenarios with observed and hypothetical vegetation.Full size tableThe simulation of these four scenarios facilitates comparison of the existing vegetation structure of each site with the corresponding counterfactual scenarios. By varying the model inputs of vegetation present at each site while holding the environmental conditions and site-specific parameters consistent, we are able to directly compare thermodynamic outcomes as a result of varying vegetation structure and determine whether the counterfactual scenario with the simulated forest is thermodynamically feasible. Model performance was judged based on comparison to observed heat fluxes, such as latent and sensible heat (see Supplementary Material, Figs. S1–S3). As detailed below, the analysis supports the conclusion that thermodynamic feasibility is an important and complementary condition to the usual considerations of resource availability, such as water and nutrients, which determines the organizing form and function of ecosystems. More

Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119. https://doi.org/10.1111/jeu.12691 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Smirnov, A. Amoebas, Lobose. In Encyclopedia of Microbiology (ed. Schaechter, M.) 191–212 (Elsevier, 2012).
Google Scholar 
Schaeffer, A. A. Taxonomy of the Amoebas: With Descriptions of Thirty-Nine New Marine and Freshwater Species (Carnegie Inst, 1926).
Google Scholar 
Page, F. C. The classification of “naked” amoebae (Phylum Rhizopoda). Arch. Protistenkd. 133, 199–217. https://doi.org/10.1016/S0003-9365(87)80053-2 (1987).Article 

Google Scholar 
Page, F. C. A New Key to Freshwater and Soil Gymnamoebae (Freshwater Biological Association, 1988).
Google Scholar 
Smirnov, A. V. & Goodkov, A. V. An illustrated list of basic morphotypes of Gymnamoebia (Rhizopoda, Lobosea). Protistology 1, 20–29 (1999).
Google Scholar 
Smirnov, A. V. & Brown, S. Guide to the methods of study and identification of soil gymnamoebae. Protistology 3, 148–190 (2004).
Google Scholar 
Bovee, E. C. & Jahn, T. L. Mechanisms of movement in taxonomy of Sarcodina. II. The organization of subclasses and orders in relationship to the classes Autotractea and Hydraulea. Am. Midland Nat. 73, 293–298. https://doi.org/10.2307/2423456 (1965).Article 

Google Scholar 
Bovee, E. C. & Jahn, T. L. Mechanisms of movement in taxonomy or sarcodina. III. Orders, suborders, families, and subfamilies in the superorder Lobida. Syst. Zool. 15, 229–240. https://doi.org/10.2307/sysbio/15.3.229 (1966).CAS 
Article 
PubMed 

Google Scholar 
Bovee, E.C. & Sawyer, T.K. Marine Flora and Fauna of the Northeastern United States. Protozoa: Sarcodina: Amoebae. (NOAA Technical Report, 1979). https://doi.org/10.5962/bhl.title.63225.Jahn, T. L. & Bovee, E. C. Mechanisms of movement in taxonomy of Sarcodina. I. As a basis for a new major dichotomy into two classes, Autotractea and Hydraulea. Am. Midl. Nat. 73, 30–40. https://doi.org/10.2307/2423319 (1965).Article 

Google Scholar 
Jahn, T. L., Bovee, E. C. & Griffith, D. L. Taxonomy and evolution of the Sarcodina: A reclassification. Taxon 23, 483–496. https://doi.org/10.2307/1218771 (1974).Article 

Google Scholar 
Cavalier-Smith, T., Chao, E.E.-Y. & Oates, B. Molecular phylogeny of Amoebozoa and the evolutionary significance of the unikont Phalansterium. Eur. J. Protistol. 40, 21–48. https://doi.org/10.1016/j.ejop.2003.10.001 (2004).Article 

Google Scholar 
Smirnov, A. et al. Molecular phylogeny and classification of the lobose amoebae. Protist 156, 129–142. https://doi.org/10.1016/j.protis.2005.06.002 (2005).CAS 
Article 
PubMed 

Google Scholar 
Amaral Zettler, L. A. et al. A molecular reassessment of the leptomyxid amoebae. Protist 151, 275–282. https://doi.org/10.1078/1434-4610-00025 (2000).CAS 
Article 
PubMed 

Google Scholar 
Bolivar, I., Fahrni, J. F., Smirnov, A. & Pawlowski, J. SSU rRNA-based phylogenetic position of the genera Amoeba and Chaos (Lobosea, Gymnamoebia): The origin of gymnamoebae revisited. Mol. Biol. Evol. 18, 2306–2314. https://doi.org/10.1093/oxfordjournals.molbev.a003777 (2001).CAS 
Article 
PubMed 

Google Scholar 
Fahrni, J. F. et al. Phylogeny of lobose amoebae based on actin and small-subunit ribosomal RNA genes. Mol. Biol. Evol. 20, 1881–1886. https://doi.org/10.1093/molbev/msg201 (2003).CAS 
Article 
PubMed 

Google Scholar 
Cavalier-Smith, T. et al. Multigene phylogeny resolves deep branching of Amoebozoa. Mol. Phylogenet. Evol. 83, 293–304. https://doi.org/10.1016/j.ympev.2014.08.011 (2015).Article 
PubMed 

Google Scholar 
Cavalier-Smith, T., Chao, E. E. & Lewis, R. 187-gene phylogeny of protozoan phylum Amoebozoa reveals a new class (Cutosea) of deep-branching, ultrastructurally unique, enveloped marine Lobosa and clarifies amoeba evolution. Mol. Phylogenet. Evol. 99, 275–296. https://doi.org/10.1016/j.ympev.2016.03.023 (2016).Article 
PubMed 

Google Scholar 
Kang, S. et al. Between a pod and a hard test: The deep evolution of amoebae. Mol. Biol. Evol. 34, 2258–2270. https://doi.org/10.1093/molbev/msx162 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tekle, Y. I. & Wood, F. C. Longamoebia is not monophyletic: Phylogenomic and cytoskeleton analyses provide novel and well-resolved relationships of amoebozoan subclades. Mol. Phylogenet. Evol. 114, 249–260. https://doi.org/10.1016/j.ympev.2017.06.019 (2017).Article 
PubMed 

Google Scholar 
Tekle, Y. I., Wang, F., Wood, F. C., Anderson, O. R. & Smirnov, A. New insights on the evolutionary relationships between the major lineages of Amoebozoa. bioRxiv https://doi.org/10.1101/2022.02.28.482369 (2022).Article 

Google Scholar 
Van Wichelen, J. et al. A hotspot of amoebae diversity: 8 new naked amoebae associated with the planktonic bloom-forming cyanobacterium microcystis. Acta Protozool. 55, 61–87. https://doi.org/10.4467/16890027AP.16.007.4942 (2016).Article 

Google Scholar 
Janicki, C. Paramoebenstudien (P. pigmentifera Grassi und P. chaetognathi Grassi). Z. Wiss. Zool. 103, 449–518 (1912).
Google Scholar 
Volkova, E. & Kudryavtsev, A. A morphological and molecular reinvestigation of Janickina pigmentifera (Grassi, 1881) Chatton 1953—an amoebozoan parasite of arrow-worms (Chaetognatha). Int. J. Syst. Evol. Microbiol. 71, 005094. https://doi.org/10.1099/ijsem.0.005094 (2021).CAS 
Article 

Google Scholar 
Page, F. C. Taxonomic criteria for limax amoebae, with descriptions of 3 new species of Hartmannella and 3 of Vahlkampfia. J. Protozool. 14, 499–521 (1967).CAS 
Article 

Google Scholar 
Page, F. C. & Blanton, R. L. The Heterolobosea (Sarcodina: Rhizopoda), a new class uniting the Schizopyrenida and the Acrasidae (Acrasida). Protistologica 21, 121–132 (1985).
Google Scholar 
Laurin, V., Labbé, N., Parent, S., Juteau, P. & Villemur, R. Microeukaryote diversity in a marine methanol-fed fluidized denitrification system. Microb. Ecol. 56, 637–648. https://doi.org/10.1007/s00248-008-9383-x (2008).CAS 
Article 
PubMed 

Google Scholar 
Page, F. C. A further study of taxonomic criteria for limax amoebae, with descriptions of new species and a key to genera. Arch. Protistenkd. 116, 149–184 (1974).
Google Scholar 
Page, F. C. Marine Gymnamoebae (Institute of Terrestrial Ecology, 1983).
Google Scholar 
Page, F. C. A light- and electron-microscopical comparison of limax and flabellate marine amoebae belonging to four genera. Protistologica 16, 57–78 (1980).
Google Scholar 
Kuiper, M. W. et al. Quantitative detection of the free-living amoeba Hartmannella vermiformis in surface water by using real-time PCR. Appl. Environ. Microbiol. 72, 5750–5756. https://doi.org/10.1128/AEM.00085-06 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smirnov, A., Chao, E., Nassonova, E. & Cavalier-Smith, T. A revised classification of naked lobose amoebae (Amoebozoa: Lobosa). Protist 162, 545–570. https://doi.org/10.1016/j.protis.2011.04.004 (2011).Article 
PubMed 

Google Scholar 
Page, F. C. & Blakey, S. M. Cell surface structure as a taxonomic character in the Thecamoebidae (Protozoa: Gymnamoebia). Zool. J. Linn. Soc. 66, 113–135. https://doi.org/10.1111/j.1096-3642.1979.tb01905.x (1979).Article 

Google Scholar 
Smirnov, A. V. & Goodkov, A. V. Paradermamoeba valamo gen. n., sp. n. (Gymnamoebia, Thecamoebidae)—a freshwater amoeba from bottom sediments. Zool. Zhurn. 72, 5–11 (1993) (In Russian with English summary).
Google Scholar 
Smirnov, A. & Goodkov, A. Ultrastructure and geographic distribution of the genus Paradermamoeba (Gymnamoebia, Thecamoebidae). Eur. J. Protistol. 40, 113–118. https://doi.org/10.1016/j.ejop.2003.12.001 (2004).Article 

Google Scholar 
Smirnov, A. V., Bedjagina, O. M. & Goodkov, A. V. Dermamoeba algensis n sp (Amoebozoa, Dermamoebidae)—an algivorous lobose amoeba with complex cell coat and unusual feeding mode. Eur. J. Protistol. 47, 67–78. https://doi.org/10.1016/j.ejop.2010.12.002 (2011).Article 
PubMed 

Google Scholar 
Bailey, G. B., Day, D. B. & McCoomer, N. E. Entamoeba motility: Dynamics of cytoplasmic streaming, locomotion and translocation of surface-bound particles, and organization of the actin cytoskeleton in Entamoeba invadens. J. Protozool. 39, 267–272. https://doi.org/10.1111/j.1550-7408.1992.tb01313.x (1992).CAS 
Article 
PubMed 

Google Scholar 
Shiratori, T. & Ishida, K. I. Entamoeba marina n. sp.; a new species of Entamoeba isolated from tidal flat sediment of Iriomote Island, Okinawa, Japan. J. Eukaryot. Microbiol. 63, 280–286. https://doi.org/10.1111/jeu.12276 (2016).Article 
PubMed 

Google Scholar 
Lahr, D. J., Laughinghouse, H. D. IV., Oliverio, A. M., Gao, F. & Katz, L. A. How discordant morphological and molecular evolution among microorganisms can revise our notions of biodiversity on Earth. BioEssays 36, 950–959. https://doi.org/10.1002/bies.201400056 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Pomorski, P. et al. Actin dynamics in Amoeba proteus motility. Protoplasma 231, 31–41. https://doi.org/10.1007/s00709-007-0243-1 (2007).CAS 
Article 
PubMed 

Google Scholar 
Rogerson, A., Anderson, O. R. & Vogel, C. Are planktonic naked amoebae predominately floc associated or free in the water column?. J. Plankton Res. 25, 1359–1365. https://doi.org/10.1093/plankt/fbg102 (2003).Article 

Google Scholar 
Kudryavtsev, A. Paravannella minima n. g. n. sp. (Discosea, Vannellidae) and distinction of the genera in the vannellid amoebae. Eur. J. Protistol. 50, 258–269. https://doi.org/10.1016/j.ejop.2013.12.004 (2014).Article 
PubMed 

Google Scholar 
Kudryavtsev, A., Völcker, E., Clauß, S. & Pawlowski, J. Ovalopodium rosalinum sp. nov., Planopodium haveli gen. nov, sp. nov., Planopodium desertum comb. nov. and new insights into phylogeny of the deeply branching members of the order Himatismenida (Amoebozoa). Int. J. Sys. Evol. Microbiol. 71, 004737. https://doi.org/10.1099/ijsem.0.004737 (2021).CAS 
Article 

Google Scholar 
Blandenier, Q. et al. Mycamoeba gemmipara nov. gen., nov. sp., the first cultured member of the environmental Dermamoebidae clade LKM74 and its unusual life cycle. J. Eukaryot. Microbiol. 64, 257–265. https://doi.org/10.1111/jeu.12357 (2017).CAS 
Article 
PubMed 

Google Scholar 
Kudryavtsev, A. & Volkova, E. Cunea russae n. sp. (Amoebozoa, Dactylopodida), another cryptic species of Cunea Kudryavtsev and Pawlowski, 2015, inhabits a continental brackish-water biotope. Eur. J. Protistol. 73, 125685. https://doi.org/10.1016/j.ejop.2020.125685 (2020).Article 
PubMed 

Google Scholar 
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).CAS 
Article 
PubMed 

Google Scholar 
Maniatis, T., Fritsch, E. F. & Sambrook, J. Molecular Cloning, A Laboratory Manual (Cold Spring Harbor Laboratory, 1982).
Google Scholar 
Kudryavtsev, A. & Pawlowski, J. Cunea n. g. (Amoebozoa, Dactylopodida) with two cryptic species isolated from different areas of the ocean. Eur. J. Protistol. 51, 197–209. https://doi.org/10.1016/j.ejop.2015.04.002 (2015).Article 
PubMed 

Google Scholar 
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA coding regions. Gene 71, 491–499. https://doi.org/10.1016/0378-1119(88)90066-2 (1988).CAS 
Article 
PubMed 

Google Scholar 
Yoon, H. S. et al. Broadly sampled multigene trees of eukaryotes. BMC Evol. Biol. 8, 14. https://doi.org/10.1186/1471-2148-8-14 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).CAS 
Article 
PubMed 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973. https://doi.org/10.1093/bioinformatics/btp348 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gouy, M., Tannier, E., Comte, N. & Parsons, D. P. Seaview version 5: A multiplatform software for multiple sequence alignment, molecular phylogenetic analyses, and tree reconciliation. In Multiple Sequence Alignment. Methods in Molecular Biology (ed. Katoh, K.) 241–260 (Humana, 2021). https://doi.org/10.1007/978-1-0716-1036-7_15.Chapter 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ronquist, F. et al. MRBAYES 3.2: Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst. Biol. 61, 539–542. https://doi.org/10.1093/sysbio/sys029 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320. https://doi.org/10.1093/molbev/msn067 (2008).CAS 
Article 
PubMed 

Google Scholar  More