Liu, J. et al. Research progress in Sapindus L. germplasm resources. World For. Res. 30, 15–21 (2017).
Sun, C., Jia, L., Xi, B., Wang, L. & Weng, X. Natural variation in fatty acid composition of Sapindus spp. seed oils. Ind. Crops Prod. 102, 97–104 (2017).
Google Scholar
Liu, J. et al. Variation in fruit and seed properties and comprehensive assessment of germplasm resources of the genus Sapindus. Sci. Silva Sin. 55, 44–54 (2019).
Xu, Y., Jia, L., Chen, Z. & Gao, Y. Advances on triterpenoid Saponin of Sapindus mukorossi. Chem. Bull. 081, 1078–1088 (2018).
Google Scholar
Basu, A., Basu, S., Bandyopadhyay, S. & Chowdhury, R. Optimization of evaporative extraction of natural emulsifier cum surfactant from Sapindus mukorossi: Characterization and cost analysis. Ind. Crops Prod. 77, 920–931 (2015).
Google Scholar
Mukhopadhyay, S., Hashim, M. A., Sahu, J. N., Yusoff, I. & Gupta, B. S. Comparison of a plant based natural surfactant with SDS for washing of As(V) from Fe rich soil. Journal of Environmental Sciences 25 (2013).
Mukhopadhyay, S. et al. Ammonium-based deep eutectic solvents as novel soil washing agent for lead removal. Chem. Eng. J. 294, 316–322 (2016).
Google Scholar
Mukherjee, S. et al. Optimization of pulp fibre removal by flotation using colloidal gas aphrons generated from a natural surfactant. J. Taiwan Inst. Chem. Eng. 53, 15–21 (2015).
Google Scholar
Shinobu-Mesquita, C. et al. Cellular structural changes in candida albicans caused by the hydroalcoholic extract from Sapindus saponaria L. Molecules 20, 9405–9418 (2015).
Google Scholar
Smułek, W. et al. Sapindus saponins’ impact on hydrocarbon biodegradation by bacteria strains after short- and long-term contact with pollutant. Colloids Surf. B 142, 207–213 (2016).
Google Scholar
Rodríguez-Hernández, D. et al. Highly potent anti-leishmanial derivatives of hederagenin, a triperpenoid from Sapindus saponaria L. Eur. J. Med. Chem. 124, 153–159 (2016).
Google Scholar
Rodriguez-Hernández, D., Demuner, A. J., Barbosa, L. C. A., Csuk, R. & Heller, L. Hederagenin as a triterpene template for the development of new antitumor compounds. Eur. J. Med. Chem. 105, 57–62 (2015).
Google Scholar
Singh, P. T. D. & Singh, M. M. Anti-Trichomonas activity of Sapindus saponins, a candidate for development as microbicidal contraceptive. J. Antimicrob. Chemother. 62, 526–534 (2008).
Google Scholar
Muntaha, S. T. & Khan, M. N. Natural surfactant extracted from Sapindus mukurossi as an eco-friendly alternate to synthetic surfactant: A dye surfactant interaction study. J. Clean. Prod. 93, 145–150 (2015).
Google Scholar
Sun, C., Jia, L., Ye, H. O., Gao, Y. & Weng, X. Geographic variation evaluating and correlation with fatty acid composition of economic characters of Sapindus spp. fruits. J. Beijing For. Univ. 12, 73–83 (2016).
Barry, C. & Cox, P. D. Biogeography: An Ecological and Evolutionary Approach (Blackwell, 1980).
Stocker, T. F. et al. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change, 95–123. http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_report_the_physical_science_basis.htm (2014).
Hatfield, J. L. & Prueger, J. H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extremes 10, 4–10 (2015).
Google Scholar
Ray, R., Gururaja, K. V. & Ramchandra, T. V. Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC. J. Environ. Biol. 32, 725–730 (2011).
Google Scholar
Lawler, J. J. Climate change adaptation strategies for resource management and conservation planning. Ann. N. Y. Acad. Sci. 1162, 79–98 (2009).
Google Scholar
Richard et al. Will plant movements keep up with climate change? Trends Ecol. Evol. (2013).
Araújo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539 (2012).
Google Scholar
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar
Zhu, G., Liu, G., Bu, W. & Gao, Y. Ecological niche modeling and its applications in biodiversity conservation. Biodiv. Sci. 1, 94–102 (2013).
Stockwell, D. & Peters, D. P. The GARP modelling system: Problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).
Google Scholar
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar
Beaumont, L. J. et al. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distribution. Ecol. Model. 186, 251–270 (2005).
Google Scholar
Liaw, A. & Wiener, M. Classification and regression by random forest. R News 23, 1–10 (2002).
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–814 (2008).
Google Scholar
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar
Li, G., Du, S. & Wen, Z. Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale. Sci. Rep. 6, 30009 (2016).
Google Scholar
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Google Scholar
Merow, C. et al. What do we gain from simplicity versus complexity in species distribution models?. Ecography 37, 1267–1281 (2014).
Google Scholar
Wang, W. et al. Assessment of potential habitat for firmiana danxiaensis, a plant species with extremely small populations in danxiashan national nature reserve based on maxent model. Scientia Silvae Sinicae (2019).
Guo, Y., Guo, J., Shen, X., Wang, G. & Wang, T. Predicting the bioclimatic habitat suitability of Ginkgo biloba L. in China with field-test validations. Forests https://doi.org/10.3390/f10080705 (2019).
Google Scholar
Huang, Z. et al. Geographic distribution and impacts of climate change on the suitable habitats of Zingiber species in China. Ind. Crops Prod. 138, 111429 (2019).
Google Scholar
Rong, Z. et al. Modeling the effect of climate change on the potential distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains. Forests https://doi.org/10.3390/f10010062 (2019).
Google Scholar
Mohammadi, S., Ebrahimi, E., Moghadam, M. S. & Bosso, L. Modelling current and future potential distributions of two desert jerboas under climate change in Iran. Ecol. Inform. 52, 7–13 (2019).
Google Scholar
Ramos, R. S., Kumar, L., Shabani, F. & Picanco, M. C. Risk of spread of tomato yellow leaf curl virus (TYLCV) in tomato crops under various climate change scenarios. Agric. Syst. 173, 524–535. https://doi.org/10.1016/j.agsy.2019.03.020 (2019).
Google Scholar
Sultana, S., Baumgartner, J. B., Dominiak, B. C., Royer, J. E. & Beaumont, L. J. Impacts of climate change on high priority fruit fly species in Australia. PLoS ONE 15, e0213820 (2020).
Google Scholar
Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).
Google Scholar
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108837 (2020).
Google Scholar
Sterne, T. K., Retchless, D., Allee, R. & Highfield, W. Predictive modelling of mesophotic habitats in the north-western Gulf of Mexico. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3281 (2020).
Google Scholar
Convertino, M., Annis, A. & Nardi, F. Information-theoretic portfolio decision model for optimal flood management. Environ. Model. Softw. 119, 258–274 (2019).
Google Scholar
Ardestani, E. G. & Mokhtari, A. Modeling the lumpy skin disease risk probability in central Zagros Mountains of Iran. Prev. Vet. Med. 176, 104887–104887. https://doi.org/10.1016/j.prevetmed.2020.104887 (2020).
Google Scholar
Hanafi-Bojd, A. A., Vatandoost, H. & Yaghoobi-Ershadi, M. R. Climate change and the risk of malaria transmission in Iran. J. Med. Entomol. 57, 50–64. https://doi.org/10.1093/jme/tjz131 (2020).
Google Scholar
Zhang, L., Jing, Z., Li, Z., Liu, Y. & Fang, S. Predictive modeling of suitable habitats for Cinnamomum Camphora (L.) presl using maxent model under climate change in China. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph16173185 (2019).
Google Scholar
Peng, L.-P. et al. Modelling environmentally suitable areas for the potential introduction and cultivation of the emerging oil crop Paeonia ostii in China. Sci. Rep. https://doi.org/10.1038/s41598-019-39449-y (2019).
Google Scholar
Li, J., Fan, G. & He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134141 (2020).
Google Scholar
Zhang, K., Zhang, Y. & Tao, J. Predicting the potential distribution of Paeonia veitchii (Paeoniaceae) in China by incorporating climate change into a maxent model. Forests https://doi.org/10.3390/f10020190 (2019).
Google Scholar
Sun, C. et al. Genetic structure and biogeographic divergence among Sapindus species: An inter-simple sequence repeat-based study of germplasms in China. Ind. Crops Prod. 118, 1–10 (2018).
Google Scholar
Mahar, K. S., Palni, L. M. S., Ranade, S. A., Pande, V. & Rana, T. S. Molecular analyses of genetic variation and phylogenetic relationship in Indian soap nut (Sapindus L.) and closely related taxa of the family Sapindaceae. Meta Gene, S2214540017300336 (2017).
Li, J., Chang, H., Liu, T. & Zhang, C. The potential geographical distribution of Haloxylon across Central Asia under climate change in the 21st century. Agric. For. Meteorol. 275, 243–254. https://doi.org/10.1016/j.agrformet.2019.05.027 (2019).
Google Scholar
Prevéy, J. S., Parker, L. E., Harrington, C. A., Lamb, C. T. & Proctor, M. F. Climate change shifts in habitat suitability and phenology of huckleberry (Vaccinium membranaceum). Agric. For. Meteorol. 280, 107803 (2020).
Google Scholar
Adeyemi, T. O., Ogundipe, O. T. & Olowokudejo, J. D. Species distribution modelling of family Sapindaceae in West Africa. Int. J. Bot. 8, 45–49 (2012).
Google Scholar
Sun, C. et al. Association of fruit and seed traits of sapindus mukorossi germplasm with environmental factors in Southern China. Forests 8, 491 (2017).
Google Scholar
Sun, C. et al. Genetic diversity and association analyses of fruit traits with microsatellite ISSRs in Sapindus. J. For. Res. 30, 197–207 (2019).
Google Scholar
Gao, Y. et al. Canopy characteristics and light distribution in Sapindus mukorossi Gaertn. are influenced by crown architecture manipulation in the hilly terrain of Southeast China. Sci. Hortic. 240, 11–22 (2018).
Google Scholar
Zhang, Y.-Q. et al. Spatio-temporal effects of canopy microclimate on fruit yield and quality of Sapindus mukorossi Gaertn. Sci. Hortic. 251, 136–149 (2019).
Google Scholar
Liu, J. et al. Potential distribution and ecological characteristics of genus Sapindus in China based on MaxEnt model. Sci. Silvae Sin. 57, 1–12. https://doi.org/10.11707/j.1001-7488.20210501 (2021).
Google Scholar
Cabral, A. L., Sales, J. D. F., Barbosa, K. F., Rodrigues, A. A. & Filho, S. C. V. Dormancy breakage and germination in Sapindus saponaria L. seeds as a function of temperature and germination substrate. Semina 40, 3345–3358 (2019).
Google Scholar
Jia, L. & Sun, C. Research progress of biodiesel tree Sapindus mukorossi. J. China Agric. Univ. 017, 191–196 (2012).
Wei, X., Dai, T., Liu, S. & Jia, L. Effects of formula fertilization on leaf nutrient dynamics and yield of Sapindus mukorossi Gaertn. J. Nanjing For. Univ. 42, 21–28 (2018).
Pal, A. K., Vaishnav, V., Meena, B., Pandey, N. & Rana, T. S. Adaptive fitness of Sapindus emarginatus Vahl populations towards future climatic regimes and the limiting factors of its distribution. Sci. Rep. 10, 1–11 (2020).
Google Scholar
Jayasinghe, S. L. & Kumar, L. Modeling the climate suitability of tea [Camellia sinensis(L.) O. Kuntze] in Sri Lanka in response to current and future climate change scenarios. Agric. For. Meteorol. 272–273, 102–117 (2019).
Google Scholar
He, X., Ning, X., Guo, Y. & Wei, H. Geographical distribution of Xanthoceras sorbifolia Bunge in China and predicting suitable area under the climate change scenario. Res. Agric. Modern. 40, 138–146 (2019).
GBIF.org (13 October 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.4d9kye.
Veloz, S. D. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J. Biogeogr. 36, 2290–2299 (2010).
Google Scholar
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
Google Scholar
Brown, J. L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700. https://doi.org/10.1111/2041-210x.12200 (2014).
Google Scholar
Beckmann, M. et al. glUV: A global UV-B radiation data set for macroecological studies. Methods Ecol. Evol. 5, 372 (2014).
Google Scholar
Mao, J. F. & Wang, X. R. Distinct niche divergence characterizes the homoploid hybrid speciation of Pinus densata on the Tibetan Plateau. Am. Nat. 177(4), 424–439 (2011).
Google Scholar
Li, G., Xu, G., Guo, K. & Du, S. Mapping the global potential geographical distribution of black locust (Robinia Pseudoacacia L.) using herbarium data and a maximum entropy model. Forests 5, 2773–2792 (2014).
Google Scholar
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar
Phillips, S. J. Transferability, sample selection bias and background data in presence-only modelling: A response to Peterson et al. (2007). Ecography 31, 272–278 (2008).
Google Scholar
Araujo, M. B., Pearson, R. G., Thuiller, W. & Erhard, M. Validation of species–climate impact models under climate change. Glob. Change Biol. 11, 1504–1513 (2005).
Google Scholar
Ikhumhen, H. O., Li, T., Lu, S. & Matomela, N. Assessment of a novel data driven habitat suitability ranking approach for Larus relictus specie using remote sensing and GIS. Ecol. Model. 432, 109221 (2020).
Google Scholar
Bosso, L., De Conno, C. & Russo, D. Modelling the risk posed by the zebra mussel Dreissena polymorpha: Italy as a case study. Environ. Manage. 60, 304–313 (2017).
Google Scholar
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