Potential distribution ranges of natural grassland in subtropical humid mountainous areas were predicted by species distribution models (SDMs) to examine the effects of true and pseudo-absence data on model performances that were scarcely assessed by using real data. Climate spaces of potential ranges of natural grassland were then constructed by principal components analysis (PCA). The distribution map projected by six model algorithms based on true absence data had all presented restricted distribution ranges of natural grassland along mountain ridges, whereas that based on pseudo-absence data presented wider distribution ranges. RF model was used to detect the effects of data record number and contribution of climate variables on model performance because of higher True Skill Statistics. Restricted distribution ranges of natural grassland projected by RF based on true absence data were similar to limited climate space quantified by PCA. However, climate variables related to occurrences of natural grassland were not consistent between RF and PCA results. Occurrences of natural grassland associated with treeline at low elevation were presumably determined by multiple climate factors at subtropical mountain ridges, such as relatively lower temperatures, heavy precipitations, and strong winds. Local climate dataset derived from meteorological stations and followed by altitudinal adjustment was available for modeling species distribution range in mountainous areas. Conclusively, true absence data had practically delineated geographical boundaries and characterized the climate environments of natural grassland. True absence data was recommended to collect along a known environmental gradient and used to construct training dataset with pseudo-absence data to improve model performance.
Keyword: High-resolution climate dataset, natural grassland, principal components analysis, species distribution models, Taiwan
Anacker, B. L., M. Gogol-Prokurat, K. Leidholm and S. Schoenig. 2013. Climate change vulnerability assessment of rare plants in California. Madro?o 60(3): 193–210. DOI: 10.3120/0024-9637-60.3.193View ArticleGoogle Scholar
Anderson?Teixeira, K.J., S.J. Davies, A.C. Bennett, E. B. Gonzalez?Akre, H.C. Muller?Landau, S.J. Wright, K.A. Salim, A.M.A. Zambrano, A. Alonso, J.L. Baltzer, Y. Basset, N.A. Bourg, E.N. Broadbent, W.Y. Brockelman, S. Bunyavejchewin, D.F.R.P. Burslem, N. Butt, M. Cao, D. Cardenas, G.B. Chuyong, K. Clay, S. Cordell, H.S. Dattaraja, X. Deng, M. Detto, X. Du, A. Duque, D.L. Erikson, C.E.N. Ewango, G.A. Fischer, C. Fletcher, R.B. Foster, C.P. Giardina, G.S. Gilbert, N. Gunatilleke, S. Gunatilleke, Z. Hao, W.W. Hargrove, T.B. Hart, B.C.H. Hau, F. He, F.M. Hoffman, R.W. Howe, S.P. Hubbell, F.M. Inman-Narahari, P.A. Jansen, M. Jiang, D.J. Johnson, M. Kanzaki, A.R. Kassim, D. Kenfack, S. Kibet, M.F. Kinnaird, L. Korte, K. Kral, J. Kumar, A.J. Larson, Y. Li, X. Li, S. Liu, S.K.Y. Lum, J.A. Lutz, K. Ma, D.M. Maddalena, J.-R. Makana, Y. Malhi, T. Marthews, R.M.Serudin, S.M. McMahon, W.J. McShea, H.R. Memiaghe, X. Mi, T. Mizuno, M. Morecroft, J.A. Myers, V. Novotny, A.A. de Oliveira, P.S. Ong, D.A. Orwig, R. Ostertag, J. den Ouden, G.G. Parker, R.P. Phillips, L. Sack, M.N. Sainge, W. Sang, K. Sri-Ngernyuang, R. Sukumar, I-F. Sun, W. Sungpalee, H.S. Suresh, S. Tan, S.C. Thomas, D.W. Thomas, J. Thompson, B.L. Turner, M. Uriarte, R. Valencia, M.I. Vallejo, A. Vicentini, T. Vr?ka, X. Wang, X. Wang, G. Weiblen, A. Wolf, H. Xu, S. Yap, J. Zimmerman 2015. CTFS?forestGEO: A worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 21(2): 528–549. DOI: 10.1111/gcb.12712View ArticleGoogle Scholar
Booth, T. H., H. A. Nix, J. R. Busby and M. F. Hutchinson. 2014. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions 20(1): 1–9. DOI: 10.1111/ddi.12144View ArticleGoogle Scholar
Breiman, L. 2001. Random forests. Mach. Learn. 45(1): 5–32. DOI: 10.1023/A:1010933404324View Article
Brunialti, G. and L. Frati. 2021. Modeling of species distribution and biodiversity in forests. Forests 12(3): 319. DOI: 10.3390/f12030319View ArticleGoogle Scholar
Chambers, J. and T. Hastie. 1992. Linear Models. Chapter 4 of statistical models in S. Wadsworth & Brooks/Cole
Chapman, D., O. L. Pescott, H. E. Roy and R. Tanner. 2019. Improving species distribution models for invasive non?native species with biologically informed pseudo?absence selection. J. Biogeogr. 46(5): 1029–1040. DOI: 10.1111/jbi.13555View ArticleGoogle Scholar
Chen, W. K. and C. Y. Tsai. 1983. The climate of Yangmingshan National Park. Yangmingshan National Park, Construction and Planning Agency Ministry of the Interior, Executive Yuan, Taipei, Taiwan
Dobrowski, S. Z. 2011. A climatic basis for microrefugia: The influence of terrain on climate. Glob. Chang. Biol. 17(2): 1022–1035. DOI: 10.1111/j.1365-2486.2010.02263.xView ArticleGoogle Scholar
Dubuis, A., J. Pottier, V. Rion, L. Pellissier, J. P. Theurillat and A. Guisan. 2011. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approaches. Divers. Distrib. 17(6): 1122–1131. DOI: 10.1111/j.1472-4642.2011.00792.xView ArticleGoogle Scholar
Dupin, M., P. Reynaud, V. Jaro??k, R. Baker, S. Brunel, D. Eyre, J. Pergl, D. Makowski, S. Thrush. 2011. Effects of the training dataset characteristics on the performance of nine species distribution models: Application to Diabrotica virgifera virgifera. Plos One 6(6): e20957 DOI: 10.1371/journal.pone.0020957View ArticleGoogle Scholar
Early, R. and D. F. Sax. 2014. Climatic niche shifts between species' native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Glob. Ecol. Biogeogr. 23(12): 1356–1365. DOI: 10.1111/geb.12208View ArticleGoogle Scholar
Elith, J. and J. R. Leathwick. 2009. Species distribution models: Ecological explanation and prediction across space and time. Ann. Rev. Ecol. Evol. Syst. 40(1): 677–697. DOI: 10.1146/annurev.ecolsys.110308.120159View ArticleGoogle Scholar
Evans, M. E., S. A. Smith, R. S. Flynn and M. J. Donoghue. 2009. Climate, niche evolution, and diversification of the “bird?cage” evening primroses (Oenothera, Sections Anogra and Kleinia). Am. Nat. 173(2): 225–240. DOI: 10.1086/595757View ArticleGoogle Scholar
Fick, S. E. and R. J. Hijmans. 2017. Worldclim 2: New 1?km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12): 4302–4315. DOI: 10.1002/joc.5086View ArticleGoogle Scholar
Fois, M., G. Fenu, A. C. Lombrana, D. Cogoni and G. Bacchetta. 2015. A practical method to speed up the discovery of unknown populations using species distribution models. J. Nat. Conserv. 24: 42–48. DOI: 10.1016/j.jnc.2015.02.001View ArticleGoogle Scholar
Germino, M. J., W. K. Smith and A. C. Resor. 2002. Conifer seedling distribution and survival in an alpine-treeline ecotone. Plant Ecol. 162(2): 157–168. DOI: 10.1023/A:1020385320738View Article
Gies, M., M. Sondermann, D. Hering and C. K. Feld. 2015. Are species distribution models based on broad-scale environmental variables transferable across adjacent watersheds? A case study with eleven macroinvertebrate species. Fundamental and Applied Limnology/Archiv f?r Hydrobiologie 186(1-2): 63–97.
DOI: 10.1127/fal/2014/0600View ArticleGoogle Scholar
Guillera?Arroita, G., J.J. Lahoz?Monfort, J. Elith, A. Gordon, H. Kujala, P.E. Lentini, M.A. McCarthy, R. Tingley, B.A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24(3): 276–292. DOI: 10.1111/geb.12268View ArticleGoogle Scholar
Hegel, T.M., S.A. Cushman, J. Evans and F. Huettmann. 2010. Current State of the Art for Statistical Modelling of Species Distributions. In: Cushman, S.A. and F. Huettmann (eds). Spatial complexity, informatics, and wildlife conservation, 273–311 pp. Springer, Tokyo. DOI: 10.1007/978-4-431-87771-4_16View ArticleGoogle Scholar
Hoch, G. and C. Korner. 2003. The carbon charging of pines at the climatic treeline: A global comparison. Oecologia 135(1): 10–21. DOI: 10.1007/s00442-002-1154-7View ArticleGoogle Scholar
Hsieh, C. F., W. C. Chao, C. C. Liao, K. C. Yang and T. H. Hsieh. 1997. Floristic composition of the evergreen broad-leaved forests of Taiwan. Nat. Hist. Res. 4: 1–16.
Kier, G., H. Kreft, T. M. Lee, W. Jetz, P. L. Ibisch, C. Nowicki, J. Mutke, W. Barthlott. 2009. A global assessment of endemism and species richness across island and mainland regions. PNAS 106(23): 9322–9327. DOI: 10.1073/pnas.0810306106View ArticleGoogle Scholar
Korner, C. 1998. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115(4): 445–459. DOI: 10.1007/s004420050540View ArticleGoogle Scholar
Lannuzel, G., J. Balmot, N. Dubos, M. Thibault and B. Fogliani. 2021. High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia. Biodivers. Conserv. 30(4): 963–990 DOI: 10.1007/s10531-021-02126-6View ArticleGoogle Scholar
Liang, W., M. Pape?, L. Tran, J. Grant, R. Washington-Allen, S. Stewart and G. Wiggins. 2018. The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift. Ecol Model 388: 1–9. DOI: 10.1016/j.ecolmodel.2018.09.018View ArticleGoogle Scholar
Liao, C.C., S.C. Kuo and C.R. Chang. 2012. Forest distribution on small isolated hills and implications on woody plant distribution under threats of global warming. Taiwania 57(3): 242–250. DOI: 10.6165/tai.2012.57(3).242View ArticleGoogle Scholar
Liao, C.C., C.R. Chang, M.T. Hsu and W.K. Poo. 2014. Experimental evaluation of the sustainability of dwarf bamboo (Pseudosasa usawai) sprout-harvesting practices in Yangminshan National Park, Taiwan. Environ. Manage. 54(2): 320–330. DOI: 10.1007/s00267-014-0296-9View ArticleGoogle Scholar
Liao, C. C. and Y. H. Chen. 2021. Improving performance of species distribution model in mountainous areas with complex topography. Ecol. Res. 36(4): 648–662. DOI: 10.1111/1440-1703.12227View ArticleGoogle Scholar
Liaw, A. and M. Wiener. 2002. Classification and regression by random forest. R news 2: 18–22.
Liu, B., E. Liang and L. Zhu. 2011. Microclimatic conditions for Juniperus saltuaria treeline in the Sygera Mountain, Southeastern Tibetan plateau. Mt. Res. Dev. 31(1): 45–53. DOI: 10.1659/MRD-JOURNAL-D-10-00096.1View ArticleGoogle Scholar
Lobo, J. M., A. Jim?nez?Valverde and R. Real. 2008. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2): 145–151. DOI: 10.1111/j.1466-8238.2007.00358.xView ArticleGoogle Scholar
Mohapatra, J., C. P. Singh, M. Hamid, A. Verma, S. C. Semwal, B. Gajmer, A.A. Khuroo, A. Kumar, M.C. Nautiyal, N. Sharma, H.A. Pandya. 2019. Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest. Biodivers. Conserv. 28(8-9): 2295–2317. DOI: 10.1007/s10531-019-01731-wView ArticleGoogle Scholar
Pearse, I. S. and A. L. Hipp. 2012. Global patterns of leaf defenses in oak species. Evolution 66(7): 2272–2286. DOI: 10.1111/j.1558-5646.2012.01591.xView ArticleGoogle Scholar
Peng, D., L. Sun, H. W. Pritchard, J. Yang, H. Sun and Z. Li. 2019. Species distribution modelling and seed germination of four threatened snow lotus (Saussurea), and their implication for conservation. Glob. Ecol. Biogeogr. 17: e00565. DOI: 10.1016/j.gecco.2019.e00565View ArticleGoogle Scholar
Peterson, A. T., J. Sober?n, R. G. Pearson, R. P. Anderson, E. Mart?nez-Meyer, M. Nakamura and M. B. Ara?jo. 2011. Ecological Niches and Geographic Distributions (MPB-49). Princeton University Press.
Porfirio, L.L., R.M. Harris, E.C. Lefroy, S. Hugh, S.F. Gould, G. Lee, N.L. Bindoff, B. Mackey, L. Kumar. 2014. Improving the use of species distribution models in conservation planning and management under climate change. Plos One 9(11): e113749. DOI: 10.1371/journal.pone.0113749View ArticleGoogle Scholar
Pradervand, J.-N., A. Dubuis, L. Pellissier, A. Guisan and C. Randin. 2014. Very high resolution environmental predictors in species distribution models: Moving beyond topography? Prog. Phys. Geog. 38(1): 79–96. DOI: 10.1177/0309133313512667View ArticleGoogle Scholar
Qian, H. 2017. Climatic correlates of phylogenetic relatedness of woody angiosperms in forest communities along a tropical elevational gradient in South America. J. Plant Ecol. 11(3): 394–400. DOI: 10.1093/jpe/rtx006View ArticleGoogle Scholar
Qiao, H., X. Feng, L. E. Escobar, A. T. Peterson, J. Sober?n, G. Zhu and M. Pape?. 2019. An evaluation of transferability of ecological niche models. Ecography 42(3): 521–534. DOI: 10.1111/ecog.03986View ArticleGoogle Scholar
Senay, S. D., S. P. Worner and T. Ikeda. 2013. Novel three-step pseudo-absence selection technique for improved species distribution modelling. Plos One 8(8): e71218. DOI: 10.1371/journal.pone.0071218View ArticleGoogle Scholar
Smith, W.K., M.J. Germino, D.M. Johnson and K. Reinhardt. 2009. The altitude of alpine treeline: A bellwether of climate change effects. Bot Rev 75(2): 163–190. DOI: 10.1007/s12229-009-9030-3View ArticleGoogle Scholar
Stevens, G. C. and J. F. Fox. 1991. The causes of treeline. Annu Rev Ecol Syst 22(1): 177–191. DOI: 10.1146/annurev.es.22.110191.001141View ArticleGoogle Scholar
Thuiller, W., D. Georges, R. Engler, F. Breiner, M. D. Georges and C. W. Thuiller. 2016. Package ‘biomod2’. Species distribution modeling within an ensemble forecasting framework.
Title, P. O. and J. B. Bemmels. 2018. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41(2): 291–307. DOI: 10.1111/ecog.02880View ArticleGoogle Scholar
Tomlinson, S., W. Lewandrowski, C. P. Elliott, B. P. Miller and S. R. Turner. 2020. High?resolution distribution modeling of a threatened short?range endemic plant informed by edaphic factors. Ecol. Evol. 10(2): 763–777. DOI: 10.1002/ece3.5933View ArticleGoogle Scholar
Tsoar, A., O. Allouche, O. Steinitz, D. Rotem and R. Kadmon. 2007. A comparative evaluation of presence?only methods for modelling species distribution. Divers. Distrib. 13(4): 397–405. DOI: 10.1111/j.1472-4642.2007.00346.xView ArticleGoogle Scholar
Wisz, M. S. and A. Guisan. 2009. Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecology 9(1): 1–13. DOI: 10.1186/1472-6785-9-8View ArticleGoogle Scholar
Xu, Y., Y. Huang, H. Zhao, M. Yang, Y. Zhuang and X. Ye. 2021. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in china. Forests 12(4): 429. DOI: 10.3390/f12040429View ArticleGoogle Scholar
Zhu, Y., W. Wei, H. Li, B. Wang, X. Yang and Y. Liu. 2018. Modelling the potential distribution and shifts of three varieties of Stipa tianschanica in the eastern Eurasian steppe under multiple climate change scenarios. Glob. Ecol. Biogeogr. 16: e00501. DOI: 10.1016/j.gecco.2018.e00501View ArticleGoogle Scholar
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