Abstract
Climate change poses major threats to global biodiversity, with a significant bearing on predicting potential suitable areas of plant species for future conservation. This study aimed to predict the potential suitable distribution areas of the endangered Toona ciliata under current and future climate change, providing the scientific-objective basis for enhanced species conservation and utilization. Adopting the Maximum Entropy (MaxEnt) approach, we predicted the valuable tree’s distribution under current and future conditions (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in China. The model incorporated seven bioclimatic variables, widely used in cognate studies and significantly influencing current T. ciliata distribution. The fitted model yielded high-quality (AUC = 0.947) and biologically meaningful results, identifying temperature as the most critical limiting factor, especially the minimum temperature of the coldest month. The MaxEnt simulation predicted excellent suitability habitats under the current climate consistent with the current actual distribution, remaining in China’s middle-subtropical region. Under three future climate-change scenarios, the model predicted an increase in excellent suitability areas. However, predicting future fragmentation of the current excellent range should raise concerns. MaxEnt can use bioclimatic data to pinpoint the optimal locations for species introduction, ex situ conservation, and cultivation of T. ciliata in China and other parts of the world.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
References
Abdelaal M, Fois M, Fenu G, Bacchetta G (2019) Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Eco Inform 50:68–75. https://doi.org/10.1016/j.ecoinf.2019.01.003
Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) Bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers Distrib 20(1):1–9. https://doi.org/10.1111/ddi.12144
Boria RA, Blois JL (2018) The effect of large sample sizes on ecological niche models: analysis using a North American rodent, Peromyscus maniculatus. Ecol Model 386:83–88. https://doi.org/10.1016/j.ecolmodel.2018.08.013
Caldwell MM, Bornman JF, Ballaré CL, Flint SD, Kulandaivelu G (2007) Terrestrial ecosystems, increased solar ultraviolet radiation, and interactions with other climate change factors. Photochem Photobiol Sci 6(3):252–266. https://doi.org/10.1039/B700019G
Chen X, Lei Y, Zhang X, Jia H (2012) Effects of sample sizes on accuracy and stability of Maximum Entropy model in predicting species distribution. Scientia Silvae Sinicae 48(1):53–59. https://doi.org/10.11707/j.1001-7488.20120110
Das J, Umamahesh NV (2022) Heat wave magnitude over India under changing climate: projections from CMIP5 and CMIP6 experiments. Int J Climatol 42(1):331–351. https://doi.org/10.1002/joc.7246
Das S, Sharangi AB (2018) Impact of climate change on spice crops. In: Sharangi AB (ed) Indian spices: the legacy, production and processing of India’s treasured export. Springer International Publishing, Cham, pp 379–404
Dyderski MK, Paź S, Frelich LE, Jagodziński AM (2018) How much does climate change threaten European forest tree species distributions? Glob Change Biol 24(3):1150–1163. https://doi.org/10.1111/gcb.13925
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Fang J, Lechowicz MJ (2006) Climatic limits for the present distribution of beech (Fagus L.) species in the world. J Biogeogr 33(10):1804–1819. https://doi.org/10.1111/j.1365-2699.2006.01533.x
Farahat EA, Refaat AM (2021) Predicting the impacts of climate change on the distribution of Moringa peregrina (Forssk.) Fiori — a conservation approach. J Mt Sci 18(5):1235–1245. https://doi.org/10.1007/s11629-020-6560-y
Feng L, Chen R, Zhu C, Yang S, Liao Y, Mai K et al (2015) Age structure and spatial distribution pattern of Toona ciliate population in northwestern Guangxi. J Northwest For Univ 30(1):46–50. https://doi.org/10.3969/j.issn.1001-7461.2015.01.08
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315. https://doi.org/10.1002/joc.5086
Franklin J (2023) Species distribution modelling supports the study of past, present and future biogeographies. J Biogeogr n/a(n/a). https://doi.org/10.1111/jbi.14617
Gaisberger H, Fremout T, Kettle CJ, Vinceti B, Kemalasari D, Kanchanarak T et al (2022) Tropical and subtropical Asia’s valued tree species under threat. Conserv Biol 36(3):e13873. https://doi.org/10.1111/cobi.13873
Gilman SE, Urban MC, Tewksbury J, Gilchrist GW, Holt RD (2010) A framework for community interactions under climate change. Trends Ecol Evol 25(6):325–331. https://doi.org/10.1016/j.tree.2010.03.002
Gornall J, Betts R, Burke E, Clark R, Camp J, Willett K et al (2010) Implications of climate change for agricultural productivity in the early twenty-first century. Phil Trans R Soc B Biol Sci 365(1554):2973–2989. https://doi.org/10.1098/rstb.2010.0158
Guo Y, Zhao Z, Qiao H, Wang R, Wei H, Wang L et al (2020) Challenges and development trend of species distribution model. Adv Earth Sci 35(12):1292–1305. https://doi.org/10.11867/j.issn.1001-8166.2020.110
Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5):773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x
Huang H, Zhong W, Yi D, Cai J, Zhang L (2020) Predicting the impact of future climate change on the distribution patterns of Toona ciliata var. pubescens in China. J Nan**g For Univ 44(3):163–170. https://doi.org/10.3969/j.issn.1000-2006.201812037
Jentsch A, Kreyling J, Beierkuhnlein C (2007) A new generation of climate-change experiments: events, not trends. Front Ecol Environ 5(7):365–374. https://doi.org/10.1890/1540-9295(2007)5[365:ANGOCE]2.0.CO;2
Ji Q, Wang R, Huang Z, Yuan J, Ren G, **ao W (2019) Effects of sample size and study range on accuracy of MaxEnt in predicting species distribution: a case study of the black-and-white snub-nosed monkey. ACTA Theriologica Sinica 39(2):126–133. https://doi.org/10.16829/j.slxb.150203
Jiang L, He Z, Liu J, **ng C, Gu X, Wei C et al (2019) Elevation gradient altered soil C, N, and P stoichiometry of Pinus taiwanensis forest on Daiyun Mountain. Forests 10(12):1089. https://doi.org/10.3390/f10121089
Kadmon R, Farber O, Danin A (2003) A systematic analysis of factors affecting the performance of climatic envelope models. Ecol Appl 13(3):853–867. https://doi.org/10.1890/1051-0761(2003)013[0853:ASAOFA]2.0.CO;2
Kharouba HM, Ehrlén J, Gelman A, Bolmgren K, Allen JM, Travers SE et al (2018) Global shifts in the phenological synchrony of species interactions over recent decades. Proc Natl Acad Sci 115(20):5211–5216. https://doi.org/10.1073/pnas.1714511115
Kidane YO, Steinbauer MJ, Beierkuhnlein C (2019) Dead end for endemic plant species? A biodiversity hotspot under pressure. Global Ecol Conserv 19:e00670. https://doi.org/10.1016/j.gecco.2019.e00670
Li K, **ao X, Li B, Wang L, Long S, Yang B et al (2023a) Effects of combined application of nitrogen, phosphorus and potassium fertilizers on the growth of Toona ciliata Roem. J Cent South Univ For Technol 43(01):50–56. https://doi.org/10.14067/j.cnki.1673-923x.2023.01.005
Li P, Que Q, Wu L, Zhu Q, Chen X (2017) Growth rhythms of Toona ciliata seedlings from different provenances. J South China Agric Univ 38(1):96–102. https://doi.org/10.7671/j.issn.1001-411X.2017.01.016
Li Y, Gu M, Liu X, Lin J, Jiang H, Song H et al (2023b) Sequencing and analysis of the complete mitochondrial genomes of Toona sinensis and Toona ciliata reveal evolutionary features of Toona. BMC Genomics 24(1):58. https://doi.org/10.1186/s12864-023-09150-6
Li Y, Li M, Li C, Liu Z (2020) Optimized Maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China. Forests 11(3):302. https://doi.org/10.3390/f11030302
Lind L, Eckstein RL, Relyea RA (2022) Direct and indirect effects of climate change on distribution and community composition of macrophytes in lentic systems. Biol Rev 97(4):1677–1690. https://doi.org/10.1111/brv.12858
Liu Q, Li Z, Wu J, Yang S, Wu Z, Li Y (2016) Regulation effects of exogenous spermine on morphology and physiology of Toona ciliata seedlings under drought stress. Chin J Ecol 35(12):3266–3272. https://doi.org/10.13292/j.1000-4890.201612.031
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2):145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Meehl GA, Washington WM, Santer BD, Collins WD, Arblaster JM, Hu A et al (2006) Climate change projections for the twenty-first century and climate change commitment in the CCSM3. J Clim 19(11):2597–2616. https://doi.org/10.1175/JCLI3746.1
Mehmood I, Bari A, Irshad S, Khalid F, Liaqat S, Anjum H et al (2020) Carbon cycle in response to global warming. In: Fahad S, Hasanuzzaman M, Alam M, Ullah H, Saeed M, Ali Khan I, Adnan M (eds) Environment, climate, plant and vegetation growth. Springer International Publishing, Cham, pp 1–15
Merow C, Smith MJ, Silander JA Jr (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36(10):1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
Morales NS, Fernández IC, Baca-González V (2017) MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A Systematic Review. PeerJ 5:e3093. https://doi.org/10.7717/peerj.3093
Mousazade M, Ghanbarian G, Pourghasemi HR, Safaeian R, Cerdà A (2019) Maxent data mining technique and its comparison with a bivariate statistical model for predicting the potential distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran. Sustainability 11(12):3452. https://doi.org/10.3390/su11123452
Moyroud N, Portet F, Baghdadi N, Mallet C, Zribi M (2018) Introduction to QGIS, QGIS and Generic Tools. ISTE, Wiley, 1–17. In Earth Systems-Environmental Sciences, QGIS in Remote Sensing Set, vol. 1, 978-1-78630-187-1
Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M et al (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol Evol 5(11):1198–1205. https://doi.org/10.1111/2041-210X.12261
Ni J (2011) Impacts of climate change on Chinese ecosystems: key vulnerable regions and potential thresholds. Reg Environ Change 11(1):49–64. https://doi.org/10.1007/s10113-010-0170-0
Nzei JM, Ngarega BK, Mwanzia VM, Musili PM, Wang Q-F, Chen J-M (2021) The past, current, and future distribution modeling of four water lilies (Nymphaea) in Africa indicates varying suitable habitats and distribution in climate change. Aquat Bot 173:103416. https://doi.org/10.1016/j.aquabot.2021.103416
Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological niches and geographic distributions. Princeton University Press, Princeton and Oxford
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pradhan P, Setyawan AD (2021) Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context. Asian J For 5(2):111–122. https://doi.org/10.13057/asianjfor/r050207
Qazi AW, Saqib Z, Zaman-ul-Haq M (2022) Trends in species distribution modelling in context of rare and endemic plants: a systematic review. Ecol Process 11(1):40. https://doi.org/10.1186/s13717-022-00384-y
Qiao W, Qiu Y, Zou J, Liu J, Chen W, Jiang J (2017) Study on variation pattern of wood traits in natural stands of Toona ciliata. J Cent South Univ For Technol 37(5):101–105. https://doi.org/10.14067/j.cnki.1673-923x.2017.05.018
Rosenzweig ML, Ziv Y (1999) The echo pattern of species diversity: pattern and processes. Ecography 22(6):614–628. https://doi.org/10.1111/j.1600-0587.1999.tb00510.x
Schwalm CR, Glendon S, Duffy PB (2020) RCP8.5 tracks cumulative CO2 emissions. Proc Natl Acad Sci 117(33):19656–19657. https://doi.org/10.1073/pnas.2007117117
Seddon AWR, Macias-Fauria M, Long PR, Benz D, Willis KJ (2016) Sensitivity of global terrestrial ecosystems to climate variability. Nature 531(7593):229–232. https://doi.org/10.1038/nature16986
Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 5:1–10. https://doi.org/10.17161/bi.v2i0.4
Stephenson NL (1990) Climatic control of vegetation distribution: the role of the water balance. Am Nat 135(5):649–670. https://doi.org/10.1086/285067
Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148(1):1–13. https://doi.org/10.1016/S0304-3800(01)00388-X
Tebaldi C, Debeire K, Eyring V, Fischer E, Fyfe J, Friedlingstein P, ... Ziehn T (2020) Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth Syst Dynamics Discuss 12;253-293. https://doi.org/10.5194/esd-12-253-2021
van Proosdij ASJ, Sosef MSM, Wieringa JJ, Raes N (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography 39(6):542–552. https://doi.org/10.1111/ecog.01509
van Vuuren DP, Stehfest E, den Elzen MGJ, Kram T, van Vliet J, Deetman S et al (2011) RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C. Clim Chang 109(1):95. https://doi.org/10.1007/s10584-011-0152-3
Wan J-Z, Wang C-J, Yu F-H (2019) Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modelling for invasive plants. Biologia 74(7):757–766. https://doi.org/10.2478/s11756-019-00215-0
Wang M, Chen B, Wu L (1999) Biological characteristics and artificial cultivation of Toona ciliata. For Sci Technol 1:14–16. https://doi.org/10.13456/j.arki.lykt.1999.01.005
Wang Y, Yan K, Teng J, Chen W, Wang L, Chen S (2016) Analysis on natural population dynamics of endangered species Toona ciliata in northwestern Hubei. J Plant Resour Environ 25(3):96–102. https://doi.org/10.3969/j.issn.1674-7895.2016.03.12
Warren DL, Matzke NJ, Iglesias TL (2020) Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. J Biogeogr 47(1):167–180. https://doi.org/10.1111/jbi.13705
Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA (2009) Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci 106(supplement_2):19729–19736. https://doi.org/10.1073/pnas.0901639106
Williams JN, Seo C, Thorne J, Nelson JK, Erwin S, O’Brien JM et al (2009) Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15(4):565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A et al (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14(5):763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x
**ao X, Yang Y, Guo H, **a L, Qi J (2019) Study on wood anatomical structure of natural Toona ciliate Roem. forests. J Cent South Univ For Technol 39(08):115–123. https://doi.org/10.14067/j.cnki.1673-923x.2019.08.017
**e C, Huang B, Jim CY, Liu D, Liu C, Zhu Z (2023) Predicting suitable habitat for the endangered plant Cephalotaxus oliveri Mast. in China. Environ Conserv 50(1):50–57. https://doi.org/10.1017/S0376892922000376
**n X-G, Wu T-W, Zhang J, Zhang F, Li W-P, Zhang Y-W et al (2019) Introduction of BCC models and its participation in CMIP6. Adv Clim Chang Res 15(5):533–539. https://doi.org/10.12006/j.issn.1673-1719.2019.039
Yost AC, Petersen SL, Gregg M, Miller R (2008) Predictive modeling and map** sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon. Eco Inform 3(6):375–386. https://doi.org/10.1016/j.ecoinf.2008.08.004
Zhan X, Li P, Hui W, Deng Y, Gan S, Sun Y et al (2019) Genetic diversity and population structure of Toona ciliata revealed by simple sequence repeat markers. Biotechnol Biotechnol Equip 33(1):214–222. https://doi.org/10.1080/13102818.2018.1561210
Zhang J-M, Song M-L, Li Z-J, Peng X-Y, Su S, Li B et al (2021) Effects of climate change on the distribution of Akebia quinata. Front Ecol Evol 9:752682. https://doi.org/10.3389/fevo.2021.752682
Zhang X, Li Y, Fang Y (2014) Geographical distribution and prediction of potential ranges of Quercus acutissima in China. Acta Botan Boreali-Occiden Sin 34(8):1685–1692. https://doi.org/10.7606/j.issn.1000-4025.2014.08.1685
Zhang Y, Zhou G, Ma L, Zhang X (2022) Growth difference analysis of 4 different provenances of Toona ciliata in Wuhan. Hubei For Sci Technol 51(2):6–12. https://doi.org/10.3969/j.issn.1004-3020.2022.02.003
Zhao Y, Deng X, **ang W, Chen L, Ouyang S (2021) Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Eco Inform 64:101393. https://doi.org/10.1016/j.ecoinf.2021.101393
Zheng X, Streimikiene D, Balezentis T, Mardani A, Cavallaro F, Liao H (2019) A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players. J Clean Prod 234:1113–1133. https://doi.org/10.1016/j.jclepro.2019.06.140
Zhou R, Ci X, **ao J, Cao G, Li J (2021) Effects and conservation assessment of climate change on the dominant group—the genus Cinnamomum of subtropical evergreen broad-leaved forests. Biodivers Sci 29(6):697–711. https://doi.org/10.17520/biods.2020482
Zou G (1994) The study of introdution and cultivation on valuable fast-growing species of Toona ciliata and Toona ciliata var. pubescens. J For Environ 3:271–276. https://doi.org/10.13324/j.cnki.jfcf.1994.03.022
Funding
This research was funded by the National Natural Science Foundation of China (grant number: 32360417), the Natural Science Foundation of Hainan Province (grant number: 423MS061, 623RC519), and the Education Department of Hainan Province (grant number: Hnky2023ZD-17).
