Advertisement

Chinese Geographical Science

, Volume 29, Issue 6, pp 1011–1023 | Cite as

Prediction of Suitable Habitat for Lycophytes and Ferns in Northeast China: A Case Study on Athyrium brevifrons

  • Yan Li
  • Wei Cao
  • Xingyuan HeEmail author
  • Wei Chen
  • Sheng XuEmail author
Article
  • 5 Downloads

Abstract

Suitable habitat is vital for the survival and restoration of a species. Understanding the suitable habitat range for lycophytes and ferns is prerequisite for effective species resource conservation and recovery efforts. In this study, we took Athyrium brevifrons as an example, predicted its suitable habitat using a Maxent model with 67 occurrence data and nine environmental variables in Northeast China. The area under the curve (AUC) value of independent test data, as well as the comparison with specimen county areal distribution of A. brevifrons exhibited excellent predictive performance. The type of environmental variables showed that precipitation contributed the most to the distribution prediction, followed by temperature and topography. Percentage contribution and permutation importance both indicated that precipitation of driest quarter (Bio17) was the key factor in determining the natural distribution of A. brevifrons, the reason could be proved by the fern gametophyte biology. The analysis of high habitat suitability areas also showed the habitat preference of A. brevifrons: comparatively more precipitation and less fluctuation in the driest quarter. Changbai Mountains, covering almost all the high and medium habitat suitability areas, provide the best ecological conditions for the survival of A. brevifrons, and should be considered as priority areas for protection and restoration of the wild resource. The potential habitat suitability distribution map could provide a reference for the sustainable development and utilisation of A. brevifrons resource, and Maxent modelling could be valuable for conservation management planning for lycophytes and ferns in Northeast China.

Keywords

Athyrium brevifrons lycophytes and ferns Maxent suitable habitat Northeast China gametophyte 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

Part of the occurrence data were provided by Northeast Biological Herbaria of Institute of Applied Ecology, Chinese Academy of Sciences. We express sincere thanks to Prof. Chang Yu and Prof. Tao Dali for the revisions and comments, and Prof. Xiong Zaiping for the advice to GIS.

References

  1. Amici V, Eggers B, Geri F et al., 2015. Habitat suitability and landscape structure: a maximum entropy approach in a mediterranean area. Landscape Research, 40(2): 208–225. doi:  https://doi.org/10.1080/01426397.2013.774329 CrossRefGoogle Scholar
  2. Baker K, Lambdon P, Jones E et al., 2014. Rescue, ecology and conservation of a rediscovered island endemic fern (Anogramma ascensionis): ex situ methodologies and a road map for species reintroduction and habitat restoration. Botanical Journal of the Linnean Society, 1749(3): 461–477. doi:  https://doi.org/10.1111/boj.12131 CrossRefGoogle Scholar
  3. Balbontin J, 2005. Identifying suitable habitat for dispersal in Bonelli’s eagle: an important issue in halting its decline in Europe. Biological Conservation, 1267(1): 74–83. doi:  https://doi.org/10.1016/j.biocon.2005.04.023 CrossRefGoogle Scholar
  4. Baldwin R A, 2009. Use of maximum entropy modeling in wildlife research. Entropy, 11(4): 854–866. doi:  https://doi.org/10.3390/e11040854 CrossRefGoogle Scholar
  5. Banks J A, 1999. Gametophyte development in ferns. Annual Review of Plant Physiology and Plant Molecular Biology, 50: 163–186. doi:  https://doi.org/10.1146/annurev.arplant.50.1.163 CrossRefGoogle Scholar
  6. Benito Garzon M, Blazek R, Neteler M et al., 2006. Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197(3–4): 383–393. doi:  https://doi.org/10.1016/j.ecolmodel.2006.03.015 CrossRefGoogle Scholar
  7. Booth T H, Nix H A, Busby J R et al., 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:  https://doi.org/10.1111/ddi.12144 CrossRefGoogle Scholar
  8. Booth T H, 2018. Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important. Austral Ecology, 43(8): 852–860. doi:  https://doi.org/10.1111/aec.12628 CrossRefGoogle Scholar
  9. Boria R A, Olson L E, Goodman S M et al., 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275: 73–77. doi:  https://doi.org/10.1016/j.ecolmodel.2013.12.012 CrossRefGoogle Scholar
  10. Brummitt N, Bachman S P, Aletrari E et al., 2015. The sampled red list index for plants, phase II: ground-truthing specimen-based conservation assessments. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1662): 20140015. doi:  https://doi.org/10.1098/rstb.2014.0015 CrossRefGoogle Scholar
  11. Brummitt N, Aletrari E, Syfert M M et al., 2016. Where are threatened ferns found? Global conservation priorities for pteridophytes. Journal of Systematics and Evolution, 54(6): 604–616. doi:  https://doi.org/10.1111/jse.12224 CrossRefGoogle Scholar
  12. Bruni I, Gentili R, De Mattia F et al., 2013. A multi-level analysis to evaluate the extinction risk of and conservation strategy for the aquatic fern Marsilea quadrifolia L. in Europe. Aquatic Botany, 111: 35–42. doi:  https://doi.org/10.1016/j.aquabot.2013.08.005 CrossRefGoogle Scholar
  13. Campbell C A, Hilderbrand R H, 2017. Using maximum entropy to predict suitable habitat for the endangered dwarf wedge-mussel in the Maryland Coastal Plain. Aquatic Conservation: Marine and Freshwater Ecosystems, 27(2): 462–475. doi:  https://doi.org/10.1002/aqc.2699 CrossRefGoogle Scholar
  14. Canestraro B K, Moran R C, Watkins J E, 2014. Reproductive and physiological ecology of climbing and terrestrial Polybotrya (Dryopteridaceae) at the La Selva biological station, Costa Rica. International Journal of Plant Sciences, 175(4): 432–441. doi:  https://doi.org/10.1086/675576 CrossRefGoogle Scholar
  15. Carnaval A C, Moritz C, 2008. Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. Journal of Biogeography, 35(7): 1187–1201. doi:  https://doi.org/10.1111/j.1365-2699.2007.01870.x CrossRefGoogle Scholar
  16. Cook C N, Morgan D G, Marshall D J, 2010. Reevaluating suitable habitat for reintroductions: lessons learnt from the eastern barred bandicoot recovery program. Animal Conservation, 13(2): 184–195. doi:  https://doi.org/10.1111/j.1469-1795.2009.00320.x CrossRefGoogle Scholar
  17. Cui Shaopeng, Luo Xiao, Li Chunwang et al., 2018. Predicting the potential distribution of white-lipped deer using the Max-Ent model. Biodiversity Science, 26(2): 171–176. (in Chinese)CrossRefGoogle Scholar
  18. Davies A J, Wisshak M, Orr J C et al., 2008. Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep Sea Research Part I: Oceanographic Research Papers, 55(8): 1048–1062. doi:  https://doi.org/10.1016/j.dsr.2008.04.010 CrossRefGoogle Scholar
  19. Dong Shiyong, Zuo Zhengyu, Yan Yuehong et al., 2017. Red list assessment of lycophytes and ferns in China. Biodiversity Science, 25(7): 765–773. (in Chinese)CrossRefGoogle Scholar
  20. Dong Yuan, Wang Jianzhong, 1991. Exploitation, Utilization and protection of wild plant resources under forest in Northeast China. Resources Science, (2): 41–45. (in Chinese)Google Scholar
  21. Elith J, 2000. Quantitative methods for modeling species habitat: comparative performance and an application to Australian plants. In: Ferson S, Burgman M (eds). Quantitative Methods for Conservation Biology. New York: Springer, 39–58.CrossRefGoogle Scholar
  22. Elith J, Graham C H, Anderson R P et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2): 129–151. doi:  https://doi.org/10.1111/j.2006.0906-7590.04596.x CrossRefGoogle Scholar
  23. Elith J, Phillips S J, Hastie T et al., 2011. A statistical explanation of MaxEnt for ecologists. Biodiversity Research, 17(1): 43–57. doi:  https://doi.org/10.1111/j.1472-4642.2010.00725.x Google Scholar
  24. Estallo E L, Sangermano F, Grech M et al., 2018. Modelling the distribution of the vector Aedes aegypti in a central Argentine city. Medical and Veterinary Entomology, 32(4): 451–461. doi:  https://doi.org/10.1111/mve.12323 CrossRefGoogle Scholar
  25. Evangelista P H, Kumar S, Stohlgren T J et al., 2008. Modelling invasion for a habitat generalist and a specialist plant species. Diversity and Distributions, 14(5): 808–817. doi:  https://doi.org/10.1111/j.1472-4642.2008.00486.x CrossRefGoogle Scholar
  26. Fick S E, Hijmans R J, 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12): 4302–4315. doi:  https://doi.org/10.1002/joc.5086 CrossRefGoogle Scholar
  27. French K J, Shackell N L, den Heyer C E, 2018. Strong relationship between commercial catch of adult Atlantic halibut (Hippoglossus hippoglossus) and availability of suitable habitat for juveniles in the Northwest Atlantic Ocean. Fishery Bulletin, 116(2): 107–121. doi:  https://doi.org/10.7755/FB.116.2.1 Google Scholar
  28. Fu Peiyun, 1995. Clavis Plantarum Chinae Boreali-Orientalis (Editio Secunda). Beijing: Science Press, 35. (in Chinese)Google Scholar
  29. Galparsoro I, Borja Á, Bald J et al., 2009. Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis. Ecological Modelling, 220(4): 556–567. doi:  https://doi.org/10.1016/j.ecolmodel.2008.11.003 CrossRefGoogle Scholar
  30. Giordano P F, Navarro J L, Martella M B, 2010. Building large-scale spatially explicit models to predict the distribution of suitable habitat patches for the Greater rhea (Rhea americana), a near-threatened species. Biological Conservation, 143(2): 357–365. doi:  https://doi.org/10.1016/j.biocon.2009.10.022 CrossRefGoogle Scholar
  31. Greer G K, McCarthy B C, 2000. Patterns of growth and reproduction in a natural population of the fern Polystichum acrostichoides. American Fern Journal, 90(2): 60–76. doi:  https://doi.org/10.2307/1547415 CrossRefGoogle Scholar
  32. Gu W D, Swihart R K, 2004. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation, 116(2): 195–203. doi:  https://doi.org/10.1016/S0006-3207(03)00190-3 CrossRefGoogle Scholar
  33. Han X Z, Ma R, Chen Q et al., 2018. Anti-inflammatory action of Athyrium multidentatum extract suppresses the LPS-induced TLR4 signaling pathway. Journal of Ethnopharmacology, 217: 220–227. doi:  https://doi.org/10.1016/j.jep.2018.02.031 CrossRefGoogle Scholar
  34. He Xingyuan, Yu Jinghua, 2016. Technology and demonstration of ecological protection and exploitation and utilization of biological resources in northeast forest region. Acta Ecologica Sinica, 36(22): 7028–7033. (in Chinese)Google Scholar
  35. Jia Xiang, Ma Fangfang, Zhou Wangming et al., 2017. Impacts of climate change on the potential geographical distribution of broadleaved Korean pine (Pinus koraiensis) forests. Acta Ecologica Sinica, 37(2): 464–473. (in Chinese)Google Scholar
  36. Khafaga O, Hatab E E, Omar K, 2011. Predicting the potential geographical distribution of Nepeta septemcrenata in Saint Katherine Protectorate, South Sinai, Egypt using Maxent. Academia Arena, 3(7): 45–50.Google Scholar
  37. Kumar S, Stohlgren T J, 2009. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and Natural Environment, 1(4): 94–98.Google Scholar
  38. Lathrop R G, Niles L, Smith P et al., 2018. Mapping and modeling the breeding habitat of the Western Atlantic Red Knot (Calidris canutus rufa) at local and regional scales. The Condor, 120(3): 650–665. doi:  https://doi.org/10.1650/CONDOR-17-247.1 CrossRefGoogle Scholar
  39. Li G Q, Du S, Guo K, 2015. Evaluation of limiting climatic factors and simulation of a climatically suitable habitat for Chinese sea buckthorn. PLoS One, 10(7): e0131659. doi:  https://doi.org/10.1371/journal.pone.0131659 CrossRefGoogle Scholar
  40. Li G Q, Du S, Wen Z M, 2016. Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale. Scientific Reports, 6: 30009. doi:  https://doi.org/10.1038/srep30009 CrossRefGoogle Scholar
  41. Li N, Wang Z, Xia L et al., 2019. Effects of long-term coastal reclamation on suitable habitat and wintering population size of the endangered Red-crowned Crane, Grus japonensis. Hydrobiologia, 827(1): 21–29. doi:  https://doi.org/10.1007/s10750-017-3341-x CrossRefGoogle Scholar
  42. Liu Baodong, Li Xinhong, 1995. Resources of economic plant pteridophyte in Northeast China. Chinese Wild Plant Resources, (4): 36–38. (in Chinese)Google Scholar
  43. Liu Dongmei, Sheng Jiwen, Wang Sihong et al., 2016. Chemical constituents from Athyrium multidentatum rhizome and their reducing capacity. Chinese Journal of Experimental Traditional Medical Formulae, 22(21): 59–62. (in Chinese)Google Scholar
  44. Lu C Y, Gu W, Dai A H et al., 2012. Assessing habitat suitability based on geographic information system (GIS) and fuzzy: a case study of Schisandra sphenanthera Rehd. et Wils. In Qinling Mountains, China. Ecological Modelling, 242: 105–115. doi:  https://doi.org/10.1016/j.ecolmodel.2012.06.002 CrossRefGoogle Scholar
  45. Lu Shugang, Chen Feng, 2013. On the pteridophyte ecological types. Journal of Yunnan University (Natural Sciences Edition), 35(3): 407–415. (in Chinese)Google Scholar
  46. MacKenzie D I, Nichols J D, Lachman G B et al., 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83(8): 2248–2255. doi:  https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 CrossRefGoogle Scholar
  47. Manel S, Williams H C, Ormerod S J, 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38(5): 921–931. doi:  https://doi.org/10.1046/j.1365-2664.2001.00647.x CrossRefGoogle Scholar
  48. Merow C, Smith M J, Silander J A 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. doi:  https://doi.org/10.1111/j.1600-0587.2013.07872.x CrossRefGoogle Scholar
  49. Nettesheim F C, Damasceno E R, Sylvestre L S, 2014. Different slopes of a mountain can determine the structure of ferns and lycophytes communities in a tropical forest of Brazil. Anais da Academia Brasileira de Ciências, 86(1): 199–210. doi:  https://doi.org/10.1590/0001-3765201495912 CrossRefGoogle Scholar
  50. Nieto-Lugilde D, Lenoir J, Abdulhak S et al., 2015. Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps. Ecography, 38(6): 578–589. doi:  https://doi.org/10.1111/ecog.00954 CrossRefGoogle Scholar
  51. Olsson O, Rogers D J, 2009. Predicting the distribution of a suitable habitat for the white stork in Southern Sweden: identifying priority areas for reintroduction and habitat restoration. Animal Conservation, 12(1): 62–70. doi:  https://doi.org/10.1111/j.1469-1795.2008.00225.x CrossRefGoogle Scholar
  52. Pearce J L, Boyce M S, 2006. Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43(3): 405–412. doi:  https://doi.org/10.1111/j.1365-2664.2005.01112.x CrossRefGoogle Scholar
  53. Pearson R G, Raxworthy C J, Nakamura M et al., 2007. ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34(1): 102–117. doi:  https://doi.org/10.1111/j.1365-2699.2006.01594.x CrossRefGoogle Scholar
  54. Peck J H, Peck C J, Farrar D R, 1990. Influences of life history attributes on formation of local and distant fern populations. American Fern Journal, 80(4): 126–142. doi:  https://doi.org/10.2307/1547200 CrossRefGoogle Scholar
  55. Peterson A T, Soberón J, Pearson R G et al., 2011. Ecological Niches and Geographic Distributions. Princeton: Princeton University Press, 172.CrossRefGoogle Scholar
  56. Phillips S J, Anderson R P, Schapire R E, 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4): 231–259. doi:  https://doi.org/10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  57. Phillips S J, Dudik M, 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2): 161–175. doi:  https://doi.org/10.1111/j.0906-7590.2008.5203.x CrossRefGoogle Scholar
  58. Phillips S J, Anderson R P, Dudik M et al., 2017. Opening the black box: an open-source release of Maxent. Ecography, 40(7): 887–893. doi:  https://doi.org/10.1111/ecog.03049 CrossRefGoogle Scholar
  59. Phillips S J, Dudík M, Robert E S, 2018. Maxent software for modeling species niches and distributions (Version 3.4.1). Available at: http://biodiversityinformatics.amnh.org/open_source/maxent/.Google Scholar
  60. Phipps W L, Diekmann M, MacTavish L M et al., 2017. Due South: a first assessment of the potential impacts of climate change on Cape vulture occurrence. Biological Conservation, 210: 16–25. doi:  https://doi.org/10.1016/j.biocon.2017.03.028 CrossRefGoogle Scholar
  61. Qi G Y, Yang L Q, Xiao C X et al., 2015. Nutrient values and bioactivities of the extracts from three fern species in China: a comparative assessment. Food & Function, 6(9): 2918–2929. doi:  https://doi.org/10.1039/C5FO00510H CrossRefGoogle Scholar
  62. Qi G Y, Liu Z G, Fan R et al., 2017. Athyrium multidentatum (Doll.) Ching extract induce apoptosis via mitochondrial dysfunction and oxidative stress in HepG2 cells. Scientific Reports, 7(1): 2275. doi:  https://doi.org/10.1038/s41598-017-02573-8 CrossRefGoogle Scholar
  63. Radosavljevic A, Anderson R P, 2014. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4): 629–643. doi:  https://doi.org/10.1111/jbi.12227 CrossRefGoogle Scholar
  64. Remya K, Ramachandran A, Jayakumar S, 2015. Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats, India. Ecological Engineering, 82: 184–188. doi:  https://doi.org/10.1016/j.ecoleng.2015.04.053 CrossRefGoogle Scholar
  65. Richard K, Abdel-Rahman E M, Mohamed S A et al., 2018. Importance of remotely-sensed vegetation variables for predicting the spatial distribution of African citrus Triozid (Trioza erytreae) in Kenya. International Journal of Geo-Information, 7(11): 429. doi:  https://doi.org/10.3390/ijgi7110429 CrossRefGoogle Scholar
  66. Sato T, 1992. Size dependency of gametophytes decay in Athyrium brevifrons Nakai during spring desiccation. Ecological Research, 7(1): 1–7. doi:  https://doi.org/10.1007/BF02348591 CrossRefGoogle Scholar
  67. Sheffield E, 1994. Alternation of generations in ferns: mechanisms and significance. Biological Review, 69(3): 331–343. doi:  https://doi.org/10.1111/j.1469-185X.1994.tb01275.x CrossRefGoogle Scholar
  68. Shen Tao, Zhang Ji, Yang Qing et al., 2017. Ecology suitability study of Gentiana rhodantha in Yunnan-Guizhou Plateau. Chinese Pharmaceutical Journal, 52(20): 1816–1823. (in Chinese)Google Scholar
  69. Testo W L, Watkins J E Jr, 2013. Understanding mechanisms of rarity in Pteridophytes: competition and climate change threaten the rare fern Asplenium scolopendrium var. americanum (Aspleniaceae). American Journal of Botany, 100(11): 2261–2270. doi:  https://doi.org/10.3732/ajb.1300150 CrossRefGoogle Scholar
  70. Vilar L, Gómez I, Martínez-Vega J et al., 2016. Multitemporal modelling of socio-economic wildfire drivers in central Spain between the 1980s and the 2000s: comparing generalized linear models to machine learning algorithms. PLoS One, 11(8): e0161344. doi:  https://doi.org/10.1371/journal.pone.0161344 CrossRefGoogle Scholar
  71. Vormisto J, Tuomisto H, Oksanen J, 2004. Palm distribution patterns in Amazonian rainforests: what is the role of topographic variation? Journal of Vegetation Science, 15(4): 485–494. doi:  https://doi.org/10.1111/j.1654-1103.2004.tb02287.x CrossRefGoogle Scholar
  72. Wang Yunsheng, Xie Bingyan, Wan Fanghao et al., 2007. Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models. Biodiversity Science, 15(4): 365–372. (in Chinese)CrossRefGoogle Scholar
  73. Wang Zhongren, Zhang Xianchun, Zhu Weiming et al., 1999. Flora Reipublicae Popularis Sinicae, vol. 3(2). Beijing: Science Press, 162–165. (in Chinese)Google Scholar
  74. Watkins J E Jr, Mack M K, Mulkey S S, 2007. Gametophyte ecology and demography of epiphytic and terrestrial tropical ferns. American Journal of Botany, 94(4): 701–708. doi:  https://doi.org/10.3732/ajb.94.4.701 CrossRefGoogle Scholar
  75. West A M, Kumar S, Brown C S et al., 2016. Field validation of an invasive species Maxent model. Ecological Informatics, 36: 126–134. doi:  https://doi.org/10.1016/j.ecoinf.2016.11.001 CrossRefGoogle Scholar
  76. Wu F, Wang M M, Xi Z et al., 2014. Study on drought stress of six common ferns in North China. Acta Horticulturae, 1035: 113–124. doi:  https://doi.org/10.17660/ActaHortic.2014.1035.13 CrossRefGoogle Scholar
  77. Wu Z Y, Raven P H, Hong D Y, 2013. Flora of China, Vol. 2–3. Beijing: Science Press; St. Louis: Missouri Botanical Garden Press, 452, 466–467.Google Scholar
  78. Xu Wenduo, 1986. The relation between the zonal distribution of types of vegetation and the climate in Northeast China. Acta Phytoecologica et Geobotanica Sinica, 10(4): 254–263. (in Chinese)Google Scholar
  79. Yang X Q, Kushwaha S P S, Saran S et al., 2013. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51: 83–87. doi:  https://doi.org/10.1016/j.ecoleng.2012.12.004 CrossRefGoogle Scholar
  80. Yi Y J, Cheng X, Yang Z F et al., 2016. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92: 260–269. doi:  https://doi.org/10.1016/j.ecoleng.2016.04.010 CrossRefGoogle Scholar
  81. Zaniewski A E, Lehmann A, Overton J M, 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling, 157(2–3): 261–280. doi:  https://doi.org/10.1016/s0304-3800(02)00199-0 CrossRefGoogle Scholar
  82. Zhang Jiping, Zhang Yili, Liu Linshan et al., 2011. Predicting potential distribution of Tibetan Spruce (Picea smithiana) in Qomolangma (Mount Everest) national nature preserve using maximum entropy niche-based model. Chinese Geographical Science, 21(4): 417–426. doi:  https://doi.org/10.1007/s11769-011-0483-z CrossRefGoogle Scholar
  83. Zhang M G, Zhou Z K, Chen W Y et al., 2014. Major declines of woody plant species ranges under climate change in Yunnan, China. Diversity and Distributions, 20(4): 405–415. doi:  https://doi.org/10.1111/ddi.12165 CrossRefGoogle Scholar
  84. Zhang Xianchun, Wei Ran, Liu Hongmei et al., 2013. Phylogeny and classification of the extant lycophytes and ferns from China. Chinese Bulletin of Botany, 48(2): 119–137. (in Chinese)CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Applied EcologyChinese Academy of SciencesShenyangChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Shenyang ArboretumChinese Academy of SciencesShenyangChina
  4. 4.Key Laboratory of Forest Ecology and Management, Institute of Applied EcologyChinese Academy of SciencesShenyangChina

Personalised recommendations