IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i10p4132-d359748.html
   My bibliography  Save this article

Species Distribution Modeling of Sassafras Tzumu and Implications for Forest Management

Author

Listed:
  • Keliang Zhang

    (Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China)

  • Yin Zhang

    (Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China)

  • Diwen Jia

    (Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China)

  • Jun Tao

    (Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou 225009, China)

Abstract

Sassafras tzumu (Chinese sassafras) is an economically and ecologically important deciduous tree species. Over the past few decades, increasing market demands and unprecedented human activity in its natural habitat have created new threats to this species. Nonetheless, the distribution of its habitat and the crucial environmental parameters that determine the habitat suitability remain largely unclear. The present study modeled the current and future geographical distribution of S. tzumu by maximum entropy (MAXENT) and genetic algorithm for rule set prediction (GARP). The value of area under the receiver operating characteristic curve (AUC), Kappa, and true skill statistic (TSS) of MAXENT was significantly higher than that of GARP, indicating that MAXENT performed better. Temperate and subtropical regions of eastern China where the species had been recorded was suitable for growth of S. tzumu . Relative humidity (26.2% of permutation importance), average temperature during the driest quarter (16.6%), annual precipitation (12.6%), and mean diurnal temperature range (10.3%) were identified as the primary factors that accounted for the present distribution of S. tzumu in China. Under the climate change scenario, both algorithms predicted that range of suitable habitat will expand geographically to northwest. Our results may be adopted for guiding the preservation of S. tzumu through identifying the habitats susceptible to climate change.

Suggested Citation

  • Keliang Zhang & Yin Zhang & Diwen Jia & Jun Tao, 2020. "Species Distribution Modeling of Sassafras Tzumu and Implications for Forest Management," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4132-:d:359748
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/10/4132/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/10/4132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    2. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    3. Stockwell, David R.B. & Noble, Ian R., 1992. "Induction of sets of rules from animal distribution data: A robust and informative method of data analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(5), pages 385-390.
    4. Chris D. Thomas & Alison Cameron & Rhys E. Green & Michel Bakkenes & Linda J. Beaumont & Yvonne C. Collingham & Barend F. N. Erasmus & Marinez Ferreira de Siqueira & Alan Grainger & Lee Hannah & Lesle, 2004. "Extinction risk from climate change," Nature, Nature, vol. 427(6970), pages 145-148, January.
    5. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    6. John Harte & Annette Ostling & Jessica L. Green & Ann Kinzig, 2004. "Climate change and extinction risk," Nature, Nature, vol. 430(6995), pages 34-34, July.
    7. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    8. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ping He & Yu Gao & Longfei Guo & Tongtong Huo & Yuxin Li & Xingren Zhang & Yunfeng Li & Cheng Peng & Fanyun Meng, 2021. "Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    2. Chunyan Cao & Jun Tao, 2021. "Predicting the Areas of Suitable Distribution for Zelkova serrata in China under Climate Change," Sustainability, MDPI, vol. 13(3), pages 1-11, February.
    3. Fahim Arshad & Muhammad Waheed & Kaneez Fatima & Nidaa Harun & Muhammad Iqbal & Kaniz Fatima & Shaheena Umbreen, 2022. "Predicting the Suitable Current and Future Potential Distribution of the Native Endangered Tree Tecomella undulata (Sm.) Seem. in Pakistan," Sustainability, MDPI, vol. 14(12), pages 1-10, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weiyu Yu & Nicola A Wardrop & Robert E S Bain & Victor Alegana & Laura J Graham & Jim A Wright, 2019. "Mapping access to domestic water supplies from incomplete data in developing countries: An illustrative assessment for Kenya," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
    2. Yuncheng Zhao & Mingyue Zhao & Lei Zhang & Chunyi Wang & Yinlong Xu, 2021. "Predicting Possible Distribution of Tea ( Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China," Agriculture, MDPI, vol. 11(11), pages 1-18, November.
    3. Chefaoui, Rosa M. & Lobo, Jorge M., 2008. "Assessing the effects of pseudo-absences on predictive distribution model performance," Ecological Modelling, Elsevier, vol. 210(4), pages 478-486.
    4. Ko, Chia-Ying & Root, Terry L. & Lee, Pei-Fen, 2011. "Movement distances enhance validity of predictive models," Ecological Modelling, Elsevier, vol. 222(4), pages 947-954.
    5. Jim Graham & Greg Newman & Sunil Kumar & Catherine Jarnevich & Nick Young & Alycia Crall & Thomas J. Stohlgren & Paul Evangelista, 2010. "Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions," Future Internet, MDPI, vol. 2(4), pages 1-11, November.
    6. Cory J. Lindgren, 2012. "Biosecurity Policy and the Use of Geospatial Predictive Tools to Address Invasive Plants: Updating the Risk Analysis Toolbox," Risk Analysis, John Wiley & Sons, vol. 32(1), pages 9-15, January.
    7. Stokland, Jogeir N. & Halvorsen, Rune & Støa, Bente, 2011. "Species distribution modelling—Effect of design and sample size of pseudo-absence observations," Ecological Modelling, Elsevier, vol. 222(11), pages 1800-1809.
    8. Rubén G Mateo & Ángel M Felicísimo & Julien Pottier & Antoine Guisan & Jesús Muñoz, 2012. "Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    9. Krzysztof S. Targiel & Maciej Nowak & Tadeusz Trzaskalik, 2018. "Scheduling non-critical activities using multicriteria approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 585-598, September.
    10. F. Castro-Llanos & G. Hyman & J. Rubiano & J. Ramirez-Villegas & H. Achicanoy, 2019. "Climate change favors rice production at higher elevations in Colombia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(8), pages 1401-1430, December.
    11. Okitonyumbe Y.F., Joseph & Ulungu, Berthold E.-L., 2013. "Nouvelle caractérisation des solutions efficaces des problèmes d’optimisation combinatoire multi-objectif [New characterization of efficient solution in multi-objective combinatorial optimization]," MPRA Paper 66123, University Library of Munich, Germany.
    12. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    13. Monica Motta & Caterina Sartori, 2020. "Normality and Nondegeneracy of the Maximum Principle in Optimal Impulsive Control Under State Constraints," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 44-71, April.
    14. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    15. Chenchen Wu & Dachuan Xu & Donglei Du & Wenqing Xu, 2016. "An approximation algorithm for the balanced Max-3-Uncut problem using complex semidefinite programming rounding," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1017-1035, November.
    16. Gengping Zhu & Matthew J Petersen & Wenjun Bu, 2012. "Selecting Biological Meaningful Environmental Dimensions of Low Discrepancy among Ranges to Predict Potential Distribution of Bean Plataspid Invasion," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    17. Uzma Ashraf & Hassan Ali & Muhammad Nawaz Chaudry & Irfan Ashraf & Adila Batool & Zafeer Saqib, 2016. "Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model," Sustainability, MDPI, vol. 8(8), pages 1-11, July.
    18. Ernst Althaus & Felix Rauterberg & Sarah Ziegler, 2020. "Computing Euclidean Steiner trees over segments," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 309-325, October.
    19. World Bank, 2003. "Argentina : Reforming Policies and Institutions for Efficiency and Equity of Public Expenditures," World Bank Publications - Reports 14637, The World Bank Group.
    20. Ceretani, Andrea N. & Salva, Natalia N. & Tarzia, Domingo A., 2018. "Approximation of the modified error function," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 607-617.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4132-:d:359748. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.