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The Predictive Performance and Stability of Six Species Distribution Models

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  • Ren-Yan Duan
  • Xiao-Quan Kong
  • Min-Yi Huang
  • Wei-Yi Fan
  • Zhi-Gao Wang

Abstract

Background: Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. Methodology: We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. Results: The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p

Suggested Citation

  • Ren-Yan Duan & Xiao-Quan Kong & Min-Yi Huang & Wei-Yi Fan & Zhi-Gao Wang, 2014. "The Predictive Performance and Stability of Six Species Distribution Models," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-8, November.
    • Handle: RePEc:plo:pone00:0112764
    DOI: 10.1371/journal.pone.0112764
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    1. Pecchi, Matteo & Marchi, Maurizio & Burton, Vanessa & Giannetti, Francesca & Moriondo, Marco & Bernetti, Iacopo & Bindi, Marco & Chirici, Gherardo, 2019. "Species distribution modelling to support forest management. A literature review," Ecological Modelling, Elsevier, vol. 411(C).
    2. Steen, Bart & Broennimann, Olivier & Maiorano, Luigi & Guisan, Antoine, 2024. "How sensitive are species distribution models to different background point selection strategies? A test with species at various equilibrium levels," Ecological Modelling, Elsevier, vol. 493(C).
    3. Huayong Zhang & Shuang Zheng & Tousheng Huang & Jiangnan Liu & Junjie Yue, 2023. "Estimation of Potential Suitable Habitats for the Relict Plant Euptelea pleiosperma in China via Comparison of Three Niche Models," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    4. Jan Altman & Kerstin Treydte & Vit Pejcha & Tomas Cerny & Petr Petrik & Miroslav Srutek & Jong-Suk Song & Valerie Trouet & Jiri Dolezal, 2020. "Tree growth response to recent warming of two endemic species in Northeast Asia," Climatic Change, Springer, vol. 162(3), pages 1345-1364, October.
    5. Karen E DeMatteo & Miguel A Rinas & Juan Pablo Zurano & Nicole Selleski & Rosio G Schneider & Carina F Argüelles, 2017. "Using niche-modelling and species-specific cost analyses to determine a multispecies corridor in a fragmented landscape," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-22, August.
    6. Ana Cristina Mosebo Fernandes & Rebeca Quintero Gonzalez & Marie Ann Lenihan-Clarke & Ezra Francis Leslie Trotter & Jamal Jokar Arsanjani, 2020. "Machine Learning for Conservation Planning in a Changing Climate," Sustainability, MDPI, vol. 12(18), pages 1-28, September.
    7. Lu, Yongquan & Liu, Guilin & Xian, Yuyang & Tang, Jiaqi & Zhong, Liming, 2024. "Climate change brings both opportunities and challenges to rural revitalization in China: Evidence from apple geographical indication predictions," Agricultural Systems, Elsevier, vol. 216(C).
    8. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.

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