Biodiversity Science ›› 2016, Vol. 24 ›› Issue (10): 1189-1196.doi: 10.17520/biods.2016265

• Orginal Article • Previous Article     Next Article

Effect of the Maxent model’s complexity on the prediction of species potential distributions

Gengping Zhu1, *(), Huijie Qiao2   

  1. 1 Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387
    2 Institute of Zoology, Chinese Academy of Sciences, Beijing 100101
  • Received:2016-09-20 Accepted:2016-10-28 Online:2016-11-10
  • Zhu Gengping

Ecological niche modeling (ENM) is widely used in the study of biological invasions and conservation biology. Maxent is the most popular algorithm and is being increasingly used to estimate species’ realized and potential distributions. Most modelers use the default Maxent setting to fit niche models, which originated from an earlier study containing 266 species, with the purpose of seeking their realized distributions. However, recent studies have shown that Maxent uses a complex machine learning method. It is sensitive to sampling bias and tends to overfit training data, and is only transferrable at low thresholds. Default settings based on Maxent outputs are sometimes not reliable, making it difficult to interpret. Using Halyomorpha halys and classical modeling approaches (i.e., niche models that were calibrated in native East Asia and transferred to North America), we tested the complexity and performance of the Maxent model under different settings of regulation multipliers and feature combinations, and chose a fine-tuned setting with the lowest complexity. We then compared the response curves and model interpolative and extrapolative validations between models calibrated using default and fine-tuned settings. Our purpose was to explore the effects of the model’s complexity on niche model performance in order to improve the development and application of Maxent in China. We argue that selection of environmental variables is crucial for model calibration, which should include ecological relevance and spatial correlation. Reducing sampling bias and delimitating a proper geographic background, together with the comparison of response curves and complexity of Maxent models built under different settings, is very important for fitting a good niche model. In the case of H. halys, the default and fine-tuned settings are different, however the response curve is much smoother in the fine-tuned model, and the omission error is lower in introduced areas when compared to default model, suggesting that the fine-tuned model reflects the response of H. halys to environmental factors more reasonably and precisely predicts the potential distribution.

Key words: ecological niche model, Maxent, model complexity, transferability, realized distribution, potential distribution

Fig. 1

Performances of native niche model of Halyomorpha halys under different settings. Black arrow indicates default setting, rededge arrow indicates the AICc-chosen setting."

Fig. 2

Comparison of response curves of Halyomorpha halys to five bioclimatic variables based on the default and fine-tuned Maxent settings"

Fig. 3

Predictions of Halyomorpha halys based on its native Maxent models calibrated on the default and fine-tuned settings"

Fig. 4

Omission rates of North American records of Halyomorpha halys based on its native Maxent models predictions using the default and fine-tuned settings."

1 Ahmed SE, McInerny G, O’Hara K, Harper R, Salido L, Emmott S, Joppa LN (2015) Scientists and software - surveying the species distribution modelling community. Diversity and Distributions, 21, 258-267.
2 Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory (eds Petrov BN, Csáki F), pp. 267-281. Akadémiai Kiadó, Budapest.
3 Barbosa FG, Schneck F (2015) Characteristics of the top-cited papers in species distribution predictive models. Ecological Modelling, 313, 77-83.
4 Elith J, Phillips SJ, Hastie T, Dudík D, Chee YE, Yates CJ (2010) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57.
5 Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.
6 Jiménez-Valverde A, Peterson AT, Soberón J, Overton JM, Aragón P, Lobo JM (2011) Use of niche models in invasive species risk assessments. Biological Invasions, 13, 2785-2797.
7 Kearney MR, Wintle BA, Porter WP (2010) Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conservation Letters, 3, 203-213.
8 Kriticos DJ, Webber BL, Leriche A, Ota N, Macadam I, Bathols J, Scott JK (2011) CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modeling. Methods in Ecology and Evolution, 3, 53-64.
9 Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M, Anderson RP (2014) ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods in Ecology and Evolution, 5, 1198-1205.
10 Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63-72.
11 Peterson AT, Soberón J (2012) Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza & Conservacao, 10, 102-107.
12 Peterson AT, Soberón J, Pearson RG, Anderson RP, Nakamura M, Martínez-Meyer E, Araújo MB (2011) Ecological Niches and Geographical Distributions. Princeton University Press, New Jersey.
13 Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
14 Phillips SJ, Dudík MM (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161-175.
15 Qiao HJ, Hu JH, Huang JH (2013) Theoretical basis, future directions, and challenges for ecological niche models. Scientia Sinica Vitae, 43, 915-927. (in Chinese with English abstract)
[乔慧捷, 胡军华, 黄继红 (2013) 生态位模型的理论基础、发展方向与挑战. 中国科学: 生命科学, 43, 915-927.]
16 Qiao HJ, Soberón J, Peterson AT (2015) No silver bullets in correlative ecological niche modeling: insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution, 6, 1126-1136.
17 Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics, 2, 1-10.
18 Vaz UL, Cunha HF, Nabout JC (2015) Trends and biases in global scientific literature about ecological niche models. Brazilian Journal of Biology, 75, 17-24.
19 Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecology Applications, 21, 335-342.
20 Warren DL, Wright AN, Seifert SN, Shaffer HB (2014) Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Diversity and Distributions, 20, 334-343.
21 Zhu GP, Bu WJ, Gao YB, Liu GQ (2012) Potential geographic distribution of brown marmorated stink bug invasion (Halyomorpha halys). PLoS ONE, 7, e31246.
22 Zhu GP, Gao YB, Zhu L (2013) Delimiting the coastal geographic background to predict potential distribution of Spartina alterniflora. Hydrobiologia, 717, 177-187.
23 Zhu GP, Redei D, Kment P, Bu WJ (2014) Effect of geographic background and equilibrium state on niche model transferability: predicting areas of invasion of Leptoglossus occidentalis. Biological Invasions, 16, 1069-1081.
24 Zhu GP, Gariepy TD, Haye T, Bu WJ (2016) Patterns of niche filling and expansion across the invaded ranges of Halyomorpha halys in North America and Europe. Journal of Pest Science, doi:10.1007/s10340-016-0786-z.
25 Zhu GP, Liu GQ, Bu WJ, Gao YB (2013) Ecological niche modeling and its applications in biodiversity conservation. Biodiversity Science, 21, 90-98. (in Chinese with English abstract)
[朱耿平, 刘国卿, 卜文俊, 高玉葆 (2013) 生态位模型的基本原理及其在生物多样性保护中的应用. 生物多样性, 21, 90-98.]
26 Zhu GP, Liu Q, Gao YB (2014) Improving ecological niche model transferability to predict the potential distribution of invasive exotic species. Biodiversity Science, 22, 223-230. (in Chinese with English abstract)
[朱耿平, 刘强, 高玉葆 (2014) 提高生态位模型转移能力来模拟入侵物种的潜在分布. 生物多样性, 22, 223-230.]
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