, Volume 74, Issue 7, pp 757–766 | Cite as

Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modelling for invasive plants

  • Ji-Zhong Wan
  • Chun-Jing Wang
  • Fei-Hai YuEmail author
Original Article


MaxEnt, a commonly used approach of species distribution modelling, is widely used to predict plant invasion at the large spatial scale based on occurrence records and environmental variables. However, the number of occurrence records, number of environmental variables, and spatial scales have a large potential to affect the ability of MaxEnt to predict invasive plant distributions. In this study, we used the area under the curve (AUC) of the receiver operator characteristics as an indicator of MaxEnt performance, and evaluated the effects of the number of occurrence records, number of environmental variables, and spatial scales on MaxEnt distribution modelling of invasive plants based on 1015 cases of invasive plants. Next, we suggested improvements for model performance. We found significant relationships between the AUC and the above-mentioned modelling parameters. Furthermore, we determined the relevant threshold values for the available MaxEnt models (i.e. AUC >0.7). We suggested using an appropriate number of occurrence records and environmental variables (e.g. >5) and covered cell sizes of 5.0 arc-min to model the distributions of invasive plants on the global scale. Our study provides practical references using MaxEnt to prevent and control plant invasion under global changes and contributes to the exploration of species distribution modelling mechanisms.


MaxEnt Modelling uncertainty Plant invasion Presence point Species distribution modelling 



This research was supported by the National Key Research and Development Program of China (2016YFC1201101) and NSFC (31800449 and 31800464).

Compliance with ethical standards

Conflict of interest

All the authors have approved the manuscript and agreed with submission to your esteemed journal. There are no conflicts of interest to declare.

Supplementary material

11756_2019_215_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 20 kb)


  1. Atwater DZ, Ervine C, Barney JN (2018) Climatic niche shifts are common in introduced plants. Nat Ecol Evol 2:34. CrossRefGoogle Scholar
  2. Austin MP (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19. CrossRefGoogle Scholar
  3. Beck J, Böller M, Erhardt A, Schwanghart W (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol Inform 19:10–15. CrossRefGoogle Scholar
  4. Bradie J, Leung B (2017) A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J Biogeogr 44:1344–1361. CrossRefGoogle Scholar
  5. Broennimann O, Treier UA, Müller-Schärer H, Thuiller W, Peterson AT, Guisan A (2007) Evidence of climatic niche shift during biological invasion. Ecol Lett 10:701–709. CrossRefGoogle Scholar
  6. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. CrossRefGoogle Scholar
  7. Feeley KJ, Silman MR (2011) Keep collecting: accurate species distribution modelling requires more collections than previously thought. Divers Distrib 17:1132–1140. CrossRefGoogle Scholar
  8. Franklin J, Davis FW, Ikegami M, Syphard AD, Flint LE, Flint AL, Hannah L (2013) Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Glob Chang Biol 19:473–483. CrossRefGoogle Scholar
  9. Gallardo B, Zieritz A, Aldridge DC (2015) The importance of the human footprint in shaping the global distribution of terrestrial, freshwater and marine invaders. PLoS One 10:e0125801. CrossRefGoogle Scholar
  10. Gallien L, Douzet R, Pratte S, Zimmermann NE, Thuiller W (2012) Invasive species distribution models–how violating the equilibrium assumption can create new insights. Glob Ecol Biogeogr 21:1126–1136. CrossRefGoogle Scholar
  11. Gueta T, Carmel Y (2016) Quantifying the value of user-level data cleaning for big data: a case study using mammal distribution models. Ecol Inform 34:139–145. CrossRefGoogle Scholar
  12. Higgins SI, Richardson DM (2014) Invasive plants have broader physiological niches. Proc Natl Acad Sci U S A 111:10610–10614. CrossRefGoogle Scholar
  13. Jiménez-Valverde A (2012) Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob Ecol Biogeogr 21:498–507. CrossRefGoogle Scholar
  14. Kramer-Schadt S, Niedballa J, Pilgrim JD, Schroder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. CrossRefGoogle Scholar
  15. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151. CrossRefGoogle Scholar
  16. Lonsdale WM (1999) Global patterns of plant invasions and the concept of invasibility. Ecology 80:1522–1536.;2 CrossRefGoogle Scholar
  17. Mainali KP, Warren DL, Dhileepan K, McConnachie A, Strathie L, Hassan G, Karki D, Shrestha BB, Parmesan C (2015) Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Glob Chang Biol 21:4464–4480. CrossRefGoogle Scholar
  18. Manzoor SA, Griffiths G, Lukac M (2018) Species distribution model transferability and model grain size–finer may not always be better. Sci Rep 8(7168).
  19. Marcer A, Pino J, Pons X, Brotons L (2012) Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales. Divers Distrib 18:1177–1189. CrossRefGoogle Scholar
  20. Merow C, Smith MJ, Silander JAA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. CrossRefGoogle Scholar
  21. Merow C, Smith MJ, Edwards TC, Guisan A, McMahon SM, Normand S, Thuiller W, Wüest RO, Zimmermann NE, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281. CrossRefGoogle Scholar
  22. Meyer C, Weigelt P, Kreft H (2016) Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol Lett 19:992–1006. CrossRefGoogle Scholar
  23. Morán-Ordóñez A, Lahoz-Monfort JJ, Elith J, Wintle BA (2017) Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions? Glob Ecol Biogeogr 26:371–384. CrossRefGoogle Scholar
  24. Moreno-Amat E, Mateo RG, Nieto-Lugilde D, Morueta-Holme N, Svenning J, García-Amorena L (2015) Impact of model complexity on cross-temporal transferability in Maxent species distribution models: an assessment using paleobotanical data. Ecol Model 312:308–317. CrossRefGoogle Scholar
  25. Mouton AM, De Baets B, Goethals PLM (2010) Ecological relevance of performance criteria for species distribution models. Ecol Model 221:1995–2002. CrossRefGoogle Scholar
  26. Padalia H, Srivastava V, Kushwaha SPS (2014) Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. In India: comparison of MaxEnt and GARP. Ecol Inform 22:36–43. CrossRefGoogle Scholar
  27. Padalia H, Srivastava V, Kushwaha SPS (2015) How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)? Environ Monit Assess 187:1–14. CrossRefGoogle Scholar
  28. Pearman PB, Guisan A, Broennimann O, Randin CF (2008) Niche dynamics in space and time. Trends Ecol Evol 23:149–158. CrossRefGoogle Scholar
  29. Pearson RG, Raxworthy CJ, Nakamura M, Peterson TA (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. CrossRefGoogle Scholar
  30. Peterson AT, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560. CrossRefGoogle Scholar
  31. Petitpierre B, Kueffer C, Broennimann O, Randin C, Daehler C, Guisan A (2012) Climatic niche shifts are rare among terrestrial plant invaders. Science 335:1344–1348. CrossRefGoogle Scholar
  32. Petitpierre B, Broennimann O, Kueffer C, Daehler C, Guisan A (2017) Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. Glob Ecol Biogeogr 26:275–287. CrossRefGoogle Scholar
  33. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. CrossRefGoogle Scholar
  34. Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME (2017) Opening the black box: an open-source release of Maxent. Ecography 40:887–893. CrossRefGoogle Scholar
  35. Powell KI, Chase JM, Knight TM (2011) A synthesis of plant invasion effects on biodiversity across spatial scales. Am J Bot 98:539–548. CrossRefGoogle Scholar
  36. Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643. CrossRefGoogle Scholar
  37. Randin CF, Dirnböck T, Dullinger S, Zimmermann NE, Zappa M, Guisan A (2006) Are niche-based species distribution models transferable in space? J Biogeogr 33:1689–1703. CrossRefGoogle Scholar
  38. Ray D, Behera MD, Jacob J (2016) Improving spatial transferability of ecological niche model of Hevea brasiliensis using pooled occurrences of introduced ranges in two biogeographic regions of India. Ecol Inform 34:153–163. CrossRefGoogle Scholar
  39. Richardson DM, Pyšek P, Rejmanek M, Barbour MG, Panetta FD, West CJ (2000) Naturalization and invasion of alien plants: concepts and definitions. Divers Distrib 6:93–107. CrossRefGoogle Scholar
  40. Shabani F, Kumar L (2015) Should species distribution models use only native or exotic records of existence or both? Ecol Inform 29:57–65. CrossRefGoogle Scholar
  41. Song W, Kim E, Lee D, Lee M, Jeon SW (2013) The sensitivity of species distribution modeling to scale differences. Ecol Model 248:113–118. CrossRefGoogle Scholar
  42. Suárez-Mota ME, Ortiz E, Villaseñor JL, Espinosa-García FJ (2016) Ecological niche modeling of invasive plant species according to invasion status and management needs: the case of Chromolaena odorata (Asteraceae) in South Africa. Pol J Ecol 64:369–383. CrossRefGoogle Scholar
  43. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. CrossRefGoogle Scholar
  44. Syfert MM, Smith MJ, Coomes DA (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS One 8:e55158. CrossRefGoogle Scholar
  45. Thuiller W (2014) Editorial commentary on ‘BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change’. Glob Chang Biol 20:3591–3592. CrossRefGoogle Scholar
  46. Turner MG (1989) Landscape ecology: the effect of pattern on process. Annu Rev Ecol Syst 20:171–197. CrossRefGoogle Scholar
  47. Wan JZ, Wang CJ, Yu FH (2016) Impacts of the spatial scale of climate data on the modeled distribution probabilities of invasive tree species throughout the world. Ecol Inform 36:42–49. CrossRefGoogle Scholar
  48. Wan JZ, Wang CJ, Tan JF, Yu FH (2017) Climatic niche divergence and habitat suitability of eight alien invasive weeds in China under climate change. Ecol Evol 7:1541–1552. CrossRefGoogle Scholar
  49. Wang Z, Rahbek C, Fang J (2012) Effects of geographical extent on the determinants of woody plant diversity. Ecography 35:1160–1167. CrossRefGoogle Scholar
  50. Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342. CrossRefGoogle Scholar
  51. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. CrossRefGoogle Scholar
  52. Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Grant EHC, Veran S (2013) Presence-only modelling using Maxent: when can we trust the inferences? Methods Ecol Evol 4:236–243. CrossRefGoogle Scholar
  53. Zhu GP, Qiao HJ (2016) Effect of the Maxent model's complexity on the prediction of species potential distributions. Biodivers Sci 24:1189–1196. CrossRefGoogle Scholar

Copyright information

© Plant Science and Biodiversity Centre, Slovak Academy of Sciences 2019

Authors and Affiliations

  1. 1.Institute of Wetland Ecology & Clone Ecology / Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and ConservationTaizhou UniversityTaizhouChina
  2. 2.State Key Laboratory of Plateau Ecology and AgricultureQinghai UniversityXiningChina
  3. 3.College of Agriculture and Animal HusbandryQinghai UniversityXiningChina

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