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Effects of Sample Size, Sample Accuracy and Environmental Variables on Predictive Performance of MaxEnt Model

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The availability of sample data, together with detailed environmental factors, has fueled a rapid increase in predictive modeling of species geographic distributions and environmental requirements. We founded that MaxEnt model has provided different descriptions of potential distributions based on different sample size, sample accuracy and environmental background. We used six combinations based on three sample data set and two kinds of environmental variables to estimate the potentially suitable areas of Brown Eared Pheasant (Crossoptilon mantchuricum) in MaxEnt model. The results show that the complex variables provided the higher AUC value and accurate potential distribution than simple variables based on the same size of samples. Complicated environmental factors combined with moderate size and accurate sample, can predict better results. The model results were scabrous based on simple environmental factors. Furthermore, big sample size and simple prediction environmental factors will reduce the prediction accuracy, whereas small samples provided a conservative description of ecological niche. Here, we highlighted that considering the big size and high accuracy of sample and many environmental factors of a species to minimize error when attempting to infer potential distributions from current data in MaxEnt model.
Rocznik
Strony
303--312
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wykr.
Twórcy
autor
  • School of Nature Conservation, Beijing Forestry University, Beijing 100083, China
autor
  • School of Nature Conservation, Beijing Forestry University, Beijing 100083, China
Bibliografia
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  • [13] Li H. Q., Yue B. S., Lian Z. M., Zhao H. F., Zhao D. L., Xiao X. M. 2012 — Modeling spring habitat requirements of the endangered Brown Eared Pheasant (Crossoptilon mantchuricum) in the Huanglong Mountains, Shaanxi Province, China — Zoological Society of Japan, Zoolog. Sci. 29: 593–598.
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  • [23] Phillips S. J., Anderson R. P., Schapire R. E. 2006 — Maximum entropy modeling of species geographic distributions — Ecol. Model. 190: 231–259.
  • [24] Phillips S. J., Dudík M. 2008 — Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation — Ecography, 31:161–175.
  • [25] Piri sahragard H., Zare Chahouki M. A. 2015 — An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province — Ecol. Model. 309–310: 64–71.
  • [26] Real R., Barbosa A. M., Porras D., Kin M. S., Márquez A. L. et al. 2003 — Relative importance of environment, human activity and spatial situation in determining the distribution of terrestrial mammal diversity in Argentina — J. Biogeogr. 30: 939–947.
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  • [29] Sánchez-Fernández D., Lobo J. M., Hernández-Manrique O. L. 2011 — Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles — Divers. and Distrib. 17: 163–171.
  • [30] Steven J. P., Robert P. A., Robert E. S. 2006 — Maximum entropy modeling of species geographic distributions — Ecol. Model. 190: 231–259.
  • [31] Thuiller W., Araujo M. B., Lavorel S. 2003 — Generalized, model vs. classification tree analysis: predicting spatial distributions of plant species at different scales — J. Veg. Sci. 14: 669–680.
  • [32] Wisz M. S., Hijmans R. J., Li J., Peterson A. T., Graham C. H., Guisan A. NCEAS Predicting Species Distributions Working Group 2008 — Effects of sample size on the performance of species distribution models — Divers. and Distrib. 14: 763–773.
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Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bfb8863d-69a2-4f32-abed-62b76470b1ff
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