The performance of state-of-the-art modelling techniques depends on geographical distribution of species
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DOI: 10.1016/j.ecolmodel.2008.10.019
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- Guo, Chuanbo & Lek, Sovan & Ye, Shaowen & Li, Wei & Liu, Jiashou & Li, Zhongjie, 2015. "Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques," Ecological Modelling, Elsevier, vol. 306(C), pages 67-75.
- Kyungeun Lee & Daeguen Kim & Jaegyu Cha & Seungbum Hong, 2023. "Fine-Scale Species Distribution Modeling of Abies koreana across a Subalpine Zone in South Korea for In Situ Species Conservation," Sustainability, MDPI, vol. 15(11), pages 1-15, June.
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- Junhee Lee & Youngjae Yoo & Raeik Jang & Seongwoo Jeon, 2023. "Mapping the Species Richness of Woody Plants in Republic of Korea," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
- Alexandra M. Thorn & Jonathan R. Thompson & Joshua S. Plisinski, 2016. "Patterns and Predictors of Recent Forest Conversion in New England," Land, MDPI, vol. 5(3), pages 1-17, September.
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Keywords
Bioclimatic modelling; Latitudinal range; Machine learning; Predictive accuracy; Prevalence; Spatial autocorrelation;All these keywords.
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