Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)
Chao Tan,
David Kay Ferguson,
Yong Yang
Abstract:Global warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hot topics in the field of biodiversity research. Species distribution modeling predicts the potential distribution of species based on species occurrence records. However, it remains ambiguous how the accuracy of the distribution data impacts on the prediction results. To address this question, we used the endangered plant species Litsea auriculata (La… Show more
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