Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)

Abstract

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 (Lauraceae) as a case study. By collecting and assembling six different datasets of Litsea auriculata, we used MaxEnt model to perform species distribution modeling and then conducted comparative analyses. The results show that the distribution of Litsea auriculata is mainly in the Dabie Mountain region, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (bio2) and temperature annual range (bio7) play important roles in the distribution of Litsea auriculata. Compared with the correct data, the dataset including misidentified specimens leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a smaller and contracted range. According to the analysis of the local protection status of Litsea auriculata, we found that only about 23.38% of this species is located within nature reserves, so there is a large conservation gap. Our study suggests that the accurate distribution data is important for species modeling, and incomplete and incorrect data normally gives rise to misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gaps of Litsea auriculata, laying the foundation for the development of rational conservation strategies for this species.