Advances in Earth Science ›› 2020, Vol. 35 ›› Issue (12): 1292-1305. doi: 10.11867/j.issn.1001-8166.2020.110
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Yanlong Guo 1( ),Zefang Zhao 2,Huijie Qiao 3,Ran Wang 3, 4,Haiyan Wei 5,Lukun Wang 5,Wei Gu 6,Xin Li 1( )
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Yanlong Guo,Zefang Zhao,Huijie Qiao,Ran Wang,Haiyan Wei,Lukun Wang,Wei Gu,Xin Li. Challenges and Development Trend of Species Distribution Model[J]. Advances in Earth Science, 2020, 35(12): 1292-1305.
Species Distribution Models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental variables database. SDMs tend to be used to simulate the potential distribution of species in a larger scale, under the confirming modeling conditions, which can be extrapolated in space and time. SDMs are now widely used in the fields of climate change biology, landscape ecology and conservation biology, and they are one of the most important tools in current biophysics research. This paper systematically discusses the current challenges and future development of species distribution model from two aspects: i.e., model building and model application. The development status and prospect of species distribution models in China are reviewed. The main viewpoints and conclusions are as follows: In the modeling practice of species distribution model, the rationality and accuracy of model results depend on many factors, including the choice of environmental variables and model algorithms, spatial and temporal scale, the interaction between environmental and geographical factors and the model extrapolation degree. Hence, researchers should understand the theoretical basis of the model, attach importance to the standardization of statistics in the process of modeling, choose and design a reasonable model algorithm. In future research, we need to consider the historical and geographical reasons for the current distribution pattern and the possible impacts of species niche shift, as well as the principles of landscape genetics and phylogeography, which should be effectively incorporated into the model framework. Meanwhile, how to use geoscience big data (remote sensing data) effectively is also one of the challenges for the development of the models.