Biodiv Sci ›› 2018, Vol. 26 ›› Issue (8): 878-891.DOI: 10.17520/biods.2018051

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Ecological niche modeling with LiDAR data: A case study of modeling the distribution of fisher in the southern Sierra Nevada Mountains, California

Zhongyi Zhou1, Ran Liu1, Shuna Shi1, Yanjun Su2, Wenkai Li1,*(), Qinghua Guo2,3   

  1. 1 School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275
    2 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093
    3 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2018-02-10 Accepted:2018-04-28 Online:2018-08-20 Published:2018-09-27
  • Contact: Li Wenkai
  • About author:# Co-first authors

Abstract:

Ecological niche modeling seeks to infer the relationship between occurrences of a species and environmental covariates and has been widely applied in biodiversity studies. Light detection and ranging (LiDAR) is a new active remote sensing technology that is being increasingly used for acquisition of 3D structural information of forests. However, its applications in ecological niche modeling are rarely studied. In this study, we wanted investigate the effectiveness of LiDAR in modeling the spatial distribution of fisher (Martes pennanti) in the southern Sierra Nevada Mountains, California. We used artificial neural networks, generalized linear model, generalized additive model, discriminative maximum entropy, and multivariate adaptive regression splines to implement the presence and background learning (PBL) method separately. We then combined all the models based on weighted average to create an ensemble model. The generative maximum entropy model was also considered for comparison. Area under the receiver operating characteristic curve (AUC) and Fpb based on presence and background data were used to evaluate the continuous and binary outputs, respectively. Our results show that the values of AUC and Fpb were 0.779 and 1.077, respectively, when only climate variables (such as temperature and precipitation) were included in the models, whereas the values of AUC and Fpb were 0.800 and 1.106, respectively, when both climate and LiDAR-derived variables (such as canopy bulk density, height to live canopy base, leaf area index, digital elevation model, slope, etc.) were included in the models. Therefore, we conclude that LiDAR-derived variables are helpful in modeling the spatial distribution of fisher, and has good potential in ecological niche modeling.

Key words: ecological niche model, light detection and ranging (LiDAR), fisher, presence and background learning (PBL), maximum entropy (MaxEnt)