Biodiversity Science ›› 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:2018-09-27
  • Li Wenkai
  • About author:# Co-first authors

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)

Fig. 1

The study area and observed localities of fishers"

Table 1

8 climate variables for predicting the spatial distribution of fishers"

Variable code
Climate variables
Bio 1 1月降水量 Precipitation in January
Bio 2 4月降水量 Precipitation in April
Bio 3 7月降水量 Precipitation in July
Bio 4 10月降水量 Precipitation in October
Bio 5 1月平均温度 Mean temperature in January
Bio 6 4月平均温度 Mean temperature in April
Bio 7 7月平均温度 Mean temperature in July
Bio 8 10月平均温度 Mean temperature in October

Table 2

9 LiDAR-derived variables for predicting the spatial distribution of fishers"

Variable code
LiDAR-derived variables
Bio 9 冠层容重 Canopy bulk density (CBD)
Bio 10 冠盖度 Canopy cover
Bio 11 树木胸径 Diameter at breast height (DBH)
Bio 12 枝下高 Height to live canopy base (HTLCB)
Bio 13 叶面积指数 Leaf area index (LAI)
Bio 14 最大树高 Maximum tree height
Bio 15 平均树高 Mean tree height
Bio 16 坡度 Slope
Bio 17 数字高程模型 Digital elevation model (DEM)

Fig. 2

Flow chart of model development and accuracy assessment"

Fig. 3

The accuracies of different models based on different environmental variables. (a) The training sample size is 20%; (b) The training sample size is 40%; (c) The training sample size is 60%; (d) The training sample size is 80%; (e) The training sample size is 100%."

Table 3

P-value of models’ AUC accuracy based on different environmental variables"

Types of models
训练样本量 The training sample size
10% 20% 30% 40% 50%
Ensemble < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
MaxEnt < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-ANN < 0.001 < 0.001 < 0.001 0.001 0.030
PBL-GAM < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-GLM < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-MARS < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-MaxEnt < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Table 4

P-value of models’ Fpb accuracy based on different environmental variables"

Types of models
训练样本量 The training sample size
10% 20% 30% 40% 50%
Ensemble 0.002 0.002 < 0.001 < 0.001 < 0.001
MaxEnt < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-ANN 0.001 < 0.001 0.073 0.277 0.736
PBL-GAM 0.025 0.002 < 0.001 < 0.001 < 0.001
PBL-GLM < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-MARS < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
PBL-MaxEnt < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Fig. 4

The importance of environmental variables based on Jackknife analysis. The meanings of the variables are shown in Table 1 and Table 2."

Fig. 5

The predicted probabilities maps of fishers by different models with different environmental variables. (a) Climate variables; (b) Climate plus LiDAR-derived variables."

Fig. 6

The predicted binary maps of fishers by different models with different environmental variables. (a) Climate variables; (b) Climate plus LiDAR-derived variables."

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