生物多样性 ›› 2018, Vol. 26 ›› Issue (8): 878-891. DOI: 10.17520/biods.2018051
周中一1, 刘冉1, 时书纳1, 苏艳军2, 李文楷1,*(), 郭庆华2,3
收稿日期:
2018-02-10
接受日期:
2018-04-28
出版日期:
2018-08-20
发布日期:
2018-09-27
通讯作者:
李文楷
作者简介:
# 共同第一作者
基金资助:
Zhongyi Zhou1, Ran Liu1, Shuna Shi1, Yanjun Su2, Wenkai Li1,*(), Qinghua Guo2,3
Received:
2018-02-10
Accepted:
2018-04-28
Online:
2018-08-20
Published:
2018-09-27
Contact:
Li Wenkai
About author:
# Co-first authors
摘要:
生态位模型通过拟合物种分布与环境变量之间的关系提供物种空间分布预测, 在生物多样性研究中有广泛应用。激光雷达(LiDAR)是一种新兴的主动遥感技术, 已被大量应用于森林三维结构信息的提取, 但其在物种分布模拟的应用研究比较缺乏。本研究以美国加州内华达山脉南部地区的食鱼貂(Martes pennanti)的分布模拟为例, 探索LiDAR技术在物种分布模拟中的有效性。生态位模型采用5种传统多类分类器, 包括神经网络、广义线性模型、广义可加模型、最大熵模型和多元自适应回归样条模型, 并使用正样本-背景学习(presence and background learning, PBL)算法进行模型校正; 同时对这5种模型使用加权平均进行模型集成, 作为第6个模型。此外, 一类最大熵模型也被用于模拟该物种的空间分布。模型的连续输出和二值输出分别使用AUC (area under the receiver operating characteristic curve)以及基于正样本-背景数据的评价指标Fpb进行评价。结果表明, 仅考虑气候因子(温度和降水)时, 7个模型的AUC和Fpb平均值分别为0.779和1.077; 当考虑LiDAR变量(冠层容重、枝下高、叶面积指数、高程、坡度等)后, AUC和Fpb分别为0.800和1.106。该研究表明, LiDAR数据能够提高食鱼貂空间分布的预测精度, 在物种分布模拟方面存在一定的应用价值。
周中一, 刘冉, 时书纳, 苏艳军, 李文楷, 郭庆华 (2018) 基于激光雷达数据的物种分布模拟: 以美国加州内华达山脉南部区域食鱼貂分布模拟为例. 生物多样性, 26, 878-891. DOI: 10.17520/biods.2018051.
Zhongyi Zhou, Ran Liu, Shuna Shi, Yanjun Su, Wenkai Li, Qinghua Guo (2018) Ecological niche modeling with LiDAR data: A case study of modeling the distribution of fisher in the southern Sierra Nevada Mountains, California. Biodiversity Science, 26, 878-891. DOI: 10.17520/biods.2018051.
变量代码 Variable code | 气候变量 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 |
表1 预测食鱼貂潜在分布的8个气候变量
Table 1 8 climate variables for predicting the spatial distribution of fishers
变量代码 Variable code | 气候变量 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 |
变量代码 Variable code | 激光雷达派生变量 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) |
表2 预测食鱼貂潜在分布的基于LiDAR的9个变量
Table 2 9 LiDAR-derived variables for predicting the spatial distribution of fishers
变量代码 Variable code | 激光雷达派生变量 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) |
图3 不同模型基于不同环境变量的模型精度对比图。(a)训练样本量为20%; (b)训练样本量为40%; (c)训练样本量为60%; (d)训练样本量为80%; (e)训练样本量为100%。
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%.
模型类型 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 |
表3 基于不同环境变量的模型AUC显著性检验P值
Table 3 P-value of models’ AUC accuracy based on different environmental variables
模型类型 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 |
模型类型 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 |
表4 基于不同环境变量的模型Fpb显著性检验P值
Table 4 P-value of models’ Fpb accuracy based on different environmental variables
模型类型 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 |
图4 基于Jackknife的环境变量重要性。变量的含义见表1和表2。
Fig. 4 The importance of environmental variables based on Jackknife analysis. The meanings of the variables are shown in Table 1 and Table 2.
图5 不同模型使用不同环境变量预测的食鱼貂概率分布图。(a)基于气候变量; (b)基于气候和LiDAR变量。
Fig. 5 The predicted probabilities maps of fishers by different models with different environmental variables. (a) Climate variables; (b) Climate plus LiDAR-derived variables.
图6 不同模型使用不同环境变量预测的食鱼貂二值分布图。(a)基于气候变量; (b)基于气候和LiDAR变量。
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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