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宓春荣,郭玉民,HuettmannFalk,韩雪松.基于物种分布模型的精确采样提高目标物种发现率——以黑颈鹤(Grus nigricollis),白头鹤(Grus monacha)为例.生态学报,2017,37(13):4476~4482 本文二维码信息
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基于物种分布模型的精确采样提高目标物种发现率——以黑颈鹤(Grus nigricollis),白头鹤(Grus monacha)为例
Species distribution model sampling contributes to the identification of target species:take Black-necked Crane and Hooded Crane as two cases the model-based sampling approach could help to reduce areas to be investigated and it can find target species more effectively re. cost and effort
投稿时间:2016-02-02  最后修改时间:2017-01-09
DOI: 10.5846/stxb201602020243
关键词物种分布模型  随机森林  精确采样  黑颈鹤  白头鹤
Key Wordsspecies distribution model (SDM)  Random Forest  sampling method  black-necked crane  hooded crane
基金项目国家自然科学基金(31570532)
作者单位E-mail
宓春荣 北京林业大学自然保护区学院, 北京 100083  
郭玉民 北京林业大学自然保护区学院, 北京 100083 guoyumin@bjfu.edu.cn 
HuettmannFalk EWHALE Lab, Department of Biology and Wildlife, Institute of Arctic Biology, University of Alaska Fairbanks(UAF), AK 99775, USA  
韩雪松 北京林业大学自然保护区学院, 北京 100083  
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摘要:
明确野生动植物的地理分布是基础生态学和应用生态学领域的一个基础但关键的步骤,为后续分析提供了重要的信息。而野生动植物分布调查是一项需要投入大量人力,精力和资金的工作,特别是稀有物种的调查。物种分布模型越来越受到广泛引用尤其是在生物保护方面。为了证明物种分布模型在野生生物调查中精确采样方法的可行性,以全球易危物种黑颈鹤和白头鹤的实际繁殖分布预测为例,使用随机森林(Random Forest)算法加以验证。比较发现物种分布模型预测实际调查分布点,随机样方法生成的随机点,系统样方法的规则点在空间相对出现概率具有显著差异(P<0.001),实际分布点具有较高的相对出现概率。该结果表明若在物种分布相对出现概率较高区域设置样方能够减少实际调查区域,有效提高发现目标物种的概率,从而减少调查投入。基于物种分布模型的精确采样方法将有效地提高我们对稀有物种分布的了解,有利于野生动植物的保护规划。
Abstract:
The identification of the geographic distribution of wildlife is fundamental in applied ecology, since it provides important information for subsequent analyses. However, the investigation of wildlife is often expensive and time consuming, especially for rare species and when using inefficient sampling designs. To determine target species more efficiently, we tried to apply model-based sampling using predictions from species distribution models (SDMs). We used black-necked (Grus nigricollis) and hooded (Grus monacha) cranes as two examples, and used the Random Forest algorithm combining the breeding location and environmental information to model the breeding geographic distribution of the two crane species. We extracted the relative index of occurrence (RIO) for the breeding locations (testing points, model-based sampling method), random point locations (random sampling method), and regular point locations (regular sampling method) from the prediction map. Then, we used boxplots and ANOVA to analyze these data; the results indicated breeding locations with higher RIOs, and a significant difference was found between the other two methods. Therefore, the model-based sampling method helped to reduce the size of the investigated areas and determine target species more effectively. To conclude, a species distribution model-based sampling method for fieldwork would help to increase our knowledge of rare species distributions. More generally, we recommend using this approach to support conservation plans.
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