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基于随机森林模型对中国东北东北虎适宜栖息地的评估。

An Evaluation of Suitable Habitats for Amur Tigers () in Northeastern China Based on the Random Forest Model.

作者信息

Gao Chunyu, Hong Yang, Sun Shiquan, Zhang Ning, Liu Xinxin, Wang Zheyu, Zhou Shaochun, Zhang Minghai

机构信息

College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China.

Heilongjiang Wildlife Research Institute, Harbin 150081, China.

出版信息

Biology (Basel). 2023 Nov 17;12(11):1444. doi: 10.3390/biology12111444.

DOI:10.3390/biology12111444
PMID:37998043
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10669879/
Abstract

Amur tigers are at the top of the food chain and play an important role in maintaining the health of forest ecosystems. Scientific and detailed assessment of the habitat quality of Amur tigers in China is the key to maintaining the forest ecosystem and also addressing the urgent need to protect and restore the wild population of Amur tigers in China. This study used the random forest method to predict the potential habitat of Amur tigers in Heilongjiang and Jilin provinces using animal occurrence sites and a variety of environmental variables. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. The results showed that the AUC value of the test set was 0.955. The true skill statistic (TSS) value is 0.5924, indicating that the model had good prediction accuracy. Using the optimal threshold determined by the Youden index as the cutoff value, we found that the suitable habitat for Amur tigers in the field was approximately 107,600 km, accounting for 16.3% of the total study areas. It was mainly distributed in the Sino-Russian border areas in the south of the Laoyeling Mountains at the junction of Jilin and Heilongjiang provinces, the Sino-Russian border areas of Hulin-Raohe in the eastern part of the Wanda Mountains, and the Lesser Khingan Mountain forest region. The habitat suitability of the Greater Khingan Mountain and the plain areas connecting Harbin and Changchun was relatively low. Prey potential richness was the most critical factor driving the distribution of Amur tigers. Compared with their prey, the potential habitats for Amur tigers in Heilongjiang and Jilin provinces were small in total areas, sporadically distributed, and had low continuity and a lack of connectivity between patches. This indicates that some factors may restrict the diffusion of the Amur tiger, whereas the diffusion of ungulates is less restricted. The Amur tigers in this area face a serious threat of habitat fragmentation, suggesting that habitat protection, restoration, and ecological corridor construction should be strengthened to increase population dispersal and exchange. We provide a reference for future population conservation, habitat restoration, construction of ecological migration corridors, and population exchange of Amur tigers.

摘要

东北虎处于食物链顶端,在维护森林生态系统健康方面发挥着重要作用。对中国东北虎栖息地质量进行科学、详细的评估,是维护森林生态系统以及满足中国东北虎野生种群保护与恢复迫切需求的关键。本研究运用随机森林方法,利用动物出现位点及多种环境变量预测黑龙江省和吉林省东北虎的潜在栖息地。随机森林是由树预测器组成的集合,其中每棵树都依赖于一个独立采样的随机向量的值,且森林中所有树的分布相同。随着森林中树的数量增多,森林的泛化误差会收敛到一个极限。树分类器森林的泛化误差取决于森林中个体树的强度以及它们之间的相关性。结果表明,测试集的AUC值为0.955。真技能统计量(TSS)值为0.5924,表明该模型具有良好的预测准确性。以约登指数确定的最优阈值作为截断值,我们发现实地中东北虎的适宜栖息地约为107,600平方千米,占研究总面积的16.3%。其主要分布在黑龙江省和吉林省交界处老爷岭山脉以南的中俄边境地区、完达山东部虎林—饶河的中俄边境地区以及小兴安岭林区。大兴安岭以及连接哈尔滨和长春的平原地区的栖息地适宜性相对较低。猎物潜在丰富度是驱动东北虎分布的最关键因素。与它们的猎物相比,黑龙江省和吉林省东北虎的潜在栖息地总面积较小,呈零星分布,连续性低且斑块之间缺乏连通性。这表明某些因素可能限制了东北虎的扩散,而有蹄类动物的扩散受到的限制较小。该地区的东北虎面临着栖息地破碎化的严重威胁,这表明应加强栖息地保护、恢复以及生态廊道建设,以增加种群扩散和交流。我们为未来东北虎的种群保护、栖息地恢复、生态迁移廊道建设以及种群交流提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/42250c5efe65/biology-12-01444-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/f916d8b9d13b/biology-12-01444-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/8be28a51589e/biology-12-01444-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/fb01a73cb07e/biology-12-01444-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/b6cef1b9ebb0/biology-12-01444-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/42250c5efe65/biology-12-01444-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/f916d8b9d13b/biology-12-01444-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/dcd71764bba2/biology-12-01444-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/017ae9f23c8f/biology-12-01444-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/8be28a51589e/biology-12-01444-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/fb01a73cb07e/biology-12-01444-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/b6cef1b9ebb0/biology-12-01444-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880c/10669879/42250c5efe65/biology-12-01444-g007.jpg

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