Factors influencing wild boar damage in Taohongling National Nature Reserve in China: a model approach
Factors influencing wild boar damage in Taohongling National Nature Reserve in China: a model approach
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DOI：
10.1007/s10344-012-0663-x
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发表时间：
2013-04-01
影响因子：
2
通讯作者：
Ying, Xia
中科院分区：
文献类型：
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作者：
Li, Lanlan;Shi, Jianbin;Ying, Xia
近年来，人类与野生动物尤其是野猪（Sus scrofa）之间的冲突在世界各地造成了严重问题。受多种因素影响，必须有效控制野猪对农业的损害。在本研究中，我们通过实地调查和社会访谈，收集了2009年11月至2010年10月在中国江西省桃红岭国家级自然保护区野猪造成的农业损失的数据。我们使用二元逻辑回归分析构建模型来预测损害风险并确定影响损害风险的因素。约8.1%的耕地受到野猪的破坏，并且卡方拟合优度检验的结果显示，对水稻、棉花和其他作物的损害并未根据各自的可用性进行分配。基于受损面积的模型（基于区域的模型）解释了五种因素（芒草、土壤条件、地形、距居民点的距离和水源），预测精度为72.1%。除了这五个因素外，基于损害频率的模型（基于频率的模型）还保留了一个附加因素（即到森林边缘的距离），预测精度为 83.1%。应用这两个模型来预测野猪对农作物的损害时需要谨慎，建议将这两个模型结合使用，以更准确地预测损害概率。
Conflicts between humans and wildlife, especially wild boar (Sus scrofa), have caused serious problems across the world in recent years. It is necessary to effectively control wild boar agricultural damage that may be influenced by many factors. In this study, we collected data on agricultural damage caused by wild boars from November 2009 to October 2010 using field surveys and social interviews in Taohongling National Nature Reserve of Jiangxi Province, China. We constructed models using binary logistic regression analysis to predict damage risks and to identify the factors influencing damage risks. About 8.1 % of croplands were damaged by wild boars, and the damage to rice, cotton, and other crops were not distributed based on their respective availability as shown by the result of a chi-square goodness-of-fit test. Five factors (Japanese silvergrass, soil conditions, terrain, distances to settlements, and water sources) were explained in a model based on damage area (area-based model) with the prediction accuracy being 72.1 %. In addition to these five factors, one additional factor (i.e. distance to forest edge) was retained in a model based on damage frequency (frequency-based model) with the prediction accuracy being 83.1 %. Caution is needed when we apply these two models to predict boar damage to crops, and it is recommended that both models be used in combination to predict the damage probabilities more accurately.