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加州鼠疫的空间分析:基于生态位模型预测其当前分布及对气候变化的潜在响应

Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change.

机构信息

Environmental Science, Policy, and Management Department, University of California, Berkeley, CA, USA.

出版信息

Int J Health Geogr. 2009 Jun 28;8:38. doi: 10.1186/1476-072X-8-38.

Abstract

BACKGROUND

Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance.

RESULTS

Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras.

CONCLUSION

Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions.

摘要

背景

鼠疫是由鼠疫耶尔森菌引起的,是加利福尼亚州和美国西部的公共卫生和野生动物健康关注问题。本研究探讨了加利福尼亚州鼠疫阳性样本的空间特征,并测试了 Maxent,这是一种可以从仅有存在数据开发基于生态位模型的机器学习方法,用于绘制鼠疫焦点的潜在分布。Maxent 模型使用加利福尼亚地松鼠(Spermophilus beecheyi)监测的血清阳性率数据作为病例点,并使用 Worldclim 生物气候数据作为预测变量进行构建,然后使用接收者操作特征曲线(AUC)统计数据进行比较和验证。此外,还将模型结果与阳性和阴性郊狼(Canis latrans)样本的位置进行了比较,以确定 Maxent 模型预测与通过野生食肉动物监测确定的鼠疫风险区域之间的相关性。

结果

基于最近的气候条件,加利福尼亚地松鼠鼠疫活动模型准确识别了病例位置(AUC 为 0.913 至 0.948),并且与郊狼样本显著相关。最终模型用于根据六个未来气候情景的集合来确定潜在的鼠疫风险区域。这些模型表明,到 2050 年,气候条件可能会降低加利福尼亚南部地区的鼠疫风险,并增加北海岸和塞拉山脉的风险。

结论

由于不同的建模方法可能会产生截然不同的结果,因此在解释未来模型预测时应谨慎。尽管如此,生态位建模仍然是探索和绘制鼠疫活动对气候变化潜在响应的有用工具。本研究中的最终模型用于根据六个未来气候情景的集合来确定潜在的鼠疫风险区域,这有助于公共管理人员决定在哪里分配监测资源。此外,Maxent 模型结果与郊狼样本显著相关,表明食肉动物监测计划将继续是跟踪鼠疫对未来气候条件的反应的重要手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d47b/2716330/9e58077844e0/1476-072X-8-38-1.jpg

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