Climate, vegetation, introduced hosts and trade shape a global wildlife pandemic
Climate, vegetation, introduced hosts and trade shape a global wildlife pandemic
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气候、植被、引进宿主和贸易塑造了全球野生动物大流行
DOI：
10.1098/rspb.2012.2506
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发表时间：
2013-02-22
影响因子：
4.7
通讯作者：
Li, Yiming
中科院分区：
文献类型：
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作者：
Liu, Xuan;Rohr, Jason R.;Li, Yiming
气候变化、国际贸易和外来物种引进等全球因素往往被认为是导致前所未有的疾病出现速度的因素，但很少有研究将这些因素划分为全球大流行的因素。虽然现代相关物种分布模型(SDMS)可用于预测新出现疾病的空间格局，但它们主要集中在基本生态位(FN)预测因子(即非生物气候和栖息地因素)，而忽略了扩散和繁殖压力预测因子(PP，释放到一个地区的非本地个体的数量)。利用一个有效的、可预测的和全球的SDM，我们发现FN和PP都对菊苣真菌Batrachochytrium endrobatidis(Bd)的分布造成了显著的、独特的变化，Bd是一种与全球200多种两栖动物物种的衰退和灭绝有关的病原体。BD与植被、总贸易量、引进两栖类宿主呈正相关，与年较差呈非线性相关，与两栖类腿类贸易量、两栖类物种丰富度无显著相关。这些发现提供了一个罕见的例子，即FN和PP因子都可以预测全球大流行。我们的模型应该有助于指导对这种致命病原体的管理，以及开发其他受FN和PP因素影响的物种入侵和病原体出现的全球预测模型。
Global factors, such as climate change, international trade and introductions of exotic species are often elicited as contributors to the unprecedented rate of disease emergence, but few studies have partitioned these factors for global pandemics. Although contemporary correlative species distribution models (SDMs) can be useful for predicting the spatial patterns of emerging diseases, they focus mainly on the fundamental niche (FN) predictors (i.e. abiotic climate and habitat factors), neglecting dispersal and propagule pressure predictors (PP, number of non-native individuals released into a region). Using a validated, predictive and global SDM, we show that both FN and PP accounted for significant, unique variation to the distribution of the chytrid fungus Batrachochytrium dendrobatidis (Bd), a pathogen implicated in the declines and extinctions of over 200 amphibian species worldwide. Bd was associated positively with vegetation, total trade and introduced amphibian hosts, nonlinearly with annual temperature range and non-significantly with amphibian leg trade or amphibian species richness. These findings provide a rare example where both FN and PP factors are predictive of a global pandemic. Our model should help guide management of this deadly pathogen and the development of other globally predictive models for species invasions and pathogen emergence influenced by FN and PP factors.