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The geography of parasite local adaptation to host communities
Ecography ( IF 5.4 ) Pub Date : 2021-05-16 , DOI: 10.1111/ecog.05730
Emily S. Bellis 1 , Chloee M. McLaughlin 2 , Claude W. dePamphilis 3 , Jesse R. Lasky 3
Affiliation  

Fitness responses to environment can shape species distributions, though opposing eco-evolutionary processes can obscure environmental effects. For example, host specificity influences parasite dynamics, but is unclear how adaptation of parasites to local host communities may scale up to continental distributions. Here, we develop a macroecological framework to determine how host community structure affects the distribution of specialist and generalist populations of Striga hermonthica, an African parasitic plant of cereal crops. Combining data from global crop production and parasite experimental trials, we find that parasites perform best on the host species that is most common in their location of origin. Moreover, niche model contrasts predict parasite specialization on two hosts that evolved alongside Striga during domestication (pearl millet and sorghum), indicating that specialist parasites may be most likely to occur where host niches differ most in multivariate environmental space. Our study demonstrates that patterns of parasite local adaptation to host communities can emerge at continental scales and that differential environmental tolerances of hosts indirectly shape the distribution of specialist and generalist parasites. By predicting spatial dynamics of parasite specialization versus generalization directly from environmental data, our approach may help inform current and future management of pests and disease.

中文翻译:

寄生虫当地适应寄主社区的地理

对环境的适应性反应可以塑造物种分布,尽管相反的生态进化过程可以掩盖环境影响。例如,宿主特异性影响寄生虫动态,但不清楚寄生虫对当地宿主社区的适应如何扩大到大陆分布。在这里,我们开发了一个宏观生态学框架,以确定寄主群落结构如何影响独脚金(一种非洲谷物作物寄生植物)的专家和通才种群的分布。结合来自全球作物生产和寄生虫实验试验的数据,我们发现寄生虫在其起源地最常见的宿主物种上表现最佳。此外,利基模型对比预测了两个同时进化的宿主的寄生虫专业化驯化期间的独脚金(珍珠粟和高粱),表明专家寄生虫可能最有可能发生在多变量环境空间中宿主生态位差异最大的地方。我们的研究表明,寄生虫对宿主社区的局部适应模式可以在大陆范围内出现,宿主的不同环境耐受性间接影响了专家和通才寄生虫的分布。通过直接从环境数据预测寄生虫特化与泛化的空间动态,我们的方法可能有助于为当前和未来的病虫害管理提供信息。
更新日期:2021-05-16
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