Leo Sarah S T, Gonzalez Andrew, Millien Virginie
a Department of Biology, McGill University, Stewart Biology Building, 1205 Docteur Penfield Ave., Montreal, QC H3A 1B1, Canada.
b Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, QC H3A 0C4, Canada.
Genome. 2016 May;59(5):349-61. doi: 10.1139/gen-2016-0039. Epub 2016 Mar 4.
Zoonotic disease transmission systems involve sets of species interacting with each other and their environment. This complexity impedes development of disease monitoring and control programs that require reliable identification of spatial and biotic variables and mechanisms facilitating disease emergence. To overcome this difficulty, we propose a framework that simultaneously examines all species involved in disease emergence by integrating concepts and methods from population genetics, landscape ecology, and spatial statistics. Multi-taxa integrated landscape genetics (MTILG) can reveal how interspecific interactions and landscape variables influence disease emergence patterns. We test the potential of our MTILG-based framework by modelling the emergence of a disease system across multiple species dispersal, interspecific interaction, and landscape scenarios. Our simulations showed that both interspecific-dependent dispersal patterns and landscape characteristics significantly influenced disease spread. Using our framework, we were able to detect statistically similar inter-population genetic differences and highly correlated spatial genetic patterns that imply species-dependent dispersal. Additionally, species that were assigned coupled-dispersal patterns were affected to the same degree by similar landscape variables. This study underlines the importance of an integrated approach to investigating emergence of disease systems. MTILG is a robust approach for such studies and can identify potential avenues for targeted disease management strategies.
人畜共患病传播系统涉及相互作用的物种集合及其环境。这种复杂性阻碍了疾病监测和控制计划的制定，而这些计划需要可靠地识别促进疾病出现的空间和生物变量及机制。为克服这一困难，我们提出了一个框架，通过整合种群遗传学、景观生态学和空间统计学的概念与方法，同时研究参与疾病出现的所有物种。多物种综合景观遗传学（MTILG）能够揭示种间相互作用和景观变量如何影响疾病出现模式。我们通过对跨多个物种扩散、种间相互作用和景观情景的疾病系统出现情况进行建模，来测试基于MTILG的框架的潜力。我们的模拟结果表明，种间依赖的扩散模式和景观特征均对疾病传播有显著影响。使用我们的框架，我们能够检测到在统计上相似的种群间遗传差异以及高度相关的空间遗传模式，这意味着物种依赖的扩散。此外，被赋予耦合扩散模式的物种受到相似景观变量的影响程度相同。这项研究强调了采用综合方法调查疾病系统出现情况的重要性。MTILG是此类研究的一种有力方法，能够识别出针对性疾病管理策略的潜在途径。
