Collier Melissa Ann, Urian Kim, Theisen Sarah, Jacoby Ann-Marie, Wilkin Sarah, Patterson Eric M, Wallen Megan, Colizza Vittoria, Mann Janet, Bansal Shweta
Department of Biology, Georgetown University, Washington, DC, USA.
Duke University Marine Laboratory, Beaufort, NC, USA.
Proc Biol Sci. 2025 Jul;292(2051):20250698. doi: 10.1098/rspb.2025.0698. Epub 2025 Jul 30.
Infectious diseases have detrimental impacts across wildlife taxa. Despite this, we often lack information on the complex spatial and contact structures of host populations, reducing our ability to understand disease spread and our preparedness for epidemic response. This is also prevalent in the marine environment, where rapid habitat changes due to anthropogenic disturbances and human-induced climate change are heightening the vulnerability of marine species to disease. Recognizing these risks, we leveraged a collated dataset to establish a data-driven epidemiological metapopulation model for Tamanend's bottlenose dolphins (), whose populations are periodically impacted by deadly respiratory disease. We found their spatial distribution and contact is heterogeneous throughout their habitat and by ecotype, which explains differences in past infection burdens. With our metapopulation approach, we demonstrate spatial hotspots for epidemic risk during migratory seasons and that populations in some central estuaries would be the most effective sentinels for disease surveillance. These mathematical models provide a generalizable, non-invasive tool that takes advantage of routinely collected wildlife data to mechanistically understand disease transmission and inform disease surveillance tactics. Our findings highlight the heterogeneities that play a crucial role in shaping the impacts of infectious diseases, and how a data-driven understanding of these mechanisms enhances epidemic preparedness.
传染病对野生生物类群有着不利影响。尽管如此，我们常常缺乏关于宿主种群复杂空间结构和接触结构的信息，这降低了我们理解疾病传播的能力以及应对疫情的准备程度。这在海洋环境中也很普遍，在那里，人为干扰和人为引起的气候变化导致的快速栖息地变化正在增加海洋物种对疾病的易感性。认识到这些风险后，我们利用一个整理好的数据集，为塔曼德宽吻海豚建立了一个数据驱动的流行病学集合种群模型，其种群周期性地受到致命呼吸道疾病的影响。我们发现，它们在整个栖息地以及按生态型划分的空间分布和接触情况是异质的，这解释了过去感染负担的差异。通过我们的集合种群方法，我们展示了迁徙季节疫情风险的空间热点，并且一些中央河口的种群将是疾病监测最有效的哨兵。这些数学模型提供了一种可推广的、非侵入性的工具，该工具利用常规收集的野生生物数据，从机制上理解疾病传播并为疾病监测策略提供信息。我们的研究结果突出了在塑造传染病影响方面发挥关键作用的异质性，以及对这些机制的数据驱动理解如何增强疫情防范能力。
