Winter Steven N, Kirchgessner Megan S, Frimpong Emmanuel A, Escobar Luis E
Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States.
Virginia Department of Wildlife Resources, Blacksburg, VA, United States.
Front Vet Sci. 2021 Aug 24;8:698767. doi: 10.3389/fvets.2021.698767. eCollection 2021.
Many infectious diseases in wildlife occur under quantifiable landscape ecological patterns useful in facilitating epidemiological surveillance and management, though little is known about prion diseases. Chronic wasting disease (CWD), a fatal prion disease of the deer family Cervidae, currently affects white-tailed deer () populations in the Mid-Atlantic United States (US) and challenges wildlife veterinarians and disease ecologists from its unclear mechanisms and associations within landscapes, particularly in early phases of an outbreak when CWD detections are sparse. We aimed to provide guidance for wildlife disease management by identifying the extent to which CWD-positive cases can be reliably predicted from landscape conditions. Using the CWD outbreak in Virginia, US from 2009 to early 2020 as a case study system, we used diverse algorithms (e.g., principal components analysis, support vector machines, kernel density estimation) and data partitioning methods to quantify remotely sensed landscape conditions associated with CWD cases. We used various model evaluation tools (e.g., AUC ratios, cumulative binomial testing, Jaccard similarity) to assess predictions of disease transmission risk using independent CWD data. We further examined model variation in the context of uncertainty. We provided significant support that vegetation phenology data representing landscape conditions can predict and map CWD transmission risk. Model predictions improved when incorporating inferred home ranges instead of raw hunter-reported coordinates. Different data availability scenarios identified variation among models. By showing that CWD could be predicted and mapped, our project adds to the available tools for understanding the landscape ecology of CWD transmission risk in free-ranging populations and natural conditions. Our modeling framework and use of widely available landscape data foster replicability for other wildlife diseases and study areas.
野生动物中的许多传染病都发生在可量化的景观生态模式之下，这些模式有助于开展流行病学监测和管理，不过人们对朊病毒疾病了解甚少。慢性消耗病（CWD）是鹿科动物的一种致命朊病毒疾病，目前影响着美国大西洋中部地区的白尾鹿种群，其发病机制以及在景观中的关联尚不清楚，这给野生动物兽医和疾病生态学家带来了挑战，尤其是在疫情爆发的早期阶段，此时慢性消耗病的检测结果稀少。我们旨在通过确定从景观条件中能够可靠预测慢性消耗病阳性病例的程度，为野生动物疾病管理提供指导。以2009年至2020年初美国弗吉尼亚州的慢性消耗病疫情作为案例研究系统，我们使用了多种算法（例如主成分分析、支持向量机、核密度估计）和数据划分方法来量化与慢性消耗病病例相关的遥感景观条件。我们使用各种模型评估工具（例如AUC比率、累积二项式检验、杰卡德相似度），利用独立的慢性消耗病数据来评估疾病传播风险的预测结果。我们还在不确定性的背景下进一步研究了模型的变化情况。我们提供了有力的证据，表明代表景观条件的植被物候数据可以预测和绘制慢性消耗病的传播风险。纳入推断的活动范围而非原始的猎人报告坐标时，模型预测效果有所改善。不同的数据可用性情景确定了模型之间的差异。通过表明慢性消耗病可以被预测和绘制，我们的项目为理解自由放养种群和自然条件下慢性消耗病传播风险的景观生态学增加了可用工具。我们的建模框架以及对广泛可用的景观数据的使用促进了其他野生动物疾病和研究区域的可重复性。
