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首页> 美国卫生研究院文献>Frontiers in Veterinary Science >The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance
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The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance

机译：核密度估计与生物物理模型的结合使用提供了一种量化鲑鱼养殖场之间连通性的方法：具有流行病学意义的空间规划和管理

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Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.
机译：水生环境中的连通性是由水动力循环和驱动链接的生物的生物学共同决定的。这些复杂的过程可以在耦合的生物物理模型中进行模拟。物理模型是指由空间显式节点定义的基础循环模型，通常包含粒子跟踪模型。然后可以为粒子提供生物学参数或行为（例如成熟度和/或存活率，垂直垂直迁移，避免或寻求行为）。然后，生物物理模型的输出可用于量化发射和/或接收粒子的节点之间的连通性。在这里，我们提出一种方法，该方法利用粒子跟踪模型的输出上的核密度估计（KDE），以量化每个节点在周围区域引起的感染或侵扰压力（IP）。由于IP是暴露时间和传染原颗粒浓度的乘积，因此使用KDE（还结合了时间和空间元素）可以更准确地捕获IP。这种方法对那些对传染媒介网络感兴趣的人特别有用，在这种情况下，IP是比从每个节点到达其他节点的粒子概率更高的连通性度量。在此，我们通过对加拿大不列颠哥伦比亚省布劳顿群岛的海虱幼虫通过鲑鱼养殖场的连通性进行建模来说明该方法。分析揭示了两个通过一个服务器场连接的服务器场子网的证据，并证明来自给定排放场的最高IP通常距离该服务器场数十公里或更远。根据农场在网络中的结构角色，我们还将农场划分为净辐射者，接受者或平衡农场。通过更好地了解这些鲑鱼养殖场如何通过其海虱幼虫相互联系，我们可以有效地集中管理工作，以最大程度地减少养殖场之间海虱的传播，就未来的养殖场位置和协调的处理工作提供建议，并最大程度地降低养殖场的影响在少年野生鲑鱼上。该方法对于捕获传染媒介网络可以为管理或预防性计划决策提供有用指导的任何系统具有广泛的适用性。

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