Doctoral Dissertation Research: Detecting Anthropogenic Forest Disturbances and Understory Density for the Study of Hantavirus in Paraguay

景观流行病学是研究景观对疾病的发生、传播和持久性的影响。因此，景观流行病学研究的质量往往取决于土地覆盖信息的准确性和可靠性。对于人畜共患疾病，这意味着需要关注构成宿主栖息地的景观方面。汉坦病毒是一种由鼠科啮齿动物寄生的人畜共患病毒，是已知其动态与景观因素有关的病原性有机体的一个例子。对于一些汉坦病毒宿主物种，森林干扰是疾病传播的重要因素。通常，使用现有遥感技术绘制的土地覆盖图只考虑森林覆盖和森林砍伐覆盖之间的二元区别。根据正在进行的研究，中等干扰的森林似乎实际上是啮齿动物群落中汉坦病毒高流行的关键栖息地类型之一。因此，需要改进方法来远程区分干扰程度，以便绘制汉坦病毒发生和传播风险地区的地图。这项研究将解决这样一个问题：我们如何改进不同类型森林和森林干扰的土地复盖图，以促进巴拉圭大西洋森林汉坦病毒的景观流行病学研究？将对代表巴拉圭姆巴拉卡尤生物圈内森林干扰梯度的选定地块进行实地勘测，以确定林下植被和树冠的结构和组成。每个样地将有一个嵌套的子样地设计，其中100x100米的样地用于测量树木，中心的较小的样地用于测量灌木密度，九个子样地用于测量地面覆盖度。这些野外数据将被用来将野外地块中确定的树冠和林下结构与从各种空间尺度上的面向对象的图像分析得出的遥感景观指标联系起来。预计这项研究将改进利用遥感图像对森林干扰和林下密度差异的估计，并对研究疾病产生影响。该项目的研究将更紧密地将遥感数据的使用与景观流行病学应用联系起来。开发的技术将适用于分析遥感数据，以更有用的方式研究汉坦病毒的生态学，以及一系列其他人畜共患病。此外，这些技术将普遍适用于森林景观中的景观生态学和生物地理学研究。目前，用遥感数据监测森林砍伐通常被视为一项二元任务，即森林砍伐和森林砍伐，但在现实中，有许多级别的森林干扰对各种自然系统可能是重要的。这项研究将提高探测和绘制不同程度和种类的森林干扰的能力，使人们能够更详细地分析这些干扰的生态影响。这项研究的其他潜在好处包括增进对巴拉圭东部大西洋森林的了解，这是一个鲜为人知的濒危生态系统和生物多样性热点。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Landscape epidemiology is the study of the effect that landscape has on disease occurrence, transmission, and persistence. Hence, the quality of landscape epidemiology research frequently depends on the accuracy and reliability of land cover information. For zoonotic diseases, this means a needed focus on the aspects of the landscape that form the habitat of the host. Hantavirus, a zoonotic virus hosted by rodents of the murine family, is one example of an etiological organism whose dynamics are known to be related to landscape factors. With some hantavirus reservoir species, forest disturbances are important factors in disease transmission. Frequently, land cover maps developed using existing remote sensing techniques consider only the binary distinction between forested and deforested cover. Based on ongoing research, it appears that forests with intermediate disturbances are in fact one of the key habitat types associated with high hantavirus prevalence in rodent communities. Improved methods are therefore needed to differentiate levels of disturbance remotely, in order to map areas at risk for the occurrence and spread of hantavirus. This research will address the question: How can we improve landcover mapping of different types of forest and forest disturbances to facilitate landscape epidemiology studies of hantavirus in the Paraguayan Atlantic Forest? Selected plots representing a gradient of forest disturbance within the Mbaracayu Biosphere in Paraguay will be field surveyed to determine understory and canopy structure and composition. Each sample plot will have a nested sub-plot design, with 100x100m plots to measure trees, a smaller plot in the center to measure shrub density, and nine subplots to measure ground cover. These field data will be used to relate the canopy and understory structures, as determined in the field plots, to remotely sensed landscape metrics derived from object-oriented image analysis at a variety of spatial scales. It is expected that this study will lead to improved estimation of forest disturbances and differences in understory density using remotely sensed imagery, with implications for studying diseases. The research in this project will more closely tie the use of remotely sensed data to landscape epidemiology applications. The developed techniques will be applicable to analyzing remotely sensed data in a way that is more useful to studying the ecology of hantavirus, as well as a range of other zoonotic diseases. Additionally, the techniques will be generally applicable to landscape ecology and biogeography studies in forested landscapes. Currently, detecting deforestation with remotely sensed data often is treated as a binary task, forested and deforested, but in reality, there are many levels of forest disturbance that can be important to various natural systems. This research will improve the ability to detect and map different levels and kinds of forest disturbance, enabling more detailed analysis of the ecological effect of those disturbances. Other potential benefits of this study include improved understanding of the Atlantic forest in Eastern Paraguay, a little known and endangered ecosystem and biodiversity hotspot. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.