Elements: Spatial Ecology Gateway

空间生态学是研究景观特征如何影响生物在其环境中的分布和运动。空间生态学网关（SEG）使研究人员，学生和野生动物管理人员能够上传生物遥测数据，通常是GPS读数，并构建家庭范围，使他们能够解释动物空间的使用。SEG的应用可以包括课堂项目，对野生动物生态学问题的基础研究，环境影响研究和减轻不利后果，如栖息地破碎或新开发导致的人类与野生动物互动增加。SEG将用户与底层的计算细节隔离开来，这样他们就可以专注于他们的科学，而不是掌握技术。SEG的用户可以选择生成二维（2D）或三维（3D）家庭范围（如果适用）。SEG使用HubZero平台构建，这是一个开源软件平台，用于构建支持科学活动的网站，并利用极限科学和工程发现环境（XSEDE）来执行计算要求更高的任务。数据可以使用其REST API从在线社区平台Movebank直接拉入SEG。SEG部署了布朗桥运动模型（BBMM），潜在路径规划（PPV）和连续时间运动建模（ctmm）的方法，为家庭范围的建设。动物轨迹的探索性分析工具包括净平方位移、运动路径曲折度和重新定位采样率与部署时间。用户可以对其数据集应用基本过滤，以将分析限制在指定的时间范围或特定的动物。SEG使用户不必决定计算细节，例如资源的选择，计算核心的数量或挂钟时间。相反，这些决定是使用内置于网关中的逻辑并基于广泛的基准研究做出的。高级网络基础设施办公室的这一奖项由NSF生物科学理事会内的生物基础设施部门共同支持，以及NSF计算机和信息科学与工程理事会内的信息和智能系统部门。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。

Spatial ecology is the study of how landscape characteristics influence the distribution and movement of organisms within their environment. The Spatial Ecology Gateway (SEG) enables researchers, students and wildlife managers to upload biotelemetry data, typically GPS readings, and construct home ranges that allow them to interpret animal space use. Applications of the SEG can include classroom projects, basic research into problems in wildlife ecology, environmental impact studies and mitigation of adverse outcomes such as habitat fragmentation or increased human-wildlife interaction resulting from new development. The SEG insulates users from the underlying computational details so that they can focus on their science rather than mastering the technology. Users of the SEG have the option to generate two-dimensional (2D) or, where applicable, three-dimensional (3D) home ranges. Users are also provided with less computationally intensive tools to perform exploratory analyses.The SEG is built using the HubZero platform, an open-source software platform for building websites that support scientific activities and leverages the Extreme Science and Engineering Discovery Environment (XSEDE) for more computationally demanding tasks. Data can be pulled directly into the SEG from the online community platform Movebank using their REST API. The SEG deploys the Brownian Bridge Movement Model (BBMM), Potential Path Volumes (PPV) and Continuous-Time Movement Modeling (ctmm) methods for home range construction. Tools for exploratory analysis of animal trajectories include net squared displacement, movement path tortuosity and relocation sampling rates versus deployment time. Users can apply basic filtering to their data sets to restrict analyses to specified time ranges or particular animals. The SEG insulates users from having to decide computational details such as choice of resource, number of compute cores or wall clock time. Rather, these decisions are made using logic built into the gateway and based on extensive benchmarking studies.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Biological Infrastructure within the NSF Biosciences Directorate, and by the Division of Information and Intelligent Systems within the NSF Computer and Information Science and Engineering Directorate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.