The Datafied Animal: Biologging, Machine Learning and Wildlife Conservation

在过去的几十年里，科学家们开发了一个不断扩大的动物网络。这个网络是一系列工具的集合，包括小型化标签、gps遥测、记录设备、网络基础设施、机器学习和针对野生动物的人工智能。这些新技术正被用于将动物生活中以前难以接近的方面转化为数据，从根本上改变了对野生动物的认识，并将生态学重塑为一门学科。关于与人类生活相关的数据化，有许多批判性的研究。然而，当这些技术被用于非人类动物的研究时，这些研究没有检查会发生什么。生成的野生动物数据有可能对保护和动物管理实践产生变革。这些倡议隐含着为非人类创造一个更好的人类世的想法，在这个想法中，人类对世界的影响被用来改善生态系统。这种环境的重塑将由来自动物网络的数据提供信息；因此，必须了解影响其生产的价值观、信仰和实践。这个项目跟踪数据，它在该领域的创建，到算法和机器学习工具的开发来分析它。该项目的主要实地工作将包括与正在开发和使用技术研究和管理野生动物的科学家和非专业人员的访谈和参与性观察研究。通过对产生野生动物大数据的技术的实证分析，以及对其设计和部署的选择，该项目将考虑生态监测是如何产生的，以及科学视线中包含和排除的内容。在此过程中，该项目还将探讨更广泛的问题，如环境和动物生命的表现是如何产生的，以及这项技术是如何改变生态科学、劳动力、专业知识概念和保护实践的。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Over the past several decades, scientists have developed an ever-expanding internet of animals. This network is a collection of tools that includes miniaturized tags, GPS-telemetry, recording devices, cyberinfrastructure, machine learning, and AI directed at wildlife. These new technologies are being used to turn previously inaccessible aspects of animal life into data, fundamentally transforming knowledge about wildlife, as well as reshaping ecology as a discipline. There have been many critical studies of datafication as related to human lives. However, these studies leave unexamined what happens when these technologies are used for the study of non-human animals. The data about wildlife being generated have the potential to be transformative for conservation and animal management practices. Implicit in these initiatives is the idea of a better Anthropocene for nonhumans, one in which the human impact on the world is used to improve ecological systems. This remaking of the environment will be informed by the data emerging from the internet of animals; it is thus imperative to understand the values, beliefs, and practices affecting its production. This project follows the data, its creation in the field, to the development of algorithms and machine learning tools to analyze it. Primary fieldwork for this project will consist of interviews and participant observation research with scientists and laypeople who are developing and using technology to study and manage wildlife. Through an empirical analysis of the technology that produces big data about wildlife and the choices that go into its design and deployment, this project will reckon with how ecosurveillance is produced and what is included and excluded from the scientific gaze. In doing so, this project will also address broader questions about how representations of the environment and animal life are produced and how this technology has changed ecological science, labor, notions of expertise, and conservation practices.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.