Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence‐only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi‐source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter‐ or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi‐source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA‐based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

中文翻译：

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在全球范围内建立物种分布和丰度的基本生物多样性变量 (EBV)

世界范围内收集了大量生物多样性数据，但收集分散的知识以评估生物多样性状况和趋势仍然具有挑战性。引入了基本生物多样性变量 (EBV) 的概念来构建全球生物多样性监测，并协调和标准化来自不同来源的生物多样性数据，以获取研究、报告和管理生物多样性变化所需的最少关键变量集。在这里，我们评估了“大数据”方法在跨分类群和时空尺度构建全球 EBV 数据产品的挑战，重点是物种分布和丰度。大多数目前可用的物种分布数据来自偶然报告的观察或调查，其中使用标准化协议重复采样仅存在或不存在数据。大多数丰度数据来自机会性种群计数或使用标准化协议的种群时间序列（例如，从单个或多个地点对同一种群进行重复调查）。跨空间、时间、分类群和不同采样方法集成这些异构、多源数据集存在巨大的复杂性。将此类数据整合到全球 EBV 数据产品中需要纠正由不完善检测和不同采样工作引入的偏差，处理不同的空间分辨率和范围，协调来自不同数据源或采样方法的测量单位，应用统计工具和模型进行空间交互或外推法，并量化数据和模型中不确定性和错误的来源。为了支持地球观测组织生物多样性观测网络 (GEO BON) 开发 EBV，我们确定了 11 个关键工作流程步骤，这些步骤将在全球研究基础设施内和跨研究基础设施构建 EBV 数据产品的过程中实施。这些工作流程步骤考虑了多个连续活动，包括各种原始数据源的识别和聚合、数据质量控制、分类名称匹配和集成数据的统计建模。我们用现有公民科学和专业监测项目的具体例子来说明这些步骤，包括 eBird、热带生态评估和监测网络、地球生命力指数和波罗的海浮游动物监测。确定的工作流程步骤适用于陆地和水生系统以及广泛的空间、时间和分类尺度。它们依赖于清晰、可查找和可访问的元数据，我们提供了当前数据和元数据标准的概述。构建全球 EBV 数据产品仍有几个挑战需要解决：(i) 开发工具和模型以组合异构、多源数据集并填补地理、时间和分类覆盖范围内的数据空白，(ii) 整合新兴方法和技术，公民科学、传感器网络、基于 DNA 的技术和卫星遥感等数据收集，(iii) 解决与数据产品结构、数据存储、工作流和生产过程/周期的执行，以及研究基础设施之间的技术互操作性，(iv) 通过开发和采用标准和工具来捕获一致的数据和元数据来实现语义互操作性，以及 (v) 通过认可开放数据确保合法的互操作性或不受使用、修改和共享限制的数据。应对这些挑战对于生物多样性研究以及评估保护政策目标和可持续发展目标的进展至关重要。修改和分享。应对这些挑战对于生物多样性研究以及评估保护政策目标和可持续发展目标的进展至关重要。修改和分享。应对这些挑战对于生物多样性研究以及评估保护政策目标和可持续发展目标的进展至关重要。