Forbes Owen, Thrall Peter H, Young Andrew G, Ong Cheng Soon
CSIRO National Research Collections Australia, Canberra, Australia.
CSIRO Data61, Canberra, Australia.
Ecol Lett. 2025 Aug;28(8):e70188. doi: 10.1111/ele.70188.
Natural history collections face a critical juncture as environmental change and biodiversity crises accelerate. While collections data are key components of eco-evolutionary and environmental research in both fundamental and applied contexts, collecting strategies remain primarily taxonomically motivated. We argue that sampling strategies must evolve to better address broader ecological challenges, through emerging applications enabled by advances in data science and digital technology. Here, we propose a flexible framework using modern statistical approaches to optimise sampling design and research prioritisation. By considering biodiversity, environmental, spatial and temporal dimensions, we demonstrate how information theory and decision science tools can support strategic collecting, databasing and digitisation efforts. Our framework provides an evidence-based pathway for collections to enhance their role as critical research infrastructure for addressing 21st century environmental challenges. To illustrate this data-driven approach to research prioritisation, we present an example based on sampling design for wombats (Vombatus ursinus) in Australia.
随着环境变化和生物多样性危机加速，自然历史藏品面临着一个关键节点。虽然藏品数据在基础和应用背景下都是生态进化和环境研究的关键组成部分，但收集策略仍主要受分类学驱动。我们认为，通过数据科学和数字技术进步带来的新兴应用，采样策略必须不断发展，以更好地应对更广泛的生态挑战。在此，我们提出一个灵活的框架，使用现代统计方法来优化采样设计和研究优先级。通过考虑生物多样性、环境、空间和时间维度，我们展示了信息论和决策科学工具如何支持战略性收集、数据库建设和数字化工作。我们的框架为藏品提供了一条基于证据的途径，以增强其作为应对21世纪环境挑战的关键研究基础设施的作用。为了说明这种数据驱动的研究优先级确定方法，我们给出一个基于澳大利亚袋熊（Vombatus ursinus）采样设计的例子。
