Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst perfect analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

中文翻译：

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大数据，大问题？如何规避生物多样性绘图中的问题并确保取得有意义的结果


我们对生物多样性的了解取决于充足的数据、可靠的方法和现实的模型。如果没有对物种分布的准确评估，我们就无法有效地瞄准和阻止生物多样性丧失。物种分布范围图是此类工作的基础，但无数研究未能解释可靠物种测绘实践的最基本假设，从而损害了其结果的可信度，并可能误导和阻碍保护和管理工作。在这里，我们使用最近文献和更广泛的保护界的例子来强调当前实践的重大缺陷及其对分析和保护管理的影响。我们详细介绍了数据过滤的不同决策如何影响分析结果，并提供实用的建议和步骤以实现更可靠的分析，同时了解可用数据可靠允许的限制以及最合适的方法。虽然鉴于有限的数据和偏差，许多分类单元不可能进行完美的分析，但确保我们在合理的范围内使用数据并理解固有的假设对于确保正确使用至关重要。通过采用和实施此类最佳实践，我们可以确保未来生物多样性分析的准确性和可比性，最终增强我们使用数据促进保护自然世界的能力。