Species distribution models (SDMs) are subject to many sources of uncertainty, limiting their application in research and practice. One of their main limitations is the quality of the distributional data used to calibrate them, which directly influences the accuracy of model predictions. We propose a standardized methodology to create maps, describing the limitations of occurrence data for covering the distribution of a species. We develop a set of tools based on the general framework of Maps of Biogeographical Ignorance to describe the main sources of data-driven uncertainty: taxonomic stability, environmental similarity, geographical proximity and temporal decay of the underlying biodiversity data. The so-derived indicators of data-driven uncertainty account for inventory completeness, taxonomic quality, time since the surveys and geographical (and environmental) distance to localities with information. These indicators form the basis of ignorance maps, which can be used to visualize the reliability of SDM projections in geographical space, to estimate the uncertainty of these predictions and to identify target survey areas. To demonstrate the application of our approach, we use data on fourteen Iberian species of Scarabaeidae dung beetles. Data-driven uncertainty is widespread even for this well-surveyed group; more than 60% of the region has distributional uncertainty values higher than 0.6, and 30% higher than 0.7. Ignorance maps can be jointly evaluated with SDM predictions to generate spatially explicit maps of uncertainty, identifying where predictions are reliable/unreliable. Neglecting such uncertainty can severely affect SDM effectiveness, as it can introduce biases and inaccuracies into the measured species–environment relationships. These errors could result in incorrect theoretical or practical applications, including ill-advised conservation actions. We therefore advocate for the routine use of ignorance maps or similar techniques as supporting information in SDM applications.

中文翻译：

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使用生物地理无知地图揭示隐藏在物种分布模型中的分布数据的不确定性

物种分布模型 (SDM) 受到许多不确定性来源的影响，限制了它们在研究和实践中的应用。它们的主要限制之一是用于校准它们的分布数据的质量，这直接影响模型预测的准确性。我们提出了一种创建地图的标准化方法，描述了覆盖物种分布的发生数据的局限性。我们基于生物地理无知地图的一般框架开发了一套工具来描述数据驱动的不确定性的主要来源：分类稳定性、环境相似性、地理邻近性和潜在生物多样性数据的时间衰减。数据驱动的不确定性的衍生指标说明了清单完整性、分类质量、自调查和地理（和环境）距离到具有信息的地方的时间。这些指标构成了无知地图的基础，可用于可视化地理空间中 SDM 预测的可靠性，估计这些预测的不确定性并确定目标调查区域。为了演示我们的方法的应用，我们使用了 14 种伊比利亚金龟科粪甲虫的数据。即使对于这个经过充分调查的群体，数据驱动的不确定性也很普遍；超过 60% 的区域分布不确定性值高于 0.6，30% 高于 0.7。无知地图可以与 SDM 预测联合评估，以生成空间明确的不确定性地图，确定预测可靠/不可靠的位置。忽视这种不确定性会严重影响 SDM 的有效性，因为它会在测量的物种 - 环境关系中引入偏差和不准确性。这些错误可能导致不正确的理论或实际应用，包括不明智的保护行动。因此，我们提倡在 SDM 应用程序中常规使用无知图或类似技术作为支持信息。