The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access ‘Big Data’ sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest—an ensemble-type model of Machine Learning following Leo Breiman’s approach of inference from predictions—we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors (‘Big Data’). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a ‘new’ baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka ‘Phantom of the North’). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.

目前可用的大灰猫头鹰（GGOW；Strix nebulosa）的分布和范围图是模糊的、矛盾的、不精确的、过时的，通常是手绘的，因此不是量化的，不是基于数据或科学的。在这项研究中，我们提出了一个概念验证，涉及最新的全球开放获取“大数据”共享、R 的开源方法和地理信息系统（OGIS 和 QGIS）的技术和生物工作流程进展的生物学应用，并用最近的六个项目进行了评估GGOW 的多证据公民科学目击事件。该工作流程可应用于任何物种栖息地模型的量化推理，例如通常应用于物种分布模型（SDM）。使用随机森林（遵循 Leo Breiman 的预测推理方法的机器学习集成型模型），我们为阿拉斯加的 GGOW 提供了一个在 Oracle 云基础设施 (OCI) 上运行的超级 SDM。这些超级 SDM 基于最佳公开数据（410 次事件 + 1% 新评估目击事件）和 100 多个环境 GIS 栖息地预测因子（“大数据”）。编译的全球开放获取数据和相关工作流程首次克服了传统使用的个人电脑和笔记本电脑的局限性。它开辟了新天地，对 GGOW、阿拉斯加以及全世界其他物种的保护和土地管理具有现实意义，作为“新”基线。由于这个研究领域仍然充满活力，超级 SDM 可能有局限性，还不是物种-栖息地关联的最终声明，但它们以量化和可测试的方式总结了有关某个主题的所有公开数据和信息，允许进行微调和调整。根据需要进行改进。至少，它们可以实现低成本的快速评估，并实现更加生态化和包容所有现有信息的巨大飞跃。使用 GGOW，我们的目标是纠正对该物种的看法，对人类世中适应城市的猫头鹰进行更具包容性、整体性和科学正确的评估，而不是神秘的荒野栖息物种（又名“北方幽灵”） ）。这样的超级 SDM 以前从未为任何鸟类创建过，为影响评估政策和全球可持续发展开辟了新的视角。