A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

• Published in: Scientific reports Vol. 14; no. 1; pp. 7213 - 16
• Main Authors: Huettmann, Falk, Andrews, Phillip, Steiner, Moriz, Das, Arghya Kusum, Philip, Jacques, Mi, Chunrong, Bryans, Nathaniel, Barker, Bryan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-03-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158/1144; 631/158/672; 704/172; Alaska; Anthropocene; Big Data; Geographic information systems; Geographical distribution; Great Gray Owl (Strix nebulosa); Habitats; Humanities and Social Sciences; Land management; Machine learning ensemble; multidisciplinary; Open access; Open source geographic information system (OGIS, QGIS); Owls; Remote sensing; Science; Science (multidisciplinary); Species; Strix nebulosa; Wilderness; United States > US; Alaska; Cloud computing; Machine learning ensemble; Oracle cloud infrastructure; Alaska; Big data; Open source geographic information system (OGIS, QGIS); Great Gray Owl; Open access; Great Gray Owl (Strix nebulosa)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-57588-9

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) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific.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.