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• W4386554016 abstract "Abstract The currently available distribution range maps for the Great Grey Owl (GGOW; Strix nebulosa ) are rather coarse, imprecise, outdated, often hand-drawn and thus not quantified or scientific even. 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 recent citizen-science sightings of the GGOW. This proposed workflow can be applied on the inference for species-habitat models 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 Great Gray Owls in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best-publicly data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors. The compiled global open access data and the associated workflow achieve for the first time to overcome limitations for traditionally used PC and laptops (technological computing limitations), breaking new ground and have real-world implications for conservation and land management for GGOW, Alaska, and other species worldwide as a ‘new’ baseline. As this research field remains dynamic, SuperSDMs are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic allowing fine-tuning and improvements as needed. At minimum, it’s a great leap forward to be more ecological and inclusive. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this human-environment inhabiting 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 up new perspectives for impact assessment policy and global sustainability." @default.
• W4386554016 created "2023-09-09" @default.
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• W4386554016 date "2023-09-08" @default.
• W4386554016 modified "2023-10-16" @default.
• W4386554016 title "Running a Super SDM (Species Distribution Model) ‘in the cloud’ for Better Habitat- Associations, Predictions and Inference: Applying Open Access Big Data, Machine Learning Ensembles on Great Gray Owls in Alaska" @default.
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• W4386554016 doi "https://doi.org/10.21203/rs.3.rs-3210815/v1" @default.
• W4386554016 hasPublicationYear "2023" @default.
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