With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots
Abstract Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their pot...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-55173-8 |
| _version_ | 1797275014847791104 |
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| author | Moriz Steiner F. Huettmann N. Bryans B. Barker |
| author_facet | Moriz Steiner F. Huettmann N. Bryans B. Barker |
| author_sort | Moriz Steiner |
| collection | DOAJ |
| description | Abstract Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm (‘Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference. |
| first_indexed | 2024-03-07T15:08:17Z |
| format | Article |
| id | doaj.art-7f47e6fb9559417288be56a8f1f57d44 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:08:17Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7f47e6fb9559417288be56a8f1f57d442024-03-05T18:49:43ZengNature PortfolioScientific Reports2045-23222024-03-0114111010.1038/s41598-024-55173-8With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspotsMoriz Steiner0F. Huettmann1N. Bryans2B. Barker3IUCN Small Mammal Specialist Group (SMSG), IUCNEWHALE Lab-Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska Fairbanks (UAF)Oracle for ResearchOracle for ResearchAbstract Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm (‘Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.https://doi.org/10.1038/s41598-024-55173-8BIG DATASquirrelsMaxentSuper species distribution models (SDMs)ROracle |
| spellingShingle | Moriz Steiner F. Huettmann N. Bryans B. Barker With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots Scientific Reports BIG DATA Squirrels Maxent Super species distribution models (SDMs) R Oracle |
| title | With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots |
| title_full | With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots |
| title_fullStr | With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots |
| title_full_unstemmed | With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots |
| title_short | With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots |
| title_sort | with super sdms machine learning open access big data and the cloud towards more holistic global squirrel hotspots and coldspots |
| topic | BIG DATA Squirrels Maxent Super species distribution models (SDMs) R Oracle |
| url | https://doi.org/10.1038/s41598-024-55173-8 |
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