Details

Autor(en) / Beteiligte

• Steiner, Moriz
• Huettmann, F
• Bryans, N
• Barker, B

Titel

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots

Ist Teil von

• Scientific reports, 2024-03, Vol.14 (1), p.5204-5204

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.