With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots.
Sci Rep
; 14(1): 5204, 2024 03 03.
Article
em En
| MEDLINE
| ID: mdl-38433273
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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Acesso à Informação
/
Big Data
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Suíça