With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel
Moriz Steiner1,2,3, F Huettmann4, N Bryans5
1IUCN Small Mammal Specialist Group (SMSG), IUCN, Rue Mauverney 28, 1196, Gland, Switzerland. moriz.steiner.work@gmail.com.
Scientific Reports
|March 3, 2024
Summary
Super Species Distribution Models (SDMs) leverage AI and Big Data for enhanced global inference, crucial for marginalized and endangered squirrel species. This approach improves conservation assessments, especially in data-deficient tropical regions.
Area of Science:
- Ecology and conservation biology
- Computational biology and bioinformatics
- Artificial Intelligence and Machine Learning
Background:
- Species Distribution Models (SDMs) are vital for understanding species-habitat associations and informing conservation policy.
- Current SDM applications, particularly those using Machine Learning (ML) and Artificial Intelligence (AI), often fail to utilize their full potential, especially in policy-making.
- Many global species, particularly squirrels, are marginalized, endangered, or data-deficient, necessitating advanced modeling techniques for effective conservation.
Purpose of the Study:
- To introduce Super Species Distribution Models (Super SDMs) that integrate ML, Open Access Big Data, and cloud computing for superior inference.
- To address limitations in current SDMs, including the 'Shallow Learning' issues associated with algorithms like maxent.
- To provide a multi-species Big Data SDM framework for global species hotspot and coldspot assessments, promoting inclusive conservation strategies.
Main Methods:
- Development of a novel workflow utilizing ML, Open Access Big Data, and cloud infrastructure.
- Application of the Super SDM framework to over 300 global squirrel species.
- Comparative analysis highlighting common issues with traditional SDMs (e.g., maxent) versus the proposed Big Data approach.
Main Results:
- Demonstration of common challenges and limitations within existing SDM methodologies, referred to as 'Shallow Learning'.
- Successful implementation of a Big Data SDM template applicable to multiple species.
- Establishment of a foundation for ensemble modeling and advancement in global species distribution assessments.
Conclusions:
- Super SDMs offer a powerful, inclusive approach to ecological inference, surpassing the capabilities of conventional methods.
- The developed framework is particularly valuable for assessing marginalized, endangered, and data-deficient species, especially in tropical ecosystems.
- This work provides a scalable template for future research and policy applications in global biodiversity assessments and conservation planning.
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