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Updated: Feb 1, 2026
16:02
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
Published on: February 10, 2023
3.3K
Predicting plant conservation priorities on a global scale
Tara A Pelletier1, Bryan C Carstens2, David C Tank3,4,5
1Department of Biology, Radford University, Radford, VA 24142.
Summary
Most plant species
Area of Science:
- Ecology and conservation biology, focusing on plant biodiversity and ecosystem health.
Background:
- The conservation status of most plant species remains unknown, hindering effective ecosystem management.
- Plants play a critical role in maintaining ecosystem health and stability.
Purpose of the Study:
- To develop a predictive machine-learning protocol for assessing the conservation status of over 150,000 land plant species.
- To identify geographic regions with the highest conservation needs, including those not currently recognized as global priorities.
Main Methods:
- Utilized open-source geographic, environmental, and morphological trait data.
- Applied a machine-learning approach to predict conservation status for a large number of plant species.
- Conducted the largest and only global assessment of conservation risk for plants to date.
Main Results:
- A significant number of unassessed plant species are predicted to be at risk.
- Identified key geographic areas requiring urgent conservation interventions.
- Highlighted regions needing conservation efforts that are not currently recognized as global priorities.
Conclusions:
- Predictive frameworks are essential for addressing the pressing need for biodiversity science and conservation.
- The developed protocol provides conservation-relevant predictions at multiple spatial and taxonomic scales.
- This study significantly advances the global understanding of plant conservation status and priorities.
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