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Updated: Feb 1, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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Published on: February 10, 2023

Predicting plant conservation priorities on a global scale.

Tara A Pelletier¹, Bryan C Carstens², David C Tank^3,4,5

¹Department of Biology, Radford University, Radford, VA 24142.

Proceedings of the National Academy of Sciences of the United States of America

|December 5, 2018

Summary

Most plant species

Keywords:

IUCN conservation plantae predictive modeling random forest

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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.