Predicting the Potential Distribution of Lagotis Medicinal Plants on the Qinghai‐Xizang Plateau, With a Maximum Entropy Model
Huiyuan Ma, Bo Wang, Xue Yang, Guoying Zhou

TL;DR
This study predicts where Lagotis medicinal plants can grow on the Qinghai-Xizang Plateau using climate data and a MaxEnt model.
Contribution
The study introduces a MaxEnt-based prediction model for Lagotis species distribution under future climate scenarios.
Findings
The MaxEnt model accurately predicted suitable areas for six Lagotis species with AUC values > 0.94.
Altitude, isothermality, and annual precipitation were key factors influencing Lagotis distribution.
Future climate scenarios suggest expanding suitable areas for some Lagotis species in the eastern, central, and southern parts of the plateau.
Abstract
Lagotis is a Tibetan medicine with high medicinal and research value in the Qinghai‐Xizang Plateau region. Predicting its potential suitable areas can provide important references for the protection and research of Lagotis. This study investigated and collected 769 site data of six species of Lagotis and 28 environmental factor data, and used the maximum entropy model (MaxEnt) to simulate the distribution of suitable areas under three climate scenarios (RCP2.6, RCP6.0, RCP8.5) in the 2050s and 2070s. The results showed that: (1) The AUC values of the model training dataset for the six Lagotis species were all > 0.94, indicating that the predictions of the model were accurate. (2) Altitude (alt), isothermality (bio3), and average annual precipitation (bio12) were the main environmental factors affecting the geographical distribution of Lagotis. (3) Under the current climate conditions,…
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Taxonomy
TopicsSpecies Distribution and Climate Change · Metabolomics and Mass Spectrometry Studies · Remote Sensing in Agriculture
