Modelling the potential distribution of African Wormwood (Artemisia afra) using machine learning algorithm-based approach (MaxEnt) in Sekhukhune District, South Africa

Artemisia afra Jacq. Ex Willd, commonly known as African wormwood, is a native medicinal plant that has been unsustainable harvested primarily for its leaves due to its medicinal properties. The unsustainable harvesting of this plant underscores the urgent need for conservation and management practices. This study, therefore, used the MaxEnt model of the potential distribution of A. afra. Location: Sekhukhune District Municipality, South Africa. We used 105 sampled records and 27 environmental variables to model the potential spatial distribution of A. afra using the MaxEnt modelling approach. The predictions were performed using current climatic and topographic conditions. A significant portion of the area, 54.46%, is highly suitable for the distribution of A. afra, with various suitability degrees. Precipitation contributed 33.6% to the suitability predictions, followed by NDVI, soil, and distance from rivers with 27.1%, 8.1%, and 5.7%, respectively. Artemisia afra is predicted to be ..., Handheld GPS was used for data collection. The collected location data was cleaned and prepared using Excel., , **README** **Title**: *Modelling the Potential Distribution of African Wormwood* (Artemisia afra) *Using a Machine Learning Algorithm-Based Approach (MaxEnt) in Sekhukhune District, South Africa* **DOI**: [https://doi.org/10.5061/dryad.dz08kps71](https://doi.org/10.5061/dryad.dz08kps71) --- ## Description of the Data and File Structure This dataset contains georeferenced field occurrence records and raster-based environmental predictors used to model the potential distribution of *Artemisia afra* using the MaxEnt algorithm. Field data were collected in the Greater Sekhukhune District Municipality, Limpopo, South Africa. Environmental variables were sourced from publicly available datasets and processed to meet the requirements of MaxEnt. ### File: `Occurrence_Data.zip` Contains presence-only occurrence records for *Artemisia afra* used in model development and validation. * **Artemisia afra train.csv** * 70% of field-verified occurrence points (training set) * Columns: *...,学名*Artemisia afra Jacq. Ex Willd.*，通用名为非洲苦艾（African wormwood），是一种本土药用植物，因其药用价值，其叶片被过度采收，已不可持续。该物种的不可持续采收凸显了开展保护与管理工作的迫切性。因此，本研究采用最大熵模型（MaxEnt）对非洲苦艾的潜在分布区进行预测。研究区域为南非塞库昆内区直辖市（Sekhukhune District Municipality）。本研究共使用105条采样记录与27个环境变量，通过最大熵建模方法构建非洲苦艾的潜在空间分布模型。模型预测基于当前气候与地形条件开展。研究区域内54.46%的面积为非洲苦艾的高度适宜分布区，整体存在多个适宜性等级。降水对适宜性预测的贡献度为33.6%，其次为归一化植被指数（NDVI，Normalized Difference Vegetation Index）、土壤因子与距河流距离，贡献度分别为27.1%、8.1%与5.7%。非洲苦艾的潜在分布预测结果为……，本研究采用手持全球定位系统（GPS）进行数据采集，所获取的点位数据经Excel软件清洗与预处理后使用。 **README** **标题**：*南非塞库昆内区基于机器学习算法（最大熵模型MaxEnt）模拟非洲苦艾（*Artemisia afra*）的潜在分布* **DOI**：[https://doi.org/10.5061/dryad.dz08kps71](https://doi.org/10.5061/dryad.dz08kps71) --- ## 数据与文件结构说明 本数据集包含经地理空间定位的野外物种出现记录，以及用于通过最大熵模型（MaxEnt）构建*Artemisia afra*潜在分布模型的栅格格式环境预测因子。野外数据采集于南非林波波省大塞库昆内区直辖市。环境变量均来自公开数据集，并经预处理以满足最大熵模型的建模要求。 ### 文件：`Occurrence_Data.zip` 包含用于模型开发与验证的非洲苦艾仅存在型物种出现记录。 * **Artemisia afra train.csv** * 占野外验证出现点位的70%（训练集） * 列信息： *...,