Data for variation of magnesium drives plant adaption to heterogeneous environments by regulating efficiency in photosynthesis on a large scale

Magnesium (Mg) is a vital nutrient for plants, and its role in photosynthesis, enzyme regulation, and resistance to environmental stress is becoming increasingly evident. However, there is a paucity of knowledge regarding the characteristics of Mg (content, density, and stock) on a large scale, particularly at the community level, which serves as a fundamental unit for linking to ecosystem functions. A leaf-branch-trunk-root-matched database of the Mg content (mg g–1) and biomass (g m–2) of plant organs across 1972 sampling sites in China was constructed based on field surveys and data compilation. Using machine learning algorithms, we comprehensively explored the spatial patterns and main influencing factors of plant Mg content and density (g m–2). Deserts exhibited higher Mg content, with the primary influencing factors being high temperature and soil Mg supply. High Mg density values occurred in forests. The spatial patterns of Mg content and density underscore the adaptation of plan..., When investigating community structure, we set up 3 or 4 sample plots for trees (20 m × 20 m), 6 for shrubs (5 m × 5 m), and 8 for herbs (1 m × 1 m). The height and breast height diameter for trees, height, basal diameter, and crown width for shrubs were investigated for biomass calculation using allometric growth equations. We collected the aboveground portion of herbs by species after drying and weighing. The biomass of trees and shrubs was calculated, and the aboveground biomass of herbs was obtained by weighing, whereas the belowground biomass was calculated using the root-crown ratio.
Healthy and fully expanded leaves and top branches (diameter < 1 cm) were collected for trees and shrubs, along with core samples at breast height (trees only). All aboveground portions of the herbs were collected. Intact fine roots (diameter < 2 mm) were excavated along lateral roots. Based on the results of the community structure investigation, samples from different species were proportional..., , # Title of Dataset: Data for variation of magnesium drives plant adaption to heterogeneous environments by regulating efficiency in photosynthesis on a large scale

## Description of the Data and file structure

We give the mean values of Mg content and density of each organ in different vegetation of China (\"`China vegetation magnesium content.csv`\" and \"`China vegetation magnesium content.csv`\"), predictions of Mg content and density (\"`Mg content rasters.zip`\" and \"`Mg density rasters.zip`\"), and code for machine learning (\"`Machine_learning.py`\") and multi-model inference (\"`Multi-model inference.R`\") as well as their example data (\"`df.xlsx`\" and \"`data.xlsx`\").

Descriptions:

### China vegetation magnesium content

Summary: The mean vegetation magnesium content under the secondary classification of the vegetation map of China based on machine learning models. The vegetation map of China here was produced by Hou (2019).
Unit: mg g–1

### China vegetation magnesium density

Summary...