The research titled "Estimating global geographical distribution and ecological niche dynamics of Ammannia coccinea under climate change based on Biomod2" investigates the invasive plant species Ammannia coccinea, which poses a threat to rice production and biodiversity. The study employs the Biomod2 platform to predict the species' distribution under current and future climate scenarios, focusing on the ecological niche dynamics influenced by environmental factors such as temperature, precipitation, and human impact.
The study utilized a total of 1138 occurrence records of A. coccinea for modeling its potential global distribution. The methodology included:
The findings emphasize the need for targeted management strategies to prevent the spread of A. coccinea in regions overlapping with key rice production areas. Proactive measures are crucial to safeguard agricultural production and contribute to achieving the United Nations Sustainable Development Goals related to food security and environmental sustainability.
The study acknowledges limitations such as potential biases in occurrence data collection and the exclusion of other ecological factors like species interactions. Future research should incorporate a broader range of ecological factors and consider biotic interactions to enhance the understanding of A. coccinea's distribution dynamics.
import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt # Load climate data and occurrence records data = pd.read_csv('climate_data.csv') # Placeholder for actual data occurrences = pd.read_csv('occurrence_records.csv') # Placeholder for actual data # Analyze correlation between environmental variables correlation_matrix = data.corr() plt.figure(figsize=(10, 8)) sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm') plt.title('Correlation between Environmental Variables') plt.show()