Applying Deep Learning For Forecasting Species Interactions In Dynamic Ecosystems

Abstract

This study investigates the application of deep learning techniques for forecasting
species interactions and ecological attributes in dynamic ecosystems.
Computational methods and data visualization tools are employed to analyze
synthetic datasets representing species interactions, live tree carbon, tree species
diversity, and climate scenario attributes. We explore the effectiveness of various
deep learning models, including Convolutional Neural Networks (CNNs),
Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), in
predicting species interactions over time. Additionally, we examine the spatial
distribution of ecological attributes, such as live tree carbon and tree species
diversity, at different simulation years using boxplots and maps generated through
data visualization libraries. Our results reveal insights into the temporal dynamics
of species interactions and attribute values under different climate scenarios. We
observe fluctuations in species interactions and attribute values over time,
highlighting the complex interplay between environmental factors and ecosystem
dynamics. The findings contribute to advancing ecological research by
demonstrating the potential applications and limitations of deep learning models
in forecasting ecological processes. Furthermore, the study provides valuable
insights for ecosystem management and conservation, facilitating informed
decision-making for maintaining ecosystem health and biodiversity in the face of
environmental changes.