A New Framework to Empower Ecosystem Assessment Through the Integration of eDNA Inventories, Graph Theory and Niche...

Area of Science:

• Ecology
• Network Analysis
• Environmental DNA (eDNA)

Background:

• Understanding ecosystem functioning requires analyzing network structure, a challenge for current ecological assessments.
• Translating complex network analysis into practical ecological assessment tools remains underdeveloped.

Purpose of the Study:

• To develop an innovative predictive model for ecological assessment by integrating environmental DNA (eDNA) data with graph theory, niche modeling, and machine learning.
• To evaluate the effectiveness of graphlet-based network topological metrics for assessing ecological conditions.

Main Methods:

• Integration of environmental DNA (eDNA) inventories with graph theory (graphlet analysis).
• Application of niche modeling and machine learning to build a predictive assessment model.
• Validation using 599 phytoplankton inventories from 186 lakes along a human-induced eutrophication gradient.

Main Results:

• Graphlet-based network topological metrics proved effective for ecological assessment, explaining 67% of anthropogenic gradient variability.
• Demonstrated a clear link between network topology metrics and ecological processes.
• The developed framework is scalable and transposable across various ecological contexts.

Conclusions:

• Graphlet-based network topology provides a robust foundation for developing novel ecological assessment tools.
• This approach enhances our understanding of ecosystem functioning through network analysis.
• The study paves the way for more comprehensive ecological assessments using eDNA data and network theory.