A network simplification approach to ease topological studies about the food-web architecture

Area of Science:

• Ecology
• Network Science
• Conservation Biology

Background:

• Food web studies are complex and time-consuming, with limited trophic interaction data available, especially for terrestrial ecosystems.
• Existing ecological data is scarce compared to general ecological datasets, hindering comprehensive food web analysis.
• The complexity of food webs poses challenges for data collection and analysis.

Purpose of the Study:

• To present a complex network strategy that simplifies food web data collection using a taxonomic key.
• To evaluate the impact of data simplification on food web topology and community detection.
• To assess the robustness of topological indices under varying levels of data simplification.

Main Methods:

• Developed a complex network strategy incorporating a taxonomic key for simplified data collection.
• Analyzed the topological structure of three distinct food webs at different simplification levels.
• Estimated the influence of data simplification on community detection algorithms.

Main Results:

• The first level of simplification largely preserved the general topological indices of the food webs.
• Key metrics such as betweenness and trophic levels remained consistent and robust even with higher levels of simplification.
• Data simplification showed minimal impact on the overall structure and key topological properties.

Conclusions:

• Generalization and standardization in food web science can increase open data availability and inter-study comparisons.
• The proposed simplification strategy supports new researchers and exploratory analyses in food web science.
• This approach offers a practical solution to the inherent complexity and data scarcity in food web studies.