# Algorithmic reconstruction of trophic networks from open-access species lists reveals key organisms in real ecosystems

## TL;DR

A new automated method uses open species lists to build realistic food webs, helping identify key species in ecosystems for conservation.

## Contribution

A novel automated protocol for reconstructing trophic networks from uncurated species lists, integrating functional traits and theoretical models.

## Key findings

- The method successfully generates biologically plausible trophic networks from RAMSAR wetland species lists.
- Medium-sized, highly mobile organisms at intermediate trophic levels are crucial for wetland ecosystem robustness.
- The approach offers a scalable and cost-efficient way to estimate ecological networks and identify keystone species.

## Abstract

Biotic interactions, crucial for understanding the ecology and evolution of species, are often conceptualized as ecological networks. However, the complexity of real ecosystems poses challenges for empirical inference, and theoretical interaction models, while informative, frequently fail to undergo empirical validation. This dual limitation creates a gap between theoretical and empirical approaches in portraying ecosystem dynamics and identifying (and protecting) key species, which are critical for conservation efforts and ecosystem management. In order to bridge this operational gap, we present a novel automated protocol capable of generating realistic trophic networks, including multilayer ones, using non-curated, freely-available species lists from real ecosystems as input data. As a proof-of-concept, we applied this method to the species lists contained in the RAMSAR database of wetland ecosystems. Our data mining algorithm enriches these species lists with functional traits, such as body size, habitat, and diet, by integrating information directly sourced from online biodiversity databases. Subsequently, a modified version of the Allometric Niche Model is used to sort species within the trophic network according to their functional traits and ecological roles. After demonstrating the algorithmic robustness of our method and the biological plausibility of the resulting ecological networks, we illustrate its potential to characterize, in a cost-efficient manner, the structure of real-world ecosystems and to identify the organisms that are crucial for maintaining that structure. In this case study, our findings indicate that the robustness of wetland ecosystems often depends on medium-sized, highly mobile organisms occupying intermediate trophic levels.

Understanding who eats whom in nature is key to conserving ecosystems, but inferring these complex food webs (trophic networks) in real environments is resource-intensive and sometimes unfeasible. Although theoretical models can generate plausible networks, they often lack direct links to real-world data. To address this, we developed a fully automated protocol that uses openly available species lists to reconstruct realistic ecological networks. Our method enriches these raw species lists with traits like body size, diet, and habitat, mined from public databases. Then, it applies a well-tested theoretical model to generate trophic networks where links represent biologically plausible interactions. We validate the networks against known ecological patterns and show that they capture the essential structural features of ecosystems. This approach offers a scalable, cost-efficient way to estimate interaction networks, identify keystone species, and assess ecosystem robustness, potentially informing biodiversity conservation and the management of vulnerable habitats worldwide.