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

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.