Scale-free correction of under-/over-reported biases in global biotic interaction network

Author

Funabashi, Masatoshi and Nonaka, Kei Aria and Minami, Tomoyuki

Abstract

Biotic interactions within ecosystems are crucial for maintaining biodiversity and various ecological functions. This study focuses on correcting biases in the Global Biotic Interaction (GloBI) database, which catalogs species interactions from a vast array of scientific literature that are generally compromised by under- and over-reporting biases. We assume the biotic interaction network has a scale-free property, and the degree distribution follows a power law. Using a linear regression model on a double-logarithmic scale, we correct the potential biases in degree centrality (DC), betweenness centrality (BC), and PageRank scores (PR). After the correction, these centrality measures better align with the power-law frequency distribution, and alleviate the initially observed bounding relationships of BC to DC and PR, thereby approaching the generally expected statistics of a scale-free network. Taking the universality of scale-free networks as the underlying property in biotic interactions, the corrected centrality scores are expected to mitigate the reporting biases and enhance the database's utility for ecological research.