Abstract
Observed biotic interactions between species, such as in pollination, predation, and competition, are determined by combinations of population densities, matching in functional traits and phenology among the organisms, and stochastic events (neutral effects).We propose optimal transportation theory as a unified view for modeling species interaction networks with different intensities of interactions. We pose the coupling of two distributions as a constrained optimization problem, maximizing both the system's average utility and its global entropy, that is, randomness. Our model follows naturally from applying the MaxEnt principle to this problem setting.This approach allows for simulating changes in species relative densities as well as to disentangle the impact of trait matching and neutral forces.We provide a framework for estimating the pairwise species utilities from data. Experimentally, we show how to use this framework to perform trait matching and predict the coupling in pollination and host–parasite networks.
Highlights
Biotic interactions between animals, plants, fungi, bacteria, viruses, etc., are incredibly complex
We propose optimal transportation theory as a unified view for modeling species interaction networks with different intensities of interactions
Using a honeybee spillover dataset from the southwest of Spain and host–parasite networks spatially distributed over Eurasia, we show that the fitted utility matrix can generalize over time and space, outcompeting the 2 | OPTIMAL TR ANSPORTATION THEORY FOR ECOLOGY
Summary
Plants, fungi, bacteria, viruses, etc., are incredibly complex. In food webs, the prey is usually smaller than the predator (Gravel et al, 2013), plants use fruit brightness as reward cues for bird species to regulate their nutrient intake (Albrecht et al, 2018), and parasitism typically depends on a complex interplay of physiology and evolutionary history between parasites and hosts (Hadfield et al, 2014). In addition to the species' traits and other properties, the observed interaction network is dependent on the abundances of the species and environmental factors (Bartomeus et al, 2016; Poisot et al, 2015). The former determines the probability that two species can encounter each other, a requirement for an interaction to occur. Because none of these mechanisms act with perfect reliability, a part of the structure in ecological networks is stochastic, justifying a probabilistic framework to model interactions
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