Co-evolution of resource trade-offs driving species interactions in a host-parasite network: An exploratory model

TL;DR

This paper introduces an adaptive dynamics model showing how co-evolution of resource trade-offs between hosts and parasites can explain patterns of specialization asymmetry in ecological networks, especially with closely related species.

Contribution

It presents a novel co-evolutionary model linking resource allocation to network structure, providing insights beyond abundance-based explanations.

Findings

Co-evolution leads to specialization asymmetry in certain network configurations.

Relatedness of parasites influences the emergence of specialization asymmetry.

Fast host mutation rates promote specialization asymmetry.

Abstract

Patterns of nestedness and specialization asymmetry, where specialist species interact mainly with generalists while generalists interact with both generalists and specialists, are often observed in mutualistic and antagonistic bi-partite ecological networks. These have been explained in terms of the relative abundance of species, using a null model that assigns links in proportion to abundance, but doubts have been raised as to whether this offers a complete explanation. In particular, host-parasite networks offer a variety of examples in which the reverse patterns are observed. We propose that the link between specificity and species-richness may also be driven by the co-evolution of hosts and parasites, as hosts allocate resources to optimize defence against parasites, and parasites to optimize attack on hosts. In this hypothesis, species interactions are a result of resource allocations. This novel concept, linking together many different arguments for network structures, is introduced through the adaptive dynamics of a simple ecological toy system of two hosts and two parasites. We analyse the toy model and its functionality, demonstrating that co-evolution leads to specialization asymmetry in networks with closely related parasites or fast host mutation rates, but not in networks with more distantly related species. Having constructed the toy model and tested its applicability, our model can now be expanded to the full problem of a larger system.