The relationship between the duration of food web evolution and the vulnerability to biological invasion

Properties of ecosystems that are vulnerable during eco-fusion

When two ecosystems with separate evolutionary histories come into contact (eco-fusion), reciprocal invasions occur during their fusion. Asymmetries in the migration direction or extinction rate then occur (e.g., during the Great American Biotic Interchange, GABI). Hypotheses have been proposed to describe this process, but the ecosystem properties have not been adequately discussed. To identify the ecosystem properties that create vulnerability to species loss during eco-fusion, we conducted computer simulations of the fusion of ecosystems with independent evolutionary histories. With asymmetrical species extinction rates, the ecosystem with a higher extinction rate had a shorter food chain, a higher ratio of animal species to plant species, and a lower ratio of carnivores to herbivores. Most ecosystems that have undergone isolated evolution are vulnerable. These results may explain the vulnerability of South America's ecosystem during the GABI and that of modern Australia.

Relevance of evolutionary history for food web structure

Explaining the structure of ecosystems is one of the great challenges of ecology. Simple models for food web structure aim at disentangling the complexity of ecological interaction networks and detect the main forces that are responsible for their shape. Trophic interactions are influenced by species traits, which in turn are largely determined by evolutionary history. Closely related species are more likely to share similar traits, such as body size, feeding mode and habitat preference than distant ones. Here, we present a theoretical framework for analysing whether evolutionary history--represented by taxonomic classification--provides valuable information on food web structure. In doing so, we measure which taxonomic ranks better explain species interactions. Our analysis is based on partitioning of the species into taxonomic units. For each partition, we compute the likelihood that a probabilistic model for food web structure reproduces the data using this information. We find that taxonomic partitions produce significantly higher likelihoods than expected at random. Marginal likelihoods (Bayes factors) are used to perform model selection among taxonomic ranks. We show that food webs are best explained by the coarser taxonomic ranks (kingdom to class). Our methods provide a way to explicitly include evolutionary history in models for food web structure.