IMBIV   05474
INSTITUTO MULTIDISCIPLINARIO DE BIOLOGIA VEGETAL
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Mechanisms driving the evolution of species-rich interaction networks
Autor/es:
GUIMARAES JR, PR; ANDREAZZI C; ASTEGIANO J
Reunión:
Conferencia; BIFI; 2018
Resumen:
Complexrepresentstructuralnetworksmeasuressubsets of the species with whom generalists interact. Modularity measures the degree of species grouping into semi-independent units. The changes on phenotypic traits driven by reciprocal selection between species (i.e., coevolution) is considered to play a key role in assembling and maintaining species interactions (Thompson 2013). Yet the underlying processes shaping the observed network structure and their consequences for species evolution remain poorly understood. Here, we explore how coevolution driven by different functional mechanisms shapes the structure and dynamics of ecological networks.networks are powerful analytical tools used to ecological interactions among species. The patterns that are widely observed in ecological are nestedness and modularity. Nestedness the degree to which specialists interact withWe use an adaptive network framework (Andreazzi et al. 2017) to study how trait matching (probability of interaction increases with trait similarity) and exploitation barriers (probability of interaction increases with trait dissimilarity) functional mechanisms drive coevolution and shape network organization. In these networks nodes represent species and each species have an attributed trait value. There are two sets of species: resources and consumers. Interactions are either antagonistic, in which consumers are benefited while resources are exploited or mutualistic, in which both consumers and resources are benefited. Phenotypic traits, such as body size, modulate the likelihood of interactions according to the functional mechanism. This approach combines the evolution of species traits due to antagonistic or mutualistic selection and feedbacks of coevolution on network dynamics.We parameterize the models with data from 122 antagonistic and 122 mutualistic empirical networks comparing scenarios with weak and strong coevolutionary selection. In Fig. 1 we show that the models tend to underestimate nestedness and overestimate modularity of both antagonistic and mutualistic networks. We also show that trait matching reproduce better the structure of antagonistic networks while explotation barriers are more related to mutualistic interactions. Weak coevolutionary selection driven by trait matching reproduce better the structure of antagonistic networks, predicting 70% of the empirical networks (Fig. 1a, 1c). Contrary to the traditional claim, we show that coevolution poorly reproduce the structure of mutualistic networks. Indeed, weak coevolutionary selection driven by exploitation barriers predict only 20% of the empirical mutualistic networks (Fig. 1b, 1d).Coevolution prevents networks from achieving a stable state and interaction rewiring varies from 0-50% of the interactions. This rewiring is drastically reduced when the functional mechanism have a strong effect on the probabilities of interactions. Strong coevolution by trait matching reduces interaction rewiring in antagonistic networks, while strong coevolution by exploitation barriers reduces interaction rewiring in mutualistic interactions. The different mechanisms also favor different trait dynamics. Exploitation barriers favor coevolutionary arm races in consumer and resource functional traits, while trait matching favors fluctuating selection dynamics in both types of networks. In summary, coevolution better predict the structure of antagonistic than mutualistic networks. Neutral processes and additional mechanisms, such as differences in abundance, dispersal, drift and speciation, may be more relevant predictors of the structure of mutualistic networks. Our results highlight the relevance of combining eco- evolutionary models with network analysis to understand the dynamics of interactions in species-rich communities and its consequences on the organization of biodiversity.