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Models that estimate missing interactions change our understanding of the structure and species roles in the largest seed-dispersal network
  • André Martinez,
  • Mathias Pires
André Martinez
Universidade Estadual de Campinas

Corresponding Author:[email protected]

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Mathias Pires
Universidade Estadual de Campinas Instituto de Biologia
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Interactions between species, such as seed-dispersal interactions, can shape many aspects of those species' life, as well as ecological patterns and evolutionary dynamics. Yet, sampling interactions in the field is challenging. Even with extensive sampling efforts we can hardly obtain a comprehensive picture of which species interact with each other. Such missing interactions can produce important gaps that affect how we perceive and interpret the network formed by species interactions and the roles of individual species within those networks. In this study we propose two methods that combine data on species interactions with information on species traits and phylogenies to estimate potentially missing interactions. We use one of the largest datasets on plant-frugivore interactions, depicting thousands of interactions between birds and plants in the Atlantic Forest hotspot, to test those methods and analyze how adding newly estimated interactions change the structure and the topological importance of the species within the seed dispersal network. We show that estimated missing interactions more than tripled the number of interactions in the network and impact the general topological properties of the network increasing nestedness and reducing modularity. Both models generated networks with a similar structure and were effective in estimating new interactions, accurately predicting known interactions without overestimating interactions in place of true absences. More importantly, added interactions changed our perception on the topological role of species, with several under-sampled species earning several interactions and becoming more central to network structure. This shows that estimating interactions can be helpful to get a more complete idea of how a network may look like and may help inform which interactions should be focused on in further sampling efforts.