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Castro, J. A.

Publications and source records attributed to Castro, J. A..

2 recordsLinked to original sources

A probabilistic view of forbidden links: their prevalence and their consequences for the robustness of mutualistic networks

The presence in ecological communities of unfeasible species interactions, termed forbidden links, due to physiological or morphological exploitation barriers has been long debated, but little direct evidence has been found. Forbidden links are likely to make ecological communities less robust to species extinctions, stressing the need to assess their prevalence. Here, we used a dataset of plant-hummingbird interactions, coupled with a Bayesian hierarchical model, to assess the importance of exploitation barriers in determining species interactions. We found evidence for exploitation barriers between flowers and hummingbirds across the 32 studied communities, however, the proportion of forbidden links changed drastically among communities, because of changes in trait distributions. The higher the proportion of forbidden links, the more they decreased network robustness, because of constraints on interaction rewiring. Our results suggest that exploitation barriers are not rare in plant-hummingbird communities and have the potential to limit the rescue of species experiencing partner extinction.

ecology↗

MetaDAG: a web tool to generate and analysemetabolic networks

We introduce MetaDAG, a web-based tool designed for metabolic network reconstruction and analysis. MetaDAG is capable of constructing metabolic networks associated with specific organisms, sets of organisms, sets of reactions, sets of enzymes, and sets of KO (KEGG Orthology) identifiers. To generate these metabolic networks, MetaDAG retrieves from the KEGG database the chemical reaction information that corresponds to the users queries. MetaDAG computes a reaction graph as a first metabolic graph model. This reaction graph is a network in which nodes represent reactions, and edges between reactions indicate the presence of a metabolite produced by one reaction and consumed by another. Next, as a second metabolic model, MetaDAG computes a directed acyclic graph called a metabolic DAG (m-DAG for short). The m-DAG is obtained from the reaction graph by collapsing all strongly connected components into single nodes. As a result, the m-DAG representation reduces considerably the number of nodes while keeping and also highlighting the networks connectivity. Both metabolic models, the reaction graph, and the m-DAGs, are displayed on an interactive web page to assist the users in visualising and analysing the networks. Furthermore, users can retrieve the nodes information linked to the KEGG database. All generated files, including images containing metabolic network information and analysis results, are available for download directly from the web page. In the Eukariotes test presented here, MetaDAG has demonstrated its effectiveness in classifying all eukaryotes from the KEGG database at both the kingdom and phyla taxonomy levels.

bioinformatics↗