IADIZA   20886
INSTITUTO ARGENTINO DE INVESTIGACIONES DE LAS ZONAS ARIDAS
Unidad Ejecutora - UE
artículos
Título:
On the parsimony of null models of plant-pollinator networks
Autor/es:
VÁZQUEZ, D. P.
Revista:
PLoS Biology
Referencias:
Año: 2007 p. 1 - 1
Resumen:
There has been much recent interest in describing the structural patterns of plant–animal mutualistic networks. Most of this work has been descriptive, aimed at documenting existing patterns. Hypotheses about the underlying causes of the observed patterns have been proposed, but few attempts have been made to evaluate those hypotheses. A recent paper by Santamaría and Rodríguez-Gironés [1] (hereafter SRG) is a bold attempt to provide such an evaluation. In particular, these authors propose that the nested pattern usually observed in mutualistic networks [2] may result from rules that dictate the phenotypic matching between interacting species. A nested pattern is one in which each species interacts only with a subset of those species interacting with more connected species [2]. Using models with two kinds of rules (complementarity traits and barrier traits), SRG simulate the process of network assembly, showing that for one of their models (the mixed model, combining two complementarity traits and two barrier traits for plants and another two and two for animals) the simulated networks exhibit a substantial degree of nestedness, similar to that observed in real-world networks. SRG also use two kinds of neutral models, asuming that probabilities of interaction are determined by the relative abundances of species, with abundances coming from a uniform distribution (neutral model) or a lognormal distribution (lognormal neutral model). The latter model generated simulated matrices with nestedness comparable to that observed in real networks, thus providing a fit to the data at least as good as that obtained for the mixed model. As SRG argue, the principle of parsimony dictates that when two alternative models can explain a certain phenomenon, the simpler one should be accepted. However, SRG reject the lognormal neutral model as more parsimonious than their mixed model. In my opinion, the three justifications provided by SRG for such rejection are questionable. I explain why I think so below.