Modelización de datos de geolocalización de especies vegetales mediante redes complejas

Resumen

We analyse the properties of real plant-based networks constructed from geospatial databases, in particular vineyards, olive trees, cork oak and prickly pear in the Iberian Pensinsula. Due to the large scale of such networks, we study the sampling dependence of the networks measures: giant component, clustering cofficient, modularity, diameter and degree distribution. To compare with, we review the sampling properties of classical synthetic networks, namely ramdom Erdos-Renyi networks and its spatial variant, the random geometric networks. Concerning sampling, the main results obtained were: 1) Clustering coefficient and Degree Distribution are independent of the sampling size, both on synthetic and real networks. 2) While modularity depends on the sampling size in synthetic networks, it behaves similarly for every size of the plant-based complex networks. Observed topological properties of real plant-based networks are framed and contextualized according to our results.