Network Science and Forest Species Interactions

It is already common knowledge that ecology benefits from advances in various fields of mathematics. Network theory [1] introduced new opportunities in exploring species interactions incorporating trophic, mutualistic, facilitative, antagonistic relationships, by targeting superior level of integration compared to pair wise approach of classical ecology. Interactions are key elements of structural community properties like biodiversity [2], community assembly rules, ecological niches, energy flow through the trophic webs, nutrient cycles to name few most important. The hidden properties of interacting species networks are revealed through metrics and analytic tools produced by network science. Other important contributions of network theory to ecological topics consist in the analysis of habitat structures at landscape and meta-ecosystem levels [3,4], disease spread, biotic invasions and, at temporal scale, co-evolution of mutualist /parasites and their hosts [5,6] and conservation ecology [7]. At the moment, we witness the emergence of a new discipline within the large domain of ecology, namely network ecology [8]. Networks are flexible conceptual models providing a systematic way to approach complexity and addressing practically any type of system with interlinked elements. Network science studies networks and is grounded in graph theory being an empirically driven domain with elements borrowed from engineering (control and information theory) statistical physics and social science [1] also a fast growing discipline. At the moment, we witness the emergence of a new discipline within the large domain of ecology, namely network ecology [8]. One special category is represented by ecological networks that are non-random, complex and heterogeneous structures, with small world and scale free topology [9,10] and with a well-defined architecture [11]. However, compared to technological or social networks, metabolic pathways or protein-gene interactions [12,13], they are relatively small in terms of number of nodes [10] and number of interactions.