What do interaction network metrics tell us about specialization and biological traits?

BLÜTHGEN, N.; FRÜND, J.; VÁZQUEZ, D. P.; MENZEL, F.

ECOLOGY

ECOLOGICAL SOC AMER

Año: 2008 vol. 89 p. 3387 - 3399

0012-9658

The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted (connectance, nestedness and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as ‘specialists’ irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the log-normal), combined with a relatively low sampling density typical for ecological data, already generates a ‘nested’ and poorly ‘connected’ network with ‘asymmetric interaction strengths’ when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter co-evolution and suggests a lower ecological redundancy than implied by nested networks.