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Advanced Taiwan Ocean Prediction System

Tseng, Tsu-Lun - ALL News | 2018-04-03 | Count:450


Title: Fluctuating interaction network and time-varying stability of a natural community (start at 13:00)

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions, arising from time-varying behavioral and/or physiological responses of individual species. Although this view is supported by a few simplified manipulative experiments, evidence from natural ecosystems is lacking. The challenge here is in tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time), and then identifying the effect of such changes on the resulting large-scale community dynamics. Here, using tools for analyzing nonlinear time series (empirical dynamics), and an extensive, 12-year, fortnightly dataset of observations on a natural marine fish community in Maizuru Bay in Kyoto, Japan, we present evidence that short-term changes in the interaction network influence the overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the interaction strengths and even signs change with time and develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. This dynamic stability measure is used to examine the linkage between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months is associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and even community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

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