Life in the branches: Population distribution and persistence in dendritic ecological networks

Spatial structure regulates and modifies processes at several levels of ecological organization (e.g. individual/genetic, population, community) and is thus a key component of complex systems, where knowledge at a small scale can be insufficient for understanding system behaviour at a larger scale.  There is a specific need to examine how dendritic habitat structure, such as that found in stream and cave networks, influences ecological processes.  Although dendritic networks are one type of ecological network, they are distinguished by two fundamental characteristics: (1) both the branches and the nodes serve as habitat, and (2) the specific spatial arrangement and hierarchical organization of these elements interacts with a species’ movement behavior to alter patterns of population distribution and abundance, and community interactions.  The fraction of movements taking place within the restrictive geometry of a network relative to out-of-network movements (e.g., from one branch to another) is undoubtedly critical to population demography. For organisms that have evolved within spatially structured systems, within-network movements can reasonably be considered as primary movement pathways, and out-of-network movements as secondary pathways, which may be critical for population persistence.  In this presentation, we (1) discuss characteristics and special properties of dendritic ecological networks, and (2) investigate the impact of network topology on population distribution, metapopulation persistence and recolonization dynamics.  Using empirical data on stream salamander distribution in 75 streams, we discuss how the stream network topology may have shaped distribution patterns in these animals.  Finally, through simulation modeling of a metapopulation, we show that out-of-network dispersal can buffer populations from extinction, and drastically reduce the time to recolonize local extinctions, though the outcome is sensitive to the network topology.