The characteristic, or natural, length scales of a spatially dynamic ecological landscape are the spatial scales at which the deterministic trends in the dynamic are most sharply in focus. Given recent development of techniques to determine the characteristic length scales (CLSs) of real ecological systems, the author explores the potential for using CLSs to address three important and vexing issues in applied ecology, viz. <ol> <li>determining the optimum scales to monitor ecological systems, </li> <li>interpreting change in ecological communities, and</li> <li>ascertaining connectivity between species in complex ecologies.</li> </ol> In summarising the concept of characteristic length scales as system-level scaling thresholds, the author emphasises that the primary CLS is, by definition, the optimum scale at which to monitor a system if the objective is to observe its deterministic dynamics at a system level. Using several different spatially explicit individual-based models, he then explore predictions of the underlying theory of CLSs in the context of interpreting change and ascertaining connectivity among species in ecological systems. Analysis of these models support predictions that systems with strongly fluctuating community structure, but an otherwise stable long-term dynamic defined by a stationary attractor, indicate an invariant length scale irrespective of community structure at the time of analysis, and irrespective of the species analyzed. In contrast, if changes in the underlying dynamic are forcibly induced, the shift in dynamics is reflected by a change in the primary length scale. Thus, consideration of the magnitude of the CLS through time enables distinguishing between circumstances where there are temporal changes in community structure but not in the long-term dynamic, from that where changes in community structure reflect some kind of fundamental shift in dynamics. In this context, CLSs emerge as a diagnostic tool to identify phase shifts to alternative stable states associated with loss of resilience in ecological systems and thus provide a means to interpret change in community composition. By extension, comparison of the CLSs of ostensibly similar communities at different points in space can reveal whether they experience similar underlying dynamics. Analysis of these models also reveals that species in the same community whose dynamics are largely independent indicate different length scales. These examples demonstrate the potential to apply CLSs in a decision-support role in determining scales for monitoring, interpreting whether change in community structure reflects a shift in underlying dynamics and therefore may warrant management intervention, and determining connectivities among species in complex ecological systems.