Indicating retrospective resilience of multi-scale patterns of real habitats in a landscape

• Published in: Ecological indicators Vol. 6; no. 1; pp. 184 - 204
• Main Authors: Zurlini, G., Zaccarelli, N., Petrosillo, I.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2006
• Subjects: Change detection; Fragility; Multi-scale domains; Retrospective resilience indication; Multi-scale domains; Retrospective resilience indication; Change detection; Fragility
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2005.08.013

Vegetation or habitat types are ecological phases, which can assume multiple states, and transformations from one type of phase to another are ecological phase transitions. If an ecological phase maintains its condition of normality in the linked processes and functions that constitute ecosystems then is believed healthy. An adaptive cycle, such as in Holling's model, has been proposed as a fundamental unit for understanding complex systems and their dynamics. Such model alternates between long periods of aggregation and transformation of resources and shorter periods that create opportunities for innovation. The likelihood of shifts among different phases largely depends on resilience; thereby, a clear and measurable definition of resilience has become paramount. Different resilience levels are expected to be intertwined with different scale ranges of real habitats, in relation to the kind and intensity of natural and human disturbances. We argue that the type, magnitude, length and timing of external pressure, its predictability, the exposure of habitats, and the habitat's inherent resistance have important interactive relationships which determine resilience at multiple scales. In this paper, we provide an operational framework to derive operational indices of short-term retrospective resilience of real grasslands in a northern Italy watershed, from multi-scale analysis of landscape patterns, to find scale domains for habitat edges where change is most likely, i.e. resilience is lowest and fragility highest. That is achieved through cross-scale algorithms like fractal analysis coupled with change detection of ecological response indices. The framework implements the integration of habitat edge fractal geometry, the fitting of empirical power functions by piecewise regressions, and change detection as a procedure to find scale domains for grassland habitat where retrospective resilience is lowest. The effects of external pressure are significantly related to habitat scale domains, resulting from the interactions among ecological, physical, and social controls shaping the systems. Grassland scale domains provide evidence and support for identifying and explaining scale invariant ecological processes at various scales, from which much insight can be gained for characterizing grassland adaptive cycles and capabilities to resist disturbances.