Landscape Frontiers, Geography Frontiers: Lessons to Be Learned

Abstract

Advancing ecotones, such as treelines and frontiers of human settlement, may share some characteristic dynamics because both include feedbacks between spatial pattern and process. Both might be examined as complex, self-organizing systems in terms of complexity theory and thus be usefully compared. A cellular automaton of advancing alpine treeline in Montana shows attractors in power-law frequency distributions of spatial and temporal pattern. Frontiers of study areas in the Amazonian region of Ecuador, analyzed using change detection of Landsat Thematic Mapper imagery, have power-law distributions of advancing deforestation. Alternative approaches in self-organized complexity, including self-organized percolation, and the inverse cascade model, and an approach to complexity involving optimization, highly optimized tolerance, are considered. Some combination of these, based on their common ancestry in percolation theory (with its ties to geocomputation), might provide insights into population-environment interactions at settlement frontiers and ecotones together, given comparisons drawn between the spatial feedbacks at alpine treeline and in Ecuador. GIScience and landscape ecology can develop synergies by building on this area of geocomputation and complexity theory, as in analysis of attractors in state spaces of spatial metrics from spatially explicit simulations and representing their uncertainty.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador

“…Critical points in the spatial structure of patterns and feedbacks can produce a system with identifiable future alternative states in which instabilities can "flip" a system into another regime of behavior by changing the processes that control patterns. Dynamics emerging from local feedback mechanisms influence the evolving patterns and system behaviors and create emergence of new system structures that can vary across space-time scales (22). Bottom-up simulations involve autonomous agents as decision makers who interact with a dynamic environment and who learn and adapt to change (23).…”

Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

“…Prior to the application of the scenarios or simulated droughts, we explore model sensitivity via a grid search of initial conditions (i.e., commodity prices, subsidy levels), and plot simulation outcomes and attractors in state space. Tracing a system's path from initial conditions to an attractor is straightforward, requiring a plot of the system's location in state space over time against key dimensions (sensu Janssen and Carpenter 1999), and many traces can in aggregate begin to provide insight into the effects of initial conditions and model parameterization on both the locations of attractors in state space and landscape configuration (Malanson et al 2006). Fig.…”

Forefronting the Socio-Ecological in Savanna Landscapes through Their Spatial and Temporal Contingencies

“…Patch dynamics are mapped via tracking the fluctuation of patch edges as patches change in shape, size, composition, and perimeter-to-area ratio. The need for these kinds of measurements in the study of disturbance ecology and nonequilibrial conditions [51,52] coincided with the development of different means to quantify landscape forms [53,54], to evaluate scale [55,56], and to create simplified landscapes as simulation models [38,57].…”

“…Those factors could originate from biophysical and/or policy regimes (including hunting, land tenure, and conservation policy), while seasonal shifts could be due to cyclic factors, such as phenology of vegetation [143][144][145][146] and/or migration for seasonal employment [54,147]. The degree of change due to yearly (inter-annual) seasonality could be compared to temporal change over intra-decadal cycles (e.g., via panel analysis as in [16,54] or via harmonic regression and wavelet analysis [144]). Of particular temporal interest might be the role of disturbances and disturbance regimes in affecting landscape patterns through time and space, which would need to be evaluated in a similar way.…”