Metrics and Models for Quantifying Ecological Resilience at Landscape Scales

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Abstract
An explicit link between the abiotic environment, the biotic components of ecosystems, and resilience to disturbance across multiple scales is needed to operationalize the concept of ecological resilience. To accomplish this, managers must be able to measure the ecological resilience of current conditions and project resilience under future scenarios of landscape change. The goal of this paper is to present metrics and describe a process for using geospatial data, landscape pattern analysis and landscape dynamic simulation modeling to evaluate ecosystem resilience at management scales. The dynamic equilibria of species abundances, community structure, and landscape patterns that are produced under a given combination of abiotic conditions, such as topography, soils, and climate, can form a foundation to define desired conditions and measure resistance and resilience. The degree of forcing required to push the system from this dynamic range is a measure of resistance, and the rate of return to the dynamic range after the perturbation is a measure of the resilience and recovery of the system. Several tools from the field of landscape ecology are useful in defining the dynamic range of an ecosystem under natural regulation and to measure the forcing required to drive departure and the rate of recovery. Simulation models provide means to quantify the expected range of species abundance, community structure, and landscape patterns under a variety of scenarios, including the natural disturbance regime, current disturbance regime, and possible future regimes under alternative management and climate scenarios. Landscape pattern analysis and multivariate trajectory analysis provide a means to quantify conditions and change vectors relative to this desired range. Together this combination of tools provides a means to define the conditions of a desired state for an ecosystem, to quantify the degree of resistance and resilience of the system to perturbation, and to measure and monitor the departure from the range of natural variability in the system dynamics.

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