State-and-transition models (STMs), conceptual models of vegetation change based on alternate state theory, are increasingly applied as tools for land management decision-making. Current methods of model construction often use differences in species composition to identify potential alternate states. However, functional group composition may provide a more mechanistic understanding of ecosystem change, and a way to expedite field data collection and model creation. We explore the utility of this approach by 1) comparing states defined by species composition to those defined by functional groups of differing complexity and 2) determining how management and environmental variation relate to species- and functional group-defined states. We sampled 36 plots with different grazing and spraying histories on a loamy soil type in Colorado sagebrush steppe. We measured plant species composition in each plot and categorized species into functional groups using three different classification schemes, which represented increasing numbers of traits and levels of complexity in functional group classification. The classifications were based on traits that affect plant response to grazing and spraying: life form, life history, resprouting ability, height, vegetative reproduction, and N-fixing ability. We identified potential states based on similarity in species and functional group cover using hierarchical cluster analysis, and explored their relationship to site history and environmental factors using logistic regression.
Results/Conclusions
We found that similarity between species- and functional group-defined states increased with the complexity of the functional groups. One species-defined state was related to aerial spraying of herbicides that killed shrubs, and two states derived from complex functional groups were related to grazing history. Other species- and complex functional group-defined states were related to environmental variation. States defined by simple functional groups were not related to management or environmental factors. We conclude that functional groups that combine several traits mechanistically related to management and disturbance identify potential alternate states that are similar to species-defined states. Functional group composition and species composition are related to different management practices. These findings suggest that a combination of species and functional group composition may be best for creating STMs.