Steven L. Garman

Alternative states of a semiarid grassland ecosystem: implications for ecosystem services

Ecosystems can shift between alternative states characterized by persistent differences in structure, function, and capacity to provide ecosystem services valued by society. We examined empirical evidence for alternative states in a semiarid grassland ecosystem where topographic complexity and contrasting management regimes have led to spatial variations in levels of livestock grazing. Using an inventory data set, we found that plots (n = 72) cluster into three groups corresponding to generalized alternative states identified in an a priori conceptual model. One cluster (biocrust) is notable for high coverage of a biological soil crust functional group in addition to vascular plants. Another (grass-bare) lacks biological crust but retains perennial grasses at levels similar to the biocrust cluster. A third (annualized-bare) is dominated by invasive annual plants. Occurrence of grass-bare and annualized-bare conditions in areas where livestock have been excluded for over 30 years demonstrates the persistence of these states. Significant differences among all three clusters were found for percent bare ground, percent total live cover, and functional group richness. Using data for vegetation structure and soil erodibility, we also found large among-cluster differences in average levels of dust emissions predicted by a wind-erosion model. Predicted emissions were highest for the annualized-bare cluster and lowest for the biocrust cluster, which was characterized by zero or minimal emissions even under conditions of extreme wind. Results illustrate potential trade-offs among ecosystem services including livestock production, soil retention, carbon storage, and biodiversity conservation. Improved understanding of these trade-offs may assist ecosystem managers when evaluating alternative management strategies.

Applying Threshold Concepts to Conservation Management of Dryland Ecosystems: Case Studies on the Colorado Plateau

Ecosystems may occupy functionally distinct alternative states, some of which are more or less desirable from a management standpoint. Transitions from state to state are usually associated with a particular trigger or sequence of triggers, such as the addition or subtraction of a disturbance. Transitions are often not linear, rather it is common to see an abrupt transition come about even though the trigger increases only incrementally; these are examples of threshold behaviors. An ideal monitoring program, such as the National Park Service’s Inventory and Monitoring Program, would quantify triggers, and be able to inform managers when measurements of a trigger are approaching a threshold so that management action can avoid an unwanted state transition. Unfortunately, both triggers and the threshold points at which state transitions occur are generally only partially known. Using case studies, we advance a general procedure to help identify triggers and estimate where threshold dynamics may occur. Our procedure is as follows: (1) Operationally define the ecosystem type being considered; we suggest that the ecological site concept of the Natural Resource Conservation Service is a useful system, (2) Using all available a priori knowledge to develop a state-and-transition model (STM), which defines possible ecosystem states, plausible transitions among them and likely triggers, (3) Validate the STM by verifying the existence of its states to the greatest degree possible, (4) Use the STM model to identify transitions and triggers likely to be detectable by a monitoring program, and estimate to the greatest degree possible the value of a measurable indicator of a trigger at the point that a state transition is imminent (tipping point), and values that may indicate when management intervention should be considered (assessment points). We illustrate two different methods for attaining these goals using a data-rich case study in Canyonlands National Park, and a data-poor case study in Wupatki National Monument. In the data-rich case, STMs are validated and revised, and tipping and assessment points are estimated using statistical analysis of data. In the data-poor case, we develop an iterative expert opinion survey approach to validate the degree of confidence in an STM, revise the model, identify lack of confidence in specific model components, and create reasonable first approximations of tipping and assessment points, which can later be refined when more data are available. Our goal should be to develop the best set of models possible given the level of information available to support decisions, which is often not much. The approach presented here offers a flexible means of achieving this goal, and determining specific research areas in need of study.

U.S. Geological Survey Science for the Wyoming Landscape Conservation Initiative - 2008 Annual Report

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