Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/1/2009
Publication Date: 10/5/2009
Citation: Bestelmeyer, B.T., Goolsby, D., Archer, S. 2009. Spatial Patterns in Alternative States and Thresholds: A Missing Link for Management of Landscapes? [abstract]. 10th Biennial Conference for Research on the Colorado Plateau, October 5-8, 2009, Flagstaff, Arizona. p. 40. Interpretive Summary:
Technical Abstract: The detection of threshold dynamics (and other dynamics of interest) would benefit from explicit representations of spatial patterns of disturbance, spatial dependence in responses to disturbance, and the spatial structure of feedbacks in the design of monitoring and management strategies. Spatially-structured ecosystems at landscape scales tend to exhibit signals of each of these processes and their interactions. The spatial pattern of disturbance is determined by natural or anthropogenic processes associated with land use. In the latter case, management units and resource use gradients within units often feature different states or indications of threshold risk. Spatial dependence in disturbance response accounts for differences in resilience caused by static environmental variations including soil and landform attributes. These factors determine the effect of disturbance on resource availability and species populations underlying transitions. The patterning of spatial dependence is detected via soil sampling and maps. Finally, the spatial structure of feedbacks accounts for how the initial state change in a location propagates over time and space, conditioned by the pattern of spatial dependence and disturbance. At fine scales, feedbacks may be detected via predictable changes in patch structure (e.g., fragmentation processes prior to crossing soil erosion thresholds). At broader scales, feedbacks may emerge between the distribution of states and broad-scale sediment redistribution (e.g., wind erosion) or even climate. We suggest that these three spatial patterns should be the basis for expanding conceptual state-and-transition models to landscape systems. These new conceptual models would contain predictions about how the likelihood of transition varies across a landscape as well as the patch-scale changes leading to, and determining the reversibility of, transitions. Both sets of predictions would be valuable guides in the design of monitoring to manage thresholds. A hierarchical approach to monitoring featuring ground-based observations coupled to remote-sensing is generally required.