Location: Range Management ResearchTitle: Degraded States, Novel Ecosystems, or Reconfigured Landscapes: How Should We View Ecosystem Change in a Changing World? Author
Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 2/1/2010
Publication Date: 2/7/2010
Citation: Bestelmeyer, B.T., Brown, J. 2010. Degraded States, Novel Ecosystems, or Reconfigured Landscapes: How Should We View Ecosystem Change in a Changing World? [abstract] Society for Range Management and Weed Science Society of America Abstracts, February 7-11, 2010, Denver, Colorado. SYM-109. Interpretive Summary:
Technical Abstract: Climate change is predicted to cause gradual warming and changes in the amount and distribution of rainfall. Thus, the default assumption should be that attributes of rangelands will change, rather than stay as they have been. The talks in this symposium have described what may happen to rangeland communities and why. Here, we explore how managers and policymakers might evaluate and react to predicted ecosystem change. First, ecosystem change can be viewed as degradation to an alternative state in which the risks of transition have increased but managerial attention can preserve functions, and some essential services, of the original ecosystem. Second, ecosystem change may inevitably produce novel ecosystems with combinations of species, functions, and ecosystem services that are distinct from ancestral ecosystems (particularly via invasive species) and that should be accepted for what they are. Third, ecosystem change may reconfigure the relationships among attributes that existed in the ancestral landscape, such that adaptive management can be used to promote a combination of new and previously emphasized functions and services in different parts of a landscape. Depending on the nature of baseline, climate-driven ecosystem responses and managers’ ability to influence them, each of these reactions should be considered. We conclude by exploring how these distinct scenarios might be captured in state-and-transition models.