Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/10/2006
Publication Date: 10/1/2006
Citation: Bestelmeyer, B.T., Tugel, A., Peacock, G., Sanchez, H. 2006. A three-tiered approach for coupled vegetation and soil sampling to develop ecological descriptions. In: Proceedings of the 2006 West Regional Cooperative Soil Survey Conference, June 19-23, 2006, Park City, Utah. 2006 Available on ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCSS/Conferences/regional/2006/West/bestelmeyer_report.doc.
Technical Abstract: Ecological site descriptions (ESDs), alongside similar land classification systems, are used to describe the breadth of plant community types, community changes, and soil surface conditions that can occur within a particular land area. Vegetation dynamic processes and management may change the identity of plant communities over time, but there are a limited number of recognizable plant communities or vegetation states that are observed to occupy a given spatial position. Soil and climate variables are the key environmental attributes predicting the plant communities that can potentially occupy site. Consequently, soil survey products are tremendously useful for developing ESDs. Specifically, ecologically-similar soil map unit components are grouped to form ecological site classes. Soil maps can thus be used to anticipate the ecological sites and possible vegetation states present in a landscape. Ecological site descriptions have been developed by the NRCS alongside soil survey efforts and in soil survey updates. These efforts have resulted in the first generation of ESDs (formerly called range site descriptions). In spite of the great utility of these products, there are now a broader range of uses and users and increased scrutiny. Users have called for improvements in the clarity of classification criteria, increasing use and documentation of empirical data in developing classifications, and refinements to classifications in order accommodate state-and-transition models of vegetation dynamics. The latter issue is particularly important, as there is increasing evidence that the expression of vegetation dynamics and ecological thresholds may be governed by surprisingly subtle differences in soil properties that may or may not be distinguished in existing ESDs (e.g., Fuhlendorf and Smeins 1998, Bestelmeyer et al., 2006). Unfortunately, there are often few data available that can be used to investigate vegetation-soil relationships as a quantitative basis for producing or refining ESDs. The dearth of suitable data has many causes including 1) the lack of any vegetation data gathering associated with soil descriptions, 2) vegetation data that cannot be associated with soil pedon data, 3) too few samples of linked vegetation and soils to represent regional variation or to observe different vegetation states on the same soil types, 4)lack of geolocation and an inability to linked data to spatial layers such as modeled climate (e.g., Daly et al., 2002), 5) vegetation attributes are not sufficient to distinguish vegetation states or other important conditions, and 6) there are no data on surface soil attributes, and thus weak linkages to state-and-transition models and rangeland health. These limitations have precluded evaluation and refinement of ESDs by the science and management communities. In this presentation, we offer a comprehensive framework to overcome data limitations in the development of ESDs and other soil-vegetation-based land classification systems. The framework has four general components 1) training and employment vegetation/rangeland specialists who understand soils in soil survey and other efforts, 2) a set of vegetation sampling protocols that matches the pace of sampling in the field, 3) setting the goal of collecting data at many points with varying levels of precision rather than collecting data at a few points with unnecessarily high precision, and 4) systematic collection of data at georeferenced points and their storage in a database that links soil and vegetation properties. We provide details about these components and an example of how data can be used to generate inferences in an ESD context.