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Developing a Framework for Evaluating Tallgrass Prairie Reconstruction Methods and Management
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Developing a Framework for Evaluating Tallgrass Prairie Reconstruction Methods and Management
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Developing a Framework for Evaluating Tallgrass Prairie Reconstruction Methods and Management
Developing a Framework for Evaluating Tallgrass Prairie Reconstruction Methods and Management
Journal Article

Developing a Framework for Evaluating Tallgrass Prairie Reconstruction Methods and Management

2018
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Overview
The thousands of hectares of prairie reconstructed each year in the tallgrass prairie biome can provide a valuable resource for evaluation of seed mixes, planting methods, and post-planting management if methods used and resulting characteristics of the prairies are recorded and compiled in a publicly accessible database. The objective of this study was to evaluate the use of such data to understand the outcomes of reconstructions over a 10-year period at two U.S. Fish and Wildlife Service refuges. Variables included number of species planted, seed source (combine-harvest or combine-harvest plus hand-collected), fire history, and planting method and season. In 2015 we surveyed vegetation on 81 reconstructions and calculated proportion of planted species observed; introduced species richness; native species richness, evenness and diversity; and mean coefficient of conservatism. We conducted exploratory analyses to learn how implied communities based on seed mix compared with observed vegetation; which seeding or management variables were influential in the outcome of the reconstructions; and consistency of responses between the two refuges. Insights from this analysis include: 1) proportion of planted species observed in 2015 declined as planted richness increased, but lack of data on seeding rate per species limited conclusions about value of added species; 2) differing responses to seeding and management between the two refuges suggest the importance of geographic variability that could be addressed using a public database; and 3) variables such as fire history are difficult to quantify consistently and should be carefully evaluated in the context of a public data repository.
Publisher
University of Wisconsin Press

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