Rhus glabra response to season and intensity of fire in tallgrass prairie
Kristy M. Hajny A , David C. Hartnett A C and Gail W. T. Wilson B
+ Author Affiliations
- Author Affiliations
A Division of Biology, Kansas State University, 104 Ackert Hall, Manhattan, KS 66506, USA.
B Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078, USA.
C Corresponding author. Email: dchart@ksu.edu
International Journal of Wildland Fire 20(5) 709-720 https://doi.org/10.1071/WF09127
Submitted: 4 November 2009 Accepted: 20 December 2010 Published: 8 August 2011
Abstract
Altered fire regimes play a key role in shrub expansion in grasslands worldwide. We assessed how season and type or intensity of fire affected the growth and demography of Rhus glabra, a common woody invader in North American mesic grasslands. Fire during any season killed 99% of ramets but stimulated new ramet recruitment from belowground buds, resulting in a near-complete turnover of ramet populations. During the first 2 years following fire, populations on spring-burned sites had the greatest post-fire ramet densities and population growth rates, and winter- and spring-burned populations showed the highest resprouting rates. However, after 10 years, R. glabra cover on summer-burned sites was 3.5 times greater than on autumn- or winter-burned treatments. Thus, short-term post-fire responses may not be good predictors of long-term changes in abundance. Low-intensity spring backfires resulted in the highest ramet population growth rates, whereas high-intensity headfires in any season resulted in slower growth, and populations burned with low-intensity winter fires declined. In addition, season of fire influenced browsing pressure, suggesting that plant responses may be partially a result of indirect effects of fire on rates of herbivory. Overall, our results demonstrate that the application of frequent autumn or winter backfires is an effective management tool for limiting R. glabra expansion in grasslands, and that long-term data are critical for management decision-making, particularly in systems characterised by high interannual climate variability.
Additional keywords: smooth sumac, shrub demography, shrub encroachment.
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