Fire effects on post-invasion spread of Chinese tallow (Triadica sebifera) in wet pine flatwood ecosystems in the southeastern United States

Abstract Quantifying invasion severity of nonnative invasive plant species is vital for the development of appropriate mitigation and control measures. We examined more than 23,250 Forest Inventory and Analysis (FIA) plots from the southern coastal states of the United States to develop an alternative method to classify and map the invasion severity of Chinese tallow (Triadica sebifera). Remeasured FIA plot-level data were used to examine the spatiotemporal changes in the presence probability and cover percentage of tallow. Four invasion severity classes were identified by using the product of presence probability and cover percentage. Chinese tallow invasion severity increased over time with 90 and 123 counties being classified into the highest severity class for the first and second measurement, respectively. Further, the invasibility of major forest-type groups by severity class was examined using the product of the county-level mean presence probability and mean cover percentage of Chinese tallow as a proxy of invasibility. Longleaf/slash pine (Pinus palustris/P. elliottii) forests were highly resilient to the Chinese tallow invasion. In contrast, elm/ash/cottonwood (Ulmus spp./Fraxinus spp./Populus deltoides) and oak/gum/cypress (Quercus spp./Nyssa spp./Taxodium spp.) forest-type groups were vulnerable to invasion. Study Implications: In the southern United States forestland, differences in invasion severity and vulnerability of forest types to Chinese tallow invasion have been observed across time and space. Our findings provide insight into spatial variations in the severity of Chinese tallow invasion and the relative susceptibility of different forest-type groups in the region to inform monitoring and management of this invasive species. High invasion severity occurs in the lower Gulf of Mexico coastal region of Texas, Louisiana, and Mississippi and the Atlantic coastal region of South Carolina and Georgia, with the longleaf/slash pine and oak/gum/cypress forest-type groups being most susceptible to Chinese tallow invasion. Based on these results, we recommend that management efforts be tailored to the different invasion severity classes. Forests in the high-severity class need a management program coordinated across different agencies and landowners to curb the increase of tallow populations to prevent stand replacing risks. The monitoring of Chinese tallow spread should focus on longleaf/slash pine, loblolly/shortleaf pine, and oak/gum/cypress groups, because the spread rate was higher in these forest-type groups. A better use of scarce resources could be to treat lands in the moderate- and low-severity classes to reduce the propagule pressure levels and post-invasion spread. For those counties with a minimal-severity condition, early detection and eradiction measures should be taken in a timely maner to prevent tallow from invading noninvaded neighboring counties. Managers may be able to treat a larger area of these lands for a given investment compared with lands already severely invaded.

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Predicting litter and live herb fuel consumption during prescribed fires in native and old-field upland pine communities of the southeastern United States

Canadian Journal of Forest Research ◽

10.1139/x2012-096 ◽

2012 ◽

Vol 42 (8) ◽

pp. 1611-1622 ◽

Cited By ~ 22

Author(s):

Angela M. Reid ◽

Kevin M. Robertson ◽

Tracy L. Hmielowski

Keyword(s):

United States ◽

Fuel Consumption ◽

Southeastern United States ◽

Pinus Palustris ◽

Fire Effects ◽

Fuel Load ◽

Prescribed Fires ◽

Old Field ◽

Fire Emissions ◽

Fuel Loads

The ability to predict fuel consumption during fires is essential for a wide range of applications, including estimation of fire effects and fire emissions. This project identified predictors of fuel consumption for the dominant fuel bed components (litter (<0.6-cm diameter dead material) and live herbs) during 217 prescribed fires in native longleaf pine ( Pinus palustris Mill.) and old-field loblolly pine ( Pinus taeda L.) – shortleaf pine ( Pinus echinata Mill.) communities in the southeastern United States. Additionally, these data were used to validate the First Order Fire Effects Model (FOFEM) fuel consumption computer model using custom and default fuel loads. Regression models using empirical data suggested that litter and live herb fuel consumption can be predicted by prefire litter and live herb fuel loads, litter and live herb fuel moisture, litter fuel bed bulk density, season of burn, years since fire, days since last rain ≥0.64 cm, relative humidity, energy release component, community type, pine and hardwood basal areas, and the Keetch–Byram drought index. FOFEM’s prediction of fuel consumption for litter, live herbs, and duff combined using default fuel loads was 1.5 times the measured fuel consumption (where duff fuel load was zero). Refinement of FOFEM’s fuel load and consumption calculations in the studied community types using the newly collected data and suggestions for model improvement would provide more accurate air quality inventories and assist in guiding appropriate regulation of prescribed fire.

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