Chinese privet (Ligustrum sinense Lour.) is a deciduous to evergreen shrub with an expansive nonnative global range. Control costs are often high, so land managers must carefully consider whether the plant’s potential negative effects warrant active management. To help facilitate this decision-making process, we reviewed and synthesized the literature on the potential ecological effects of L. sinense invasion. We also identified research gaps in need of further study. We found ample evidence of negative relationships between L. sinense invasion and native plant communities. While observational studies are not able to confirm whether L. sinense is driving these relationships, experimental evidence suggests that there is a cause–effect relationship. Of particular concern is the possibility that L. sinense could suppress forest regeneration and cause areas to transition from forest to L. sinense–dominated shrublands. Although this outcome would obviously impact a wide variety of wildlife species, empirical evidence of negative effects of L. sinense on wildlife are limited, and some species may actually benefit from the additional cover and foraging opportunities that L. sinense can provide. Further research on the potential effects of L. sinense invasion on large-scale forest structure and wildlife populations is needed. In areas where L. sinense invasion is a concern, evidence suggests early detection and management can mitigate control costs.
There is emerging interest in using prescribed fire to manage bottomlands for wildlife habitat, invasive species control, and overall forest function. We evaluated the feasibility of conducting prescribed fires in bottomland hardwood forests in west-central Alabama as part of a broader strategy to control the invasive shrub Chinese privet (Ligustrum sinense). We used 22 small-scale plots (0.04 hectares) in areas with residual slash from privet cutting operations and initiated prescribed fires on each to assess the overall feasibility and the relation of in-stand weather (i.e., microclimate), stand composition, and litter measurements to fire behavior. Overall, prescribed fire ignition was difficult, and only half the trials successfully burned >10 percent of the plot. We found that stand composition was most correlated with percent plot burned, and plots with higher proportions of tree species with flammable leaf traits (e.g., Quercus spp.) tended to burn best. Although further investigation is warranted, managers interested in using prescribed fire for bottomland hardwoods likely face short time windows and limited forest conditions in which fires can be reliably set. Study Implications There is increasing interest in using prescribed fires in bottomland hardwood forests. This exploratory study evaluated whether prescribed fires could be reliably set in bottomlands. Prescribed fires were difficult to establish and tended to be very patchy with fire spread related to tree canopy composition (because of differences in leaf litter flammability) and litter loads. Results suggest that it would be difficult to apply fire on a large scale in bottomland hardwood forests and that small-scale fires could only be set under certain conditions.
Chinese privet (Ligustrum sinense) is a common invasive shrub in hardwood forests of the southeastern US and has been shown to negatively affect native herbaceous and woody plants. The ability to map the distribution of L. sinense on a property could help land managers plan and budget for control operations. We evaluated whether freely available moderate resolution multispectral imageries (Landsat 8 and Sentinel 2) and open-source GIS software (QGIS with the Semi-Automatic Classification Plugin) could be effective tools for this application. These tools are widely used by remote sensing and mapping professionals; however their adoption by field-level land managers appears limited, and their utility for mapping L. sinense invasions is untested. We evaluated how satellite type, image acquisition date, classification algorithm, and L. sinense cover affected detection accuracy. We found that Sentinel 2 imagery from March tended to produce good results, especially when analyzed using the maximum likelihood algorithm. Our best classifier obtained an overall accuracy of 92.3% for areas with ≥ 40% L. sinense cover. We recommend that land managers interested in applying this tool use an adaptive process for developing training polygons and test multiple images and classification algorithms in order to achieve optimal results.
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