{'fraction_non_alphanumeric': 0.026749888542131076, 'fraction_numerical': 0.0026749888542131075, 'mean_word_length': 5.32112676056338, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Role of Soil and Landscape Factors in Chinese Privet (Ligustrum sinense) Invasion in the Appalachian Piedmont

Abstract There is a limited understanding about the ecological mechanisms that enable certain plant species to become successful invaders of natural areas. This study was conducted to determine the soil and landscape characteristics that correlate with invasion of Chinese privet (CHP), and to develop a model to predict the probability of CHP invasion in Piedmont forests. A landscape ecosystem classification (LEC) system—based on the percentage of clay in the B horizon, depth to maximum clay (cm), exposure, terrain shape, and aspect (degrees)—was used to determine the soil moisture characteristics of invaded and uninvaded plots. Additional measurements included the cover classes of CHP and other species, litter depth (cm), slope (degrees), overstory basal area (m2 ha−1), and soil chemical properties. CHP invasion was negatively correlated with overstory basal area and slope and positively with litter depth and pH. A stepwise logistic regression model containing these four variables was highly sensitive, with an overall accuracy of 78%. Given the accuracy of this model, we propose that it can be used to calculate the probability of invasion in a given area, provided that some basic, readily obtainable site characteristics are known. Nomenclature: Chinese privet, Ligustrum sinense Lour. Management Implications: Chinese privet (CHP) (Ligustrum sinense Lour) is one of the most common woody invasive alien plant species in the Appalachian Piedmont. An improved understanding of the factors that correlate with CHP invasion will benefit land managers in the region, as this species can reduce native diversity, alter forest structure, and be costly to control. We found CHP invasion to be negatively correlated with overstory basal area and slope and positively correlated with litter depth and pH. A model containing these four variables was highly sensitive, being able to predict CHP invasion 78% of the time. By identifying the areas that are most likely to be invaded, this model could facilitate early detection and control of CHP, thereby slowing its spread and helping to conserve native flora and fauna.