Predicting the invasion of the exotic species Paulownia tomentosa following fire in pine and oak-pine forests of the Appalachian mountains. Dane M. Kuppinger, Peter S. White, and Michael A. Jenkins University of North Carolina at Chapel Hill, University of North Carolina at Chapel Hill, National Park Service, Great Smoky Mountains National Park CoefficientDFDevianceResidual DF Residual Dev Pr(ChiSq) NULL Densio Bare.soil Site Topo.position Slope Litter Max litter depth Densio:slope Densio:site Distribution of Paulownia tomentosa in the US  The variables used in the first series of splits (densio, site, bare.soil, topographic position, and slope) in the classification trees were also found to be the significant predictors in both types of regression models. The congruence of such divergent methods for analyzing the data suggest that these variables are indeed the most significant ones.  The high level of residual variation left in both regression models suggests that there is a good deal of interaction between the variables that is captured by the classification tree, but not the regressions.  The classification trees show that there are complicated interactions between different variables. Regardless, the trees are able to predict the invasion of Paulownia with a fair overall success rate (89.82%). The accurate classification rate for Paulownia’s absence was however significantly higher (94%) than the accuracy of its presence prediction (77%).  The significance of topographic position as a whole and the variation in the proportion of sites within each topographic position that are invaded suggests that there may be a watershed scale pattern to invasion that needs further clarification.  The inclusion of site as a significant variable suggests that there is some spatial component of P. tomentosa’s invasion not captured by the data analyzed so far. Flowers of Paulownia tomentosa CoefficientDFDevianceResidual DF Residual Dev Pr(ChiSq) NULL Densio Bare.soil Site Topo.position Slope While fire has become a valuable management tool in recent years, understanding and minimizing the detrimental effects of prescribed fire is becoming critical as its use increases. Of these detrimental effects, invasion by exotic species is particularly alarming as it presents the potential to undermine the beneficial effects of prescribed fires. As fires are once again becoming part of the landscape, it has become increasingly important to determine the landscape, watershed and stand variables that favor the spread of exotic species into natural areas following fire. Paulownia tomentosa is one such exotic species that has been observed to invade natural areas following burns in the Southern Appalachian Mountains. We hypothesized that, at the stand level, canopy cover and ground cover variables that impact the ability of P. tomentosa to become established (put down a primary root) will significantly affect the number of stems encountered. Our results indicate that remaining canopy cover is the most significant predictor of invasion success. Other significant variables include: bare soil exposure, slope, site, and topographic position. Abstract 1.Do site specific environmental variables as measured by topographic position, slope, aspect, elevation, and herbaceous cover, have an impact on Paulownia invasion as measured by its presence or absence, the number of seedlings, or their height? 2.Do the effects of fire severity as measured by the surviving canopy, understory or shrub cover, or ground-surface cover (litter, mineral and organic soil exposure) influence the pattern of Paulownia invasion as measured by its presence or absence, the number of seedlings, or their height? Research Questions  P. tomentosa was first introduced to the United States from SE Asia in  In the US it has been used for lumber, pulp, as an ornamental in the horticulture industry, to reclaim strip mines, and as goat browse.  It possesses many life history traits often observed in invasive species including: rapid growth, high seed production, a short juvenile period, and rapid colonization following disturbance.  Dense populations of P. tomentosa have been found in areas of high human disturbance for years. In the past decade it has also begun to appear following fires in forest habitats in the National Forests of the S. Appalachians and the Smoky Mountain National Park. Background information Field Sampling  10x10m plots placed every 50m along transects run across the slope and stratified by fire intensity and landscape position.  Each plot was subdivided into 4 5x5m modules and ground coverage was recorded for 9 biotic and abiotic variables by cover class.  Slope, aspect, topographic position, cover by strata, and overall cover over breast height (measured with a densiometer, coded as densio in the analysis) was recorded for each plot.  Tree dbh’s and shrub and herb coverages were recorded for each sub-module.  The number and height of all P. tomentosa stems were recorded.  A GIS position was taken for each plot. Data Analysis  Densiometer readings for the whole plot were assumed to be representative of each module and these readings were combined with the per module ground cover measurements.  Classification trees were built using S-Plus to look at the ability of recorded variables to predict the presence or absence of P. tomentosa on a per module basis (n=516).  Two regression models (logistic and negative binomial) were developed using presence/absence and stem counts respectively. Variables were stepwise removed from the models and ANOVA tests and AIC calculations were used to test the significance of each variable.  Results from these two models were compared with the classification tree to look at the predictive power of the different approaches. Methods Our analysis shows that the dominant factors affecting the invasion of P. tomentosa after fire, both in terms of its presence and the number of stems encountered, are canopy cover, amount of bare soil exposure, site, slope, and topographic position. These results support the initial hypothesis that canopy cover would be the most important determinant of invasion success, but the presence of variables other than bare soil in the second and third splits within the classification tree, suggests that factors other than soil exposure may be more important determinants of invasion success. Indeed sites without P. tomentosa have a higher mean area of bare soil exposure than sites with it. The difference however is not large and may represent the impact of other variables such as soil stability. The significance of the site variable suggests that there is a landscape scale spatial component affecting invasion success not accounted for by the variables currently included in the model. The tendency of the trees to over predict the occurrence of P. tomentosa may be a result of its ongoing invasion. Those plots without P. tomentosa but identified as suitable for P. tomentosa invasion may represent areas where invasion will occur in the future if current conditions persist. The high level of accuracy with which Paulownia’s absence can be predicted with the classification tree suggests that this approach clearly identifies the conditions which prevent invasion and that the approach may be a useful tool for conservationists. Discussion and Conclusions Special thanks goes to my advisor Dr. Peter White and the rest of my committee for their assistance throughout this project. My field assistant, john johnson, has been a wonderful help and very tolerant of long hours and wet conditions. Jack Weiss deserves credit for his help with statistical issues, the Plant Ecology grad students for support and feedback, and my wife, Ellen, and newborn son, Quetzal for their love, support, and patience. Funding was provided by the Joint Fire Science Program. Acknowledgments Table 1. ANOVA results for the negative binomial regression model: # Paulownia stems ~ densio + bare.soil + site + topo.position + slope + litter + Max litter depth + densio*slope + densio*site Table 2. ANOVA results for the logistic general linear model: Paulownia Pres/Abs ~ densio + bare.soil + site + topo.position + slope Additional variables and interaction terms were tested and found to be non-significant. Unpruned tree, Proportional length branches Results and Analyses Actual (x axis) vs. predicted (y axis) number of plots with Paulownia Full treePruned tree Red - 100% accurate prediction Blue - >90% accurate prediction Green - >80% accurate prediction Both graphs give values in relation to tree size (# leaves) Graph #1 (top): Rate of misclassifications for whole sample Graph #2 (bottom): Deviance of pooled subsample misclassification rates Full Tree Statistics Overall misclassification rate = 8.21% Overall classification accuracy = 91.78% Accuracy of presence prediction = 77.85% Accuracy of absence prediction = 92.88% Pruned Tree Statistics Overall misclassification rate = 10.17% Overall classification accuracy = 89.82% Accuracy of presence prediction = 77.14% Accuracy of absence prediction = 94.05%