Predicting Ailanthus altissima presence across a managed forest landscape in southeast Ohio

• Main Authors: Louis R. Iverson, Joanne Rebbeck, Matthew P. Peters, Todd Hutchinson, Timothy Fox
• Format: Article
• Language: English
• Published: SpringerOpen 2019-10-01
• Series: Forest Ecosystems
• Subjects: Ohio; Random Forest; CART; Maxent; Landscape model; Non-native invasive species
• Online Access: http://link.springer.com/article/10.1186/s40663-019-0198-7

Abstract Background The negative impacts of the exotic tree, Ailanthus altissima (tree-of-heaven, stink tree), is spreading throughout much of the Eastern United States. When forests are disturbed, it can invade and expand quickly if seed sources are nearby. Methods We conducted studies at the highly dissected Tar Hollow State Forest (THSF) in southeastern Ohio USA, where Ailanthus is widely distributed within the forest, harvests have been ongoing for decades, and prescribed fire had been applied to about a quarter of the study area. Our intention was to develop models to evaluate the relationship of Ailanthus presence to prescribed fire, harvesting activity, and other landscape characteristics, using this Ohio location as a case study. Field assessments of the demography of Ailanthus and other stand attributes (e.g., fire, harvesting, stand structure) were conducted on 267 sample plots on a 400-m grid throughout THSF, supplemented by identification of Ailanthus seed-sources via digital aerial sketch mapping during the dormant season. Statistical modeling tools Random Forest (RF), Classification and Regression Trees (CART), and Maxent were used to assess relationships among attributes, then model habitats suitable for Ailanthus presence. Results In all, 41 variables were considered in the models, including variables related to management activities, soil characteristics, topography, and vegetation structure (derived from LiDAR). The most important predictor of Ailanthus presence was some measure of recent timber harvest, either mapped harvest history (CART) or LiDAR-derived canopy height (Maxent). Importantly, neither prescribed fire or soil variables appeared as important predictors of Ailanthus presence or absence in any of the models of the THSF. Conclusions These modeling techniques provide tools and methodologies for assessing landscapes for Ailanthus invasion, as well as those areas with higher potentials for invasion should seed sources become available. Though a case study on an Ohio forest, these tools can be modified for use anywhere Ailanthus is invading.