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 qui...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 LiDARderived 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.展开更多
基金Funding was provided by the Joint Fire Science Program(US)(for field assistance)primarily the Northern Research Station of the USDA Forest Service(for author salaries)
文摘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 LiDARderived 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.
