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

作     者：Louis R.Iverson Joanne Rebbeck Matthew P.Peters Todd Hutchinson Timothy Fox 

作者机构：Northern Research Station USDA Forest Service 

出 版 物：《Forest Ecosystems》 (森林生态系统（英文版）)

年 卷 期：2019年第6卷第4期

页      面：261-273页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 070303[理学-有机化学] 0703[理学-化学] 

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) 

主　　题：Ohio Random Forest CART Maxent Landscape model Non-native invasive species 

摘      要：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 ***: 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 ***: 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 ***: 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