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. 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. 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. 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.

中文翻译：

---

预测俄亥俄州东南部整个管理的森林景观中的臭椿altissima的存在

异国情调的树臭椿（Ailanthus altissima）（天堂树，臭树）的负面影响正蔓延到美国东部大部分地区。当森林受到干扰时，如果种子源在附近，森林就会入侵并迅速扩张。我们在美国俄亥俄州南部东南部高度解剖的焦油空心森林（THSF）进行了研究，臭椿在森林中广泛分布，数十年来一直在进行采伐，并在研究区域的约四分之一处开了规定的火。我们的目的是使用俄亥俄州的这一地点作为案例研究，开发模型来评估臭椿的存在与规定的火灾，收成活动和其他景观特征之间的关系。对臭椿和其他林分属性（例如火灾，采伐，在整个THSF的400米网格上的267个样地上进行了林分结构），并通过在休眠季节通过数字空中草图绘制来识别臭椿种子源。统计建模工具使用随机森林（RF），分类和回归树（CART）和Maxent评估属性之间的关系，然后对适合臭椿存在的栖息地进行建模。在模型中总共考虑了41个变量，包括与管理活动，土壤特征，地形和植被结构有关的变量（来自LiDAR）。臭椿存在的最重要预测指标是近期木材采伐的某种度量，可以是采伐历史记录（CART）或LiDAR派生的树冠高度（Maxent）。重要的，在任何THSF模型中，规定的火灾或土壤变量均未显示为臭椿是否存在的重要预测指标。这些建模技术提供了工具和方法，用于评估臭椿入侵的景观，以及在可获得种子源时具有较高入侵潜力的地区。尽管以俄亥俄州的森林为案例研究，但可以修改这些工具，以便在臭椿入侵的任何地方使用。