Large-scale control of invasive plants can benefit strongly from reliable assessment of spatial variation in plant invasibility. With this knowledge, limited management resources can be concentrated in areas of high invasion risk. We assessed the influence of spatial environments and proximity to roads on the invasibility of African mustard (Brassica tournefortii Gouan) over the 280,000-ha Barry M. Goldwater Range West in southwestern Arizona, USA. We used presence/absence data of B. tournefortii acquired from a vegetation classification project, in which lands were mapped to the level of vegetation subassociations. Logistic regression models suggested that spatial environments represented by the subassociations, not proximity to roads, represented the only factor significantly explaining B. tournefortii presence. We then used the best model to predict B. tournefortii invasibility in each subassociation. This prediction indicates management strategy should differ between the western part and the central to eastern part of the range. The western range is a large spatial continuum with intermediate to high invasion risk, vulnerable to an untethered spread of B. tournefortii. Controlling efforts should focus on preventing existing local populations from further expansion. The central and eastern ranges are a mosaic varying strongly in invasion risk. Control efforts can take advantage of natural invasion barriers and further reduce connectivity through removal of source populations connected with other high-risk locations via roads and other dispersal corridors. We suggest our approach as one effective way to combine vegetation classification and plant invasion assessment to manage complex landscapes over large ranges, especially when this approach is used through an iterative prediction–validation process to achieve adaptive management of invasive plants.

中文翻译：

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植被分类能够推断植物入侵性的中尺度空间变化

对入侵植物的大规模控制可以从对植物入侵性空间变化的可靠评估中受益匪浅。有了这些知识，有限的管理资源就可以集中在入侵风险高的地区。我们评估了空间环境和靠近道路对非洲芥菜侵入性的影响（芸薹属Gouan）在美国亚利桑那州西南部 280,000 公顷的 Barry M. Goldwater Range West 上空。我们使用了存在/不存在数据B.tournefortii从植被分类项目中获得，其中土地被映射到植被子协会的水平。逻辑回归模型表明，子关联所代表的空间环境，而不是靠近道路，是唯一能显着解释的因素B.tournefortii在场。然后我们使用最好的模型来预测B.tournefortii每个子关联中的不可渗透性。这一预测表明，该范围的西部和中部到东部的管理策略应该不同。西部山脉是一个大的空间连续体，具有中到高入侵风险，容易受到不受限制的B.tournefortii. 控制工作应侧重于防止现有当地人口进一步扩大。中部和东部山脉是一个镶嵌式，入侵风险差异很大。控制工作可以利用自然入侵障碍，并通过清除通过道路和其他分散走廊与其他高风险地点相连的源种群来进一步减少连通性。我们建议将我们的方法作为结合植被分类和植物入侵评估来管理大范围内复杂景观的一种有效方法，特别是当这种方法通过迭代预测-验证过程来实现入侵植物的适应性管理时。