Prioritizing management of invasive plants is important for large land management entities, such as federal and state public land stewards, because management resources are limited and multiple land uses and management objectives are differentially impacted. Management decisions also have important consequences for the likelihood of success and ultimate cost of control efforts. We applied multi-criteria decision analysis methods in a geographic information system using natural resource and land use data from Fort Bragg, North Carolina. Landscape-scale prioritization was based on a hierarchical model designed to increase invasive plant management efficiencies and reduce the risk of impacts to key installation management goals, such as training-land management and protected species conservation. We also applied spatial sensitivity analyses to evaluate the robustness of the prioritization to perturbations of the model weights, which were used to describe the relative importance of different elements of the hierarchical model. Based on stakeholders' need for confidence in making management investments, we incorporated the results of the sensitivity analysis into the decision-making process. We identified high-priority sites that were minimally affected by the weight perturbations as being suitable for up-front management and evaluated how adopting this strategy affected management area, locations, and costs. We found that incorporating the results of the sensitivity analysis led to a reduced management area, different target locations, and lower costs for an equal area managed. Finally, we confirmed the distinctiveness of the approach by comparing this same subset of prioritized sites with locations representing species-centric strategies for three invasive plants and their aggregate distribution. By supplying pragmatic information about the localized effects of weighting uncertainty, spatial sensitivity analyses enhanced the invasive plant management decision-making process and increased stakeholder confidence.

As climates change, some plant species will need to migrate across landscapes fragmented by unsuitable environments and human activities to colonize new areas with suitable climates as previously habited areas become uninhabitable. Previous modelling of plant's migration potential has generally assumed that climate changes at a constant rate, but this ignores many potentially important aspects of real climate variability. In this study, a spatially explicit simulation model was used to investigate how interannual climate variability, the occurrence of extreme events and step changes in climate might interact with gradual long-term climate change to affect plant species’ capacity to migrate across fragmented landscapes and persist. The considered types of climate variability generally exacerbated the negative effects of long-term climate change, with a few poignant exceptions where persistence of long-lived trees improved. Strategic habitat restoration ameliorated negative effects of climate variability. Plant functional characteristics strongly influenced most results. Any modelling of how climate change may affect species persistence, and how actions such as restoration may help species adapt, should account for both short-term climate variability and long-term change.

A field-scale study of the spatially explicit interaction between the carabid Poecilus cupreus Linnaeus, and two common aphid species (Sitobion avenae (Fabricius) and Metopolophium dirhodum (Walker)) in winter wheat was conducted. All three species showed considerable spatial pattern at the field scale. Activity-density of P. cupreus was an order of magnitude higher in the central part of the field compared to its periphery. Where P. cupreus activity-density was highest, S. avenae and M. dirhodum population peaks were delayed. Additionally, in the case of M. dirhodum, lower maximum counts were evident where P. cupreus activity-density was highest. An analysis of the movement of individual P. cupreus using release–recapture indicated that those beetles within the centre of the field exhibited reduced displacement, which may have caused the generation or maintenance of spatial pattern. Crop density was also measured throughout the field. Although crop density had no large-scale spatial pattern, its variability at the small-scale was consistent with an influence on aphid population dynamics. This study demonstrates empirically that both large-scale spatially explicit and small-scale localized processes influenced aphid population dynamics simultaneously.