Surveillance protocols for management of invasive plants: modelling Chilean needle grass (Nassella neesiana) in Australia

Accounting for spatially heterogeneous conditions in local-scale surveillance strategies: case study of the biosecurity insect pest, grape phylloxera (Daktulosphaira vitifoliae (Fitch))

BACKGROUND

Surveillance strategies are often standardized and completed on grid patterns to detect pest incursions quickly; however, it may be possible to improve surveillance through more targeted observation that accounts for landscape heterogeneity, dispersal and the habitat requirements of the invading organism. We simulated pest spread at a local scale, using grape phylloxera (Daktulosphaira vitifoliae (Fitch)) as a case study, and assessed the influence of incorporating spatial heterogeneity into surveillance compared with current, standard surveillance strategies.

RESULTS

Time to detection and spread within and beyond the vineyard were reduced by conducting surveys that target sampling effort in soil that is highly suitable for the invading pest in comparison with standard surveillance strategies. However, these outcomes were dependent on the virulence level of phylloxera because phylloxera is a complex pest with multiple genotypes that influence spread and detectability.

CONCLUSION

Targeting surveillance strategies based on local-scale spatial heterogeneity can decrease the time to detection without increasing the survey cost, and surveillance that targets highly suitable soil is the most efficient strategy for detecting new incursions. In addition, combining targeted surveillance strategies with buffer zones and hygiene procedures, and updating surveillance strategies as additional species information becomes available, will further decrease the risk of pest spread. © 2018 Society of Chemical Industry.

Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species

Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single- and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models—CHMs; and Bi-Temporal Band Ratios—BTBRs) for the detection and mapping of the highly problematic Asian knotweeds (Fallopia japonica; Fallopia × bohemica) in two different landscapes (i.e., open vs. highly heterogeneous areas). The idea was to develop a simple classification procedure using the Random Forest classifier in eCognition, usable in various contexts and requiring little training to be used by non-experts. We also rationalized errors of omission by applying simple “buffer” boundaries around knotweed predictions to know if heterogeneity across multi-date images could lead to unfairly harsh accuracy assessment and, therefore, ill-advised decisions. Although our “crisp” satellite results were rather average, our UAV classifications achieved high detection accuracies. Multi-date spectral indices and CHMs consistently improved classification results of both datasets. To the best of our knowledge, it was the first time that UAV-generated CHMs were used to map invasive plants and their use substantially facilitated knotweed detection in heterogeneous vegetation contexts. Additionally, the “buffer” boundary results showed detection rates often exceeding 90–95% for both satellite and UAV images, suggesting that classical accuracy assessments were overly conservative. Considering these results, it seems that knotweed can be satisfactorily mapped and monitored via remote sensing with moderate time and money investment but that the choice of the most appropriate method will depend on the landscape context and the spatial scale of the invaded area.

Dispersal and the design of effective management strategies for plant invasions: matching scales for success

Dispersal of propagules makes invasions a fundamentally spatial phenomenon, and to be effective, management actions to control or eradicate invasive species must take this spatial structure into account. While there is a vibrant literature linking detailed dispersal measurements to the rate of invasive spread, and a separate literature focused on incorporating management into invasive models in order to improve the control of weeds, there are relatively fewer manuscripts incorporating state-of-the-art dispersal modeling and management modeling together to provide on-ground recommendations for structuring effective management. In this paper, we perform a generalized analysis of a spatially explicit, individual-based simulation model of invasion management with empirically determined dispersal processes, illustrated with the example of Miconia calvescens in the Australian Wet Tropics rain forest, to explore how matching the spatial scale of management to the spatial scale of the dispersal processes underpinning invasion influences the success of management. We find that management strategies designed to maximize the number of weeds removed from the management region, either in the first year of management or over longer periods, provide a poor estimate of the spatial scale of management that maximizes the probability of eradication. We show that achieving a goal of certainty of eradication requires exceeding a minimal spatial scale of management and total management resourcing. We generalize these results to examine how the spatial scale of dispersal drives the spatial scale of effective management strategies. These results show that to be effective, management of dispersal-driven invasions must occur at spatial scales determined by the scale of dispersal processes, and resourced accordingly. It illustrates how those scales might be calculated for a specific case for which detailed dispersal data are available and generalizes the result to highlight how dispersal scale drives the scale of effective management. The results highlight the importance of understanding the ecological drivers of invasion to structure effective management.