There has been ongoing discussion around the necessity for quantitative models in ecology. The use of quantitative modeling is well established in some areas of endeavour, such as Before-After-Control-Impact (BACI) studies, but not in others, in particular the field of invasion ecology. In weed risk analysis, semi-quantitative models (scoring systems, with or without weighting procedures) help policy makers to assess the risk (hazard) posed by individual weed species. Such systems are available to assess weed risk management feasibility at larger geographic scales. However, nothing is available to assist on-ground practitioners in prioritising weed control at the individual site level. Interestingly, the fundamental problem of model choice was solved in the early 2000s by sociological researchers (Dana and Dawes 2004), who demonstrated that qualitative models actually outperformed quantitative models, as long as all of the important factors in the system had been identified. An earlier attempt to establish this finding in the weed invasion literature (Panetta and Cacho 2014) has not been successful. In this paper, I use the results from an ongoing project (“Future-proofing Australia’s National Post-Border Weed Risk Management System”) to develop a model that combines both qualitative and semi-quantitative approaches. This model should be fit-for-purpose by practitioners at the site level, as well as by policy makers charged with allocating scarce resources at larger geographic scales
Major fires and floods have enormous impacts on natural ecosystems and are predicted to increase in frequency with global warming. Land managers need to make decisions on the prioritisation of weeds for control in post-disturbance landscapes, but little is available in the way of guidance to support timely decision making. Semi-quantitative models (e.g., scoring systems) have been employed routinely in weed risk assessment, which considers the potential impacts posed by weeds, as well as the likelihood of these impacts being realised. Some progress has been made in the development of similar models addressing the topic of weed risk management. Under conditions prevailing after major disturbances, changes (both positive and negative) can be expected in the multiple factors that determine weed management feasibility, relative to pre-disturbance conditions. A semi-quantitative model is proposed that is based on the key factors that contribute to weed management feasibility in post-disturbance environments, along with annotated modules that could be used by land managers in both post-fire and post-flood situations. The fundamental challenge for weed management in these scenarios lies in the identification of differences between weeds and native species in relation to (1) patterns of seedling emergence; and (2) detectability relative to the growth stage. These two factors will determine the timing of control actions that are designed to address the trade-off between weed control and off-target damage during the period when both types of plants are recovering from a major disturbance event. The model is intuitively sound, but field testing is required to determine both its practical value and any necessary improvement.
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