Search

Managing the potential weed risks associated with the entry of new plant species into a country is dependent on a comprehensive regulatory system to control entry and a reliable system for predicting potential weediness in advance of release. The weed risk assessment (WRA) system developed in Australia has been used by many countries to assess weed risks. Evaluation of this system using test data sets of plants with known characteristics has shown that it performs well in identifying species that may be weedy but has a significant rate of false positives. However, the costs associated with allowing weedy species to establish and spread significantly exceed the benefits forgone by rejecting useful, non-weedy species particularly when longer time scales are considered.

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1 year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.

We evaluated the applicability of the Australian Weed Risk Assessment (AWRA) system in Japan. Native weeds (n = 117) and introduced plants (n = 142), whose weed status was classified by 20 plant experts, were assessed using a slightly modified version of the AWRA system designed to fit Japanese conditions. A receiver operating characteristic (ROC) curve for the system, when classifying two-thirds of the 259 taxa as weeds or non-weeds, was plotted and the area under the ROC curve was calculated. The area was 0.88 and significantly greater than 0.5. Thus, the validity of the system to classify plants was proven. The best cut-off level for the WRA score using Youden’s index was 10. When taxa whose AWRA scores were greater than 10 were regarded as weeds, the sensitivity and specificity were 0.88 and 0.78, respectively. These values were verified with the remaining one-third of the taxa. From these findings, the modified AWRA system was considered to be effective for use in Japan. However, further studies are required to set the best cut-off level in terms of maximising the benefits gained from using the system. A second screening test associated with the cut-off level also needs to be developed.

The detectability of invasive organisms influences the costs and benefits of alternative control strategies, and the feasibility of eradicating an infestation. Search theory offers a mathematically rigorous framework for defining and measuring detectability taking account of searcher ability, biological factors and the search environment. In this paper, invasive species detectability is incorporated into a population simulation model. The model is applied to a base set of parameter values that represent reasonable values for a hypothetical weed. The analysis shows the effects of detectability and search effort on the duration of an eradication program. For a given level of detectability and search time, the analysis shows that the variables with the greatest influence on the duration of the eradication effort are search speed, kill efficiency, germination rate and seed longevity. Monte Carlo simulations are performed on a set of four weed scenarios, involving different combinations of plant longevity, seed longevity and plant fecundity. Results of these simulations are presented as probability distributions and allow us to calculate how the probability of eradication will be affected by search strategy.

Using the Australian Weed Risk Assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means of explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’ or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high-expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect.

The C4 grasses Cynodon dactylon L. Pers., Chloris gayana Kunth, Paspalum dilatatum Poir. and Pennisetum clandestinum Hochst. were analysed for their contents of protein, extracted by and soluble in neutral buffer solutions, and for the major soluble protein of the chloroplast, fraction 1 protein. Conditions avoiding oxidative changes were used. In the four C4 grasses, soluble protein was 26–30% of the total protein, and fraction 1 protein was 11–16% of the soluble protein. In three C3 grasses, soluble protein was 33–48% of total protein, and fraction 1 protein was 29–43% of soluble protein. The comparative solubilities and ease of extraction of C3 and C4 grasses is discussed in relation to nitrogen retention in grazing ruminants.