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Impact assessment with different scoring tools: How well do alien amphibian assessments match?
(2017)
Classification of alien species' impacts can aid policy making through evidence based listing and management recommendations. We highlight differences and a number of potential difficulties with two scoring tools, the Environmental Impact Classification of Alien Taxa (EICAT) and the Generic Impact Scoring System (GISS) using amphibians as a case study. Generally, GISS and EICAT assessments lead to very similar impact levels, but scores from the schemes are not equivalent. Small differences are attributable to discrepancies in the verbal descriptions for scores. Differences were found in several impact categories. While the issue of disease appears to be related to uncertainties in both schemes, hybridisation might be inflated in EICAT. We conclude that GISS scores cannot directly be translated into EICAT classifications, but they give very similar outcomes and the same literature base can be used for both schemes.
Prioritisation of high-impact species is becoming increasingly important for management of introduced species (‘neobiota’) because of their growing number of which, however, only a small fraction has substantial impacts. Impact scores for prioritising species may be affected by the type of effect model used. Recent studies have shown that environmental co-variation and non-linearity may be significant for effect models of biological invasions. Here, we test for differences in impact scores between simple and complex effect models of three invasive plant species (Heracleum mantegazzianum, Lupinus polyphyllus, Rosa rugosa). We investigated the effects of cover percentages of the invasive plants on species richness of invaded communities using both simple linear effect models (‘basic models’) and more complex linear or nonlinear models including environmental co-factors (‘full models’). Then, we calculated impact scores for each invasive species as the average reduction of species richness predicted by basic and full effect models. All three non-native species had negative effects on species richness, but the full effect models also indicated significant influence of habitat types. Heracleum mantegazzianum had uniform linear effects in all habitats, while effects of L. polyphyllus interacted strongly with habitat type, and R. rugosa showed a marked non-linear relationship. Impact scores were overestimated by basic effect models for H. mantegazzianum and R. rugosa due to disregard of habitat effects and non-linearity, respectively. In contrast, impact of L. polyphyllus was underestimated by the basic model that did not account for the strong interaction of invader cover and habitat type. We conclude that simple linear models will often yield inaccurate impact scores of non-native species. Hence, effect models should consider environmental co-variation and, if necessary, non-linearity of the effects of biological invasions on native ecosystems.
For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk.