Comparing agglomerative clustering and three weed classification frameworks to assess the invasiveness of alien...

To prioritize weed management at the catchment scale, information is required on the species present, their relatively frequency, abundance, and likely spread and impact. The objective of this study was to classify the invasiveness of alien species that have invaded the Upper Burdekin Catchment in Queensland, Australia, at three spatial scales. A combination of three published weed classification frameworks and multivariate techniques were employed to classify species based on their frequency and cover at a range of spatial scales. We surveyed the Upper Burdekin Catchment for alien species, and for each species determined the following distribution indices - site frequency, total cover, transect frequency per site frequency and quadrat frequency per site frequency, cover per quadrat when present, cover per transect when present, and cover per site when present. These indices capture the effect of species abundance and frequency between sites (site frequency and total cover), within sites (transect frequency per site and cover per transect when present), and within transects (quadrat frequency per site frequency and cover per site). They were used to classify the species into seven groups using a hierarchical cluster analysis. The relationship between the indices was explored to determine how effective the small scale, site-specific indices were at predicting the broader, landscape-scale patterns. Strong correlations were observed between transect frequency per site and frequency (r ² = 0.89) and cover per transect when present and total cover (r ² = 0.62). This suggests that if a weed is abundant at the site level, it has the potential to occupy large areas of the catchment. The species groupings derived from the application of the three published weed classification frameworks were compared graphically to the groupings derived from the cluster analysis. One of the frameworks classified species into three groups. The other two frameworks classified species into four groups. There was a high degree of subjectivity in applying the frameworks to the survey data. Some of the data were of no relevance to the classification frameworks and were therefore ignored. We suggest that the weed classification frameworks should be used in conjunction with existing multivariate techniques to ensure that classifications capture important natural variations in observed data that may reflect invasion processes. The combined use of the frameworks and multivariate techniques enabled us to aggregate species into categories appropriate for management.