The risk posed by invasive alien species is determined primarily by two factors: distribution (occupancy) and abundance (density). However, most ecological studies use distribution data for monitoring and assessment programs, but few incorporate abundance data due to financial and logistical constraints. Failure to take into account invaders’ abundance may lead to imprecise pest risk assessments. Since 2003 as part of the Annual Pest Distribution Survey (APDS) exercise in the state of Queensland, Australia, government biosecurity officials have collected data on distribution and abundance of more than 100 established and emerging weeds. This data acquisition was done at spatial grid sizes of 17–50 × 17–50 km and across a very broad and varied geographical land area of ~2 × 106 km2. The datasets provide an opportunity to compare weed dynamics at large-medium spatial scales. Analysis of the APDS datasets indicated that weed distributions were highest in regions along the southern and central, coastal parts of Queensland, and decreased in the less populated inland (i.e. western) and northern parts of the state. Weed abundance showed no discernible landscape or regional trends. Positive distribution–abundance relationships were also detected at multiple spatial scales. Using both traits of weed abundance and distribution, we derived a measure of invasion severity, and constructed, for several (64) weed species, ‘space-for-time’ invasion curves. State-wide and in each of Queensland’s 10 regions, we also categorised the invasion stages of these weeds. At the grassroots of local government area or regional levels, the derived invasion curves and stage categories can provide policy direction for long-term management planning of Queensland’s priority weeds.
Herbarium records provide comprehensive information on plant distribution, offering opportunities to construct invasion curves of introduced species, estimate their rates and patterns of expansions in novel ranges, as well as identifying lag times and hence “sleeper weeds”, if any. Lag times especially have rarely been determined for many introduced species, including weeds in the State of Queensland, Australia as the trait is thought to be unpredictable and cannot be screened for. Using herbarium records (1850–2010), we generated various invasiveness indices, and developed simple invasion and standardised proportion curves of changes in distribution with time for ~ 100 established and emerging weed species of Queensland. Four major periods (decades) of increased weed spread (spikes) were identified: 1850s, 1900–1920, 1950–1960 and 2000–2010, especially for grasses and trees/shrubs. Many weeds with spikes in spread periods did so only 1–2 decadal times, except for a few species with higher spike frequencies > 6; the majority of these spikes occurred recently (1950–1990). A significant proportion (~ 60%) of Queensland’s weeds exhibit non-linear increase in spread with time, and hence have lag phases (mean: 45.9 years; range: 12–126 years); of these lag-phase species, 39% are “sleeper” weeds with > 50 years of lag time (mainly trees/shrubs and grasses). Twelve traits of invasiveness, including lag time and species-specific/historical factors were screened, of which frequency of invasion waves, spread rates and residence time were the main drivers of weeds’ distribution. The low predictive power of lag time on weed distribution suggests that retrospective analyses offer little hope for a robust generalisation to identify weeds of tomorrow.
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