Spatial extent of invasiveness and invasion stage categorisation of established weeds of Queensland, Australia
Olusegun O. Osunkoya
A D , Claire Lock A B , Joshua C. Buru C , Brad Gray A and Moya Calvert A
+ Author Affiliations
- Author Affiliations
A Invasive Plant and Animal Science Unit, Biosecurity Queensland, Department of Agriculture and Fisheries, EcoSciences Precinct, Dutton Park, Brisbane, Qld 4102, Australia.
B Weed Risk Consultant, Invasive Species, Biosecurity Unit, NSW Department of Primary Industries PMB 2, Grafton, NSW 2462, Australia.
C School of Biology and Environmental Science, Faculty of Science & Engineering, Queensland University of Technology, Gardens Point Campus, Brisbane, Qld 4000, Australia.
D Corresponding author. Email: olusegun.osunkoya@daf.qld.gov.au
Australian Journal of Botany 68(8) 557-573 https://doi.org/10.1071/BT20066
Submitted: 15 June 2020 Accepted: 6 October 2020 Published: 30 November 2020
Abstract
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.
Keywords: abundance–distribution relationship, invasion curve, life-history traits, pest risk assessment, spatial scale, Queensland, weeds.
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