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Australian Journal of Botany Australian Journal of Botany Society
Southern hemisphere botanical ecosystems
RESEARCH ARTICLE

Spatial extent of invasiveness and invasion stage categorisation of established weeds of Queensland, Australia

Olusegun O. Osunkoya https://orcid.org/0000-0001-6525-3605 A D , Claire Lock A B , Joshua C. Buru https://orcid.org/0000-0001-5669-8071 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.


References

Akin-Fajiye M, Gurevitch J (2018) The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across northeastern USA. Biological Invasions 20, 3009–3023.
The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across northeastern USA.Crossref | GoogleScholarGoogle Scholar |

Antunes PM, Schamp B (2017) Constructing standard invasion curves from herbarium data—toward increased predictability of plant invasions. Invasive Plant Science and Management 10, 293–303.
Constructing standard invasion curves from herbarium data—toward increased predictability of plant invasions.Crossref | GoogleScholarGoogle Scholar |

Auld BA, Johnson SB (2014) Invasive alien species plant management Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 9, 1–12.
Invasive alien species plant managementCrossref | GoogleScholarGoogle Scholar |

Bajwa AA, Nguyen T, Navie S, O’Donnell C, Adkins S (2018) Weed seed spread and its prevention: the role of roadside wash down. Journal of Environmental Management 208, 8–14.
Weed seed spread and its prevention: the role of roadside wash down.Crossref | GoogleScholarGoogle Scholar | 29241067PubMed |

Behrendorff L, Harris SM, Muirhead IF (2019) Towards eradication: the history and management of bitou bush on K’gari‐Fraser Island, Australia. Ecological Management & Restoration 20, 92–100.
Towards eradication: the history and management of bitou bush on K’gari‐Fraser Island, Australia.Crossref | GoogleScholarGoogle Scholar |

Booy O, Mill AC, Roy HE, Hiley A, Moore N, Robertson P, Bullock R (2017) Risk management to prioritise the eradication of new and emerging invasive non-native species. Biological Invasions 19, 2401–2417.
Risk management to prioritise the eradication of new and emerging invasive non-native species.Crossref | GoogleScholarGoogle Scholar |

Bradley BA (2013) Distribution models of invasive plants over-estimate potential impact Biological Invasions 15, 1417–1429.
Distribution models of invasive plants over-estimate potential impactCrossref | GoogleScholarGoogle Scholar |

Bradley BA, Allen JM, O’Neill MW, Wallace RD, Bargeron CT, Richburg JA, Stinson K (2018) Invasive species risk assessments need more consistent spatial abundance data. Ecosphere 9, e02302
Invasive species risk assessments need more consistent spatial abundance data.Crossref | GoogleScholarGoogle Scholar |

Breiman L, Friedman J, Olshen R, Stone C (1984) ‘Classification and Regression Trees (CART).’ (Wadsworth: Pacific Grove, CA, USA)

Brummer TJ, Maxwell BD, Higgs MD, Rew LJ (2013) Implementing and interpreting local‐scale invasive species distribution models. Diversity & Distributions 19, 919–932.
Implementing and interpreting local‐scale invasive species distribution models.Crossref | GoogleScholarGoogle Scholar |

Buckley HL, Freckleton RP (2010) Understanding the role of species dynamics in abundance–occupancy relationships. Journal of Ecology 98, 645–658.
Understanding the role of species dynamics in abundance–occupancy relationships.Crossref | GoogleScholarGoogle Scholar |

Campbell S, Vogler WD, Brazier D, Vitelli J, Brooks SJ (2019) Weed leucaena and its significance, implications and control. Tropical Grasslands 7, 280–289.
Weed leucaena and its significance, implications and control.Crossref | GoogleScholarGoogle Scholar |

Catford JA, Vesk PA, Richardson DM, Pyšek P (2012) Quantifying levels of biological invasion: towards the objective classification of invaded and invasible ecosystems. Global Change Biology 18, 44–62.
Quantifying levels of biological invasion: towards the objective classification of invaded and invasible ecosystems.Crossref | GoogleScholarGoogle Scholar |

Caton BP, Koop AL, Fowler L, Newton L, Kohl L (2018) Quantitative uncertainty analysis for a weed risk assessment system. Risk Analysis 38, 1972–1987.
Quantitative uncertainty analysis for a weed risk assessment system.Crossref | GoogleScholarGoogle Scholar | 29509965PubMed |

Colautti R, Parker JD, Cadotte MW, Pyšek P, Brown CS, Sax D, Richardson D (2014) Quantifying the invasiveness of species. NeoBiota 21, 7–27.
Quantifying the invasiveness of species.Crossref | GoogleScholarGoogle Scholar |

Crawford PH, Hoagland BW (2009) Can herbarium records be used to map alien species invasion and native species expansion over the past 100 years? Journal of Biogeography 36, 651–661.
Can herbarium records be used to map alien species invasion and native species expansion over the past 100 years?Crossref | GoogleScholarGoogle Scholar |

Crowley SL, Hinchliffe S, McDonald RA (2017) Conflict in invasive species management. Frontiers in Ecology and the Environment 15, 133–141.
Conflict in invasive species management.Crossref | GoogleScholarGoogle Scholar |

Dallas TA, Pöyry J, Leinonen R, Ovaskainen O (2019) Temporal sampling and abundance measurement influences support for occupancy– abundance relationships. Journal of Biogeography 46, 2839–2849.
Temporal sampling and abundance measurement influences support for occupancy– abundance relationships.Crossref | GoogleScholarGoogle Scholar |

Delisle F, Lavoie C, Jean M, Lachance D (2003) Reconstructing the spread of invasive plants: taking into account biases associated with herbarium specimens. Journal of Biogeography 30, 1033–1042.
Reconstructing the spread of invasive plants: taking into account biases associated with herbarium specimens.Crossref | GoogleScholarGoogle Scholar |

Dhileepan K, Callander J, Shi B, Osunkoya OO (2018) Biological control of parthenium (Parthenium hysterophorus): the Australian experience Biocontrol Science and Technology 28, 970–988.
Biological control of parthenium (Parthenium hysterophorus): the Australian experienceCrossref | GoogleScholarGoogle Scholar |

Ehrenfeld JG (2010) Ecosystem consequences of biological invasions Annual Review of Ecology and Systematics 41, 59–80.
Ecosystem consequences of biological invasionsCrossref | GoogleScholarGoogle Scholar |

Fan Z, Moser WK, Crosby MK, Yu W, Zhang Y, Hansen MH, Fan SX (2018) Mapping the invasion stage and invasiveness of major non-native invasive plants in the upper Midwest forestlands, USA. Mathematical and Computational Forestry & Natural-Resource Sciences 10, 68–79.

Fleming PJ, Ballard G, Reid NC, Tracey JP (2017) Invasive species and their impacts on agri-ecosystems: issues and solutions for restoring ecosystem processes. The Rangeland Journal 39, 523–535.
Invasive species and their impacts on agri-ecosystems: issues and solutions for restoring ecosystem processes.Crossref | GoogleScholarGoogle Scholar |

Foxcroft LC, van Wilgen NJ, Baard JA, Cole NS (2017) Biological invasions in South African national parks. Bothalia 47, a2158
Biological invasions in South African national parks.Crossref | GoogleScholarGoogle Scholar |

Freckleton R, Gill J, Noble D, Watkinson A (2005) Large‐scale population dynamics, abundance–occupancy relationships and the scaling from local to regional population size. Journal of Animal Ecology 74, 353–364.
Large‐scale population dynamics, abundance–occupancy relationships and the scaling from local to regional population size.Crossref | GoogleScholarGoogle Scholar |

Froese JG, Pearse AR, Hamilton G (2019) Rapid spatial risk modelling for management of early weed invasions: balancing ecological complexity and operational needs. Methods in Ecology and Evolution 10, 2105–2117.
Rapid spatial risk modelling for management of early weed invasions: balancing ecological complexity and operational needs.Crossref | GoogleScholarGoogle Scholar |

Gassó N, Thuiller W, Pino J, Vilà M (2012) Potential distribution range of invasive plant species in Spain NeoBiota 12, 25–40.
Potential distribution range of invasive plant species in SpainCrossref | GoogleScholarGoogle Scholar |

Gaston KJ (1999) Implications of interspecific and intraspecific abundance-occupancy relationships. Oikos 86, 195–207.
Implications of interspecific and intraspecific abundance-occupancy relationships.Crossref | GoogleScholarGoogle Scholar |

Gaston KJ, Blackburn TM, Greenwood JJ, Gregory RD, Quinn RM, Lawton JH (2000) Abundance–occupancy relationships. Journal of Applied Ecology 37, 39–59.
Abundance–occupancy relationships.Crossref | GoogleScholarGoogle Scholar |

Kriticos DJ, Randall RP (2001) A comparison of systems to analyse potential weed distributions. In ‘Weed Risk Assessment’. (Eds RH Groves, FD Panetta, JG Virtue) pp. 61–79. (CSIRO Publishing: Melbourne, Vic., Australia)

Kriticos D, Beautrais J, Dodd M (2018) WRASP: a spatial strategic weed risk analysis tool reveals important subnational variations in weed risks. Weed Research 58, 398–412.
WRASP: a spatial strategic weed risk analysis tool reveals important subnational variations in weed risks.Crossref | GoogleScholarGoogle Scholar |

Lacasella F, Marta S, Singh A, Stack Whitney K, Hamilton K, Townsend P, Gratton C (2017) From pest data to abundance‐based risk maps combining eco‐physiological knowledge, weather, and habitat variability. Ecological Applications 27, 575–588.
From pest data to abundance‐based risk maps combining eco‐physiological knowledge, weather, and habitat variability.Crossref | GoogleScholarGoogle Scholar | 27859850PubMed |

Mack RN, Simberloff D, Mark Lonsdale W, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecological Applications 10, 689–710.
Biotic invasions: causes, epidemiology, global consequences, and control.Crossref | GoogleScholarGoogle Scholar |

Mackay H, Keskitalo ECH, Pettersson M (2017) Getting invasive species on the political agenda: agenda setting and policy formulation in the case of ash dieback in the UK. Biological Invasions 19, 1953–1970.
Getting invasive species on the political agenda: agenda setting and policy formulation in the case of ash dieback in the UK.Crossref | GoogleScholarGoogle Scholar |

Margolis M, Shogren JF, Fischer C (2005) How trade politics affect invasive species control. Ecological Economics 52, 305–313.
How trade politics affect invasive species control.Crossref | GoogleScholarGoogle Scholar |

Moser WK, Fan Z, Hansen MH, Crosby MK, Fan SX (2016) Invasibility of three major non-native invasive shrubs and associated factors in Upper Midwest US forest lands. Forest Ecology and Management 379, 195–205.
Invasibility of three major non-native invasive shrubs and associated factors in Upper Midwest US forest lands.Crossref | GoogleScholarGoogle Scholar |

Ngugi MR, Neldner VJ (2017) Assessing the invasion threat of non-native plant species in protected areas using herbarium specimen and ecological survey data: a case study in two rangeland bioregions in Queensland. The Rangeland Journal 39, 85–95.
Assessing the invasion threat of non-native plant species in protected areas using herbarium specimen and ecological survey data: a case study in two rangeland bioregions in Queensland.Crossref | GoogleScholarGoogle Scholar |

Osawa T, Akasaka M, Kachi N (2019) Facilitation of management plan development via spatial classification of areas invaded by alien invasive plant. Biological Invasions 21, 2067–2080.
Facilitation of management plan development via spatial classification of areas invaded by alien invasive plant.Crossref | GoogleScholarGoogle Scholar |

Osunkoya OO, Froese JG, Nicol S, Perrett C, Moore K, Callander J, Campbell S (2019a) A risk‐based inventory of invasive plant species of Queensland, Australia: regional, ecological and floristic insights. Austral Ecology 44, 1123–1138.
A risk‐based inventory of invasive plant species of Queensland, Australia: regional, ecological and floristic insights.Crossref | GoogleScholarGoogle Scholar |

Osunkoya OO, Froese JG, Nicol S (2019b) Management feasibility of established invasive plant species in Queensland, Australia: a stakeholders’ perspective. Journal of Environmental Management 246, 484–495.
Management feasibility of established invasive plant species in Queensland, Australia: a stakeholders’ perspective.Crossref | GoogleScholarGoogle Scholar | 31200182PubMed |

Pearson DE, Ortega YK, Eren Ö, Hierro JL (2016) Quantifying ‘apparent’ impact and distinguishing impact from invasiveness in multispecies plant invasions. Ecological Applications 26, 162–173.
Quantifying ‘apparent’ impact and distinguishing impact from invasiveness in multispecies plant invasions.Crossref | GoogleScholarGoogle Scholar | 27039517PubMed |

Pyšek P, Jarošík V, Müllerová J, Pergl J, Wild J (2008) Comparing the rate of invasion by Heracleum mantegazzianum at continental, regional, and local scales. Diversity & Distributions 14, 355–363.
Comparing the rate of invasion by Heracleum mantegazzianum at continental, regional, and local scales.Crossref | GoogleScholarGoogle Scholar |

Pyšek P, Křivánek M, Jarošík V (2009) Planting intensity, residence time, and species traits determine invasion success of alien woody species Ecology 90, 2734–2744.
Planting intensity, residence time, and species traits determine invasion success of alien woody speciesCrossref | GoogleScholarGoogle Scholar | 19886483PubMed |

Schoeman J, Buckley Y, Cherry H, Long R, Steadman K (2010) Inter‐population variation in seed longevity for two invasive weeds: Chrysanthemoides monilifera ssp. monilifera (boneseed) and ssp. rotundata (bitou bush). Weed Research 50, 67–75.
Inter‐population variation in seed longevity for two invasive weeds: Chrysanthemoides monilifera ssp. monilifera (boneseed) and ssp. rotundata (bitou bush).Crossref | GoogleScholarGoogle Scholar |

Scott JK, Batchelor KL, Webber BL (2019) Long term monitoring of recruitment dynamics determines eradication feasibility for an introduced coastal weed. NeoBiota 50, 31–53.
Long term monitoring of recruitment dynamics determines eradication feasibility for an introduced coastal weed.Crossref | GoogleScholarGoogle Scholar |

Shabbir A, Bajwa AA, Dhileepan K, Zalucki M, Khan N, Adkins S (2018) Integrated use of biological approaches provides effective control of parthenium weed. Archives of Agronomy and Soil Science 64, 1861–1878.
Integrated use of biological approaches provides effective control of parthenium weed.Crossref | GoogleScholarGoogle Scholar |

Sindel, B (2009) Fireweed in Australia: directions for future research. Report to the Bega Valley Fireweed Association. Bega Valley Fireweed Association, Bega, NSW, Australia

Standards Australia International Ltd (2006) National post-border weed risk management protocol. AS-NZ HB 294-2006. (Standards Australia International Ltd: Sydney, NSW, Australia) Available at https://www.standards.org.au/standards-catalogue/sa-snz/publicsafety/ob-007/hb--294-2006 [Verified 8 October 2020]

Sutherst RW (2003) Prediction of species geographical ranges Journal of Biogeography 30, 805–816.
Prediction of species geographical rangesCrossref | GoogleScholarGoogle Scholar |

Taylor S, Kumar L (2013) Potential distribution of an invasive species under climate change scenarios using CLIMEX and soil drainage: a case study of Lantana camara L. in Queensland, Australia. Journal of Environmental Management 114, 414–422.
Potential distribution of an invasive species under climate change scenarios using CLIMEX and soil drainage: a case study of Lantana camara L. in Queensland, Australia.Crossref | GoogleScholarGoogle Scholar | 23164541PubMed |

Thompson K, Hodgson JG, Gaston KJ (1998) Abundance–range size relationships in the herbaceous flora of central England. Journal of Ecology 86, 439–448.
Abundance–range size relationships in the herbaceous flora of central England.Crossref | GoogleScholarGoogle Scholar |

Václavík T, Meentemeyer RK (2012) Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion. Diversity & Distributions 18, 73–83.
Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion.Crossref | GoogleScholarGoogle Scholar |

van Kleunen M, Bossdorf O, Dawson W (2018) The ecology and evolution of alien plants. Annual Review of Ecology and Systematics 49, 25–47.
The ecology and evolution of alien plants.Crossref | GoogleScholarGoogle Scholar |

van Klinken RD, Morin L, Sheppard A, Raghu S (2016) Experts know more than just facts: eliciting functional understanding to help prioritise weed biological control targets. Biological Invasions 18, 2853–2870.
Experts know more than just facts: eliciting functional understanding to help prioritise weed biological control targets.Crossref | GoogleScholarGoogle Scholar |

Webb TJ, Noble D, Freckleton RP (2007) Abundance–occupancy dynamics in a human dominated environment: linking interspecific and intraspecific trends in British farmland and woodland birds. Journal of Animal Ecology 76, 123–134.
Abundance–occupancy dynamics in a human dominated environment: linking interspecific and intraspecific trends in British farmland and woodland birds.Crossref | GoogleScholarGoogle Scholar | 17184360PubMed |

Wilson JR, Caplat P, Dickie IA, Hui C, Maxwell BD, Nunez MA, Robertson MP (2014) A standardized set of metrics to assess and monitor tree invasions. Biological Invasions 16, 535–551.
A standardized set of metrics to assess and monitor tree invasions.Crossref | GoogleScholarGoogle Scholar |