iÃÌÊ > Ì Û>Ã Ûi iÃÃ ÃÃiÃÃ i Ì - VRO

AuthorsJohn E.R. Weiss B. Sc. (Hons.)Linda J. Iaconis B. App. Sc.

Contributing StaffJim Backholer B. Agr. Sc. (Hons.)Les BouldCarmen Sporle

� The State of Victoria, Department of Natural Resources and Environment, 2002.

DisclaimerThe recommendations in this document are intended only to assist the reader.

The information contained in this publication is based on knowledge available, andunderstanding at the time of writing. However, due to continual advances inknowledge, readers are reminded of the need to ensure that the information they relyupon is current.

This publication may be of assistance to you but the staff at the Keith Turnbull ResearchInstitute, Department of Natural Resources and Environment, does not guarantee thatthe publication is without flaw of any kind or is wholly appropriate for your particularpurposes and therefore disclaims all liability for any error, loss or other consequencewhich may arise from you relying on any information in this publication.

Pest Plant Invasiveness Assessment

Prepared by the Keith Turnbull Research Institute, 2002. 2

Contents

Contents .............................................................................................................................2

List of Figures....................................................................................................................3

List of Tables .....................................................................................................................3

Acronyms...........................................................................................................................4

1. Project Background ........................................................................................ 51.1 Weed Management Within Parks Victoria .........................................................51.2 The Pest Plant Assessment System ....................................................................51.3 Project Objectives................................................................................................6

2. Method, Results and Discussion of thePest Plant Invasiveness Assessment ............................................................. 7

2.1 Invasiveness Potential of Pest Plants...................................................................7 2.1.1 Introduction .......................................................................................................7 2.1.2 Invasiveness Criteria .........................................................................................7 2.1.3 Results and Discussion of the Invasiveness Index ..........................................122.2 Present and Potential Distribution of Pest Plants ..............................................15 2.2.1 Introduction .....................................................................................................15 2.2.2 Present Distribution.........................................................................................16 2.2.3 Potential Distribution ......................................................................................17 2.2.4 Results and Discussion of the Present and Potential Distribution

Maps ..................................................................................................................20

3. References ..................................................................................................... 23

Appendix 1: Invasive Potential Criteria and Intensity Ratings ......................................24

Appendix 2: Table of Risk Ratings, National Status and InvasivenessIndices......................................................................................................29

Appendix 3: Examples of Present and Potential Distribution Maps ..............................35



List of Figures

Figure 1: Group weightings of invasiveness. ...................................................................9

Figure 2: Criterion weightings of invasiveness. ...............................................................9

Figure 3: Individual criterion total weightings expressed in groups ..............................10

Figure 4: Frequency graph of invasiveness indices........................................................13

Figure 5: Distribution of Spartina anglica in the United States of America.....................16

Figure 6: Distribution of Spartina anglica (left) and S. x. townsendii(right) in Victoria. ...................................................................................17

Figure 7: Potential distribution of Spartina anglica (left) andS. x. townsendii (right) in Australia, according to climatic parameters. .....17

Figure 8: Potential distribution of Spartina anglica (left) andS. x. townsendii (right) in Victoria, according to climatic parameters. ....... .18

Figure 9: Dialogue box from CLIMATE® showing the climatic parameters used interrestrial weed modeling. The eight rainfall parameters are not includedwhen modeling the potential climatic range of aquatic weeds .....................18

Figure 10: Potential distribution of Spartina anglica (left) andS. x. townsendii (right) in Victoria, according to climatic parameters,susceptible land uses and BVTs. ..............................................................19

Figure 11: Potential distribution of Spartina anglica and S. x. townsendiiin Eastern Victoria, according to climatic parameters, susceptible landuses and BVTs. ........................................................................................19

Figure 12: Invasion graph indicating stages of expansion of a newspecies into a habitat. ..............................................................................21

Figure 13: Total cost of plant invasions. Costs of early expenditure(Area A) and the resulting benefit (Area B). ..........................................22

List of Tables

Table 1: Group and criteria weightings for determining invasive potential ........................... 8

Table 2: Summary of the biological data used to determine the intensityrankings for each criterion and the overall score ofSpartina anglica and S. x. townsendii. .......................................................11

Table 3: Assessed species, grouped according to their invasiveness indices ....................14



Acronyms

AHP Analytical Hierarchal ProcessARI Arthur Rylah InstituteBVTs Broad Vegetation TypesCMA Catchment Management AuthorityEVCs Ecological Vegetation ClassesFIS Flora Information System (Viridans 1999)GIS Geographic Information SystemKTRI Keith Turnbull Research InstituteIPMS Integrated Pest Management System (previously known as PMIS)PMIS Pest Management Information SystemPV Parks Victoria



1. Project Background

1.1 Weed Management Within Parks VictoriaParks Victoria (PV) is responsible for the management of 16 percent of the State andhas a critical role as the custodian of natural values and for the development of highquality and innovative environmental management in the State’s parks and reserves.PV is required to conduct its management to achieve the objectives of a range oftreaties, conventions, Acts, regulations and policies, including:

� ensuring that threats to all indigenous flora and fauna occurring in the parks andreserves system are managed, in accordance with the Flora and Fauna GuaranteeAct (1988); and

� the eradication or control of exotic flora and fauna in the parks and reserves is inline with the National Parks Act (1975) and the Catchment and Land Protection Act(1994).

In 2000/2001, PV’s Environmental Management Program included 402 pest plantmanagement projects across the State accounting for 28 percent of PV’s natural valuesmanagement budget. To ensure PV directs its resources to weed species of highest riskpotential to particular environmental values, an assessment of the invasive potential ofpest plant species is required.

1.2 The Pest Plant Assessment SystemIn Victoria, there are over 1200 plant species reported in literature as weeds(Ross 2000). It has been estimated that only approximately ten percent of naturalisedplant species become weeds of significant economic and ecological impact (Williamsonand Fitter 1995). It is therefore unrealistic and unnecessary to expect that all weeds canand should be controlled.

To make informed decisions about the best way to control weeds on public land, it isnecessary that the relative importance and potential impact of each weed be determined.It is essential that this is done prior to the allocation of priority works or funding.Decisions based on limited factual data and emotional reactions, will almost certainlyresult in unnecessary expenditure of resources and damage to the environment throughinappropriate use of control measures.

The Pest Plant Assessment System, developed for Victoria by the KTRI, is a riskassessment process that allows for the prioritisation of weeds based on a combinationof the following factors:

(1) Assessing the plant’s invasiveness;

(2) Comparing the plant’s present and potential distribution; and

(3) Determining the impacts of the plant on social, environmental and agriculturalvalues.



1.3 Project ObjectivesThe objective of this project is to determine the invasive and current and potentialdistribution of 112 pest plant species using the method already established by KTRI(factors (1) and (2) above). The species were selected by PV on the basis of:

� risk rating for environmental weeds (Carr et al 1992);

� pest plant species new to a district, park or area of the park;

� number of hectares per pest plant species proposed to be controlled in2001/02; and

� number of parks in which each pest plant species occurs.The selected species are listed alphabetically, with some updates to the nomenclatureused in the report, at the start of Appendix 3.

This report documents:

1. The method, results and discussion of the invasiveness score and current andpotential distribution for the 112 environmental weeds selected by PV (Section 2);

2. Tabulated data for the 112 weed species including environmental risk rating(according to Carr et al 1992), national status, and invasiveness score (Appendix 2);and

3. Present and potential distribution maps for the 112 weeds (Appendix 3), which havealso been provided in digital format.

The data compiled in this report on the invasive potential of pest plants will assist withthe development of a pest plant risk assessment method. This method will be consistentwith Victoria’s Pest Plant Assessment System, incorporating additional measures ofimpact on environmental, social and economic values, and is being developed under aseparate, but related project.



2. Method, Results and Discussion of the Pest PlantInvasiveness Assessment

2.1 Invasiveness Potential of Pest Plants2.1.1 IntroductionMany researchers have focused on the relative invasiveness of species as an indicator ofpotential spread rate. Invasiveness can be defined as the ability to establish, reproduce,and disperse within an ecosystem. Plant propagules arrive at a new site with certaininherent characteristics that previously enabled their successful survival and continuedreproduction throughout their evolutionary history. There is no single suite ofcharacteristics which make a plant invasive, rather there are several predisposing factorsthat act either alone or together to increase the chance of a plant becoming invasive.

Many researchers have also agreed that the following biological attributes of a plantspecies are associated with invasiveness.

� Ecological status; a generalist or specialist plant.Most common and noxious weeds in southern Australia are generalist andopportunistic rather than requiring specific niches or special habitat requirements.

� ‘Weedy’ phenology and biology; such as competitive growth, seed dispersalmechanisms, seed dormancy and propagule production.Major weeds can have attributes such as high seed production, rapid vegetativespread, long-lived seeds, staggered germination, competitive growth and long-distance seed dispersal. However, there is no defined group of ecological andbiological attributes that can be used to identify all major weeds. Differentattributes may be important for different plant families and different ecosystems.

� Wide native range.Within a genus the more important weeds may have a wider native range.

� Taxonomic position; members of generally ‘weedy’ plant families.Certain plant families such as Poaceae (grasses), Asteraceae (eg. daisies, thistles),Iridaceae (irises) and Brassicaceae (eg. mustards, turnips) are noted for havingmany ‘weedy’ species.

� Effective modes of reproduction and genetic variation.Plant species that vegetatively reproduce or self-pollinate have the potential tostart new populations from a single, isolated plant. However, high levels ofinbreeding in self-pollinators may limit their adaptability compared to cross-pollinators.

2.1.2 Invasiveness CriteriaCriteria for a generic model to assess the potential invasiveness of weeds weredetermined at workshops by national participants at the Arthur Rylah Institute (ARI),in June 1998 (Table 1). A working group at the Keith Turnbull Research Institute(KTRI) then used an expert system, relying on multi-criteria analysis/analyticalhierarchical process (AHP) (Saaty 1995), to develop a decision tree that allows forgroups and criteria to be weighted according to importance (Table 1). Basically, theAHP is a method of breaking down a complex unstructured situation into its componentparts; arranging these parts into a hierarchical order; and assigning numerical values tosubjective judgements to determine which variables have the highest priority and shouldbe acted upon to influence the outcome of the situation. AHP also facilitates effective



decisions on complex issues by simplifying and expending the intuitive decision makingprocess.

Table 1: Group and criteria weightings for determining invasive potential.

GROUP CRITERIA GROUP & CRITERIAWEIGHTINGS

Establishment 0.5Germination requirements? 0.085Establishment requirements? 0.671Disturbance requirements? 0.244

Growth/competitive ability 0.096Life form? 0.06Allelopathic properties? 0.09Tolerates herbivory pressure? 0.472Normal growth rate? 0.192Stress tolerances? 0.185

Reproduction 0.119Reproductive system? 0.047Propagule production? 0.46Seed longevity? 0.256Reproductive period? 0.101Time to reproductive maturity? 0.136

Dispersal 0.284Number of mechanisms? 0.333How far do propagules disperse? 0.667

Comparing the major groups (i.e. establishment, growth/competitive ability,reproduction and dispersal), the working group indicated that the ability of a plant toestablish in an ecosystem was by far the most important indicator of its invasiveness,followed by the plant’s ability to disperse, then its reproduction strategy, and finally itsgrowth/competitive ability. Some of the reasons (and comments made during theweighting process) for this ranking are mentioned below:� “It doesn’t matter how many seeds/propagules the plant produces, if they can not

establish themselves they are not going to be invasive.”� “The ability to disperse great distances, no matter how they reproduce, gives the

plant a better chance of finding a suitable location for it to establish.”� “The more locations it establishes in, (i.e. multi loci), the quicker the plant will

invade an area.”

These group weightings can be expressed graphically as shown below in Figure 1.



Figure 1: Group weightings of invasiveness.

Within each group, the individual criterion (Appendix 1) were compared and weightedagainst each other. For instance, within the dispersal group, the working group decidedthat the distance propagules disperse was twice as important as the mechanisms fordispersal. The results of the intra-group criteria weightings are illustrated in Figure 2.

Figure 2: Criterion weightings of invasiveness.

Within the invasiveness hierarchy, the weightings of individual criterion are multipliedby the groups weight (eg. distance of dispersal x dispersal � 0.667 x 0.284 = 0.189).The total weightings for individual criterion are illustrated in Figure 3.

0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6

1

GR

OU

PS

G R O U P W E I G H T I N G S

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Gro

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D is ta nce o f d ispe rsa lN u m be r o f d isp ersa l m echa n ism s

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S tre ss to le ran ceN o rm al g row th ra teTo lera te s h erb ivoryA lle lo pa th ic p rope rtiesL ife fo rm

H o w m uch d is turb an ceE s ta b lishm e nt re qu irem e ntsG erm inatio n requ ire m en ts

D is pe rs al

R ep ro d u c tio n

G ro w th &C o m pe titive Ab ility

E s ta b lis h m en t



Figure 3: Individual criterion total weightings expressed in groups.

The invasive hierarchy and weightings indicate that the most important invasivebiological features of a plant is its’ ability to establish and disperse widely. The plant’sability to withstand herbivore pressure, number of propagules produced and seedlongevity are of lesser importance. Other biological features such as its life formand reproductive strategies are of much less importance. This was acknowledgedduring the workshops at ARI, where serious weeds from a wide range of life forms(grasses, shrubs, trees, climbers etc) and reproductive systems (vegetative, asexual,self pollinating, cross pollinating etc) were identified.

The criteria are assigned intensity ratings or definitions of ‘high’, ‘medium-high’,‘medium’, ‘medium-low’ and ‘low’ (Appendix 1), which are used to score each species.The scores for each criterion and weightings are then tallied and calculated to producea final ‘invasiveness index’ for each species. An example of this process is shownbelow; a summary of biological data was collated to determine the ‘invasiveness index’of Spartina anglica and S. x townsendii (Table 2). The overall score is only relative toscores of other plants run through the same process, but can be used to rank species asto their potential invasiveness or rate of spread.

The information to rate each criterion is sourced from databases, journal articles, flora’sof the world (books or articles describing the species of a particular country or region),online information, and any other sources. There is much available informationon some species (eg. declared noxious species), and very scant information for others(eg. grasses and new and emerging weed species). Where there is an information ‘gap’for a particular criterion, a ‘medium’ (M) ranking is given to indicate ‘unknown’.Although the invasiveness assessments are undertaken using the best availableinformation, they are only as accurate as the information that is used. Therefore, as webecome more informed about a species, reassessment may be necessary.

0 . 0 0 0 0 .1 0 0 0 .2 0 0 0 . 3 0 0 0 .4 0 0 0 .5 0 0 0 .6 0 0 0 . 7 0 0 0 .8 0 0

Gro

ups

W e ig h t

D is ta n c e o f d is p e r s a lN u m b e r o f d is p e r s a l m e c h a n is m s

R e p r o d u c t iv e a g eR e p r o d u c t iv e p e r io dS e e d lo n g e v i t yN u m b e r o f p r o p a g u le sR e p r o d u c t iv e m o d e

S t r e s s t o le r a n c eN o r m a l g r o w th r a teT o le r a te s h e r b iv o r yA l le lo p a t h ic p r o p e r t ie sL i f e f o r m

H o w m u c h d is t u r b a n c eE s t a b l i s h m e n t r e q u i r e m e n tsG e r m in a t io n r e q u i r e m e n ts

D i s p e r s a l

R e p r o d u c t i o n

G r o w t h &C o m p e t i t i v e A b i l i t y

E s t a b l i s h m e n t

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2.1.3 Results and Discussion of the Invasiveness IndexThe invasiveness scores, as well as risk rating according to Carr et al (1992) and national status, for all112 species are given in Appendix 2. The closer the invasiveness index to the maximum score of one,the more invasive a species is. To give the score some perspective, a comparison can be made with the‘non-weedy’ plant Red or Chinese Hibiscus (Hibiscus rosa-sinensis). A common plant grownthroughout southern Australia, it scored 0.43 but only because it has such a high chance of dispersal bydeliberate transport of cuttings/seeds greater than one kilometre. When humans were excluded fromdispersal, the invasiveness score dropped to 0.31.Spartina anglica scored 0.61 and S. x townsendii 0.58. In relation to the other assessed weeds, bothSpartina species scored less than the mean invasive index of the 112 species assessed, which was 0.69.S. anglica scored an equivalent rank to plants such as Angled onion (Allium triquetrum), Slender thistle(Carduus tenuiflorus), and Stinkwort (Dittrichia graveolens), while S. x townsendii scored equivalent toArrowhead (Sagittaria graminea). Many species are considerably more invasive than Spartina, such asPampas grass (Cortaderia selloana) with a score of 0.95, White willow (Salix alba) with a score of0.93, and Grey sallow and Crack willow (Salix babylonica and S. cinerea respectively) which bothscored 0.92 (Appendix 2).

As demonstrated above, an expert system, such as the Pest Plant Assessment System, provides aranking of plants from most invasive to least. The invasiveness indices are not meant to be anabsolute number relating to distance, but to give a relative indication of rate of spread of a plant in itspreferred habitat. To make the invasiveness scores more useful, one can group the resulting indicesof the 112 assessed species into categories (eg. ‘very high’, ‘high’, ‘medium’ invasiveness, etc).Such a process can assist land managers to determine which species, or groups of species, to targetmanagement efforts and resources at for maximum gain. There are several methods of breaking thescore range of zero to one to form ‘invasiveness categories’, with some more relevant than others.

One method is to group the species, ordered from lowest to highest invasiveness index, according topercentile rankings. For instance, using 25 percentiles to break the species into four groups (i.e. indicesbetween the 0-25, 25-50, 50-75 and 75-100 percentiles respectively). Such an approach results in aneven number of species in each category, however did not well reflect the range or ‘natural groupings’of indices when applied to the 112 assessed species. As the majority of assessed species have indices inthe mid range (i.e. between the 25-75 percentiles), species at the lower and higher ends of this midrange were dispersed into the lower and high groups (ie. 0-25 and 75-100 percentiles respectively) toallow for an even distribution of the scores. This elevated the cut-off score for the lowest group andreduced the cut-off score for the highest group to accommodate the extra species falling either side ofthis mid group. Furthermore, if subsequent species are assessed and grouped with the 112 species, the25 percentiles are likely to change. Therefore, a method which arbitrarily fixed the ‘cut off’ points isrequired.

A frequency distribution graph of the scores of all 200 species that have been assessed to date byKTRI was produced (Figure 4). Evaluation of the figure shows indications of some natural breaks inthe data. These ‘natural groupings’ produced the five groups used to categorise the 112 speciesconsidered in this report (Table 3): moderately invasive (species with scores <0.5), moderately-highly invasive (species with scores between 0.5-0.59), highly invasive (species with scores between0.6-0.79), very highly invasive (species with scores between 0.8-0.89), and extremely invasive(species with scores >0.9). As expected, few species occur in the moderately and extremely invasivecategories, while the vast majority (73 of the 112 assessed species) occur in the middle highlyinvasive category (Table 3).

Pest

Pla

nt In

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ss A

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Pre

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the

Kei

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Figu

re 4

: Fr

eque

ncy

grap

h of

inva

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ness

indi

ces.

024681012

0.40.4

20.4

40.4

60.4

80.50.5

20.5

40.5

60.5

8

0.60.6

20.6

40.6

60.6

80.70.7

20.7

40.7

60.7

80.80.8

20.8

40.8

60.8

80.90.9

20.9

40.9

60.9

8

1

Scor

e

Frequency

Pest

Pla

nt In

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Tab

le 3

: A

sses

sed

spec

ies,

grou

ped

acco

rdin

g to

thei

r inv

asiv

enes

s ind

ices

.M

oder

atel

y In

vasiv

eSc

ore

(<0.

5)M

oder

atel

y/H

ighl

y In

vasiv

eSc

ore

(0.5

-0.5

9)H

ighl

y In

vasiv

eSc

ore

(0.6

-0.7

9)H

ighl

y In

vasiv

eSc

ore

(0.6

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2.2 Present and Potential Distribution of Pest Plants2.2.1 IntroductionCurrent and potential distributions are another major component required in the decisionsupport system and AHP, to predict the status of a weed. The greater the potentialdistribution of a weed, the greater the potential impact and management costs.To ensure the most cost-effective use of resources, invasive species that have thegreatest potential range should be targeted. Prioritisation is also important, as it isunrealistic to expect that all weeds can be controlled with limited available resources.Knowledge of potential distribution is furthermore important for devising managementprograms. Land managers can be alerted to the risk of weed invasion and measures canbe enforced to prevent the introduction of weed propagules into new areas. Lowpriority can be given to areas where the weed might fail to persist, or be of littleeconomic, environmental or social importance.

Two of the major factors influencing weed distribution are climate and land use.Weed species are typically more invasive in regions that are climatically similar to theirnative environment. Climate limits distribution according to how temperature andmoisture stresses affect the weed's life cycle. Different land uses (eg. cropping,perennial pasture and forestry) have different disturbance regimes that favour differentgroups of weeds. Having determined the climatic preferences of a weed it is necessaryto overlay these on a map of the weed’s associated land use in Victoria. The areas ofthe state that are potentially at risk from this weed can then be identified.



2.2.2 Present DistributionInformation on the weed’s present distribution, both overseas and in Australia,is collected from databases, journal articles, flora’s of the world (books or articlesdescribing the species of a particular country or region), online information, and any othersources. Spartina is used herein as an example to highlight the variety of sources andprocess used to determine a weed’s present. The information was sourced from books,conference proceedings, online databases, and a CD database.

Spartina anglica and S. x townsendii were first collected at Lymington Hampshire,United Kingdom in 1892, and then in the Isle of Wight in 1893 (Hubbard 1974). By 1900it occurred in scattered patches from Chichester Harbour to Poole Harbour (Hubbard1974). By 1907 many thousands of tidal mud-flats from Sussex to East Dorset wereinfested (Hubbard 1974). It also appeared on the north coast of France (Hubbard 1974),and has spread through marshes in Germany, Denmark, the Netherlands, New Zealand,China, and the west coast states of the United States of America (Figure 4)(Wu et al 1999).

Figure 5: Distribution of Spartina anglica in the United States of America.(From USDA Plant Database http://plants.usda.gov/plantproj/plants/index.html).

Spartina is widely distributed in New Zealand, with S. anglica occurring from latitudes35025'S to near 470S (Shaw and Gosling 1996).

In Australia, Spartina appears to be restricted to Tasmania, Victoria and South Australia(Bridgewater 1996). In Tasmania it is widespread in estuaries, river mouths andespecially along the north coast (Wells 1996). The worst affected areas of Tasmania arePort Sorell and the Tamar River (Wells 1996). In Victoria, Spartina is found along theGippsland coast in Corner Inlet, Waratah Bay and Andersons Inlet, on the eastern side ofWesternport Bay and near the heads of Port Phillip Bay (Williamson 1996) (Figure 5).



Figure 6: Distribution of Spartina anglica (left) and S. x townsendii (right) in Victoria.(Shaded squares indicate quadrats in which one or more plants of a particular specieshave been recorded. The species does not necessarily occupy the entire shaded area).(From Wild Plants of Victoria, Viridans).

2.2.3 Potential DistributionInformation on Australian and overseas distributions were imported into a climatematching program, CLIMATE®, to predict potential distribution in Australia. Using thelocalities where a species occurs overseas and within Australia, the potential climaticrange of any species can be overlaid upon Australia's climatic regions. The maps belowillustrate the climatic regions most suitable for Spartina in Australia and Victoria(Figures 6 and 7).

Figure 7: Potential distribution of Spartina anglica (left) and S. x townsendii (right)and in Australia, according to climatic parameters. (Areas in red indicate a 80%+ matchwith the preferred climate of the plant species, dark green 70%, light green 60% andyellow 50%).



Figure 8: Potential distribution of Spartina anglica (left) and S. x townsendii (right) inVictoria, according to climatic parameters. (Areas in red indicate a 80%+ match withthe preferred climate of the plant species, yellow 70%, orange 60% and green 50%).

The 16 climatic parameters that are used to determine potential distribution can begrouped into temperature or rainfall parameters (Figure 8).

Aquatic weeds are modeled for potential climatic range slightly differently thanterrestrial species. Rainfall is obviously not a major criterion for determining thepotential range of aquatic species, especially submergents, although it may play a keyrole in triggering certain biological properties (eg. freshwater flood events appear tostimulate flowering in Spartina) (Strong pers. comm.). Thus rainfall parameters areexcluded when predicting the climatic range. Water temperature is generally moremoderate and has fewer fluctuations than air temperature and provides a more accurateprediction for modeling aquatic species. However, the required data is usuallyunknown. Therefore, modeling the climatic range of aquatic species has included eightair temperature parameters that provide at least some indication of potential range. Theprocess is, consequently, more uncertain and likely to overestimate the species’ actualpotential range.

Figure 9: Dialogue box from CLIMATE® showing the climatic parameters used interrestrial weed modeling. The eight rainfall parameters are not included whenmodeling the potential climatic range of aquatic weeds.



The climatic overlays are then used to determine the potential range of the plant speciesby linking or intersecting them with susceptible land uses and broad vegetation types(BVTs) or wetlands using the ArcView Geographic Information System (GIS) program.This refines the potential distribution maps produced using the climate matchingprogram, as plants are limited by other factors, such as disturbance regimes associatedwith land uses. The resulting maps (Figures 9 and 10) illustrate the potential range ofSpartina in Victoria.

Figure 10: Potential distribution of Spartina anglica (left) and S. x townsendii (right)in Victoria, according to climatic parameters, susceptible land uses and BVTs.

Areas in red indicate a very high probability that Spartina could establish inwatercourses and wetlands within this region, yellow a high, and orange a mediumprobability of establishment.

Figure 11: Potential distribution of Spartina anglica and S. x townsendii in EasternVictoria, according to climatic parameters, susceptible land uses and BVTs.



The potential distribution maps are estimates and are only as reliable as the data they arebased on. As more records are collected on where the plants occur, the predictions willbecome more accurate. It is expected, consequently, that there are potential distributionmaps that do not yet fully represent existing or potential distribution. Also, for somespecies there may be insufficient data to undertake potential distribution mapping.For instance, only a small number of current distribution records were found forSalix x rubens; too few to produce a meaningful potential distribution map. For thesespecies, a provisional coarse climate map is produced but the next stage of matchingonto susceptible land types is not completed.

2.2.4 Results and Discussion of the Present and Potential Distribution MapsPresent and potential distribution maps for each of the 112 species assessed are given inAppendix 3. These have also been provided to PV in digital format. As 74 of thespecies had already been assessed by KTRI, these maps include CatchmentManagement Authority (CMA) boundaries. The 38 species assessed as part of thisproject have been mapped using PV district boundaries.

The present distribution of weeds is generally underrepresented in the databasesused (i.e. herbarium records, Wild Plants of Victoria (Viridans 1999), and to a lesserextent IPMS/PMIS), with the exception of priority weeds such as Serrated tussock(Nassella trichotoma). Conversely, the modeled potential distribution of weeds is likelyto be overestimated. This occurs as the broad scale (i.e. 1:250,000) of the statewidedatabases used, merges minor differences into the larger BVTs or land uses for eachgrid. Microhabitats within a vegetation or land use type maybe unsuitable for theparticular weed species, and microhabitats outside the identified susceptible land use orvegetation type may be suitable but not recognised (eg. roadsides, small riparian orvegetation corridors). More detailed map layers, such as the soon to be introducedEcological Vegetation Classes (EVCs), will produce better quality predictions.

The many weeds recorded as occurring along roadsides presents another majorlimitation when predicting potential distribution. Victoria has over 170,000 kilometresof roads, however to include all these roads within the image would not be suitable, as itwould be too cluttered and meaningless. Thus, some potential distribution images maynot include the occurrence of weeds within a region, if they only occur along roadsides.For example, Horehound (Marrubium vulgare) can occur along roadsides withincropping regions, but is unable to withstand cultivation. Similarly, some riparian weedsmay occur along small rivers, streams and water channels, but are not included inthe riparian or riverine vegetation classes of the BVT GIS layer, as they are too smallor scattered to be detected, and so don’t appear on the predicted potentialdistribution maps.

The above limitations highlight the need for suitable actions to be undertaken.Where information on a weeds present distribution is known but not recorded, recordsneed to be updated to ensure management and monitoring are effectively undertaken.An accurate comparison of a weeds present distribution with its potential distributionallows managers to make decisions on the course of actions to take. The ratio of presentand potential distribution provides an indication as to what stage the weed is at.Another way of expressing this is the relative position of the species on its invasiongraph (Figure 11).



Figure 12: Invasion graph indicating stages of expansion of a new species into ahabitat. (Adapted from Groves (1992) and Hobbs (1991)).

Weeds that have reached or nearly reached the full limits of their distribution, are not amajor concern in terms of potential spread and impacts. Whereas, weeds currentlyoccupying a small area of their potential range, or in the ‘lag or sleeper’ phase, shouldbecome a management priority. Indeed, a powerful weapon against weed invasionsarising from existing infestations is early intervention (Figure 12). Early interventionnot only achieves better in government/land manager investment, but also reduces costsof control and impact on the triple bottom line (social, environmental and agriculturalvalues).



Figure 13: Total cost of plant invasions. Costs of early expenditure (Area A) and theresulting benefit (Area B) (Adapted from Hobbs and Humphries (1995)).

Two parts of the Pest Plant Invasiveness Assessment process have been finalised, theinvasiveness assessments and the current and potential distributions within Victoria.The results of this project will provide a valuable tool for determining the risks that pestplants pose to environmental values and for prioritising weed management activitieswithin PV.

Time

Early

Late

A

B

Intervention



3. References:Bridgewater, P. 1996. Opening Address. In: Proceedings of the Australasian Conference

on Spartina Control. Department of Conservation and Natural Resources, Victoria.

Carr, G.W., Yugovic, J.V. and Robinson, K.E. 1992. Environmental Weed Invasions inVictoria: Conservation and Management Implications. Department of Conservation andNatural Resources and Ecological Horticulture, Victoria.

Groves, R.H. 1992. Weed ecology and spread. In: Proceedings of the First InternationalWeed Control Congress (Volume 1). Weed Science Society of Victoria, Melbourne.

Hobbs, R.J. 1991. Disturbance a precursor to weed invasion in native vegetation. PlantProtection Quarterly 6(3): 99-104.

Hobbs, R.J. and Humphries, S.E. 1995. An Integrated Approach to the Ecology andManagement of Plant Invasions. Conservation Biology 9(4): 761-770.

Hubbard, C.E. 1974. Grasses: A Guide to Their Structure, Identification, Uses, andDistribution in the British Isles (Revised Edition). Penguin Books, United Kingdom.

Ross, J.H. 2000. A Census of the Vascular Plants of Victoria. Royal Botanic Gardens,Victoria.

Saaty, T. 1995. Decision Making for Leaders: The Analytical Hierarchy Process forDecisions in a Complex World. RWS Publications, Pittsburgh.

Shaw, W.B. and Gosling, D.S. 1996. Spartina Control in New Zealand: An Overview. In:Proceedings of the Australasian Conference on Spartina Control. Department ofConservation and Natural Resources, Victoria.

Strong, D.R. 2002. Professor, Evolutionary and Ecology Department. University ofCalifornia, Davis, United States of America.

USDA PLANTS Profile (Online Database). 2000. Spartina anglica C.E. Hubbard. USDANatural Resources Conservation Service. Available: http://plants.usda.gov/plants/index.html(31 May 2000).

Viridans. 1999. Wild Plants of Victoria. Viridans Biological Databases, Victoria.

Wells, A. 1996. Rice Grass in Tasmania: An Overview. In: Proceedings of theAustralasian Conference on Spartina Control. Department of Conservation and NaturalResources, Victoria.

Williamson, M. and Fitter, A. 1995. The varying success of invaders. Ecology 77:11661-1666.

Williamson, R. 1996. Spartina in Victoria: An Overview. In: Proceedings of theAustralasian Conference on Spartina Control. Department of Conservation and NaturalResources, Victoria.

Wu, M., Hacker, S., Ayres, D. and Strong, D.R. 1999. Potential of Prokelisia spp. AsBiological Control Agents of English Cordgrass, Spartina anglica. Biological Control 16:267-273.



Appendix 1:

Invasive Potential Criteriaand Intensity Ratings

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

25

Cri

teri

aR

ank

Inte

nsity

Rat

ing

Wei

ght

Est

ablis

hmen

tG

erm

inat

ion

/pro

pagu

leH

Opp

ortu

nist

ic g

erm

inat

or, c

an g

erm

inat

e or

strik

e/ se

t roo

t at a

ny ti

me

whe

neve

r wat

er is

ava

ilabl

e.1.

00re

quire

men

ts?

MH

Req

uire

s nat

ural

seas

onal

dis

turb

ance

s suc

h as

seas

onal

rain

fall,

sprin

g/su

mm

er te

mpe

ratu

res f

orge

rmin

atio

n.0.

67

MU

nkno

wn.

0.50

ML

Req

uire

s uns

easo

nal o

r unc

omm

on n

atur

al e

vent

s for

ger

min

atio

n (e

g. fl

oodi

ng, f

ire).

0.33

LR

equi

res s

peci

fic e

nviro

nmen

tal f

acto

rs th

at a

re n

ot p

art o

f an

annu

al c

ycle

to g

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e (e

g. sp

ecifi

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res,

flood

s, fir

e O

R h

uman

cau

sed

dist

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nce

such

as p

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).0

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ling/

pro

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est

ablis

h w

ithou

t add

ition

al fa

ctor

s.1.

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tabl

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ent

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cano

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over

0.67

requ

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(i.e.

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ater

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ML

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s mor

e sp

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c re

quire

men

ts to

est

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h (e

g. o

pen

spac

e or

bar

e gr

ound

with

acc

ess t

o lig

htan

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rain

fall)

.0.

33

LR

equi

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and

ver

y sp

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c fa

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s suc

h as

nut

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wat

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at a

re d

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ely

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high

ly e

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0

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eg. m

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s).

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is re

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o oc

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n re

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ely

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inor

dis

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al e

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stem

s (eg

. wet

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s, rip

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n,riv

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en w

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); in

vig

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s OR

in w

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stab

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0.67

MU

nkno

wn.

0.50

ML

Esta

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hes i

n hi

ghly

dis

turb

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atur

al e

cosy

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s (eg

. roa

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ildlif

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rrid

ors,

or a

reas

whi

chha

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ater

impa

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y hu

man

s suc

h as

tour

ist a

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or c

amps

ites)

OR

in o

verg

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d pa

stur

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grow

ing

or p

atch

y cr

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ajor

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ance

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ired

with

littl

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om o

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lity

Life

form

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erge

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sem

i aqu

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33L

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Pla

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vasi

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sses

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ting

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pro

perti

es.

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one.

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lity

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ory

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vour

ed b

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ing

pres

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as n

ot e

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nd n

ot u

nder

a b

ioco

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eed

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uctio

n un

der m

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essu

re (w

here

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e =

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ot o

vers

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.

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sum

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epro

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her

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ut st

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apab

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geta

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e pr

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(by

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or t

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s); w

eed

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ated

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rm.

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Mod

erat

ely

rapi

d gr

owth

that

will

equ

al c

ompe

titiv

e sp

ecie

s of t

he sa

me

life

form

.0.

67M

Unk

now

n.0.

50M

LM

axim

um g

row

th ra

te le

ss th

an m

any

spec

ies o

f the

sam

e lif

e fo

rm.

0.33

LSl

ow g

row

th, w

ill b

e ex

ceed

ed b

y m

any

othe

r spe

cies

.0

Stre

ss to

lera

nce

ofes

tabl

ishe

d pl

ants

toH

Hig

hly

resi

stan

t to

at le

ast t

wo

of (d

roug

ht, f

rost

, wat

erlo

ggin

g, fi

re a

nd sa

linity

) not

susc

eptib

le to

mor

e th

an o

ne (c

anno

t be

drou

ght o

r wat

erlo

ggin

g).

1.00

fros

t, dr

ough

t, w

ater

logg

ing,

salin

ity, f

ire?

MH

Hig

hly

tole

rant

of a

t lea

st tw

o of

(dr

ough

t, fr

ost,

wat

erlo

ggin

g, fi

re a

nd sa

linity

) and

may

be to

lera

ntof

ano

ther

. Su

scep

tible

to a

t lea

st o

ne.

0.67

MU

nkno

wn.

0.50

ML

Tole

rant

to a

t lea

st tw

o an

d su

scep

tible

to a

t lea

st o

ne.

0.33

LM

aybe

tole

rant

of o

ne st

ress

, sus

cept

ible

to a

t lea

st tw

o.0

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

27

Cri

teri

aR

ank

Inte

nsity

Rat

ing

Wei

ght

Rep

rodu

ctio

n

Rep

rodu

ctiv

e sy

stem

?H

Bot

h ve

geta

tive

and

sexu

al re

prod

uctio

n.1.

00M

HV

eget

ativ

e re

prod

uctio

n.0.

67M

Unk

now

n.0.

50M

LSe

xual

(sel

f and

cro

ss p

ollin

atio

n).

0.33

LSe

xual

but

eith

er c

ross

or s

elf p

ollin

atio

n.0

Num

ber o

fH

Abo

ve 2

000.

1.00

prop

agul

es p

rodu

ced

MH

1000

-200

0.0.

67pe

r flo

wer

ing

even

t?M

Unk

now

n.0.

50M

L50

-100

0.0.

33L

Less

than

50.

0

Prop

agul

e lo

ngev

ity?

HG

reat

er th

an 2

5% o

f see

ds c

an su

rviv

e ov

er 2

0 ye

ars i

n th

e so

il.1.

00M

HG

reat

er th

an 2

5% o

f see

ds su

rviv

e 10

-20

year

s in

the

soil,

OR

low

er v

iabi

lity

but s

urvi

ves o

ver 2

0ye

ars.

0.67

MU

nkno

wn.

0.50

ML

Gre

ater

than

25%

of s

eeds

surv

ive

5-10

yea

rs in

the

soil,

OR

low

er v

iabi

lity

but s

urvi

ve 1

0-20

yea

rs.

0.33

LG

reat

er th

an 2

5% o

f see

ds su

rviv

e 5

year

s, O

R v

eget

ativ

ely

repr

oduc

es.

0R

epro

duct

ive

perio

d?H

Mat

ure

plan

t pro

duce

s via

ble

prop

agul

es fo

r 10

year

s or m

ore,

OR

spec

ies f

orm

s sel

f-su

stai

ning

mon

ocul

ture

s.1.

00

MH

Mat

ure

plan

t pro

duce

s via

ble

prop

agul

es fo

r 3–1

0 ye

ars.

0.67

MU

nkno

wn.

0.50

ML

Mat

ure

plan

t pro

duce

s via

ble

prop

agul

es fo

r onl

y 1–

2 ye

ars.

0.33

LM

atur

e pl

ant p

rodu

ces v

iabl

e pr

opag

ules

for o

nly

1 ye

ar.

0

Tim

e to

reac

hH

Rea

ches

mat

urity

and

pro

duce

s via

ble

prop

agul

es in

und

er a

yea

r.1.

00re

prod

uctiv

e m

atur

ity?

MH

Prod

uces

pro

pagu

les b

etw

een

1-2

year

s afte

r ger

min

atio

n.0.

67M

Unk

now

n.0.

50M

L2-

5 ye

ars.

0.33

LG

reat

er th

an 5

yea

rs to

reac

h se

xual

mat

urity

.0

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

28

Cri

teri

aR

ank

Inte

nsity

Rat

ing

Wei

ght

Dis

pers

alN

umbe

r of d

ispe

rsal

mec

hani

sms?

HV

ery

light

, win

d di

sper

sed

seed

s, O

R b

ird d

ispe

rsed

seed

s OR

has

edi

ble

frui

t tha

t is r

eadi

ly e

aten

by

high

ly m

obile

ani

mal

s.1.

00

MH

Prop

agul

es sp

read

by

win

d, w

ater

, ani

mal

s (no

t bird

s), l

ight

veh

icul

ar tr

affic

.0.

67M

Unk

now

n.0.

50M

LPr

opag

ules

spre

ad b

y at

tach

ing

to h

uman

s or a

nim

als.

0.33

LPr

opag

ules

mai

nly

spre

ad b

y gr

avity

.0

Prob

abili

ty (o

r cha

nce)

HV

ery

likel

y th

at so

me

prop

agul

es w

ill d

ispe

rse

grea

ter o

ne k

ilom

etre

.1.

00th

at p

ropa

gule

s will

MH

Few

pro

pagu

les w

ill d

ispe

rse

grea

ter t

han

one

kilo

met

re b

ut m

any

will

reac

h 20

0-10

00 m

etre

s.0.

67di

sper

se to

a d

ista

nce

MU

nkno

wn.

0.50

grea

ter t

han

one

kilo

met

re?

ML

Ver

y fe

w to

non

e w

ill d

ispe

rse

to o

ne k

ilom

etre

, mos

t 20-

200

met

res.

0.33

LV

ery

unlik

ely

to d

ispe

rse

grea

ter t

han

200

met

res,

mos

t les

s tha

n 20

met

res.

0



Appendix 2:

Table of Risk Ratings, National Statusand Invasiveness Indices

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

30

Scie

ntifi

c N

ame

Com

mon

Nam

eR

isk

Rat

ing

Nat

iona

l Sta

tus

Inva

sive

Inde

xA

caci

a ba

ileya

naC

oota

mun

dra

wat

tleV

Non

e0.

79A

cer

pseu

dopl

atan

usSy

cam

ore

map

leV

Non

e0.

72A

gros

tis c

apill

aris

Bro

wn-

top

bent

VN

one

0.65

Aila

nthu

s al

tissi

ma

Tree

-of-h

eave

nP

Vic

, NSW

, WA

0.89

Alli

um tr

ique

trum

Ang

led

onio

nV

Vic

, SA

, Tas

0.61

Alo

e sa

pona

ria

Alo

eS

Non

e0.

63A

ltern

anth

era

philo

xero

ides

Alli

gato

r wee

dN

one

Vic

, NSW

, QLD

, ACT

, SA

, NT,

WA

, Tas

0.69

Ant

hoxa

nthu

m o

dora

tum

Swee

t ver

nal-g

rass

VN

one

0.66

Asp

hode

lus

fistu

losu

sO

nion

-wee

dS

Vic

, NSW

, SA

, NT,

Tas

0.54

Bri

za m

axim

aLa

rge

quak

ing-

gras

sV

Non

e0.

74C

ardu

us p

ycno

ceph

alus

Slen

der t

hist

leS

Vic

, Tas

0.72

Car

duus

tenu

iflor

usSl

ende

r thi

stle

PV

ic, S

A, T

as0.

61C

arth

amus

lana

tus

Saff

ron

this

tleP

Vic

, NSW

, QLD

, NT,

WA

, Tas

0.51

Cen

taur

ea c

alci

trap

aSt

ar th

istle

SV

ic, N

SW0.

41C

estr

um p

arqu

iG

reen

poi

sonb

erry

Non

eV

ic, N

SW, Q

LD0.

73C

hrys

anth

emoi

des

mon

ilife

ra s

sp. m

onili

fera

Bon

esee

dV

Vic

, NSW

, QLD

, SA

, WA

0.80

Cir

sium

aca

rna

Sold

ier t

hist

leN

one

Vic

, SA

0.52

Cir

sium

arv

ense

Cal

iforn

ian

this

tleN

one

Vic

, NSW

, SA

, WA

, Tas

0.70

Cir

sium

vul

gare

Spea

r thi

stle

SV

ic, S

A, T

as0.

69C

onvo

lvul

us a

rven

sis

Com

mon

bin

dwee

dN

one

Vic

, SA

, WA

0.78

Cop

rosm

a re

pens

New

Zea

land

mirr

or-b

ush

VN

one

0.82

Cor

tade

ria

sello

ana

Pam

pas g

rass

VN

SW, W

A, T

as0.

95C

oton

east

er g

lauc

ophy

llus

Cot

onea

ster

VN

SW0.

81C

rata

egus

mon

ogyn

aH

awth

orn

VV

ic, S

A0.

70C

uscu

ta c

ampe

stri

sG

olde

n do

dder

SV

ic, N

SW, S

A, W

A, T

as0.

73C

ynar

a ca

rdun

culu

sA

rtich

oke

this

tleV

Vic

, SA

, WA

, Tas

0.66

Cyt

isus

sco

pari

usEn

glis

h br

oom

VV

ic, N

SW, S

A, A

CT, T

as0.

73

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

31

Scie

ntifi

c N

ame

Com

mon

Nam

eR

isk

Rat

ing

Nat

iona

l Sta

tus

Inva

sive

Inde

xD

atur

a st

ram

oniu

mC

omm

on th

orn-

appl

eS

Vic

, QLD

, WA

, NT,

Tas

0.61

Del

aire

a od

orat

aC

ape

ivy

VN

SW0.

72D

iplo

taxi

s te

nuifo

liaSa

nd m

usta

rdP

Vic

, SA

0.57

Dip

ogon

lign

osus

Dip

ogon

VW

A0.

82D

ipsa

cus

fullo

num

Wild

teas

elP

Vic

0.55

Ditt

rich

ia g

rave

olen

sSt

inkw

ort

PV

ic0.

61E

chiu

m p

lant

agin

eum

Pate

rson

's cu

rse

SV

ic, N

SW, S

A, W

A, N

T, T

as0.

66E

chiu

m v

ulga

reV

iper

's bu

glos

sP

Vic

, NSW

, Tas

0.66

Ehr

hart

a er

ecta

Pani

c ve

ldt g

rass

VN

one

0.77

Em

ex a

ustr

alis

Spin

y em

exS

Vic

, NSW

, QLD

, SA

, WA

, NT,

Tas

0.53

Era

gros

tis c

urvu

laA

fric

an lo

ve-g

rass

VV

ic, N

SW, S

A, W

A, A

CT,

Tas

0.71

Eri

ca lu

sita

nica

Span

ish

heat

hV

Non

e0.

77F

oeni

culu

m v

ulga

reFe

nnel

VV

ic, T

as0.

57G

alen

ia p

ubes

cens

Gal

enia

SN

SW0.

61G

enis

ta li

nifo

liaFl

ax-le

af b

room

VV

ic0.

70G

enis

ta m

onsp

essu

lana

Mon

tpel

lier b

room

VV

ic, N

SW, S

A, A

CT, T

as0.

70H

eder

a he

lixIv

yV

Non

e0.

76H

iera

cium

aur

antia

cum

Ora

nge

haw

kwee

dN

one

Non

e0.

62H

omer

ia fl

acci

daC

ape

tulip

(one

-leaf

)V

Vic

, NSW

, SA

, WA

, Tas

0.74

Hyp

eric

um a

ndro

saem

umTu

tsan

VV

ic, W

A0.

81H

yper

icum

per

fora

tum

St Jo

hn's

wor

tV

Vic

, NSW

, WA

, Tas

0.66

Hyp

eric

um te

trap

teru

mSq

uare

-ste

m S

t Joh

n's w

ort

PV

ic0.

55Il

ex a

quifo

lium

Hol

lyV

Non

e0.

75Ju

ncus

acu

tus

Shar

p ru

shV

Vic

0.52

Junc

us e

ffusu

sSo

ft ru

shV

Non

e0.

69La

vand

ula

stoe

chas

Topp

ed la

vend

erS

Vic

0.68

Leuc

anth

emum

vul

gare

Ox-

eye

dais

yP

Vic

0.72

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

32

Scie

ntifi

c N

ame

Com

mon

Nam

eR

isk

Rat

ing

Nat

iona

l Sta

tus

Inva

sive

Inde

xLe

yces

teri

a fo

rmos

aH

imal

ayan

hon

eysu

ckle

VN

one

0.80

Ligu

stru

m v

ulga

reEu

rope

an p

ivet

SN

one

0.75

Loni

cera

japo

nica

Japa

nese

hon

eysu

ckle

VN

one

0.83

Lyci

um fe

roci

ssim

umA

fric

an b

ox-th

orn

VV

ic, N

SW, Q

LD, S

A, W

A, N

T, T

as0.

67M

arru

bium

vul

gare

Hor

ehou

ndV

Vic

, NSW

, SA

, WA

, Tas

0.77

Mel

iant

hus

com

osus

Tufte

d ho

neyf

low

erN

one

Vic

0.49

Myr

ioph

yllu

m a

quat

icum

Parr

ot's

feat

her

PW

A, T

as0.

63M

yrsi

phyl

lum

asp

arag

oide

sB

ridal

cre

eper

VN

SW, S

A, W

A, T

as0.

79N

asse

lla h

yalin

aFi

ne n

eedl

e-gr

ass

SN

one

0.48

Nas

sella

nee

sian

aC

hile

an sp

ear-

gras

sV

NSW

0.69

Nas

sella

tric

hoto

ma

Serr

ated

tuss

ock

VV

ic, N

SW, S

A, A

CT, T

as0.

76O

nopo

rdum

aca

nthi

umH

eral

dic

this

tleN

one

Vic

, NSW

, Tas

0.57

Ono

pord

um a

caul

onSt

emle

ss o

nopo

rdon

SV

ic, N

SW, W

A, T

as0.

53O

punt

ia fi

cus-

indi

caPr

ickl

y-pe

arP

Non

e0.

76O

punt

ia r

obus

taW

heel

cac

tus

SV

ic, N

SW, Q

LD, S

A, W

A, N

T0.

66O

punt

ia s

tric

taC

omm

on p

rickl

y pe

arS

Vic

, NSW

, QLD

, SA

, WA

, NT

0.76

Oxa

lis p

es-c

apra

eSo

urso

bV

Vic

, SA

, Tas

0.74

Oxy

lobi

um la

nceo

latu

mO

xylo

bium

SN

one

0.62

Pas

palu

m d

ilata

tum

Pasp

alum

VN

one

0.73

Pen

nise

tum

cla

ndes

tinum

Kik

uyu

VN

one

0.67

Pha

lari

s aq

uatic

aTo

owoo

mba

can

ary-

gras

sV

Non

e0.

73P

hysa

lis v

isco

saPr

airie

gro

und

cher

ryN

one

Vic

, NSW

0.73

Pin

us p

inas

ter

Clu

ster

pin

eV

Non

e0.

79P

inus

rad

iata

Mon

tere

y pi

neV

Non

e0.

78P

ittos

poru

m u

ndul

atum

Swee

t pitt

ospo

rum

VN

SW, W

A0.

79P

olyg

ala

myr

tifol

iaM

yrtle

-leaf

milk

wor

tV

Non

e0.

76P

opul

us a

lba

Whi

te p

opla

rP

Non

e0.

66

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

33

Scie

ntifi

c N

ame

Com

mon

Nam

eR

isk

Rat

ing

Nat

iona

l Sta

tus

Inva

sive

Inde

xP

runu

s lu

sita

nica

Portu

gal l

aure

lS

Non

e0.

77R

esed

a lu

teol

aW

eld

Non

eV

ic, T

as0.

54R

ham

nus

alat

ernu

sIta

lian

buck

thor

nV

Non

e0.

82R

osa

rubi

gino

saSw

eet b

riar

VV

ic, N

SW, S

A, T

as0.

79R

ubus

frut

icos

us a

gg.

Bla

ckbe

rry

Non

eV

ic, N

SW, Q

LD, S

A, W

A, A

CT, T

as0.

77Sa

gitta

ria

gram

inea

Arr

owhe

adS

SA, W

A, T

as0.

58Sa

lix a

lba

Whi

te w

illow

SN

SW, S

A, A

CT

0.93

Salix

bab

ylon

ica

Wee

ping

will

owS

Non

e0.

93Sa

lix c

iner

eaG

rey

sallo

wV

NSW

, SA

, AC

T0.

92Sa

lix x

rub

ens

Cra

ck w

illow

VN

SW, S

A, A

CT

0.92

Salp

ichr

oa o

riga

nifo

liaPa

mpa

s lily

-of-

the-

valle

yV

Vic

, Tas

0.76

Scol

ymus

his

pani

cus

Gol

den

this

tleN

one

Vic

0.50

Sene

cio

jaco

baea

Rag

wor

tP

Vic

, NSW

, SA

, WA

, Tas

0.66

Sene

cio

pter

opho

rus

Win

ged

grou

ndse

lP

Vic

0.68

Sily

bum

mar

ianu

mV

arie

gate

d th

istle

SV

ic, S

A, W

A, T

as0.

76So

lanu

m e

laea

gnifo

lium

Silv

erle

af n

ight

shad

eN

one

Vic

, NSW

, SA

, Tas

0.67

Sola

num

linn

aean

umA

pple

of S

odom

SV

ic, W

A, T

as0.

36So

llya

hete

roph

ylla

Blu

ebel

l cre

eper

VN

one

0.81

Spar

tina

angl

ica

Spar

tina

VN

one

0.61

Spar

tina

x to

wns

endi

iSp

artin

aV

Non

e0.

58Sp

orob

olis

indi

cus

Rat

-tail

gras

sS

NSW

0.71

Trad

esca

ntia

alb

iflor

aW

ande

ring

cree

per

VN

SW0.

59Tr

ibul

us te

rres

tris

Cal

trop

Non

eV

ic, S

A, W

A, N

T, T

as0.

61U

lex

euro

paeu

sG

orse

VV

ic, N

SW, S

A, W

A, A

CT,

Tas

0.83

Ver

basc

um th

apsu

sG

reat

mul

lein

SV

ic0.

47V

erba

scum

vir

gatu

mTw

iggy

mul

lein

SN

one

0.64

Vin

ca m

ajor

Blu

e pe

riwin

kle

VN

one

0.67

Pest

Pla

nt In

vasi

vene

ss A

sses

smen

t

Pre

pare

d by

the

Kei

th T

urnb

ull R

esea

rch

Inst

itute

, 200

2.

34

Scie

ntifi

c N

ame

Com

mon

Nam

eR

isk

Rat

ing

Nat

iona

l Sta

tus

Inva

sive

Inde

xV

ulpi

a br

omoi

des

Squi

rrel

-tail

fesc

ueV

Non

e0.

67W

atso

nia

mer

iana

Wild

wat

soni

aS

Vic

, SA

, WA

0.40

Xan

thiu

m s

pino

sum

Bat

hurs

t bur

rS

Vic

, NSW

, QLD

, SA

, WA

, NT,

Tas

0.58

Xan

thiu

m s

trum

ariu

mN

oogo

ora

burr

Non

eV

ic, N

SW, Q

LD, S

A, W

A, N

T, A

CT, T

as0.

62L

egen

dR

isk

Rat

ing

(acc

ordi

ng to

Car

r et

al 1

992)

:V

- ve

ry se

rious

thre

at to

one

or m

ore

vege

tatio

n fo

rmat

ion

in V

icto

riaS

- ser

ious

thre

at to

one

or m

ore

vege

tatio

n fo

rmat

ion

in V

icto

riaP

- pot

entia

l thr

eat t

o on

e or

mor

e ve

geta

tion

form

atio

n in

Vic

toria

Non

e - N

ot p

rese

nt in

risk

ass

essm

ent

Nat

iona

l Sta

tus:

Vic

- N

oxio

us in

Vic

toria

NSW

- N

oxio

us in

New

Sou

th W

ales

Tas -

Nox

ious

in T

asm

ania

SA -

Nox

ious

in S

outh

Aus

tralia

WA

- N

oxio

us in

Wes

tern

Aus

tralia

QLD

- N

oxio

us in

Que

ensl

and

NT

- Nox

ious

in N

orth

ern

Terr

itory

AC

T - N

oxio

us in

Aus

tralia

n C

apita

l Ter

ritor

y



Appendix 3:

Examples of Present and PotentialDistribution Maps



List of species with present and potential distribution maps in the order they occur inAppendix 3. Updates to names are indicated.

Botanical name:

Acacia baileyana*Acer pseudoplatanus*Agrostis capillaris*Ailanthus altissimaAllium triquetrumAloe saponaria* = A. maculataAlternanthera philoxeroidesAnthoxanthum odoratum*Asphodelus fistulosus*Briza maxima*Carduus pycnocephalusCarduus tenuiflorusCarthamus lanatus*Centaurea calcitrapaCestrum parquiChrysanthemoides monilifera ssp. moniliferaCirsium acarna = Picnomon acarnaCirsium arvense*Cirsium vulgareConvolvulus arvensisCoprosma repens*Cortaderia selloanaCotoneaster glaucophyllus*Crataegus monogynaCuscuta campestrisCynara cardunculusCytisus scopariusDatura stramoniumDelairea odorata*Diplotaxis tenuifoliaDipogon lignosus*Dipsacus fullonumDittrichia graveolensEchium plantagineumEchium vulgareEhrharta erecta*Emex australisEragrostis curvulaErica lusitanica*Foeniculum vulgareGalenia pubescensGenista linifoliaGenista monspessulanaHedera helix*Hieracium aurantiacum



Homeria flaccida = Moraea flaccidaHypericum androsaemumHypericum perforatumHypericum tetrapterum*Ilex aquifolium*Juncus acutusJuncus effusus*Lavandula stoechasLeucanthemum vulgareLeycesteria formosaLigustrum vulgare*Lonicera japonica*Lycium ferocissimumMarrubium vulgareMelianthus comosusMyriophyllum aquaticum*Myrsiphyllum asparagoides = Asparagus asparagoidesNassella hyalinaNassella neesianaNassella trichotomaOnopordum acanthiumOnopordum acaulonOpuntia ficus-indicaOpuntia robustaOpuntia strictaOxalis pes-capraeOxylobium lanceolatum* = Callistachys lanceolataPaspalum dilatatum*Pennisetum clandestinum*Phalaris aquatica*Physalis viscosaPinus pinaster*Pinus radiata*Pittosporum undulatum*Polygala myrtifolia*Populus alba*Prunus lusitanica*Reseda luteolaRhamnus alaternus*Rosa rubiginosaRubus fruticosus agg.Sagittaria gramineaSalix albaSalix babylonicaSalix cinereaSalix x rubensSalpichroa origanifoliaScolymus hispanicusSenecio jacobaea



Senecio pterophorusSilybum marianumSolanum elaeagnifoliumSolanum linnaeanumSollya heterophylla*Spartina anglicaSpartina x townsendiiSporobolus indicus = S. africanusTradescantia albiflora* = T. fluminensisTribulus terrestrisUlex europaeusVerbascum thapsusVerbascum virgatum*Vinca major*Vulpia bromoides*Watsonia merianaXanthium spinosumXanthium strumarium = X. occidentale; X. orientale* Indicates a species with PV district boundaries on the potential distribution maps; the others

species, previously assessed by KTRI, have the CMA boundaries overlayed onto the potentialdistribution maps.



Maps in Appendix 3 are located in a separate document.

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