A weed risk assessment model for use as a biosecurity tool evaluating plant introductions
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Journal of Environmental Management (1999) 57, 239–251
Article No. jema.1999.0297, available online at http://www.idealibrary.com on
A weed risk assessment model for use
as a biosecurity tool evaluating plant
introductions
P. C. Pheloung†‡ , P. A. Williams§* , S. R. Halloy¶
New plant taxa from around the world continue to be imported into Australia and New Zealand. Many of
these taxa have the potential to become agricultural or environmental weeds and this risk needs to be
assessed before allowing their entry. A weed risk assessment system is described that uses information
on a taxon’s current weed status in other parts of the world, climate and environmental preferences, and
biological attributes. The system is designed to be operated by quarantine personnel via a user-friendly
computer interface.
The model was tested against experts’ scores for weediness for 370 taxa present in Australia, representing
both weeds and useful taxa from agriculture, the environment, and other sectors. The model was judged on
its ability to correctly ‘reject’ weeds, ‘accept’ non-weeds, and generate a low proportion of taxa which could
not be decisively categorised, termed ‘evaluate’. More than 70% of the taxa were rejected or accepted. All
taxa classified as serious weeds, and most minor weeds, were rejected or required further evaluation, while
only 7% of non-weeds were rejected. The model was modified to New Zealand conditions and evaluated
against the opinions of several groups of experts and against economic measures. The model produced a
weediness score very similar to the mean of the experts scores. The latter were highly variable: agriculturalists
tended to accept known weeds, conservationists tended to reject most adventive taxa, and only botanists
produced scores similar to the model. The model scores also tended to be independent of economic value
as measured in this study. The model could be adapted for use as a screening tool in any region of the world.
1999 Academic Press
Keywords: Australia, New Zealand, weed risk assessment, plant introductions, biosecurity,
quarantine, computer model.
Introduction potential (reviewed by Mack, 1996), but bor-
der authorities urgently need an objective,
Ł Corresponding author
credible, and publicly acceptable risk assess-
Weeds have major impacts on economies and ment system to predict the weediness, or † Agriculture Western
natural environments world wide, including invasive potential, of the thousands of poten- Australia, 3 Baron-Hay Crt,
South Perth WA 6151,
Australia (Groves, 1986) and New Zealand tial new entries. Australia
(Williams and Timmins, 1990). Many of these There have been several symposia (e.g.
weeds have been purposely introduced as Drake et al., 1989) and a growing research ‡ Present address:
Australian Quarantine
potential new crops, ornamentals, or as novel- literature on invasive plant taxa, but until Inspection Service, GPO
ties (Esler, 1988; Lonsdale, 1994). To counter recently there was considerable pessimism Box 858, Canberra,
the threat to agriculture or the environment that potentially invasive species could be ACT 2601, Australia
from new plant taxa, regulatory authori- identified (Crawley, 1987). However, progress § Landcare Research,
ties have a statutory responsibility to ensure has recently been made in identifying the Private Bag 6, Nelson,
that all plant taxa proposed to be imported, key characteristics of potential weeds in New Zealand
which are not already prohibited and are not Australia (Noble, 1988; Scott and Panetta, ¶ Crop and Food, Private
already established, be evaluated for their 1993), New Zealand (Esler et al., 1993), Bag, Mosgiel, New
potential to damage the productive capac- South Africa (Richardson et al., 1990), Zealand
ity or environment of these countries. There Britain (Williamson and Fitter, 1996), and Received 27 August 1998;
are many approaches to predicting weed north America (Mack 1996; Reichard and accepted 23 April 1999
0301–4797/99/120239C13 $30.00/0 1999 Academic Press240 P. C. Pheloung et al.
Hamilton, 1997), or within closely related authority that satisfies many of these crite-
groups of taxa including herbs (Forcella et al., ria: the Weed Risk Assessment model (WRA).
1986; Williamson, 1993) or woody seed plants We then report on a test of the applicabil-
(Rejmanek and Richardson, 1996). Predic- ity of a slightly altered version of the model
tions from a non-experimental approach are that incorporates environmental differences
most accurate if they are based on informa- between Australia and New Zealand: the
tion that includes the biology of the invad- New Zealand Weed Risk Assessment Model
ing species, the bioclimatic features of the (New Zealand WRA). In testing the New
recipient and source regions, and the evo- Zealand WRA we examine the sources of
lutionary history of both (e.g. Richardson expert bias that are inherent in the con-
et al., 1990). A synthesis of this informa- cept of weediness (Perrins et al., 1992) and
tion can lead to general conceptual models of demonstrate that the model minimises these
plant invasiveness (Rejmanek, 1995) which difficulties.
in turn can be applied to screening procedures The model could be used to investigate
such as decision trees to categorise poten- the nature of weediness, but here we simply
tial plant imports (Panetta, 1993; Tucker and describe how it works and the steps we took
Richardson, 1995; Reichard, 1996; Reichard to ensure it would function as a publicly
acceptable screening tool.
and Hamilton, 1997). Regulatory authorities
responsible for biosecurity need a tool that
synthesizes much of this disparate and some- Methods and results
times theoretical information. One approach
is a computer based spreadsheet model pro- Producing the WRA
ducing a score for weediness, which can then
be converted to an entry recommendation for The basis of the WRA is the answers to
a specified taxon. 49 questions (Appendix 1) based on the main
An acceptable biosecurity assessment sys- attributes and impacts of weeds. These are
tem should satisfy a number of requirements combined into a scoring system which in
(Hazard, 1988; Panetta, 1993). It should be the absence of any evidence to the contrary,
calibrated and validated against a large num- gives an equal weight to nearly all questions
ber of taxa already present in the recipient (Appendix 2). These cover a range of weedy
country and representing the full spectrum of attributes in order to screen for taxa that are
taxa likely to be encountered as imports into likely to become weeds of the environment
that country. It must effectively discriminate and/or agriculture. The questions are divided
into three sections producing identifiable
between weeds and non-weeds, such that the
scores that contribute to the total score
majority of weeds are not accepted, non-
(Appendix 2).
weeds are not rejected, and the proportion
Biogeography (section A) encompasses
of taxa requiring further evaluation is kept
the documented distribution, climate
to a minimum. As international trade agree-
preferences, history of cultivation, and
ments require that prohibited taxa should
weediness of a plant taxon elsewhere in the
fit the definition of a quarantine pest before world, i.e. apart from the proposed recipient
they can be excluded by quarantine regula- country. Weediness elsewhere is a good
tions (Anon., 1994, 1995; Walton and Parnell, predictor of a taxon becoming a weed in new
1996), the system must be based on explicit areas with similar environmental conditions
assumptions and scientific principles so that (Forcella and Wood, 1984; Panetta and
the country cannot be accused of applying Mitchell, 1991a,b). The question concerning
unjustified non-tariff trade barriers. Ideally the history of cultivation recognises the
the system should be capable of identifying important human component of propagule
which land use system the taxon is likely to pressure (Richardson et al., 1994; Williamson
invade, to assist in an economic evaluation and Fitter, 1996), but such data are obviously
of its potential impacts. Finally, the sys- never available for the proposed new
tem must be cost effective to the prospective country. The global distribution and climate
importer and the border control authority. preferences, where these are available, are
Here, we first describe a model designed used to predict a potential distribution in the
specifically for the Australian quarantine recipient country.A weed risk assessment model 241 Undesirable attributes (section B) are char- A benchmark against which to compare the acteristics such as toxic fruits and unpalat- model is problematic because there are no ibility, or invasive behaviour, such as a absolute values for weediness of individual climbing or smothering growth habit, or the taxa (Perrins et al., 1992). However, those ability to survive in dense shade. familiar with a taxon in a country can offer a Biology/ecology (section C) are the attri- meaningful opinion on the actual or potential butes that enable a taxon to reproduce, weediness of the taxon in that country, spread, and persist (Noble, 1988), such as against which to compare the score from the whether the plant is wind dispersed or animal model. (The sources of bias in this procedure dispersed, and whether the seeds would are examined in evaluating the New Zealand survive passage through an animal’s gut. WRA.) A further 12 scientists therefore Availability of information is often very defined the weed status or usefulness of taxa limited for new species which can restrain they were familiar with. Taxa were given a the utility of screening systems. To ensure rank of 0, 1 or 2 and this was used to classify that at least some questions were answered them as non-weeds, minor weeds, and serious for each section, the WRA system requires weeds, respectively. The mean score was used the answers to two questions in section A, to assess the performance of the WRA model; two in section B, and six in section C before it again, potential bias is examined in testing will give an evaluation and recommendation. the New Zealand WRA. The recommendation can be compared with the number of questions answered as an indication of its reliability, which obviously Results of the WRA improves as more questions are answered. Answers to the questions provide a poten- The majority of the 370 taxa (81%) are tial total score ranging from 14 (benign perceived as weeds in some context, but taxa) to 29 (maximum weediness) for each less than half are considered serious weeds. taxon. The total score is partitioned between Useful taxa (63%) including both weeds answers to questions considered to relate pri- (45%) and non-weeds (18%), and non-useful marily to agriculture, to the environment, or taxa (37%) were included in the sam- common to both (Appendix 1). The total scores ple. Weeds from all sectors—agriculture, are converted to one of the three possible rec- environment, horticulture, garden and ser- ommendations by two critical score settings. vice areas—were well represented (data not The lower critical score, 0, separates accept- shown). able taxa from those requiring evaluation, The cumulative frequency distributions of and the higher critical score, 6, separates WRA scores, for each of the survey classifica- taxa requiring evaluation from those that tions (Figure 1), were used to investigate the should be rejected. Evaluation could mean effect of different pairs of critical scores on either obtaining more data and re-running the distribution of WRA recommendations. the model, or undertaking further investiga- The range of scores for non-weeds overlaps tions such as field trials (Mack, 1996). the range for serious weeds, so it is impos- To provide data for defining these score sible to define any set of critical scores that settings, six Australian scientists in a range reject all serious weeds while accepting all of the fields ran the model to assess the non-weeds. However, it is possible to ensure weed potential of taxa they were familiar that none of the serious weeds are accepted by with, ranging from non-weedy beneficial taxa setting the accept score at 0 or less. Similarly, to serious weeds. In addition, all taxa that less than 10% of non-weeds will be rejected if have a noxious status in Australia were the reject score is greater than 6. This would assessed using the information provided by mean that 29% of the taxa assessed in this Parsons and Cuthbertson (1992). Assessors study would fall between these extremes and treated each taxon as if it had not yet require evaluation. Lowering the minimum arrived in Australia, such that serious weeds, reject score to 6 would reduce this proportion for example, were assessed purely on their to 22% but increase the proportion of rejected weed status outside Australia. The taxa were non-weeds to 15%, which is less desirable, classified as agricultural or environmental since some of these are regarded as useful weeds. (Figure 1). Consequently, the critical scores,
242 P. C. Pheloung et al.
Proportion of species scoring the amount shown or less (%)
100
80
60 Accept Reject
40
20
0
–15 –10 –5 0 5 10 15 20 25 30
Weed risk assessment score
Figure 1. Cumulative frequency of taxa receiving a particular WRA score or less for each survey
classification. Survey categories: ( M ), serious weeds (139); ( ž ), minor weeds (147); ( ), non-weeds
(84); ( ), all plants (370).
0 and 6, were used to convert the WRA scores Table 2. Correlations of the agriculture and
into the recommendations–‘accept’, ‘evaluate’ environment components of the WRA score
and the total WRA score, with the experts
and ‘reject’. These settings recommended all classification of taxa as weeds of agriculture
serious weeds and most minor weeds (84%) or environment. All PA weed risk assessment model 243
‘a range of soil conditions’ because infertile The WRA database contains replicate
soils are not as extensive in New Zealand. As scores from the six independent Australian
anticipated, several questions that remained experts. These were used to estimate the vari-
the same needed to be answered differently ance of the model scores, on the assumption
for Australia and New Zealand; e.g. in that a similar degree of variability would be
Australia there are many parrots which found in the scores from experts using the
destroy seeds, and so certain new plants New Zealand WRA model.
are more likely to have natural enemies in Next, 13 New Zealand experts, 12 of whom
Australia than in New Zealand. None of were not involved with the model, classified
the modified questions required alterations many of the same 291 taxa into the categories
to the scoring system, so that the outcome of non-weed, minor weed, and major weed.
scores were directly comparable. The outcome The categories were not defined, and neither
of these changes was the New Zealand were the taxa classified as environmental or
WRA model. agricultural weeds as in Australia. However,
respondents were asked not to consider
economic value, i.e. we were interested only in
their opinion as to whether taxa were weeds
Testing the New Zealand WRA in any land-use system. This classification
was transformed into the numbers 0, 0Ð5 and
We assessed the ability of the model to iden- 1Ð0, respectively. The experts were classified
tify weeds of a range of land use systems when by one of us (P.A.W.) as botanists (seven),
used in a country for which it was not origi- agriculturalists (three), and conservationists
nally designed, in this instance New Zealand. (three). This classification was unknown to
We also tested the New Zealand WRA—in them, to avoid any conscious bias. This
a manner that was equally relevant to allowed an evaluation of speciality bias with
WRA—by asking whether it was better than respect to classification, and how it affected
an expert’s opinion? ‘Better’ we define as variation. Each expert reviewed between
efficacy, ease of use, and cost of use but only twenty-seven and 291 taxa, with most doing
efficacy was tested, i.e. the capability to score between 140 and 250 taxa. Two hundred and
correctly with respect to the benchmark, and seventy-four taxa were classified by more
with a low variation when used by several than one expert.
individuals. Finally, 195 taxa for which data were avail-
We tested whether the New Zealand WRA able were scored for relative economic value,
was more biased by economic or environ- based on the average of three evaluations.
mental considerations than the collective (1) The area of the taxon cultivated from
opinion, as represented by an average score, 1842 to 1991 was obtained from New Zealand
of a group of experts representing a wide statistics as processed by Halloy (1994). The
range of interests. This potentially large ele- logarithm of this area was standardised to a
ment of subjectivity was tested with a series range of 0 to 1. (2) An estimate of an eco-
of cross-checks on variation and bias using nomic value of any type was scored as 0 to 5
three data sets referred to the same taxa. by one of us (S.R.H.) then standardised 0 to 1.
Firstly, the New Zealand WRA model was (3) Taxa were scored according to their pres-
run on 198 taxa used in testing the WRA ence or absence in the New Zealand Nursery
model that are also present in New Zealand Register 1995/1996, on the assumption that
(the majority of which are adventive), plus a listing represents value as an ornamental
any additional noxious plants in New Zealand but not as a major economic plant. Scores
(Esler et al., 1993). Responses to the model were 1 if the taxon was present, 0Ð5 if only
questions for the resulting 291 taxa were the genus was present but with probability of
made by one of us (P.A.W) as if they were not the taxon being present, 0 if the genus was
yet in New Zealand, using the international absent. This value was standardised 0 to 0Ð2.
literature, floras, and databases. The scores The data were divided into five classes, and
of 14 to 29 for each taxon were standardised the taxon score outcomes for New Zealand
from 0 to 1 for comparison with the other data WRA and the experts were compared within
sets, using the thresholds: accept244 P. C. Pheloung et al.
WRA within each economic class is expressed ‘evaluate’ category of the three expert groups,
as percentages. as against 21% for the model, shows that
Lastly, the total scores for the WRA and all expert groups have a tendency to either
New Zealand WRA models derived in the two accept or reject a larger proportion of taxa
countries were compared for the 198 taxa than the model (Table 3).
common to both. There are differences in recommendations
between the New Zealand WRA model and
the average experts’ evaluation (Figure 2).
Results of testing the New Zealand
Sixty-four percent of taxa did not change,
WRA and overall 93% of taxa either rejected
by the model or requiring evaluation were
The combined scores and their variability
also not accepted by the experts. Only 4%
show a strong similarity between the scores
of taxa that were accepted by the model
from the New Zealand WRA model (0Ð538)
were rejected by the expert averages. Two
and the mean scores for the 13 experts
differences between the New Zealand WRA
(0Ð545), and for the deviations, and the dis-
outcomes and the expert outcomes (Figure 2)
tributions (Table 3). When converted to out-
require examination.
comes the results are again very similar. That
the higher proportion of taxa rejected by both (1) A greater permissiveness in the model
the New Zealand WRA model and the experts as compared with the experts could allow
score (mean 62Ð0%) is higher than might be introduction of weeds if the model alone were
expected from a random selection of taxa used as a screening tool. Eight percent of
partly reflects the addition of all the noxious taxa were accepted by the model but classed
weeds of New Zealand to the database. as ‘reject’ or ‘evaluate’ by the expert averages.
Replicate answers to the New Zealand Taxa accepted by the model but rejected by
WRA model are less variable (CV 8Ð2%) experts include: Chasmanthe floribunda (Sal-
than the mean expert scores (CV 58Ð7%) isb.) N. E. Brown, Echium candicans L., Era-
(Table 3). The high variation in taxon scores grostis tef Trott and Scabiosa atropurpurea
is explained by the variability among the L. Taxa rated as ‘evaluate’ include Betula
individual respondents, even within expert pendula Roth, Festuca rubra L., Gliricidia
groups. Agriculturalists gave the lowest sepium (Jacq.) Walp and Lathyrus ochrus L.
scores for weediness (0Ð438), conservationists These taxa have several features in common.
the highest (0Ð673). Conservationists’ scores They received a low number of responses
were also less variable than those of the (two or three), their expert score variation
other groups (19Ð5% cf. 31Ð3% and 33Ð8%). was high (data not presented), and most had
The outcome is that conservationists tend to model scores on or near the thresholds. It
reject most taxa while agronomists accept a is significant that no taxon with an average
higher proportion. The 5–6% of taxa in the expert score of major weed was accepted by
Table 3. Comparisons of scores from NZWRA and the expert groups: (a) the average of all taxa scored and
the variation; (b) the average level of variability of scores for individual taxa from NZWRA and the experts for
taxa with two or more scores; (c) the outcomes for all taxa expressed as a rounded percentage
NZ Model Mean Botanists Agriculturalists Conservationists
expert
(a) All taxa scores
N 291 291 288 251 291
Mean 0Ð538 0Ð545 0Ð520 0Ð438 0Ð673
SD taxa score 0Ð204 0Ð238 0Ð307 0Ð311 0Ð258
(b) Individual taxa
N 291 291 208 181 224
Mean SD of score 0Ð041 0Ð231 0Ð115 0Ð099 0Ð087
CV % of score 8Ð2 58Ð7 31Ð3 33Ð8 19Ð5
(c) All taxa outcomes
Accept 16 17 25 35 7
Evaluate 21 20 5 6 4
Reject 63 63 70 59 89A weed risk assessment model 245
80
70
Proportion of model outcomes which
differed from experts outcomes
60
50
40
30
20
10
0
Expert: No change Evaluate Reject Accept Reject Accept Evaluate
Model: Accept Evaluate Reject
Figure 2. A comparison of the outcomes from the NZWRA model vs. the outcomes from the expert groups,
expressed as a percentage of the total outcomes for each group. , agriculturalists (251); , botanists
(288); , conservationists (291); , all (291).
the New Zealand WRA model, nor any taxon Scoring by the model and by the experts
rated as a noxious plant in New Zealand. varied according to the economic value of
(2) A greater severity of assessment by the the taxon (Figure 3). The mean expert score
model than by the experts could result in showed very little bias, with only a slight
benign taxa being rejected. The model was tendency to accept more taxa (16Ð7%) of
more cautious than the experts in 19% of intermediate economic value (246 P. C. Pheloung et al.
80
Difference in accepted vs. rejected (%)
60
40
20
0
–20
–40
–60
0A weed risk assessment model 247
availability of experts may be limiting, but Zealand context Agrostis stolonifera L., Lupi-
any smaller number of experts—especially if nus polyphyllus and Pinus contorta. Where
they were from one interest group—would a taxon may have significant economic ben-
be likely to give a biased answer with efits, a further evaluation of its weediness
respect to a particular sector (Perrins et al., potential may include experimental stud-
1992). ies (Williamson, 1993; Scott and Panetta,
The bias displayed by different groups of 1993). Economic value should be scored in
experts differs between the two countries for a transparently separate exercise and bal-
which we have information. Conservation- anced against weediness in appropriate risk
ists in Britain, for example, are less inclined assessment evaluations (Walton and Parnell,
than the average to consider a taxon to be 1996).
a weed (Perrins et al., 1992; Williamson, We conclude that the weed risk assess-
1993), presumably because many weeds have ment model with explicit scoring of biolog-
redeeming features such as colourful flow- ical, ecological, and geographical attributes
ers. In contrast, New Zealand conservation- is a useful biosecurity tool for detecting
ists are inclined to consider such taxa as potentially invasive weeds in many areas of
weeds. Those with botanical training distin- the world.
guish clearly between the native and alien
floras, often to the exclusion of the latter
in the compilation and collection of botani-
Acknowledgement
cal data (Raven and Engelhorn, 1971). The
interplay between these two floras is a major This work is a largely collaborative effort to
issue on oceanic islands (Simberloff, 1995) gather and process information on several hun-
dred plant taxa. Mark Boersma, Richard Carter,
such as New Zealand, and all alien taxa are David Cooke, Roger Cousens, Jon Dodd, Bryan
considered weeds to the extent that they Hacker, Surrey Jacobs, Mark Lonsdale, Michael
are perceived to impact on native ecosys- Mulvaney, Dane Panetta, Tom Parnell, Bruce Pen-
tems, irrespective of any redeeming features gelly, Darren Phillips, Rod Randall and Craig
Walton all contributed to the development of
(Williams and Timmins, 1990). The situation the model. Thanks also to the anonymous New
could well be different, yet unpredictable, in Zealand experts.
other countries. In Argentina and Chile for The Australian Quarantine and Inspection
example, some wild adventive taxa have an Service (AQIS) provided funding for resources
and technical assistance in Australia and the
economic value because of their potential to
Ministry of Agriculture and Fisheries in New
provide food for poor people (E. Rapoport, per- Zealand. Final preparation of the paper was
sonal communication to P.A.W.), and there is funded by the Foundation for Research, Science
less inclination to consider them to be weeds. and Technology in New Zealand under contract
In contrast to these differing judgments, the (No. 9625).
model would be less influenced by the eco-
nomic value of a taxon than any group of
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