Selecting non-target species for arthropod biological control agent host range testing: Evaluation of a novel method

Accepted Manuscript Selecting non-target species for arthropod biological control agent host range testing: evaluation of a novel method B.I.P. Barratt, J.H. Todd, L.A. Malone PII: DOI: Reference:

S1049-9644(15)30056-6 http://dx.doi.org/10.1016/j.biocontrol.2015.11.012 YBCON 3354

To appear in:

Biological Control

Received Date: Revised Date: Accepted Date:

20 August 2015 16 November 2015 30 November 2015

Please cite this article as: Barratt, B.I.P., Todd, J.H., Malone, L.A., Selecting non-target species for arthropod biological control agent host range testing: evaluation of a novel method, Biological Control (2015), doi: http:// dx.doi.org/10.1016/j.biocontrol.2015.11.012

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Selecting non-target species for arthropod biological control agent host range testing: evaluation of a novel method Barratt, B.I.P.1,2, Todd, J.H.2,3, Malone, L.A.3, 1

AgResearch Invermay, PB 50034, Mosgiel, NZ

2

Better Border Biosecurity NZ

3

Plant and Food Research, Auckland, NZ

ABSTRACT Regulators often require risk assessment to ascertain biosafety of biocontrol agents before approval for release. Selecting the most informative non-target species for host range testing can be challenging. Here we compare traditional test list selection with a more objective method that selects species from a dataset of invertebrates from the receiving environment. A model, PRONTI (Priority Ranking of Non-Target Invertebrates) ranks species using five criteria: hazard, exposure, potential ecological impacts from exposure, anthropocentric value and testability. For a case study, we used the braconid parasitoid Microctonus aethiopoides Loan released in New Zealand in 1982 for biocontrol of the pest weevil Sitona discoideus Gyllenhal. We compared species prioritised by PRONTI as worthy of testing with those selected prior to release. Several species which have been attacked in the field by M. aethiopoides since its release ranked highly suggesting that if PRONTI had been available pre-release, better predictions of non-target attack might have been made. The investment in time needed to adopt PRONTI needs to be balanced against its objectivity when comparing it with current conventional methods.

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Key words: biosafety, biological control, entomophagous biological control agents, PRONTI, prioritising species, decision-support INTRODUCTION In many countries, regulatory approval to introduce a biological control agent (BCA) into a new area depends upon the results of a risk assessment and demonstration of environmental biosafety. Legislation implemented by regulatory agencies often requires evidence that the proposed new organism will not adversely affect non-target (NT) species and ecosystems in the receiving area. In New Zealand, for example, this process is governed by the HSNO Act (1996). Sheppard et al. (2003) reviewed the regulatory requirements in several other countries and noted that regulators are becoming more risk-averse to biological control. Furthermore, it is generally accepted that to meet regulatory biosafety standards, biological control programmes against arthropod targets should employ only highly specialist predators (e.g. Booth et al. (1995)) or relatively host-specific parasitoids (Barratt et al. 2007b; Sands 1997). There is now a considerable body of literature and web-based tools on aspects of the design and implementation of host range testing of arthropod BCAs. For example, contributions in Follett and Duan (2000); Lockwood et al. (2001); Van Driesche and Reardon (2004); Withers and Barton-Browne (2004); Bigler et al. (2006) and the web-based information resource ‘Biocontrol Information Resource for EPA Applicants’ (BIREA) (Barratt et al. 2007a) are available to provide guidance for biological control practitioners. However, it can still be difficult to select which species should undergo host range testing, and to justify that those selected are adequate for regulators to make decisions about the release of the BCA. Recently, the PRONTI (priority ranking of non-target invertebrates) tool, developed to aid in the selection of test species for assessing non-target risks from genetically modified plants (Todd et al. 2008), was modified for use with entomophagous BCAs (Todd et al. 2015). 2

Here we present a case study which provides an opportunity to compare NT species selection methods and, with hindsight, to evaluate their relative effectiveness in predicting NT risks. The parasitoid Microctonus aethiopoides Loan (Hymenoptera: Braconidae; Euphorinae) was introduced into New Zealand in 1982 for biological control of the lucerne weevil, Sitona discoideus Gyllenhal (Coleoptera: Curculionidae) (Stufkens et al. 1987). The parasitoid originated from Morocco and was introduced into Australia in 1977 (Aeschlimann 1983b), and from there a consignment was sent to New Zealand (Stufkens et al. 1987). Before release in Australia, there was no host range testing carried out because Aeschlimann (1983a) quoting Loan (1975) confirmed that the only hosts of M. aethiopoides were weevils in the genera Sitona and Hypera and claimed that records of parasitism of other hosts such as chrysomelids (Smith 1953) were incorrect. However, Shaw and Huddleston (1991) subsequently proposed an evolutionary pathway for euphorine braconids in which larval Chrysomelidae were the original hosts, followed by a switch to adult chrysomelids feeding on the same plants, and through the same process to other taxa feeding in the same microhabitat. Thus some level of uncertainty remains about the status of chrysomelids as potential hosts of M. aethiopoides.

The biological control programme for S. discoideus in New Zealand pre-dated current HSNO legislation. In 1982, decisions about biocontrol introductions were made by the New Zealand government’s Department of Scientific and Industrial Research (DSIR) in consultation with the Ministry of Agriculture and Fisheries (MAF). To bring new insects into New Zealand, an import permit was required from MAF under various pieces of legislation, but the director of the DSIR’s Entomology Division could make the final decision to release them from quarantine (Longworth 1987). Although there was no formal prescribed requirement for

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testing, researchers at the time did consider NT impacts, especially risks to beneficial species. While there are no published data on laboratory host range testing carried out for M. aethiopoides pre-release in New Zealand, unpublished records are available (Stufkens pers. comm.). The species that were selected to investigate the potential impacts of M. aethiopoides are listed in Table 1, and, excluding the target S. discoideus, were all weed BCAs. No parasitism of any of these non-target species was recorded in the laboratory in choice or no-choice tests. Published information on the natural host range of M. aethiopoides was also available in the pre-1982 literature from overseas studies (Table 2) and this suggested that in its natural range, M. aethiopoides was restricted to the genera Hypera (Curculionidae: Hyperinae: Hyperini) and Sitona (Curculionidae: Entiminae: Sitonini) as hosts, neither of which occur naturally in New Zealand. Similarly, the subfamily Hyperinae does not occurin New Zealand, and although the subfamily Entiminae is well represented, the entimine tribe Sitonini is not. This might be one reason why no New Zealand entimine species were selected for NT testing with M. aethiopoides prior to release in 1982.

M. aethiopoides established well following its introduction to New Zealand (Goldson et al. 1993) and by 2007 between 17% and 78% of field-collected S. discoideus from Otago province were found to be parasitized (Barratt et al. 2007c). However, the parasitoid was also recorded in field collections of a further 16 NT weevil species, including several native species (Barratt 2004; Barratt et al. 2007c). Subsequent investigation has shown that a better understanding of the native host range of M. aethiopoides in Morocco, and its performance after introduction to Australia, may have allowed for improved prediction of NT impacts in New Zealand (Barratt et al. 2012).

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Compared with the 1980s, methods of host range test species selection have become the subject of more careful consideration since biosafety of biological control has become an increasing requirement by regulators. Essentially new methods involve analysis of taxonomic (phylogenetic) and ecological affinities with the addition of species of particular economic or iconic value, e.g. Kuhlmann et al. (2006); Babendreier et al. (2006); van Lenteren et al. (2006a). More recently the automated PRONTI method has been developed (Todd et al. 2015; Todd et al. 2008) whereby a screening model is applied to a reasonably comprehensive dataset of invertebrate species from the receiving environment. The model takes a set of predetermined selection criteria, applies them to each species in the dataset, and produces a PRONTI score for each. These scores are then used to rank the species in order of best to worst fit with the criteria. The criteria can be altered to suit different environmental protection goals, but for the New Zealand situation they comprise assessments for each species potentially at risk (hazard and exposure), including consideration of mechanisms whereby organisms can reduce their risk, estimates of ecological and anthropocentric value, and the practicality of including them as a test species (testability).

Our objective in this study was to determine how closely the species prioritised by the PRONTI model corresponded with those that were selected by traditional methods. If we had used the PRONTI model in 1982, would we have selected a different set of test species from those actually selected at the time? For this case study we also had the benefit of information available from post-1982 studies carried out with M. aethiopoides in its natural range and new areas of introduction, and have used this to address the secondary question of the predictive power of PRONTI to prioritise NT species that were not considered in the original test species selection but subsequently were found to be affected by the BCA post-release. The conclusion from this and other similar case study comparisons of the PRONTI model

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will be used to evaluate PRONTI and determine whether it represents a more robust system for selecting test species for risk assessment, with additional biosafety benefits, or whether the current, more intuitive methods are adequate. MATERIAL AND METHODS PRONTI uses data which have been entered into Eco Invertebase, a Microsoft Access® 2007 database holding information on species known from the literature to be present in the receiving environments where the target species occurs (in this case pasture and lucerne forage crops and native tussock grasslands). While this clearly cannot be a comprehensive list, it included many pasture and forage crop species that had already been entered into Eco Invertebase. It also included: •

Insects that were tested in laboratory studies pre-release (Table 1) excluding Disonycha argentinensis Jacoby, which was released but did not establish in New Zealand

•

Insects taxonomically related to the target species at subfamily level

•

Insects known to occur in the same environments as the target species

•

Valued species (e.g. other BCAs)

•

Potential natural enemies of the target species

•

Pest species that might develop outbreak populations if the target species was suppressed

•

Representatives of insect taxa which have been recorded in the literature to be hosts of Microctonus species (for the purposes of this study these data were restricted according to the state of our knowledge in 1982).

The information entered for each species included taxonomy, known food or host species, known natural enemies, ecological function, population density, dry weight, mobility,

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reproductive rate, generation time, social and cultural value, origin (native/exotic) and rearing and bioassay information. For each of these attributes a selection was made from a multiple choice drop-down list, and the database automatically allocated a score from 0-10 for each selection. Each entry in the database was referenced from the literature as far as possible. Details of these attributes are given in (Todd et al. 2015).

Once data were entered, the PRONTI model used the scores allocated by the database for each parameter in the formula below to calculate an overall score for priority ranking:

PRONTI score =

   

× S + V + T

(Equation 1)

Where H = hazard; E = exposure; R = resilience (ability of the species to mitigate the risk); S = the status of the species in the receiving environment; V = the value of the species and T = the ‘testability’ of the species.

Each parameter in the equation was derived from several attributes collated for each species at the data entry stage. For example, hazard (H) can be the potential for non-target attack by the biological control agent; exposure (E) factors which affect the likelihood of a non-target species coming into contact with the BCA; resilience (R) combined aspects of individual and population ability to mitigate risk, including migration potential, high intrinsic rate of increase, potential for a host immune response, predator avoidance/cryptic colouration, diapause at a time when the BCA is present etc. Species status (S) combines estimates of biomass, number of food web links and key ecological function (pollinator, pest etc.). To examine the relative influence of the parameters in the equation on the ranking of species, ranked lists were also made using four reduced equations: 7

(H x E)   

(Equation 3)

        

(Equation 2)

× V

(Equation 4)

× V + T

(Equation 5)

Ranking the species using equation 2 shows the influence of the risk of the BCA (i.e., the score obtained by each species for the hazard posed by the BCA (H) multiplied by the score for the likelihood the species would be exposed (E) to that hazard) on each species’ ranking. To illustrate the influence of including the estimated ability of each species to be resilient to the risk, equation 3 ranks the species with the risk scores reduced by the resilience score of each species (R). Equation 4 adds the score for anthropocentric value (V) for each species, and equation 5 adds a testability score (T). The final PRONTI ranking of the species is achieved by adding the status (S) score for each species in the environment into the equation (i.e., Equation 1).

Uncertainty in PRONTI scores was calculated for each species, as described in Todd et al (2015). Briefly, if data were unavailable for any of the species attributes, this was recorded in the database as ‘unknown’ and assigned a score of 5, whereas if information on an attribute was available it would be assigned a score between 0 and 10, other than 5. Additionally, some data gaps could be filled with estimates. For example, where the dry weight and length of a species were unknown, an estimated length was selected from a range of options in the database and dry weight calculated from this allowing an estimate of biomass to be calculated by the model. Uncertainty was calculated for each species by determining the number of 8

attributes that were ‘unknown’ or estimated for that species, expressed as a percentage of the total number of attributes scored.

Three ‘dummy’ (hypothetical) species were added to the database to verify that the PRONTI equation was operating accurately: Dummy 1 was a species at ‘high risk’ of attack from M. aethiopoides that had high-scoring attributes for the other parameters, and a low level of uncertainty in its scores; Dummy 2 was a species for which there was a high level of uncertainty in all its attributes and, therefore, also its scores; and Dummy 3 was at minimal risk of attack from M. aethiopoides, with low uncertainty in its scores. So it would be expected that Dummy 1 would appear near the top of the PRONTI list, Dummy 2 would appear near the middle, and Dummy 3 would appear near the bottom of the list of ranked species if the model worked correctly.

Data were entered into Eco Invertebase using information on S. discoideus and M. aethiopoides that was available before 1982 so that we could evaluate the ranking of species by the PRONTI model in comparison with the test species selection that was actually carried out before M. aethiopoides was released in New Zealand. However, the taxonomic placements of species in the database follow more current literature, for example that used in Barratt et al. (2012) which was informed by more recent phylogenetic studies such as Hundsdörfer et al. (2009) and McKenna et al. (2009). We have also used the current PRONTI definition of V, which probably places a higher value on native species than was the case in 1982. The HSNO Act (1996) specifically calls for consideration of impacts of new organisms on ‘native and valued exotic species’. In the 1980s, the value of beneficial species such as BCAs that had already been introduced was considered (Stufkens pers. comm.), but effects on NT organisms in New Zealand’s natural environment were still a topic for discussion

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(Longworth 1987). However, it is likely that if there had been any native weevils in the genera Hypera or Sitona in New Zealand then these would have been selected for host range testing in 1982. RESULTS In total, 82 species in the database were used for the analysis. The output from the PRONTI model is shown in Table 3 and the distribution of PRONTI scores plotted in order of priority ranking is shown in Figure 1. The placements of the dummy species in the list (Table 3) confirmed that the PRONTI model worked as expected. The target host, S. discoideus, was the highest ranking species as would be expected because it is a common host in the natural range of M. aethiopoides and the only species in New Zealand in 1982 that was known to be a host. The species in descending rank fell roughly into four groups, or steps, with similar rankings, as indicated in Table 3 and clearly visible in Figure 1. The highest ranked group, species 3-11 were all entimine weevils (mostly in the tribe Leptopiini) except for Steriphus diversipes lineatus (Pascoe) (Cyclominae: Listroderini), an adventive species. Most of the weevils in the genus Irenimus that were in the database were included in this group, and it can be seen in Table 3 that these species all received high ‘risk scores’ (calculated using Equation 2). The next group, species 12-18, were also weevils, again comprising entimines and cyclomines, and although these species also had high risk scores, the scores these species obtained for the other criteria were not as high as those obtained by the species above them in the list. This is illustrated in Table 4 where scores for two species in the second group with a similar rank for Equation 1, are also shown using Equations 2-5. Irenimus compressus (Broun) obtained a high risk score (Equation 2) but scored more highly for resilience (R) than some other Irenimus species because it has a wider geographical range in New Zealand, 10

reducing its rank (Equation 3). The endemism and testability of I. compressus kept the ranking high in Equations 4 and 5, but its relatively low abundance in the receiving ecosystems, and therefore lower species status, reduced its score in Equation 1. By contrast, Catoptes cuspidatus Broun, another native entimine, had a lower risk score because it is restricted in its geographical range in New Zealand, which reduces its resilience to the risk. However, it is endemic which increases its value, and it is common in its natural range with a number of known links to other species; it is reasonably testable and of comparatively high status, resulting in a ranking of 12 on the PRONTI list. The rankings for the three dummy species are also included in Table 4, reinforcing the value of these in showing that the model is working as expected. The risk scores of the next, larger group of species, 19-58, were all Coleoptera except for one lepidopteran, Wiseana cervinata (Walker), a very common pasture pest species. The top six species in this group were again entimines and cyclomines, but then other weevil subfamilies were represented, and other families especially Carabidae and Chrysomelidae. These species generally have lower risk scores, however species 19 – 25 are in this group because their scores for other attributes are lower although their risk scores are still quite high (Table 3). The final group of species, 59-85, comprise all of the other insect orders that were included in the database, and these all had risk scores that were less than 15% of that of the target species. Also shown in Table 3 are species (marked by shading) in the database that we now know, from post-release studies, are attacked by M. aethiopoides in the field. These species have all been ranked in the top 30 of the PRONTI list, with ten of the 12 species included in the top 17 of the list. The amount of uncertainty ranged between 6 and 50% (Table 3, Figure 1). The lowest amount of uncertainty was for the hepialid W. cervinata (Lepidoptera: Hepialidae). This species is a very well-studied pasture pest, but with attributes unlikely to place it at risk from 11

M. aethiopoides, consequently it was ranked 54 th by the model. The taxon with the highest percentage uncertainty was the endemic chrysomelid Atrichatus ochraceus Sharp for which there was a lack of biological and ecological information. It was ranked at 31 largely as a result of the uncertainty about whether chrysomelids might be suitable hosts for M. aethiopoides. DISCUSSION Traditionally, test species selection for risk assessment has involved a study of the most closely related species to the target pest that could be at risk, and especially those species that occur in the same environment as the target. In weed biological control, a process of ‘centrifugal phylogenetic testing’ where plants most closely related to the target are tested first, and then progressively more distantly related are tested to develop a profile of the host range of the proposed biological control agent (Wapshere 1974). In insect biological control, the greater number of species that would need to be considered makes this process more difficult, but studies have certainly been strongly guided by taxonomic and ecological affinities between the target species and potential non-target species that occur in the new intended range (Kuhlmann et al. 2006). In addition, valued species are also often included. The list is then reduced in length by excluding species which have different spatial distributions, inconsistent phenological attributes or cannot be collected for testing (Kuhlmann et al. 2006). Cobo et al. (2014) used a ‘multicriteria analysis’ to select NT tephritid flies for risk assessment with potential biological control agents for the olive fruit fly, Bactrocera oleae (Rossi). These authors scored a number of criteria relating to phylogenetic affinity, size, parts of the plant attacked, larval phenology and abundance, etc. mainly on a 0-3 scale, and scores were weighted for importance in the risk assessment, and the three highest scoring tephritids were chosen for host range tests. The PRONTI model uses

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a similar “scoring” approach to produce a test species list that includes as many organisms as possible from the receiving environment. The objectivity, consistency and reliance on published information in this process are attractive to regulators, since for these reasons it can be defended. PRONTI also enables all species to be compared equally using a number of different selection criteria. However, unless there is an existing comprehensive and accessible database, obtaining the published data for each species can be time-consuming so this method needs to be more useful than conventional test species selection methods if this time investment is to be worthwhile. In this case study, the original 1982 species test list was limited to valued species, that Kuhlmann et al. (2006) termed ‘safeguard’ species. There was no consideration of native species, probably because the known host range in the Mediterranean area of origin showed no overlap with the New Zealand native fauna at genus or tribal level, as it was understood at the time. The species tested in 1982 were included in the database to be ranked by the PRONTI model, and the only species that ranked above 30 in the PRONTI list was R. conicus, a thistle BCA. Subsequently we have found this to be a host for M. aethiopoides (Barratt et al. 1997) but at the time of year the original tests were undertaken, R. conicus was in diapause and largely immobile. Motionless hosts are not usually susceptible to attack by M. aethiopoides, even in laboratory tests in close confinement (Phillips 2002). None of the other species selected for testing in 1982 have been found to be hosts of M. aethiopoides since its release. The list produced by the PRONTI method should be used to support decisions on which species are selected for non-target testing. There are several ways of doing this, and taking the top species in the list might result in several species in the same genus being selected, as would be the case in our study. This might be less informative than, for example, selecting

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test species from each of the five top-ranked genera, and from those, choosing from adjacent species, the one with the lowest level of uncertainty. If regulators require beneficial or otherwise particularly valued species to be included, then those from further down the list with the highest risk scores could be selected. So given some predetermined ‘rules’ such as those mentioned above and assuming that about 5 species should be tested in the first instance, plus the top-ranked beneficial, the test species list in our study would be: 1. Nicaeana cervina Broun (top-ranked genus) 2. Irenimus stolidus Broun (2nd-ranked genus; species with lowest uncertainty in the group ranked 4-6) 3. Naupactus cervinus (Boheman) (3rd-ranked genus) 4. Steriphus variabilis Broun (4th-ranked genus, lowest uncertainty and endemic) 5. Catoptes cuspidatus Broun (fifth-ranked genus, lowest uncertainty) 6. Rhinocyllus conicus (Fröhlich) (top-ranked beneficial) Clearly this list is very different from those that were selected for testing at the time, except for R. conicus. However, it is important to take into account the different values and regulatory requirements guiding test species selection. If more current methods of test species selection were used, e.g. as suggested by Van Lenteren et al. (2006b); Kuhlmann et al. (2006) then it is likely that more account would be taken of species with taxonomic and ecological affinities to the target as well as beneficial species, and hence a priority list more similar to that generated by PRONTI might have resulted. Had PRONTI been used by researchers in the 1980s, could it have predicted what has happened post-release? It is difficult to speculate precisely what species a PRONTI model carried out pre-release of M. aethiopoides would have prioritised for testing. While we have attempted to only use biological and ecological information that was available in 1982 to

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populate the database, it was difficult to completely transport ourselves back to that time when there were conflicting versions of subfamily and tribal classification of weevils. For that reason we have used current taxonomic nomenclature in this contribution. However, the grouping of genera within tribes has probably not changed substantially, so almost certainly grassland-dwelling native species that are found in mixed populations with S. discoideus, such as Irenimus and Nicaeana, would have been placed relatively highly in the ranking of species. So it is likely that the adoption of PRONTI in 1982 would have indicated that M. aethiopoides would be capable of attacking a wider range of hosts in New Zealand than in its native range in Morocco. All species in the list above except one are known to have been attacked in the field by M. aethiopoides. The exception is N. cervinus which has not been examined from field collections, and has not been tested in the laboratory. Considerably more information is available now than in the 1980s. Further work has been carried out on the NT host range of M. aethiopoides, including: laboratory and field host range (Barratt et al. 1997); phenology and susceptibility of NT native weevils in New Zealand (Barratt et al. 2000); factors affecting parasitism of NT species (Barratt and Johnstone 2001); modelling field impacts (Barlow et al. 2004; Barratt et al. 2010); research in the native range, and improved phylogenetic analyses (Barratt et al. 2012). We have evidence now, albeit from a single record, that in Australia a species of Prosayleus (Leptopiini) is a host (Barratt et al. 2012). We know that in New Zealand Leptopiini are well represented, but also that in Morocco they are poorly represented, so earlier research in Morocco would not have alerted us to this potential risk in New Zealand. Given our improved understanding of phylogenetic relationships in the Entiminae, and focus on adverse impacts on native species and ecosystems, if PRONTI or a conventional method of test species selection were used now, native New Zealand Leptopiini would almost

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certainly be included in host range tests. NT parasitism recorded in such tests would have alerted researchers to potential environmental impacts. The ‘acid test’ for PRONTI is to determine whether it would rank highly any species that would not have been considered for testing using conventional test species selection methods. In this case study, PRONTI has not produced such an example, although the ranking of Carabidae between ranks 26 and 32 might not have been considered a risk, and their position in the ranking might be higher than predicted intuitively. This has reflected the potential for indirect risks arising from competition between carabid beetles and M. aethiopoides since the target species, S. discoideus may be a prey item for these beetles. While this example is not particularly compelling for this case study, it does illustrate the potential for PRONTI to alert biological control practitioners to possible indirect impacts that might not be intuitive. As noted above, regulators are likely to be attracted to the scientific justification, consistency and documentation that adoption of PRONTI can provide, reducing reliance on ‘expert opinion’. In addition, biological control practitioners might see benefit in being able to justify to regulators that it is not necessary to test certain species, perhaps iconic or highly valued species, if they appear well down in the ranking. In practical terms, data entry into Eco Invertebase is time-consuming, and hence, costly, although this becomes increasingly less onerous as more species are added to the database because the information is then available for use with future BCA risk assessments. This investment in time needs to be balanced against the alternative which relies on a more subjective, less inclusive compilation of test species lists.

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ACKNOWLEDGEMENTS We thank Kim Crook for species research and data entry; Libby Burgess and John Kean for valuable comments on an earlier draft; funding from the research collaboration Better Border Biosecurity (B3), via AgResearch Core funding. REFERENCES Aeschlimann JP (1980) The Sitona (Col.: Curculionidae) species occurring on Medicago and their natural enemies in the Mediterranean region. Entomophaga 25 (2):139-153 Aeschlimann JP (1983a) Notes on the variability of Microctonus aethiopoides Loan (Hymenoptera: Braconidae: Euphorinae). Contributions of the American Entomological Institute 20:329-335 Aeschlimann JP (1983b) Sources of importation, establishment and spread in Australia of Microctonus aethiopoides Loan (Hymenoptera: Braconidae), a parasitoid of Sitona discoideus Gyllenhal (Coleoptera: Curculionidae). J Austr Entomol Soc 22 (4):325-331 Alonso-Zarazaga MA, Sanchez-Ruiz M (2002) Revision of the Trichosirocalus horridus (Panzer) species complex, with description of two new species infesting thistles (Coleoptera: Curculionidae: Ceutorhynchinae). Australian Journal of Entomology 41:199-208 Babendreier D, Bigler F, Kuhlmann U (2006) Current status and constraints in the assessment of nontarget effects. In: Bigler F, Babendreier D, Kuhlmann U (eds) Environmental impact of invertebrates for biological control of arthropods – methods and risk assessment, F. . CABI Publishing, Wallingford, UK, pp 1-13 Barlow ND, Barratt BIP, Ferguson CM, Barron MC (2004) Using models to estimate parasitoid impacts on non-target host abundance. Environ Entomol 33 (4):941-948 Barratt BIP (2004) Microctonus parasitoids and New Zealand weevils: comparing laboratory estimates of host ranges to realised host ranges. In: Van Driesche R, Reardon R (eds) Assessing host ranges for parasitoids and predators used for classical biological control: a guide to best practice. USDA Forest Service, Morgantown, WV, pp 103-120 Barratt BIP, Berndt LA, Dodd SL, Ferguson CM, Hill RH, Kean JM, Teulon DAJ, Withers TM (2007a) BIREA - Biocontrol Information Resource for EPA Applicants. www.b3nz.org/birea/. Barratt BIP, Berndt LA, Dodd SL, Hill RH, Kean JM, Teulon DAJ, Withers TM, Ferguson CM, Harrison L, Pottinger B, Perry JH (2007b) BIREA – Biocontrol information resource for ERMA New Zealand applicants. NZ Plant Prot 60:314 Barratt BIP, Evans AA, Ferguson CM, Barker GM, McNeill MR, Phillips CB (1997) Laboratory nontarget host range of the introduced parasitoids Microctonus aethiopoides and Microctonus hyperodae (Hymenoptera: Braconidae) compared with field parasitism in New Zealand. Environ Entomol 26 (3):694-702 Barratt BIP, Evans AA, Ferguson CM, McNeill MR, Addison P (2000) Phenology of native weevils (Coleoptera: Curculionidae) in New Zealand pastures and parasitism by the introduced braconid, Microctonus aethiopoides Loan (Hymenoptera: Braconidae). NZJ Zool 27 (2):93110 Barratt BIP, Ferguson CM, Bixley AS, Crook KE, Barton DM, Johnstone PD (2007c) Field parasitism of nontarget weevil species (Coleoptera : Curculionidae) by the introduced biological control agent Microctonus aethiopoides Loan (Hymenoptera : Braconidae) over an altitude gradient. Environ Entomol 36 (4):826-839. doi:10.1603/0046-225x(2007)36[826:fponws]2.0.co;2 Barratt BIP, Howarth FG, Withers TM, Kean J, Ridley GS (2010) Progress in risk assessment for classical biological control. Biol Contr 52:245–254 17

Barratt BIP, Johnstone PD (2001) Factors affecting parasitism by Microctonus aethiopoides Loan (Hymenoptera: Braconidae) and parasitoid development in natural and novel host species. Bull Entomol Res 91:245-253 Barratt BIP, Oberprieler RG, Barton D, Mouna M, Stevens M, Alonso-Zarazaga MA, Vink CJ, Ferguson CM (2012) Could research in the native range, and non-target host range in Australia, have helped predict host range of the parasitoid Microctonus aethiopoides Loan (Hymenoptera: Braconidae), a biological control agent introduced for Sitona discoideus Gyllenhal (Coleoptera: Curculionidae) in New Zealand? BioControl 57 (6):735-750. doi:10.1007/s10526-012-9453-3 Bigler F, Babendreier D, Kuhlmann U (eds) (2006) Environmental impact of arthropod biological control: methods and risk assessment. CABI Publishing, Delemont, Switzerland Booth RG, Cross A, Fowler SV, Shaw RH (1995) The biology and taxonomy of Hyperaspis pantherina (Coleoptera: Coccinellidae) and the classical biological control of its prey, Orthezia insignis (Homoptera: Ortheziidae). Bull Entomol Res 85:307-314 Cobo A, González-Núñez M, Sánchez-Ramos I, Pascual S (2014) Selection of non-target tephritids for risk evaluation in classical biocontrol programmes against the olive fruit fly. Journal of Applied Entomology. doi:10.1111/jen.12145 Follett PA, Duan JJ (eds) (2000) Nontarget effects of biological control. Kluwer Academic Publishers, Norwell, USA Ge D, Gómez-Zurita J, Chesters D, Yang XD, Vogler AP (2012) Suprageneric systematics of flea beetles (Chrysomelidae: Alticinae) inferred from multilocus sequence data. Molecular Phylogenetics and Evolution 62 (3):793-805. doi:http://dx.doi.org/10.1016/j.ympev.2011.11.028 Goldson SL, Proffitt JR, Barlow ND (1993) Sitona discoideus (Gyllenhal) and its parasitoid Microctonus aethiopoides Loan: a case study in successful boiological control. In: Corey S, Dall D, Milne W (eds) Pest control and sustainable agriculture. CSIRO, Division of Entomology, Canberrra, Australia, pp 236-239 HSNO Act (1996) Hazardous Substances and New Organisms Act. http://rangi.knowledgebasket.co.nz/gpacts/public/text/1996/an/030.html. Hundsdörfer AK, Rheinheimer J, Wink M (2009) Towards the phylogeny of the Curculionoidea (Coleoptera): reconstructions from mitochondrial and nuclear ribosomal DNA sequences. Zoologischer Anzeiger 248:9–31 Kuhlmann U, Schaffner U, Mason PG (2006) Selection of non-target species for host specificity testing. In: Bigler F, Babendreier D, Kuhlmann U (eds) Environmental impact of invertebrates for biological control of arthropods: methods and risk assessment CABI Publishing, Wallingford, Oxford, pp 15-37 Loan C, Holdaway FG (1961) Microctonus aethiops (Nees) auctt. and Perilitis rutilis(Nees) auctt. (Hymenoptera: Braconidae), European parasites of Sitona weevils (Coleoptera: Curculionidae). Canadian Entomologist 93:1057-1079 Loan CC (1975) A review of Haliday species of Microctonus (Hym. : Braconidae, Euphorinae). Entomophaga 20 (1):31-41 Lockwood JA, Howarth FG, Purcell M (eds) (2001) Balancing nature: Assessing the impact of importing non-target biological control agents (An international perspective). Thomas Say Publications in Entomology: proceedings. Thomas Say Publications, Lanham, Maryland, USA Longworth JF (1987) Biological control in New Zealand: policy and procedures. New Zealand Entomologist 10:1-7 Matsumura Y, Yao I, Beutel RG, Yoshizawa K (2014) Molecular phylogeny of the leaf beetle subfamily Criocerinae (Coleoptera: Chrysomelidae) and the correlated evolution of reproductive organs. Arthropod Syst Phylogeny 72 (2):95-110 McKenna DM, Sequeira AS, Marvaldi AE, Farrell BD (2009) Temporal lags and overlap in the diversification of weevils and flowering plants. Proc Nat Acad Sci 106 (17):7083–7088

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Phillips CB (2002) Observations of oviposition behaviour of Microctonus hyperodae Loan and M. aethiopoides Loan (Hymenoptera: Braconidae: Euphorinae). J Hymenopt Res 11 (2):326-337 Sands DPA (1997) The 'safety' of biological control agents: assessing their impact on beneficial and other non-target hosts. Memoirs of the Museum of Victoria 56:611-615 Shaw MR, Huddleston T (1991) Classification and biology of braconid wasps, vol 7, Part 11. Handbooks for the identification of Bristish insects. Royal Entomological Society of London, London Sheppard AW, Hill RL, DeClerck-Floate RA, McClay A, Olckers T, Quimby PC, Zimmermann HG (2003) A global review of risk-benefit-cost analysis for the introduction of classical weed biological control agents against weeds: a crisis in the making? Biocontrol News and Information 24 (4):91N-108N Smith OJ (1953) Species, distribution, and host records of the braconid genera Microctonus and Perilitus (Hymenoptera: Braconidae). Ohio Journal of Science 53:173-178 Stufkens MW, Farrell JA, Goldson SL Establishment of Microctonus aethiopoides, a parasitoid of the sitona weevil in New Zealand. In: Popay AJ (ed) Proceedings of the 40th New Zealand Weed and Pest Control Conference, Quality Inn, Nelson, 1987. The New Zealand Weed and Pest Control Society, pp 31-32 Todd J, Barratt BIP, Tooman L, Beggs JR, Malone LA (2015) Selecting non-target species for risk assessment of entomophagous biological control agents: evaluation of the PRONTI decisionsupport tool. Biol Contr 80:77-88 Todd JH, Ramankutty P, Barraclough EI, Malone LA (2008) A screening method for prioritizing nontarget invertebrates for improved biosafety testing of transgenic crops. Env Biosafety Res 7:35-56 Van Driesche R, Reardon R (eds) (2004) Assessing host ranges for parasitoids and predators used for classical biological control: a guide to best practice, vol FHTET-2004-03. USDA Forest Service, Morgantown, West Virginia van Lenteren JC, Bale J, Bigler F, Hokkanen HMT, Loomans AJM (2006a) Assessing risks of releasing exotic biological control agents of arthropod pests. Ann Rev Entomol 51 (1):609-634 Van Lenteren JC, Cock MJW, Hoffmeister TS, Sands DPA (2006b) Host specificity in arthropod biological control, methods for testing and interpreting the data. In: Bigler F, Babendreier D, Kuhlmann U (eds) Environmental impact of invertebrates for biological control of arthropods: methods and risk assessment. CAB Publishing, Wallingford, Oxford, pp 38-63 Wapshere AJ (1974) A strategy for evaluating the safety of organisms for biological weed control. Ann appl Biol 77:201-211 Withers TM, Barton-Browne L (2004) Behavioral and physiological processes affecting the outcome of host range testing. In: Van Driesche R, Reardon R (eds) Assessing host ranges for parasitoids and predators used for classical biological control: a guide to best practice. USDA Forest Service, Morgantown, West Virginia, USA, pp 40-55

19

Table 1. Records of host range tests (all within Coleoptera) carried out with Microctonus aethiopoides in the laboratory in 1982. All tests included choice and no-choice treatments (M. Stufkens, pers. comm.).

Test species

Common name

Family: Subfamily: Tribe/group*

Parasitism

Lema cyanella (L.)

Californian thistle

Chrysomelidae: Criocerinae

No

Chrysomelidae: Alticinae:

No

leaf beetle Longitarsus jacobaeae

Ragwort flea beetle

(Waterhouse)

Longitarsus group

Disonycha argentinensis

Alligator weed flea

Chrysomelidae: Alticinae:

Jacoby

beetle

Disonycha group

Rhinocyllus conicus

Nodding thistle

Curculionidae: Lixinae: Cleonini

No

(Frölich)

receptacle weevil

Trichosirocalus horridus

Nodding thistle

Curculionidae: Ceutorhynchinae

No

(Panzer)

crown weevil

Sitona discoideus

Lucerne weevil

Curculionidae: Entiminae:

Yes

Gyllenhal**

No

Sitonini

* Taxonomic placements are as in the current literature (Alonso-Zarazaga and Sanchez-Ruiz 2002; Hundsdörfer et al. 2009; Ge et al. 2012; Matsumura et al. 2014) **target species

20

Table 2. Natural range field hosts of Microctonus aethiopoides known prior to 1982, modified from (Barratt et al. 2012) Host species Hypera meles (F.) Hypera nigrirostris (F.) Hypera postica Gyllenhal Sitona hispidulus (F.) Sitona humeralis Steph. Sitona lineatus L. Sitona crinitus (Hbst.) Sitona puncticollis Steph. Sitona sulcifrons (Thunb.) Sitona obsoletus (Gmelin) Sitona tenuis Rosenhauer Sitona discoideus (Glyllenhal)

Weevil family: subfamily: tribe Curculionidae: Hyperinae: Hyperini Curculionidae: Hyperinae: Hyperini Curculionidae: Hyperinae: Hyperini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini Curculionidae: Entiminae: Sitonini

Location

Reference

France

Loan (1975)

France

Loan (1975)

France, Croatia

Loan (1975); Loan and Holdaway (1961)

Mediterranean, Europe, Sweden France, Mediterranean

Aeschlimann (1980); Loan (1975); (Loan and Holdaway (1961) Loan (1975); Loan and Holdaway (1961)

Mediterranean

Loan (1975); Aeschlimann (1980)

Mediterranean

Aeschlimann (1980); Loan and Holdaway (1961)

Mediterranean

Aeschlimann (1980)

Mediterranean

Aeschlimann (1980)

Mediterranean

Aeschlimann (1980)

Mediterranean

Aeschlimann (1980)

Mediterranean

Aeschlimann (1980); Loan (1975)

21

Table 3 Output from the PRONTI model with species ranked using their PRONTI scores (Equation 1). Uncertainty is shown as a percentage of attributes used to produce the score that were unknown or estimated. The “risk scores” (H x E) (Equation 2) are also shown. Horizontal lines indicate the groups of species that are visible in Figure 1. Species which are shaded are those now known to be hosts of M. aethiopoides in New Zealand following its release in 1982. Rank numbers with an asterisk denote species tested in 1982 (see Table 1) Rank

Species Name

Order

Family: subfamily: tribe

Origin

1* 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28* 29 30* 31 32

Sitona discoideus Gyllenhal Dummy 1 Nicaeana cervina Broun Irenimus duplex (Broun) Irenimus aemulator Broun Irenimus stolidus Broun Naupactus cervinus (Boheman) Irenimus aequalis (Broun) Irenimus albosparsus (Broun) Irenimus egens (Broun) Steriphus diversipes lineatus (Pascoe) Catoptes cuspidatus Broun Naupactus leucoloma (Boheman) Catoptes censorious Pascoe Irenimus compressus (Broun) Steriphus variabilis Broun Listronotus bonariensis (Kuschel) Otiorhynchus ovatus L. Otiorhynchus rugosostriatus (Goeze) Listroderes difficilis Germain Anagotus graniger Broun Nonnotus albicans Broun Irenimus vexator (Broun) Otiorhynchus sulcatus (F.) Rhinoncus australis Oke Megadromus antarcticus (Chaudoir) Hypharpax antarcticus (Castelnau) Rhinocyllus conicus (Frölich) Holcaspis hudsoni Britton Longitarsus jacobaeae (Waterhouse) Atrichatus ochraceus Sharp Mecodema fulgidum Broun

Coleoptera

Curculionidae: Entiminae: Sitonini

Self-introduced

Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera

Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Naupactini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Cyclominae: Listroderini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Naupactini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Cyclominae: Listroderini Curculionidae: Cyclominae: Listroderini Curculionidae: Entiminae: Otiorhynchini Curculionidae: Entiminae: Otiorhynchini Curculionidae: Cyclominae: Listroderini Curculionidae: Cyclominae: Aterpini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Leptopiini Curculionidae: Entiminae: Otiorhynchini Curculionidae: Curculioninae: Ceutorhynchini Carabidae: Harpalinae Carabidae: Harpalinae Curculionidae: Lixinae: Cleonini Carabidae: Harpalinae Chrysomelidae: Galerucinae Chrysomelidae: Eumolpinae Carabidae: Trechinae

Endemic Endemic Endemic Endemic Self-introduced Endemic Endemic Endemic Self-introduced Endemic Self-introduced Endemic Endemic Endemic Self-introduced Self-introduced Self-introduced Self-introduced Endemic Endemic Native Self-introduced Self-introduced Endemic Endemic Introduced BCA Endemic Introduced BCA Endemic Endemic

PRONTI score 25841 25597 17273 15445 15211 14785 14431 14335 13732 13584 13295 11451 11416 11178 11062 11006 10707 10575 8841 8086 7891 7699 7312 7255 6891 6722 6404 6112 6106 5750 5732 5729

% uncertainty 8 6 8 21 20 16 24 13 19 10 28 23 12 27 14 19 12 11 27 16 24 10 19 19 34 33 37 13 41 18 52 29

Risk score 4620 6128 3600 3600 3600 3600 3692 3744 3408 3600 3692 2115 3692 2115 3744 3360 3475 3360 2100 3500 2159 2160 2130 2100 2542 1536 1820 2875 1536 1972 1832 1536

22

33 34 35* 36 37 38 39 40 41 42 43 44 45 46 47 48 49* 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67

Cicindela tuberculata F. Megadromus enysi (Broun) Trichosirocalus horridus Panzer Metaglymma moniliferum Bates Hypharpax australis Bates Mecodema howittii Castelnau Chrysolina quadrigemina (Suffrian) Linogeraeus urbanus (Boheman) Baeosomus amplus (Broun) Notagonum submetallicum (White) Athor arcifer Broun Adalia bipunctata L. Clivina vagans Putzeys Chrysolina hyperici (Forster) Harpalus affinis (Shrank) Rhytisternus miser (Chaudoir) Lema cyanella (L.) Dummy 2 Paropsis charybdis Stal Coccinella leonina F. Coccinella undecimpunctata L. Wiseana cervinata (Walker) Bruchidius villosus (F.) Thyreocephalus orthodoxus Olliff Mecyclothorax rotundicollis (White) Exapion ulicis Forster Microctonus zealandicus Shaw Eristalis tenax (L.) Agasicles hygrophila Selman and Vogt Anoteropsis hilaris L. Koch Nabis kinbergii Reuter Melanostoma fasciatum (Macquart) Anzacia gemmea Dalmas Mermessus fradeorum (Berland) Nabis maoricus Walker

Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera Coleoptera

Carabidae: Cicindelinae Carabidae: Harpalinae Curculionidae: Curculioninae Carabidae: Trechinae Carabidae: Harpalinae Carabidae: Trechinae Chrysomelidae: Chrysomelinae Curculionidae: Curculioninae Curculionidae: Curculioninae Carabidae: Platyninae Curculionidae: Curculioninae Coccinellidae: Coccinellinae Carabidae: Scaritinae Chrysomelidae: Chrysomelinae Carabidae: Harpalinae Carabidae: Pterostichinae Chrysomelidae: Criocerinae

Endemic Endemic Introduced BCA Endemic Self-introduced Endemic Introduced BCA Self-introduced Native Native Endemic Self-introduced Self-introduced Introduced BCA Self-introduced Self-introduced Introduced BCA

Coleoptera Coleoptera Coleoptera Lepidoptera Coleoptera Coleoptera Coleoptera Coleoptera Hymenoptera Diptera Coleoptera Araneae Hemiptera Diptera Araneae Araneae Hemiptera

Chrysomelidae Coccinellidae: Coccinellinae Coccinellidae: Coccinellinae Hepialidae Chrysomelidae Staphylinidae Carabidae Brentidae Braconidae: Euphorinae Syrphidae Chrysomelidae Lycosidae Nabidae Syrphidae Gnaphosidae Linyphiidae Nabidae

Self-introduced Endemic Introduced BCA Endemic Introduced Introduced Endemic Introduced BCA Native Introduced Introduced Endemic Self-introduced Native Endemic Self-introduced Endemic

5726 5674 5467 5451 5409 5243 5139 5122 5025 4947 4868 4704 4692 4666 4596 4558 4519 4423 4400 4354 4350 4348 4329 4247 3986 3852 2633 1562 1496 1378 1373 1218 1213 1180 1139

25 37 21 19 37 37 19 36 29 37 23 23 33 10 37 33 21 94 15 14 14 6 12 29 33 17 42 27 17 34 29 29 49 45 35

1536 1536 1800 1536 1820 1536 1798 1905 1575 1536 1575 1386 1920 1624 1456 1536 2332 1350 1740 1440 1620 1150 1590 1536 1820 2250 660 324 638 418 429 297 363 341 403

23

68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

Haplinis diloris (Urquhart) Araeoncus humilis Blaclwall Haplinis exigua Blest & Vink Melangyna novaezealandiae (Macquart) Diplocephalus cristatus (Blackwall) Erigone wiltoni Locket Haplinis inexacta (Blest) Microctonus alpinus Shaw Tenuiphantes tenuis (Blackwall) Laetesia bellissima Millidge Clubiona clima Forster Haplinis fucatinia (Urquhart) Diploplecta simplex Millidge Novanapis spinipes (Forster) Erigone prominens Bösenberg & Strand Microctenonyx subitaneus (O.P.Cambridge) Diploplecta communis Millidge Microctonus falcatus Shaw Dummy 3

Araneae Araneae Araneae Diptera

Linyphiidae Linyphiidae Linyphiidae Syrphidae

Endemic Self-introduced Endemic Endemic

1123 1037 1023 985

45 44 47 28

363 324 341 297

Araneae Araneae Araneae Hymenoptera Araneae Araneae Araneae Araneae Araneae Araneae Araneae

Linyphiidae Linyphiidae Linyphiidae Braconidae: Euphorinae Linyphiidae Linyphiidae Clubionidae Linyphiidae Linyphiidae Anapidae Linyphiidae

Self-introduced Self-introduced Endemic Endemic Self-introduced Endemic Endemic Endemic Endemic Endemic Self-introduced

980 908 883 847 837 816 814 798 797 791 767

40 37 43 23 28 46 47 47 46 43 37

396 330 275 468 330 225 297 275 225 330 270

Araneae

Linyphiidae

Self-introduced

686

39

330

Araneae Hymenoptera

Linyphiidae Braconidae: Euphorinae

Endemic Endemic

671 593 153

46 23 8

225 416 60

24

Table 4 Examples of ranking positions for two weevil species and three dummy species by the complete PRONTI model (Equation 1) and for (Equations 2-5), showing the influence of different elements of the model. Scores are made up of H = hazard, E = exposure, R = resilience, V = value, T = testability and S = species status (see text for definitions)

Ranking using each equation Irenimus

Catoptes

compressus

cuspidatus

Dummy 1

Dummy 2

Dummy 3

1 [(HxE)/R]x(V+T+S)

15

12

2

50

86

2 (HxE)

4

26

1

57

86

3 (HxE)/R

12

18

3

54

86

4 [(HxE)/R]xV

4

12

1

46

86

5 [(HxE)/R]x(V+T)

6

17

2

57

86

Equation

26

Fig 1 PRONTI scores plotted in order of rank, with the percentage uncertainty in the score for each species also indicated. 30000

100 Pronti score 90 % uncertainty

25000

80

PRONTI score

60 15000

50 40

10000

% uncertainty

70

20000

30 20

5000

10 0

0 1

11

21

31

41 51 Species rank

61

71

81

27

Highlights

• • • •

Biosafety risk assessment depends upon selecting appropriate test species Traditional test species selection is compared here with a PRONTI tool PRONTI was used to rank 82 species that occur in pasture and forage environments for testing with Microctonus aethiopoides, a parasitoid of lucerne weevil PRONTI prioritized a number of non-target weevils that are now know to be attacked in the field

28