Biological Control 35 (2005) 215–226 www.elsevier.com/locate/ybcon

ScientiWc advances in the analysis of direct risks of weed biological control agents to nontarget plants

A.W. Sheppard a,¤, R.D. van Klinken b, T.A. Heard b

a CSIRO European Laboratory, Campus International de Baillarguet, 34980 Montferrier-sur-Lez, France b CSIRO Entomology Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068 Brisbane, Australia

Received 17 December 2004; accepted 20 May 2005 Available online 21 July 2005

Abstract

Research on host speciWcity testing protocols over the last 10 years has been considerable. Traditional experimental designs have been reWned and interpretation of the results is beneWting from an improved understanding of agent behavior. The strengths, weak- nesses, and best practice for the diVerent test types are now quite clearly understood. Understanding the concept of fundamental host range (the genetically determined limits to preference and performance) and using this to maximize reliability in predicting Weld host speciWcity following release (behavioral expression of the fundamental host range under particular conditions) are still inconsistently understood or adopted despite having been identiWed as the critical steps in analyzing the threats posed by biological control agents to the agriculture and biodiversity of novel environments. This needs to be consistently understood and applied so the process of testing can follow a recognized process of risk analysis from hazard identiWcation (identifying life stages of the agent that pose a threat and deWning their fundamental host range) to uncertainty analysis based on the magnitude (predicted Weld host speciWcity fol- lowing release) and likelihood of threats (predicted actual damage and impact) to nontargets. Modern molecular techniques are answering questions associated with subspeciWc variation in biological control agents with respect to host use and the chance of host shifts of agents following release. Guidelines for assessment of nontarget impacts need to recognize and adopt such recent develop- ments and emphasize a general increased understanding of the evolution of host choice and the phylogenetic constraints to shifts in host use. This review covers all these recent advances for the Wrst time in one document, highlighting how inconsistent interpretation by biological control practitioners can be avoided. Crown copyright  2005 Published by Elsevier Inc. All rights reserved.

Keywords: Host speciWcity; Risk assessment; Fundamental host range; Nontarget impacts; IntraspeciWc variation; Host shifts; Host speciWcity testing

1. Introduction exotic biological control agents within the International Plant Protection Convention (Sheppard et al., 2003a). Countries that conduct research on classical weed These procedures generally consist of host speciWcity biological control have regulatory procedures for decid- assessment of agents followed by an independent deci- ing the release of exotic biological control agents based sion made by regulatory bodies. As such the regulatory on assessed direct threat agents may pose to nontarget procedures are the direct conduit of understanding from organisms. Other countries gain a similar protection stakeholders and biological control practitioners through a code of conduct for the import and release of through to policy makers and regulators, who represent current societal values (Briese, 2005). The basic scientiWc aim is to predict nontarget damage by such agents to * Corresponding author. Fax: +33 4 67 59 90 40. economic and native species following release and to E-mail address: [email protected] (A.W. Sheppard). prevent the release of agents likely to cause unacceptable

1049-9644/$ - see front matter. Crown copyright  2005 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.biocontrol.2005.05.010 216 A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 damage (Withers, 1999). This may be either through What has been lacking has been a synthesis of these nontarget colonization or temporary “spill-over” onto developments into a consistent analytical pathway for nontargets by less speciWc life stages. However, in order direct nontarget risk assessment. This review aims to to make informed decisions, regulators need to under- achieve this by discussing developments over the past 10 stand and have conWdence in a transparent and recog- years in the science behind risk assessment of direct non- nized scientiWc process of this type of risk analysis in target impacts of weed biological control agents. We addition to the results themselves (Briese, 2005). hope this review will assist not only scientiWc reviewers The regulatory processes used vary in the degree of but also regulators and policy makers to clearly under- prescription. Prescriptive processes have the advantage stand and practitioners correctly undertake best possible of transparency, but the disadvantage of requiring fre- practice. quent modiWcation to keep pace with scientiWc advances and inXexibility when dealing with diverse organisms. 1.1. DeWnitions The process adopted in most countries allows signiWcant degrees of freedom for the practitioner to design the tests Host range of a herbivore or plant pathogen is the set and interpret the results, however approval is usually of species that it can perceive, accept and/or use depen- required for test plant lists and the process for construct- dent on the environmental setting, while we argue host ing the list can be very prescriptive. Since the 1970s the speciWcity is best used to describe how preference focus of this risk has globally changed from commercial () and performance (arthropods and micro- plants to include native species. The change in focus also organisms) vary within the host range for a deWned varies between countries. Rare and threatened native agent population or strain (van Klinken, 2000a). The species are a key focus of the host speciWcity assessment fundamental host range deWnes the absolute limits of a and the test plant list in the USA, for example, and the species host range and has historically been poorly importance of spill-over (agents damaging but not devel- deWned (van Klinken, 2000a). Fundamental host range is oping on nontargets) appears to receive less emphasis in a broader concept than previously used “physiological the USA than in some other countries (Sheppard et al., host range” (e.g., Cullen, 1990; Harris and McEvoy, 2003a). 1995) as it acknowledges the need for appropriate In the USA, the procedure for assessing the direct behavioral stimuli for host acceptance rather than just threats to nontargets as part of the release application meeting simple physiological requirements (van Klinken, process is publicly described in greatest detail in the 2000a). Fundamental host range includes all hosts that, form of a Technical Advisory Group (TAG) reviewer’s given synchronous phenology, are used by a test organ- manual (www.aphis.usda.gov/ppq/permits/tag) orga- ism when no alternative is oVered, i.e., independent of nized by the regulatory agency to assist reviewers of any environmental setting. Fundamental host range can applications. This is therefore the document that ensures be determined for any aspect of an ’s or pathogen’s regulator understanding and conWdence in the assess- interaction with its host (Nechols et al., 1992; van Klin- ment process. As a consequence, the manual is used by ken, 2000a,b). Preference for a particular host is the like- USA practitioners as a default guide to the release appli- lihood of acceptance based on the capacity of tested cation process and therefore a reference for require- agents to detect a particular host (Singer, ments for agencies that make such applications. The 2004). Agent performance will also vary between hosts information within is extensive and science-based. By based on host suitability and resistance. oVering a much more detailed public-domain descrip- Host use by the tested agent following release (Weld tion of this risk assessment process than available in host speciWcity) will vary with environment (availability other countries, however, the TAG reviewer’s manual of hosts in time and space), genetic variation in host sus- needs to be a dynamic document that changes with sci- ceptibility (Müller-Schärer et al., 2004), and the interac- entiWc advances in the process of risk analysis in biologi- tion between these and agent behavior and physiological cal control and changes in societal values. condition (motivation, age, experience, and capacity to Historically, testing philosophies have been inconsis- perceive all available hosts). Risk analysis includes the tently deWned (van Klinken, 2000a) and testing use of host speciWcity testing and Weld host-use studies to approaches inconsistently applied (Sheppard, 1999). make pre-release relativity-based predictions of likeli- Confusion for those outside the process has been com- hood (e.g., “highly improbable,” “most likely”) that the pounded by inconsistent terminology deWnition sur- agent threatens particular plants or groups of plants rounding the terms host range and host speciWcity. In the based on predicted Weld host speciWcity in the environ- last 10 years Wve books or conference proceedings have ment where it will be released (Barratt and Moeed, 2005; been published that address scientiWc developments for van Klinken, 2000a). Risk analysis and therefore host assessing threats to nontarget species (Follett and Duan, speciWcity testing do not provide deWnitive predictions 1999; SpaVord-Jacob and Briese, 2003; Van Driesche on whether or not a particular agent will be “safe” (Bri- et al., 2000; Wajnberg et al., 2000; Withers et al., 1999). ese, 2005). Screening plants for safety would be very A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 217 hard to achieve without testing all plant species within ing in many regulatory procedures. There is now little the expected host range of a potential agent. scientiWc or risk analytical basis for including such cate- gories unless there are arguments for biochemical simi- larity (see category 5 in Table 1). 2. Tools for risk analysis Biogeographic overlap with the target or the likely Wnal distribution of the agent is also relevant for inclu- 2.1. Test plant lists sion on the test list within the framework of phylogenetic separation, along with ecological, phenological, and The host speciWcity test plant list is generally morphological similarity to the target. Such nontargets approved independently by a regulator appointed tech- are more likely to be used by agents following release. A nical advisory committee. Briese (1996, 2003, 2005) biochemical basis to some host speciWcity evolution is explains in depth the signiWcance of recent advances in evident in some groups (e.g., Bruchidae; Kergoat et al., plant phylogeny and how these can lead to improved test 2004) making this another possible criterion for selecting plant lists. The traditional centrifugal phylogenetic test plants, where possible, in categories of more distant approach, based on historic taxonomic hierarchies based phylogenetic separation (category 5, Table 1). The argu- on morphological similarity (Wapshere, 1974) has been ments about biochemical similarity and host range in surpassed by these recent advances (Kelch and McClay, specialist phytophagous , for example, remain 2004). Lists are best constructed from degrees of phylo- unclear, however, as secondary chemical proWles in some genetic separation in published molecular phylogenies other groups are not clearly related to host use (Mábel, whether or not these correlate with existing concepts of 2003). Conversely, therefore, testing closely related genus, tribe, family, etc. (Briese, 2005) (Table 1). The plants with contrasting biochemical proWles may also concept of testing safeguard species of distant phyloge- help determine host range. netic relatedness (Wapshere, 1974) also becomes redun- While preferential selection of economic or rare and dant, as such species do not add to the statistical threatened test plant species on to the test list (e.g., TAG strength of the risk analysis (Briese, 2003, 2005; Briese category 4) is justiWable, providing they Wt well with and Walker, 2002). Despite this, safeguard categories, these selection criteria (Briese, 2003), systematically test- including native and economic species in the same order ing them is not relevant for risk analysis (Briese, 2005). (e.g., TAG category 5) or in other orders (e.g., TAG cate- Host speciWcity testing is an assessment tool for predict- gory 6) with some morphological or biochemical similar- ing the likelihood of nontarget damage based on all ity with the target, or any plant on which congeners of potential nontargets available in the new environment, the agent have been previously found to feed and repro- rather than a means to deWne whether or not a particular duce (e.g., TAG category 7) are still mandatory for test- plant or group of plants will be safe from damage.

2.2. Test types and designs Table 1 Categories of test plants relevant for inclusion in host speciWcity test- ing to ensure eVective risk assessment based around phylogenetic test- The accuracy of host speciWcity testing is constrained ing of fundamental host range by the environmental conditions of the test. These can • Category 1: subspeciWc levels of genetic variation in the target species inXuence the behavioral response of the agent or the vir- • Category 2: species in the same phylogenetic clade as the target weed, ulence of the pathogen. While clinical no-choice tests including, where appropriate, native and economically important present the only option for passive aerially distributed W species that show signi cant biogeographic overlap and ecological, pathogens, a range of test types is available for assess- morphological or biochemical similarity • Category 3: species in clades with a single degree of phylogenetic ment of arthropod agents. separation from the clade of the Target Weed, including, where Tests to describe fundamental host range need to be appropriate, native and economically important species that show carefully designed to eliminate eVects of motivation, signiWcant biogeographic overlap and ecological, morphological or experience, and learning. Host perception and accep- biochemical similarity • tance for feeding and/or oviposition are important deter- Category 4: species in phylogenetic clades with two or more degrees W of separation of the Target Weed suYcient to cover species closely minants of host speci city for dispersing discriminatory related to the target (i.e., to the level of related families), including adult (occasionally larval) arthropods, including some or where appropriate, native, and economically important species that all behaviors associated with habitat location, pre- and show signiWcant biogeographic overlap and ecological, post-alighting host perception, post-alighting host morphological or biochemical similarity acceptance, and host use (Heard, 2000; Marohasy, 1998). • Category 5: species with speciWc biochemical similarity to the target weed, for agents with evidence of host acceptance behavior Host acceptance may broaden with age. An understand- associated with that speciWc plant biochemistry, including, where ing of host selection behaviors and eVects of motivation, appropriate, native and economically important species that show prior learning, and experience should provide the basis signiWcant biogeographic overlap and ecological or morphological for selection, design, and interpretation of host speciWc- similarity ity tests. If nontarget hosts are identiWed, tests must help 218 A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 predict the Weld host speciWcity on these hosts relative to capita population growth rates from no-choice trials the target (van Klinken, 2000a). Multiple names have provide data on the suitability of diVerent hosts for sup- been applied to tests, so clear deWnitions as deWned by porting agent populations. No-choice oviposition tests Heard and van Klinken (1998) help simplify the process. assess the threat of agent colonization. Aberrant behav- The characteristics, strengths, and weaknesses of the ior may occur in these tests, if there is not careful atten- three basic test design types; no-choice tests (single test tion to appropriate cage hygiene and size, nonetheless all species; Hill, 1999), choice tests (multiple test species; unexpected results need careful interpretation (Hill, Edwards, 1999), and Weld tests (Briese, 1999), have 1999). recently been outlined and each test type is prone to behavioral induced outcomes (Table 2; see Marohasy, 2.2.2. Choice tests 1998). Beyond host suitability, ranking hosts for preference Statistics are rarely used to analyze test results, but in arthropods usually requires the use of choice tests given the greater strength of quantitative risk analysis (Singer, 2004). Choice tests are valuable for highly (Lonsdale et al., 2000; Sheppard et al., 2003a) and a need mobile discriminatory life stages (Sheppard, 1999) or for a relative assessment of risk, tests require statistically where test plants are small (although cuttings or plant rigorous experimental designs and should be analyzed, parts can be used for large test plants) and where test at least when there is clear variation in the response vari- plant phenology can be synchronized. Choice tests allow ables. assessment of how motivation, prior experience, and learning aVect preference, but should not be conducted 2.2.1. No-choice tests alone as they may inaccurately predict Weld host range No-choice tests are where life stages are conWned onto (Haines et al., 2004). Choice tests come in two distinct one species at a time (Hill, 1999). Agents must be con- forms, the traditional choice test in which the choice Wned on the test plant until death (sometimes referred to includes the target (control) species and “choice-minus- as starvation tests for feeding) or at least for suYcient target” tests where multiple nontarget species are pre- time to reach a highly deprived state. They are the only sented in the same arena without the target (Heard and test design for passively distributed pathogens, and can van Klinken, 1998; Marohasy, 1998). be used for both discriminatory and developmental Choice-with-target tests have target and test plants stages of most arthropods (Sheppard, 1999). In arthro- present concurrently and provide basic assessment of pods where host discrimination and feeding takes place preference rank of test plants relative to the target. As in diVerent life stages, no-choice discrimination (e.g., ovi- preferred hosts can mask less-preferred hosts or prevent position) tests may be the only test required if only the arthropods from becoming suYciently deprived to use target is selected. Starvation tests are more generally them, such tests are best accompanied by “choice-minus- used as it is easier to get clear results. In addition to target” tests. determining the fundamental host range, no-choice tests Choice-minus-target tests allow for a range of designs can also provide valuable information for both patho- as plants may be oVered either concurrently or sequen- gens and arthropods on the relative suitability of hosts tially. Indeed in concurrent designs, the preference rank for development (e.g., survival, development rate, and between hosts can be determined if necessary by sequen- size of life stages, and fecundity and longevity of adults) tially removing the most preferred host (Edwards, 1999). and for arthropod oviposition (number and frequency of Test duration aVects the level of deprivation insects oviposition, egg batch sizes, etc.) and adult feeding (e.g., reach and so is critical for ensuring conWdence in the frequency and duration of feeding). Estimating per results (Edwards, 1999). Alternatively, agent density can

Table 2 Arthropod behavioral causes of false outcomes in diVerent test types modiWed from Marohasy (1998) Test type False –ve scenarios False +ve scenarios Field choice Agents not deprived All cage tests Cage reduced motivation Normal host perception behavior disrupted Cage induced egg-dumping Cage choice Prior experience Sensitization Central inhibition Central excitation Repetition induced associative learning Habituation to non-host deterrents Transfer of host stimuli Cage no-choice Habituation to non-host deterrents Cage sequential Prior experience and time dependent deprivation with respect to test sequence Repetition induced associative learning A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 219 be kept above that which can be supported by the avail- and uncertainty analysis (stage c); (see Sheppard et al., able resources on the known acceptable hosts (Heard, 2003a) and so its consistent adoption would better align 1999). As with no-choice tests, there are cage size and the testing to risk analysis. Testing of plant pathogens condition issues, so aberrant results require careful inter- has always had a more standardized focus on describing pretation. fundamental host range of the agent (Barton, 2004). This structure is adopted for the remainder of this paper. 2.2.3. Field tests Field tests, carried out in the native range, are almost always choice tests with conditions that maximize the 4. Life stage speciWcity requirements—what to test? opportunity for natural Weld behavior within natural or augmented Weld populations of the agent. These tend to The life stage(s) most likely to pose a threat to non- be used for screening multiple potential agents or for targets include all stages that select host plants. These seeking clariWcation or reWnement of results from other are usually mobile fecund adult females in the case of types of tests (Briese, 1999, 2005; Clement and Cristof- arthropods (also males if the adult feeding is signiWcant) aro, 1995). They are particularly useful for agents that or dormant and wind-dispersed spore stages for patho- are hard-to-rear, of unknown speciWcity, highly mobile, gens. Basic understanding of how these stages detect, or agents that prove highly sensitive to artiWcial experi- select, and start damaging a new host is needed for each mental conditions (Sheppard, 1999). High agent density agent. For pathogens, how the spores are dispersed, and is an important and common constraint to experimental the environmental conditions (humidity, temperature, design as some degree of agent deprivation is required direct contact with the host, etc.) for spore germination (Briese, 1999). This makes the experimental design criti- and host penetration are important. For arthropods this cal for conWdence in the results. Briese (1999) proposed a may require basic studies of host perception and accep- two-phase design. The Wrst phase includes the target tance behaviors for oviposition or feeding, as agents do (choice), followed by a second phase where the target is not choose between hosts but undergo a sequence of physically removed (choice-minus-target), forcing the behavioral steps which lead either to acceptance or rejec- agents to use the test plants or emigrate out of the sys- tion of each potential host encountered. If the discrimi- tem. The strength of Weld tests is that they allow for nat- natory stage of the arthropod does not feed and the ural host perception and acceptance behavior fundamental host range of this stage (e.g., for oviposi- minimizing acceptance of plants normally outside the tion) is limited to the target weed, then no further testing Weld host speciWcity. Field tests should not be the only should be necessary. test used, but rather used with caution as the last test in a Damaging life stage(s) either through the infective hierarchy of tests when they are aimed at conWrming growth stages of pathogens or direct feeding by usually speciWcity of a valuable agent (e.g., Anonymous, 1999). the immature and adult stages in arthropods also require testing. The only exception is that outlined above when discriminatory stages are nonfeeding and have been 3. ScientiWc risk analysis approach shown to be extremely speciWc so there is no opportunity for evolutionary change in host range following release. van Klinken (2000a) reviewed and analyzed the role In other cases, however, it is not suYcient in the assess- of host speciWcity testing of arthropod agents in assess- ment to consider only the discriminatory stages of ing nontarget impacts. Host speciWcity testing of arthro- potential agents even if the most damaging stages do not pod agents is most often aimed at directly estimating the move between hosts (Withers et al., 1999), because, at agent’s Weld host speciWcity and likelihood of nontarget least in arthropods, host preference is not always corre- attack once released into the new environment. A review lated with agent performance. New environments can of test use supports this (Sheppard, 1999). Van Klinken modify host perception and acceptance behaviors argued that, as a one-step process, the testing environ- (Singer, 2004), leading to rapid evolutionary changes in ment cannot simulate each of the possible environments host preference within the host range (Singer et al., the agent is likely to encounter following release and so 1992). Arthropods may also pose a hazard from trans- lacks scientiWc rigor. He therefore outlined a more rigor- mission of disease or other antagonistic organisms to ous three stage process for all assessment of nontarget hosts. impacts. These are: (a) deWning and understanding the life stages of the agents that will require testing, (b) determination of the fundamental host range of the rele- 5. Determination of fundamental host range vant life stages of the agent, and (c) prediction of the Weld host speciWcity in the new environment following Despite rarely being done, host speciWcity testing release. This process parallels the two stages of formal should Wrst determine the fundamental host range of the risk analysis; i.e., hazard identiWcation (stage a and b) potential agent’s life history stages that could result in 220 A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 nontarget impact (van Klinken, 2000a). This is the most Singer, 2004; Withers and Barton Browne, 1998; Withers conservative estimate of risk, being the widest range of et al., 2000) has identiWed three reasons: (a) the disrup- hosts that the organism can accept and/or use. Both the- tion of the natural sequence of host perception and ory and available evidence suggest that the likelihood of acceptance behaviors, (b) the motivational status of the rapid evolutionary change in the fundamental host agent at the time of testing, and (c) the eVect that prior range of an organism is negligible (van Klinken and experience and learning of particular individuals may Edwards, 2002). have on host selection (Table 3). Such concerns are less- Fundamental host range is best determined through ened, however, when the primary aim of testing is to the use of no-choice tests conducted for the duration of objectively determine the fundamental host range such the life of the insect stage being tested, eVectively exclud- that motivational status, eVects of prior experience and ing possible eVects of prior experience and learning, and learning are excluded (van Klinken, 2000a). Poor experi- maximizing motivation levels (van Klinken and Heard, mental estimation of Weld host range for agents observed 2000). Fundamental host range may vary considerably to be speciWc (e.g., in adult oviposition) in the Weld is a for diVerent behaviors and activities including oviposi- key issue if other life stages accept nontargets (e.g., tion (and discriminatory steps within this), initiation of Heard et al., 2004). Furthermore, behavioral phenom- larval feeding, larval development, and adult feeding ena, such as habituation to non-host deterrents, associa- (van Klinken, 2000b). tive learning, and sensitization to host stimuli (Table 3), Care is required in both the design and interpretation need to be carefully considered within the context of the of no-choice trials to ensure that the fundamental host release environment in order to assess the likelihood of ranges of the desired aspects of life history are indeed them being expressed, and subsequently result in nontar- being described. There are many examples where experi- get attack and impact. mental arenas do not allow the discriminatory steps within an organism’s life history to take place. Experi- 6.2. Ecological causes for contrasting fundamental and mental conditions (e.g., lighting; Barton Browne and Weld host ranges Withers, 2002; Withers et al., 2000) may aVect agent motivation and thus its host acceptance (Barton Browne 6.2.1. Absence of target and Withers, 2002; Heard, 2000; Marohasy, 1998). Also, A nontarget species may be able to support only part oviposition behavior, for example, may not be fully of the agent’s lifecycle, and therefore will not be a host expressed (especially distance cues) leading to indiscrim- under natural conditions in the absence of the target. inate oviposition even though Weld observations suggest Even if the nontarget species can support agent popula- oviposition is highly speciWc. If non-dispersing stages are tions under laboratory conditions, the nontarget may not host speciWc, this could lead to premature rejection not do so in the Weld if it is too rare for the agent popula- of a speciWc agent. In these cases, other methods, such as tion to maintain a positive growth rate, or because mor- native-range Weld tests (Anonymous, 1999; Briese, 1999) tality factors prevent population growth. For example, or wind tunnel trials (Keller, 1990), may be required to the Xubida infusellus (Walker), introduced against see full expression of the discriminatory phase. Table 3 Heard’s (2000) types of arthropod behavioral constraint in host speci- 6. DiVerences between fundamental and Weld host ranges Wcity testing and the consequence they may have on test results (for particular test types) While the diVerences between fundamental and Weld Cause Consequence host ranges in plant pathogens can be largely explained External stimuli by host quality or environmental diVerences between the Host perception behavior disrupted False +ves (cages) laboratory settings of the tests and the Weld situation Non-host odor masks host odor False ¡ves (choice tests) Preferred host odor masks lesser False +ves (choice tests) (Barton, 2004), in arthropods there are a number of hosts causes now recognized that relate to arthropod behavior Experience, learning, and motivation and other ecological factors. Associative learning False +ves (choice tests) Habituation to non-host deterrents False +ves (naive Wrst instars) 6.1. Incorrect characterization of fundamental host range Sensitization to host stimuli False +ves (choice tests) Central excitation False +ves (choice tests) A common concern when predicting Weld host range Central inhibition False ¡ves (choice tests) W Predisposition False ¡ves (experienced agents) is that the results of host speci city tests may overesti- Time dependent deprivation False ¡ves (short tests) mate (generation of false positives) or underestimate Time of day/temperature False ¡ves (constant conditions) (generation of false negatives) Weld host range (IPPC, Age False ¡ves 1996; Marohasy, 1998). Recent research (Barton Browne (long-lived/multi-stage) and Withers, 2002; Heard, 2000; Marohasy, 1998; Cage False +ves (escape behavior) A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 221

Eichhornia crassipes (Mart.) Solms (water hyacinth), 7. Predicting Weld speciWcity and relative nontarget attacked nontarget Monochoria spp. during testing, but impacts minimal Weld impact was predicted as these species are too short-lived and ephemeral for the moth to complete Predicting Weld speciWcity, the level of damage on its life cycle or sustain populations. Monochoria spp. and those species relative to the target plant, and the likely the target also have allopatric distributions so spill-over ecological nontarget impacts of that damage, is termed was not predicted to occur (Julien et al., 2001). analyzing the uncertainty in a risk analysis. The magni- tude of the threat (predicted host speciWcity) and the 6.2.2. Presence of target likelihood of such threats occurring (predicting impacts) In the presence of the target, nontargets may be used are the two components of this. Where the Weld host (Greathead, 1968; Jayanth et al., 1993) or more heavily range is likely to include nontarget species, making accu- used (Rand and Louda, 2004) because of spill-over eVects. rate predictions is the most challenging component of Even if perceived, however, susceptible nontargets may the risk analysis, being based on the combined outcome not be acceptable if their preference rank is lower than of relative acceptability and suitability of hosts, and other hosts present (Singer, 2004). This can also depend being sensitive to behavioral mechanisms. For example, on the relative availability of each host, which can change prior experience can predispose agents to certain hosts if, for example, the target is successfully controlled, and (Barton Browne and Withers, 2002; Withers et al., 2000), result in the use of hosts of lower preference. In this case, or even reverse preference rankings (Szentesi and Jermy, host preferences between acceptable hosts may evolve 1990) relative to naïve insects. rapidly (van Klinken and Edwards, 2002). Results from laboratory, semi-natural or Weld choice tests assist in predicting Weld speciWcity by describing 6.2.3. Asynchrony preferences, and how they may be modiWed by factors Susceptible stages of the nontarget may be asynchro- such as prior experience, insect age, and the relative nous with the activity period of the agent (Hasan and availability of hosts. Predictions must relate to the envi- Delfosse, 1995; Julien et al., 2001). In some groups, the ronments into which the agent will be released and may synchrony of the agent to a particular developmental spread. A wide range of factors need to be considered for stage of hosts can deWne population Weld speciWcity. such predictions, including plant quality, insect and When Jolivet (2002) presented very short-lived females plant phenology, and the relative availability of other of the wild-radish Xower-bud gall midges (Gephyraulus host species in time and space. Predictions based on host raphanistri (KieVer)) with artiWcially resynchronized speciWcity could range from: (a) incidental feeding on Canola (Brassica napus L.) Xower buds, galls appeared nontargets resulting from spill-over eVects from high on them despite having never been recorded in the wild agent abundance (e.g., Teleonemia on Lantana; Great- (M. Skuhravá, Praha, Czech Republic, personal commu- head, 1968), (b) development on nontarget plants in the nication). Similarly, apparently highly speciWc Weld pop- presence of the target (e.g., (Busck) ulations of the broom seed beetle (Bruchidius villosus F.) on Neptunia major (Benth.) Windler; Q. Paynter and P. exhibit a broader Weld host range when provided with Taylor, CSIRO Entomology, Australia, unpublished resynchronized close relatives (A. Sheppard and T. Tho- data or Rhinocyllus conicus (Frölich) on Cirsium undula- mann, unpublished data). The experimental design must tum (Nutt.) Spreng.; Rand and Louda, 2004), to (c) pop- therefore allow full expression of fundamental host ulation build up on nontarget species in the absence of range to ensure an eVective risk analysis. the target (examples in Louda et al., 2003). Spill-over eVects are generally transitory. They are 6.2.4. Geographical incompatibility likely to be temporary and local if the target is con- Nontarget species may occur in habitats or climates trolled, but permanent and widespread if not. They can unsuitable for the agent. Hodkinson (1997) showed prove unacceptable if associated with additional likeli- that climate-restricted host exploitation by the willow hood of disease transmission (Fowler et al., 2000). Esti- psyllid, Cacopsylla groenlandica Sulc. Nontargets may mating the agent per capita population growth rate on be biogeographically separated from the agent popula- the target and each susceptible test plant species is one tion (e.g., by mountains, water or desert). Such barriers way to assist likelihood estimations. This provides a can be breached with time, however, as has happened measure of whether nontargets will support agent pop- with Cactoblastus cactorum (Berg) onto nontarget ulations as well as, and in the absence of, the target and Opuntia spp. by arriving in Florida from the Caribbean can suggest whether the agent can reach a density on (Bennett and Habeck, 1995; Louda et al., 2003). Like- the nontargets suYcient to suppress their populations. wise, strong habitat preferences may prevent an agent Predicting whether a nontarget host can support agent from using potential nontargets hosts, e.g., in Drosoph- populations is particularly important where nontargets ila magnaquinaria Wheeler (Kibota and Courtney, are allopatric from the target, and likely to continue to 1991). be so. 222 A.W. Sheppard et al. / Biological Control 35 (2005) 215–226

Estimating likelihood of impact on nontargets to C. scoparius were originally tested. The test results, requires understanding of the population-level conse- which turned out to be erroneous (Haines et al., 2004), quences of attack relative to similar consequences for the agreed with the Weld speciWcity of the original popula- target. The impact on nontargets will also not necessarily tion. Following release, the agent exhibited a broader be proportional to the level of damage. The ratio of host range than in the tests. Subsequent surveys over the agent attack rate to intrinsic rate of increase of the non- whole native range of this species found disparate popu- target will determine the level of impacts on nontargets. lations can exhibit high Weld speciWcity to diVerent hosts For example, rare nontargets with low population even within the presence of other suitable hosts (A. growth rates will suVer proportionally higher impacts Sheppard and T. Thomann, unpublished data). Local from agents than target species with higher population host abundance and seed production phenology are con- growth rates for the same level of damage (Holt and sidered to be causing this locally restricted, but region- Hochberg, 2000). ally variable, Weld host speciWcity, despite such Evolutionary consequences should also be consid- populations showing equally broad host ranges under ered. There is some likelihood of rapid evolution in host no-choice conditions (Haines, 2004). If phenological syn- preference where the new environment has diVering con- chrony alone restricts local Weld host speciWcity, then the ditions of host quality and quantity (van Klinken and chance of host shifts within the fundamental host range Edwards, 2002). Finally, all such predictions need to be could be high if: (a) there is genetic variability in agent evaluated in the Weld following release as risk evaluation phenology and/or (b) the new environment presents con- is a key and necessary component of risk analysis (Lons- ditions that change the synchrony for interactions dale et al., 2000; Sheppard et al., 2003a,b). between agents and potential hosts. Such occurrences appear to be not uncommon in bruchids (Fox, 2000). 7.1. IntraspeciWc variation in agents and host shifts Similarly asynchrony between agents and targets is not uncommon following releases (e.g., Harris, 1980; Pit- IntraspeciWc variation within the agent species can cairn, 2001; Woodburn and Cullen, 1996). Reliable host lead to variation in expressed Weld host speciWcity within records from throughout the native range have always the fundamental host range. Whether this can lead to provided valuable evidence for risk analysis. shifts in expressed Weld host speciWcity following release Second, cases also occur where subspeciWc popula- is hard to predict. Risks can be restricted by ensuring tions of an agent show higher speciWcity than the species low variation in the tested population and restricting as a whole and this is maintained in no-choice tests releases to individuals from only this population. (Evans and Gomez, 2004; Fumanal et al., 2004). In such Such variation generally occurs in two forms, varia- cases, variation is more likely to be associated with sig- tion in phenology and in host speciWcity. First, disparate niWcant measurable genetic distance between popula- populations of the same agent species may show appar- tions. The taxa may be in the early stages of sympatric ent high speciWcity to diVerent host species or genera, speciation. Fumanal et al. (2004) have found subspeciWc while the species as a whole uses hosts over a broader variation in fundamental host range of morphologically range (Briese and Sheppard, 1992; Klein and Seitz, identical crown-gall weevil, Ceutorhynchus assimilis Pay- 1994). Similarly, when the recorded Weld host range for kull, populations feeding within the Brassicaceae, such the species as a whole conXicts with that exhibited by a that some populations are monospeciWc while others are local population, then this is an indication intraspeciWc stenophagous within the family. This variation is sup- variation in host use may be important (Haines, 2004). ported by molecular clade separation and cross-breeding This high variation in Weld host speciWcity within the studies. Within the species, reliable diVerence in genetic native range may be unrelated to genetic distance or markers between clades tightly maps on to observed reproductive isolation between populations. Variation host speciWcity and there is evidence of reproductive may result from local phenological adaptation to a par- incompatibility between individuals from diVerent ticular locally abundant host that restricts host use for a clades (M-C Bon, USDA-ARS-EBCL, Montferrier-sur- given population despite other suitable hosts being pres- Lez, France, personal communication). ent (Haines, 2004). Such apparent speciWcity is usually There is currently no reason to suppose genetically relatively easy to detect as even speciWc populations based subspeciWc variation in host range or speciWcity is exhibit the same host speciWcity (host range, suitability, any more evolutionarily unstable than between-species and preferences) in no-choice tests when phenological diVerences if, for example, a monophagous population asynchrony between agents and hosts is eliminated of a polyphagous species was maintained in isolation (Zwölfer and Priess, 1983). from other populations of the same species (Singer, Bruchidius villosus, a seed beetle released in several 2004). Consistent restrictions in fundamental host range countries against Scotch broom, Cytisus scoparius (L.) at the subspeciWc level, with support from molecular Link, had various synonyms at the time of testing in the data, can open doors to biological control for clonal 1980s. Only populations that were restricted in the Weld weeds, notably Rubus spp. (Evans and Gomez, 2004) and A.W. Sheppard et al. / Biological Control 35 (2005) 215–226 223 for the reconsideration of agents previously thought too agent life history (e.g., adult oviposition or larval feed- generalist (Fumanal et al., 2004). ing) that could pose a threat are identiWed, and the fun- The biggest risk is that apparent intraspeciWc varia- damental host ranges of these stages are determined tion is in fact resulting from the presence of one or more (hazard identiWcation). Testing should account for crypto-species within the sampled populations and behavioral factors such as motivation, learning, and extreme care and genetic analyses are required to avoid prior experience and allow the best possible predictions these going undetected prior to release (e.g., Alonso- of Weld host range, relative use of nontargets, and poten- Zarazaga and Sánchez-Ruiz, 2002; Balciunas and Ville- tial nontarget impacts within the release environment gas, 1999). (uncertainty analysis). Conducting rigorous risk analysis In general, these studies suggest population diVer- of nontarget impacts remains challenging, but under- ences in Weld host speciWcity within the fundamental host standing has increased to a point where best possible range of a species may be commoner than we thought. practice can be applied to this process. Phylogenetic dating of such host shifts, however, sug- gests they happen on longer timescales (Futuyma, 2000) than would be considered relevant to the relatively Acknowledgments immediate problems of managing invasive pest species. Host shifts outside the fundamental host range of spe- The authors thank USDA-ARS, USDA-CSREES- cialist natural enemies are, in all evidence, exceedingly IFAFS, and the Center for Invasive Plant Management rare. Absence of evidence comes from post-release retro- for organizing the Denver conference on “Science and spective evaluations of biological control agents (Louda Decision making in biological control of Weeds” and for W et al., 2003; Pemberton, 2000; van Klinken and Edwards, nancial support of the senior author to attend the meet- 2002), other recorded cases of host shifts in phytopha- ing and present this review. The Australian Government gous arthropods (Marohasy, 1996), phylogenetic pat- and the Australian Cooperative Research Centre for terns of host range for generalists and specialists, along Australian Weed Management for support of activities with studies of genetic variability in host speciWcity that contribute to this paper. Alan Kirk and Rouhollah (Futuyma, 2000), and recent understanding of the Sobhian for discussions and John Scott and two anony- behavioral mechanisms aVecting changes in host prefer- mous referees for comments on the manuscript. ence (Singer, 2004). References

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