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
b
CSIRO European Laboratory, Campus International de Baillarguet, 34980 Montferrier-sur-Lez, France
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, weaknesses, 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 following 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 developments 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
Countries that conduct research on classical weed
biological control have regulatory procedures for deciding the release of exotic biological control agents based
on assessed direct threat agents may pose to nontarget
organisms. Other countries gain a similar protection
through a code of conduct for the import and release of
*
Corresponding author. Fax: +33 4 67 59 90 40.
E-mail address: [email protected] (A.W. Sheppard).
exotic biological control agents within the International
Plant Protection Convention (Sheppard et al., 2003a).
These procedures generally consist of host speciWcity
assessment of agents followed by an independent decision made by regulatory bodies. As such the regulatory
procedures are the direct conduit of understanding from
stakeholders and biological control practitioners
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
economic and native species following release and to
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
nontarget colonization or temporary “spill-over” onto
nontargets by less speciWc life stages. However, in order
to make informed decisions, regulators need to understand and have conWdence in a transparent and recognized scientiWc process of this type of risk analysis in
addition to the results themselves (Briese, 2005).
The regulatory processes used vary in the degree of
prescription. Prescriptive processes have the advantage
of transparency, but the disadvantage of requiring frequent modiWcation to keep pace with scientiWc advances
and inXexibility when dealing with diverse organisms.
The process adopted in most countries allows signiWcant
degrees of freedom for the practitioner to design the tests
and interpret the results, however approval is usually
required for test plant lists and the process for constructing the list can be very prescriptive. Since the 1970s the
focus of this risk has globally changed from commercial
plants to include native species. The change in focus also
varies between countries. Rare and threatened native
species are a key focus of the host speciWcity assessment
and the test plant list in the USA, for example, and the
importance of spill-over (agents damaging but not developing on nontargets) appears to receive less emphasis in
the USA than in some other countries (Sheppard et al.,
2003a).
In the USA, the procedure for assessing the direct
threats to nontargets as part of the release application
process is publicly described in greatest detail in the
form of a Technical Advisory Group (TAG) reviewer’s
manual (www.aphis.usda.gov/ppq/permits/tag) organized by the regulatory agency to assist reviewers of
applications. This is therefore the document that ensures
regulator understanding and conWdence in the assessment process. As a consequence, the manual is used by
USA practitioners as a default guide to the release application process and therefore a reference for requirements for agencies that make such applications. The
information within is extensive and science-based. By
oVering a much more detailed public-domain description of this risk assessment process than available in
other countries, however, the TAG reviewer’s manual
needs to be a dynamic document that changes with scientiWc advances in the process of risk analysis in biological control and changes in societal values.
Historically, testing philosophies have been inconsistently deWned (van Klinken, 2000a) and testing
approaches inconsistently applied (Sheppard, 1999).
Confusion for those outside the process has been compounded by inconsistent terminology deWnition surrounding the terms host range and host speciWcity. In the
last 10 years Wve books or conference proceedings have
been published that address scientiWc developments for
assessing threats to nontarget species (Follett and Duan,
1999; SpaVord-Jacob and Briese, 2003; Van Driesche
et al., 2000; Wajnberg et al., 2000; Withers et al., 1999).
What has been lacking has been a synthesis of these
developments into a consistent analytical pathway for
direct nontarget risk assessment. This review aims to
achieve this by discussing developments over the past 10
years in the science behind risk assessment of direct nontarget impacts of weed biological control agents. We
hope this review will assist not only scientiWc reviewers
but also regulators and policy makers to clearly understand and practitioners correctly undertake best possible
practice.
1.1. DeWnitions
Host range of a herbivore or plant pathogen is the set
of species that it can perceive, accept and/or use dependent on the environmental setting, while we argue host
speciWcity is best used to describe how preference
(arthropods) and performance (arthropods and microorganisms) vary within the host range for a deWned
agent population or strain (van Klinken, 2000a). The
fundamental host range deWnes the absolute limits of a
species host range and has historically been poorly
deWned (van Klinken, 2000a). Fundamental host range is
a broader concept than previously used “physiological
host range” (e.g., Cullen, 1990; Harris and McEvoy,
1995) as it acknowledges the need for appropriate
behavioral stimuli for host acceptance rather than just
meeting simple physiological requirements (van Klinken,
2000a). Fundamental host range includes all hosts that,
given synchronous phenology, are used by a test organism when no alternative is oVered, i.e., independent of
any environmental setting. Fundamental host range can
be determined for any aspect of an insect’s or pathogen’s
interaction with its host (Nechols et al., 1992; van Klinken, 2000a,b). Preference for a particular host is the likelihood of acceptance based on the capacity of tested
arthropod agents to detect a particular host (Singer,
2004). Agent performance will also vary between hosts
based on host suitability and resistance.
Host use by the tested agent following release (Weld
host speciWcity) will vary with environment (availability
of hosts in time and space), genetic variation in host susceptibility (Müller-Schärer et al., 2004), and the interaction between these and agent behavior and physiological
condition (motivation, age, experience, and capacity to
perceive all available hosts). Risk analysis includes the
use of host speciWcity testing and Weld host-use studies to
make pre-release relativity-based predictions of likelihood (e.g., “highly improbable,” “most likely”) that the
agent threatens particular plants or groups of plants
based on predicted Weld host speciWcity in the environment where it will be released (Barratt and Moeed, 2005;
van Klinken, 2000a). Risk analysis and therefore host
speciWcity testing do not provide deWnitive predictions
on whether or not a particular agent will be “safe” (Briese, 2005). Screening plants for safety would be very
A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
hard to achieve without testing all plant species within
the expected host range of a potential agent.
2. Tools for risk analysis
2.1. Test plant lists
The host speciWcity test plant list is generally
approved independently by a regulator appointed technical advisory committee. Briese (1996, 2003, 2005)
explains in depth the signiWcance of recent advances in
plant phylogeny and how these can lead to improved test
plant lists. The traditional centrifugal phylogenetic
approach, based on historic taxonomic hierarchies based
on morphological similarity (Wapshere, 1974) has been
surpassed by these recent advances (Kelch and McClay,
2004). Lists are best constructed from degrees of phylogenetic separation in published molecular phylogenies
whether or not these correlate with existing concepts of
genus, tribe, family, etc. (Briese, 2005) (Table 1). The
concept of testing safeguard species of distant phylogenetic relatedness (Wapshere, 1974) also becomes redundant, as such species do not add to the statistical
strength of the risk analysis (Briese, 2003, 2005; Briese
and Walker, 2002). Despite this, safeguard categories,
including native and economic species in the same order
(e.g., TAG category 5) or in other orders (e.g., TAG category 6) with some morphological or biochemical similarity with the target, or any plant on which congeners of
the agent have been previously found to feed and reproduce (e.g., TAG category 7) are still mandatory for testTable 1
Categories of test plants relevant for inclusion in host speciWcity testing to ensure eVective risk assessment based around phylogenetic testing of fundamental host range
• Category 1: subspeciWc levels of genetic variation in the target species
• Category 2: species in the same phylogenetic clade as the target weed,
including, where appropriate, native and economically important
species that show signiWcant biogeographic overlap and ecological,
morphological or biochemical similarity
• Category 3: species in clades with a single degree of phylogenetic
separation from the clade of the Target Weed, including, where
appropriate, native and economically important species that show
signiWcant biogeographic overlap and ecological, morphological or
biochemical similarity
• Category 4: species in phylogenetic clades with two or more degrees
of separation of the Target Weed suYcient to cover species closely
related to the target (i.e., to the level of related families), including
where appropriate, native, and economically important species that
show signiWcant biogeographic overlap and ecological,
morphological or biochemical similarity
• Category 5: species with speciWc biochemical similarity to the target
weed, for agents with evidence of host acceptance behavior
associated with that speciWc plant biochemistry, including, where
appropriate, native and economically important species that show
signiWcant biogeographic overlap and ecological or morphological
similarity
217
ing in many regulatory procedures. There is now little
scientiWc or risk analytical basis for including such categories unless there are arguments for biochemical similarity (see category 5 in Table 1).
Biogeographic overlap with the target or the likely
Wnal distribution of the agent is also relevant for inclusion on the test list within the framework of phylogenetic
separation, along with ecological, phenological, and
morphological similarity to the target. Such nontargets
are more likely to be used by agents following release. A
biochemical basis to some host speciWcity evolution is
evident in some groups (e.g., Bruchidae; Kergoat et al.,
2004) making this another possible criterion for selecting
test plants, where possible, in categories of more distant
phylogenetic separation (category 5, Table 1). The arguments about biochemical similarity and host range in
specialist phytophagous insects, for example, remain
unclear, however, as secondary chemical proWles in some
other groups are not clearly related to host use (Mábel,
2003). Conversely, therefore, testing closely related
plants with contrasting biochemical proWles may also
help determine host range.
While preferential selection of economic or rare and
threatened test plant species on to the test list (e.g., TAG
category 4) is justiWable, providing they Wt well with
these selection criteria (Briese, 2003), systematically testing them is not relevant for risk analysis (Briese, 2005).
Host speciWcity testing is an assessment tool for predicting the likelihood of nontarget damage based on all
potential nontargets available in the new environment,
rather than a means to deWne whether or not a particular
plant or group of plants will be safe from damage.
2.2. Test types and designs
The accuracy of host speciWcity testing is constrained
by the environmental conditions of the test. These can
inXuence the behavioral response of the agent or the virulence of the pathogen. While clinical no-choice tests
present the only option for passive aerially distributed
pathogens, a range of test types is available for assessment of arthropod agents.
Tests to describe fundamental host range need to be
carefully designed to eliminate eVects of motivation,
experience, and learning. Host perception and acceptance for feeding and/or oviposition are important determinants of host speciWcity for dispersing discriminatory
adult (occasionally larval) arthropods, including some or
all behaviors associated with habitat location, pre- and
post-alighting host perception, post-alighting host
acceptance, and host use (Heard, 2000; Marohasy, 1998).
Host acceptance may broaden with age. An understanding of host selection behaviors and eVects of motivation,
prior learning, and experience should provide the basis
for selection, design, and interpretation of host speciWcity 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
the target (van Klinken, 2000a). Multiple names have
been applied to tests, so clear deWnitions as deWned by
Heard and van Klinken (1998) help simplify the process.
The characteristics, strengths, and weaknesses of the
three basic test design types; no-choice tests (single test
species; Hill, 1999), choice tests (multiple test species;
Edwards, 1999), and Weld tests (Briese, 1999), have
recently been outlined and each test type is prone to
behavioral induced outcomes (Table 2; see Marohasy,
1998).
Statistics are rarely used to analyze test results, but
given the greater strength of quantitative risk analysis
(Lonsdale et al., 2000; Sheppard et al., 2003a) and a need
for a relative assessment of risk, tests require statistically
rigorous experimental designs and should be analyzed,
at least when there is clear variation in the response variables.
2.2.1. No-choice tests
No-choice tests are where life stages are conWned onto
one species at a time (Hill, 1999). Agents must be conWned on the test plant until death (sometimes referred to
as starvation tests for feeding) or at least for suYcient
time to reach a highly deprived state. They are the only
test design for passively distributed pathogens, and can
be used for both discriminatory and developmental
stages of most arthropods (Sheppard, 1999). In arthropods where host discrimination and feeding takes place
in diVerent life stages, no-choice discrimination (e.g., oviposition) tests may be the only test required if only the
target is selected. Starvation tests are more generally
used as it is easier to get clear results. In addition to
determining the fundamental host range, no-choice tests
can also provide valuable information for both pathogens and arthropods on the relative suitability of hosts
for development (e.g., survival, development rate, and
size of life stages, and fecundity and longevity of adults)
and for arthropod oviposition (number and frequency of
oviposition, egg batch sizes, etc.) and adult feeding (e.g.,
frequency and duration of feeding). Estimating per
capita population growth rates from no-choice trials
provide data on the suitability of diVerent hosts for supporting agent populations. No-choice oviposition tests
assess the threat of agent colonization. Aberrant behavior may occur in these tests, if there is not careful attention to appropriate cage hygiene and size, nonetheless all
unexpected results need careful interpretation (Hill,
1999).
2.2.2. Choice tests
Beyond host suitability, ranking hosts for preference
in arthropods usually requires the use of choice tests
(Singer, 2004). Choice tests are valuable for highly
mobile discriminatory life stages (Sheppard, 1999) or
where test plants are small (although cuttings or plant
parts can be used for large test plants) and where test
plant phenology can be synchronized. Choice tests allow
assessment of how motivation, prior experience, and
learning aVect preference, but should not be conducted
alone as they may inaccurately predict Weld host range
(Haines et al., 2004). Choice tests come in two distinct
forms, the traditional choice test in which the choice
includes the target (control) species and “choice-minustarget” tests where multiple nontarget species are presented in the same arena without the target (Heard and
van Klinken, 1998; Marohasy, 1998).
Choice-with-target tests have target and test plants
present concurrently and provide basic assessment of
preference rank of test plants relative to the target. As
preferred hosts can mask less-preferred hosts or prevent
arthropods from becoming suYciently deprived to use
them, such tests are best accompanied by “choice-minustarget” tests.
Choice-minus-target tests allow for a range of designs
as plants may be oVered either concurrently or sequentially. Indeed in concurrent designs, the preference rank
between hosts can be determined if necessary by sequentially removing the most preferred host (Edwards, 1999).
Test duration aVects the level of deprivation insects
reach and so is critical for ensuring conWdence in the
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
Field choice
All cage tests
Agents not deprived
Cage reduced motivation
Cage choice
Prior experience
Central inhibition
False +ve scenarios
Normal host perception behavior disrupted
Cage induced egg-dumping
Sensitization
Central excitation
Repetition induced associative learning
Habituation to non-host deterrents
Transfer of host stimuli
Cage no-choice
Cage sequential
Prior experience and time dependent deprivation with respect to test sequence
Habituation to non-host deterrents
Repetition induced associative learning
A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
be kept above that which can be supported by the available resources on the known acceptable hosts (Heard,
1999). As with no-choice tests, there are cage size and
condition issues, so aberrant results require careful interpretation.
2.2.3. Field tests
Field tests, carried out in the native range, are almost
always choice tests with conditions that maximize the
opportunity for natural Weld behavior within natural or
augmented Weld populations of the agent. These tend to
be used for screening multiple potential agents or for
seeking clariWcation or reWnement of results from other
types of tests (Briese, 1999, 2005; Clement and Cristofaro, 1995). They are particularly useful for agents that
are hard-to-rear, of unknown speciWcity, highly mobile,
or agents that prove highly sensitive to artiWcial experimental conditions (Sheppard, 1999). High agent density
is an important and common constraint to experimental
design as some degree of agent deprivation is required
(Briese, 1999). This makes the experimental design critical for conWdence in the results. Briese (1999) proposed a
two-phase design. The Wrst phase includes the target
(choice), followed by a second phase where the target is
physically removed (choice-minus-target), forcing the
agents to use the test plants or emigrate out of the system. The strength of Weld tests is that they allow for natural host perception and acceptance behavior
minimizing acceptance of plants normally outside the
Weld host speciWcity. Field tests should not be the only
test used, but rather used with caution as the last test in a
hierarchy of tests when they are aimed at conWrming
speciWcity of a valuable agent (e.g., Anonymous, 1999).
3. ScientiWc risk analysis approach
van Klinken (2000a) reviewed and analyzed the role
of host speciWcity testing of arthropod agents in assessing nontarget impacts. Host speciWcity testing of arthropod agents is most often aimed at directly estimating the
agent’s Weld host speciWcity and likelihood of nontarget
attack once released into the new environment. A review
of test use supports this (Sheppard, 1999). Van Klinken
argued that, as a one-step process, the testing environment cannot simulate each of the possible environments
the agent is likely to encounter following release and so
lacks scientiWc rigor. He therefore outlined a more rigorous three stage process for all assessment of nontarget
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 relevant life stages of the agent, and (c) prediction of the
Weld host speciWcity in the new environment following
release. This process parallels the two stages of formal
risk analysis; i.e., hazard identiWcation (stage a and b)
219
and uncertainty analysis (stage c); (see Sheppard et al.,
2003a) and so its consistent adoption would better align
the testing to risk analysis. Testing of plant pathogens
has always had a more standardized focus on describing
fundamental host range of the agent (Barton, 2004). This
structure is adopted for the remainder of this paper.
4. Life stage speciWcity requirements—what to test?
The life stage(s) most likely to pose a threat to nontargets include all stages that select host plants. These
are usually mobile fecund adult females in the case of
arthropods (also males if the adult feeding is signiWcant)
or dormant and wind-dispersed spore stages for pathogens. Basic understanding of how these stages detect,
select, and start damaging a new host is needed for each
agent. For pathogens, how the spores are dispersed, and
the environmental conditions (humidity, temperature,
direct contact with the host, etc.) for spore germination
and host penetration are important. For arthropods this
may require basic studies of host perception and acceptance behaviors for oviposition or feeding, as agents do
not choose between hosts but undergo a sequence of
behavioral steps which lead either to acceptance or rejection of each potential host encountered. If the discriminatory stage of the arthropod does not feed and the
fundamental host range of this stage (e.g., for oviposition) is limited to the target weed, then no further testing
should be necessary.
Damaging life stage(s) either through the infective
growth stages of pathogens or direct feeding by usually
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
shown to be extremely speciWc so there is no opportunity
for evolutionary change in host range following release.
In other cases, however, it is not suYcient in the assessment to consider only the discriminatory stages of
potential agents even if the most damaging stages do not
move between hosts (Withers et al., 1999), because, at
least in arthropods, host preference is not always correlated with agent performance. New environments can
modify host perception and acceptance behaviors
(Singer, 2004), leading to rapid evolutionary changes in
host preference within the host range (Singer et al.,
1992). Arthropods may also pose a hazard from transmission of disease or other antagonistic organisms to
hosts.
5. Determination of fundamental host range
Despite rarely being done, host speciWcity testing
should Wrst determine the fundamental host range of the
potential agent’s life history stages that could result in
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A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
nontarget impact (van Klinken, 2000a). This is the most
conservative estimate of risk, being the widest range of
hosts that the organism can accept and/or use. Both theory and available evidence suggest that the likelihood of
rapid evolutionary change in the fundamental host
range of an organism is negligible (van Klinken and
Edwards, 2002).
Fundamental host range is best determined through
the use of no-choice tests conducted for the duration of
the life of the insect stage being tested, eVectively excluding possible eVects of prior experience and learning, and
maximizing motivation levels (van Klinken and Heard,
2000). Fundamental host range may vary considerably
for diVerent behaviors and activities including oviposition (and discriminatory steps within this), initiation of
larval feeding, larval development, and adult feeding
(van Klinken, 2000b).
Care is required in both the design and interpretation
of no-choice trials to ensure that the fundamental host
ranges of the desired aspects of life history are indeed
being described. There are many examples where experimental arenas do not allow the discriminatory steps
within an organism’s life history to take place. Experimental conditions (e.g., lighting; Barton Browne and
Withers, 2002; Withers et al., 2000) may aVect agent
motivation and thus its host acceptance (Barton Browne
and Withers, 2002; Heard, 2000; Marohasy, 1998). Also,
oviposition behavior, for example, may not be fully
expressed (especially distance cues) leading to indiscriminate oviposition even though Weld observations suggest
oviposition is highly speciWc. If non-dispersing stages are
not host speciWc, this could lead to premature rejection
of a speciWc agent. In these cases, other methods, such as
native-range Weld tests (Anonymous, 1999; Briese, 1999)
or wind tunnel trials (Keller, 1990), may be required to
see full expression of the discriminatory phase.
6. DiVerences between fundamental and Weld host ranges
While the diVerences between fundamental and Weld
host ranges in plant pathogens can be largely explained
by host quality or environmental diVerences between the
laboratory settings of the tests and the Weld situation
(Barton, 2004), in arthropods there are a number of
causes now recognized that relate to arthropod behavior
and other ecological factors.
6.1. Incorrect characterization of fundamental host range
A common concern when predicting Weld host range
is that the results of host speciWcity tests may overestimate (generation of false positives) or underestimate
(generation of false negatives) Weld host range (IPPC,
1996; Marohasy, 1998). Recent research (Barton Browne
and Withers, 2002; Heard, 2000; Marohasy, 1998;
Singer, 2004; Withers and Barton Browne, 1998; Withers
et al., 2000) has identiWed three reasons: (a) the disruption of the natural sequence of host perception and
acceptance behaviors, (b) the motivational status of the
agent at the time of testing, and (c) the eVect that prior
experience and learning of particular individuals may
have on host selection (Table 3). Such concerns are lessened, however, when the primary aim of testing is to
objectively determine the fundamental host range such
that motivational status, eVects of prior experience and
learning are excluded (van Klinken, 2000a). Poor experimental estimation of Weld host range for agents observed
to be speciWc (e.g., in adult oviposition) in the Weld is a
key issue if other life stages accept nontargets (e.g.,
Heard et al., 2004). Furthermore, behavioral phenomena, such as habituation to non-host deterrents, associative learning, and sensitization to host stimuli (Table 3),
need to be carefully considered within the context of the
release environment in order to assess the likelihood of
them being expressed, and subsequently result in nontarget attack and impact.
6.2. Ecological causes for contrasting fundamental and
Weld host ranges
6.2.1. Absence of target
A nontarget species may be able to support only part
of the agent’s lifecycle, and therefore will not be a host
under natural conditions in the absence of the target.
Even if the nontarget species can support agent populations under laboratory conditions, the nontarget may
not do so in the Weld if it is too rare for the agent population to maintain a positive growth rate, or because mortality factors prevent population growth. For example,
the moth Xubida infusellus (Walker), introduced against
Table 3
Heard’s (2000) types of arthropod behavioral constraint in host speciWcity testing and the consequence they may have on test results (for
particular test types)
Cause
Consequence
External stimuli
Host perception behavior disrupted False +ves (cages)
Non-host odor masks host odor
False ¡ves (choice tests)
Preferred host odor masks lesser
False +ves (choice tests)
hosts
Experience, learning, and motivation
Associative learning
Habituation to non-host deterrents
Sensitization to host stimuli
Central excitation
Central inhibition
Predisposition
Time dependent deprivation
Time of day/temperature
Age
Cage
False +ves (choice tests)
False +ves (naive Wrst instars)
False +ves (choice tests)
False +ves (choice tests)
False ¡ves (choice tests)
False ¡ves (experienced agents)
False ¡ves (short tests)
False ¡ves (constant conditions)
False ¡ves
(long-lived/multi-stage)
False +ves (escape behavior)
A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
Eichhornia crassipes (Mart.) Solms (water hyacinth),
attacked nontarget Monochoria spp. during testing, but
minimal Weld impact was predicted as these species are
too short-lived and ephemeral for the moth to complete
its life cycle or sustain populations. Monochoria spp. and
the target also have allopatric distributions so spill-over
was not predicted to occur (Julien et al., 2001).
6.2.2. Presence of target
In the presence of the target, nontargets may be used
(Greathead, 1968; Jayanth et al., 1993) or more heavily
used (Rand and Louda, 2004) because of spill-over eVects.
Even if perceived, however, susceptible nontargets may
not be acceptable if their preference rank is lower than
other hosts present (Singer, 2004). This can also depend
on the relative availability of each host, which can change
if, for example, the target is successfully controlled, and
result in the use of hosts of lower preference. In this case,
host preferences between acceptable hosts may evolve
rapidly (van Klinken and Edwards, 2002).
6.2.3. Asynchrony
Susceptible stages of the nontarget may be asynchronous with the activity period of the agent (Hasan and
Delfosse, 1995; Julien et al., 2001). In some groups, the
synchrony of the agent to a particular developmental
stage of hosts can deWne population Weld speciWcity.
When Jolivet (2002) presented very short-lived females
of the wild-radish Xower-bud gall midges (Gephyraulus
raphanistri (KieVer)) with artiWcially resynchronized
Canola (Brassica napus L.) Xower buds, galls appeared
on them despite having never been recorded in the wild
(M. Skuhravá, Praha, Czech Republic, personal communication). Similarly, apparently highly speciWc Weld populations of the broom seed beetle (Bruchidius villosus F.)
exhibit a broader Weld host range when provided with
resynchronized close relatives (A. Sheppard and T. Thomann, unpublished data). The experimental design must
therefore allow full expression of fundamental host
range to ensure an eVective risk analysis.
6.2.4. Geographical incompatibility
Nontarget species may occur in habitats or climates
unsuitable for the agent. Hodkinson (1997) showed
that climate-restricted host exploitation by the willow
psyllid, Cacopsylla groenlandica Sulc. Nontargets may
be biogeographically separated from the agent population (e.g., by mountains, water or desert). Such barriers
can be breached with time, however, as has happened
with Cactoblastus cactorum (Berg) onto nontarget
Opuntia spp. by arriving in Florida from the Caribbean
(Bennett and Habeck, 1995; Louda et al., 2003). Likewise, strong habitat preferences may prevent an agent
from using potential nontargets hosts, e.g., in Drosophila magnaquinaria Wheeler (Kibota and Courtney,
1991).
221
7. Predicting Weld speciWcity and relative nontarget
impacts
Predicting Weld speciWcity, the level of damage on
those species relative to the target plant, and the likely
ecological nontarget impacts of that damage, is termed
analyzing the uncertainty in a risk analysis. The magnitude of the threat (predicted host speciWcity) and the
likelihood of such threats occurring (predicting impacts)
are the two components of this. Where the Weld host
range is likely to include nontarget species, making accurate predictions is the most challenging component of
the risk analysis, being based on the combined outcome
of relative acceptability and suitability of hosts, and
being sensitive to behavioral mechanisms. For example,
prior experience can predispose agents to certain hosts
(Barton Browne and Withers, 2002; Withers et al., 2000),
or even reverse preference rankings (Szentesi and Jermy,
1990) relative to naïve insects.
Results from laboratory, semi-natural or Weld choice
tests assist in predicting Weld speciWcity by describing
preferences, and how they may be modiWed by factors
such as prior experience, insect age, and the relative
availability of hosts. Predictions must relate to the environments into which the agent will be released and may
spread. A wide range of factors need to be considered for
such predictions, including plant quality, insect and
plant phenology, and the relative availability of other
host species in time and space. Predictions based on host
speciWcity could range from: (a) incidental feeding on
nontargets resulting from spill-over eVects from high
agent abundance (e.g., Teleonemia on Lantana; Greathead, 1968), (b) development on nontarget plants in the
presence of the target (e.g., Neurostrota gunniella (Busck)
on Neptunia major (Benth.) Windler; Q. Paynter and P.
Taylor, CSIRO Entomology, Australia, unpublished
data or Rhinocyllus conicus (Frölich) on Cirsium undulatum (Nutt.) Spreng.; Rand and Louda, 2004), to (c) population build up on nontarget species in the absence of
the target (examples in Louda et al., 2003).
Spill-over eVects are generally transitory. They are
likely to be temporary and local if the target is controlled, but permanent and widespread if not. They can
prove unacceptable if associated with additional likelihood of disease transmission (Fowler et al., 2000). Estimating the agent per capita population growth rate on
the target and each susceptible test plant species is one
way to assist likelihood estimations. This provides a
measure of whether nontargets will support agent populations as well as, and in the absence of, the target and
can suggest whether the agent can reach a density on
the nontargets suYcient to suppress their populations.
Predicting whether a nontarget host can support agent
populations is particularly important where nontargets
are allopatric from the target, and likely to continue to
be so.
222
A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
Estimating likelihood of impact on nontargets
requires understanding of the population-level consequences of attack relative to similar consequences for the
target. The impact on nontargets will also not necessarily
be proportional to the level of damage. The ratio of
agent attack rate to intrinsic rate of increase of the nontarget will determine the level of impacts on nontargets.
For example, rare nontargets with low population
growth rates will suVer proportionally higher impacts
from agents than target species with higher population
growth rates for the same level of damage (Holt and
Hochberg, 2000).
Evolutionary consequences should also be considered. There is some likelihood of rapid evolution in host
preference where the new environment has diVering conditions of host quality and quantity (van Klinken and
Edwards, 2002). Finally, all such predictions need to be
evaluated in the Weld following release as risk evaluation
is a key and necessary component of risk analysis (Lonsdale et al., 2000; Sheppard et al., 2003a,b).
7.1. IntraspeciWc variation in agents and host shifts
IntraspeciWc variation within the agent species can
lead to variation in expressed Weld host speciWcity within
the fundamental host range. Whether this can lead to
shifts in expressed Weld host speciWcity following release
is hard to predict. Risks can be restricted by ensuring
low variation in the tested population and restricting
releases to individuals from only this population.
Such variation generally occurs in two forms, variation in phenology and in host speciWcity. First, disparate
populations of the same agent species may show apparent high speciWcity to diVerent host species or genera,
while the species as a whole uses hosts over a broader
range (Briese and Sheppard, 1992; Klein and Seitz,
1994). Similarly, when the recorded Weld host range for
the species as a whole conXicts with that exhibited by a
local population, then this is an indication intraspeciWc
variation in host use may be important (Haines, 2004).
This high variation in Weld host speciWcity within the
native range may be unrelated to genetic distance or
reproductive isolation between populations. Variation
may result from local phenological adaptation to a particular locally abundant host that restricts host use for a
given population despite other suitable hosts being present (Haines, 2004). Such apparent speciWcity is usually
relatively easy to detect as even speciWc populations
exhibit the same host speciWcity (host range, suitability,
and preferences) in no-choice tests when phenological
asynchrony between agents and hosts is eliminated
(Zwölfer and Priess, 1983).
Bruchidius villosus, a seed beetle released in several
countries against Scotch broom, Cytisus scoparius (L.)
Link, had various synonyms at the time of testing in the
1980s. Only populations that were restricted in the Weld
to C. scoparius were originally tested. The test results,
which turned out to be erroneous (Haines et al., 2004),
agreed with the Weld speciWcity of the original population. Following release, the agent exhibited a broader
host range than in the tests. Subsequent surveys over the
whole native range of this species found disparate populations can exhibit high Weld speciWcity to diVerent hosts
even within the presence of other suitable hosts (A.
Sheppard and T. Thomann, unpublished data). Local
host abundance and seed production phenology are considered to be causing this locally restricted, but regionally variable, Weld host speciWcity, despite such
populations showing equally broad host ranges under
no-choice conditions (Haines, 2004). If phenological synchrony alone restricts local Weld host speciWcity, then the
chance of host shifts within the fundamental host range
could be high if: (a) there is genetic variability in agent
phenology and/or (b) the new environment presents conditions that change the synchrony for interactions
between agents and potential hosts. Such occurrences
appear to be not uncommon in bruchids (Fox, 2000).
Similarly asynchrony between agents and targets is not
uncommon following releases (e.g., Harris, 1980; Pitcairn, 2001; Woodburn and Cullen, 1996). Reliable host
records from throughout the native range have always
provided valuable evidence for risk analysis.
Second, cases also occur where subspeciWc populations of an agent show higher speciWcity than the species
as a whole and this is maintained in no-choice tests
(Evans and Gomez, 2004; Fumanal et al., 2004). In such
cases, variation is more likely to be associated with signiWcant measurable genetic distance between populations. The taxa may be in the early stages of sympatric
speciation. Fumanal et al. (2004) have found subspeciWc
variation in fundamental host range of morphologically
identical crown-gall weevil, Ceutorhynchus assimilis Paykull, populations feeding within the Brassicaceae, such
that some populations are monospeciWc while others are
stenophagous within the family. This variation is supported by molecular clade separation and cross-breeding
studies. Within the species, reliable diVerence in genetic
markers between clades tightly maps on to observed
host speciWcity and there is evidence of reproductive
incompatibility between individuals from diVerent
clades (M-C Bon, USDA-ARS-EBCL, Montferrier-surLez, France, personal communication).
There is currently no reason to suppose genetically
based subspeciWc variation in host range or speciWcity is
any more evolutionarily unstable than between-species
diVerences if, for example, a monophagous population
of a polyphagous species was maintained in isolation
from other populations of the same species (Singer,
2004). Consistent restrictions in fundamental host range
at the subspeciWc level, with support from molecular
data, can open doors to biological control for clonal
weeds, notably Rubus spp. (Evans and Gomez, 2004) and
A.W. Sheppard et al. / Biological Control 35 (2005) 215–226
for the reconsideration of agents previously thought too
generalist (Fumanal et al., 2004).
The biggest risk is that apparent intraspeciWc variation is in fact resulting from the presence of one or more
crypto-species within the sampled populations and
extreme care and genetic analyses are required to avoid
these going undetected prior to release (e.g., AlonsoZarazaga and Sánchez-Ruiz, 2002; Balciunas and Villegas, 1999).
In general, these studies suggest population diVerences in Weld host speciWcity within the fundamental host
range of a species may be commoner than we thought.
Phylogenetic dating of such host shifts, however, suggests they happen on longer timescales (Futuyma, 2000)
than would be considered relevant to the relatively
immediate problems of managing invasive pest species.
Host shifts outside the fundamental host range of specialist natural enemies are, in all evidence, exceedingly
rare. Absence of evidence comes from post-release retrospective evaluations of biological control agents (Louda
et al., 2003; Pemberton, 2000; van Klinken and Edwards,
2002), other recorded cases of host shifts in phytophagous arthropods (Marohasy, 1996), phylogenetic patterns of host range for generalists and specialists, along
with studies of genetic variability in host speciWcity
(Futuyma, 2000), and recent understanding of the
behavioral mechanisms aVecting changes in host preference (Singer, 2004).
223
agent life history (e.g., adult oviposition or larval feeding) that could pose a threat are identiWed, and the fundamental host ranges of these stages are determined
(hazard identiWcation). Testing should account for
behavioral factors such as motivation, learning, and
prior experience and allow the best possible predictions
of Weld host range, relative use of nontargets, and potential nontarget impacts within the release environment
(uncertainty analysis). Conducting rigorous risk analysis
of nontarget impacts remains challenging, but understanding has increased to a point where best possible
practice can be applied to this process.
Acknowledgments
The authors thank USDA-ARS, USDA-CSREESIFAFS, and the Center for Invasive Plant Management
for organizing the Denver conference on “Science and
Decision making in biological control of Weeds” and for
Wnancial support of the senior author to attend the meeting and present this review. The Australian Government
and the Australian Cooperative Research Centre for
Australian Weed Management for support of activities
that contribute to this paper. Alan Kirk and Rouhollah
Sobhian for discussions and John Scott and two anonymous referees for comments on the manuscript.
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This review highlights signiWcant advances in tools
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