Host range testing methods
There are several important factors to consider when designing experiments to assess the host range of a prospective biological agent. These are discussed below.
Test designs for weed biocontrol
As mentioned above, the assessment of plants as potential hosts for herbivorous insects began over 70 years ago. A variety of tests have been developed, although those most in favour have changed over time due to changes in the perspective of biocontrol practitioners and developments in the study of insect behaviour. Tests used have focused on oviposition, adult feeding, larval development and survival, oogenesis and multi-generation population persistence, and host preference. There have been a number of thorough reviews that describe the different test method types and discuss and detail the appropriateness of each test type for different types of weed biocontrol agents (Harris and Zw�lfer 1968, Cullen 1989, Wapshere 1989, Heard and Van Klinken 1998, Marohasy 1998, Briese 1999, Sheppard 1999, Heard 2000, van Driesche and Murray 2004). Each of these papers makes a valuable contribution to the appropriateness of different types of test design. For this reason a thorough debate of the pros and cons of various testing designs according to the biology and behaviour of the individual weed biocontrol agent is beyond the scope of this website. Instead, bullet points will be used to summarise main points below and for more details you should consult the above papers.
What we do need to emphasise in applications to the EPA is that the applicant is open and clear in justifying both the way they arrived at the test species selection list as well as the reasoning for approaching the testing methodology in terms of the host selection behaviour and biology of the potential agent. Wapshere (1989) provides a very clear and logical explanation of how both could be approached, and applicants are urged to read that paper.
The possible agents that may be chosen as potential weed biocontrol agents is very large, so there are no strict guidelines on how to undertake testing. Careful consideration of the behavioural characteristics of both the:
- damaging life stage/s - responsible for the physical damage to the plant;
- dispersive life stage/s - responsible for selecting which plant will or will not be attacked.
The importance of testing the host specificity of both life stages separately is that the dispersive life stage has the ability to choose the plant species that will be contacted by the other life stages. For instance, in many cases it is the adult female insect that is mobile and actively undertakes host searching and host choice for oviposition. Often internally feeding or virtually immobile larvae do not have the ability to move far enough to choose a different, perhaps more palatable species of plant. In such cases, host specificity testing that establishes both the host preference of the adult female, and then the survival and feeding ability of the larva, will capture all the vital information required to make an accurate risk assessment.
There are examples where the reproducing female may be immobile on a plant part, yet produces highly dispersive crawlers or ballooning larvae that themselves move according to weather conditions such as wind and the proximity of other plant species will have a significant influence on what plants they contact. In such a case, host specificity testing that establishes the preference of the neonate larvae to settle on the different plants, and then the survival and reproductive potential of the resultant individuals, will capture all the information required to make an accurate risk assessment.
In other cases the reproductive female may be only loosely selective in choosing a certain habitat in which to oviposit, and the larvae may be highly mobile with the ability to locate different individuals and species of plants through some or all of their damaging life stages. In such a case, host specificity testing will need to both establish the feeding preferences of the mobile life stages of the larvae between different plants, and the oviposition preferences of the adults, as well as the survival and reproductive potential of individuals on individual plant species.
A flow chart generalising decisions involved in the selection of an initial host specificity testing procedure for weed biocontrol agents has been presented by Sheppard (1999). However, Withers (1997) strongly insists that the results of initial screening trials undertaken should not be used to reduce the plant list used for testing in subsequent trial types. This is because host specificity testing on both the dispersive and damaging life stages need to be gathered independently for a quantitative risk analysis to be undertaken.
Test designs for arthropod biological control agents
Similarly to weed biocontrol, there are some extremely valuable publications describing the design of host range tests for parasitoids and predators (van Driesche and Murray 2004). A common generalisation is that estimation of the host range of entomophagous biocontrol agents (parasitoids and predators) is more complex than for phytophagous weed biological control agents. This is primarily because there is an additional trophic layer involved and often an intimate and specific relationship between the target and test organisms and their substrate (usually their food plant). An important consequence of this intimate and specific relationship between the host or prey of entomophagous agents and their substrate is that prior experience of the substrate can affect the organism's responsiveness to cues from this and other substrates.
A second complicating factor for endoparasitoids is that it is not possible, in most cases, to inoculate all test organisms with eggs or neonates to determine "suitability", though exceptions do exist (Morehead and Feener 2000, Fuester et al. 2001). Thus, a program to determine the host range of parasitoids is denied one of the most powerful tools (the so-called physiological host range test on larvae) that is used in determination of host range of phytophagous agents (Hill 1999, van Klinken 2000). This means that, in the host range testing of parasitoids, it is important to employ test procedures that will maximize the probability that the test species will be accepted for oviposition in the first place. This is vital for an accurate risk assessment (Withers and Barton-Browne 2004). It is possible in some cases, however, to distinguish between behavioural host acceptance and physiological compatibility of parasitoids and test species. For example, McNeill et al. (2000) used a pathogen on the ovipositor of parasitoids as an indicator of an oviposition attempt in a test species. Parallel exposure of untreated parasitoids to the test insects gave an estimate of physiological compatibility.
Failing a high tech method such as this, only the careful dissection of all exposed hosts to identify parasitoid ovi-or larvi-position will reliably ascertain whether a failure to develop a parasitoid was the result of a physiological host immune response or a lack of oviposition. This is a laborious and time-consuming exercise but may be necessary, along with carefully chosen controls, to prove that a suitable testing method has been utilised.
Replication
There is no perfect number to choose for how many replicates of a test to undertake. Statistical power analyses exist that can more accurately predict the number of replicates that need to be undertaken to generate data with certain probabilities of type I or type II errors. This calculation is based on the variance from the mean obtained in an a priori trial run or the mortality obtained in tests a posteriori (Hoffmeister 2005). It is possible that variance in biological parameters such as survival and development may differ according to the suitability of the host, and if so, different levels of replication may be required for different test plants.
It is important to avoid pseudo-replication. This can come about in host specificity testing if a shortage of test species results in long-lived individuals being utilised repeatedly in different tests. Pseudo-replication can also occur if the statistical testing is undertaken upon individual insects when in fact the data were gathered from insects caged together as a group (Hoffmeister et al. 2006). Hence, the samples are not independent. As a general rule of thumb true replication is achieved at the 'cage' level within which each individual test was run. Hoffmeister et al. (2006) goes as far as to say that it is essential that insects for the tests on non-target species do not come from one rearing container while control individuals come from another. Also it is essential that non-target hosts are not always tested in the same container or field cage or on the same plant, while target hosts are tested in another cage or on another plant. Equally important is that the location of experimental units (cages) within a chamber or laboratory, need to be randomised to avoid confounding effects of differences in temperature and light conditions etc. Similarly, tests with control and test insects should be carried out in parallel and not at different times. Randomisation of testing order or random assignment to plants or test cages assures that pseudo-replication can be avoided (Hoffmeister 2005). When writing applications to the EPA, it is unwise to expect the reviewers to assume that experiments have been carried out according to 'best practice', rather it is preferable to mention in detail that such steps were taken in the methods section of the host range testing reports.
Controls
Positive controls are essential for oviposition and feeding trials, to validate negative responses by showing that the group from which the test biocontrol agents were drawn, had the capacity for oviposition or feeding. Individuals used in controls should either be:
- different insects from the same rearing batch that are exposed to the target pest simultaneously with the main experiment, or
- the same insects used in the main experiment that, after exposure to a non-target species, are re-used by exposing them to the target host to demonstrate their physiological readiness to respond positively to a highly ranked host (van Driesche and Murray 2004).
Negative controls, in which test species are not exposed to parasitoids, or plants not exposed to weed agents, are needed to detect background mortality of test species that is unrelated to the biocontrol agent. For feeding tests with predators, controls in which only water is provided show whether the test prey species provides any nutritional benefit to the predator by assessing survival time with prey versus water alone. For tests measuring survival and development, performance on a host for survival and development of known suitability or an artificial diet could be used as a standard against which reduced survival in a poorer host can be gauged (van Driesche and Murray 2004).
Rigorous positive controls are absolutely critical for the reliable host range testing of parasitoids and predators, particularly when no-choice tests are used. Parasitoids can differ in the way they mature their eggs prior to oviposition. This can be expressed according to the "ovigeny index" (Withers and Barton-Browne 2004). The main implication of the ovigeny index on host range testing methods is for those species that emerge with no or very few mature eggs. There is a risk that young females may not oviposit in any host because of a lack of mature eggs. Futhermore, even young females carrying some mature eggs might not oviposit in lower ranked hosts because of a low egg load. Positive controls needs to be females from the same rearing group exposed to the target pest to confirm egg laying ability of the parasitoid cohort used.
No-choice tests
The no-choice test design was the first approach used in early weed biocontrol projects (van Driesche and Murray 2004). No-choice tests can be used with adult insects to assess feeding, survival to maturity and the willingness to oviposit. With larvae no-choice tests measure the ability of a plant to support the development of the immature life stages. The accuracy of the data obtained from no-choice tests obviously relies upon providing in non-limiting quantities the required part of the plant, whether it be bud, leaf, flower, seed pod etc, in optimum physiological condition. For species with larvae that do not move between plants, use of the no-choice test is ideal because the only choice available to the larva in the field would also be to either feed or die. The strength of no-choice tests is that negative results are very robust and provide convincing evidence that a test species is not likely to be used as a field host, provided of course that the experimental design includes an environment that permits normal behaviour of the biological control agent, as evidenced by a positive response (or excellent larval survival) to the normal host that is provided as a control. Use of no-choice tests early in the testing sequence provides a strong rationale for classifying un-attacked test species as non-hosts.
Unfortunately no-choice development tests sometimes indicate that herbivorous insect larvae can feed successfully on plant species that adult insects did not find or accept for oviposition. Sometimes this is as a result of time-dependent changes in responsiveness, that can be viewed as a high motivation to feed or oviposit when starvation or death is imminent (Barton-Browne and Withers 2002). Also positive responses within no-choice tests can sometimes be argued to have been artificially induced by confinement. The cage environment may bring the agent into close contact with a test species such that important host finding steps may be skipped (since the insect is literally put on the host or very near to it), allowing oviposition or adult feeding to occur on test species that might not have been approached or utilised in the field. This issue can be particularly important with parasitoids (van Driesche and Murray 2004).
So often no-choice tests alone cannot be used for risk assessment without incorporating other assay methods, or potentially safe and effective agents may be rejected unnecessarily Hill (1999). Often when used on phytophagous larvae to test development, no-choice tests are becoming known as the method for ascertaining the "physiological host range" or "fundamental host range" of an insect (van Klinken 2000). Using this terminology reminds the reader or reviewer that the risk assessment is not complete based on no-choice data alone, and data incorporating other testing methods will also be required to reliably predict the "field host range" (van Klinken 2000). Undoubtedly the most important benefit of no-choice tests is the tendency for them to reveal lower-ranked (lower preference or lower palatability) hosts whose status within the physiological host range can be missed if too much reliance is given to the result of choice tests (Withers 1999).
Often survival from immature larva to adult is not an absolute measure obtained from no-choice tests. Instead more careful monitoring is often conducted to obtain survivorship curves on the range of test plant species. When some survival to pupation or adulthood does occur, a single measure of percentage survival to adult of the cohort is often insufficient to enable accurate interpretation of the data. Some insects feeding on sub-optimal hosts conserve body mass at the expense of time taken to reach full size, others conserve development time at the expense of final body size (Barton-Browne 1995). The repercussions for these two strategies can be significant, so where a degree of physiological suitability for development is revealed for an insect feeding on a non-target plant in a no-choice test, both time taken to develop, as well as final body size, recorded in relation to gender, needs to be recorded and analysed appropriately to aid interpretation of the data.
Positive controls are essential to validate negative responses by showing that the group from which the test agents were taken had the capacity for oviposition or feeding. In parasitoids, negative controls are also required, in which test species are not exposed to parasitoids. These are held to detect mortality of test herbivores that is unrelated to the direct action of parasitism (van Driesche and Murray 2004).
Hill (1999) argued that the size and mobility of the agent should influence the choice of test design. Where agents are large, and mobile females can choose to move freely between plants with relative ease, and with reasonable frequency, choice tests may be more appropriate than no-choice tests. In this situation females can select or reject a host, and respond by arresting movement or dispersing once more. No-choice tests may be a more appropriate test for target species that are small relative to their host and disperse passively, or conversely are relatively immobile. In this case the choices available to an individual are to feed or to not to feed, a scenario that can be effectively tested by no-choice tests.
Choice tests
In choice tests, two or more plant or host species are presented to the biocontrol
agent simultaneously, and the comparable response is a measure of preference between the
two options ( Choice tests rose to a height of popularity in the 70's and 80's because
practitioners felt that the no-choice test (especially larval starvation tests)
were resulting in too many cases in which a promising agent was rejected when a
wider than expected physiological host range was being predicted in the
laboratory. Hence there was a time when testing for weed agents relied on the
results of choice tests alone. Sometimes choice tests were only conducted on
those species that had revealed positive results in no-choice tests, apparently
in the mistaken belief that subsequent lack of attack in choice tests would
identify which of the positive responses in the no-choice data set were
"erroneous" (Fornasari et al. 1991). This line of reasoning has since been shown
to be inappropriate as research has now revealed many cases where choice tests
failed to reveal a lower but significant host association (Marohasy 1998).
Choice tests have often been used to compare oviposition responses in weed
biocontrol agents, parasitoids and predators. In some cases the polyphagy of a
species is clearly revealed when all non-target species are attacked at rates
similar to the known field host. However, in choice tests, sometimes agents will
show strong oviposition preferences towards the target pest or weed, or even
fail to attack non-target hosts at all. From such results it is tempting to
conclude that the agent is host specific, whereas a more accurate
interpretation may be that the preference ranking (or hierarchy) of the agent is
that the target host sits most highly ranked out of those test species presented to
it.
The greatest concern with choice tests is that they have clearly been shown
(Withers et al. 2000, Barton-Browne and Withers 2002) to carry an
unsatisfactorily high risk that there will be no attack on less stimulating
hosts (less preferred or lower ranked) in choice tests that are run for too
short a time or when the insect is introduced to the test in an un-stimulated
(such as satiated in relation to food, or not ready to oviposit in relation to
reproductive) state. Consequently, such tests can fail to reveal the fundamental
host range, and there is a risk of a false negative result. On this basis
Barton-Browne and Withers (2002) strongly recommended that choice tests should never be
used as the only type of test for assessing risk to non-target hosts, and that
they should not be used for a 'screening' phase of host specificity testing
programs, if the intention is the use the results to reduce the host list for
further testing.
Another way of expressing this, is that for the results of a choice oviposition
test to be predictive of field events, (1) the agent must experience the choice
in the field � that is, the non-target (lower ranked) host must not be the only
possible host encountered, or (2) the non-target (low ranked) host must be so
non-preferred that even agents deprived of their preferred hosts for
considerable periods will keep searching rather than attack the low ranked
species. Since these conditions may not always be met, inferring that a species
not attacked in a choice oviposition test is not a host will lead to some
unpredicted impacts (van Driesche and Murray 2004). Despite these concerns
choice tests remain valuable, and in some cases, absolutely crucial for helping
to assess the likely risk of non-target attack in the field, when we are reliant
on laboratory testing methods alone.
A variation on the no-choice and choice test design is called the sequential
test, in which the natural enemy is exposed to a series of test species, one at
a time. These were called "sequential no-choice tests" by (Withers and Mansfield 2005,
but "sequential choice tests" by van Driesche and Murray (2004). The sequence
in which species are presented in such tests can vary. For example, the
presumably high ranking target species can be presented before or after the
presentation of one or a sequence of non-target species, or both. Another
possibility is that insects are given access to the target species between each
test with a different non-target species. Furthermore the duration of the
experimental regime, times during presentations and between them, can vary
(Barton-Browne and Withers 2002). All these factors can potentially have a
significant effect on the outcome of the test.
A theoretical analysis of the potential outcomes of some sequential no-choice
test types (Barton-Browne and Withers 2002) concluded that the outcome of the
test varied according to the period of time for which the insects were given
no-choice access, particularly access to host B. For instance, if the
parasitoids oviposited during the first access to the target host (which was
often the aim � to ensure the parasitoid was physiologically and behaviourally
ready to oviposit), it may not accept the non-target host when it was first
presented with it (note the generality of this outcome will depend upon the
species reproductive biology and behaviour, and what latency period occurs
between ovipositions). Generally we can conclude that the probability the
parasitoid will accept the non-target host during the test will depend on
whether the test was run for sufficient length of time for time-dependent
processes (Barton-Browne and Withers 2002) to act upon the parasitoid to lower
its acceptance threshold to a level whereby the non-target host stimulated
attack behaviour (Withers and Mansfield 2005).
The general conclusion around sequential no-choice tests is that they should
only be utilised cautiously when the physiology and behaviour of the parasitoid
or weed biocontrol agent is very well documented and understood in terms of its
temporal pattern of oviposition. Otherwise the validity of the results will be
questionable (van Driesche and Murray 2004).
A complicated test design for phytophagous insects was used to establish the
rank order (or preference rank) of plant species for oviposition in butterflies
(Wiklund 1981). The insects were confined in a large field cage and each day the
plant array was altered to ascertain the preference ranking. A similar design
has been demonstrated where tethered lepidopterans have been successfully
manipulated to ascertain the length of their discrimination phase with regards
to different host species (Singer 1982, Jallow and Zalucki 1996) for quantifying
the host preferences. However such experiments are relatively technically
complicated and not all insects are suited to this type of experimental
manipulation or to longer running assays. Marohasy (1998) suggests a
similar method of sequential choice test where the plant species that receives
the most eggs progressively is removed until there is not oviposition on any
plant species. This has been referred to as the Preference Ranking Test
(van Driesche and Murray 2004). This type of method is designed to overcome the
problem of false negative results owing to unresponsiveness to lower ranked
plants species in choice tests. This method has been trialled with some success
(Solarz and Newman 1996, Withers et al. 2000) but whether such complicated
sequential designs to reveal host preference rankings are worth the effort and
can be safely interpreted will depend upon the circumstances and biology of each
agent.
Most of the traits to be analysed in biocontrol do not follow a Gaussian
('Normal') distribution, and thus standard t-tests, analysis of variance (ANOVA)
or regression analyses cannot be used with validity to statistically test the
effect of a treatment. All these common 'classical' methods assume that the
distribution of residuals around the fitted model (i.e., the error distribution)
is normal. Thus data need to be appropriately transformed (Fernandez 1992) to
first achieve a Gaussian distribution or preferably different statistical
approaches have to be used (Hoffmeister 2005).
Non-parametric tests like Mann-Whitney U tests or Kruskal-Wallis tests are often
the most appropriate test for host specificity testing data, and are more than
adequate if the results are clear-cut. However non-parametric tests lack
statistical power, which can mean that real, but more minor, differences
existing between, for example, test species and target (control) species will
tend to be interpreted as not significantly different by a non-parametric test,
when in fact this might not be the case. Non-parametric tests will almost
certainly have insufficient power to analyse data if sample sizes were forced to
be small due to other factors out of the researchers' control. Generalized
Linear Models can be used to predict responses both for dependent variables that
are not normally distributed and for dependent variables which are nonlinearly
related to the predictors (Hoffmeister 2005). Expert statistical advice will
probably be required to both appropriately utilise and interpret such models.
Choice tests are often invaluable for undertaking tests on the preference
hierarchies shown by ovipositing or feeding insects. However choice tests are
problematic for statistical analyses (Mansfield and Mills 2004, Withers and Mansfield 2005).
The same individual insect is confronted with both target and
non-target species, and we usually wish to obtain comparative acceptance rates
for both target and non-target species as a result of such a test. Thus, a
repeated measure design must be used. This renders the data dependent (not
independent as required for Gaussian statistics, see above). The acceptance of
non-target hosts or prey within such a test may well depend upon the frequency
of target and non-target hosts within the cage. Therefore every target that is
eaten or accepted and that is not immediately replaced alters the experimental
conditions of the experiment, and the acceptance of any given host or prey may
depend on the current ratio of availability of alternative hosts or prey. Thus
if exploited hosts or prey cannot be replaced immediately, simultaneous choice
tests may become almost impossible to interpret (Hoffmeister 2005).
An alternative to simultaneous choice tests are sequential no-choice tests
(Singer 1986). This design does have appropriate statistical analysis methods
available to treat and analyse the data (Hoffmeister 2005). However sequential
no-choice tests generate a multitude of potential behavioural complications
which may be almost impossible to control for or adequately measure
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Behavioral and physiological processes affecting the outcome of host range testing.
Pp. 40-55 In: Assessing host ranges for parasitoids and predators used for classical biological control: a guide to best practice, R.G. Van Driesche and R. Reardon (Ed.) USDA Forest Service, Morgantown, West Virginia.
Withers T.M., Barton-Browne L. and Stanley J. (2000).
How time-dependent processes can affect the outcome of assays.
Pp. 27-41 In: Host-specificity testing of exotic arthropod biological control agents: the biological basis for improvement in safety, R.G. Van Driesche, T. Heard, A.S. McClay and R. Reardon (Ed.) USDA Forest Service Bulletin, Morgantown, West Virginia, USA.
Withers T.M., McFadyen R.E. and Marohasy J. (2000).
Importation protocols and risk assessment for weed biological control agents in Australia: The example of Carmenta nr ithacae.
Pp. 195-214 In: Nontarget Effects of Biological Control, P.A. Follett and J.J. Duan (Ed.) Kluwer Academic Publishers, Norwell, MA.
Sequential testing
Parameters that can be measured
This section is designed to provide ideas of the range of parameters that might be
included within an EPA application to summarise the results of host range testing.
In reality an in depth discussion of each parameter is not possible here, as the biology
of each target and agent organism will need to dictate the appropriate methods used.
Weed biocontrol
Insect biocontrol (van Driesche and Murray 2004)
There are almost infinite numbers of parameters that could be measured in host range tests.
With parasitoids and predators some common ones are included in the above list. A subset
of these are discussed in detail in van Driesche and Murray (2004). The applicant is encouraged to
consult this resource as well as being familiar with any behavioural or ecological
research on the target or closely related insects. Behavioural research papers can provide
invaluable information on the host searching and oviposition or feeding behaviour
of the agent. The most appropriate parameters to record can then be identified for each of
the host range tests undertaken.
Statistical analysis
References
Next page
Background information