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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. top

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 Zwölfer 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:

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. top

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. top

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. top

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:

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.top

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. top

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 (. The target species is often, but not always, one of the choices offered. This method is most commonly used to compare oviposition preferences but can also be used for comparing feeding preferences of adults and sometimes of larvae that are sufficiently mobile to move between hosts.

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. top

Sequential testing

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. top

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

  • Oviposition – number of eggs laid
  • Qualitative severity of damage from adult feeding
  • Percentage adult survival when fed only that species
  • Larval development rate fed only that species
  • Larval final body size/ pupal weight fed only that species
  • Sex ratio resulting from larval feeding on only that species
  • Percentage larval survival to pupa or adult fed only that species
  • Oogenesis after larva fed only that species
  • Multi-generation population persistence fed only that species
  • Latency to oviposit or initiate feeding
  • Proportion of population accepting species for oviposition
  • Proportion of population accepting species for feeding
  • Proportion of population located on that plant over time
  • Comparative levels of plant damage The most useful parameters to be measured in any host range test of a weed biocontrol agent is necessarily going to be dictated by the biology and behaviour of the agent. Many of the research papers referred to in this website can assist with designing the most appropriate test and therefore the parameters to be measured. We therefore urge applicants to be familiar with the relevant literature and utilise any specific methods that have been shown to be relevant to biology and behaviour of their proposed biocontrol agent.

    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.top

    Statistical analysis

    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 (Barton-Browne and Withers 2002, Withers and Barton-Browne 2004, Withers and Mansfield 2005). top

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    Withers T.M. and Barton-Browne L. (2004). 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.

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