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Native range studies during a biological control project continue to be the foundation for demonstrating agent specificity and high damage potential, and maximizing chances of agent establishment. However, such studies can be operationally difficult and expensive. As such, detailed efficacy assessments in the native range have been largely carried out against targets from temperate regions, although this is now changing (Sheppard, 2003).

Molecular Tools for Determining Variation, Identification, Matching and Native Range

Molecular DNA characterization tools changed the way we understanding genetic variation in the target and agent species, thereby assisting selection of specific agent strains or biotypes. Unfortunately, these tools coincide with decreasing availability of alpha taxonomic experts for naming potential agents. Using the molecular tools to generate genetic bar-coding of agents may therefore be needed to replace such skills while still ensuring characterization of what to release. Molecular techniques are also used to define the evolutionary center of origin in the native range because this will be the area where there is highest genetic variability (e.g., Arundo donax, A. Kirk pers. comm.; Goolsby et al, in press). The center or origin will also be the area where the greatest choice in agents and strains will be found to match the target weed genotypes for specificity.

Table 1.

Worldwide non-tropical weeds that have had some control success using classical biological control and the agents associated with that success (adapted from Denoth et al. (2002) and MacFadyen (2003) with recent additions from the literature).

Target Country Agent species in rough order of importance

(Beetles in bold)

Acacia saligna S. Africa Uromycladium tepperianum (pathogen)

Acacia longifolia S. Africa Melanterius ventralis, Trichilogaster acacaelongifoliae Acacia cyclops S. Africa Melanterius servulus

Acacia melanoxylon S. Africa Melanterius acaciae

Ageratina riparia Hawaii Entyloma ageratinae (pathogen), Oidaematophorus beneficus Procecidochares alani

Alternanthera philoxeroides USA Agasicles hygrophila, Amynothrips andersoni, Arcola malloi

Azolla filiculoides S. Africa Stenopelmus rufinasus

Baccharis halimifolia Australia Megacyllene mellyi, Hellensia balanotes, Rhopalomyia californica,Trirhabda bacharidis

Calluna vulgaris NZ Lochmaea suturalis

Carduus nutans Canada Rhinocyllus conicus, Trichosirocalus mordelo

Centaurea maculosa USA Sphenoptera jugoslavica, Agapeta zoegana

Centaurea diffusa Canada Larinus minutus, Agapeta zoegana, Sphenoptera jugoslavica, Urophora

affinis

Centaurea solstitialis USA Eustenopus villosus

Chondrilla juncea Australia Puccinia chondrillina (pathogen) Echium plantagineum Australia Mogulones larvatus, Longitarsus echii Emex australis Hawaii Perapion antiquum

Euphorbia esula Canada Aphthona cyparissiae, Aphthona nigriscutis

Hakea sericea S. Africa Erytenna consputa Hydrilla verticillata USA Hydrellia pakistanae

Hypericum perforatum Australia Chrysolina hyperici, Chrysolina quadrigemina Linaria dalmatica Canada Mecinus janthinus

Lythrum salicaria USA Galerucella calmariensis, Galerucella pusilla

Pistia stratiotes Australia Neohydronomus affinis

Rubus argutus Hawaii Croesia zimmermani, Priophorus morio, Schreckensteinia festaliella Rubus constrictus Chile Phragmidium violaceum (pathogen)

Salvia aethiopis USA Phrydiuchus tau

Sida acuta Australia Calligrapha pantherina Senecio jacobaeae NZ Longitarsus jacobaeae

Sesbenia punicea S. Africa Neodiplogrammus quadrivittatus Solanum elaeagnifolium S. Africa Leptinotarsa texana

Sonchus arvensis Canada Cystiphora sonchi

Tribulus terrestris USA Microlarinus lareynii, Microlarinus lypriformis

The amount of target weed genetic variability should determine an appropriate genetic structure for agent introductions (Burdon and Thrall, 2004; Evans and Gomez, 2004). Conducting surveys should be combined with molecular studies to show any need for and achieve matching of target genotypes to agent strains. Sub-specific

matching of native and introduced populations of the target using molecular tools is vital to avoid mismatches and have helped locate specific and damaging natural enemies, including arthropod agents for C. juncea (Cullen and Moore 1983), M. quinquenervia (Giblin-Davis et al. 2001), and Lygopodium microphyllum (Goolsby et al., in press). Highly intimate gene-for-gene type resistance-pathogenicity relationships (leading to highly specific pathotypes) are well documented for pathogen-host interactions in weed systems (Ellison et al., 2004; Espiau et al., 1998). Where the target shows high genetic variability due to restricted out-crossing, the exact match in the native range for the specific forms/genotypes of the weed that occur in the introduced country may have to be found to source the most pathogenic strain or cocktail of strains of the pathogen for successful control. Conversely, intraspecific variation in host plants with regard to disease resistance may not be important where there is strong out-crossing in the target population. In such circumstances, releasing genetically diverse founder populations of the pathogen is still considered the best strategy to avoid evolution of target resistance (Thrall and Burdon 2004). Finding Potential Agents

Quantitative surveying approaches for finding potential biological control agents generate data sets that can be useful for judging agent efficacy of both arthropods and pathogens. For arthropods, abundances in time and space and levels of parasitism are useful for understanding agent population dynamics (see below), but also estimates can be obtained for per capita impacts that are useful when conceptualizing agent efficacy (Cullen, 1995). Surveys for potential pathogen biological control agents should also quantify some level of disease incidence and severity to assist agent selection based on efficacy.

Trap gardens where the invasive weed genotypes are planted out in the native range to attract specific pathogen strains are a key tool for plant pathogens, but also for locating agents that are effective at dispersing widely and finding weed populations. Assessments within the gardens can clarify the agent’s ability to damage the target (Berner and Bruckart, 2005; Shishkoff and Bruckart, 1996) or cause disease (pathogenicity) for pathogens (e.g., den Breeÿen and Morris, 2003) under environmental conditions relevant for the range of introduction.

Climate Matching

Climate matching software has provided a means for selecting the “most suitable” agent strain or when “better adapted” strains are required in ineffective programs, and there is a need to minimize environmental causes for failed establishment and ineffectiveness (Goolsby et al., 2004). Climate matching works by improving the predictability of the interactions among the agent, the target weed, and climate, but to be effective: a) climates between the native and exotic ranges need to be comparable enough; and b) gross climate variability needs to be the driving force behind agent-target weed dynamics in key parts of the exotic range (Walter and Zalucki, 1998). As such, climatic matching appears to be a reasonable strategy to adopt for pathogens given a frequent strong dependency on certain environmental conditions to cause severe damage (Morin et al., in press).

Zalucki and van Klinken (in press) propose that standard agent prospecting activities can easily collect suitable data (host plant and potential agent distribution and abundance in time and space) for building climate null models that can suggest whether climate does predict areas of greatest potential impact of released biological control agents. This has been achieved for some pests. Agent selection predictions based on climate matching may, however, be misleading if other mechanisms such as multi-species interactions (e.g., Pimm, 1991) are the main driving force behind agent-target weed dynamics in the exotic range (van Klinken, 2004). Therefore, discrepancies in the predictions of gross climate models will indicate other factors, such as more subtle environmental drivers, seasonal target-agent asynchrony, variation in target and agent habitat requirements or weed-agent-natural enemy dynamics are otherwise important. Indeed, it should not be forgotten that hosts may also be rare in some parts of the native range because the conditions are ideal for disease epidemics or suppression by arthropods. Such modeling may be helped by laboratory-based estimates of physiological thresholds and optima for development, but these can be a costly exercise. Choosing the best strain based on controlled temperature laboratory studies as an imitation of likely field conditions, however, is fraught with difficulties, and it may just be simpler to collect based on match climates (Morin et al., in press). Furthermore, some fungal pathogens can indeed adapt to different climates following release and consequently generate strains that have slightly different temperature optima to infect their host than in the native range (Prakash and Heather, 1986).

Climate matching, therefore, does not consistently improve success because gross climate is only one likely key factor that influences host-natural enemy dynamics. Retrospective analyses of past programs have shown where climatic variation was important, but it seems that generalities are hard, and we are no closer to predicting where climate will be important in driving ecological dynamics than population ecologists are. Each instance will need to be assessed on a case-by-case basis. Opportunities prevail, however, with the increasing availability of spatial data not only on climate, but also on vegetation, land-use, and soil types, etc., that present a range of as yet rarely exploited opportunities for improving the value of spatial modeling based on native versus exotic range survey

information. Biological control along with other ecological studies on invasive species will continue to be at the forefront of testing the role of climate and the environment in natural-enemy-host interactions.

Native Range Agent Population Dynamics: Top Down versus Bottom Up

Many argue that studying the population dynamics of potential agents in the native range is the most profitable endeavor for selecting effective agents (Harris, 1991; Gassmann, 1996). Where potential biocontrol agents are found to be resource limited their populations should expand rapidly following release into high density invasive weed stands. This can be shown by comparing potential agent densities across natural and artificial target stands of variable biomass per unit area. Conversely, evidence of top down regulation in the population dynamics of potential agents by their natural enemies and parasitoids is also very valuable because if such agents escape these through release into the target range their densities and impacts are likely to increase. Indeed, top down regulated agents are the only way a target, which is also common and widespread in its native range, is likely to be controlled. Successful insect biological control programs provide examples of how enemy free space allows insect herbivores to be pests, but also shows that insect herbivores without their natural enemies can be successful weed biological control agents. However, it is hard to predict the levels of parasitism following release and whether these will render the agents ineffective (Van Klinken and Burwell, 2005). Some such agents can still be very abundant in the exotic range even with relatively diverse adopted parasitoid communities (Goolsby et al., in press). Also, there is some evidence to suggest that natural enemies with high parasitoid and predation levels in their native range may also attract high numbers, too, in the exotic range (Cornell and Hawkins, 1993). Agents that are relatively exposed or un-protected, however, have repeatedly been shown to be hard to establish due to the unpredictable, but often significant predation from generalist predators in the target range (Sheppard, 2003; van Klinken, 2005).

Impact Studies

Impact studies in the native range where densities of potential agents are experimentally manipulated on their host plants in the field or in cages can measure per capita impacts as well as agent abundances to help estimate likely impacts for given densities following release (Briese, 1996). Varying the density of the target in such studies can also help determine which agents are resource limited in the native range and so should increase their impact when faced with higher target densities (Sheppard, 2003).

Watson (1991) also recommended the use of field experiments in the weed’s native range to assess impacts of candidate pathogens. Such native range studies have however only seldom been used (Hasan and Aracil, 1991; Brun et al., 1995). Considerable resources were invested to demonstrate the effectiveness of P. chondrillina in adversely affecting host plant populations in the native range (Hasan and Wapshere, 1973; Wapshere, 1985).

Impact studies under controlled conditions can also be used to measure pathogen impacts (Berner and Bruckhart, 2005) and to compare different agent strains (Ellison et al., 2004). For pathogen aggressiveness is made up of several components ranging from the ability to overwinter and to remain infectious during dissemination, to the length of generation time and amount of spores produced (Shaner et al., 1992; Welz, 1988). The relative fitness of pathogen strains (the relative contribution an individual makes to the next generation’s gene pool; Shaner et al., 1992) can be estimated by measuring and comparing the various components of aggressiveness between strains (Welz, 1988). However, such comprehensive comparisons of strain’s aggressiveness have never been undertaken in a weed biological control program, presumably due to cost and problems with in vivo predictions from in vitro studies.

Specificity Assessment

Finally, it should not be forgotten that native range studies can be the only way to complete a full risk assessment required for demonstrating field host specificity following release. A minority of useful agents only show their full potential host range in the field (e.g., Bruchdius villosus)(Sheppard et al. in press). Also, Arytinnis hakani, the French broom psyllid exhibits surprising differences between its fundamental and field host range (Sheppard and Thomann, 2003) that require field validation prior to release. Gephyraulus raphanistri, a flower-bud galling cecidomyiid, considered as a biological control agent for wild radish, exhibits a host flowering phenology- driven field specificity that was only understood through intensive native range field studies (J. Vitou et al., unpublished data). Each of these examples hinged on a priori studies in the native range.

EXOTIC RANGE STUDIES