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Erschienen in: BioControl 1/2018

Open Access 16.11.2017 | Review

Theoretical contributions to biological control success

verfasst von: Peter B. McEvoy

Erschienen in: BioControl | Ausgabe 1/2018

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Abstract

Ecologists have long tried, with little success, to develop ecological theory for biological control. Biological control illustrates how science often follows, rather than precedes, technological advances. A scientific theory of biological control remains a worthy and achievable goal. We need to (1) combine deductions from mathematical models with rigorous empiricism measuring and modeling the effects of abiotic and biotic environmental drivers on demography and population dynamics of real biological control systems in the field, (2) use a wider range of model systems to explore how population structure, movement, spatial heterogeneity, and external environmental conditions influence population and community dynamics, and (3) combine deductive and inductive approaches to address the day-to-day concerns of biocontrol scientists including how to rear and release a control organism, suppress the target organism, and minimize harm to non-target organisms. Further progress will require more fundamental research in population and community ecology directly relevant to biological control.
Hinweise
Handling Editor: Jacques Brodeur.

Introduction

Biological control, which aims to use living organisms to suppress pests, weeds, and disease-causing organisms while minimizing harm to the environment, is a well-established component of integrated pest management and a valuable ecosystem service (Heimpel and Mills 2017). The history of biological control resembles the ups and downs of other technologies, rising over decades in a flush of optimism for a “silver bullet” (a simple and seemingly magical solution to a complicated problem) and falling in recent decades (Cock et al. 2016) prompting soul searching about the adequacy of evidence that biological control is necessary, safe, and effective (McEvoy and Coombs 2000). Environmental values and regulatory intransigence may contribute to the recent decline in activity and accomplishments of biological control (Moran and Hoffmann 2015). Wavering theoretical interest, and intellectual market forces may also contribute to the observed decline. Therefore, for both scientific and sociological reasons, it is timely that we review progress in shoring up the theoretical foundations of biological control technology.
Ecological theory developed for biological control has historically tended to reinforce certain biases in biological control applications by emphasizing (1) classical (= importation) biological control rather than conservation and augmentation to highlight the ‘natural’ self-sustaining dynamics of population interactions, (2) parasitoids over predators, pathogens, and herbivores to simplify the coupling of enemy and host, (3) perennial over ephemeral ecosystems to achieve the continuity of interactions envisioned in most models, (4) specialists over generalists to approximate the theoretician’s delight of a tightly-coupled predator–prey system, (5) local dynamics ignoring patterns and processes operating on more global scales (to satisfy the assumption of perfect mixing, like chemicals in a chemostat), (6) homogeneity of individuals with respect to their characteristics (neglecting how population structure influences population dynamics), and (7) the long-term, asymptotic behavior of biological control systems, ignoring many of the day-to-day decisions confronting biological control scientists (finding and screening candidate control organisms; rearing, releasing and establishing selected control organisms; increasing and redistributing control organisms; suppressing the target organism; and fostering ecological succession to replace the target organism with a more desirable community). Fortunately, recent work has begun to relax the classical assumptions and limiting conditions of biological control theory.
Mathematical investigations of biological control systems have centered on the detailed analysis of Nicholson-Bailey, Lotka–Volterra, and other simple model systems (Barlow 1999; Gurr and Wratten 2000; Hassell 1978; Hawkins and Cornell 1999; Heimpel and Mills 2017; May and Hassell 1988; Mills and Getz 1996; Murdoch et al. 2003), and readers are referred to Heimpel and Mills (2017) for a recent overview. However, progress in new directions has been restricted. As reviewed by Kingsland (1985), these models of ecological interactions were created nearly 70 years ago in a theoretical vacuum, they have withstood the test of time, and they are still widely used today. However, it is time to expand beyond the classical forms, and I shall attempt to identify how field experiments coupled with modeling help guide the directions in which expansion must proceed. In particular, I shall focus on parallel developments in biological control of arthropods and weeds that relax in various ways one of the fundamental constraints of the classical literature, the assumption of population homogeneity with respect to all characteristics. Population structure (the distribution within a population of such properties as genotypic and phenotypic traits, age, size, stage of development, spatial location, gender, and social role) is not merely the fine tuning of population theory. The description and elaboration of the consequences of population structure for the ecological and evolutionary dynamics of consumer-resource interactions has proven to be fundamental for understanding, predicting, and managing biological control systems. Of particular importance is how population structure influences the encounters between control organisms and target organisms and the dynamics of population interactions.
Here I review theoretical contributions to biological control success, showing where significant theoretical progress is being made, and suggesting priorities for further investigation. To counter skepticism about the value of theory in biological control, I begin by reminding us of why a scientific theory of biological control is a worthy goal. I then show how (1) stronger ties between theory, mathematical modeling, observational studies, and field experiments linking population structure to ecological dynamics are helping to temper the possible (identified by deductions from mathematical models) with the actual (confirmed by rigorous empiricism), and (2) better quantitative documentation of biological control outcomes is spawning rigorous inductive approaches to theory building. Finally, I suggest how we should prioritize research aimed at generalizing from these recent results.

The possible role of theory in biological control practice

Ecologists have long sought to develop ecological theory as an explanation and guide for biological control. The broad justifications for these efforts should be clear. Theory building is the hallmark of scientific progress. Not only would a scientific theory of biological control improve the effectiveness and safety of biological control, it would also help biological control compete in the marketplace of ideas that determines which disciplines thrive (attracting fertile young minds, ample funding, and public support) or wither.
Theory helps transform the details of case studies into more powerful abstractions. An over-arching theory would help integrate (1) multiple classes of enemy-pest interaction (parasitoid-host, predator–prey, pathogen-host, herbivore-plant), (2) multiple ways of enhancing control organism effectiveness (introduction, augmentation, and conservation of control organisms), (3) multiple settings where biological control is practiced (annual and perennial plant systems in greenhouse or field; aquatic or terrestrial environments), that often devolve into separate subcultures communicating weakly at the margins. A general theory of biological control would make investigation of these special cases easier. Theory helps identify problems to be solved and suggests ways to go about investigating them. For example, reaction–diffusion models used as a framework for studying animal movement helped redefine an effective predator from one that eats quickly, to one that moves effectively through a heterogeneous environment to quickly suppress an incipient pest outbreak and prevent a wider epidemic (Grunbaum 1998; Kareiva 1984; Kareiva and Odell 1987). Theory helps identify relevant details for models and explanations. Typically, some details of pest-enemy interactions are suppressed, so that other features can be exposed, analyzed, and understood (Levin 1991). In the foregoing example of modeling predator movement, the details of predator reproduction were suppressed to highlight the blend of random and non-random movement contributing to effective searching behavior. Theory helps provide a more reliable basis for comparison and extrapolation to new situations. The rival view, and the bane of theory building, is that each biological control system is unique and must be dealt with on a case-by-case basis. Classically, the best predictor of success for a given biological control program is evidence of its success elsewhere in similar situations (Crawley 1989), thereby taming the bugbear of ‘context dependence’ but leaving outcomes unexplained and overall success rates of new programs destined to remain low.
Theory ideally clarifies the link between assumptions and conclusions. The best control strategy may vary with assumptions, forcing us to be clear which assumptions we are making and to decide which assumptions are most reasonable. For example, the best control organism for creating a low and stable pest-enemy equilibrium over the long term (the classical goal assumed for successful biological control) (Murdoch et al. 1985) may not be the best enemy for reducing the mean and variance in pest densities in the short-term, transient phases of dynamics before a hypothetical equilibrium is reached (a plausible, alternative assumption for ecosystems frequently reset by disturbance) (Kidd and Amarasekare 2012; Mills 2012). Similarly, the best strategy for suppressing population growth rates of the target organism may not be best for reducing spatial spread rates, which combine population growth and dispersal rates (Shea et al. 2010). Theory provides parsimonious and sometimes mechanistic explanations for biological control outcomes. The best example demonstrating the mechanisms leading to strong and stable suppression of pest density is the wasp Aphytis melinus DeBach, 1959 (Hymenoptera: Aphelinidae) used to control California red scale Aonidiella auranti (Maskell 1879) (Hemiptera: Diaspididae) in citrus Citrus spp. (Sapindales: Rutaceae) in California, USA (Murdoch et al. 2003). Ideally, theory building combines natural history, mathematical, experimental, and observational approaches, showing how (1) the strengths of one approach compensate for the weaknesses of another, and (2) the more closely each relates to the other, then the more powerful each becomes. In practice, however, mathematical modeling investigations have proceeded independent of empirical studies, often settling for only casual correspondence between the behavior of model and natural systems. In a celebrated example, unstable parasitoid-host interactions in simple models can be stabilized by non-random patterns of parasitism leading to the necessary and sufficient levels of aggregation in the risk of parasitism (Hassell and May 1973, 1974). Many natural interactions of parasitoids and hosts appear to have the required level of aggregation (Pacala and Hassell 1991; Walde and Murdoch 1988). However, the possible mechanisms leading to aggregation are many, and it is difficult to see how they could be screened or manipulated to increase the odds of success without recourse to measuring and modeling mechanisms generating aggregation in the field (Ives 1995; Wajnberg et al. 2016). The preferred approach would be to analyze the foraging behavior, incorporate the behavior into mathematical models to project the consequences for population dynamics, test by field experiments whether the models correctly described the underlying processes, perturb (or render inoperative) the candidate regulatory mechanism in field populations and compare with experimental controls to confirm the role in dynamics, and then seek ways to manipulate behavior to achieve more favorable population dynamics.
Theory complements and extends the verbal, natural-history intuition that has been the mainstay of biological control since its inception (Godfray and Waage 1991). Who would have thought, based on verbal intuition alone, that there could be ‘too much of a good thing’ as in pest-enemy interactions that are too strong (leading to dangerous oscillations in pest population density) or too complex (multiple enemy species introductions leading to enemy interference) for biocontrol success? Theory building in biological control should strive (in equal measure) to improve the understanding, prediction, and management of biological control systems. Theory for biological control should advance hand-in-hand with practice (a ‘coevolutionary’ model for developing theory and practice together) (Barot et al. 2015), rather than advancing sequentially from ‘blue sky research’ to applications expected to arise serendipitously sometime in the future (a ‘sequential’ model of developing theory before practice) (Courchamp et al. 2015). Theory building embodies scientific reasoning as a kind of dialogue between the possible and the actual, between what might be and what is in fact the case (Jacob 1982; Medawar 1967) as established ultimately by rigorous empiricism. Finally, what is the alternative? Without theory, biological control will continue to rely on ad hoc procedures based mainly on inadequately documented past experiences (May and Hassell 1988).

The actual role of theory in biological control practice

While theoretical development holds promise for biological control, it has produced little of practical value in the past. The beginning of biological control as a recognized discipline is usually traced to the introduction of the Vedalia beetle, Rodolia cardinalis (Mulsant, 1850) (Coleoptera: Coccinellidae), into California from Australia in 1888 to control the cottony cushion scale, Icerya purchasi Maskell, 1878 (Hemiptera: Margarodidae). Up to 2006, there have been 7094 introductions involving 2677 invertebrate biological control organisms for control of arthropods and weeds, leading to some spectacular successes and many more failures (Cock et al. 2010; Heimpel and Mills 2017). The beginnings of biological control were pragmatic and empirical, and the practice remains so today to a considerable degree. The history of biological control supports the argument that technology often leads to science rather than always following science (Sawyer 1996).
The mathematical theory applied to biological control (predator–prey, parasitoid-host, pathogen-host) is nearly as old as biological control technology itself. Multiple theoretical ecologists have bravely suggested how theory might gain a foothold in biological control, while at the same time acknowledging that ecology has contributed little of practical value to biological control, with most of the insights instead flowing from biological control to ecology (Kareiva 1996; Lawton 1985). Ecologists who have spent most of their careers developing and testing biological control theory (Murdoch et al. 2003) offer this counsel of despair: ‘In spite of its successes, and its more numerous failures, there are few if any, general principles, or even rules of thumb, to guide the efforts of biological control. Nor is there a general scientific basis for biological control [...]. Whether theory can inform this process and make it more “scientific” is still an open question.’
To temper this skepticism, I review three signs of an emergent scientific theory of biological control owing to theoretical research that (1) combines deductions from mathematical models with rigorous investigation of the links between environmental drivers, population structure, and ecological dynamics in real biological control systems in the field, (2) uses a wider range of model systems to explore how population structure, movement, spatial heterogeneity, and external environmental conditions influence population and community dynamics, and (3) addresses the day-to-day concerns of biocontrol scientists including optimal ways to rear and release a control organism, suppress the target organism, and minimize harm to non-target organisms.

Signs of an emergent scientific theory of biological control

Tempering deductive approaches by rigorous empiricism

Mathematical theory applied to biological control has been built mainly on deductive approaches that have rarely been rigorously tested in real biological control systems operating in the field. Most of these studies have been retrospective, but prospective studies are starting to emerge, as illustrated by the use of demographic models to inform selection of biocontrol agents for garlic mustard (Alliaria petiolata (M. Bieb.) Cavara & Grande) (Brassicaceae) (Davis et al. 2006; Evans et al. 2012). One study, involving two parasitoids for control of mango mealy bug (Rastrococcus invadens Williams) (Hemiptera: Pseudococcidae), was designed to be prospective but became retrospective when biological control success was achieved faster than the system could be modeled (Godfray and Waage 1991). Modeling today, building on deductive approaches of the past, is being combined with rigorous empiricism. Here I illustrate the power of combining modeling with rigorous empiricism (including field experiments at the population and community level) for two well-studied cases, one for control of arthropods and one for weeds.

Biological control of arthropods: parasitoid-host interactions

In biological control of insects, the best-studied system is a parasitoid-host system represented by a wasp Aphytis melinus used to control California red scale Aonidiella aurantii, a pest of multiple species of citrus (Murdoch et al. 2003). Competition between multiple enemy species did not undermine success: The more effective natural enemy Aphytis melinus competitively displaced a less effective natural enemy Aphytis lingnanensis Compere, 1955. The basic approach used in investigating this system was to analyze the foraging behavior, determine the consequences of behavior for population dynamics using mathematical models, and finally to test by field experiments whether the models correctly described the underlying processes.
Two (among many) hypotheses were tested with a single field experiment (Murdoch et al. 1996). The first was the refuge hypothesis, which grew out of empirical observations showing that a refuge exists for California red scale at the interior of the tree where scales are impervious to parasitism and suggesting that the parasitoid-host interaction might be stabilized by this spatial refuge for California red scale from parasitism. The refuge accounts for ~ 90% of the total scale population, it is a source of scale recruits to the exterior of the tree, and scale density in the refuge is less variable than at the exterior. Investigators hypothesized that the population at the exterior of the tree might be stabilized by the continuous flux of immigrants from the tree interior to the tree exterior. The refuge concept is a popular idea, widely invoked in population dynamics and biological control (Berryman et al. 2006), but until this study, its contribution to equilibrium levels and stability had not been tested in real biological control systems in a rigorous way.
The second was the metapopulation hypothesis—which holds scale populations may be unstable at local scales of observation (i.e. characterized by wide amplitude oscillations, extinctions, and colonization events), but are stabilized at global scales by sufficient asynchrony in local population fluctuations, and sufficient migration among local populations, to provide for persistence of the ensemble of locally unstable, interacting populations (Kean and Barlow 2000; Levins 1969). Within many agricultural systems, it is widely assumed that insect pests and their natural enemies are forced to persist as a metapopulation, undergoing frequent local extinctions and recolonizing patches following disturbances caused by harvesting or insecticides. However, until this study, the metapopulation hypothesis had not been rigorously tested for biocontrol systems.
The two possibilities—the refuge hypothesis and the metapopulation hypothesis—were rejected by a single, elegant 2 × 2 factorial experimental design to test what stabilizes this parasitoid-host interaction: refuge, metapopulation, or some combination. The experiment compared trees ‘open’ (uncaged) and ‘closed’ (caged) to migration, compounded with ‘refuge’ and ‘refuge removed’ trees. The experiment ran for 17 months (three generations of scale and nine generations of wasp), adequate to measure population dynamics. Stability was operationally defined as the inverse of variability, where variability was defined as the coefficient of variation in scale numbers. Removing the refuge actually decreased variability (increased stability), so the refuge is not stabilizing. Closing off a tree to migration had no detectable effect on variability, so the interaction is not stabilized by metapopulation dynamics. This study provides valuable lessons on how to understand complex systems at all levels of biological organization. Where possible to do so, perturb the system experimentally by selectively removing the candidate regulatory mechanism (or making it inoperative) and comparing the dynamics with that in an unmanipulated control treatment.
Feasible remaining mechanisms for stability were explored using a pulse, density-perturbation experiment that might uncover both density-dependence and the regulating mechanisms causing return to equilibrium. The experiment was coupled with a detailed day-to-day stage-structured model of the system of interactions incorporating candidate regulatory mechanisms, with parameters estimated independent of the experiment (Murdoch et al. 2005). The model distinguished scale stages and their differential use by Aphytis, and it addressed three remaining mechanisms by which the system might be stabilized: (1) there is a long-lived invulnerable adult scale stage, (2) the wasp Aphytis develops (about three times) faster than the scale, (3) an attack on older immature scale yields more parasitoid offspring than an attack on younger immature scale (the developmental ‘gain mechanism’)—for example, attack on older scale yields one or more female parasitoid offspring directly, while host-feeding on the youngest scale yields fewer offspring indirectly by providing the adult female with nutrients for egg development. Investigators followed the dynamics of these outbreak populations, together with caged and uncaged control populations, over three to five scale ‘development times’ (time intervals from scale birth to adult). Three separate experiments gave the same result. Control of the outbreak and stability (return to equilibrium density) occurred rapidly over a time period equal to about ~ three scale generations—illustrating the dynamic stability of this system. Little variation in density was observed thereafter. The model reproduced the dynamics of the perturbed populations with remarkable accuracy, and the model result was robust to substantial changes in parameter values and even to structural changes in the model. To generalize these results, it was possible to survey other parasitoid-host systems for control of scale insects to discover whether similar ecological attributes are shared across taxonomically-related biological control systems (Murdoch et al. 2005). Few other cases have been studied in sufficient detail, but 16 cases of biological control of coccids appear to share four main features: (1) the interaction is persistent, and perhaps stable, (2) control is attributed (at least locally) to a single parasitoid, (3) the pest has an invulnerable stage, and (4) the enemy development time is shorter than that of the pest.
The takeaway lessons from this decades-long study are (1) the mechanisms regulating biological control systems cannot simply be deduced from mathematical models, they must also be confirmed by rigorous empiricism including field observations and manipulative experiments, (2) this parasitoid-host interaction is highly stable and is regulated at the level of the individual citrus tree, (3) stabilizing mechanisms are linked to stage structure (developmental responses and invulnerable stages), (4) the stage-structured model developed for this system faithfully describes the return to equilibrium following a pulse, experimental perturbation (Murdoch et al. 2005). It is an open question whether conclusions from this study can be generalized beyond parasitoids interacting with scale hosts, mainly because too few systems have been studied in sufficient detail. However, from what we know so far, none of the other biological control systems targeting arthropod pests that have been examined for strong suppression and stability of interactions (winter moth in Nova Scotia, larch sawfly in Manitoba, olive scale in California, walnut aphid in California, and California red scale in Australia) matches the remarkable, intrinsic stability at a local spatial scale exhibited by the Aphytis-red scale system in California (Murdoch et al. 1985). Furthermore, theoretical rules of thumb developed from these studies concentrate on pest suppression and ignore stability (Murdoch et al. 2003), suggesting that investigating the causes of stability is an activity of leisure rather than of necessity.

Biological control of weeds: herbivore-plant interactions

In biological control of weeds, a well-studied example is an herbivore-plant system involving multiple herbivore species—the cinnabar moth Tyria jacobaeae (L.) (Lepidoptera: Erebidae), and the ragwort flea beetle Longitarsus jacobaeae (Waterhouse) (Coleoptera: Chrysomelidae) interacting with a shared target host plant tansy ragwort Jacobaea vulgaris Gaertn.) (Asterales: Asteraceae) and non-target host plants related to the target. The caterpillars of the cinnabar moth feed on foliage and flower buds (technically capitula). The adults do not feed. The larvae of the flea beetle feed mainly on roots, and adults feed on foliage. Additional control organism species have been introduced for biological control of tansy ragwort, but they play a minor role in regulating ragwort abundance. The entire community represents an Old Association—the weed, the enemies, and background vegetation of perennial pasture grasses all originated from Europe and have been transported around the world to Australia, New Zealand, and North America. The tansy ragwort system has been studied in the plant and herbivores’ native home in Europe (mainly by investigators in England and The Netherlands) as well as abroad in North America (both USA and Canada), New Zealand, and Australia. Studying invasive species in both native and introduced ranges is a longstanding practice in biological control and recently recommended by ecologists for studying the mechanisms that regulate ecological and evolutionary dynamics of invasive plants (Hierro et al. 2005; Williams et al. 2010). Recently, the tansy ragwort system has been used to study the role of rapid evolution in ecological dynamics (McEvoy et al. 2012b; Rapo et al. 2010; Szucs et al. 2012).
The success of biological control must be measured before it can be explained. Early studies documented variability in the biological control of tansy ragwort on a regional scale, identified its causes, and quantitatively evaluated overall success in surveys of field populations in Western Oregon spanning 12 years and 42 sites (McEvoy et al. 1991). The system of interactions led to strong, stable suppression of ragwort to < 1% of ragwort’s former regional abundance, and the speed of control (6–8 years) did not vary with precipitation, elevation, land use, or timing (year) of natural enemy release. In the vernacular favored by ecologists, the outcome was robust, not highly ‘context dependent.’ A local pulse-perturbation experiment showed that introduced insects, within one ragwort generation, can depress the density, biomass, and reproduction of ragwort to < 1% of populations protected from natural enemies (McEvoy et al. 1991). Stability was not confirmed by modeling. However, the stability of this system has been repeatedly demonstrated empirically by pulse perturbation experiments, a generally accepted method of demonstrating population regulation (Murdoch et al. 2005; Murdoch 1970). Create an upsurge in ragwort abundance and natural enemies readily colonize and return the population to pre-perturbation levels in ragwort populations exposed to natural enemies in open cages. This outcome contrasts with persistence ragwort populations at high levels of abundance when protected from natural enemies by closed cages (James et al. 1992; McEvoy et al. 1991; McEvoy and Rudd 1993; McEvoy et al. 1993). Stabilizing mechanisms, whatever they might be, are not found at local scales (e.g. an agronomic field), but at more global, regional scales of observation. The stabilization of ragwort density is not strictly a deterministic process, but perhaps better described by stochastic boundedness (Murdoch et al. 1985): the ceiling on fluctuations in weed density steadily declined after releasing biological control organisms (McEvoy et al. 1991). Strong suppression of ragwort abundance triggered a successional process leading to replacement of ragwort by a plant community dominated by perennial grasses introduced long ago from Europe (McEvoy et al. 1991), but the community changes also allowed recovery of populations of a North American native herb (the hairy stemmed checker mallow Sidalcia hirtipes C.L. Hitchc., Malvaceae) of conservation concern (Gruber and Whytemare 1997). An invulnerable stage for ragwort exists, represented by a large, persistent seed bank buried in soil (McEvoy et al. 1991). Factors regulating the number of individuals entering, remaining within, and leaving the seed bank of ragwort populations are fairly well known (McEvoy et al. 1991, 1993; McEvoy and Rudd 1993); the consequences of the seed bank for ragwort population dynamics have been investigated using structured population models, under the simplifying but unsatisfactory assumption of a constant annual rate of recruitment of individuals from the seed bank (Dauer et al. 2012). A recipe for neutralizing a seed bank is to minimize disturbance and maintain a competitive grass cover. However, the exposure of populations to various levels of each regulating factor remains to be quantified in a landscape context. The seed bank did not prevent local extinction of actively-growing stages of ragwort populations over the time period of observation, but over the longer term it might buffer against environmental perturbations and reduce the probability of extinction under conditions recently identified in structured population models parameterized for marsh thistle Cirsium palustre (L.) Scop. (Asterales, Asteraceae) and its seed bank (Eager et al. 2014).
Theory helps identify problems to be solved and suggests ways to go about investigating them. Our theoretical approach was inspired by the theory of activator-inhibitor systems of reaction–diffusion equations that has been used to describe pattern formation in numerous applications in biology, chemistry, and physics. Reaction–diffusion models first gained a foothold in ecology for modeling spatial spread of organisms and genes (Fisher 1937; Skellam 1951), and subsequently for investigating the roles of spatial heterogeneity and movement in the dynamics of predator–prey interactions (Grunbaum 1998; Kareiva and Odell 1987), and in modeling the spread and biological control of invasive species (Fagan et al. 2002; Hastings et al. 2005; Shigesada and Kawasaki 1997). In our framework, the causes of strong pest suppression and persistence of pest-enemy interactions do not rest solely with the natural enemy. The driving forces in our biological control system were initially assumed, and ultimately confirmed, to be disturbance, colonization, and local interactions (resource limitation, plant competition, and herbivory). Patchy disturbances to vegetation and soil remove biomass, open up space, recycle limiting resources, and set the stage for colonization and occupancy. Colonization plays an organizing role, depending on the ratio of diffusion coefficients of the long-range inhibitor to the short-range activator. Local interactions, including the ‘top-down’ effect of herbivory combine with the ‘bottom-up’ effect of plant competition (resource limitation), set in motion the process of community succession that leads to replacement of tansy ragwort with a background vegetation composed mainly of perennial grasses. We incorporated these assumptions in the Activation-Inhibition Hypothesis developed to describe the workings of biological weed control systems: (1) The Activation Hypothesis is that localized disturbance and buried seed (more generally a source of propagules) combine to create incipient weed outbreaks; (2) The Inhibition Hypothesis is that insect herbivory and interspecific plant competition combine to oppose increase and spread of incipient weed outbreaks; (3) The Stability Hypothesis is that the balance in short-range activation and long-range inhibition leads to a general condition of local instability and stable average spatial concentration of the weed.
Our study was implemented as a very large, long-term factorial experimental design varying the timing and intensity of disturbance in the activation phase; observing the timing of arrival by activator and inhibitor in open field plots in the colonization phase; varying the levels of the inhibitors including herbivory by ragwort flea beetle and the cinnabar moth, and interspecific plant competition. One version of this design yielded a total of 24 treatment combinations of two disturbance times (a pulse perturbation achieved by tilling of soil in fall or spring) × two cinnabar moth levels (plots exposed or protected with cages) × two flea beetle levels (exposed or protected) × three plant competition levels (a press perturbation whereby background vegetation was continually removed, clipped to mimic grazing, or left unaltered), with each of 24 treatment combinations replicated four times for a total of 96 experimental plots (McEvoy et al. 1993). We periodically censused insects and plants by stage for six years or ~ 2–3 ragwort generations (ragwort ranges from biennial to short-lived perennial) and six insect generations (the insects are univoltine), long enough to observe population dynamics of interacting plants and insects for each of the 24 combinations of disturbance-herbivory-plant competition treatments.
The theory of structured population models helped us translate variation in environment factors or drivers (disturbance, plant competition, herbivory) into changes in ragwort vital rates and then project the consequences of changes in vital rates into changes in population growth rates (Caswell 2001). Such models have been widely used for managing populations, whether for harvesting, controlling, or conserving (Caswell 2001; Morris and Doak 2002). Population growth was projected using a linear deterministic, stage-structured population model parameterized independently from field populations representing each experimental treatment combination. This single-species model developed to project ragwort dynamics is incomplete—it does not incorporate density-dependence (which is nonetheless implicit in the pulse-perturbation experiments and in field estimates of parameters) and stochasticity (reflected in the region-wide observational studies), and the ragwort population is not explicitly coupled in the model with each of the interacting populations (insects and other plant species featured in the experiment). Our linear, deterministic model best represents a population growing exponentially following disturbance, and it permits elasticity values to be easily obtained analytically, while elasticity methods for non-linear models are more difficult. This simple model performed remarkably well for projecting the speed of control, the time taken to eliminate an incipient outbreak (Dauer et al. 2012; McEvoy and Coombs 1999), a valuable metric for management. Perturbation analysis (including elasticity, sensitivity, decomposition of effects) provided a framework for ‘targeted life cycle disruption’ as a control strategy, identifying which life cycle transitions are potentially most influential on ragwort’s population growth rate, and which of these transitions are actually the most variable and amenable to management manipulations (Dauer et al. 2012; McEvoy and Coombs 1999). The standard approach in biological weed control is to target plant parts like roots, shoots, leaves, and seeds (a way to kill individuals), but targeting life cycle transitions is now generally recognized as a more reliable way to control populations (Shea et al. 2010). The recommended targeting of life cycle transitions (for this short-lived perennial modeled with a one year time step, the ‘biennial’ transitions include the probability a juvenile alive at time t will survive and develop to adult at time t + 1, and the fertility parameter or the number of juvenile offspring surviving at time t + 1 per adult alive at time t) proved remarkably robust to variation across 15 combinations of disturbance timing and community configuration, ranging for a single-species ragwort population to a multi-species community with a ragwort population interacting with populations of the cinnabar moth, the ragwort flea beetle, and interspecific plant competitors within the background vegetation (Dauer et al. 2012). Once again, the bugbear of ‘context dependence’ was brought to bay.
Our work with the ragwort system was motivated by a desire to test assumptions and predictions of ecological theories offered as explanations for biological control (McEvoy et al. 1993). We sought answers to three broad questions that have nagged ecologists for decades. First, we asked: do herbivores impose a low, stable pest-enemy equilibrium at a local spatial scale, or does local extinction occur? We found local pest extinction (defined from an economic perspective as eliminating all actively-growing stages outside the seed bank at the scale of an agronomic field) occurs and is compatible with success. We used speed of control (i.e. time to local extinction) as a metric for measuring success, on the grounds that even transient weed populations can cause economic and environmental damage. The speed of control was remarkably insensitive to scale: a 40,000-fold increase in spatial scale of the ragwort infestation (from 0.25 to 10,000 m2) yields less than a three-fold increase in the time to local extinction of the weed population (from 1–3 to 5–6 year) (McEvoy and Rudd 1993). We conclude with others that transient rather than equilibrium dynamics may be of more practical significance for biological control in systems frequently reset by disturbance (Hastings 2001; Kidd and Amarasekare 2012). Second, we asked: is success more likely from a single ‘best’ herbivore species or from the combined effects of multiple herbivore species? We found that the ragwort flea beetle was by far the more effective regulator of ragwort abundance (Dauer et al. 2012; McEvoy and Coombs 1999); the flea beetle epitomizes the ‘search and destroy’ strategy (Dauer et al. 2012; McEvoy and Coombs 1999), offered as an alternative to creating a low, stable equilibrium on a local spatial scale (Murdoch et al. 1985). We found that the cinnabar moth makes a relatively small but detectable contribution to ragwort suppression (Dauer et al. 2012; McEvoy and Coombs 1999), thereby providing marginal support for the ‘complementary enemies’ model (Murdoch et al. 1985). The ragwort flea beetle attacks perennial rosettes that live up to five years. The rosette stage suffers relatively little mortality from a less successful control organism, the cinnabar moth. The ragwort flea beetle reduces all transitions in the ragwort life cycle graph used to develop the model. The cinnabar moth reduces only of one (from flowering plants to rosettes) of the two transitions (the ‘biennial’ transitions) identified as potentially most influential for ragwort population growth (Dauer et al. 2012; McEvoy and Coombs 1999). Third, we asked: is plant competition necessary to augment the impact of natural enemies on the dynamics of weed populations? When we began our study, it was not clear whether natural enemies and interspecific plant competition act sequentially or simultaneously: that is, natural enemies clearly cause weed suppression, but after that, interspecific plant competition possibly maintains low weed density until disturbance reoccurs. We found that herbivory and competition act simultaneously to inhibit an increase in weed abundance following disturbance, and herbivore colonization of ragwort was not reduced by the weed hiding in the background vegetation. We concluded that strong resource limitation at all target organism densities (due to a combination of intraspecific and interspecific plant competition) is a feature distinguishing biological control of weeds from biological control of arthropods.
This framework of activator-inhibitor systems provides a natural way to (1) incorporate the multiple forces driving biological control (disturbance, colonization, and local interactions including ‘bottom-up effects’ of plant competition and ‘top-down effects’ of herbivory), (2) show how perturbations in these processes give rise to plant invasions, and (3) develop biological control systems to oppose the establishment, increase, and spread of invasive species. It has several advantages over standard practice for explaining and managing biological control of invasive species. First, the activation-inhibition model avoids the “either-or” fallacy of false dichotomies common in the literature on biological invasions. For example, it combines processes often studied singly as contributors to invasions and biological control including disturbance (Hobbs and Huenneke 1992), propagule pressure (Simberloff 2009), perturbation of top-down effects of natural enemies (Keane and Crawley 2002) or bottom-up effects of plant competition (Callaway and Ridenour 2004). The analysis of interacting causes is fundamentally different from the discrimination of alternative causes. As a second advantage, the activation-inhibition model transcends the oversimplification of causes and cures for biological invasions often attributed to biological control: namely, absence of enemies is the cause of invasions, and addition of enemies alone provides the cure. Even textbooks in biological control now question the ‘enemy release hypothesis’ as a universal explanation for invasiveness (Heimpel and Mills 2017). As a third advantage, the activator-inhibitor framework replaces the phenomenology of ‘context dependence’ with mechanistic explanations based on the action and interaction of disturbance, colonization, and local organism interactions, the forces driving the dynamics of nearly all ecological systems (Levin 1989). Finally, the activator-inhibitor framework incorporates environmental disturbance, movement and spatial heterogeneity. This motivated the gathering of detailed information on movement showing that the relative dispersal ability of the host plant (McEvoy and Cox 1987) is much less than that of the natural enemy species [the cinnabar moth T. jacobaeae, the ragwort flea beetle L. jacobaeae, and the ragwort seed head fly Botanophila seneciella (Meade, 1892)] under field conditions (Harrison et al. 1995; Rudd and McEvoy 1996). Thus, natural enemies can easily overtake and suppress a weed population spreading autonomously. Interestingly, the activator-inhibitor recognizes differences in plants and insects in major modes of colonization, pitting the stronger spatial-averaging ability of the insects (by dispersal) against the stronger temporal-averaging ability of the plants (by dormancy, iteroparity, and perenniality). In their separate ways, the insects and plants rely on life history features (dispersal, dormancy, perenniality, and iteroparity) to average out local unpredictability in the environment.

Convergent features of biological control of arthropods and weeds

Complementary approaches to the study of biological control systems reviewed here yield convergent results for biological control of California red scale and tansy ragwort (Table 1). Each control organism achieved strong and stable suppression of the target organism. When a pulse perturbation creates an upsurge in target organism density, control organisms rapidly colonize and return the target organism to pre-perturbation levels after a few host generations. Persistence of interactions is not exclusively the property of the consumer-resource interaction, but also of the environment (which in each case is infrequently disturbed and favorable to population growth of the control organisms). Multiple control-organism species were introduced in each case, but ultimately control can be attributed entirely (in the case of red scale) or primarily (in the case of ragwort) to a single control-organism species at the local scales examined (other control organism species may become important at more global spatial or temporal scales). Each control organism is specialized on its target organism, and the control organisms do not eat each other (as in other cases involving cannibals, auto-parasitoids, hyper-parasitoids, facultative hyper-parasitoids, intraguild predators). Host specialization permits tight coupling with target organism dynamics and helps minimize effects on non-target organisms.
Table 1
Common features in detailed cases studies for biological control of arthropods and weeds
 
Aphytis on Red Scale
Longitarsus on Ragwort
Patterns in the dynamics
 Target-organism suppression is strong and fast; interaction is persistent and stable
Yes
Yes
 Control is attributed (entirely or primarily) to a single species of control organism at a local scale
Yes
Yes
Single control organism
 Is specialized on the target organism
Yes
Yes
 Does not eat other control organisms
Yes
Yes
 Is not self-limited at relevant densities
Yes
Yes
 Has a shorter generation time and a faster population growth rate than the target organism
Yes
Yes
 Rapidly increases when and where the target organism does
Yes
Yes
 Leaves no refuge areas
No
Yes
 Has a higher rate of successful search
Yes
Yes
Control organism attacks
 An earlier host stage
Yes
Yes
 Host stage that lasts longer
Yes
Yes
 Host stage (or life cycle transition) that suffers lower mortality from other causes
Yes
Yes
Target organism
 Is resource limited at relevant densities
No
Yes
 Has an invulnerable stage
Yes
Yes
Environment
 Is favorable for growth of control organism population
Yes
Yes
 Is infrequently reset by disturbance
Yes
Yes
The rapid rates of colonization and faster population growth rates of the control organism are necessary and sufficient to counter the colonization and population growth rates of the target organism. Each control organism counters increases in target-organism density when and where they occur, and neither control organism is self-limited at the relevant densities. A refuge exists for California red scale, although it is not stabilizing. No refuge from parasitism has been identified for the case of the ragwort flea beetle, which has a higher rate of successful search than a less effective control organism, the cinnabar moth. Each control organism attacks an earlier host stage compared with less successful control organisms. Each control organism attacks an actively growing juvenile stage that lasts longer. Each target organism has an invulnerable stage, seed buried in soil in the case of ragwort, and adult scale in the case of red scale.
Three differences stand out when comparing biological control of arthropods and weeds. First, strong resource limitation in the target organism at relevant densities is found in weeds but not arthropod pests. Ragwort population growth is slowed by plant competition for resources at all ragwort densities; control of ragwort promptly leads to its replacement by a background vegetation of other plant species. Indeed, all vegetation is resource-limited to a considerable degree (Harper 1977). By contrast, control of California red scale does not automatically lead to its replacement by another herbivore species and resources do not limit pest population growth at relevant densities. Second, variation in host quality (sensitivity of a control organism’s population growth rate to variation in biochemical composition or other relevant features of the host) is more important for consumers of weeds than for consumers of arthropods. Changes in host plant quality such as induced resistance can regulate herbivore population dynamics depending on the time lag between damage and the onset of induced resistance. The time lag depends on characteristics such as the strength of induced resistance and the mobility of the herbivore (Underwood 1999). Third, there has been a longstanding theoretical interest in parasitoid-host population dynamics, while herbivore-plant population dynamics was long neglected. Today, coupled herbivore-plant models (incorporating both relevant details of population structure and feedback between plant and herbivore populations) have been developed, analyzed, and applied in biological weed control (Buckley et al. 2005). At least 136 studies have measured and modeled the influence of abiotic and biotic drivers of plant demography and population dynamics, but 71% of these studies examined only one driver. Of 181 cases (the number of cases 181 exceeds the number of studies 136 because some studies included more than one driver type) herbivory has been examined in 26%, disturbance in 25%, competition in 16%, climatic factors in 15%, other abiotic factors in 12%, mutualists in 5%, and pathogens in 1% (Ehrlen et al. 2015). Our study of the ragwort system appears to be the only case where the independent and interacting effects of environmental drivers of disturbance, plant competition, herbivores (two species in this case) on plant demography and population growth have been examined together using a field experiment.

Inductive approaches

Building on inductive approaches, the historical record of biological control successes and failures is being quantitatively examined with more statistical rigor. Pioneering attempts to develop and analyze databases such as the Silwood (Crawley 1986; Moran 1985) and BIOCAT (Cock et al. 2016; Greathead and Greathead 1992) data bases had a number of shortcomings due to (1) subjective measures of success (e.g. partial, substantial, complete control); (2) overreliance on expert opinion (which nonetheless carries the weight of experience); (3) analyzing attributes singly, ignoring correlations and interactions among them; (4) a lack of randomization and replication; (5) sample bias (the analysis of past cases is of little use for unprecedented applications, and it prejudices future possibilities); (6) a lack of independence in repeated introductions of the same natural enemies for control of the same target organism at different locations; (7) a lack of phylogenetic independence due to inappropriate use of species with the same clade as ‘replicates’; and (8) incomplete knowledge leaving many of the relevant details influencing the dynamics unknown. Several investigators have called attention to such shortcomings (Waage and Greathead 1988; Waage and Mills 1992), and recent investigators provide thoughtful discussion of contributions and corrections for some of these biases (Heimpel and Mills 2017). Below, I show how more rigorous inductive approaches are spurring the development of ecological theory likely to improve the practical effectiveness and safety of biological control. I highlight studies that predict three transitions in the biological control process from the introduction and establishment of control organisms, to suppression of the target organisms, to the manifestation of any non-target effects.
Control organisms must be established before they can contribute to success, and all too many control organism introductions fail to establish (Heimpel and Mills 2017). Release strategies (seeking to optimize the number and size of release populations that can be made from a finite release stock) have been a fertile area for theoretical investigation for increasing rates of establishment (Grevstad 1999; Memmott et al. 1996, 1998; Shea and Possingham 2000) as recently reviewed (Heimpel and Mills 2017; McEvoy et al. 2012a). Theoretical work indicates the optimal release strategy depends on the functional form of the relationship between the number of individuals released and establishment probability, and this relationship depends on the relative influences of Allee effects (reduction in population growth rate at low density) and environmental variability (stochastic variation in population growth rates) on released populations (Grevstad 1996, 1999). When an Allee effect is prevalent, there will be a strong dependence of establishment on release size and the optimal strategy is to make few, larger releases. Where environmental variability has a large influence, then establishment probability will probably be weakly dependent on release size and a strategy of many small releases will be optimal (Grevstad 1996, 1999). A recent empirical study revisited release strategies (Grevstad et al. 2011) by analyzing release and establishment records for 74 biocontrol agents introduced against 31 weeds in Oregon, USA. The main findings were that (1) establishment was not affected by release size over the range of release sizes typically used, underscoring the foresight and reliable intuition of biological control practitioners; (2) biocontrol agent species vary in how readily individual releases lead to establishment; and (3) biocontrol programs often use fewer initial releases than is optimal to obtain a high probability of overall establishment. Ecologists have long recognized the value of biological control systems for studying the dynamics of interacting populations (Hassell 1978; Murdoch et al. 2003). Here is another reason why ecologists and evolutionary biologists should study biological control systems: they provide valuable information on the patterns and causes of variation in colonization success.
Next, consider the likelihood that established control organisms will suppress the target organism. The success of established control organisms in suppressing the target organism can be predicted based on attributes of control organism and environment. New Zealand investigators (Paynter et al. 2016, 2012) conducted an analysis of the conditions leading to effective weed suppression based on (1) a large sample (232 agents released for 80 weeds worldwide); (2) a continuous, quantitative response variable (proportional reduction in target weed abundance), and (3) a screening of seven candidate explanatory variables. The first explanatory variable (number of control organism species) below was continuous and variables 2–7 were plant or environment traits treated as categorical variables (yes/no): (1) number of control organism species, (2) abundant in native range, (3) presence of potential non-targets, (4) life cycle (temperate annual), (5) reproduction (asexual), (6) habitat stability, (7) ecosystem (aquatic). The analysis isolated three predictors of success: (1) aquatic ecosystem, (2) asexual weed reproduction, (3) not a major weed in native range. Cross classification by these predictors yielded a sliding scale for probability of success. For example, the floating aquatic fern Salvinia molesta D.S. Mitchell (Salviniaceae) offers the best possible combination of traits (aquatic, asexual reproduction, not abundant in native range), but tansy ragwort J. vulgaris is a ‘difficult weed’ with the worst possible combination of traits (terrestrial, sexual reproduction, abundant in native range). For improvements in this approach, we should look to modeling the four-way interactions in attributes of the control organism × target organism × potential nontarget organisms × recipient environment. The powerful disease triangle concept in pathology can be extended to biological control: successful biocontrol requires a virulent control organism, a susceptible host, and a permissive environment. As it stands, the New Zealand model for predicting success has two distinct advantages over past inductive approaches based on data bases in biological control: (1) it is quantitative—it escapes some but not all (e.g. not a major weed in the native range) of the subjectivity used to judge outcomes in the past, (2) it is multi-factorial—combining attributes of organisms and their environments. However, we must be flexible in applying this framework for decision making. There is no reason to avoid difficult weeds: if the benefits are large enough, they may offset the predicted higher risk of failure, as illustrated by successful biological control of ragwort.
Finally, consider the likelihood that an established control organism will attack non-target organisms. Recent work has shown that risk to non-target organisms can be predicted by host specificity and other tests conducted prior to release (Paynter et al. 2015). Consumer preference (oviposition and feeding behavior) and performance (demography) must be coordinated for effective host use, and measuring these attributes is part of the best practices for host specificity testing and safety screening prior to release. New Zealand investigators showed that a combination of relative preference and performance on test and target plants in laboratory tests predicts the risk of non-target attack in the field for arthropod weed biocontrol agents (Paynter et al. 2015). Measures of relative preference multiplied by relative performance on test and target plants in laboratory tests yield a combined risk score. ‘Non-targets’ in this study include both natives as well as other exotics (not originally declared to be targets), that sometimes yields values > 1. Combined risk score predicts the risk of non-target attack in the field for arthropods used for weed biocontrol. The point of transition from very low to very high probability of non-target attack serves as a working ‘threshold for concern’ and a possible index to inform regulatory decisions. The results of this study reinforce confidence in current host range testing that complies with best practices as a way to exclude candidate control organisms that are likely to use valued non-target organisms. This proof of concept study was based on a small sample (12 weeds, 23 agents) restricted to a single, island region (New Zealand), so these results must await further confirmation with larger samples from mainland areas. The remaining uncertainties, acknowledged by these authors, include adverse indirect effects and rapid evolution in host and climatic ranges. These are areas where ecologists could help (Fowler et al. 2012), but these areas are scarcely addressed by the standard ecology theory applied to biological control.

Conclusion

Developing a scientific theory of biological control is a worthy goal to aid understanding, prediction, and management of biological control systems. Theory transforms the details of case studies into more powerful abstractions, identifies topics of interest and suggests ways to go about investigating them, develops parsimonious yet mechanistic explanations, clarifies the link between assumptions and conclusions, and complements the verbal intuition that has been the mainstay of biological control since its inception.
Further progress toward this goal requires that we (1) combine deductions from mathematical models with rigorous empiricism measuring and modeling the effects of abiotic and biotic environmental drivers on demography and population dynamics of real biological control systems in the field, (2) use a wider range of model systems to explore how population structure, movement, spatial heterogeneity, and environmental conditions influence population and community dynamics, and (3) combine deductive and inductive approaches to address the day-to-day concerns of biocontrol scientists including optimal ways to release a control organism, suppress the target organism, and minimize harm to non-target organisms. There is a need for more foundational research in population and community ecology of direct relevance to biological control to create a more reliable basis for understanding, prediction, and management.

Acknowledgements

I wish to thank International Organization for Biological Control (IOBC) for allowing me to participate in the workshop on biological control held in Engelberg, Switzerland in October 2015. I wish to thank F. Grevstad, L. Buergi, and several anonymous reviewers for improvements in this paper made possible by their comments. I am deeply grateful to National Science Foundation and the United States Department of Agriculture for supporting our foundational research in biological control.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Metadaten
Titel
Theoretical contributions to biological control success
verfasst von
Peter B. McEvoy
Publikationsdatum
16.11.2017
Verlag
Springer Netherlands
Erschienen in
BioControl / Ausgabe 1/2018
Print ISSN: 1386-6141
Elektronische ISSN: 1573-8248
DOI
https://doi.org/10.1007/s10526-017-9852-6

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