REVIEW

Theoretical contributions to biological control success

Peter B. McEvoy

Received: 15 May 2017 / Accepted: 6 November 2017 / Published online: 16 November 2017

� The Author(s) 2017. This article is an open access publication

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 biolog-

ical control systems in the field, (2) use a wider range

of model systems to explore how population structure,

movement, spatial heterogeneity, and external envi-

ronmental conditions influence population and com-

munity dynamics, and (3) combine deductive and

inductive approaches to address the day-to-day con-

cerns of biocontrol scientists including how to rear and

release a control organism, suppress the target organ-

ism, and minimize harm to non-target organisms.

Further progress will require more fundamental

research in population and community ecology

directly relevant to biological control.

Keywords Consumer-resource dynamics �Ecological theory for biological control � Fieldexperiments � Demography and population dynamics �Parasitoid-host interactions � Herbivore-plantinteractions

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 seeminglymagical 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 andHoffmann 2015).Waver-

ing 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

Handling Editor: Jacques Brodeur.

P. B. McEvoy (&)

Botany and Plant Pathology, Oregon State University,

Corvallis, OR 97331, USA

e-mail: mcevoyp@science.oregonstate.edu

123

BioControl (2018) 63:87–103

https://doi.org/10.1007/s10526-017-9852-6

biological control applications by emphasizing (1)

classical (= importation) biological control rather than

conservation and augmentation to highlight the ‘nat-

ural’ self-sustaining dynamics of population interac-

tions, (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 preda-

tor–prey system, (5) local dynamics ignoring patterns

and processes operating on more global scales (to

satisfy the assumption of perfect mixing, like chem-

icals 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 biolog-

ical 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). Fortu-

nately, 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

andMills 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. Pop-

ulation 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, obser-

vational studies, and field experiments linking popu-

lation 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.

88 P. B. McEvoy

123

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 bio-

logical control is practiced (annual and perennial plant

systems in greenhouse or field; aquatic or terrestrial

environments), that often devolve into separate sub-

cultures 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 effec-

tive predator from one that eats quickly, to one that

moves effectively through a heterogeneous environ-

ment 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 fore-

going 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 behav-

ior. 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 unex-

plained and overall success rates of new programs

destined to remain low.

Theory ideally clarifies the link between assump-

tions 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 dynam-

ics before a hypothetical equilibrium is reached (a

plausible, alternative assumption for ecosystems fre-

quently 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 biologi-

cal control outcomes. The best example demonstrating

the mechanisms leading to strong and stable suppres-

sion of pest density is the wasp Aphytis melinus

DeBach, 1959 (Hymenoptera: Aphelinidae) used to

control California red scale Aonidiella auranti (Mas-

kell 1879) (Hemiptera: Diaspididae) in citrus Citrus

spp. (Sapindales: Rutaceae) in California, USA (Mur-

doch 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 weak-

nesses of another, and (2) the more closely each relates

to the other, then the more powerful each becomes. In

practice, however, mathematical modeling investiga-

tions have proceeded independent of empirical stud-

ies, often settling for only casual correspondence

between the behavior of model and natural systems. In

a celebrated example, unstable parasitoid-host inter-

actions 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

Theoretical contributions to biological control success 89

123

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, nat-

ural-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 bio-

logical control systems. Theory for biological control

should advance hand-in-hand with practice (a ‘coevo-

lutionary’ 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 docu-

mented 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 technol-

ogy 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 acknowl-

edging 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 envi-

ronmental conditions influence population and com-

munity 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.

90 P. B. McEvoy

123

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 para-

sitoids for control of mango mealy bug (Rastrococcus

invadens Williams) (Hemiptera: Pseudococcidae),

was designed to be prospective but became retrospec-

tive 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 (in-

cluding 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 conse-

quences 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 empir-

ical 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 (Ber-

ryman 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 sys-

tems, it is widely assumed that insect pests and their

natural enemies are forced to persist as a metapopu-

lation, 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 9 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. Remov-

ing the refuge actually decreased variability (increased

Theoretical contributions to biological control success 91

123

stability), so the refuge is not stabilizing. Closing off a

tree to migration had no detectable effect on variabil-

ity, so the interaction is not stabilized by metapopu-

lation 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 experi-

ment that might uncover both density-dependence and

the regulating mechanisms causing return to equilib-

rium. The experiment was coupled with a detailed

day-to-day stage-structured model of the system of

interactions incorporating candidate regulatory mech-

anisms, with parameters estimated independent of the

experiment (Murdoch et al. 2005). The model distin-

guished 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 mecha-

nism’)—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 repro-

duced 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 manipu-

lative 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 herbi-

vore species—the cinnabar moth Tyria jacobaeae (L.)

(Lepidoptera: Erebidae), and the ragwort flea beetle

Longitarsus jacobaeae (Waterhouse) (Coleoptera:

92 P. B. McEvoy

123

Chrysomelidae) interacting with a shared target host

plant tansy ragwort Jacobaea vulgarisGaertn.) (Aster-

ales: 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, andNorthAmerica. 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 biolog-

ical 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 quantita-

tively 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 abun-

dance, 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 demon-

strating population regulation (Murdoch et al. 2005;

Murdoch 1970). Create an upsurge in ragwort abun-

dance 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 pro-

tected 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 biolog-

ical control organisms (McEvoy et al. 1991). Strong

suppression of ragwort abundance triggered a succes-

sional 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 invulner-

able 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 unsatis-

factory 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

Theoretical contributions to biological control success 93

123

identified in structured population models parameter-

ized 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, chem-

istry, 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; Kar-

eiva 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 interac-

tions 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 (re-

source 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 interac-

tions, including the ‘top-down’ effect of herbivory

combine with the ‘bottom-up’ effect of plant compe-

tition (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) 9 two cinnabar moth

levels (plots exposed or protected with cages) 9 two

flea beetle levels (exposed or protected) 9 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 sin-

gle-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 esti-

mates 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,

94 P. B. McEvoy

123

deterministic model best represents a population

growing exponentially following disturbance, and it

permits elasticity values to be easily obtained analyt-

ically, 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 analy-

sis (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 recom-

mended 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 biolog-

ical 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 environ-

mental 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 equi-

librium dynamics may be of more practical signifi-

cance 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

Theoretical contributions to biological control success 95

123

in weed abundance following disturbance, and herbi-

vore 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, colo-

nization, 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 establish-

ment, increase, and spread of invasive species. It has

several advantages over standard practice for explain-

ing 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 contrib-

utors 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 activa-

tion-inhibition model transcends the oversimplifica-

tion 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 phe-

nomenology of ‘context dependence’ with mechanis-

tic explanations based on the action and interaction of

disturbance, colonization, and local organism interac-

tions, the forces driving the dynamics of nearly all

ecological systems (Levin 1989). Finally, the activa-

tor-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. Interest-

ingly, 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, iteropar-

ity, and perenniality). In their separate ways, the

insects and plants rely on life history features (disper-

sal, dormancy, perenniality, and iteroparity) to aver-

age 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 envi-

ronment (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.

96 P. B. McEvoy

123

The rapid rates of colonization and faster popula-

tion growth rates of the control organism are necessary

and sufficient to counter the colonization and popu-

lation growth rates of the target organism. Each

control organism counters increases in target-organ-

ism density when and where they occur, and neither

control organism is self-limited at the relevant densi-

ties. 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 com-

pared 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 bio-

logical 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 com-

petition 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 Califor-

nia 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

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

Theoretical contributions to biological control success 97

123

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 resis-

tance 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 competi-

tion, herbivores (two species in this case) on plant

demography and population growth have been exam-

ined 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 short-

comings due to (1) subjective measures of success

(e.g. partial, substantial, complete control); (2) over-

reliance 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 rela-

tionship 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

98 P. B. McEvoy

123

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) bio-

control 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 environ-

ment. 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 abun-

dance), 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 environ-

ment 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 prob-

ability 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 9 target organism 9 potential non-

target organisms 9 recipient environment. The pow-

erful 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

Theoretical contributions to biological control success 99

123

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 confir-

mation 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 conclu-

sions, 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 biolog-

ical 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 understand-

ing, 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.

Funding This work is supported by Foundational Program

grant no. 2016-67013-24929/project accession no. 1009109

from the USDA National Institute of Food and Agriculture.

Open Access This article is distributed under the terms of the

Creative Commons Attribution 4.0 International License (http://

creativecommons.org/licenses/by/4.0/), which permits unre-

stricted 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 Com-

mons license, and indicate if changes were made.

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Peter B. McEvoy is professor of ecology and biological

control at Oregon State University, USA. His research spans

ecology, evolution, and behavior of insect–plant interactions,

and the science, policy, and technology of biological control

for management of invasive species.

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