ARTICLE IN PRESS

Basic and Applied Ecology 6 (2005) 571—584

KEYWORDPopulationdynamics;Herbivore;Parasitoid;ConservatNatural enAgro-ecosyBottom-upBenefit;Success ra

1439-1791/$ - sdoi:10.1016/j.

�CorrespondE-mail addr

www.elsevier.de/baae

Using lower trophic level factors to predictoutcomes in classical biological control of insectpests

Paul Grossa,�, Bradford A. Hawkinsb, Howard V. Cornellc,Balakrishna Hosmaned

aDepartment of Natural Sciences, National-Louis University, 2840 Sheridan Road, Evanston, IL 60201, USAbDepartment of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USAcDepartment of Environmental Science and Policy, University of California, Davis, One Shields Avenue, Davis,CA 95616, USAdDepartment of Statistics, Northern Illinois University, De Kalb, IL 60115, USA

Received 9 January 2005; accepted 9 May 2005

S

ion;emies;stems;;

te

ee front matter & 200baae.2005.05.006

ing author. Tel.: +1 84ess: pgross@nl.edu (P

SummaryImportation of exotic natural enemies for biological control of insect pests entails risksto the environment. Pre-release estimates of the likelihood of achieving successfulcontrol would be helpful in avoiding ineffective importations. Based on strongevidence of multi-trophic level interactions in terrestrial ecosystems, we testedwhether variation in ecological and biological factors found at the plant and herbivoretrophic levels (levels one and two) could be used to create a simple, empirically basedformula, capable of estimating the probability of successful biological control againstholometabolous insect pests. We constructed a database consisting of 828 records ofbiological control attempts against 91 pest insect species and used stepwise logisticregression to test whether five basic features of the ecosystem, crop, and pest(habitat type, crop use, pest order, pest feeding niche, and damage severity) werecorrelated with rates of successful control. Natural enemy characteristics were notincluded in the model. The final model included 10 significant independent variables,nine of which were two-way interactions; all five basic ecosystem features appearedin significant interactions. The model provided good estimates of historical successrates against pest species in the data set. In a further test, the model was able tocorrectly rank amenability to biological control for 10 pest species not included in theoriginal data set. These results provide evidence that lower trophic level factors canbe useful in the search for a predictive formula for biological control.& 2005 Gesellschaft fur Okologie. Published by Elsevier Gmbh. All rights reserved.

5 Gesellschaft fur Okologie. Published by Elsevier Gmbh. All rights reserved.

7 905 2389; fax: +1 847 256 1057.. Gross).

ARTICLE IN PRESS

P. Gross et al.572

ZusammenfassungDie Einfuhrung von exotischen naturlichen Feinden zur biologischen Kontrolle vonInsektenschadlingen beinhaltet Risiken fur die Umwelt. Eine Abschatzung derWahrscheinlichkeit, ob eine erfolgreiche Kontrolle erreicht werden kann, ware vorder Freisetzung hilfreich um ineffektive Einfuhrungen zu vermeiden. Auf derGrundlage von deutlichen Hinweisen auf multitrophische Interaktionen zwischenden Leveln in terrestrischen Okosystemen untersuchten wir, ob die Variation vonokologischen und biologischen Faktoren, die auf den trophischen Leveln der Pflanzenund Herbivoren gefunden werden (Level eins und zwei), benutzt werden kann, umeine einfache Formel auf empirischer Grundlage zu finden, mit der man in der Lage istdie Wahrscheinlichkeit einer erfolgreichen biologischen Kontrolle von holometabolenInsekten abzuschatzen. Wir konstruierten eine Datenbasis, die aus 828 Datensatzenuber Versuche biologischer Kontrolle von 91 Insektenschadlingsarten bestand, undbenutzten eine schrittweise logistische Regression um zu testen, ob funf grundlegendeEigenschaften des Okosystems, der Feldfruchte und der Schadlinge (Habitattyp,verwendete Feldfrucht, Ordnung der Schadlinge, Nahrungsnische der Schadlinge undAusmaß der Schadigung) mit den Raten einer erfolgreichen Kontrolle korreliert waren.Die Eigenschaften der naturlichen Feinde wurden nicht in das Modell eingebracht. Dasendgultige Modell beinhaltete 10 signifikante unabhangige Variablen, von denen neunwechselseitige Interaktionen waren. Samtliche der funf grundlegenden Okosystemei-genschaften erschienen in signifikanten Interaktionen. Das Modell lieferte guteAbschatzungen der historischen Erfolgsraten gegen Schadlingsarten in dem Datensatz.In einem weiteren Test konnte das Modell die Zuganglichkeit fur biologische Kontrollebei 10 Schadlingsarten richtig einordnen, die nicht im ursprunglichen Datensatzvorhanden waren. Diese Ergebnisse liefern den Beweis, dass Faktoren der unterentrophischen Level bei der Suche nach einer Vorhersageformel fur biologische Kontrollenutzlich sein konnen.& 2005 Gesellschaft fur Okologie. Published by Elsevier Gmbh. All rights reserved.

Introduction

Inadvertent transport of invasive species con-tinues to rank as one of the greatest threats toagricultural and native ecosystems. For over 100years, entomologists have used ‘‘classical biologicalcontrol’’, i.e., importation of exotic natural ene-mies, to control populations of invasive insect pestsaround the world. Although classical biologicalcontrol is based, in part, on concepts developedin basic ecology, a general ecological modelcapable of predicting success and failure has neverbeen developed. The need for such a model isacute, as recent studies have documented agrowing list of negative (direct and indirect) effectsof imported natural enemies on native, non-targetinsect species (e.g., Barron, Barlow, & Wratten,2003; Follett & Duan, 2000; Henneman & Memmott,2001; Howarth, 1991; Kellogg, Fink, & Brower,2003; Louda, Pemberton, Johnson, & Follett, 2003;Munro and Henderson, 2002; Schellhorn, Kuhman,Olson, & Ives, 2002; van Driesche, Nunn, Kreke,Goldstein, & Benson, 2003; Wajnberg, Scott, &Quimby, 2001). The potential for harmful ecologicalrepercussions means that those making decisionsabout whether to import natural enemies will beexpected to weigh probable benefits (including the

benefit of avoiding alternate pest managementmethods) against the environmental risks of im-portation (Cory & Myers, 2000; Heimpel et al.,2004; Louda & Stiling, 2003). There is thus a needfor quantitative methods that can provide esti-mates of risks and/or expected benefits of biologi-cal control programs (Hopper, 2001).

There has been some discussion about ways toreduce the risks of natural enemy importations tonon-target insect species (e.g., DeNardo & Hopper,2004; Sands & van Driesche, 2000; Stiling, 2004;Strong & Pemberton, 2000). Calculating expectedbenefits is equally important (Heimpel et al., 2004;Louda et al., 2003; Louda & Stiling, 2003). Anessential component on the benefits side is theability to estimate the probability that a givenimportation will result in ‘‘success’’, i.e., suppres-sion of the pest’s population to an equilibriumdensity below its economic injury level. The goal ofthis study was to test a new method for making pre-release estimates of the probability of achievingsuccessful biological control.

The traditional approach for explaining andpredicting results of introductions for biologicalcontrol has involved experiments and modeling toincrease understanding of the basic ecologicalfactors controlling predator–prey and parasitoid–-

ARTICLE IN PRESS

Biocontrol prediction using pest and habitat characteristics 573

host population dynamics. Identification of naturalenemy characteristics most likely to result inregulation of pest populations at low densities hasbeen viewed as the key to improving biologicalcontrol. There has been a lengthy debate over theattributes of the ideal agent (Barlow, 1999; Bed-dington, Free, & Lawton, 1978; DeBach, 1964;Greathead, 1986; Mills, 2001) and extensive re-search on the biology, behavior, and ecology ofinsect parasitoids and predators. However, it hasproven difficult to reduce the complexities ofpest–enemy interactions to a simple generalformula capable of predicting success or failure(Andow, Ragsdale, & Nyvall, 1997; Hawkins &Cornell, 1999; Mackauer, Ehler, & Roland, 1990;Mills, 2000, 2001; Waage & Mills, 1992).

Advances in the study of three trophic levelinteractions in terrestrial ecosystems suggested analternative approach. In the 25 years since Price etal. (1980) argued that most pest–enemy interac-tions are influenced by host plant characteristics,an enormous body of information on multi-trophiclevel interactions has developed (e.g., Tscharntke& Hawkins, 2002). Two conclusions that can bedrawn from this research are: (1) that three trophiclevel interactions are ubiquitous in terrestrialecosystems and (2) that first trophic level char-acteristics, not only of the host plant, but also atthe level of the plant community and surroundinglandscape, often affect the vulnerability of insectherbivores to their natural enemies. We also knowthat biological and behavioral characteristics of theherbivore itself can influence the ease with whichits population can be suppressed by naturalenemies (see below). If lower trophic level factors,i.e., characteristics of the landscape, plant com-munity/habitat, host plant, and pest, have strongeffects on pest–enemy population dynamics, thenthese factors should be useful in predicting out-comes of biological control programs.

A great deal of evidence supports the idea thatlower trophic level factors strongly influence theability of natural enemies to suppress pest popula-tions. For example, biological characteristics ofinsect herbivores, such as taxonomic position(Lane, Mills, & Getz, 1999; Stiling, 1990), fecundity(Stiling, 1990), generations per year (Stiling, 1990),and defensive abilities (Dyer & Gentry, 1999; Evans& Schmidt, 1990; Gross, 1993) are known to affecttheir vulnerability to natural enemies and/orbiological control outcomes. A particularly impor-tant factor is the feeding niche of the herbivore(e.g., folivore, borer, root feeder, etc.), which hasstrong effects on the size and composition of theherbivore’s natural enemy fauna (Hawkins, 1994)and the ability of that fauna to suppress herbivore

populations (Gross, 1991; Hawkins & Gross, 1992;Stiling, 1990).

Effects of host plant characteristics on pest–en-emy interactions are varied and widespread.Numerous studies have documented effects ofplant structure, chemistry, nutrient status, phenol-ogy, and life history (reviewed in Bergman & Tingey,1979; Boethel & Eikenbary, 1986; Hare, 2002;Poppy, 1997; Price et al., 1980; Turlings, Gouin-guene, Degen, & Fritzsche-Hoballah, 2002).

There is also evidence that the local plantcommunity and farming method can affect naturalenemy populations. Factors found to influenceenemy effectiveness or population size includelocal plant species and structural diversity, hostplant abundance, whether the crop is annual orperennial, intercropping, strip cutting, frequencyand degree of disturbance, organic versus conven-tional farming methods, and availability of variousresources, including alternative hosts or prey,nectar, honeydew, pollen, cover, shade, overwin-tering sites, and refugia from pesticides (Andow,1991; Barbosa, 1998; Burel & Baudry, 1995; Drink-water, Letourneau, Workneh, van Bruggen, &Shennan, 1995; Gilstrap, 1997; Gurr & Wratten,1999; Hunter, 1992; Kareiva, 1983; Landis, Wrat-ten, & Gurr, 2000; Pickett & Bugg, 1998; Sheehan,1986; Tscharntke & Kruess, 1999; Wissinger, 1997).

Landscape level factors can also influence (posi-tively or negatively, Wratten, Gurr, Landis, Irvin, &Berndt, 2000) natural enemy effectiveness. Incontrast to some herbivorous pests, many naturalenemies require resources that lie beyond theedges of cultivated fields (Ekbom, 2000). Land-scape factors known to influence natural enemyeffectiveness include the perimeter/area ratios offields (Ostman, Ekbom, & Bengtsson, 2001), pre-sence of perennial plants in nearby habitats (Landiset al., 2000; Ostman et al., 2001), habitat diversityin the surrounding area (Kruess, 2003), andproximity to non-crop areas such as hedges, fallowfields, uncultivated field margins, woods, or grass-land (Elliott, Kieckhefer, & Beck, 2002; Kruess,2003; Thies & Tscharntke, 1999; Wratten, vanEmden, & Thomas, 1998). In general, many of theecosystem level characteristics thought to beimportant for conservation of biodiversity in gen-eral, e.g., habitat size and connectivity (Kareiva &Wennergren, 1995), are also likely to help inmaintaining effective natural enemy populations(Kruess & Tscharntke, 1994; Letourneau, 1998).

Taken together, this abundant evidence indicatesthat herbivore-enemy population dynamics areinfluenced by many factors acting at trophic levelsone and two. The result is that pest species vary intheir amenability to biological control. Reviews of

ARTICLE IN PRESS

P. Gross et al.574

biological control attempts have consistently re-vealed major effects of pest biology (e.g. order)and the habitat/ecosystem (e.g., field crops,orchards, forests) on rates of establishment andsuccess (Beirne, 1975; Dyer & Gentry, 1999; Great-head, 1986; Gross, 1991; Hall & Ehler, 1979; Hall,Ehler, & Bisabri-Ershadi, 1980; Hawkins & Gross,1992; Lloyd, 1960; Mackauer et al., 1990; Stiling,1990; Turnbull & Chant, 1961).

The goals of the study were: (1) to evaluate iflower trophic level factors could be helpfulin estimating the probability of biological controlsuccess, and (2) to develop a general and easy-to-use prediction formula, based on simple habitat,crop, and pest characteristics. Because thegoal was to evaluate the predictive power oflower trophic level factors, variables describingnatural enemy characteristics (e.g., fecundity,numerical response, number released, climatematching, life history, etc.) were excludedfrom the analysis.

Aside from their possible effects on biologicalcontrol outcomes, use of lower trophic level factorswould have two other advantages: (1) factorssuch as type of habitat, type of crop, pest order,pest feeding niche, etc., are in general, mucheasier to identify than details of natural enemybehavior and reproductive biology, and (2) thenature of lower trophic level factors is thatthey influence, and are influenced by, factorsat higher trophic levels. Therefore, if not a directcause of differences in enemy effectiveness,they are likely to be correlated to many otherfactors, some of which will influence the success ofbiological control.

Materials and methods

Dataset

We constructed a data set comprising 828 recordsof biological control successes and failures for 91insect pest species. The records were taken fromBIOCAT the most comprehensive compilation ofworldwide information available at the time thestudy was begun. BIOCAT lists 4769 biologicalcontrol attempts dating from 1870 to 1990 (Great-head & Greathead, 1992). We consulted standardreferences (Baker, 1972; Clausen, 1978; Johnson &Lyon, 1991; Metcalf & Metcalf, 1993) and primaryliterature to add information about the biology andfeeding behavior of each pest species, the cropplants it damages, and the agro-ecosystems itinhabits.

The scope of the data set was limited by threefactors:

(1)

To provide some uniformity, we limited the datato pest species in the four largest insect orders:Lepidoptera, Coleoptera, Diptera, and Hyme-noptera. Insects in these holometabolous ordersoccupy somewhat similar ranges of feedingniches and biological control attempts againstpest species in these orders have usuallyinvolved parasitoids rather than predators.Over 95% of the attempts in the data set usedparasitoids. We excluded Homoptera becausethese insects differ in feeding behavior (e.g.,piercing/sucking rather than chewing feedingbehavior) and in the frequent use of predatorsfor biological control. Factors influencing suc-cess against Homopteran pests and similarinsects are likely to differ from those affectingthe four orders chosen for this study.

(2)

We also excluded attempts in which the agentwas an egg-attacking parasitoid, i.e., a para-sitoid that oviposits into the egg stage of thehost and completes development in the egg,larva, or pupal stage of the host. After hatch-ing, most insect larvae move away from theoviposition site and feed elsewhere on theplant. As a result, features of the feeding niche(e.g., protection within hard plant tissues) thatinfluence a pest’s vulnerability to larval andpupal parasitoids may have little effect on itsvulnerability to egg-attacking parasitoids.

(3)

The data set was limited to pest species forwhich information on all independent variableswas available. In addition to the variablesdescribed in Table 1, we initially required dataon maximum percent parasitism and numberparasitoid species attacking the pest in itscountry of origin; there was evidence that thisinformation could increase the precision ofpredictions about biological control success(Hawkins & Cornell, 1999; Hawkins & Gross,1992). A preliminary multiple regression, how-ever, revealed no significant contribution ofmaximum percent parasitism when other in-dependent variables were included in themodel. Although statistically significant, thenumber of parasitoid species in the country oforigin was dropped because these data arefrequently unavailable.

Variables

We used a binomial dependent variable, whichwas based on the outcomes, as given in BIOCAT, of

ARTICLE IN PRESS

Table 1. Independent variables tested in logistic regression of biological control outcomes

Independent variable Trophic rank Levels of each variable Values used in regression

Habitat type 1 HAB1� HAB2�

Field crops, pasture 1 0Orchards, vineyards, urban 0 1Forest 0 0

Crop use 1 Food (human or animal) 0Fiber, ornamental 1

Pest order 2 H� L� C�

Hymenoptera 1 0 0Lepidoptera 0 1 0Coleoptera 0 0 1Diptera 0 0 0

Niche category 1 & 2 External on leaves 1Leafminers 1Shelter makersy 1Switches from leaf feeding to boring 0Borersz 0Root feeders 0

Damage severity 1 & 2 Direct damagey 0Indirect damage 1

�Dummy variables created for categorical variables with three or more levels. HAB1 and HAB2 refer to habitat type; H, L, and C referto the insect pest’s order.yLeaf folders, leaf rollers, leaf tiers, and web-making insects.zLarvae tunnel in stems, buds, or fruits.yDirect pests are those that feed on a marketable part of the host plant.

Biocontrol prediction using pest and habitat characteristics 575

biological control attempts against each of the 91pest species. An attempt represented the introduc-tion of a single biological control agent into a singlegeographic region. Outcomes in which the intro-duced enemy failed to become established or failedto reduce pest numbers were scored as failures;any evidence of pest suppression, including ‘‘par-tial’’, ‘‘substantial’’, or ‘‘complete’’ control, wasscored as a success. The number of attemptsagainst each pest species ranged from 1 to 92(mean ¼ 9.1, SD ¼ 15.3). The probability of suc-cessful biological control for each pest species wasdefined as successful attempts/total attempts.

Four criteria were considered when choosingpredictor variables: (1) ability to reflect majorecological features of the first trophic level(including habitat or ecosystem characteristics),the second trophic level, or the plant–herbivoreinteraction. Given the limitations inherent inanalyzing a historical data set such as BIOCAT, wewere less interested in which particular variableswere significant than in the power of lower trophiclevel factors, as a whole, to influence biologicalcontrol success. We therefore deliberately choserather coarse independent variables that werelikely to be correlated with other lower trophiclevel factors; (2) previous evidence of importance

to biological control success; (3) ease with whichthe value could be determined by biological controlpractitioners; and (4) probable correlation witheconomic injury levels. Judgments of success inbiological control depend on whether pest popula-tion densities fall below an economic threshold. Allother things being equal, plant and habitat factorsthat lower the threshold (e.g., use as food forhumans, use as an ornamental in urban areas,damage to marketable parts of the plant) shouldmake biological control success less likely.

Predictors tested (Table 1) were:

(1)

Habitat type (Lloyd, 1960; Hall et al., 1980;Greathead, 1986; Mackauer et al., 1990; Stil-ing, 1990; Gross, 1991).

(2)

Crop use, i.e., whether a crop is used for food,fiber, or ornament. To our knowledge, a crop’suse has never been tested for its effects onbiological control success. However, it shouldbe related to many potentially importantfactors, such as plant growth form, economicinjury level, landscape features, habitat per-manence, plant species and structural diversity,intensity of management practices, and thetypes of insect feeding that are likely to resultin economic losses.

ARTICLE IN PRESS

P. Gross et al.576

(3)

Pest order (Gross, 1991; Hall & Ehler, 1979; Hallet al., 1980; Lane et al., 1999; Stiling, 1990).

(4)

Pest feeding niche (Gross, 1991; Hawkins &Gross, 1992). Gross (1991) found that the rateof success in biological control programs againstleaf feeding Lepidoptera was nearly three timesgreater than that for Lepidoptera feeding inmore concealed locations.

(5)

‘‘Damage severity’’. We categorized pests as‘‘direct’’ if the pest damaged marketable partsof the plant, or ‘‘indirect’’ if the damage was tonon-marketable parts of the plant (Mackauer etal., 1990; Turnbull & Chant, 1961). For exam-ple, for pests of fruit trees, those speciesfeeding on the fruits were considered directpests, whereas foliage feeders were classifiedas indirect. For most ornamental plants, leaffeeders were classified as direct pests since thefoliage is the marketable commodity. Thus,gypsy moth, Lymantria dispar (L.), was classi-fied as a direct pest because it damagesaesthetically valuable foliage in urban environ-ments. Pests that caused severe damage to non-marketable parts of the plant were considereddirect if the damage frequently resulted indeath of the plant. In general, populationdensities of direct pests must be brought tolower levels than densities of indirect pests,making successful biological control less likelyfor direct pests (Turnbull & Chant, 1961).

Statistical analysis

We tested the model with logistic regression, atechnique appropriate for a binomial dependentvariable (Agresti, 1995; Hosmer & Lemeshow, 1989;SAS Institute, 1995). The five independent vari-ables, i.e., eight main effect terms (includingdummy variables, Table 1) and all 24 meaningfultwo-way interactions, were screened using step-wise logistic regression (PROC Logistic, SAS version6.12). The final model was used to generatepredicted values (PROC Logistic, SAS version 6.12)and 95% confidence intervals (PROC GENMOD, SASversion 6.12) for each pest species.

Additional test of the model

To further test the regression model, we con-structed a new data set composed of pest specieslisted in the BIOCAT database that had not beenincluded in the group of 91 pest species used todevelop the logistic regression model. Thesepest species had failed to qualify for the originaldata set because information on one potential

independent variable, number of parasitoid speciesattacking the pest in its native region, wasunknown for these species. A pest species(Lepidoptera and Coleoptera only) was included inthe test data set if results of at least 10 biologicalcontrol attempts against it were reported inBIOCAT. Only 10 pest species met the requirementsand were included in the test data set. Unlike manyof the pests in the original data set, nearly allinhabit tropical regions. Host plants include sugar-cane, coconut palm, various vegetables, coffee,and small grains.

Results

Logistic regression analysis of biological controloutcomes against the 91 pest species in the originaldata set identified 10 statistically significant pre-dictors (a ¼ 0.05, Table 2). The overall model washighly significant; the likelihood ratio statistic(Hosmer & Lemeshow, 1989) was 78.586 (10 df,P ¼ 0:0001, n ¼ 828 attempts against 91 pestspecies). The model predicted a wide range ofsuccess rates, ranging from 0.0005 for Liriomyzatrifolii Burgess, a polyphagous leafminer of vege-table crops, to 0.660 for Phytomyza ilicis Curtis(holly leafminer) and Pristiphora geniculata (Har-tig) (mountain-ash sawfly). Predicted probabilitiesof success for the 14 pest species subject to themost biological control attempts agreed reasonablywell with observed success rates (Fig. 1).

With 10 significant predictor variables, nine ofwhich were interaction terms, it was impossible todisentangle the contribution of each variable to thepredictions. As a group, the significant interactionsused all five of the original independent variables,with ‘‘habitat’’ and ‘‘pest order’’ appearing mostoften. However, our aim was not to discern specificcausal mechanisms, but rather to test the useful-ness of lower trophic level factors as a whole. Mostimportant is that a combination of variablesdescribing features of the ecosystem, host plant,and pest, with no information about the naturalenemies used in the Introduction, was able toaccount for much of the historical variation insuccess rates in classical biological control againstholometabolous insect pests (Fig. 1; a lack of fittest was not significant: Goodness of fitstatistic ¼ 5.0328, 4 df, P ¼ 0:2839, PROC Logistic,SAS).

When applied to the test data set containing thepest species not used in the original analysis, thelogistic regression model (based on the original 91pest species) correctly identified the 10 pest

ARTICLE IN PRESS

Table 2. Analysis of maximum likelihood estimates for logistic regression on records of success and failure in classicalbiological control against holometabolous pest species

Independent variable DF Parameter estimatey SE P

Intercept 1 �1.7076 0.1461 0.0001HAB2� 1 �0.9236 0.4403 0.0359H��HAB1� 1 6.1721 1.7616 0.0005L�� damage severity 1 4.0590 0.8106 0.0001L�� crop use 1 �2.4564 0.5656 0.0001C�� damage severity 1 6.0555 1.4283 0.0001C�� crop use 1 �4.8188 1.4323 0.0008Niche category�HAB1� 1 0.8530 0.4021 0.0339Damage severity�HAB1� 1 �6.6617 1.4066 0.0001Damage severity�HAB2� 1 �3.3337 0.9298 0.0003Crop use�HAB2� 1 3.2942 0.6923 0.0001

The dependent variable had a value of 1 for attempts resulting in partial, substantial or complete success and 0 for all other outcomes.�Dummy variable (see Table 1).yParameter estimates can be used to create a general prediction equation that will give the probability that importation of a naturalenemy species will provide at least partial control of a pest. For any given pest species in one of the four major insect orders, use Table1 to plug in values for each independent variable in the equation. The resulting equation will give the predicted logit transformedprobability of success. Then use the following formula to back-transform the logit value to a probability: Predicted probability ofsuccess ¼ elogit/(1+elogit), where e is the natural logarithm. An approximate 95% confidence interval for the predicted probability canbe calculated as ¼ {ea/(1+ea), eb/(1+eb)} where a ¼ loge (P/1�P)�1.96 {SE of loge (P/1�P)} and b ¼ loge (P/1�P)+1.96 {SE of loge (P/

1�P)}, and the SE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPki¼0

x2i varðbiÞ þ 2Pi o

Pjxixj covðbi; bjÞ

s.

Observed and predicted (with 95% confidence intervals) proportion of biological control

attempts providing pest suppression.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Choris

tone

ura

fum

ifera

na

Diatra

ea sa

ccha

ralis

Graph

olita

mole

sta

Lym

antri

a dis

par

Ostrini

a nu

bilali

s

Pectin

opho

ra g

ossy

piella

Phtho

rimae

a op

ercu

lella

Pieris

rapa

e

Rhyac

ionia

buoli

ana

Hyper

a po

stica

Popilli

a jap

onica

Bactro

cera

olea

e

Liriom

yza

trifo

lii

Neodip

rion

serti

fer

Pest species

Pro

po

rtio

n a

ttem

pts

su

cces

sfu

l

Predicted

Observed

Figure 1. Observed and predicted (with 95% confidence intervals) proportion of biological control attempts providingpartial, substantial, or complete suppression of the pest. Predictions are based on the logistic regression modeldescribed in Table 2. Pest species shown were those for which importation biological control had been attempted atleast 14 times (a natural break point in the data). For six of these (Choristoneura fumiferana, Grapholita molesta,Lymantria dispar, Pectinophora gossypiella, Rhyacionia buoliana, and Liriomyza trifolii) the observed success rate waszero.

Biocontrol prediction using pest and habitat characteristics 577

ARTICLE IN PRESS

Ability of regression model to predict biological control success rates against 10 pest species not in original data set

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.1 0.2 0.3 0.4 0.5

Predicted proportion of attempts successfulOb

serv

ed p

rop

ort

ion

att

emp

ts s

ucc

essf

ul

r = 0.85

P = 0.0018

Figure 2. Ability of logistic regression model to predict biological control success rates against 10 pest species notincluded in original data set.

P. Gross et al.578

species’ relative responsiveness to biological con-trol attempts. The predicted proportion of at-tempts achieving success per pest species wassignificantly correlated to the observed proportion(Fig. 2). However, the model consistently over-estimated the proportion of attempts that actuallyprovided successful control. The poorest predic-tion, by far, was for the coconut leaf hispa,Brontispa longissima (Gestro). The model predictedthat biological control should have been successfulin about half the attempts, but it was successfulonly three times in 12 attempts.

Discussion

Results of this study indicate that lower trophiclevel variables could be helpful in the search for asimple method of predicting outcomes in biologicalcontrol. Considering the inherent difficulty ofpredicting the outcomes of perturbations to ecolo-gical systems (Lonsdale, Briese, & Cullen, 2001),the logistic regression model (Table 2) was able toprovide reasonably good estimates of the prob-ability of control (i.e., proportion of biologicalcontrol attempts that provided some level ofcontrol) of pest species in the data set. In asubsequent test, the same model was able tocorrectly rank amenability to biological controlfor 10 pest species not included in the original dataset.

The predictive ability of the equation was some-what surprising considering that some variablesknown to influence biological control outcomes

were not included. For example, biological controlsuccess is partly a function of the number ofpredator or parasitoid individuals released (Beirne,1975; Ehler & Hall, 1982; Hopper & Roush, 1993;Grevstad, 1999) and there was wide variation inthis variable within the data set. In addition,explicit information about natural enemy charac-teristics was deliberately excluded from the re-gression. Either lower trophic level factors directlyaffect the ability of the natural enemy to regulatethe pest population at a level below its economicinjury level, or they are correlated with otherfactors that do. In either case, the results indicatethat lower trophic level variables could be useful inestimating the likelihood of success of biologicalcontrol programs. (Because all levels of successwere pooled, we cannot rule out the possibility thatthe ecological requirements for complete successdiffer from those required for partial or substantialsuccess.)

Several previous analyses of biological controlrecords have provided evidence that host plant andhabitat characteristics can affect the likelihood ofbiological control success. Our results are generallyconsistent with results of these studies, althoughour analysis provides evidence of a more complexpicture. In particular, the large proportion ofinteraction terms among the significant indepen-dent variables (previous analyses of habitat effectsdid not test interactions) suggests that simple,across the board rules about ecological keys tosuccessful biological control may not exist. Factorshelpful to success against pests in certain habitattypes, feeding niches or damage severity categories(i.e., direct versus indirect) may be harmful in

ARTICLE IN PRESS

Biocontrol prediction using pest and habitat characteristics 579

programs against pests in other habitat types,feeding niches, or damage severity categories.For example, the widely accepted notion thatbiological control works best in orchards and worstin field crops (Beirne, 1975; Hall et al., 1980;Stiling, 1990) held for borers and root feeders, butnot for leaf-feeders (see niche category�HAB1,Table 2); it held for indirect pests, but successagainst direct pests was highest in field crops andpastures (see interactions of damage severity withHAB1 and with HAB2, Table 2). Gilstrap (1997)stressed that enemy attributes useful in permanenthabitats may be detrimental in ephemeral croppingsystems. Unfortunately, multi-trophic level inter-actions are often complex; yet their influence onnatural enemy effectiveness cannot be ignored.

The results of this study also support the ideathat second trophic level factors are important tobiological control success. Our analysis identifiedfive significant interactions involving pest order,four involving damage severity, and one involvingpest-feeding niche (Table 2). Stiling (1990) identi-fied seven pest characteristics that were related torates of establishment of parasitoids: order, fe-cundity, generations per year, mobility, nativeversus introduced, feeding niche (internal versusexternal), and diet breadth. Dyer and Gentry(1999) found that, in biological control attemptsusing predators, rates of success are much higheragainst cryptically colored lepidopteran and co-leopteran larvae than against brightly coloredlarvae; they are higher against smooth larvae thanhairy larvae. For biological control attempts usingparasitoids, they found that rates of success aremuch greater against pests with narrow dietbreadths than broad and greater for gregariouslyfeeding larvae than for solitary larvae. Gross (1991)and Hawkins and Gross (1992) found rates ofsuccess against leaf-feeding Lepidoptera and Co-leoptera pests to be at least double that of borersand root feeders.

The logistic regression equation presented inTable 2 is the first general model capable ofpredicting the probability of biological controlsuccess for a wide range of ecological conditionsand pest taxa. Until a more precise model isdeveloped, growers and pest management scien-tists may find the model helpful in quickly evaluat-ing the potential of biological control as amanagement tool for specific pest species (seeinstructions in footnote of Table 2). All predictorvariables are easily obtained, requiring only themost basic information about the crop system andpest biology (Table 1).

The regression model may be less reliable fortropical than for temperate zone pest species. In

the second test of the model against mainlytropical pest species, the model overestimatedthe probability of biological success. The over-estimates may have resulted from economic andgeographic differences between regions whereattempts took place in the original data set andthose in the test data set. The average success ratein the test sample (mean ¼ 0.075, n ¼ 10) was onlyabout one-half that found in the original data set(mean ¼ 0.16, n ¼ 91).

Note that the prediction equation is designed fortarget selection rather than enemy selection.Historically, target selection has received far lessattention than natural enemy selection. Wilson andHuffaker (1976) argued that success in biologicalcontrol was related to the amount of researchconducted rather than the type of pest, habitat, orclimate; target pests, they concluded, ‘‘shouldseldom, if ever, be excluded on principley’’ Underthis paradigm, the major challenges facing biologi-cal control workers were choice of enemy speciesand methods of rearing and release. However, themultitudinous and pervasive effects of lowertrophic level factors, their correlation with eco-nomic injury levels, and the results of this andother retrospective analyses (e.g., Lloyd, 1960;Turnbull & Chant, 1961; Beirne, 1975; Hall & Ehler,1979; Hall et al., 1980; Greathead, 1986; Mackaueret al., 1990; Stiling, 1990; Gross, 1991; Hawkins &Gross, 1992; Dyer & Gentry, 1999) indicate thatsome pest species are intrinsically more amenableto biological control than others. Given theecological risks of importation, it will be importantto make a priori estimates of the relative suitabilityof different target pests/ecosystems to biologicalcontrol. The formula developed in this study (Table2) begins to provide a quantitative method fordoing so, at least for programs aiming to controlholometabolous pests with parasitoids.

The precision of the model’s predictions couldprobably be improved by including additionalindependent variables. Several pests (e.g., Dyer &Gentry, 1999; Stiling, 1990) and natural enemy(e.g., Kimberling, 2004; Stiling, 1990) character-istics not tested in this study are known to berelated to rates of establishment or success inbiological control. Other pest characteristics,thought to be generally important to herbivorepopulation dynamics in natural systems, might alsoprove useful as predictors (e.g., Dyer & Gentry,1999). For example, in caterpillars, traits such ascoloration (cryptic versus warning), hairiness,gregariousness, host plant range, and whetherdevelopment occurs in spring or summer, arerelated to a species’ propensity to reach outbreakdensities (Hunter, 1991, 1995). Also, many defen-

ARTICLE IN PRESS

P. Gross et al.580

sive characteristics (e.g., egg mass architecture,armor, evasive behaviors, etc.) affect vulnerabilityto natural enemies (e.g., Dyer & Gentry, 1999;Gross, 1993; Sheehan, 1991).

As a form of applied ecology, biological controlhas long relied on basic ecological principles toprovide guidelines for selection of biological con-trol agents. Suggested guidelines have come fromexperimental and theoretical analyses of enemyfunctional, aggregative, and numerical responses,searching efficiency, fecundity, and dispersal abil-ity. In addition, biological control workers haveoften relied on intuitively reasonable ecologicalcriteria such as degree of prey specialization,impact on pest populations in the source region,climate matching, performance in laboratory eva-luations, fecundity, and tolerance of climaticextremes (Hopper, 2003). However, there is dis-agreement about which characteristics are mostimportant. For example, recent articles havequestioned the importance of climate matching(Van Klinken, Fichera, & Cordo, 2003), availabilityof open niches (Sivinski & Martin, 2003), highparasitoid fecundity (Lane et al., 1999), andappropriate functional responses (Fernandez-Arhex& Corley, 2002; Kindlmann & Dixon, 2001; Lester &Harmsen, 2002).

Despite such disagreement, nearly all guidelinesto biocontrol practice begin with one basic assump-tion about insect population regulation: that pestoutbreaks occur because effective enemy speciesare absent. In this study, we tested the usefulnessof a contrasting assumption about insect populationecology, namely that landscape, community, plantand pest characteristics strongly influence thepotential of natural enemies to exert control indifferent ecosystems. The success of the regressionmodel, which was based exclusively on lowertrophic level variables, is consistent with the viewthat bottom-up factors strongly influence parasi-toid–host population dynamics. It also suggests thatconsideration of lower trophic level factors couldimprove the precision of pre-release estimates ofthe probability of successful control.

Because the data set came from a survey ratherthan a controlled experiment, it is not possible todraw conclusions about the specific ecologicalmechanisms most responsible for successful biolo-gical control. Predictor variables used in theregression model could have been correlated withmany other factors, including factors found at thethird trophic level. For example, the fundamentallife history division between idiobiosis and koino-biosis, in parasitoids, is closely related to whetherthe host uses a concealed or exposed feeding niche(Askew & Shaw, 1986). In addition, the explanatory

power of interactions involving ‘‘crop use’’, ‘‘nichecategory’’, and ‘‘damage severity’’ probably de-rived, in part, from the relationship of thesevariables to the economic injury level. The ex-istence of such correlations suggests that theability of the model to predict success ratesoccurred, not only because the predictor variablesin the regression model directly influence naturalenemy effectiveness, but also because the natureof lower trophic level factors is their influence on,and correlation with, countless other factors actingall trophic levels (see Hunter & Price, 1992). It wasprimarily for this reason that lower trophic levelvariables were investigated, as a major goal of thestudy was to find a simple predictive model.

Although the analysis could not separate correla-tional effects from direct effects of lower trophiclevel factors on biocontrol success, it is noteworthythat the model was able to predict rates of successwithout explicit inclusion of natural enemy char-acteristics. Had the regression failed to signifi-cantly predict biological control outcomes, thiswould have supported an exclusive research focuson pest–enemy interactions. Its success lends somesupport to the idea that features of the pest andthe environment in which the pest lives may be asimportant to biological control success as enemycharacteristics like fecundity and host-specificity.

Acknowledgments

We thank E. Conner, N. Mills, P.W. Price and fouranonymous reviewers for comments on earlierversions of the manuscript, K. Duggan for clericalassistance, D. LeBlond of Abbott Laboratories foradvice on SAS programming, and the Department ofComputer Services at National-Louis University.

References

Agresti, A. (1995). An introduction to categorical dataanalysis. New York: Wiley.

Andow, D. A. (1991). Vegetational diversity and arthro-pod population response. Annual Review of Entomol-ogy, 36, 561–586.

Andow, D. A., Ragsdale, D. W., & Nyvall, R. F. (1997).Ecological interactions and biological control.Boulder: Westview.

Askew, R. R., & Shaw, M. R. (1986). Parasitoid commu-nities: their size, structure and development. In J.Waage, & D. Greathead (Eds.), Insect Parasitoids (pp.225–264). London: Academic Press.

Baker, W. L. (1972). Eastern forest insects, MiscellaneousPublication No. 1175, US Department of Agriculture,Forest Service, Washington, DC.

ARTICLE IN PRESS

Biocontrol prediction using pest and habitat characteristics 581

Barbosa, P. (Ed.). (1998). Conservation biological con-trol. New York: Academic Press.

Barlow, N. D. (1999). Models in biological control: A fieldguide. In B. A. Hawkins, & H. V. Cornell (Eds.),Theoretical approaches to biological control (pp.43–68). Cambridge: Cambridge University Press.

Barron, M. C., Barlow, N. D., & Wratten, S. D. (2003).Non-target parasitism of the endemic New Zealandred admiral butterfly (Bassaris gonerilla) by theintroduced biological control agent Pteromalus pupar-um. Biological Control, 27(3), 329–335.

Beddington, J. R., Free, C. A., & Lawton, J. H. (1978).Characteristics of successful natural enemies inmodels of biological control of insect pests. Nature,273, 513–519.

Beirne, B. P. (1975). Biological control attempts byintroductions against pest insects in the field inCanada. Canadian Entomologist, 107, 225–236.

Bergman, J. M., & Tingey, W. M. (1979). Aspects ofinteraction between plant genotypes and biologicalcontrol. Bulletin of the Entomological Society ofAmerica, 25, 225–229.

Boethel, D. J., & Eikenbary, R. D. (1986). Interactions ofplant resistance and parasitoids and predators ofinsects. Chichester: Ellis Horwood.

Burel, F., & Baudry, J. (1995). Farming landscapes andinsects. In D. M. Glen, M. P. Greaves, & H. M. Anderson(Eds.), Ecology and integrated farming systems (pp.203–220). New York: Wiley.

Clausen, C. P. (Ed.), (1978). Introduced parasites andpredators of arthropod pests and weeds: A worldreview. United States Department of Agriculturehandbook 480.

Cory, J. S., & Myers, J. H. (2000). Direct and indirectecological effects of biological control. Trends inEcology and Evolution, 15(4), 137–139.

Debach, P. (1964). Biological control of insect pests andweeds. New York: Reinhold.

DeNardo, E. A. B., & Hopper, K. R. (2004). Using theliterature to evaluate parasitoid host ranges: A casestudy of Macrocentrus grandii (Hymenoptera: Braco-nidae) introduced into North America to controlOstrinia nubilalis (Lepidoptera: Crambidae). Biologi-cal Control: Theory and Application in Pest Manage-ment, November, 280–295.

Drinkwater, L. E., Letourneau, D. K., Workneh, F. A., vanBruggen, H. C., & Shennan, C. (1995). Fundamentaldifferences between conventional and organic tomatoagroecosystems in California. Ecological Applications,5, 1098–1112.

Dyer, L. A., & Gentry, G. (1999). Predicting natural-enemy responses to herbivores in natural and mana-ged systems. Ecological Applications, 9, 402–408.

Ehler, L. E., & Hall, R. W. (1982). Evidence forcompetitive exclusion of introduced natural enemiesin biological control. Environmental Entomology, 11,1–4.

Ekbom, B. (2000). Interchanges of insects betweenagricultural and surrounding landscapes. In B. Ekbom,M. E. Irwin, & Y. Robert (Eds.), Interchanges of insects

between agricultural and surrounding landscapes (pp.1–3). Dordrecht, Boston, London: Kluwer AcademicPublishers.

Elliott, N. C., Kieckhefer, R. W., & Beck, D. A. (2002).Effect of aphids and surrounding landscape on theabundance of Coccinellidae in cornfields. BiologicalControl, 24, 214–220.

Evans, D. L., & Schmidt, J. O. (Eds.). (1990). Insectdefenses: Adaptive mechanisms and strategies of preyand predators. Albany, NY: State University of NewYork Press.

Fernandez-Arhex, V., & Corley, J. C. (2002). Thefunctional response of parasitoids and its implicationsfor biological control. Biocontrol Science and Technol-ogy, 13, 403–413.

Follett, P. A., & Duan, J. J. (Eds.). (2000). Nontargeteffects of biological control. Norwell, MA: KluwerAcademic Publishers.

Gilstrap, F. E. (1997). Importation biological control inephemeral crop habitats. Biological control, 10,23–29.

Greathead, D. J. (1986). Parasitoids in classical biologicalcontrol. In J. Waage, & D. Greathead (Eds.), Insectparasitoids (pp. 289–319). London: Academic Press.

Greathead, D. J., & Greathead, A. H. (1992). Biologicalcontrol of insect pests by insect parasitoids andpredators: The BIOCAT database. Biocontrol Newsand Information, 13, 61–68.

Grevstad, F. S. (1999). Factors influencing the chance ofpopulation establishment: Implications for releasestrategies in biocontrol. Ecological Applications,9(4), 1439–1447.

Gross, P. (1991). Influence of target pest feeding niche onsuccess rates in classical biological control. Environ-mental Entomology, 20(5), 1217–1227.

Gross, P. (1993). Insect behavioral and morphologicaldefenses against parasitoids. Annual Review of En-tomology, 38, 251–273.

Gurr, G. M., & Wratten, S. D. (1999). ‘Integratedbiological control’: A proposal for enhancing successin biological control. International Journal of PestManagement, 45(2), 81–84.

Hall, R. W., & Ehler, L. E. (1979). Rate of establishment ofnatural enemies in classical biological control. Bulle-tin of the Entomological Society of America, 25,280–282.

Hall, R. W., Ehler, L. E., & Bisabri-Ershadi, B. (1980). Rateof success in classical biological control of arthropods.Bulletin of the Entomological Society of America, 26,111–114.

Hare, J. D. (2002). Plant genetic variation in tritrophicinteractions. In T. Tscharntke, & B. A. Hawkins (Eds.),Multitrophic level interactions (pp. 8–43). Cam-bridge: Cambridge University Press.

Hawkins, B. A. (1994). Pattern and process in host–par-asitoid interactions. Cambridge, England: CambridgeUniversity Press.

Hawkins, B. A., & Cornell, H. V. (1999). Theoreticalapproaches to biological control. Cambridge, UK:Cambridge University Press.

ARTICLE IN PRESS

P. Gross et al.582

Hawkins, B. A., & Gross, P. (1992). Species richness andpopulation limitation in insect parasitoid–host sys-tems. American Naturalist, 139, 417–423.

Heimpel, G. E., Ragsdale, D. W., Venette, R., Hopper, K.R., O’Neil, R. J., Rutledge, C. E., et al. (2004).Prospects for importation biological control of thesoybean aphid: Anticipating potential costs andbenefits. Annals of the Entomological Society ofAmerica, 97, 249–258.

Henneman, M. L., & Memmott, J. (2001). Infiltration of aHawaiian community by introduced biological controlagents. Science, 293, 1314–1316.

Hopper, K. R. (2001). Research needs concerningnon-target impacts of biological control intro-ductions. In E. Wajnberg, J. K. Scott, & P. C. Quimby(Eds.), Evaluating indirect ecological effectsof biological control (pp. 39–56). New York: CABIPublishing.

Hopper, K. R. (2003). United States department ofagriculture—agricultural research service research onbiological control of arthropods. Pest ManagementScience, 59, 643–653.

Hopper, K. R., & Roush, R. T. (1993). Mate finding,dispersal, number released, and the success ofbiological control introductions. Ecological Entomol-ogy, 18, 321–331.

Hosmer, D. W., Jr., & Lemeshow, S. (1989). Appliedlogistic regression. New York: Wiley.

Howarth, F. G. (1991). Environmental impacts of classicalbiological control. Annual Review of Entomology, 36,485–509.

Hunter, A. F. (1991). Traits that distinguish out-breaking and non-outbreaking Macrolepidopterafeeding on northern hardwood trees. OIKOS, 60,275–282.

Hunter, M. D. (1992). Interactions within herbivorecommunities mediated by the host plant: The key-stone herbivore concept. In M. D. Hunter, T. Ohgushi,& P. W. Price (Eds.), Effects of resource distributionon animal–plant interactions (pp. 288–325). SanDiego, CA: Academic Press, Inc.

Hunter, A. F. (1995). Ecology, life history, and phylogenyof outbreak and nonoutbreak species. In N. Cappucci-no, & P. W. Price (Eds.), Population dynamics: Newapproaches and synthesis (pp. 41–64). San Diego:Academic Press.

Hunter, M. D., & Price, P. W. (1992). Playing chutes andladders: Heterogeneity and the relative roles ofbottom-up and top-down forces in natural commu-nities. Ecology, 73(3), 724–732.

Johnson, W. T., & Lyon, H. H. (1991). Insects that feed ontrees and shrubs (2nd ed., revised). Ithaca, NY:Comstock Publishing Associates, a division of CornellUniversity Press.

Kareiva, P. (1983). Influence of vegetation texture onherbivore populations: Resource concentration andherbivore movement. In R. F. Denno, & M. S. McClure(Eds.), Variable plants and herbivores in natural andmanaged systems (pp. 259–289). New York: AcademicPress.

Kareiva, P., & Wennergren, U. (1995). Connecting land-scape patterns to ecosystem and population pro-cesses. Nature, 373, 299–302.

Kellogg, S. K., Fink, L. S., & Brower, L. P. (2003).Parasitism of native luna moths, Actias luna (L.)(Lepidoptera: Saturniidae) by the introduced Compsi-lura concinnata (Meigen) (Diptera: Tachinidae) inCentral Virginia, and their hyperparasitism by trigo-nalid wasps (Hymenoptera: Trigonalidae). Environ-mental Entomology, 32(5), 1019–1027.

Kimberling, D. N. (2004). Lessons from history: Predictingsuccesses and risks of intentional introductions forarthropod biological control. Biological invasions, 6,301–318.

Kindlmann, P., & Dixon, A. F. G. (2001). When and whytop–down regulation fails in arthropod predator–preysystems. Basic & Applied Ecology, 2(4), 333–340.

Kruess, A. (2003). Effects of landscape structure andhabitat type on a plant–herbivore–parasitoid commu-nity. Ecography, 26, 283–290.

Kruess, A., & Tscharntke, T. (1994). Habitat fragmenta-tion, species loss, and biological control. Science, 264,1581–1584.

Landis, D. A., Wratten, S. D., & Gurr, G. M. (2000).Habitat management to conserve enemies of arthro-pod pests in agriculture. Annual Review of Entomol-ogy, 45, 175–201.

Lane, S. D., Mills, N. J., & Getz, W. M. (1999). The effectsof parasitoid fecundity and host taxon on thebiological control of insect pests: The relationshipbetween theory and data. Ecological Entomology, 24,181–190.

Lester, P. J., & Harmsen, R. (2002). Functional andnumerical responses do not always indicate the mosteffective predator for biological control: An analysisof two predators in a two-prey system. Journal ofApplied Ecology, 39, 455–468.

Letourneau, D. K. (1998). Conservation biology: Lessonsfor conserving natural enemies. In P. Barbosa (Ed.),Conservation biological control (pp. 9–38). New York:Academic Press.

Lloyd, D. C. (1960). The significance of the type of hostplant crop in successful biological control of insectpests. Nature, London, 187, 430–431.

Lonsdale, W. M., Briese, D. T., & Cullen, J. M. (2001). Riskanalysis and weed biological control. In E. Wajnberg,J. K. Scott, & P. C. Quimby (Eds.), Evaluating indirectecological effects of biological control (pp. 185–210).New York: CABI Publishing.

Louda, S. V., Pemberton, R. W., Johnson, M. T., & Follett,P. A. (2003). Nontarget effects—the Achilles’ heel ofbiological control? Retrospective analyses to reducerisk associated with biocontrol introductions. AnnualReview of Entomology, 48, 365–396.

Louda, S. V., & Stiling, P. (2003). The double-edged swordof biological control in conservation and restoration.Conservation Biology, 18, 50–53.

Mackauer, M., Ehler, L. E., & Roland, J. (Eds.). (1990).Critical issues in biological control. Andover, Hants:Intercept.

ARTICLE IN PRESS

Biocontrol prediction using pest and habitat characteristics 583

Metcalf, R. L., & Metcalf, R. A. (1993). Destructive anduseful insects: Their habits and control. New York:McGraw-Hill, Inc.

Mills, N. J. (2000). Biological control: The needfor realistic models and experimental appro-aches to parasitoid introductions. In M. E. Hochberg,& A. R. Ives (Eds.), Parasitoid population biology (pp.217–234). Princeton: Princeton University Press.

Mills, N. J. (2001). Factors influencing top–down controlof insect pest populations in biologicalcontrol systems. Basic and Applied Ecology, 2,323–332.

Munro, V. M. W., & Henderson, I. M. (2002). Nontargeteffect of entomophagous biocontrol: Shared parasit-ism between native lepidopteran parasitoids and thebiocontrol agent Trigonospila brevifacies (Diptera:Tachinidae) in forest habitats. Environmental Ento-mology, 31(2), 388–396.

Ostman, O., Ekbom, B., & Bengtsson, J. (2001). Land-scape heterogeneity and farming practice influencebiological control. Basic and Applied Ecology, 2,365–371.

Pickett, C. H., & Bugg, R. L. (Eds.). (1998). Enhancingbiological control: Habitat management to promotenatural enemies of agricultural pests. Berkeley, CA:University of California Press.

Price, P. W., Bouton, C. E., Gross, P., McPheron, B. A.,Thompson, J. N., & Weis, A. E. (1980). Interactionsamong three trophic levels: Influence of plants oninteractions between insect herbivores and naturalenemies. Annual Review of Ecology and Systematics,11, 41–65.

Poppy, G. (1997). Tritrophic interactions: Improvingecological understanding and biological control? En-deavor, 21(2), 61–65.

Sands, D. P. A., & van Driesche, R. G. (2000). Evaluatingthe host range of agents for biological control ofarthropods: Rationale, methodology, and interpreta-tion. In R. van Driesche, T. Heard, A. McClay, & R.Reardon (Eds.), Host specificity testing of exoticarthropod biological control agents—the biologicalbasis for improvement in safety. Proceedings of the Xinternational symposium on biological control ofweeds (pp. 69–83). US Department of Agric. ForestHealth Tech. Entomol. Team.

SAS Institute Inc. (1995). Logistic regression examplesusing the SAS system, Version 6. Cary, NC: SASInstitute Inc.

Schellhorn, N. A., Kuhman, T. R., Olson, A. C., & Ives, A.R. (2002). Competition between native and intro-duced parasitoids of aphids: Nontarget effects andbiological control. Ecology, 83(10), 2745–2757.

Sheehan, W. (1986). Response by specialist and generalistnatural enemies to agroecosystem diversification: Aselective review. Environmental Entomology, 15,456–461.

Sheehan, W. (1991). Host range patterns of hymenopter-an parasitoids of exophytic lepidopteran folivores. InE. Bernays (Ed.), Insect–plant interactions (pp.209–247). Boca Raton, FL, USA: CRC Press.

Sivinski, J., & Martin, A. (2003). The evolution ofovipositor length in the parasitic Hymenoptera andthe search for predictability in biological control.Florida Entomologist, 86(2), 143–150.

Stiling, P. (1990). Calculating the establishment rates ofparasitoids in classical biological control. AmericanEntomologist, 36, 225–230.

Stiling, P. (2004). Biological control not on target.Biological Invasions, 6, 151–159.

Strong, D. R., & Pemberton, R. W. (2000). Biologicalcontrol of invading species—risk and reform. Science,288, 1969–1970.

Thies, C., & Tscharntke, T. (1999). Landscape structureand biological control in agroecosystems. Science,285, 893–895.

Tscharntke, T., & Hawkins, B. A. (Eds.). (2002). Multi-trophic level interactions. Cambridge: CambridgeUniversity Press.

Tscharntke, T., & Kruess, A. (1999). Habitat fragmenta-tion and biological control. In B. A. Hawkins, & H. V.Cornell (Eds.), Theoretical approaches to biologicalcontrol (pp. 190–205). Cambridge, UK: CambridgeUniversity Press.

Turlings, T. C. J., Gouinguene, S., Degen, T., & Fritzsche-Hoballah, M. E. (2002). The chemical ecology ofplant–caterpillar–parasitoid interactions. In T.Tscharntke, & B. A. Hawkins (Eds.), Multitrophic levelinteractions (pp. 148–173). Cambridge: CambridgeUniversity Press.

Turnbull, A. L., & Chant, D. A. (1961). The practice andtheory of biological control of insects in Canada.Canadian Journal of Zoology, 39, 697–753.

Van Driesche, R. G., Nunn, C., Kreke, N., Goldstein, B., &Benson, J. (2003). Laboratory and field hostpreferences of introduced Cotesia spp. parasitoids(Hymenoptera: Braconidae) between native and in-vasive Pieris butterflies. Biological Control, 28(2),214–221.

Van Klinken, R. D., Fichera, G., & Cordo, H. (2003).Targeting biological control across diverse landscapes:The release, establishment, and early success of twoinsects on mesquite (Prosopis spp.) insects in Austra-lian rangelands. Biological Control, 26(1), 8–20.

Waage, J. K., & Mills, N. J. (1992). Biological control. InM. J. Crawley (Ed.), Natural enemies: The populationbiology of predators, parasites, and diseases (pp.412–430). Oxford, UK: Blackwell Scientific Publica-tions.

Wajnberg, E., Scott, J. K., & Quimby, P. C. (Eds.). (2001).Evaluating indirect ecological effects of biologicalcontrol. New York: CABI Publishing.

Wilson, F., & Huffaker, C. B. (1976). The philosophy,scope, and importance of biological control. In C. B.Huffaker, & P. S. Messenger (Eds.), Theory andpractice of biological control (pp. 3–15). New York:Academic Press.

Wissinger, S. A. (1997). Cyclic colonization in predictablyephemeral habitats: A template for biologicalcontrol in annual crop systems. Biological control,10, 4–15.

ARTICLE IN PRESS

P. Gross et al.584

Wratten, S. D., Gurr, G. M., Landis, D., Irvin, N. A.,Berndt, L. A. (2000). Conservation biological controlof pests: Multi-trophic-level effects. In: Hoddle, M. S.(Ed.), Proceedings of the California conference onbiological control (pp. 73–80), July 11–12, TheHistoric Mission Inn, Riverside, CA: 2 CCBC II. USImprint.

Wratten, S. D., van Emden, H. F., & Thomas, M. B. (1998).Within-field and border refugia for the enhancementof natural enemies. In C. H. Pickett, & R. L. Bugg(Eds.), Enhancing biological control: Habitat manip-ulation to promote natural enemies of agriculturalpests (pp. 375–403). Berkeley, CA: University ofCalifornia Press.