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Evaluation of natural enemies for biological control: A behavioral approach
TREE vol. 5, no. 6, June 1990
Humana de Ronddnia: Impactos, Limites Planejamento, Conselho National de Desenvolvimento Cientifico e Tecnol6gico
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ICNPql 19 Hecht, S.B. ( 1981 I Stud. Third World Sot. 13,61-108 20 Uhl, C., Buschbacher, RJ. and Serrao, E.A.S. (198811. Ecol. 76, 633-681 21 Buschbacher, R.I. II9871 Biotropica 19, 200-207 22 Sanchez, P.A., Bandy, D.E., HugoVillachica. I. and Nicholaides, 1.1.. Ill II9821 Science 216,821-827 23 Nicholaides, I./., Ill et a/. ( 19851 Bioscience 35, 279-285 37, 24 Fearnside, P.M. ( 19871 Bioscience 209-2 I4 25 Fearnside, P.M. ( 1988) Bioscience ?8.
525-527 26 Fearnside, P.M. (19851 in Key Environments: Amazonia (Prance, G.T and Lovejoy, T.E.. edsl, pp. 393-418, Pergamon Press I I, 27 Fearnside. P.M ( 1986) lnterciencia 229-236 Involution: 28 Ceertz, C. ( 1967) Agricultural The Process of Ecological Change in Indonesia. University of California Press 29 janzen, D.H. II9731 Science 182. 1212-1219 30 Nye, P.H. and Greenland, D.I. II9601 The Soil Under Shifting Cultivation (Technical Communication No. 511. Commonwealth Bureaux of Soils 31 Fearnside. P.M (19791 The Simulation of Carrying Capacity for Human Agricultural
Evaluationof NaturalEnemiesfor BiologicalControl:A Behavioral Approach Robert F, Luck The success of biological pest control has stimulated the development of analytical models that explore the dynamics of natural enemies and their hosts or prey. These models seek to identify those geueral characteristics of the natural enemy, host or prey population that lead to economic pest control. Because the models are strategic in nature, they are of limited value in identifying the specific attributes of an effective Giological control agent prior to its introduction. Empirical/y developed criteria have also been of limited predictive value because they too provide only general guidelines. Behavioral ecology and foraging and sexratio theories may be useful adjuncts to these approaches, 6y identifying the evolutionary constraints and thus helping to define better the attri6utes of an effective natural enemy.
Biological control uses natural enemies, usually arthropods, to regulate agricultural and forestry pests and weeds to densities below those of economic concern. There are three methods that may be applied: (I) classical biological control, in which exotic natural enemies are introduced to reduce permanently a pest, usually of foreign origin; Robert Luck is at the Dept of Entomology, University of California, Riverside, CA 92521, USA.
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(2) augmentative biological control, in which insectary-reared natural enemies supplement indigenous populations or initiate natural enemy populations for a limited period, e.g. a growing season; and ( 31 conservation of indigenous predator or parasitoid populations, in which the important natural enemies are identified and husbanded by the appropriate management practices’ *. Development of practice and theory Biological control began in 1888 with the introduction of the vedalia beetle (Rodolia cardinalis) into California, from Australia, for the suppression of the cottony cushion scale (Icerya purchasi). This scale threatened the state’s young citrus industry, and the beetle’s introduction successfully suppressed it. The control has remained intact for some 100 years, even though the beetle and the scale can still be found in California’s citrus groves?. This spectacular success stimulated the initiation of other control projects. By 1986, there had been I 162 successful introductions of predators or parasitoids; 25% of these have successfully regulated the target pest, 69% have provided intermittent or partial control (i.e.
Populations in the Humid Tropics: Program and Documentation, lnstituto National de Pesquisas da AmazBnia IINPA) 32 Fearnside, P.M. (19831 in The Dilemma of Amazonian Development (Moran. E.F., ed.1, pp. 279-295, Westview Press 33 Fearnside, P.M. (1985) Hum. Ecol. 13. 331-339 34 Energy Studies Unit and Resource Use Institute (19841 Carrying Capacity Assessment: A Resource Accounting Methodology for Assessing the Sustainability of National Economies in the Context of Population, Resources, Environment and Development (Report KEN-1?/297.21.021, Energy Studies Unit of Strathclyde University 35 lanzen. D.H II9721 Nat Hist 81,80-90
control during some seasons, for some generations, some cultivars or in some climatic zones], and 6% have failed to provide any control at al14. Even where there was only partial success, or where the parasitoid became established but failed to effect control, pest densities were often reduced (whilst still causing economic losses). The traditional paradigm that emerged to explain the efficacy of classical biological control viewed it as a reduction in pest density to a new, and stable, equilibrium below that which had prevailed before the agent’s introduction (Fig. I IV. About 250 successful projects appeared to reflect this pattern”*. An analysis of six of these projects that had sufficient data to test the paradigm showed that the pest’s density after the enemy’s introduction was stable and reduced to less than 2% of that which prevailed before?. However, a second analysis of these same six projects along with three others revealed that only one of the nine evinced a stable equilibrium; the remainder were equivocal and could be as readily explained by alternative processes such as local extinctiorP. Successful biological control of arthropod pests has led to the inference that many natural arthropod populations are likewise regulated by predators and parasitoids? after all, predators and parasitoids are a pervasive feature of most arthropod communities. Thus, the paradigm that characterizes classical biological control seems generalizable to indigenous arthropod
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populations7. In this view, classical biological control is simply a special case of a general pattern in which populations are regulated t:y density-dependent processes, a major class of which involves r: redator-prey or parasitoid-host interactionsi,2,8. The challenge, then, for a general theory of population dynamics as it relates to biological control is twofold: (I) to explain when and how natural enemies regulate their host or prey populations, especially in light of the several alternative hypotheses that deny densitydependent processes as essential for population regulation9,r0; and (2) to develop techniques by which effective natural enemies can be detected and evaluated for use i:r introduction, augmentation or inoculative release programs, or by H hich indigenous natural enemies can be identified for purposes of cL3nservation. Two general approaches have evolved to address these challenges: (I 1 the development of mathematical models as metaphors of the parasitoid-host interaction, uith the purpose of identifying processes that regulate the host popul;ltion6,7,“,‘2; and (2) studies of laboratory or field populations using life tables and analytical techniques that seek out density-dependent processes associated with the regulation of these populations, especially those involving natural enemies13. The latter approach asslimes a linkage between effective biological control, density dependence and (host) population reguI; tion14. Unfortunately, space limitations preclude a review of lifetable methods or the analytical techniques used to study laboratory or field populations. In general, however, they have proved problematical and frustratingly enigrnatic in identifying attributes of e’fective natural enemies or in documenting which, if any, natural enemies regulate host or prey populations and the circumstances ulder which they can be expected to do SO’*,‘~-‘~, Models of the parasitoid-host interaction Models incorporating natural e lemies are usually modifications 0;’ one originally developed by Nicholson and Baileys,lR, which as-
sumes that: ( I ) host and parasitoid populations have nonoverlapping generations; (2) the parasitoid population randomly searches all the areas containing hosts; (3) every host within the population has the same risk of attack; (4) only one egg matures per host; (5) every time a host is encountered it is parasitized, even if it has been previously parasitized; and (61 only the supernumerary eggs dies. These and other assumptions yield a Poisson (random) distribution of parasitizations (eggs) amongst hosts, the zero term of which describes the probability of a host escaping parasitism. Although an equilibrium density exists for both the host and the parasitoid population, it is unstable. Slight perturbations of either population lead to increasing oscillations and eventual annihilation of both populations. Additional density-dependent mortality to either the host or the parasitoid population can stabilize the interaction, i.e. the populations return to their equilibrium densities following perturbations. the Nicholson-Bailey Clearly, model does not describe the experience of successful biological control. The introduction of a parasitoid can, in itself, regulate a host population’,2,4 whereas the modeled interaction cannot. Therefore, the basic model has been modified to identify processes contributing to the stability or persistence of the interaction. Aggregation of the parasitoid population at denser host patches8 or independent of host density7*8f’I, refuges for a portion of the host population19, a decrease in the percentage of hosts parasitized with increasing host density finverse density dependence) within a generation20, asynchrony between the parasitoid and host populations8, and sex-ratio variation in the parasitoid population via local parental control (local mate competition or sib mating)21,22 or with increasing parasitoid density2*, are all modifications that have been found to stabilize the modeled interaction. They share a common feature: individual hosts vary in their risk of attack’ 1,23,and the attacks are concentrated only on certain hosts. This is represented in the model by a negative binomial distribution of
NO
OF GENERATIONS
Fig. I. A graphical model depicting the traditional view of the expected changes in pest (host) and natural enemy fparasitoid) densities arising from a successful classical biological control project. The parasitoid was introduced in the second generation (near the originl. The dashed line represents the density at which the pest species will cause economic losses; at or above this density (the economic or intervention threshold) it is economic to suppress the pest. In this case, the introduced parasitoid suppresses the pest population to, and maintains it at an equilibrium below, the density of economic concern. Redrawn from Ref 43.
parasitoid eggs per host”,rR. The skewness in the distribution of the number of attacks (i.e. eggs) per host, independent of host density2?, evinces itself as intraspecific competition amongst the parasitoids17 and yields stability7. Stability in these models increases as the parasitoid population attacks a progressively smaller fraction of the host population (i.e. increasing aggregation)23z24. Because the parasitoid population does not redistribute itself during a generation, only between each generation24, the attacks become increasingly less efficient per capita with increasing parasite density. The attacks are concentrated on the same absolute number of hosts and, thus, intraspecific competition increases amongst the parasitoid population. However, stability in this interaction comes at a price: the more aggregated the attacks and the more stable the interaction, the higher the equilibrium density of the host population (Fig. 2). This occurs because an increasing fraction of the host population escapes parasitism to initiate the next generation24 This situation poses a conundrum for biological control. Presumably, under field conditions an effective natural enemy must both reduce a pest’s density and counteract the stochastic events that perturb it. Thus, the message from these models is disturbing: you can either reduce a pest’s density substantially (high attack rate) or obtain a highly stable interaction (aggregated attacks), but you cannot achieve both. Yet classical biological
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Fig. 2. The relationship between the degree of aggregation of parasitoid eggs fk of the negative binomial1 and the equilibrium density of the host in the modified Nicholson-Bailey model. The portion of the equation contained within the parentheses is the probability that a host escapes parasitism, assuming a negative binomial distribution in attacks per host with a constant clumping parameter, k. H,, H,,,, Pi, and P,+, are the host IHI and parasitoid (PI densities in the current ft) and subsequent ft+ 1) generations, respectively. h is the host’s net fecundity, that is the number of eggs that give rise to reproducing adults; it incorporates all mortality factors encountered during development except parasitism. a denotes the parasitoid’s percapita searching efficiency. As k+O. the number of eggs per host becomes highly aggregated and hosts are more likely to contain multiple eggs than would be expected by chance (i.e. compared with a binomial distribution based on the same percentage of parasitized hostsl. As k-m, the number of eggs per host becomes randomly dispersed based on a Poisson distribution (i.e. the hosts contain the expected number of solitary and multiple eggsl. The negative binomial presumably mimics the aggregation of parasitoid attacks on certain hosts; the smaller the value of k, the more aggregated the distribution of parasitoid eggs per host and the more stable the parasitoid-host interaction because of the competition within the parasitoid population for hosts. Only one egg survives per host.
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Fig. 3. The relationship between a female parasitoid’s size, measured as its mean hind tibia length, and its total lifetime fecundity (a presumed measure of a wasp’s reproductive potentiall. These data are for Aphytis melinus (Hymenoptera: Aphelinidae), a parasitoid of California red scale, Aonidie//a aorantii IHomoptera: Diaspididael.
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control has been, and remains, an effective control tactic - as the large number of successes attest. Thus, an apparent conflict exists, the heart of which is the assumed theoretical trade-off between heterogeneity in the host’s risk of attack and the reduction in the host’s density. Moreover, under field conditions, such heterogeneity is common in most parasitoid-host interactions, even in those that do not regulate host populations; thus, it does not uniquely define a population under biological control. Although, in the model, the pest’s equilibrium density increases with increasing stability, it can be depressed by adding linear density dependence to the host populationa; however, such self-regulation by the host seems unlikely at densities that are economically acceptable and typical of successful classical biological control projects, The criteria for identifying successful biological control agents provided by these models differ little from those previously provided by experienced practitioners: ( I I synchrony or slight asynchrony (21a high with the host population; intrinsic rate of increase relative to that of the host; (31 high searching efficiency; (4) interference (intraspecific competition I amongst the parasitoids; (5) aggregation on host patches; and (61 significant dispersal ability7,‘4,25. For biological control purposes, these criteria are too general to provide much guidance in selecting and identifying effective natural enemies for specific cases. It is unreasonable to. expect these models to provide such specific guidance, since they are designed to be strategic and to characterize the interaction at the population leve126. They are not meant to identify individual attributes that define a successful biological control agent. Behavioral ecology: a complementary approach Behavioral ecological studies of parasitoids may prove a useful adjunct to modeling the hostparasitoid interaction because they can provide the structure and constraints for these models27-3i. When a wasp parasitizes a host, it is detiding its potential contribution to future generations. This potential is tightly linked to the number and
quality of hosts that it chooses to parasitize, since these hosts provide the resources for the parasitoid’s offspring. However, most population models involving hostparasitoid interactions assume that hosts are equally acceptable regardless of their quality and the rate at which they are encountered. Such homogeneity in host populations under field conditions is unlikely32. If host quality varies, then so will the parasitoid’s reproductive potential, density and sex ratio3j. Moreover, this variation in host quality need not be a linear function of host density. In those parasitoid species that attack a host in a nongrowing stage (egg or pupal or that arrest fparalysel a host’s development prior to parasitization, the size of the parasitoid’s offspring is often correlated with the size or quality of the host at the time of attack29,34. A female wasp’s reproductive success (net lifetime fecundity) is strongly correlated with its body size (Fig. 31. A male wasp’s reproductive potential is assumed to be less so19. Thus, the selection of a host on which to lay an egg is tantamount to making a choice about the reproductive potential of an offspringj5. Host selection is all the more critical when a female’s reproductive potential is more affected by being small than is that of a male. Under these conditions, natural selection favors wasps that allocate female eggs to large hosts and male eggs to small hosts29. This is functionally possible because female wasps arise from fertilized eggs, and male wasps from unfertilized eggs. Fertilization occurs at oviposition and is under the control of the female’D Moreover, the choice of host size by a female is relative and depends on the distribution of host sizes available. Thus, the fraction of female eggs allocated to a given host size varies29. Yet there is a minimum size below which few female eggs are allotted (Ref. 37; R.F. Luck and D.S. Yu, unpublished). Consequently, the sex ratio of the parasitoid population can vary as a function of the size distribution of hosts and can be strongly male biased !> 80% males) (Ref. 29; R.F. Luck and D.S. Yu, unpublished). Identification of the minimum host size to which wasp eggs are allocated has explained the competitive displacement of
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a less effective biological control agent by a more effective cong,ener33,37, and has shown why biological control is effective in one (.alifornia region and not in a nother3R. Understanding the value of hosts of differing quality in terms of the reproductive potential for a wasp’s offspring allows the host population to be evaluated in the field from the parasitoid’s perspective. In one instance the host is known to vary irl size depending on the plant cultivar39, location within the plant37,39 aid the temperature pattern tinder which it grows40. Since the parasitoid prefers hosts of large s ze, it is reticent about using hosts that are smaller, even though these hosts will produce some low-quality female offspring (R.F. Luck and D.S. Lu, unpublished). Thus, the intensty with which a parasitoid popuI;:tion exploits the host population ic. largely a function of host density aid size distribution: the smaller t*le distribution of host sizes, the Icss intense (efficient) the exploiti:ltiOn.
In contrast, species that parasitize hosts that continue to grow after the parasitoid has deposited its egg cannot gauge the amount of food ultimately available to the developir,g offspring. In these species the choice of host appears to be predicijted, in part, on minimizing the risk 0’ intraspecific and interspecific competition - for example, by reducing the developmental time of o ‘fspring within the host, rather than by maximizing their fecundity4’. Theory and growing empirical evid ence suggest that host selection is closely linked to the reproductive success (i.e. fitness) of a parasitoid’s o*‘fspring3’,34,42. When placed in this fr.amework, behavioral observations 011 how readily a given host stage or size is located, recognized and accepted, coupled with the offspring’s survival and developmental time in that host and the realized fecundity 01 female wasps arising from it, c,.ln be used to rank potential host resources. Assessing the temporal and spatial availability of these resources in the field, then, is much more likely to predict whether a given natural enemy will be effective and the degree of success that c;n be expected. This approach, along with the assessment of host
location cues (kairomones), can provide specific criteria useful in identifying potentially effective natural enemies. But this will still not guarantee success since other factors, often stochastic in nature, may be involved. Although a behavioral ecological approach has received little research attention in biological control, it shows much promise. A fruitful avenue might be to investigate host-parasitoid interactions in well-documented cases of classical biological control involving both effective and ineffective natural enemy introductions, with attention to the phylogenetic relationships of both host and natural enemy. Alternatively, an ongoing control project might be used to test predictions derived from concurrent research seeking to develop useful criteria. This approach, when combined with population sampling, experimental manipulation and modeling, will substantially improve our understanding of the hostparasitoid interaction, yield more useful criteria for predicting effective natural enemies, and provide realistic structure for a more host-parasitoid and prey-predator models. Acknowledgements I thank Drs Len Nunney and Richard Stouthamer. University of California, Riverside, and Dr Marilyn Houck, University of Arizona, Tucson. This work was supported, in part, by an NSF grant BSR 86-1304-o I and BARD grant IS- 1397-87.
References
I DeBach, P., ed. I19641 Biological Control of Insect Pests and Weeds, Chapman & Hall 2 Huffaker, C.B. and Messenger, P.S., eds ( I9761 Theory and Practice of Biological Control, Academic Press 3 Doutt, R.L. II9641 in Biological Control of insect Pests and Weeds (DeBach, P., ed.1, pp. 21-42, Chapman & Hall 4 Greathead, D.I. 11986) in insect Parasitoids (Waage, 1.K. and Greathead, D.I., eds), pp. 289-3 18, Academic Press 5 Beddington, I.R., Free, CA. and Lawton, J.H. (1978) Nature 273, 513-519 6 Murdoch, W.W., Chesson, j. and Chesson. P.L. I I9851 Am. Nat. 125, 344-366 7 Strong, D.H., Lawton, I.H. and Southwood, T.R.E. t 19841 Insects on P/ants, Harvard University Press 8 Hassell, M.P. ( 1978) The Dynamics of Arthropod Predator-Prey Systems, Princeton University Press 9 den Boer, P.1. ( I9681 Acta Biotheor. 18. 165-194 IO Dempster, I.P. t 19831 Bio/. Rev. 58, 461-481 I I May, R.M. I I9781 /. Anim. Do/. 47,
833-843 I2 Strong, D.R. ( I9881 Trends Ecol. Evol. 3, 277-280 13 Southwood, T.R.E. (19781 Ecological Methods, Chapman & Hall I4 Huffaker, C.B., Simmonds, F.J. and Laing, I.E. (19761 in Theoryand Practice of Biological Control (Huffaker, C.B. and Messenger, P.S., edsl, pp. 41-78, Academic Press I5 Hassell, M.P. II9851 1. Anim. Eco/. 54, 323-334 I6 Hassell, M.P. II9861 Trends Eco/. &vo/. 4, 90-93 I7 Murdoch, W.W. and Reeve, J.D. 119871 Oikos 50, 137-l 4 I 18 Murdoch, W.W. in Applied Population Biology (Botsford, L. and lain, S., edsl, Junk Publications (in press) I9 Murdoch, W.W. and Oaten. A. (19751 Adv. Ecol. Res. 9, l-l 25 20 Hassell, M.P. (19841 /MA /. Math. Appl. Med. Bio/. I, 123-l 33 21 Hassell, M.P., Waage, J.K. and May, R.M. ( 1983) I. An/m. Ecol. 52,889-904 22 Comins, H.N. and Wellings, P.W. ( 19851 1. Anim. Ecol. 54, 583-594 23 Chesson, P.L. and Murdoch, W.W. (19861 Am. Nat. 127. 696-7 I5 24 Murdoch, W.W. and Stewart-Oaten, A. (I9891 Am. Nat. 134, 288-310 25 Waage, I.K. and Hassell, M.P. (I9821 Parasitology 84. 24 l-268 26 May, R.M. and Hassell, M.13 (19881 Phi/OS. Trans. R. Sot. London Ser. B 3 18, 129-169 27 Hassell. M.P. and May, R.M. (I9851 in Behavioural Ecology (Sibly, R.M. and Smith, R.H., edsl, pp. 3-32, Blackwell Scientific Publications 28 Stevens. D.W. and Krebs, I R. t 19861 foraging Theory, Princeton University Press 29 Charnov, E.L. (19821 The Theory of Sex Allocation, Princeton University Press 30 Waage, I.K. and Codfray, H.C.I. (19851 in Behavioural Ecology ISibly, R.M. and Smith, R.H.. edsl, pp. 449-470, Blackwell Scientific Publications 31 Waage, I.K. ( 19861 in insect Parasitoids (Waage, I.K. and Greathead. D.I., eds), pp. 63-95, Academic Press Ecology of 32 Lomnicki, A. ( 19881 Population Individuals. Princeton University Press 33 Parker, G.A. ( I9861 in Behavioural Ecology (Sibly, R.M. and Smith, R.H., edsl, pp. 33-58, Blackwell Scientific Publications 34 Opp, S.B. and Luck, R.F. II9861 Ann. Entomol. Sot. Am. 79, 700-704 35 Green, R.F. t 198211. Theor. Bio/. 95,43-48 36 Flanders, S.E. (19561 lnsectes Sot. 3, 325-334 37 Luck, R.F. and Podoler. H. ( 19851 Ecology 66, 904-9 I3 38 Luck, R.F. ( 1986) in integrated Pest Control in Citrus Groves ICavalloro, R. and Di Martino, E., eds), pp. 355-364, A.A Balkema 39 Hare, I.D., Yu, D.S. and Luck, R.F Ecology (in press) 40 Yu. D.S. and Luck, R.F. 119881 Environ. Entomol. I 7, 154-l 6 I 41 Yu, D.S., Luck, R.F. and Murdoch, W.W. Ecol. Entomol. (in press) 42 van Alphen, J.J.M. and Vet, L.E.M. (19861 in insect Parasitoids IWaage, 1.K. and Greathead, D.J., eds), pp. 23-6 I, Academic Press 43 Samways, M.j. II981 1Biological Control of Pests and Weeds, Edward Arnold
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ICNPql 19 Hecht, S.B. ( 1981 I Stud. Third World Sot. 13,61-108 20 Uhl, C., Buschbacher, RJ. and Serrao, E.A.S. (198811. Ecol. 76, 633-681 21 Buschbacher, R.I. II9871 Biotropica 19, 200-207 22 Sanchez, P.A., Bandy, D.E., HugoVillachica. I. and Nicholaides, 1.1.. Ill II9821 Science 216,821-827 23 Nicholaides, I./., Ill et a/. ( 19851 Bioscience 35, 279-285 37, 24 Fearnside, P.M. ( 19871 Bioscience 209-2 I4 25 Fearnside, P.M. ( 1988) Bioscience ?8.
525-527 26 Fearnside, P.M. (19851 in Key Environments: Amazonia (Prance, G.T and Lovejoy, T.E.. edsl, pp. 393-418, Pergamon Press I I, 27 Fearnside. P.M ( 1986) lnterciencia 229-236 Involution: 28 Ceertz, C. ( 1967) Agricultural The Process of Ecological Change in Indonesia. University of California Press 29 janzen, D.H. II9731 Science 182. 1212-1219 30 Nye, P.H. and Greenland, D.I. II9601 The Soil Under Shifting Cultivation (Technical Communication No. 511. Commonwealth Bureaux of Soils 31 Fearnside. P.M (19791 The Simulation of Carrying Capacity for Human Agricultural
Evaluationof NaturalEnemiesfor BiologicalControl:A Behavioral Approach Robert F, Luck The success of biological pest control has stimulated the development of analytical models that explore the dynamics of natural enemies and their hosts or prey. These models seek to identify those geueral characteristics of the natural enemy, host or prey population that lead to economic pest control. Because the models are strategic in nature, they are of limited value in identifying the specific attributes of an effective Giological control agent prior to its introduction. Empirical/y developed criteria have also been of limited predictive value because they too provide only general guidelines. Behavioral ecology and foraging and sexratio theories may be useful adjuncts to these approaches, 6y identifying the evolutionary constraints and thus helping to define better the attri6utes of an effective natural enemy.
Biological control uses natural enemies, usually arthropods, to regulate agricultural and forestry pests and weeds to densities below those of economic concern. There are three methods that may be applied: (I) classical biological control, in which exotic natural enemies are introduced to reduce permanently a pest, usually of foreign origin; Robert Luck is at the Dept of Entomology, University of California, Riverside, CA 92521, USA.
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(2) augmentative biological control, in which insectary-reared natural enemies supplement indigenous populations or initiate natural enemy populations for a limited period, e.g. a growing season; and ( 31 conservation of indigenous predator or parasitoid populations, in which the important natural enemies are identified and husbanded by the appropriate management practices’ *. Development of practice and theory Biological control began in 1888 with the introduction of the vedalia beetle (Rodolia cardinalis) into California, from Australia, for the suppression of the cottony cushion scale (Icerya purchasi). This scale threatened the state’s young citrus industry, and the beetle’s introduction successfully suppressed it. The control has remained intact for some 100 years, even though the beetle and the scale can still be found in California’s citrus groves?. This spectacular success stimulated the initiation of other control projects. By 1986, there had been I 162 successful introductions of predators or parasitoids; 25% of these have successfully regulated the target pest, 69% have provided intermittent or partial control (i.e.
Populations in the Humid Tropics: Program and Documentation, lnstituto National de Pesquisas da AmazBnia IINPA) 32 Fearnside, P.M. (19831 in The Dilemma of Amazonian Development (Moran. E.F., ed.1, pp. 279-295, Westview Press 33 Fearnside, P.M. (1985) Hum. Ecol. 13. 331-339 34 Energy Studies Unit and Resource Use Institute (19841 Carrying Capacity Assessment: A Resource Accounting Methodology for Assessing the Sustainability of National Economies in the Context of Population, Resources, Environment and Development (Report KEN-1?/297.21.021, Energy Studies Unit of Strathclyde University 35 lanzen. D.H II9721 Nat Hist 81,80-90
control during some seasons, for some generations, some cultivars or in some climatic zones], and 6% have failed to provide any control at al14. Even where there was only partial success, or where the parasitoid became established but failed to effect control, pest densities were often reduced (whilst still causing economic losses). The traditional paradigm that emerged to explain the efficacy of classical biological control viewed it as a reduction in pest density to a new, and stable, equilibrium below that which had prevailed before the agent’s introduction (Fig. I IV. About 250 successful projects appeared to reflect this pattern”*. An analysis of six of these projects that had sufficient data to test the paradigm showed that the pest’s density after the enemy’s introduction was stable and reduced to less than 2% of that which prevailed before?. However, a second analysis of these same six projects along with three others revealed that only one of the nine evinced a stable equilibrium; the remainder were equivocal and could be as readily explained by alternative processes such as local extinctiorP. Successful biological control of arthropod pests has led to the inference that many natural arthropod populations are likewise regulated by predators and parasitoids? after all, predators and parasitoids are a pervasive feature of most arthropod communities. Thus, the paradigm that characterizes classical biological control seems generalizable to indigenous arthropod
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populations7. In this view, classical biological control is simply a special case of a general pattern in which populations are regulated t:y density-dependent processes, a major class of which involves r: redator-prey or parasitoid-host interactionsi,2,8. The challenge, then, for a general theory of population dynamics as it relates to biological control is twofold: (I) to explain when and how natural enemies regulate their host or prey populations, especially in light of the several alternative hypotheses that deny densitydependent processes as essential for population regulation9,r0; and (2) to develop techniques by which effective natural enemies can be detected and evaluated for use i:r introduction, augmentation or inoculative release programs, or by H hich indigenous natural enemies can be identified for purposes of cL3nservation. Two general approaches have evolved to address these challenges: (I 1 the development of mathematical models as metaphors of the parasitoid-host interaction, uith the purpose of identifying processes that regulate the host popul;ltion6,7,“,‘2; and (2) studies of laboratory or field populations using life tables and analytical techniques that seek out density-dependent processes associated with the regulation of these populations, especially those involving natural enemies13. The latter approach asslimes a linkage between effective biological control, density dependence and (host) population reguI; tion14. Unfortunately, space limitations preclude a review of lifetable methods or the analytical techniques used to study laboratory or field populations. In general, however, they have proved problematical and frustratingly enigrnatic in identifying attributes of e’fective natural enemies or in documenting which, if any, natural enemies regulate host or prey populations and the circumstances ulder which they can be expected to do SO’*,‘~-‘~, Models of the parasitoid-host interaction Models incorporating natural e lemies are usually modifications 0;’ one originally developed by Nicholson and Baileys,lR, which as-
sumes that: ( I ) host and parasitoid populations have nonoverlapping generations; (2) the parasitoid population randomly searches all the areas containing hosts; (3) every host within the population has the same risk of attack; (4) only one egg matures per host; (5) every time a host is encountered it is parasitized, even if it has been previously parasitized; and (61 only the supernumerary eggs dies. These and other assumptions yield a Poisson (random) distribution of parasitizations (eggs) amongst hosts, the zero term of which describes the probability of a host escaping parasitism. Although an equilibrium density exists for both the host and the parasitoid population, it is unstable. Slight perturbations of either population lead to increasing oscillations and eventual annihilation of both populations. Additional density-dependent mortality to either the host or the parasitoid population can stabilize the interaction, i.e. the populations return to their equilibrium densities following perturbations. the Nicholson-Bailey Clearly, model does not describe the experience of successful biological control. The introduction of a parasitoid can, in itself, regulate a host population’,2,4 whereas the modeled interaction cannot. Therefore, the basic model has been modified to identify processes contributing to the stability or persistence of the interaction. Aggregation of the parasitoid population at denser host patches8 or independent of host density7*8f’I, refuges for a portion of the host population19, a decrease in the percentage of hosts parasitized with increasing host density finverse density dependence) within a generation20, asynchrony between the parasitoid and host populations8, and sex-ratio variation in the parasitoid population via local parental control (local mate competition or sib mating)21,22 or with increasing parasitoid density2*, are all modifications that have been found to stabilize the modeled interaction. They share a common feature: individual hosts vary in their risk of attack’ 1,23,and the attacks are concentrated only on certain hosts. This is represented in the model by a negative binomial distribution of
NO
OF GENERATIONS
Fig. I. A graphical model depicting the traditional view of the expected changes in pest (host) and natural enemy fparasitoid) densities arising from a successful classical biological control project. The parasitoid was introduced in the second generation (near the originl. The dashed line represents the density at which the pest species will cause economic losses; at or above this density (the economic or intervention threshold) it is economic to suppress the pest. In this case, the introduced parasitoid suppresses the pest population to, and maintains it at an equilibrium below, the density of economic concern. Redrawn from Ref 43.
parasitoid eggs per host”,rR. The skewness in the distribution of the number of attacks (i.e. eggs) per host, independent of host density2?, evinces itself as intraspecific competition amongst the parasitoids17 and yields stability7. Stability in these models increases as the parasitoid population attacks a progressively smaller fraction of the host population (i.e. increasing aggregation)23z24. Because the parasitoid population does not redistribute itself during a generation, only between each generation24, the attacks become increasingly less efficient per capita with increasing parasite density. The attacks are concentrated on the same absolute number of hosts and, thus, intraspecific competition increases amongst the parasitoid population. However, stability in this interaction comes at a price: the more aggregated the attacks and the more stable the interaction, the higher the equilibrium density of the host population (Fig. 2). This occurs because an increasing fraction of the host population escapes parasitism to initiate the next generation24 This situation poses a conundrum for biological control. Presumably, under field conditions an effective natural enemy must both reduce a pest’s density and counteract the stochastic events that perturb it. Thus, the message from these models is disturbing: you can either reduce a pest’s density substantially (high attack rate) or obtain a highly stable interaction (aggregated attacks), but you cannot achieve both. Yet classical biological
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Fig. 2. The relationship between the degree of aggregation of parasitoid eggs fk of the negative binomial1 and the equilibrium density of the host in the modified Nicholson-Bailey model. The portion of the equation contained within the parentheses is the probability that a host escapes parasitism, assuming a negative binomial distribution in attacks per host with a constant clumping parameter, k. H,, H,,,, Pi, and P,+, are the host IHI and parasitoid (PI densities in the current ft) and subsequent ft+ 1) generations, respectively. h is the host’s net fecundity, that is the number of eggs that give rise to reproducing adults; it incorporates all mortality factors encountered during development except parasitism. a denotes the parasitoid’s percapita searching efficiency. As k+O. the number of eggs per host becomes highly aggregated and hosts are more likely to contain multiple eggs than would be expected by chance (i.e. compared with a binomial distribution based on the same percentage of parasitized hostsl. As k-m, the number of eggs per host becomes randomly dispersed based on a Poisson distribution (i.e. the hosts contain the expected number of solitary and multiple eggsl. The negative binomial presumably mimics the aggregation of parasitoid attacks on certain hosts; the smaller the value of k, the more aggregated the distribution of parasitoid eggs per host and the more stable the parasitoid-host interaction because of the competition within the parasitoid population for hosts. Only one egg survives per host.
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Fig. 3. The relationship between a female parasitoid’s size, measured as its mean hind tibia length, and its total lifetime fecundity (a presumed measure of a wasp’s reproductive potentiall. These data are for Aphytis melinus (Hymenoptera: Aphelinidae), a parasitoid of California red scale, Aonidie//a aorantii IHomoptera: Diaspididael.
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control has been, and remains, an effective control tactic - as the large number of successes attest. Thus, an apparent conflict exists, the heart of which is the assumed theoretical trade-off between heterogeneity in the host’s risk of attack and the reduction in the host’s density. Moreover, under field conditions, such heterogeneity is common in most parasitoid-host interactions, even in those that do not regulate host populations; thus, it does not uniquely define a population under biological control. Although, in the model, the pest’s equilibrium density increases with increasing stability, it can be depressed by adding linear density dependence to the host populationa; however, such self-regulation by the host seems unlikely at densities that are economically acceptable and typical of successful classical biological control projects, The criteria for identifying successful biological control agents provided by these models differ little from those previously provided by experienced practitioners: ( I I synchrony or slight asynchrony (21a high with the host population; intrinsic rate of increase relative to that of the host; (31 high searching efficiency; (4) interference (intraspecific competition I amongst the parasitoids; (5) aggregation on host patches; and (61 significant dispersal ability7,‘4,25. For biological control purposes, these criteria are too general to provide much guidance in selecting and identifying effective natural enemies for specific cases. It is unreasonable to. expect these models to provide such specific guidance, since they are designed to be strategic and to characterize the interaction at the population leve126. They are not meant to identify individual attributes that define a successful biological control agent. Behavioral ecology: a complementary approach Behavioral ecological studies of parasitoids may prove a useful adjunct to modeling the hostparasitoid interaction because they can provide the structure and constraints for these models27-3i. When a wasp parasitizes a host, it is detiding its potential contribution to future generations. This potential is tightly linked to the number and
quality of hosts that it chooses to parasitize, since these hosts provide the resources for the parasitoid’s offspring. However, most population models involving hostparasitoid interactions assume that hosts are equally acceptable regardless of their quality and the rate at which they are encountered. Such homogeneity in host populations under field conditions is unlikely32. If host quality varies, then so will the parasitoid’s reproductive potential, density and sex ratio3j. Moreover, this variation in host quality need not be a linear function of host density. In those parasitoid species that attack a host in a nongrowing stage (egg or pupal or that arrest fparalysel a host’s development prior to parasitization, the size of the parasitoid’s offspring is often correlated with the size or quality of the host at the time of attack29,34. A female wasp’s reproductive success (net lifetime fecundity) is strongly correlated with its body size (Fig. 31. A male wasp’s reproductive potential is assumed to be less so19. Thus, the selection of a host on which to lay an egg is tantamount to making a choice about the reproductive potential of an offspringj5. Host selection is all the more critical when a female’s reproductive potential is more affected by being small than is that of a male. Under these conditions, natural selection favors wasps that allocate female eggs to large hosts and male eggs to small hosts29. This is functionally possible because female wasps arise from fertilized eggs, and male wasps from unfertilized eggs. Fertilization occurs at oviposition and is under the control of the female’D Moreover, the choice of host size by a female is relative and depends on the distribution of host sizes available. Thus, the fraction of female eggs allocated to a given host size varies29. Yet there is a minimum size below which few female eggs are allotted (Ref. 37; R.F. Luck and D.S. Yu, unpublished). Consequently, the sex ratio of the parasitoid population can vary as a function of the size distribution of hosts and can be strongly male biased !> 80% males) (Ref. 29; R.F. Luck and D.S. Yu, unpublished). Identification of the minimum host size to which wasp eggs are allocated has explained the competitive displacement of
73EE vol. 5, no. 6, June
1990
a less effective biological control agent by a more effective cong,ener33,37, and has shown why biological control is effective in one (.alifornia region and not in a nother3R. Understanding the value of hosts of differing quality in terms of the reproductive potential for a wasp’s offspring allows the host population to be evaluated in the field from the parasitoid’s perspective. In one instance the host is known to vary irl size depending on the plant cultivar39, location within the plant37,39 aid the temperature pattern tinder which it grows40. Since the parasitoid prefers hosts of large s ze, it is reticent about using hosts that are smaller, even though these hosts will produce some low-quality female offspring (R.F. Luck and D.S. Lu, unpublished). Thus, the intensty with which a parasitoid popuI;:tion exploits the host population ic. largely a function of host density aid size distribution: the smaller t*le distribution of host sizes, the Icss intense (efficient) the exploiti:ltiOn.
In contrast, species that parasitize hosts that continue to grow after the parasitoid has deposited its egg cannot gauge the amount of food ultimately available to the developir,g offspring. In these species the choice of host appears to be predicijted, in part, on minimizing the risk 0’ intraspecific and interspecific competition - for example, by reducing the developmental time of o ‘fspring within the host, rather than by maximizing their fecundity4’. Theory and growing empirical evid ence suggest that host selection is closely linked to the reproductive success (i.e. fitness) of a parasitoid’s o*‘fspring3’,34,42. When placed in this fr.amework, behavioral observations 011 how readily a given host stage or size is located, recognized and accepted, coupled with the offspring’s survival and developmental time in that host and the realized fecundity 01 female wasps arising from it, c,.ln be used to rank potential host resources. Assessing the temporal and spatial availability of these resources in the field, then, is much more likely to predict whether a given natural enemy will be effective and the degree of success that c;n be expected. This approach, along with the assessment of host
location cues (kairomones), can provide specific criteria useful in identifying potentially effective natural enemies. But this will still not guarantee success since other factors, often stochastic in nature, may be involved. Although a behavioral ecological approach has received little research attention in biological control, it shows much promise. A fruitful avenue might be to investigate host-parasitoid interactions in well-documented cases of classical biological control involving both effective and ineffective natural enemy introductions, with attention to the phylogenetic relationships of both host and natural enemy. Alternatively, an ongoing control project might be used to test predictions derived from concurrent research seeking to develop useful criteria. This approach, when combined with population sampling, experimental manipulation and modeling, will substantially improve our understanding of the hostparasitoid interaction, yield more useful criteria for predicting effective natural enemies, and provide realistic structure for a more host-parasitoid and prey-predator models. Acknowledgements I thank Drs Len Nunney and Richard Stouthamer. University of California, Riverside, and Dr Marilyn Houck, University of Arizona, Tucson. This work was supported, in part, by an NSF grant BSR 86-1304-o I and BARD grant IS- 1397-87.
References
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833-843 I2 Strong, D.R. ( I9881 Trends Ecol. Evol. 3, 277-280 13 Southwood, T.R.E. (19781 Ecological Methods, Chapman & Hall I4 Huffaker, C.B., Simmonds, F.J. and Laing, I.E. (19761 in Theoryand Practice of Biological Control (Huffaker, C.B. and Messenger, P.S., edsl, pp. 41-78, Academic Press I5 Hassell, M.P. II9851 1. Anim. Eco/. 54, 323-334 I6 Hassell, M.P. II9861 Trends Eco/. &vo/. 4, 90-93 I7 Murdoch, W.W. and Reeve, J.D. 119871 Oikos 50, 137-l 4 I 18 Murdoch, W.W. in Applied Population Biology (Botsford, L. and lain, S., edsl, Junk Publications (in press) I9 Murdoch, W.W. and Oaten. A. (19751 Adv. Ecol. Res. 9, l-l 25 20 Hassell, M.P. (19841 /MA /. Math. Appl. Med. Bio/. I, 123-l 33 21 Hassell, M.P., Waage, J.K. and May, R.M. ( 1983) I. An/m. Ecol. 52,889-904 22 Comins, H.N. and Wellings, P.W. ( 19851 1. Anim. Ecol. 54, 583-594 23 Chesson, P.L. and Murdoch, W.W. (19861 Am. Nat. 127. 696-7 I5 24 Murdoch, W.W. and Stewart-Oaten, A. (I9891 Am. Nat. 134, 288-310 25 Waage, I.K. and Hassell, M.P. (I9821 Parasitology 84. 24 l-268 26 May, R.M. and Hassell, M.13 (19881 Phi/OS. Trans. R. Sot. London Ser. B 3 18, 129-169 27 Hassell. M.P. and May, R.M. (I9851 in Behavioural Ecology (Sibly, R.M. and Smith, R.H., edsl, pp. 3-32, Blackwell Scientific Publications 28 Stevens. D.W. and Krebs, I R. t 19861 foraging Theory, Princeton University Press 29 Charnov, E.L. (19821 The Theory of Sex Allocation, Princeton University Press 30 Waage, I.K. and Codfray, H.C.I. (19851 in Behavioural Ecology ISibly, R.M. and Smith, R.H.. edsl, pp. 449-470, Blackwell Scientific Publications 31 Waage, I.K. ( 19861 in insect Parasitoids (Waage, I.K. and Greathead. D.I., eds), pp. 63-95, Academic Press Ecology of 32 Lomnicki, A. ( 19881 Population Individuals. Princeton University Press 33 Parker, G.A. ( I9861 in Behavioural Ecology (Sibly, R.M. and Smith, R.H., edsl, pp. 33-58, Blackwell Scientific Publications 34 Opp, S.B. and Luck, R.F. II9861 Ann. Entomol. Sot. Am. 79, 700-704 35 Green, R.F. t 198211. Theor. Bio/. 95,43-48 36 Flanders, S.E. (19561 lnsectes Sot. 3, 325-334 37 Luck, R.F. and Podoler. H. ( 19851 Ecology 66, 904-9 I3 38 Luck, R.F. ( 1986) in integrated Pest Control in Citrus Groves ICavalloro, R. and Di Martino, E., eds), pp. 355-364, A.A Balkema 39 Hare, I.D., Yu, D.S. and Luck, R.F Ecology (in press) 40 Yu. D.S. and Luck, R.F. 119881 Environ. Entomol. I 7, 154-l 6 I 41 Yu, D.S., Luck, R.F. and Murdoch, W.W. Ecol. Entomol. (in press) 42 van Alphen, J.J.M. and Vet, L.E.M. (19861 in insect Parasitoids IWaage, 1.K. and Greathead, D.J., eds), pp. 23-6 I, Academic Press 43 Samways, M.j. II981 1Biological Control of Pests and Weeds, Edward Arnold
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