The Foraging Behavior of Anax Junius (Odonata: Aeschnidae) and Its Potential As a Behavioral Endpoint in Pesticide Testing Sandra Kaye Brewer Iowa State University

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1997 The foraging behavior of Anax junius (Odonata: Aeschnidae) and its potential as a behavioral endpoint in pesticide testing Sandra Kaye Brewer Iowa State University

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The foraging behavior of Anax Junius (Odonata: Aeschnidae) and its potential as a behavioral endpoint in pesticide testing

Sandra Kaye Brewer

A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY

Major: Animal Ecology (Limnology) Major Professor: Gary J. Atchison

Iowa State University Ames, Iowa 1997

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Graduate College Iowa State University

This is to certify that the Doctoral dissertation of Sandra Kaye Brewer has met the dissertation requirements of Iowa State University

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DEDICATION

Incipit. in beatae memoriae mi matre ex quo didici magnum, opus mi. Hoc erat in votis: non est tanti. et etiam forsan et haec olim meminisse iuvabit.

Acta est tabula, de bone et malo. haid ignota loquor hie et ubigue.- Placet!! Finis, coronat opus! Nunc est bibendum. iv

TABLE OF CONTENTS ABSTRACT GENERAL INTRODUCTION 1 Dissertation Organization 7 Literature Cited 7 A TEST FOR SWITCHING BEHAVIOUR AS A POTENTIAL MECHANISM OF OPTIMAL FORAGING THEORY 12 Abstract 12 Materials and Methods 16 Results 22 Discussion 26 Acknowledgments 33 References 33 THE EFFECTS OF CHLORPYRIFOS ON CHOLINESTERASE ACTIVITY AND FORAGING BEHAVIOR IN THE DRAGONFLY Anax Junius (ODONATA) 50 Abstract 50 Introduction 50 Materials and Methods 54 Results 60 Discussion 62 Acknowledgments 66 References 67 GENERAL CONCLUSIONS 81 Literature Cited 83

GENERAL ACKNOWLEDGMENTS 86 V

ABSTRACT

I examined the foraging behavior of the green darner dragonfly nymph.

Anax Junius, to assess the suitability of odonate foraging behavior as a sensitive endpoint for monitoring contaminant exposure. Odonates are potentially excellent organisms for work in biomonitoring programs because: 1) they have complex foraging strategies that tightly couple sensory cues and mechanical responses: 2) their ecology is well-studied: and 3) they are often the dominant invertebrate predator in fishless systems. Thus they have the potential to structure communities through predation. I tested 1) the possibility that nymphs may regulate populations through frequency-dependent foraging and 2) the relationship between head capsule cholinesterase (ChE) levels in nymphs exposed to an organophosphorus insecticide and subsequent foraging behavior. Functional response models were developed for nymphs fed exclusively midges or amphipods and for nymphs fed mixed prey. Nymphs fed only midges exhibited a sigmoidal type III response (a prerequisite for frequency-dependent foraging): nymphs fed only amphipods exhibited a hyperbolic type II response. These results were reversed in mixed prey trials. Regardless of densities of prey offered, a higher percentage of midges were eaten compared to amphipods. Dragonflies did follow a simplified optimal foraging model based on energy maximization but they did not consistently exhibit frequency-dependent selection. ChE levels were slightly elevated in treated nymphs and foraging behaviors between treated and control groups were not statistically significant, most likely because of high variability in ChE activities within the control group and across treated groups. My results suggest vi that certain animals may be unsuitable for routine pesticide monitoring and prior to incorporating any species into a biomonitoring program, careful consideration of the species' sensitivity to the contaminant needs to be considered. Further work is needed to determine other factors that may influence ChE levels in species as well. 1

GENERAL INTRODUCTION Because behavior is a biological product of complex physiological interactions, it is an attribute readily altered by stress (Warner et al. 1966: Beitinger 1990). Normal behavioral patterns are critical to the survival of the organism, population dynamics, and community interactions. Disruptions of normal behavioral patterns can be used in place of. or in addition to. traditional endpoints (death, immobility, etc.) because contaminants have been shown to affect many behaviors of terrestrial and aquatic organisms (Little 1990). By integrating biochemical and ecological interactions, behavioral toxicology may provide a better assessment of environmental contamination than traditional methods. Sublethal effects are especially important considerations in aquatic ecosystems where more organisms are exposed to sublethal than acutely toxic concentrations (Kleerokoper 1976: Niimi 1990). Behavioral toxicity tests may provide inexpensive and accurate methods for assessing a chemical's potential to alter aquatic communities (Sandheinrich and Atchison 1990). The development of behavioral endpoints in toxicity assessment has improved the sensitivity and versatility of toxicological studies (Little 1990). Behavioral endpoints are often more sensitive indicators of sublethal toxicity than traditional endpoints (e.g. Sullivan et al. 1978: Giattina and Garton 1983: Goodrich and Lech 1990: Little and Finger 1990). Behavioral responses may also be used to assess the risk of exposure because animals can avoid or "prefer" a contaminated area (Burris et al. 1990: Gray 1990). In addition to their integrative nature and ecological relevance, behavioral tests are generally non-destructive: thus long-term monitoring is possible, which allows for studies on adaptive responses and 2 recovery. Finally, the same organisms used in a behavioral test may also be studied by researchers in related fields (e.g. biochemists, geneticists, physiologists, ecologists), increasing our understanding of how organisms and the environment interact. Although any behavioral response that can be quantified has the potential to be used as a biomarker in the assessment of stress (Beitinger 1990) not all behaviors are equally valid candidates. Any behavioral response or change in behavior must be well described and be biologically relevant to the organism, i.e., somehow contribute to the fitness of the individual. Ecologically relevant behaviors include reactions of individuals of a single species (i.e., avoidance-attraction responses, learning, locomotion), preference for physical-chemical gradients (e.g.. temperature, oxygen), interactions within a species (i.e., social behaviors, reproduction) as well as interactions among multiple species (competition, predator-prey interactions). Research on the effects of toxicants on invertebrate behavior has traditionally focused on pest species that are either physically or economically harmful to humans because more effective pesticides may be designed if we understand how insecticides affect arthropod behavior. Recently, studies have started to examine the effects of a wide range of contaminants on aquatic invertebrates that are not pest species. Changes in predator-prey interactions may be sensitive and ecologically important indicators of sublethal toxicity (Woltering et al. 1978) because these interactions incorporate behavioral components of both predators and prey (Heath 1995). Predator-prey interactions are the result of a complex series of events between the predator and its prospective prey 3

that are governed by information received by the visual, olfactory, gustatory, tactile, and electric receptors. Predators must integrate these signals to locate, pursue, capture, and ingest prey without becoming prey themselves. Prey must elude detection or else avoid capture and assimilation. A toxicant may affect any receptor or may directly influence the central nervous system, for instance by changing the balance of neural transmitters. Predation is also an important factor in structuring aquatic communities. Large invertebrates often dominate areai lacking vertebrate predators (Zaret 1980). Odonate nymphs are dominant invertebrate predators in freshwater littoral zones and wetlands (Ball and Hayne 1952; Wissinger 1988) and like other aquatic invertebrates, they are attractive prey items to larger invertebrates and fish (Werner and Hall 1976, 1977: Johnson 1993). Because Odonata have well-defined, easily observable behaviors they are good subjects for behavioral studies and field enclosure experiments, providing insights into the ecology of generalized predators with intermediate positions in food webs (Johnson 1991). Furthermore, nymphs are restricted to the aquatic habitat in which they hatch, allowing one to assess behaviors within a habitat with conditions in that area. Comparisons among areas and among years can be used to assess strengths of temporal and spatial density dependence (Van Buskirk 1993). There has been extensive work on the behavioral ecology of odonates. Research has focused on antipredator behaviors (Pierce 1988: McPeek 1990a. b): differential vulnerability to fish predation (Morin 1984: Pierce et al. 1985: Pierce 1988): intraspecific aggression (Baker 1980: Rowe 1985: Harvey and Corbet 1986: Crowley et al. 1988) including both intraguild predation 4

(Folsom and Collins 1984: Blois 1985: Johnson 1986: Robinson and Wellborn 1987: Wissinger and McGrady 1993) and interference competition (Crowley et al. 1987: McPeek and Crowley 1987: Convey 1988: Crowley and Martin 1989: Anholt 1990: Gribbin and Thompson 1990). Research on frequency-dependent prey selection (switching behavior: apostatic prey selection) has been done in the disciplines of genetics, ecology, animal behavior and psychology (Allen 1988). There have been several reviews of apostatic behavior. Reviews by Murdoch and Oaten (1975), Lawton et al. (1974), Greenwood and Elton (1979) and Sherratt and Harvey (1993) were concerned primarily with predator selection on different prey species, while Allen's review (1988) focused on selection of morphs within a population. Frequency-dependent selection has the potential to change genetic makeup within populations and species diversity within communities (Clarke 1962: Murdoch 1969). There have been several studies, with conflicting results, on switching behavior of odonate nymphs. Thompson (1978) demonstrated that Ischnura elegans consume prey taxa in roughly the same proportions as their relative abundances in the environment. Lawton et al. (1974) presented the damsel fly nymph Ischnura elegans with two species of cladocera Wdphnia and

Smocephalus) at various prey densities. J. elegans appeared to switch although the model designed to compensate for prey depletion did not adequately describe the prey selection. However, other studies have found discrepancies between prey abundance and consumption (Lawton 1970: Johnson 1982: Folsom and Collins 1984). Most predators have a varied diet but seldom do they take different prey types in proportions similar to those in which the prey occur (Allen 1988). Predators may preferentially feed on 5 the more common prey (negative frequency-dependent selection), or a predator may ignore common forms and concentrate on the rare forms (positive frequency-dependent selection). Some studies have failed to demonstrate switching in dragonfly nymphs even though there was a slight preference in prey selection as prey density increased (Sherratt and Harvey 1993: Blois-Heulin 1990). Akre and Johnson

(1979) also used Daphnia and Simocephalus as prey for another damsel fly. Anomalagrion hastatum. Damsel flies exhibited negative frequency-dependent selection when alternate prey were available at absolute densities similar to those of the primary prey. This behavior was attributed to alternate search modes (ambush or stalk) used by the nymphs: the different search modes altered encounter frequencies with the motile Daphnia and the sessile Simocephalus. However. Baker (1980) suggested that the apostatic behavior exhibited in Akre and Johnson's work may be from changes in the location where a damsel fly feeds rather than in the damsel fly's search speed. Col ton (1987) was also unable to demonstrate switching in Enallagma aspersum fed Daphnia and Simocephalus. Subsequent work with Enallagma also failed to demonstrate switching behavior (Chowdhury et al. 1989). Another damsel fly species, Pyrrhosoma. also appears not to forage in a frequency- dependent manner (Harvey and White 1990). Despite the importance of dragonflies to the aquatic environment and the complexity of their foraging behavior, little work has been done using these organisms in behavioral toxicity studies. Behaviors are influenced by a wide range of factors around and within animals; however, all of these influences operate through the nervous system (Heath 1995). Therefore neurotoxicants may be expected to alter behavioral patterns of an organism. 6

My work tested the suitability of the dragonfly. Anax Junius, for use

in behavioral toxicity testing. A. Junius are widespread in North America and their ecology has been well studied (Folsom and Collins 1982a,b: 1984:

Bergelson 1985; Wissinger 1988: 1992). A. Junius are visual predators that actively stalk prey and possess discernable foraging behaviors i.e.. orientations, pursuits, and captures. The toxicant I worked with was the organophosphorus (OP) insecticide, chlorpyrifos. OPs are the most widely used neurotoxicant pesticides in North America (Grue et al. 1983) and have largely replaced the organochlorine pesticides. The mode of action of OP pesticides is the inhibition of acetylcholinesterase (AChE), which is essential for normal functioning of the central and peripheral nervous system. Chlorpyrifos has been used to control a variety of arthropod pests including fire ants, turf and ornamental plant insects, horn flies, and cockroaches. It is currently the most commonly used termiticide on the market. In Iowa, chlorpyrifos. which is sold under the brand names of Lorsban® and Dursban®, is one of the most heavily used insecticides on corn (Hartzler and Wintersteen 1991). Although currently banned in the U. S. for direct application in aquatic environments (U. S. EPA 1986), chlorpyrifos continues to enter aquatic environments through aerial drift and run-off. My work was two-fold: the first project was designed to determine if A. Junius forage in a frequency-dependent manner: the second project was designed to see if there was a relationship between cholinesterase level in

A. Junius, exposed to chlorpyrifos at environmental concentrations, and foraging behaviors. 7

Di ssertati on Organi zati on

My dissertation is organized into two chapters. The first chapter deals with the potential for frequency-dependent foraging to act as a mechanism for optimal foraging in the dragonfly nymph. A. Junius. The

chapter is formatted as a paper submitted to Animal Behaviour. The focus of the second chapter is examing the effect of an organophosphorus

insecticide on cholinesterase levels and foraging behavior in /4. Junius.

This chapter is formatted as a paper for submission to Archives of Environmental Toxicology and Chemistry. General conclusions follow the second paper. Literature Cited Allen, J. A. 1988. Frequency-dependent selection by predators. Phil. Trans. R. Soc. Lond. 3196:485-503. Anholt, B. R. 1990. An experimental separation of exploitative and interference competition in a damselfly. Ecology 71:1483-1493. Akre, B. G., and D. M. Johnson. 1979. Switching and sigmoid functional response curves by damselfly naiads with alternative prey available. J. Anim. Ecol. 48:703-720. Baker, R. L. 1980. Use of space in relation to feeding areas by zygopteran nymphs in captivity. Can. J. Zool. 58:1060-1065. Ball. R. C.. and D. W. Hayne. 1952. Effects of the removal of the fish population on the fish-food organisms of a lake. Ecology 33:41-48. Bergelson. J. M. 1985. A mechanistic interpretation of prey selection by Anax Junius larvae (Odonata: Aeshnidae). Ecology 66:1699-1705. Beitinger, T. L. 1990. Behavioral reactions for the assessment of stress in fishes. J. Great Lakes Res. 16:495-528. Blois, C. 1985. The larval diet of three anisopteran (Odonata) species. Freshwat. Biol. 15:505-514. Blois-Heulin. C. 1990. Influence of prey densities on prey selection in Anax imperator larvae (Odonata:AeshnidaeK Aq. Insects 12:209-217. 8

Burris, J. A.. M. S. Bamford, and A. J. Stewart. 1990. Behavioral responses of marked snails as indicators of water quality. Environ. Toxicol. Chem. 9:69-76. Chowdhury, S. H., P. S. Corbet, and I. F. Harvey. 1989. Feeding and prey selection by larvae of Enallagma cyathigerum (Carpenter) (Zygoptera: a Coenagrionidae) in relation to size and density of prey. Odonatologica 18:1-13. Clarke, B. C. 1962. Natural selection in mixed populations of two polymorphic snails. Heredity 17:319-345. Colton, T. F. 1987. Extending functional response models to include a second prey type# an experimental test. Ecology 68:900-912. Convey. P. 1988. Competition for perches between larval damsel flies: the influence of perch use on feeding efficiency, growth rate and predator avoidance. Freshwat. Biol. 19:15-28. Crowley, P. S., P. M. Dillon, D. M. Johnson, and C. N. Watson. 1987. Intraspecific interference among larvae in a semi voltine dragonfly population. Oecologia 71:447-456. ., S. Gillett, and J. H. Laden. 1988. Contests between larval damselflies: empirical steps toward a better ESS model. Anim. Behav. 36:1469-1510. , P. S., and E. K. Martin. 1989. Functional responses and interference within and between year classes of a dragonfly population. J. N. Amer. Benthol. Soc. 8:211-221. Folsom, T. C., and N. C. Collins. 1982a. An index of food limitation in the field for the larval dragonfly Anax Junius (Odonata: Aeshnidae). Freshwat. Invert. Biol. 1:25-32. Folsom. T. C., and N. C. Collins. 1982b. Food availability in nature for the larval 6ragonf]y Anax Junius (Odonata: Aeshnidae). Freshwat. Invert. Biol. 1:33-40. Folsom, T. C., and N. C. Collins. 1984. The diet and foraging behavior of the larval dragonfly Anax Junius (Aeshnidae), with an assessment of the role of refuges and prey activity. Oikos 42:105-113. Giattina, J. D., and R. R. Garton. 1983. A review of the preference- avoidance responses of fishes to aquatic contaminants. Residue Rev. 87:43-90. Goodrich, M. S., and J. J. Lech. 1990. A behavioral screening assay for Daphnia magna: a method to assess the effects of xenobiotics on spacial orientation. Environ. Toxicol. Chem. 9:21-31. 9

Gray, R. H. 1990. Fish behavior and environmental assessment. Environ. Toxicol. Chem. 9:53-68. Greenwood, J. J. D.. and R. A. Elton. 1979. Analyzing experiments on frequency-dependent selection by predators. J. Anim. Ecol. 48:721- 737. Gribben, S. D., and D. J. Thompson. 1990. Asymmetric intraspecific competition among larvae of the damsel fly Ischnura elegans (Zygoptera: Coenagrionidae). Ecol. Entomol. 15:37-42. Grue, C. E., W. J. Fleming, D. G. Busby, and E. F. Hill. 1983. Assessing hazards of organophosphate pesticides to wildlife. Trans. N. Amer. Wildl. Conf. 48:200-220. Hartzler, R.. and W. Wintersteen. 1991. A survey of pesticids used in Iowa crop production in 1990. Iowa State University Extension Pamphlet Pm-1441. Harvey, I. F., and P. S. Corbet. 1986. Territorial interactions between larvae of the dragonfly. Pyrrhosoma nywphula: outcome of encounters. Anim. Behav. 34:1550-1561. ., and S. A. White. 1990. Prey selection by larvae of Pyrrhosoma nymphula (Sulzer) (Zygoptera: Coenagrionidae). Odonatologica 16:17- 25. Heath, A. G. 1995. Water pollution and fish physiology. 2nd edition. CRC Press. Boca Raton, Florida. USA. pp. 359. Johnson, J. H. 1982. Diet composition and prey selection of Cordulegaster maculata Sel. larvae (Anisoptera: Cordulegasteridae). Not. Odonatol. 1:151-153. Johnson. D. M. 1986. The life history of Tetragoneura cynosura (Say) in Bays Mountain Lake, Tennessee, United States (Anisoptera: Corduliidae). Odonatol. 15:81-90. 1991. Behavioral ecology of larval dragonflies and damsel flies. Trends Ecol. Evol. 6:8-13. Johnson, F. 1993. Intraguild predation and cannibalism in odonate larvae: effects of foraging behavior and zooplankton availability. Oikos 66:80-87. Kleerokoper, H. 1976. Effects of sublethal concentrations of pollutants on the behavior of fish. J. Fish. Res. Board Can. 33:2036-2039. Lawton, J. H. 1970. Feeding and food energy assimilation in larvae of the damsel fly. Pyrrhosoma nymphula (Sulz.) (Odonata: Zygoptera). J. Anim. Ecol. 39:669-689. 10

Lawton.. J. R. Beddington. and R. Bonser. 1974. Switching in invertebrate predators. In M. B. Usher and M. H. Williamson, eds. Ecological Stability. Chapman and Hall, London. England, pp. 141-158. Little, E. E., 1990. Behavioral toxicology: stimulating challenges for a growing discipline. Environ. Toxicol. Chem. 9:1-2. ., and S. E. Finger. 1990. Swimming behavior as an indicator of sublethal toxicity in fish. Environ. Toxicol. Chem. 9:13-20.

McPeek, M. A. 1990a. Behavioral differences between Engallma species (Odonata) influencing differential vulnerability to predators. Ecology 71:1714-1726.

1990b. Determination of species composition in the Engallma damsel fly assemblages of permanent lakes. Ecology 71:83-89. ., and P. H. Crowley. 1987. The effects of density and relative size on the aggressive behavior movement, and feeding of damsel fly larvae (Odonata: Coenagriondiae). Anim. Behav. 35:1051-1061. Morin, P. J. 1984. Odonate guild composition: experiments with colonization history and fish predation. Ecology 65:1866-1873. Murdoch, W. W. 1969. Switching in general predators: experiments on predator specificity and stability of prey populations. Ecol. Monogr. 39:335-354. ., and A Oaten. 1975. Predation and population stability. Adv. Ecol. Res. 9:1-131. Niimi, A. J. 1990. Review of biochemical methods and other indicators to assess fish health in aquatic ecosystems containing toxic chemicals. J. Great Lakes Res. 16:529-541. Pierce, C. L. 1988. Predator avoidance, microhabitat shift, and risk- sensitive foraging in larval dragonflies. Oecologia 77:81-90. Pierce., P. H. Crowley, and D. M. Johnson. 1985. Behavior and ecological interactions of larval Odonata. Ecology 66:1504-1513. Robinson, J. V., and G. A. Wellborn. 1987. Mutual predation in assembled communites of odnate species. Ecology 68:921-927. Rowe. R. J. 1985. Intraspecific interactions of New Zealand damsel fly larvae I: Xanthocnemis zealandica. Ischnura aurora. an6'Australestes colensonis (Zygoptera: Coenagrionidae: Lestidae). New. Zeal. J. Zool. 12:1-15. Sandheinrich, M. B., and G. J. Atchison. 1990. Sublethal toxicant effects of fish foraging behavior: empirical vs. mechanistic approaches. Environ. Toxicol. Chem. 9:107-119. 11

Sherratt, T. N.. and I. F. Harvey. 1989. Predation by larvae of Pantala flavescens (Odonata) on tadpoles of Phynomedusa trinitatis and Physalaemus pustulosus: the Influence of absolute and relative density of prey on predator choice. Oikos 56:170-176. Sullivan. J. F.. G. J. Atchison, 0. J. Kolar, and A. W. Mcintosh. 1978. Changes in predator-prey behavior of fathead minnows iPimephales promelas) and largemouth bass Wcropterus salmondes) caused by cadmium. J. Fish. Res. Board Can. 35:446-451. Thompson, D. J. 1978. The natural prey of larvae of the damsel fly, Ischnura elegans (Odonata). Freshwat. Biol. 8:377-384. U. S. EPA. 1986. Ambient water quality criteria for chlorpyrifos. EPA 440/5-86-005. EPA, Washington, DC. Van Buskirk, J. 1993. Population consequences of larval crowding in the dragonfly Aeshna juncea. Ecology 74:1950-1958. Warner, R. E., K. K. Peterson, and L. Borgman. 1966. Behavioral pathology in fish: a quantitative study of sublethal pesticide toxication. J. Appl. Ecol. 3(Suppl.):223-247. Werner, E. E., and D. J. Hall. 1976. Niche shifts in sunfishes: experimental evidence and significance. Science 191:404-406. ., and . 1977. Competition and habitat shift in two sunfishes (Centrarchidae). Ecology 58:869-876. Wissinger. S. A. 1988. Spatial distribution, life history and estimates of survivorship in a fourteen-species assemblage of larval dragonflies (Odonata: Anisoptera). Freshwat. Biol. 20:329-340. Wissinger, S. A., and J. McGrady. 1993. Intraguild predation and competition between larval dragonflies: direct and indirect effects on shared prey. Ecology 74:207-218. Woltering, D.M., J. L. Hedtke, and L. J. Weber. 1978. Predator-prey interactions of fishes under the influence of ammonia. Trans. Amer. Fish. Soc. 107:500-504. Zaret, T. M. 1980. Predation and Freshwater Communities. Yale University Press, New Haven, Conneticut, USA. 12

A TEST FOR SWITCHING BEHAVIOUR AS A POTENTIAL MECHANISM OF OPTIMAL FORAGING THEORY

A paper submitted to Animal Behaviour Sandra K. Brewer and Gary J. Atchison Department of Animal Ecology. Iowa State University. Ames. Iowa 50011. U.S.A. ABSTRACT Optimal diet theory predicts what types of prey a predator is likely to consume or ignore based on maximising net energy gain. but. the theory makes no assumptions as to what mechanisms determine prey selection. The possibility that frequency-dependent predation (switching) may be a mechanism for optimal foraging was investigated for green darner dragonfly nymphs (Anax .iunius). Functional response models were developed for dragonfly nymphs fed exclusively midges (Chironomus ripari us) or amphipods (Hvallela azteca) and for nymphs fed both prey. A prerequisite for switching behavior is a type III (sigmoidal) functional response. Nymphs fed only midges exhibited a type III response: nymphs fed only amphipods exhibited a type II (asymptotic) response. These responses were reversed in mixed prey trials. Dragonflies oriented towards and pursued amphipods more often than midges in single prey trials, but when both prey were offered together, nymphs oriented towards and pursued midges more often at higher mixed prey densities. Regardless of the density of prey offered, a higher percentage of midges were eaten compared to amphipods. Dragonflies did follow a simplified optimal diet model based on net energy maximisation but did not exhibit switching behaviour. Consequently, the role of frequency-dependent foraging as a potential mechanism for optimal foraging was not assessed. 13

Several theories have been suggested to explain how predators forage, and consequently, how they may regulate prey densities. Three of the most intensively studied theories are optimal diet, functional response, and frequency-dependent selection. Optimal diet models have been extensively used to explain prey selection. They predict the most profitable prey species for a predator based on benefit (e.g.. energy) to cost (e.g.. searching and handling times) ratios. Prey with high energy return per unit searching plus handling time are preferred to those with low energy return per unit searching plus handling time. Evolutionary arguments in favor of optimal foraging have been substantiated from theoretical and experimental studies of both predator and parasitoid behavior for a wide range of taxa (Werner and Hall 1974: Kacelnik 1984: Schmid-Hempel et al. 1985). Optimal foraging theory proposes no specific mechanism for determining prey selection, but one or more mechanisms must exist for a predator to select a particular foraging strategy. For some predators. this mechanism may be frequency-dependent predation. Frequency-dependent predation has also been termed switching (Murdoch 1969). Switching describes the response of a predator to varying densities of two or more prey types. If a predator consumes disproportionately more of the most common prey type and switches to a second prey type as the first becomes rare, then neither prey population greatly fluctuates in number. There have been several excellent reviews of switching behaviour (Lawton et al. 1974; Murdoch and Oaten 1975; Greenwood and Elton 1979; Allen 1988; Sherratt 1993: Sherratt and Harvey 1993). The preferred way to determine switching behaviour is to incorporate both functional response data and preference models (Cock 1978). The 14

functional response is the relationship between the frequency of prey eaten by a predator per unit time and density of prey. Rolling (1959) suggested three types of functional response: type I, where predation rate increases at a constant rate to a maximum as prey density increases: type II. where predation rate decreases proportionately as prey density increases and results in an asymptotic curve: and type III, where predation rate accelerates to a threshold prey density and then decelerates to an asymptote. This sigmoidal type III response is prerequiste for switching behaviour and can potentially stabilize prey populations because the proportion of the prey population consumed by the predator increases with the prey density. Invertebrate predators have traditionally been thought to exhibit only type II functional responses because type III responses require learning or the development of a search image or both. Earlier studies of Odonata nymphs showed that nymphs have type II responses to single prey (Cothran and Thorp 1985: Blois-Huelin 1990). However, a few studies have demonstrated type III responses for invertebrates (e.g., Lawton et al. 1974). Murdoch and Oaten (1975) have proposed that generalist invertebrate predators should show a type III response, if additional prey species are present: this is switching behavior. There have been numerous studies on switching in invertebrates (see review by Sherratt 1993). Allen (1976) predicted the phenomena would be common in nature, but the evidence is relatively rare in odonate nymphs. Thompson (1978) demonstrated that Ischnura eleqans nymphs consume prey in roughly the same proportions as their relative abundances in the environment. Other studies have not found proportional changes between 15 prey abundance and consumption (Lawton 1970: Folsom and Collins 1984). Sherratt and Harvey (1993) and Blois-Huelin (1990) failed to demonstrate switching in dragonfly nymphs even though there was a slight preference in prey selection as prey density increased. Lawton et al. (1974) presented Ischnurea eleqans with two genera of cladocera (Daohnia and Simoceohalus) at various densities. Ischnura appeared to switch, although the model did not adequately describe the prey selection. Anomalaqrion hastatum. however, when offered the same prey, did exhibit frequency-dependent selection when alternate prey were available at absolute densities similar to those of the primary prey (Arke and Johnson 1979). We were interested in determining if switching behavior could help explain the mechanism(s) used by a predator to forage optimally. We show the results of laboratory experiments designed to assess: (1) the functional response of dragonfly nymphs: (2) if nymphs follow a simplified optimal foraging strategy: (3) if nymphs exhibit switching behaviour: and (4) whether this behaviour is a potential mechanism of optimal foraging. We worked with Anax iunius (Odonata) nymphs because dragonfly nymphs are important invertebrate predators in many freshwater littoral zones and wetlands (Benke and Benke 1975: Patterson 1994) and can reach standing stocks 2 to 3 fold greater that of their prey (Benke 1976). Hence, odonates may potentially act as "keystone species" (Paine 1980) in the regulation of prey communities through functional response and switching behaviour (Cothran and Thorp 1985). Study Organism ^ iunius is widespread in North America and its ecology has been well studied (Folsom and Collins 1982a,b: Wissinger 1988, 1992). Nymphs of 16

^ .1urn"US are visual predators that actively stalk prey and possess clearly discernable foraging behaviours, i.e., orientations, pursuits, strikes, and captures. We describe the functional response of .iunius to changes in density of midge (Chironomus ripari us) and amphipod (Hvallela azteca) prey and test for switching between these two prey by comparing results of functional responses to a preference model that takes prey depletion into account (Manly et al. 1972). We used benthic prey because foraging behaviors of ^ iunius are more easily discerned when the dragonfly is oriented in a horizontal plane (Bergelson 1985). We expected ^ .iunius to select for midges because midges are larger (12-14 mm long), soft-bodied, and move more slowly than the smaller (6-8 mm long), fast-swimming amphipods that possess a hard exoskeleton. Consequently, amphipods should be harder to catch and handle than midges. According to switching theory, if ^ iunius is a selective predator, they should consume midges in direct proportion to their relative density. Under optimal diet guidelines, midges should be consumed in direct proportion to their absolute density, and at high absolute densities odonates should select midges regardless of their relative density.

MATERIALS AND METHODS Culture Methods F-instar (penultimate) to F-2 instar A^ iunius nymphs (head width mean ± sd 8.1 ± 0.8 mm; n=90) were obtained during the summer of 1994 from

Connecticut Valley Biological Supply Company, Southhampton, MA or were collected from a marsh near Ames, lA. Nymphs were housed individually in

470-ml styrofoam containers under a 16h light:8h dark photoperiod at 12 °C. 17

Nymphs were removed from the chambre and held under ambient laboratory

temperatures (22 °C) for 48 h prior to being used in a foraging trial.

Dragonflies were maintained on a diet consisting of cladocerans. small aquatic insects (except for midges), and small fish: they were fed daily to satiation. To standardize hunger levels during foraging bouts, prey were withheld for 24 h immediately before the trial.

Experimental Design Experimental chambres for both functional response and switching

experiments were 30 x 15 x 6.5 cm (Ixwxh) clear Plexiglas™ containers that

held 3 L of charcoal-filtered water. Each chambre contained 0.16-cm mesh fiberglass screen along the bottom to provide a "tarsushold" for the odonates and partial refuge for the prey. Laboratory-cultured fourth- instar midges or field-collected adult amphipods were stocked in the chambres and allowed to acclimate for 0.5 h prior to the introduction of

the odonate. Dragonflies that molted < 5 d from a foraging bout were

excluded from the analysis. Foraging bouts for functional response and switching experiments were 1 h. The number of prey ingested was recorded. Ingestions from mixed-prey trials were also used to determine switching behavior. Additionally, the number of orientations, pursuits, strikes, misses and sequence of prey types captured were recorded either by direct observation (.57% of trials) or from videotapes (43^). These foraging behaviors were operationally defined and readily observable (Table I). Capture efficiency was defined as the number of ingestions divided by the number of strikes. Pairwise multiple comparisons were done to compare results between odonates fed midges or amphipods at each density. Ingestion data and sequence of prey 18 capture were used 1n determing optimal diet decisions.

Functional Response Experiments Functional responses (proportion of prey ingested at each density) were determined separately for both ripari us and tL azteca. Ten replicates were performed at each of 3 prey densities. 20. 40. 80 organisms/3 L. We chose a high number of replications per treatment rather than more treatments to minimize potential variation among odonates within a treatment. Each predator was randomly assigned a prey density and trials were randomized. Each predator was used only once.

Switching Experiments Prey were stocked at 3 densities and three ratios per density for a total of nine treatment combinations (Table II). The experimental protocol was identical to the functional response. There were three replicates of each treatment. Initial numbers of prey offered and numbers of prey remaining were recorded. The mean number of prey left per trial was calculated after the trial. Optimal Diet Determination In a modified optimal diet model proposed by Hubbard et al. (1982). a predator confronted with two prey types, m and a. where m is more profitable, has the option to take either the most profitable prey or a combination of both types together. The predator should take m alone when;

CtjNjej) > (1 + aAT,«,) (1+ + ttaNJha) where: a„. are the rate of predator search for each prey (attack coefficient). T^a are the handling time for each prey. e„. e^ are the expected fitness gain for each prey (i.e.. calories), and N„. are the 19 number of each prey available. We used caloric values from Cummins and Wuycheck (1971) along with our estimates of attack coefficients and handling times derived from foraging trials to determine prey rankings.

Statistical Analysis Our objectives posed two distinct statistical issues: (1) what were the types (shapes) of the functional response curves for nymphs fed exclusively one prey type or presented with both prey types (model selection), and (2) what were the best estimates of the parameters (attack coefficients and handling times) for these functional responses (hypothesis testing) (Juliano 1993). Model selection is best determined by using logistic regression of the proportion of prey eaten vs. number of prey offered to ascertain the general slope of the functional response (Trexler et al. 1988: Trexler and Travis 1993). Hypothesis testing uses nonlinear least squares regression on the number of prey eaten vs. the number of prey offered to estimate parameters of the functional response (Juliano and Williams 1987). The attack coefficient is the product of the animal's rate of searching and probability of finding a prey item. It combines both the encounter and attack components of predation (Hoiling 1959). The attack coefficient and handling times were initially estimated by a "nonparametric parameter space method" for the random predator equation (Juliano and Williams 1987). This approach is derived from a method used for the Michaelis-Menton enzyme kinetics equation and subsequently modified for Hoi ling's disc equation (Williams and Juliano 1985). The method is based upon a nonparametric protocol for estimating slopes (Hollander and Wolfe 1973). The test makes no assumption about the shape of the error 20 distribution (Hollander and Wolfe 1973). Juliano and Williams (1987) showed the test to be the preferred method of analyzing functional response data when data depart greatly from normality, heterogeneity of error is pronounced, and for small sample sizes. We used a = 0.05 to judge statistical significance throughout the paper.

Functional Response We used logistic regressions on the proportions of prey eaten (N^) versus the number of prey offered (N) in order to fit the relationship between the ratio of (N^/N) vs N to distinguish type II from type III functional responses (Trexler et al. 1988). In this method, the probability of the observed values arising from the parameter estimates is maximised. We tested for best fit with both squared and cubed values for the number of initial prey. Cubic expressions provide a good starting point for fitting a logistic regression (Trexler and Travis 1993) and can provide a better fit to a type III functional response (Trexler et al. 1988) than simple quadratic expressions. Because we could not replace prey as they were eaten, exploitation was a factor in the experiment. The random predator equation of Rogers (1972) takes depletion into account. For type II functional responses, the appropriate model is:

Ne = N (1 - e where Ne = number of prey eaten. N = initial density of prey. T = total exposure time. H = handling time, and a = attack coefficient or instantaneous rate of discovery. For type III functional responses, the precise form of the random 21

predator equation depends on whether a is a function of initial or current

prey density (Hassell et al. 1977). The easiest form assumes a is a

function of initial density and the random predator equation becomes; Ne=N{l-exp[(d+bN)(HNe-T)/(l-cN)]}

where b, c. and d are constants that define the relationship of a to N.

Switching Exper iments Functional responses for odonates preying on both midges and amphipods were determined with the same methods as for single prey functional response models.

Preference Preference was predicted from the equation of Manly et al. (1972) which compensates for prey depletion. This index is approximately normally distributed (Manly 1974: Chesson 1978, 1983) and assumes odonates confronted with x number of midges and y number of amphipods act such that the probability of the next prey being a midge or amphipod, respectively is: = x/(x + Ay) Pg = ay/(x + Ay) where A is a positive quality reflecting predator preference. The value of

A is estimated by:

A = log [x/(Z-u)]/log(y-u) where x and y are the numbers of midges and amphipods, respectively, that are presented: Z is the total number of prey not consumed, and u is the mean number of midges left per trial.

When both prey are presented in equal numbers, P„ = 1/(1+A).

Substituting B for P„ in the equation, B is then the probability that the 22 first prey taken is a midge if the predators act in the same way with 50^ presentation as they do at the experimental frequency (Manly et al. 1972).

Thus. B provides a convenient probabilistic interpretation of the A values.

RESULTS When prey were presented separately, odonates oriented towards and pursued amphipods more often than midges at each density but. only at the lowest density were amphipods attacked at a higher rate (Table III). For all densities, there were more orientations than pursuits; most, but not every noticed prey was pursued regardless of density. Odonates struck more than twice as often on midges once a pursuit began. There was a trend towards higher strike:pursuit ratio as prey density increased. There were significantly more ingestions of midges than amphipods at the two highest densities. The only significant difference in capture efficiency was at a density of 20 prey/3 L density, which was also the only density where nymphs struck more at amphipods and missed more of them: strikes and misses were not statistically different. There were significant differences in odonate foraging when nymphs were simultaneously presented with both prey types (Table IV). At absolute densities of 20 prey/3 L. ingestions of midges were statistically greater at the ratio of 10 midge;10 amphipod. Capture efficiencies were significantly different at ratios of 4:16 and 16:4; in both cases, odonates were more efficient at capturing midges. In all cases, regardless of density, midges were preferred over amphipods. The greatest number of significant differences occurred at a density of 40 prey/3 L. Odonates oriented towards and pursued amphipods more than 23 midges at presentations of absolute ratios of 8 midges:32 amphipods and 20 midges:20 amphipods. Furthermore, odonates foraging at the ratio of 20:20 struck at and missed amphipods more often resulting in a significantly higher capture efficiency for midges than amphipods. At the highest absolute ratio (32:8) midges were eaten significantly more than amphipods although there were no significant differences in other foraging behaviours. Because odonates had an extraordinarily low capture efficiency for midges at this treatment, the capture efficiency was higher for amphipods only at the ratio of 32 midge:8 amphipod. Nymphs fed at the highest absolute density of 80 prey/3 L had significant differences in only the lowest ratio for midges (16:64) where odonates oriented towards and pursued amphipods more often. Nonetheless, capture efficiency was significantly greater for midges. Functional Response Experiments Both single-prey functional response models had nonsignificant cubic parameter estimates (estimate=0). Both models were reran without this parameter, but a significant lack of fit remained (for midge x,^=264.2, p=0.0001, n=3 prey densities: for amphipods x^l67.8. p=0.0001. n=3 prey densities). The high scatter around the observed means is consistent with this lack of fit (Fig 1,2). Estimates of were 0.024 and 0.027 h for midges and amphipods, respectively. Attack coefficients were estimated to be 3.26 for midges and 1.32 for amphipods. Functional responses were based on the sign of the linear and quadratic parameter estimates for the models (Juliano 1993). Odonates fed exclusively midges exhibited a significant type III functional response (Fig. 1) as evidenced by a positive linear parameter and negative quadratic 24 parameter (Table V). Conversely, nymphs fed only amphipods had a type II functional response (Fig. 2) because the signs for the linear and quadratic parameters were reversed (Table V). Functional response models developed for nymphs fed both midges and amphipods also had a significant lack of fit (for midges x^33.4. p=0.0001. n=3 prey densities: amphipods x^l2-15, p=0.0069, n=3 prey densities).

Again, there was a large scatter around observed means that is consistent with the lack of fit (Fig. 3). Nymphs, when presented with both prey types simultaneously, exhibited a type II response for midges and a type III response for amphipods based on linear and quadratic parameter estimates (Table V).

Switching Experiments Our original objective was to determine switching behavior by incorporating both functional response data and preference models (Cock 1978). However, this method assumes that the searching behavior of a predator remains constant in the presence of either prey and when both prey are presented together. Because odonates differed in their functional responses when presented with different prey combinations, we had to use the preference model by Manly et al. (1972) which compensates for prey depletion.

In the absence of preference, the regression of B against the frequency of midges presented is a horizontal straight line at B = 0.5.

If odonates prefer midges, then B values are constant, and the regression of B on frequency is a horizontal straight line above 0.5. If odonates favor the rarer amphipods, the regression is below 0.5 and has a positive 25 slope. For either case, switching is indicated when the regression crosses the straight line at 0.5.

Odonates preferred midges (B values always above 0.5) and did not favor rarer prey (slope=-0.004): (Fig. 4). The slope was not significantly different from 0 (F=4.00 p=0.09).

Optimal Foraging Caloric content for midges and amphipods were 6050 and 4079 calories/g ash free dry weight, respectively (Cummins and Wuycheck 1971). Substituting these values for e^ and e^. midges would yield 252,083 calories/h of handling time compared to 151,074 calories/h for amphipods. Hence, if odonates foraged optimally, they should select midges. Indeed, odonates ate significantly more midges than amphipods when prey were presented separately at the two highest densities (Table 3) as predicted by the theory. The decision to maximise net energy gain, however, is based on both handling time and predator search rate (a). Because high densities of non- cryptic prey will increase a for visual predators, dragonflies should choose midges at all densities tested, except at 16 midges:64 amphipods. At all of the equal presentations of prey (10:10, 20:20 and 40:40), dragonflies did prefer midges, especially at high midge densities. At no density did nymphs eat less than 50^ of the midges offered and at only the 20:20 presentation did they eat more than 50% of the amphipods offered. They did disportionately eat amphipods however, at both 4:16 and 8:32 presentations. At the 4:16 ratio they consumed all midges within 45 min and may have been forced to eat amphipods (Fig. 5). Similiarly. at 8:32 nymphs ate 75^ of the midges within 30 min and were left with the majority of the 26 amphipods U2%) so they also may have been forced to eat amphipods later in the trial (Fig. 5). Across all mixed prey densities. TAX of the total midges offered were eaten compared to 30X for amphipods. DISCUSSION We expected dragonflies to orient towards, pursue, and strike amphipods more frequently than midges because amphipods are faster moving and hence are more noticeable to visual predators like ^ .iunius. However, amphipods have hard exoskeletons that might make them less susceptible to capture. Thus, we anticipated odonates would miss amphipods more often than midges. We expected that nymphs would ingest more midges than amphipods and have a higher capture efficiency for midges. With one exception (32 midge;8 amphipod), we saw this pattern across 8 combinations. The trials with high strike rates may have lowered capture efficiency, thus creating apparently nonsignificant differences in capture efficiency. This is supported by the significant differences in midge-only trials, where significantly more midges were eaten at the two highest densities compared to amphipod-only trials. However, the corresponding capture efficiencies between midges and amphipods were not significant. The results from trials with mixed prey are harder to explain. Although nymphs oriented towards, pursued, attacked, and missed with greater frequency whichever prey type was most numerous, nymphs had a higher capture efficiency for midges at all relative proportions except at the 32 midge:8 amphipod ratio. Generally, If a nymph oriented on a prey, the nymph pursued it. Nymphs usually attacked pursued midges, often with multiple strikes. This was most evident at high midge densities. Nymphs that missed an amphipod on the first strike were more likely to end the 27 encounter and orient on a different prey item. Because ^ iunius is a visual predator, it is possible that the most numerous prey are the most visible or that a faster moving amphipod may attract the attention of a nymph but then move quickly out of the odonate's field of vision, leaving a slower but more easily captured midge in the visual field. Determining the functional response of a predator is always a challenging task. Comparative curve fitting, least-square analysis of linear models, and logit analysis have been used to distinguish functional responses for laboratory-generated data. We used a logistic regression and initial estimates of handling time and attack coefficients generated from a nonparametric parameter space method. This is the preferred method of analyses with small sample sizes (Juliano and Williams 1987). We used a high number of replicates to reduce within-treatment variability with the trade-off being fewer treatments. However, large variability remained, which most likely contributed to the significant lack of fit of the models. It is also possible that additional densities would have improved the fit. There are several possible mechanisms that may explain our results. Nymphs may change hunting strategies in the presence of both prey. Akre and Johnson (1979) suggested that nymphs of Anomalaorion hastatum. shifted from an ambush strategy in areas of mobile, abundant prey to an active search in areas of sessile prey, thereby increasing encounter rates. Similarly. Crowley (1979) found another odonate nymph, Ishnura vertical is. spent more time actively searching when no prey or sessile prey were offered than when mobile prey were available, although the time spent walking was small. Although we did not perform activity budgets on our nymphs to test for changes in hunting strategy, it is possible that a 28

faster moving amphipod may make a slower moving midge appear to be sessile, thus providing an opportunity for the nymphs to change hunting strategy. It is also possible that the prey somehow interacted to create differential susceptibility to mortality, although we did not specifically test this. We chose to work with benthic prey because it was easier to determine orientations and pursuits when odonates are oriented in a horizontal plane. However, ripari us may not be a natural prey for ^ .iunius (see Folsom and Collins 1984). Consequently, nymphs may not be familiar with midges and in order to establish that midges were an acceptable prey, sampled midges in greater numbers when midges were presented solo. This stimulation would lead to a type III response. However, nymphs may readily recognize amphipods because amphipods will occur on benthic surfaces. Thus, nymphs may more quickly take amphipods. This recognition coupled with the small confines of the enclosures resulted in nymphs being constrained only by handling time when feeding exclusively on amphipods, creating a type II response. In a separate study, we fed ^ iunius midges during a-l-mo acclimation period. Nymphs exhibited a weak type II response and had higher capture efficiencies than nymphs in this study. This suggests that prey familiarity may influence components of the foraging response and hence functional response (Brewer 1997). Components of the foraging sequence may also change in the presence of multiple prey. The formation of search images may be faster for more abundant or obvious prey (Tinbergen 1960). This is a matter of experience and learning. Interference between search images may also occur if the search image for a given prey is created faster, retained longer, or reduces the quality or retention time of the search image for another prey 29

(Pietrewicz and Kamil 1981: Ender 1988). The result of this interference is that former prey is likely to be consumed in greater quantities, depending on its density and frequency. Although there is no direct evidence for interference between search images, it is usually assumed to be true merely because of finite brain size (Kamil 1989). There are no other published reports for ^ .iunius fed rioarius or tL azteca. so we are unable to make direct comparisons between our estimates for attack coefficients and handling time. Gresens et al. (1982) fed L. tentans (a larger midge than rioarius) to Celithemis fasciata. a dragonfly about 1/2 the size of ^ .iunius. Our estimated attack coefficient was higher and handling time was faster than that of Celithemis fasciata. Differences in predator size may partly explain this discrepancy. Small prey, relative to the size of the predator, may be easier to successfully capture and ingest. However, fasci ata fed daphnids still exhibited lower attack coefficients and longer handling times (Cothran and Thorp 1985) than we estimated. Prey that are large relative to the predator should take longer to subdue, eat, and digest (creating a longer handling time). However, these same prey may be seen further away, move faster, or have higher capture efficiency (any of which would increase attack coefficients) or may escape more easily (decrease a: Thompson 1975). This may help explain our estimates for midges and amphipods. Because midges are brightly-colored and larger than amphipods, they may be expected to be seen further away, which would raise their respective attack coefficient. Furthermore, odonates did have higher capture efficiency with them. Amphipods may have had a lower attack coefficient due to their greater mobility and better 30

escape ability. We saw several odonates capture but not successfully subdue amphipods. What is surprising is the relatively small difference (11 s) between the handling times for midges and amphipods. We anticipated that midges would have a much lower handling time because they are soft-bodied. However, odonates almost exclusively manipulated their prey in order to eat them from the anterior or posterior end. Midges are longer than amphipods and it is possible that, although midges are easier to catch and manipulate than amphipods (most amphipods that escaped capture did so while being manipulated by the odonate). consuming a midge lengthwise is more time consuming. Cornell (1976) suggested the following conditions should favor switching: (1) patchy distribution of prey in space or time: (2) non- sessile searching behavior: (3) non-tactile prey detection. Murdoch and coworkers had conflicting results when they tested for switching behavior in two species of invertebrate predators. They predicted that switching will only occur when: (1) predators have search and selection behavior: (2) available prey are distinctly different: and. (3) predators have to hunt differently to obtain each species (Murdoch 1969: Murdoch and Marks 1973). Colton (1987) used Daohnia and Simoceohalus in tests with Enallaama aspersum. but the damsel fly showed no evidence of switching. Harvey and White (1990) were also unable to demonstrate switching behavior in Pvrrhosoma nvmohula fed Aedes larvae. Cothran and Thorp (1985) showed that Celithemis fasciata showed no preference for either Daohnia magna or Chironomus tentans when no prey refuge was available. However, when refuge was available to the midges. Daohnia were consumed in a 3:1 ratio. 31

The ability to learn is also a prerequisite for switching to occur (Lawton et al. 1974; Akre and Johnson 1979), and experience has been shown to modify choice in Odonata (Bergelson 1985: Blois and Clarec 1985). It is possible that our experiment did not last long enough for iunius to learn to differentiate prey. It is also possible prey densities did not reach the critical level needed to initiate switching behavior. It is intuitive that if a predator ingests "optimal" prey below a certain threshold, then, although the remaining prey remain profitable, the predator must include other prey types in the diet (switch) or else risk hunger or even starvation because the "optimal" prey are harder to locate due to increases in search time. Because our odonates did not exhibit switching behavior, we are not able to hypothesize on the potential for switching behavior to affect optimal foraging. Hubbard et al. (1982) suggested switching and optimal foraging are different aspects of the same process. Odonates did follow the rules for ranking prey profitability and foraging according to the maximisation of net energy intake. We expected that dragonflies would prefer midges and consume amphipods when midges were depleted and nymphs were not yet satiated. Indeed, nymphs did consume midges first in almost all bouts (82%). Optimal foraging predicted avoiding amphipods at most densities, however at low midge densities, odonates did eat amphipods, but only after depleting midges. It is important to remember, however, that switching behavior and optimal foraging deal with different levels of behavior and are not directly comparable. Switching theory emphasizes causal mechanisms such as learning and prey preference, whereas optimal diet theory does not entail a 32

specific mechanism when it assumes "correct" prey selection results in higher fitness rewards. Hubbard et al. (1982) suggested that both optimal diet and random predator equation models demonstrate that the intensity of switching behavior is dependent on the degree of prey exploitation: high exploitation will lead to low apparent selection, and vice versa. Switching behavior is believed to allow predators to stabilize prey populations and even promote diversity within populations. Optimal foraging, however, does not suggest the precise form of the selection, and. therefore, the stability is very likely to be based on factors which change exploitation, such as changes in hunting strategy (Baker 1980). the density of the prey (Akre and Johnson 1979), numbers of predators, and interference among predators (Crowley and Martin 1989). The potential implications of switching behavior for ecology and population genetics depend on whether switching is an underlying mechanism for optimal foraging or if they are different explanations for the same phenomenon. We are aware of only one study (Hubbard et al. 1982) that directly attempted to separate switching from optimal diet. Switching has not been conclusively demonstrated for any arthropod taxon (see review by Sherratt and Harvey 1993). Presently, most of the support for switching comes from its similarity to apostatic selection by predators on different morphs of a prey species. It is reasonable that switching may an artifact to optimal foraging because if a predator favors the prey that maximises benefits, eventually the density of that prey will drop below some critical threshold. The predator will then be forced to switch to another prey. 33

ACKNOWLEDGEMENTS Journal paper No. J-17301 of the Iowa Agriculture and Home Economics Experiment Station. Ames. lA, USA, Project 3307 and supported by Hatch Act and State of Iowa funds. Funding was provided by the United States Environmental Protection Agency Cooperative Agreement # CR 820078. We thank Joel Coats. Brent Daniel son. Joseph Morris, Clay Pierce, Mark Sandheinrich and Kenneth Shaw for reviewing an earlier draft. Joseph Morris and Steve Miranda provided valuable statistical advice. The manuscript benefitted from discussions with William Mitchell and Peter Smallwood. REFERENCES Allen. J. A. 1976. Further evidence for apostatic selection by wild passerine birds-9:1 experiments. Heredity. 36. 173-180. Allen. J. A. 1988. Frequency-dependent selection by predators. Phi 1. Trans. Roval Soc. • London B319. 485-503. Akre. B. G.. and D. M. Johnson. 1979. Switching and sigmoid functional response curves by damsel fly naiads with alternative prey available. J. Anim. Ecol.. 48, 703-720. Baker. R. L. 1980. Use of space in relation to feeding areas by zygopteran nymphs in captivity. Can. J. Zool.. 58. 1060-1065. Benke, A. C. 1976. Dragonfly production and prey turnover. Ecology. 57. 915-927. Benke, A. C., and S. S. Benke. 1975. Comparative dynamics and life histories of coexisting dragonfly populations. Ecology. 56, 302-317. Bergelson. J. M. 1985. A mechanistic interpretation of prey selection by Anax iunlus larvae (Odonata; Aeschnidae). Ecology. 66. 1699-1705. 34

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Table I. Operational definitions of odonate foraging behavior

Behavior Definition

Orientation nymph moves to bring prey in line with the eyes

Pursuit movement directed towards the prey

Strike movement of the labium or labial palps towards the prey

Capture grasping and bringing prey to mouth

Miss strike fails to contact prey or prey dropped prior to reaching mouth

Ingestion consumption of prey 41

Table II. Absolute and relative densities of Chironomus ripari us and Hvallela azteca used in switching experiments.

Number of prey/3L Proportion of prey items Cripari us:azteca)

20 i~4 i~l 4:1

40 1:4 1:1 4:1

80 1:4 1:1 4:1 Table HI. Mean (SEM) of orientations (0„. Oa), pursuits (P„,. P,). strikes (S„, SJ. ingestions (l^. 1,). misses (M„. MJ and capture efficiency (CE„. CE^) for A. iunius nymphs presented with three absolute densities of midges (m) or amphipods (a) (n=10). Density is prey/3L

Number offered 0, 0, P, P, S„ S, I, M, M, CE, CE,

20 24 58" 19 54" 39 57 17 17 22 39 0.43 0.29' (1.5) (8.7) (1.2) (6.5) (9.8) (7.0) (0.7) (1.1) (9.4) (6.0) (0.08) (0.04) 40 51 79 44 63 111 70 35 16" 77 53 0.32 0.23 (5.1). (18.5) (5.0) (15.7) (33.6) (18.1) (2.1) (2.6) (32.8) (17.6) (0.06) (0.52) 80 65 128* 59 104 144 120 39 26* 95 91 0.27 0.22 (8.4) (21.2) (8.3) (19.0) (40.5) (17.6) (3.8) (3.5) (40.0) (15.0) (0.06) (0.45)

*= p < 0.05. **= p < 0.0001 for comparisons between each behaviour within a density. Table IV. Mean (SEM) of orieni".ations (0), pursuits (P). strikes (S), ingestions (I), misses (M). and capture efficiency (CE) for A. iunius nymphs presented with nine ratios of midges.amphipods (n=3).

AKcoT • • • •' . I % it , I i 1 ' 4:16 ^ ^ ^ y-30 ~ ~ ~0.8:0.1)'"

*= p < 0.05, **= p < 0.0001 for comparisons between each behaviour within a ratio. Table IV. Mean (SEM) of orientations (0), pursuits (P), strikes (S), ingestions (I), misses (M). and capture efficiency (CE) for A. iunius nymphs presented with nine ratios of midges:amphipods (n=3).

Absolute Ratio 0 P S I M CE 4:16 5:19 5:18 5:30 4:4 1.3:26.3 0.8:0.13* (0.6:7.8) (0.7:8.1) (0.9:14.4) (0:0.6) (0.9:14) (0.1:0.2) 8:32 11:36* 9:32* 13:53 6:11 7:42 0.46:0.21 (3.9:6.2) (2.6:6.1) (5.2:19) (1.2:3.1) (4:19.2) (0.2:0.08) 16:64 18:54* 17:51* 19:57 14:20 5:37 0.7:0.35* (1.1:8.6) (1.4:9.8) (0.9:173) (0.33:4.7) (0.6:16) (0.2:0.1) 10:10 13:13 12:12 14:18 9:3* 6:15 0.64:0.17 (1.2:7.0) (1.2:7.7) (2.0:14) (0.3:1.0) (2.3:13.2) (0.11:0.26) 20:20 22:43* 22:42* 27:49* 17:11 10:36* 0.6:0.2* (0.3:1.8) (0.9:23) (5.8:5.1) (2.1:1.3) (4.5:4.5) (0.09:0.01) 40:40 42:27 41:26 143:34 24:5 118:29 0.17:0.15 (16.2:11.5) (15.5:11.4) (108:14) (8.0:2.0) (104:11) (0.16:0.1) 16:4 11:9 10:8 18:9 9:0.3 9:8 0.5:0.03* (2.9:4.9) (3.2:4.2) (8.4:4.7) (3.8:0.32) (4.6:4.3) (0.09:0.02) 32:8 130:22 56:22 295:35 19:3* 272:30 0.06:0.09 (34.3:16.9) (37:17) (270:24) (4.1:1.8) (263:21.5) (0.23:0.08) 64:16 61:36 53:35 123:44 34:6 88:39 0.27:0.14 (8.4:10) (5.0:10.6) (44:13) (6.4:0.9) (48:12) (0.15:0.03)

*= p < 0.05, **= p < 0.0001 for comparisons between each behaviour within a ratio. Table V. Parameter, parameter estimates, standard error of the mean (SEM). and p-values for single and mixed prey functional response models developed on Anax .iunius nymphs

Midge:amphipod together Midge only Amphipod only Midge Amphipod

Parameter Estimate SEM P > x' Estimate SEM p >X^ Estimate SEM p > Estimate SEM p >X^

linear 0.0390 0.0114 0.0006 -0.1149 0.0137 0. 0001 -0.0433 0.0484 0.3705 0.2259 0.09740. 0204 quadratic -0.0006 0.0001 0.0001 0.0009 0.0001 0,,0001 0.0004 0.0016 0.7844 -0.0080 0.00300,,0085 45

O)c

s <4- o 0.6 cc. C-

0.4 yce i

I 0 20 40 6C SO

No. of midges offered

Figure 1. Single prey functional response for Anax iunius nymphs fed

midges. The inset figure is the hypothetical type III functional

response based on Juliano (1993). Error bars are one standard

devi ati on. 0 20 40 60 60

No. of amphipods offered (No./3 L)

Figure 2. Single prey functional response for Anax Junius nymphs fed

amphipods. The inset figure is the hypothetical type II

functional response based on Juliano (1993). Error bars are one

standard deviation. 47

No. of midges presentee

16 22 ^ No. of amphipods offered

Figure 3. Mixed prey functional response for Anax iunius nymphs fed midges

(3a) and amphipods (3b) simultaneously (see Table IV for absolute

ratios). Error bars are one standard deviation. 48

1.0 intercept = 0.87 slope = -0.004 r^ = 0.29

O.S

UCU 0 S r— I L t i 0.7 1-

0.6 -

0.5

16 32 ^ 6^

No. of midges presented

Figure 4. Preference determinations of A. iunius for midges in mixed prey trials (see Table IV for absolute ratios). Dotted lines are %% confidence intervals. 16

12-

&. 8

4 -

0 - nm MiUil 15 45 ID

4:16 10:10 IG: CA 20:21

Phases of foraging bout (min)

Figure 5. Preference determinations of Anax Junius for midges in

mixed prey trials (see Table IV for absolute ratios),

numbers are in black: amphipods are in stippled. 50

THE EFFECTS OF CHLORPYRIFOS ON CHOLINESTERASE ACTIVITY AND FORAGING BEHAVIOR IN THE DRAGONFLY. mX JUNIUS (ODONATA)

A paper to be submitted to Archives of Environmental Contamination and Toxicology Sandra K. Brewer and Gary J. Atchison Iowa State University, Department of Animal Ecology. Ames. lA 50011 Abstract We examined changes in head capsule cholinesterase (ChE) and foraging behavior in nymphs of the green darner dragonfly. Anax Junius, exposed for

24 h to 0.2. 0.6 and 1.0 ng/L of the organophosphorus insecticide, chlorpyrifos [0.0-diethyl 0-(3.5,6-trichloro-2-pyridyl) phosphorothioate]. ChE activities were generally slightly elevated in treated nymphs but most foraging behaviors between treated and control groups were not statistically different. High variability in ChE activities within the control group and across treated groups precluded determination of significant relationships between ChE and foraging behaviors. Introduction Modern agriculture relies heavily on insecticides, shifting in the past 25 years from organochlorine (DC) insecticides to organophosphorus (OP), carbamate, and more recently, pyrethroid insecticides (Fairchild et al. 1992). These newer classes of insecticides, although generally less persistent and less likely to bioaccumulate than their DC predecessors, remain highly toxic to many non-target organisms, including those in wetlands associated with agricultural landscapes. The northern prairie wetlands of North America form an extensive complex ranging from north-central Iowa, through western Minnesota, the 51

Dakotas. and northern Montana into the southern portions of the Canadian provinces of Manitoba, Saskatchewan, and Alberta (Hubbard 1988). This region is intensively cultivated, and agricultural insecticides are extensively applied to grain and row crops that support the agricultural economy (Johnson 1986). The topography of the region results in the potential for wetlands to become sinks for insecticides via runoff, leaching, or drift. The area also provides highly valuable habitat for many species that are obligate users of prairie-wetland landscapes (Murkin and Batt 1987). most noticeably waterfowl. The region is a prime nesting and staging area, producing 50-80^ of North American duck populations (Batt et al. 1989). Wetlands provide an invertebrate food base that is critical for waterfowl production. However, potential effects of insecticides on prairie wetland ecology are largely unexplored. The mode of action of OPs and carbamates is inhibition of cholinesterase (ChE) enzymes that are essential for normal functioning of the central and peripheral nervous systems. Grue et al. (1983) suggested that the relationship between cholinesterase inhibition and its subsequent effects on the physiology and behavior of animals could provide a basis for predicting ecotoxicological consequences of OPs. Behavioral alterations in mammals in response to OPs appear to be inversely related to brain ChE when ChE levels are below 40-60^ of normal (Grue et al. 1983). Subsequent studies on bird and fish behavior have substantiated this result (Galindo et al. 1985: Pavlov et al. 1992; Hart 1993) but there have not been similar studies done to date on the foraging behavior of aquatic invertebrates. Dragonfly nymphs are important invertebrate predators in many freshwater littoral zones and wetlands (Benke and Benke 1975: Patterson 52

1994) and can reach standing stocks 2 to 3 times that of their prey (Benke 1976). Hence, odonates may potentially act as "keystone species" (Paine

1980) in regulation of some aquatic prey communities. The odonate A.

Junius is widespread in North America, and its ecology has been well studied (Bergelson 1985; Wissinger 1992). Nymphs of A. Junius are visual predators that actively stalk prey and possess discernable foraging behaviors, i.e., orientations, pursuits, strikes and captures (Table 1). Odonate nymphs have been shown to be sensitive to insecticides (Marshall and Rutschky 1974: Wallace et al. 1987; Takamura et al. 1991: Dieter et al. 1996). Foraging behavior has been shown to be a sensitive endpoint to stress (Beitenger 1990: Fernandez-Casalderrey et al. 1994: Atchison et al. 1996). Feeding rate may potentially be a useful endpoint: a variety of contaminants at low concentrations have been shown to affect feeding rate (Heath 1995). Furthermore, a change in foraging behavior caused by sublethal exposure to a stressor may directly affect individual growth, reproduction or survival rates. Because foraging is a direct link among trophic levels, any alterations in feeding behavior may have consequences at the community level through alterations in composition and numbers of predators and their prey. Jones et al. (1991) suggested that if environmental stressors quickly cause changes in feeding rates, such changes could be measured at individual and community levels before direct effects in population parameters could be measured. The number of prey a predator kills is a function of prey density and is called the functional response (Holling, 1959). Generally, the number of prey eaten in a given time approaches an asymptote as prey density 53

increases (Rolling 1959). There are several types of curves that can model differences in the proportion of available prey consumed in a specified time. The most ecologically important functional responses are types II and III. A type II response occurs when the number of prey eaten approaches the asymptote hyperbolically as prey density increases. Hyperbolic curves represent a constant proportion of prey eaten up to the maximum, regardless of prey density, followed by a declining proportion of prey killed. The leveling off of the curve is a function of handling time (how long it takes the predator to capture, subdue and ingest the prey). Type III responses occur when the number of prey eaten approaches the asymptote as a sigmoid function. This represents an increase in the proportion eaten (density dependence) up to the inflection point of the curve, followed by a decrease in the proportion eaten. Functional responses are used to address several ecological issues. On a purely descriptive level, the type (shape) of the functional response is of interest. The response type determines whether density dependent predation is a stabilizing factor in predator-prey population dynamics. Another area of interest is determining the best parameter estimates (handling times and attack coefficients) of the response. This is a important question in resource competition or predation work. Finally, one can compare the model parameters of several functional responses to see if they differ significantly. These comparisons are important in comparing different predator species or age classes or when looking for predator-prey coevolution. It is also appropriate to examine and compare model parameters when determining toxicant effects on predator foraging. Changes in handling times or attack coefficients may indicate early sublethal toxicity and may affect predator-prey dynamics by altering the foraging strategies and techniques of the predator. We investigated the effects of chlorpyrifos [0,0-diethyl 0-(3.5.6- trichloro-2-pyridyl) phosphorothioate] on foraging behavior and head ChE in nymphs of /4. Junius. Our objectives were to determine: 1) functional responses (proportion of prey eaten relative to proportion offered) of nymphs exposed to three different concentrations of an organophosphorus insecticide and offered five different densities of prey; 2) foraging behaviors that may be affected by insecticide exposure, and; 3) levels of ChE inhibition at which foraging changes occurred. We used the organophosphorus insecticide chlorpyrifos, which is a broad-spectrum organophosphorus insecticide used widely in a variety of agricultural and urban pest control situations (Racke et al. 1994). In Iowa, chlorpyrifos is one of the most widely used insecticides on corn (Hartzler and Wintersteen, 1991). Materials and Methods

Culture Methods

Penultimate A. Junius (mean head width ± relative standard deviation, 7.25 mm ± 10?; n=60) were collected from a prairie wetland near

Ames, lA in the summer of 1995 and were acclimated to laboratory conditions for 1 mo. Nymphs were housed individually in 250-ml glass containers filled with carbon-filtered municipal water. Culture water was changed weekly. Standard procedures were used to determine water chemistries for both culture and replacement water (APHA 1992). Determinations were done on pooled samples of culture water. Week-old culture water had the following characteristics (x ± standard error of the mean (SEM): n=8): pH. 55

7.2 ± 0.04; hardness. 162 mg/L ± 4.3 mg/L as CaCOj: total alkalinity. 43 mg/L ±1.0 mg/L as CaCOj: dissolved oxygen 5.8 ± 0.2 mg/L; NH3-N 2.28 ±

0.3 mg/L.

The pH of replacement water was 8.4 ± 0.2: dissolved oxygen was 9.8

± 0.2 mg/L. Hardness and total alkalinity averaged 167 ± 4.81 mg/L as

CaCOj and 47 mg/L ±1.1 mg/L CaCOa, respectively. NH3-N was never detected in replacement water. Because A. Junius is territorial, containers were partitioned from each other with cardboard to prevent nymphs from observing each other. Nymphs were maintained on a 16 h light:8 h dark photoperiod in an environmental chamber. Nymphs were fed daily to satiation with aquatic invertebrates and larval fish, but were not fed 24 h prior to the trial in which they were used. Foraging Trials A 1.0 mg/L stock solution of technical grade (98.7^) chlorpyrifos was prepared by diluting 1 mg/ml of chlorpyrifos dissolved in technical grade acetone to 1 L with carbon-filtered water. Dilution water had the same characteristics as the replacement water described above. Nymphs were randomly assigned to one of three test concentrations

(0.2. 0.6. 1.0 ^ig/L) or a control. Because wetland biota are typically exposed to pesticides through short-term spikes of concentrations followed by rapid to gradual decreases in initial concentrations, we used a static 24-h exposure. Exposure chambers were acetone-rinsed, acid-washed 250 ml glass containers filled with 80 ml of test solution or control. Chambers were separated from each other with cardboard partitions. Each chamber 56 held one nymph. Nymphs which molted 5 d prior to a foraging bout were not used. We used a 4 x 5 factorial design with 4 treatments and 5 prey densities. Prey densities were 5. 10, 20, 40. or 80 prey/3L. We ran triplicate foraging trials for each prey density tested at each concentration for a total of 60 trials. Nymphs were used only once; immediately after each trial the nymph was frozen at -80 °C for use in head cholinesterase analyses. Experimental arenas for foraging trials were 30 x 15 x 6.5 cm (Ixwxh) clear Plexiglas™ containers that held 3 L of charcoal-filtered water.

Each chamber contained 0.16-cm mesh fiberglass screen along the bottom to provide a "tarsushold" for nymphs and partial refuge for prey. Laboratory- cultured fourth-instar midges were stocked in the arenas and allowed to acclimate for 0.5 h prior to introduction of the nymph. All trials were videotaped in order to document foraging behaviors and their time of occurrence. Foraging bouts were 30 min and the number of orientations, pursuits, strikes, captures, ingestions, and misses were recorded. In addition, latency to feed, and time at rest were recorded. These foraging behaviors were operationally defined and readily observable (Table 1). We did pairwise Student-Newmans Kuehls multiple comparisons to distinguish differences between nymphs exposed to different concentrations of toxicant at each density (a = 0.05).

Model Selection Three replicates were performed at each of 5 prey densities (5. 10. 20. 40. 80 organisms/3 L). Each predator was randomly assigned a prey 57 density and trials were randomized. Each predator was used only once. We used logistic regression of proportion of prey eaten (Ng) against number of prey offered (N) in order to fit the relationship between the ratio of (Ne/N) vs N to distinguish type II from type III functional responses (Trexler et al. 1988). In this method the probability of the observed values arising from the parameter estimates is maximized. We tested for best fit with both squared and cubed values for the number of initial prey. Cubic expressions provide a good starting point for fitting a logistic regression (Trexler and Travis 1993) and can provide a better fit to a type III functional response (Trexler et al. 1988) than simple quadratic expressions. Because it was not possible to replace prey as they were eaten, exploitation was a factor in the experiment. The random predator equation of Rogers (1972) takes depletion into account and for type II functional responses the appropriate model is:

Ne = N (1 - e where Ng = number of prey eaten, N = initial density of prey, T = total exposure time. H = handling time, and a= attack coefficient or instantaneous rate of discovery. For type III functional responses the precise form of the random predator equation depends on whether a is a function of initial or current prey density (Hassell et al. 1977). The easiest form assumes a is a function of initial density, and the random predator equation becomes: Ne=N {1-exp [ (d+bN) (HNg-T) / (1-cN) ]} where b, c. and d are constants that define the relationship of a to N. 58

Chemical Analysis We collected 5-nil water samples at 1 h and 24 h during each of the 24-h chlorpyrifos exposures. Samples were collected in acetone-rinsed, acid-washed glass vials with teflon cap-liners. Vials were wrapped in aluminum foil and stored at 4 °C until analysis (<1 wk) to verify actual exposure concentrations.

We used the chlorpyrifos RaPID Assay™ (Ohmicron Environmental

Diagnostics. Newton, PA, USA) to determine concentrations. This method is a enzyme-linked immunosorbent assay (ELISA) where chlorpyrifos was detected colorimetrically by a RPA-1 RaPID analyzer (Ohmicron Environmental Diagnostics, Newton, PA, USA) set at 450 nm. The Analyzer automatically calculated the calibration curve and chemical concentrations. Quality control consisted of procedural blanks, calibration standards, and duplicate samples. Any samples with a C.V. greater than 10^ were reanalyzed. The levels of quantification for the assay were 0.1 to 3.0 ^g/L.

Cholinesterase Determination Head cholinesterase was determined using modifications from Ellman et al. (1961), Hill and Fleming (1982), and Hooper et al. (1989). All chemical reagents were purchased from Sigma Chemical. St. Louis, MO.

Nymphs were removed from the ultracold freezer (-80 °C), head capsules were immediately severed, and capsule contents were removed by cutting along each cranial suture from base to snout and across the snout tip to remove the exoskeleton. We removed contents with acid-rinsed stainless steel forceps and quickly rinsed the material to remove any 59 liquified chromatophore pigment. Material was blotted dry and weighed to the nearest 0.1 mg to determine appropriate dilution factors. Tissue samples were diluted 100-fold with ice-cold pH 7.4 Trizma buffer and homogenized with a motorized teflon pestle and glass tube. We kept homogenates on ice until analysis. We modified the colorimetric ChE assay procedure of Hill and Flemming (1981) to assay the smaller activity units of ChE in the samples. We used 1 mM acetylthiocholine iodide (AThCh) as substrate: 0.323 mM 5.5'- dithiobis(2-nitrobenzoic acid) (DTNB) provided the colored anion product. Both reagents were kept on ice and protected from light until use. Check standards (pooled Chironotnus riparius homogenates stored in liquid nitrogen at -80 °C) were run with each analysis to insure quality control. All samples and check standards were run in triplicate. If the coefficient of variation (C.V.) exceeded 10^ for any given sample, we ran the sample again; if the check standard exceeded 10%. we repeated the entire run. One-third of the samples needed to be repeated: the check standard never exceeded 10%. Samples were analyzed at 405 nm with a 96-well kinetic plate reader (THERMO max brand. Molecular Devices Corp.) fitted with an internal incubator set at 25 °C, which was computer controlled (Softmax, Molecular

Devices Corp.). Sample wells were filled with either 30 |4.1 of nymph homogenate or check standard plus 20 ^1 DTNB. 30 y.^ AThCh and enough pH

8.0 Trizma buffer (prewarmed to 25 °C) to total 250 |il in each well.

The enzymatic reaction proceeded for 2 min and optical densities (O.D.) for each well were recorded every 7 s. Changes in O.D./min were 60 automatically determined, and we calculated enzyme activity, expressed as

^mol of AThCh hydrolyzed min"' g'^ tissue, from these values (Ellman et al.

1961). Significant differences in ChE activities were determined with a one-Way ANOVA (a = 0.05). Results

Foraging Trials The only significant result in foraging behavior for dose effects was nymphs dosed at 0.6 ng/L spent significantly more time at rest (p=0.01).

Prey density effects were more numerous (Table 2). Nymphs offered the two highest densities had significantly more (p<0.0001) orientations, pursuits, strikes, and ingestions than nymphs offered the three lowest prey densities. Similarly, nymphs offered the highest prey density missed significantly more ( p=0.002) than nymphs offered the two lowest densities. Nymphs offered the lowest density spent the most time at rest. All functional response models had nonsignificant cubic parameter estimates for each treatment (estimate=0). Models were rerun without this parameter but a significant lack of fit remained (control xM3.8, p=0.003:

0.2 ug/L chlorpyrifos x^24.1, p=0.0001; 0.6 ug/L chlorpyrifos x^=172.6, p=0.0001: 1.0 ug/L x^57.5, p=0.0001). The high scatter around the observed means is consisent with this lack of fit (Fig. 1). Estimates of handling times were 0.014, 0.020, 0.15, and 0.31 hr and estimates of attack coefficients were 0.89, 2.8, 0.19, and 12.8 for control, 0.2, 0.6, and 1.0 ug/L chlorpyrifos, respectively. Functional responses were based on the sign of the linear and quadratic parameter estimates for the models (Juliano 1993). Control 61 nymphs tended towards a type II response based on the negative linear parameter and positive quadratic parameter, however these parameter estimates were not statistically significant (Table 3). Nymphs exposed to 0.2 and 1.0 ug/L chlorpyrifos had type III responses based on parameter estimates: however these estimates were statistically significant only for nymphs exposed to 0.2 ug/L. Nymphs exposed to 0.6 ug/L had a statistically significant type II response, but nymphs offered 7 prey/L did not consume any prey (Fig Ic) which may have contributed to a false model. Because there were no significant dose effects for important foraging behaviors (orientations, pursuits, strikes, ingestions) we pooled ingestions by density (n=12) and reran the functional response models. In this case, there was an overall trend towards a type II response (Fig 2: linear parameter estimate = -0.075 p value = 0.01: quadratic parameter estimate 0.00291, p value = 0.001) although both models gave a significant lack of fit due to high variation within trials.

Cho7 inesterase Ana lysis

Head Choi inesterase was not inhibited by exposure to the chlorpyrifos concentrations used in our experiment. Indeed. ChE was slightly higher in animals exposed to 0.2, 0.6. and 1.0 ^g/L chlorpyrifos than controls, although these differences were not statistically significant (p=0.19) (Table 4). There was a wide range in activity levels for all exposure concentrations.

One nymph in the 0.2 ug/L experiments had an extremely high cholinesterase activity level (14.25 [imol AThCh min'^g'^) compared to the rest of the animals within that treatment (range 0.31 to 4.84 fimol AThCh min'^g'M. The sample was reanalyzed with similar results. 62

Discussion

Aquatic macroinvertebrates are thought to be useful indicators of toxic stress because of their trophic roles and importance in nutrient cycling. Furthermore, aquatic insects may be a good choice for monitoring OP contamination because they are often more sensitive to cholinesterase- inhibiting chemicals than terrestrial insects (Edwards and Fisher 1991: Detra and Collins 1991). However, dragonfly nymphs were much more sensitive to changes in prey density than to changes in insecticide concentrations. A wide variety of predators have been shown to be sensitive to changes in prey density. We were unable to conclusively demonstrate a statistical functional response because of the wide scatter of observed means around the predicted curve, and the concentrations of chlorpyrifos used were insufficient to induce toxicity based on our measures of ChE or most of our behavioral endpoints. The noticable exceptions were handling time and attack coefficients. Handling times geometrically increased with increasing concentration of the pesticide. This is surprising because we saw a trend towards Type III responses with increasing concentrations, although organisms limited in their rate of food acquisition by handling times typically exhibit a Type II functional response. It appears that although these large increases in handling time were insufficient to alter functional responses, exposure to the pesticide may have had subtle, sublethal consequences. The attack coefficient is an estimate of the area covered by a predator in a given unit of time (search rate). A higher attack coefficient implies that an animal has a faster rate of search. Attack coefficients increased with increasing concentrations with the exception of 63

0.6 |ag/L, where none of the dragonflies ate in one trial. This inactivity in foraging most likely dropped the attack coefficients because an animal that is not pursuing prey will not have a measurable rate of searching for prey. As for the general trend towards faster search rates, it is possible that hungry predators that encounter prey with longer handling times may increase their rates of search in an attempt to locate prey that may be more quickly consumed. We are not aware of any studies that have specifically attempted to study the effects of handling times on attack coefficients. Determining the functional response of a predator is a challenging task. Comparative curve fitting, least-square analysis of linear models and logit analysis have been used to distinguish functional responses for laboratory generated data. We used a logistic regression and initial estimates of handling time and attack coefficients generated from a nonparametric parameter space method which is the preferred method of analyses with small sample sizes (Juliano and Williams 1987). Based on our previous work with this species (Brewer 1997) we decided to use more prey densities to have more points with which to fit a functional response, with the trade-off being fewer replicates per treatment. However, large variability remained, which most likely contributed to the significant lack of fit of the models. Because of the limited number of similar studies on odonate nymphs, we were unable to find any other studies that used our test organism. Thus, it is difficult to speculate on the sublethal toxicity of our exposure levels. However, we exposed 6 additional nymphs to 3 p.g/L chlorpyrifos and found a 45^ inhibition in ChE levels (mean ChE activity= 1.39 lamol 64

AThCh min'^g'"'. cv=98.4, range=0.39-4.18 ^mol AThCh min'-g'^).

We did not expect the mild elevations of ChE levels in nymphs exposed in the range of 0.2 to 1.0 ^ig/L. although similar results were observed for the stonefly Claassenia sp. exposed to 0.8 to 10.5 i^g/L of fenitrothion for 24- and 48 h, as well as the amphipod Hyallela azteca exposed to 0.5 ^ig/L of azinphosmethyl (Day and Scott 1990). The red crayfish. Procambarus clarkii , has also been shown to have short-term increases in ChE activity upon exposure to low concentrations of the OP trichlorfon, followed by an abrupt drop in ChE after 4 d (Repetto et al. 1988). It is possible that our reported ChE activities may have decreased over time if the exposure period had been longer. The high variation we had within treatment groups precluded the establishment of correlations between ChE levels and foraging behaviors. Several authors (e.g., Johnson and Wallace 1987: Robertson et al. 1995) have reported high variability between species and within a species, making it difficult to reach statistically significant differences between organisms exhibiting a response and those used as controls (or obtained from reference sites). Therefore, preliminary studies are necessary to establish "normal" ranges of ChE activities for a particular species before attempting to document exposures as well as determining the sample size needed to detect differences. We were unable to demonstrate significant reductions in ChE activities or foraging ability of nymphs exposed to several sublethal concentrations likely to occur in the environment (Cid Montanos and Van Hattum 1995). Nonetheless, the concentrations we selected have been shown to adversely affect the prey base of A. Junius (van den Brink et al. 1996: 65

van Wijngaarden et al. 1996), therefore the potential exists for adverse indirect effects. Hurlbert (1972) reported chlorpyrifos caused greater reductions in predacious invertebrates (including dragonflies) than herbaceous invertebrates, and predators recovered more slowly. These differences were not entirely due to the differential toxicity of chlorpyrifos but also involved the indirect effects of changes in the food base.

Because A. Junius nymphs are very sensitive to changes in prey density, a decrease in prey forage could have severe ecological impacts on both immature and adult dragonflies because adult size in insects is determined by the size of the nymph or larva. Glasser (1979) suggested that such disturbances may reduce the abundance of preferred (energetically profitable) prey to a level where less profitable items are included in the diet. Clearly, generalist predators like dragonflies would be better able to withstand such ecosystem alterations and indeed some dragonflies have the ability to survive periods of extreme food stress (Lawton et al. 1980). However, Lawton et al. (1980) also found that increased food availability was correlated with increased size at adulthood. This is ecologically important because in many insects, adult size influences the ability to successfully defend a mating territory, and larger individuals are usually more successful (Hildrew and Townsend 1980: O'Neil 1983). Furthermore, female size is thought to be positively correlated with fecundity. Nymphal dragonflies may be under selective pressures to maximize feeding rates in order to maximize optimal growth to avoid predation and rapidly obtain optimum adult size because adults that fed well as nymphs are larger than those that fed poorly (Pickup and Thompson 1984) and behavioral strategies 66

that allow nymphs to become larger and hence molt into larger adults can increase short-term mating success (Fincke 1982: Harvey and Corbet 1985: Tsubake and Ono 1987). Behavior is the product of complex physiological interactions and is a biological attribute readily altered by stress (Warner et al. 1966: Beitinger 1990). Normal behavioral patterns are critical to the survival of the organism, population dynamics, and community interactions. By integrating biochemical and ecological interactions, behavioral toxicology is believed to be capable of providing additional insights of environmental contamination than traditional methods. Warner et al. (1966) were the first to suggest the use of behavioral studies to assess the impact of environmental contaminants because behavior is considered to be a sensitive indicator. Our study did not conclusively demonstrate behavior as a sensitive indicator because of high variation in foraging bouts. This problem may be eliminated by using an animal model that feeds throughout a foraging bout (e.g. filter feeders) rather than an eposidie feeder like we used. Nonetheless, we believe this synthesis of behavioral ecology with toxicology is an important step towards "relat[ing] the problems of ecotoxicology to their ecological context" (Moriarity 1983). Acknowledgements Journal paper No. J- of the Iowa Agriculture and Home Economics Experiment Station. Ames, lA, USA, Project 3307 and supported by Hatch Act and State of Iowa funds. Funding was provided by the United States Environmental Protection Agency Cooperative Agreement # CR 820078. We thank Joel Coats. Joseph Morris, Clay Pierce, Roger Bachmann, and Kenneth Shaw for editorial review. Steve Miranda and Joseph Morris provided 67

statistical advice. References

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Table 1. Operational definitions of odonate foraging behavior.

Behavior Definition

Orientation movement on the part of the dragonfly that results in the prey being in line with the eyes

Pursuit movement directed towards the prey

Stri ke movement of the labium or labial palps towards the prey

Capture grasping and bringing of prey to mouth

Miss either a strike that fails to contact prey or dropping prey prior to bringing it to the mouth

Ingestion consumption of prey 75

Table 2. Dose and prey density effects (mean ± sd: n = 3: 30 min

observation) on foraging behaviors of Anax Junius exposed to chlorpyrifos. Prey density is in numbers of prey/L: means are based on total number of prey offered (density x 3). Exposures

are in ng/L, time to feed and time at rest are in seconds, p-

values for prey density are listed below the respective behavior.

"Exposure"

Endpoint Prey contro1 U.2 O.b 1.0 p-value Density (dose)

onentations

p < 0.0001 2 6 ± 1 4 ± 3 3 ± 1 8 + 2 p = 0.36

3 7 ± 4 6 ± 3 6 ± 6 11 ± 6

7 15 ± 12 23 ± 8 3 + 3 24 + 5

13 34 ± 5 38 ± 8 45 + 36 38 + 4

27 35 ± 21 44 ± 38 26 ± 14 45 ± 37

pursuits

p < 0.0001 2 5 r 0 4 ± 2 3 + 1 6 ± 1 p = 0.34

3 6 ± 4 5 ± 3 6 ± 6 8 ± 4

7 12 ± 9 21 + 6 1 ± 2 22 ± 3

13 25 ± 8 34+ 5 39 + 4 33 + 5

27 32 ± 21 37 + 38 23 + 13 43 ± 38

strikes

p < 0.0001 2 7 ± 1 8 + 5 4 ± 2 7 + 3 p = 0.10

3 5 ± 3 5 + 3 5 ± 6 9 ± 5

7 26 ± 11 37 ± 20 0.7 ± 1 37 + 23

13 28 ± 7 62 + 14 60 ± 25 57 ± 30

27 50 ± 39 78 + 68 28 ± 17 105 + 102 76

Table 2. (continued)

Prey

Endpoint Density control 0.2 0.5 1.0 p-value (dose)

ingestions

p < 0.0001 2 5 ± 0 3 ± 1 3 ± 1 3 ± 2 p = 0.47

3 4 ± 3 4 ± 2 4 ± 5 7 ± 3

7 11 ± 8 18 ± 4 0 + 0 16 + 2

13 19 ± 1 29 ± 4 36 ± 3 29 ± S

27 31 ± 21 39 ± 33 20 + 11 31 ± 27

misses

p < 0.002 2 2 ± 0.5 2 ± 3 0.7 + 0.6 3 ± 1 p - 0.05

3 1 ± 1 2 + 2 0 ± 0 1 ± 2

7 9 + 11 19 ± 17 0.7 ± 1.1 26 ± 19

13 10 ± 5 33 ± 16 22 + 21 30 ± 25

27 19 ± 18 35 ± 31 8 ± 6 70 ± 74 time to feed

p = 0.10 2 73 ± 14 857 ± 758 541 ± 380 225 + 261 p = 0.41

3 600 ± 630 246 ± 305 445 ± 760 137 ± 139

7 497 ± 738 42 ± 4 0 + 0 22 ± 15

13 45 ± 3 35 ± 11 72 ± 46 98 ± 72

27 5 ± 52 11 + 13 321 + 446 613 + 755 time at rest

p - 0.0003 2 301 ± 57 1005 + 190 1629 + 162 331 ± 157 p = 0.02

3 659 ± 471 950 + 374 1260 + 149 397 ± 55

7 583 ± 251 284 ± 104 385 ± 246 725 ± 356

13 365 ± 59 84 ± 81 231 + 105 424 ± 271

27 122 ± 52 495 ± 660 693 + 333 9 ± 10 77

Table 3. Parameter, parameter estimates standard error of the mean (SEM) and p values for single prey functional response models developed

on ^ iunius nymphs exposed to 0, 0.2. 0.6, or 1.0 ^ig/L

chlorpyrifos.

Exposure Parameter Response Cone. (^g/L) Parameter Estimate SEM P >

linear -0.0456 0.062 0.46 II

quadratic 0.000 0.002 0.64

linear 0.279 0.066 0.0001 0.2 III

quadratic -0.008 0.0019 0.0001

linear -0.564 0.097 0.0001 0.6 II

quadratic 0.0196 0.0030 0.0001

linear 0.070 0.057 0.21 1.0 III

quadratic -0.002 0.0017 0.24 78

Table 4. Nominal and actual concentrations of chlorpryifos and corresponding head capsule cholinesterase (ChE) levels in /I.

Junius nymphs. Numbers in parentheses are coefficients of

variation. ChE activities are expressed in ^irnol AThCh

hydrolyzed min'^ gram'^ of head capsule tissue.

Water Concentration (|ag/L) ChE Activity Percent Nominal Actual n mean (C.V.) Range Inhibition

0 0 (0) 15 2.52 (34.3) 0.77-3.64

0.2 0.23 (0.06) 15 3.48 (90.9)' 0.31-14.25'

0.6 0.61 (0.04) 15 2.83 (35.9) 0.93-4.74

1.0 1.22 (0.07) 15 2.84 (42.5) 0.73-5.65

'includes high value of 14.2b; without this value mean CC.V) were 2.73 (48.9) "includes high value of 14.25: without this value range was 0.31-4.84. 79

1.0 rA

0.5 -

I cd; ea 0) (/) O) G.Q h •c

oo.

20 80 0 10 No. of midges presented

Figure 1. Functional response of Anax Junius nymphs exposed to 0 i^g/L

chlorpyrifos (A). 0.2 |ig/L chlorpyrifos(B), 0.6 {j.g/L

chlorpyrifos (C) or 1.0 ^ig/L chlorpyrifos(D). Error bars are

standard deviations. 80

1.0

0.8 -

cOJ ts OJ 1 a. 0.6 -

oc.

0.4 -

0.2 -

20 40 60 SC

No. of prey offered (No./3 L)

Figure 2. Functional response of Anax iunius nymphs across across chlorpyrifos treatment groups by dose. Error bars are standard deviations. 81

GENERAL CONCLUSIONS Behavioral testing has been criticized, in part because there is a lack of test standardization and field validation. Furthermore, most behaviors between individuals are variable and make statistical inferences difficult. "Within-individual" variation can be as large as "between- individual" variation. High variability combined with the usually small sample size required of detailed behavioral observations, make it difficult to detect not only subtle but in some instances even large-scale changes in behavior. Regardless, behavioral endpoints have great potential in toxicology work and routine monitoring programs because behavior is an integrated process that both coordinates and is the result of multiple influences (e.g. genetics, biochemical, physiological, environmental). Ecologically relevant behaviors (behaviors that influence subsequent fitness) are the most potentially useful endpoints. Aquatic macroinvertebrates are thought to be good indicators of toxic stress because of their trophic role and importance in nutrient cycling. Furthermore, aquatic insects may be a good choice for monitoring OP contamination because they are more sensitive to cholinesterase-inhibiting chemicals than terrestrial insects (Edwards and Fisher 1991: Detra and Collins 1991). However, aquatic invertebrates have been shown to exhibit a wide range of sensitivities to organophosphorus insecticides. Flannagan et al. (1978) reported a regression model to predict ChE activity from exposure concentration of fenitrothion in stoneflies. However, Villar et al. (1994) were unable to establish a correlation between ChE activity and 82 behavioral or morphological effects, or mortality in the planarian DugesU tigrind. For any proposed behavioral endpoint, it is important to carefully consider the ecological importance of the behavior as well as factors that may modify it. For example. Murdoch and Oaten (1975) proposed that invertebrates that are generalist predators (such as A. Junius) should show a type III functional response if additional prey species are present. However, my work suggests that functional response may be dependent on the specific prey type and not merely the presence of an alternative prey. Furthermore, although it is intuitively obvious that a predator may "prefer" one prey type, eventually the predator will be forced to include other prey items if the preferred prey density drops below a critical threshold, or else the predator risks starvation. However, evidence of switching behavior remains controversial, and the odonates in my study did not consistently forage in a frequency-dependent manner. It is also important to consider the sensitivity of the proposed test organism to the contaminant. Although the ChE activities I obtained were in the range of values for other invertebrates, the variability I observed in the activity of ChE controls was higher than similar studies (Moultin et al. 1996). This high variability may preclude establishing practical guidelines for demonstrating organophosphate exposure with odonates. A. Junius also experienced a mild elevation in ChE levels in dragonflies exposed to a proven ChE inhibitor. Similar results have been observed for stoneflies (Day and Scott 1990) and the red crayfish.

Procambarus clarkii (Repetto et al. 1988). It is possible that my reported 83

ChE activities may have decreased over time if the exposure period had been longer. The lack of inhibition in ChE activity (or the occasionally reported increase) in some taxa exposed to documented ChE inhibitors at supposed lethal or sublethal concentrations may be the result of 1) the insecticides were not toxic at the concentration used: 2) ChE does not respond in a predictable manner to sublethal concentrations of inhibitors: or 3) other factors intrinsic to the test organism (e.g. age. sex. weight, general health) or extrinsic (e.g. test conditions, temperature, food availability) may influence ChE activity in poorly understood ways that require further research and explanation if we are to use ChE inhibition as a successful biomonitoring tool in OP or carbamate exposure assessments. The fact that other studies have demonstrated switching behavior in Odonata (Lawton et al. 1974: Thompson 1978). and widely different LCjo values for Odonata exposed to OP pesticides (Federle and Collins 1976: Muirhead-Thomson 1987: Ward et al. 1995) strongly suggest that any biomonitoring program needs to carefully evaluate not only which species to monitor, but how sensitive proposed species are to the hazard and the applicability of the species' response to other species in the same taxa. Likewise, hazard assessment programs need to consider the sensitivities of proposed endpoints and other factors that may influence these endpoints. Literature Cited

Day, K. E., and I. M. Scott. 1990. Use of acetylcholinesterase activity to detect sublethal toxicity in stream invertebrates exposed to low concentrations of organophosphate insecticides. Aquatic Toxicology 18:101-104. 84

Detra. R. L.. and W.J. Collins. 1991. The relationship of parathion concentration, exposure time, cholinesterase inhibition and symptoms of toxicity in midge larvae (Chironomidae: Diptera). Environ. Toxicol. Chem. 10:1089-1095. Edwards. C. A., and S. W. Fisher. 1991. The use of cholinesterase measurements in assessing the impoacts of pesticides on terrestrial and aquatic invertebrates. In P. Mineau. ed.. Cholinesterase- inhibiting Insecticides: Their impact on Wildlife and the Environment. Elsevier, New York, NY. USA, pp. 256-275. Flannagan, J. F.. W. L. Lockhart, D. G. Cobb, and D. Metner. 1978. Stonefly (Plecoptera) head cholinesterase as an indicator of exposure to fenitrothion. Manitoba Entomol. 12:42-48. Federle. P. F., and W. J. Collins. 1976. Insecticide toxicity to three insects from Ohio ponds. Ohio J. Sci. 76:19-24. Lawton. J. H.. J. R. Beddington, and R. Bonser. 1974. Switching in invertebrate predators. In M. B. Usher and M. H. Williamson eds. Ecological Stability. Champam and Hall. London, UK pp. 141-158. Moulton, C. A., W. J. Fleming, and C. E. Purnell. 1996. Effects of two cholinesterase-inhibiting pesticides on freshwater mussels. Environ. Toxicol. Chem. 15:131-137. Murdoch, W. W., and A. Oaten. 1975. Predation and population stability. Adv. Ecol. Res. 9:1-131. Muirhead-Thomson, R. C. 1987. Invertebrate reaction to pesticides under laboratory and experimental conditions. In R. C. Muirhead-Thomson, ed.. Pesticide impact on stream fauna with special reference to macroinvertebrates. Cambridge University Press. Cambridge, England, pp. 61-116. Repetto, 6., P. Sanz, and M. Repetto. 1988. In vivo and in vitro effect of trichlorfon on esterases of the red crayfish Procambarus clarkii. Bull. Environ. Contam. Toxicol. 41:597-603. Thompson, D. J. 1978. The natural prey of larvae of the damselfly, Ischnura elegans (Odonata: Zygoptera). Freshwater Biology 8:377-384. Villar, D.. M. Gonzalez. M. J. Gualda, and D. J. Schaeffer. 1994. Effects of organophosphorus insecticides on Dugesia tigrina: cholinesterase activity and head regeneration. Bull. Environ. Contam. Toxicol. 52:319-324. Ward, S., A. H. Arthington. and B.J. Pusey. 1995. The effects of a chronic application of chlorpyrifos on the macroinvertebrate fauna in an outdoor artificial stream system: species responses. Ecotoxicol. Environ. Safety. 30:2-23. 85

GENERAL ACKNOWLEDGEMENTS I thank my major professor. Dr. Gary Atchison for his subtle but untiring efforts in helping me to think about aquatic science in new (for me) ways. I also thank him for his patience and willingness to let me create and explore my own "webs of serendipitous" research. Most of all. I thank him for allowing me to join his research team, thus enabling me to finish my degree. There are some debts that simply can not be repaid. I also thank my committee members; Dr. Joseph Morris, who was invaluable for statistical advice, computer hardware and software diagnoses, good coffee and conversations; Dr. Joel Coats, who graciously donated the chlorpyrifos and helped me to consider the roles and relationships the broad field of toxicology plays in society; Dr. Clay Pierce was very helpful with early discussions on dragonfly culture, care, and how to avoid being bitten by "the wee beasties"; Dr. Kenneth Shaw provided good insights in the realm of behavioral ecology: and Dr. Roger Bachmann. who recruited me to Iowa State, allowed me to add to my field experience and to re-define the word "eutrophic". I also thank my entire committee for "open-door" policies and for providing me with fine examples of professionalism. I also am indebted to my "honorary" committee member Dr. Brent Danielson for always having time to discuss science and scholarship, and to provide career advice and suggestions. I greatly benefitted both personally and professionally from our discussions. Dr. John Downing also provided an excellent example of professionalism, and I thank him for providing unlimited access to his time. I am particularly indebted to Lyn Van De Pol for her patience. support, never-ending help with paperwork, clarifications and unceasingly 86

going above-and-beyond the call of duty. I also thank Dr. Bill Clark for a long-ago. far-away conversation which was important for clarifying my academic future. I am especially grateful to the entire Department of Animal Ecology for support, encouragement, and understanding. I also thank Drs. Walter Hyde and Catherine Kozak, Jill Hubbard and everyone at Racing Chemistry for their support, friendship, encouragement and inside look at "life in the contract laboratory." It would have been a rougher summer without you. No one survives graduate school without the support of one's friends. I especially need to thank my officemates both past and present (you know who you are) for tolerating the ever-increasing "stacks of knowledge" I accumulated over the course of this journey. In particular, Jeannette Schafer was a virtual "Job" of officemates and a patient friend. Shih- hsiung (Steve) Liang was not only a great source of inspiration for office decorating but also was a font of intellectual and philosophical conversations, which I will always value and from which I learned a great deal. Julie Schreiber was always the optimist everyone needs and always had a positive perspective. Anjeanette Perkins always provided a philosophical opinion and a good shoulder. Sheryl Beauvais was a "friend among friends" and helped make the transition from the "riverworld of bluffs" to the "agriworld of plains" much better as well as providing innumerable true stories of the "real world". Terry Dukerschein was a terrific sounding-board, keeper of "focus" and a quintessential friend. Last, but never least, I thank my family for their love and humorous comments about my 20-something grade during my journey. This is why the cards and presents were almost always late and holidays were always short. iv

TABLE OF CONTENTS ABSTRACT v GENERAL INTRODUCTION 1 Dissertation Organization 7 Literature Cited 7 A TEST FOR SWITCHING BEHAVIOUR AS A POTENTIAL MECHANISM OF OPTIMAL FORAGING THEORY 12 Abstract 12 Materials and Methods 16 Results 22 Discussion 26 Acknowledgments 33 References 33 THE EFFECTS OF CHLORPYRIFOS ON CHOLINESTERASE ACTIVITY AND FORAGING BEHAVIOR IN THE DRAGONFLY Anax Junius (ODONATA) 50 Abstract 50 Introducti on 50 Materials and Methods 54 Results 60 Discussion 62 Acknowledgments 66 References 67 GENERAL CONCLUSIONS 81 Literature Cited 83 GENERAL ACKNOWLEDGMENTS 86 V

ABSTRACT

I examined the foraging behavior of the green darner dragonfly nymph.

Anax Junius, to assess the suitability of odonate foraging behavior as a sensitive endpoint for monitoring contaminant exposure. Odonates are potentially excellent organisms for work in biomonitoring programs because: 1) they have complex foraging strategies that tightly couple sensory cues and mechanical responses: 2) their ecology is well-studied; and 3) they are often the dominant invertebrate predator in fishless systems. Thus they have the potential to structure communities through predation. I tested 1) the possibility that nymphs may regulate populations through frequency-dependent foraging and 2) the relationship between head capsule cholinesterase (ChE) levels in nymphs exposed to an organophosphorus insecticide and subsequent foraging behavior. Functional response models were developed for nymphs fed exclusively midges or amphipods and for nymphs fed mixed prey. Nymphs fed only midges exhibited a sigmoidal type III response (a prerequisite for frequency-dependent foraging): nymphs fed only amphipods exhibited a hyperbolic type II response. These results were reversed in mixed prey trials. Regardless of densities of prey offered, a higher percentage of midges were eaten compared to amphipods. Dragonflies did follow a simplified optimal foraging model based on energy maximization but they did not consistently exhibit frequency-dependent selection. ChE levels were slightly elevated in treated nymphs and foraging behaviors between treated and control groups were not statistically significant, most likely because of high variability in ChE activities within the control group and across treated groups. My results suggest vi

that certain animals may be unsuitable for routine pesticide monitoring and prior to incorporating any species into a biomonitoring program, careful consideration of the species' sensitivity to the contaminant needs to be considered. Further work is needed to determine other factors that may influence ChE levels in species as well.