The Population Dynamics of Anthocoris confusus in a Laboratory Cage Ecosystem

The Population Dynamics of Anthocoris confusus in a Laboratory Cage EcosystemAuthor(s): H. F. EvansSource: Journal of Animal Ecology, Vol. 45, No. 3 (Oct., 1976), pp. 773-789Published by: British Ecological SocietyStable URL: http://www.jstor.org/stable/3580 .

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773

THE POPULATION DYN-AMICS OF ANTHOCORIS CONFUSUS IN A LABORATORY CAGE ECOSYSTEM

BY H. F. EVANS*

Hope Department of Entomology, University Museum, Oxford

INTRODUCTION

Consideration of single aspects of predation can give only a rough outline of the way in which the predator reacts to its prey and to other predators. Previous studies of predatory insects attempted to elucidate some of the basic processes of predator-prey interactions. Generally, they concluded that there are likely to be only a few important components which govern the outcome of a predator prey interaction. For example, functional responses to prey density (Holling 1959, 1966), efficiency of capture (Dixon & Russel 1972; Evans 1976), the way in which the predator reacts to prey distribution (Banks 1957; Dixon 1958). Those studies were carried out in artificial situations and, in some cases, the results were used to augment field observations, for example, Dixon & Russel (1972) used laboratory estimates of speed of movement and range of perception to calculate the area covered by anthocorids in the field.

Similar studies of insect parasites have been complemented by long term observations of laboratory ecosystems. Those studies (Hassell & Huffaker 1969; Takahashi 1961; Podoler 1974) involved interactions, over several generations, between parasites and hosts with discrete and synchronized generations. The effects of different mortality factors were quite easily measured, particularly the results of parasitism. One of the major contributors to parasite population stability was interference between parasite individuals leading to a density-dependent decrease in fecundity as parasite density increased (Hassell & Varley 1969; Hassell 1971 a,b). There is now some evidence that interference occurs in natural ecosystems. For example, a female Rhyssa persuasoria L. drives off intruding females from the same area of tree trunk (Spradbery 1970) while ignoring its cleptoparasite, Pseudorhyssa sternata Merrill (Spradbery 1969).

Studies of predator-prey interactions are considerably more difficult to carry out. Prey are either removed from the system or rendered unrecognizable making it difficult to assess the degree of predation. Predator eggs can be difficult to find, particularly in a field situation, and often cannot be related to adult density. Additionally, in the field, insect predators normally have a maximum of two generations per year. Consequently data for inter-generation relationships (Hassell 1966) can take many years to accumulate. The present study was an attempt to overcome these difficulties by allowing predators and prey to interact over a number of generations within a relatively short time scale in the laboratory. The major aims were to pinpoint intra- and inter-generation relationships which affect the predator's success and, if possible, to relate these to data from natural situations. Anthocoris confusus Reuter was used as the predator and Aulacorthum circumflexus Buckton as its aphid prey.

* Present address: N.E.R.C., Unit of Invertebrate Virology, 5 South Parks Road, Oxford OXI 3UB.



774 Population dynamics of predatory Anthocoris

MATERIALS AND METHODS

Main cage ecosystem

The interaction was studied in a wooden framed cage measuring 90 x 90 x 75 cm. The cage had top and sides covered with clear plastic sheeting, and large nylon gauze sleeves in two opposite sides allowing easy access to all plants. A reinforced hardboard frame with thirty-six holes was included to hold plant pots in fixed positions. Pots rested on their rims flush with the surface of the hardboard thus creating a 'false floor' with only the plants above the surface.

Duration of each phase ot prey manipulation

Phase I No mortality Phase I: Phase m

4 pi

E 3

0 ~~~~~~~~~~~~~~~~~~~~~~~~Anthocorid _O L Or V

Time (weeks)

FIG. 1. The log, densities of egg, fifth instar and adult Anthocoris confusus and of the aphid prey Aulacorthum circumfiexus for the duration of the interaction. The three phases of

prey manipulation are also shown.

Plant replacement

Initial stocking of the cage was carried out using plants which were in three age cat- egories (four, five and six weeks old). Each week the twelve oldest (all, by this time, seven weeks old) were removed and replaced with the same number of four-week old plants. This maintained an even age distribution of plants in the ecosystem.

Aphid numbers were allowed to build up on the plants for five weeks before anthocorids were added to the ecosystem. This gave an indication of the rate of increase of the aphid population each week in the absence of predators. On the sixth week twenty-four gravid female Anthocoris confusus were introduced.

Sampling procedure

Sub-sampling during the first predator generation was found to be inadequate. There- fore, in later generations every plant in the ecosystem was examined each week to give an absolute count of total numbers and age distribution of both anthocorid and aphid. Very accurate counts of anthocorid egg numbers were achieved using a binocular micro-



H. F. EVANS 775

scope during examination of the broad bean plants. Eggs deposited in the twelve plants removed weekly were allowed to develop in separate containers and emergent nymphs were replaced in the ecosystem.

Manipulation of the prey population

Early observations indicated that the anthocorids were unable to reduce aphid numbers sufficiently to prevent destruction of the host plants by the aphids. Consequently, a proportion of the aphid population was removed each week to maintain it within limits which would prevent plant destruction.

During the first phase of this introduced mortality factor, aphids present on the twelve plants removed weekly were not replaced in the cage (Fig. 1, Phase I). During the six weeks following Phase I no mortality was applied. Later (Phase II), 9000 of the adults were removed from all plants. Finally (Phase III), the mortality was extended to include removal of all aphids in excess of 600 from each plant. Aphids removed in Phase III were taken in proportion to the age distribution of nymphal stages present. This introduced density-dependent mortality prevented a rapid and destructive increase in aphid numbers.

Discreteness of predator generations

The long oviposition period of A. confusus (up to fifty-six days with a mean of twenty- four days at 22? C in the laboratory) would quickly result in overlapping of predator generations in a laboratory system. In order to avoid this, old adult anthocorids were removed from the ecosystem when fifth instar nymphs of the following generation appeared. Fifth instar nymphs counted in one week had entered the adult stage by the time the following sample was taken (development time of fifth instar was seven days).

Estimation of the numbers of predators entering each stage

It was difficult to estimate accurately the total numbers entering each predator stage. Predators sometimes passed through two instars during the time between samples. Therefore, estimation of numbers in each stage by a simple additive process on the day of sampling was unreliable. This problem was overcome by using the method of Richards & Waloff (Richards 1959; Richards, Waloff & Spradbery 1960; Richards & Waloff 1961). The method is based on obtaining accurate estimates of both the initial population (usually the egg stage) and the duration of each stage, which in this case was obtained from experiments carried out under identical conditions of temperature and photoperiod.

SINGLE GENERATION EXPERIMENTS

Initial observations in the main ecosystem cage gave an indication of where predator mortality was greatest. Many first instar nymphs were dying but the cause was not clear. Experiments were therefore set up to investigate these factors during single generations of the predator. A sampling regime similar to that used in the main cage was used but sampling was carried out every two days. Additional observations were made to investigate the amount of mortality occurring off the plant.

MATERIALS AND METHODS-SINGLE GENERATION EXPERIMENT

The cage used for the single generation experiments measured 50 x 50 x 16 cm and was




constructed in two parts, a removable glass cover and a base holding seven 12-7 cm diameter plant pots. Four 5-5 cm diameter plastic containers with 1-3 cm sides were placed on the soil, one beneath each broadly expanded leaf of the plant. The sides of each container had been coated with Fluon (I.C.I., Herts., U.K.), so that any predators dropping off the plant into the containers would be unable to climb out and could be counted when sampling took place. The four containers covered all the soil directly below the plants except for a small area within a circle of radius 1 cm around the stem.

Each run of the experiment was set up by placing in the cage seven plants four weeks old, each infested with at least 100 Aulacorthum circumflexus. The plants were given two days to acclimatize and to allow the aphid numbers to build up. Seven gravid female anthocorids were then placed in the cage. Sampling was carried out every two days and varied slightly depending on the type of experiment.

Experiment 1

All developmental stages of the anthocorid still on the plant were counted. Those anthocorids which had dropped into the containers beneath the plant were counted but not replaced on the plant.

Experiment 2

Samples were taken as in Experiment 1 but anthocorids which had dropped into the containers were replaced on the plant. These experiments were designed to quantify soil mortality and give its extreme values.

The two experiments indicated that some early instar nymphs may fall from the plants on to the soil below. Such nymphs, which were not counted during regular sampling in the main cage, were nevertheless still alive and capable of entering the interaction at a later stage. It was therefore necessary to calculate factors which would allow for the numbers of nymphs on the soil that were missed at the time of sampling. A full discussion of the derivation of these factors is given in the appendix. Factors of 2-0 and 1 2 were used to estimate the total numbers of first and second instar nymphs from the numbers actually counted during sampling. The corrected figures were used in Richards & Waloff's method for the number entering each stage (Richards & Waloff 1961; Richards et al. 1960).

RESULTS

Figure 1 shows the densities in logarithms of adults, eggs and fifth instar nymphs of Anthocoris confusus from week to week in the ecosystem. Manipulation of the predator population resulted in the generations remaining distinct with approximately the same time between peaks. The aphid densities shown are the means of the logarithmic densities just after the applied mortality of one week and just before that of the following week.

Life tables

Table 1 is a composite life table for generations two to eight of the predator. Direct counts of egg sterility and cannibalism of first instars at eclosion were made during binocular examination of the broad bean plants. These observed mortalities were used in the life table but all other mortalities were calculated using the method of Richards & Waloff (1961). Densities of the anthocorid stages are expressed in logarithms and mortalities expressed as k-values. A k-value is the difference between log. density before the mortality and log. density following the mortality (Varley & Gradwell 1968).



H. F. EVANS 777

Eight mortality stages were recognized in this study and since they acted in sequence, total generation mortality (K) can be expressed as

K = ko+kl+k2+k3+k4+k5+k6+k7

The k-values correspond to mortalities acting on each stage of the predator and, where sufficient data are available, these are further divided.

K = generation mortality; ko = difference between observed and expected egg production; k, = egg mortality; k2= cannibalism of first instar nymphs at eclosion; k3 = first instar mortality; k4 = second instar mortality; k5 = third instar mortality; k6= fourth instar mortality; k7 = fifth instar to adult mortality.

Table 1. Composite life table for Anthocoris confususfrom generation 2 to 8

Log. density Generations of each stage 2 3 4 5 6 7 8

Eggs I 3 476 2916 3 518 3 393 3 160 3 738 3 445 kO value 0 891 0 078 0 418 0 705 0 380 0 690 0 705

Eggs II 2 585 2 838 3 100 2 688 2 780 3 048 2740 k1 0 005 0 004 0.003 0 002 0 002 0 008 0 002

Eggs III 2580 2 834 3 097 2686 2778 3 040 2738 k2 0.036 0 048 0 037 0.058 0.043 0 057 0 046

Nymphs I 2544 2786 3 060 2 628 2735 2983 2692 k3 0 470 0 470 0 508 0 482 0 473 0 617 0 493

Nymphs II 22053 2 316 2 552 24146 2 262 2 366 24199 k4 00080 0 000 0 082 0 035 0 000 0 003 0 109

Nymphs III 1 973 2 316 2 470 24111 2-262 2 363 2 090 k5 0 170 0 039 0 182 04172 04107 0 381 0 327

Nymphs IV 1-803 2 277 2 288 1 939 2 155 1 982 1V763 k6 00285 04191 0 055 04191 04182 0.000 0-150

Nymphs V 1 518 2086 2233 1.748 1 973 1 982 1.613 k7 0175 0323 0485 0204 0350 0266 0271

Adults 1 343 1 763 1 748 1 544 1.623 1 716 1 342 Total K 2 112 14153 1 770 1.849 1.537 2022 2-103

Key-factor analysis

Morris (1959) originally defined key-factors as those which 'cause a variable ... mortality and appear to be largely responsible for the observed changes in population density' in successive generations. A graphical method of key-factor analysis was devised by Varley & Gradwell (1960). Figure 2 shows the k-values acting on the predator for seven generations. Examination of the graph indicates that ko, reduction of female fecundity, is responsible for the major changes in total K and is therefore the key-factor. Other k-values are very small or do not seem to effect great changes in K. Egg mortality (kl) was extremely small and has been omitted.

Density relationships

Definitions and tests for density-dependent population changes are given by Varley & Gradwell (1968, 1970). The relationship between the k-value and the log. density of the stage on which it acts gives an indication of the way in which mortality changes with population levels. Figures 3-9 show the k-values plotted against their respective log. densities (k, omitted). Mortalities ko and k7 give linear regressions with well defined posi- tive slopes indicatingthat these mortalities act in a direct density-dependent manner causing greater proportionate mortality at higher densities. The significance of the relationships




3-

2 -

l _ *\ *\ * ko_O *

0

0~~~~~~

7~~~~~~~"

I I I I

a) S ~~S~~-; -

k5

0 5

2 3 4 5 6 7 8 Anthocorid generation

FIG. 2. Graphical key-factor analysis of the mortality factors acting on the predator generation. K = total generation mortality, ko = egg production lower than expected, k= egg sterility and disease, k2 = cannibalism of first instar nymphs at eclosion, k3 = first instar mortality, k4 = second instar mortality, k5 = third instar mortality, k6 =

fourth instar mortality, k7 = fifth instar to adult mortality.

1'0 - Fig. 3

0* - 0O5 - Fig. 4

0 1 0*. .l* 2 3 4 2 3 4

Log-expected egg density Log. fertile egg density

FIG. 3. The relationship between ko and log. expected egg density. y = 0785x-2 10; 95% limits 0 773.

FIG. 4. The relationship between k2 and log. number of eggs surviving k1.



H. F. EVANS 779

1.0 - Fig. 5

@ ~~~~~~* F_0_5 - _ _ _ _ _05_ Fg._6

.0 _ _ _ _ _ _ _ _ _ _0 2 3 4 1 2

Log. emerged first instor density Log. second instor density

FIG. 5. The relationship between k3 and log. first instar nymphs emerging. y = 0157x+ 0069; 95% limits 0 259.

FIG. 6. The relationship between k4 and log. second instar density.

Generation Generation 0.5 - Fig. 7 number 05- Fig. 8 number

5 4 1 -. *5 *6e4

0 _ _

Log. third instar density Log. fourth instor density

FIG. 7. The relationship between k5 and log. third instar density. FIG. 8. The relationship between k6 and log. fourth instar density. y = 0-136x+ 0426;

95%0 limits 0 484.

0-5 -

0~~~~

Log. fifth instor density

FIG. 9. The relationship between k7 and log. fifth instar density. y = 0337x-0 337; 95% limits 0241.

5- (a) 3 (b)

txy<0*95 ,bx10-95 -/ b=1l0 ,> byx>Q095

C~~~~~~~~~~~~~~~~~~~~~~~~~~~C

Log. thybyx <o 9/

2 3 4 5 0 1 2 3

Log. expected egg density Log. fifth instor density

FIG. 10. Tests for the validity of the density-dependent relationships of ko (reduced fecundity (a)) and k7 (fifth instar to adult mortality (b)) (Varley & Gradwell 1968).




was tested by plotting log. densities following mortality (N2) against log. densities before mortality (N1). A significant density-dependent relationship results in both the calculated regressions of log. N1 on log. N2 and log. N2 on log N1 falling on the same side of, and being significantly different from, a slope of b = 1 (Varley & Gradwell 1968). Figure 10 indicates that ko was not significantly density-dependent while k7 was significant.

The differences between observed and expected anthocorid egg numbers is used to give the ko values. The figure for expected eggs (103 per female) is based on separate laboratory experiments under identical conditions of temperature and photoperiod. Although the test of density-dependence of ko was not significant it must be borne in mind that the figures used were the results of seven generations only.

1.0 _

y=0968X -0947 0'8 _

0-6 - 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ 0

0 0 O4 -

. 0

0-2 _-/ :

0~~~~~~~~~~ I ~ ~~ *,; 0..

05 10 1-5 Log.weekly adult density

FIG. 11. The relationship between ko and log. adult anthocorid density on a weekly basis.

The relationship expressed on a weekly basis gives a clearer indication of the way in which egg production is reduced when anthocorid density rises. Figure 11 shows weekly k-value for reduced fecundity plotted against total weekly adult density for the duration of the study. This confirms a density dependent reduction in fecundity with a slope of about 09. However, at very low adult densities (one to four) there is no effect on fecundity and the egg production bears no relationship to adult density. This effect occurred independently of female mortality during the preceding week. If females die between sampling intervals then the fecundity of those surviving would tend to be over estimated. In spite of this, there was reduced fecundity indicating interference between adults resulting in lower egg production per female. Premature death of females will result in a reduction in the degree of interference. The result will be a larger egg production per female, compensating, at least in part, for early death of females.

The direct density-dependence of k7 was found to be significant when tested by Varley & Gradwell's method (Fig. 10). The k-value includes both fifth instar and early adult mortality. The detailed single-generation experiments described above indicated that fifth instar mortality was small (mean k7 = 0-071) and showed no significant relationship to fifth instar density. It was therefore likely that much of this mortality occurred in the early adult stage prior to egg production. Figure 12 shows the relationship between the observed and expected weekly change in adult densities. The expected change in adult density is based on the assumption that since the fifth instar nymphs take seven days to develop those that were present at the time of sampling in one week will have moulted



H. F. EVANS 781

to adults by the time of the next sample. Thus adult numbers were expected to increase by the same amount as the number of fifth instar nymphs present the previous week. The observed change in adult density was the differences between adult totals from week to week.

60

a 40 Y O-58x-8.67 950 limits 0-241

20 /

0

/0

n 0/.--.--* - --

-2 .0

2.0 40 60 80 100

Expected adult increase (week (n+l) -week n)

FIG. 12. The relationship between observed and expected adult anthocorid density on a weekly basis.

Fig. 13

P 05 0.5 F:9 14

0 0 2 2 2

Log. peak adult onthocorid densRy Log. peak adult anthocorid density acting on first instar acting on third instar

FIG. 13. The relationship between k3 and log. peak adult anthocorid density acting on the first instar. y = 0 222x+020; 95%4 limits 0i194.

FIG. 14. The relationship between k5 and log. peak adult anthocorid density acting on the third instar. y = 0'497x-0-468; 95%O limits 0.187.

A slope of less than one indicates that either there were fewer adults appearing than expected (i.e. late fifth instar or early adult mortality) or that the adults already present were dying between samples, or both. The latter mortality also contributed to ko in the following generation.

Of the remaining mortality factors k1 (egg sterility), k2 (cannibalism of nymphs at eclosion (Fig. 4)), k4 (second instar mortality (Fig. 6)) and k6 (fourth instar mortality (Fig. 8)) were all small and independent of density. First instar mortality (k3) was quite large (Fig. 5) but was not related to first instar density. Thus although k3 represents a large mortality it has little effect on population regulation. Results from the single generation experiment showed that the major cause of k3 was cannibalism on the plant and




death of first instars after dropping off the plant. Larger anthocorids are most likely to cannibalise first instar nymphs and, of these, adult anthocorids are the only stage present during the entire first instar recruitment period. Figure 13 indicates that there is a slight correlation between the peak adult density and k3, and hence adults may cause part of k3 (cannibalism contributed up to 25% of the first instar mortality in the single generation experiments).

Mortality of third instar anthocorids, k5, acted in a delayed density-dependent manner (Fig. 7). In the single generation experiments, k5 was very small, dropping off the plant was rare and cannibalism was the main cause of mortality. Figure 14 shows that there is a strong and significant correlation between k5 and the log. peak adult anthocorid density. This mortality is the result of cannibalism by the adults of the previous generation which accounts for the delayed form of the density relationship.

0*4 (?) (b) (c)

0 -O

. *.

0-4 - (d) e)(f

?'-0-4 %

D _ s t

-0 8 I I I I I II I . III I i1 I I I I 0 0-4 0-8 1.2 1f6 2-0 2 4 0 0'4 0-8 1.2 1-6 2.0 2-4 0 0 4 0-8 1f2 1f6 2-0 2.4

Log. anthocorid density

FIG. 15. The relationships between the k-values for aphid population change (log. aphid density after manipulation in one week-log. aphid density prior to manipulation in next week) and the log. anthocorid density present each -week. All stages of the predator have been included. (a) First instar, y = 0 002x-0-146, 95% limits 0 11; (b) second instar, y = 0-133x-0-289, 95% limits 0-142; (c) third instar, y = 0122x-0-244, 95%0 limits, 0-147; (d) fourth instar, y = 0'121x-0-209, 9500 limits 0 199; (e) fifth instar, y = 0128x-

0-246, 9500 limits 0-120; (f) adult, y = 0 294x-0-471, 95% limits 0-198.

Prey density changes

Figure 1 shows the way in which the prey population changed from week to week in the ecosystem, allowing for the weekly mortality introduced at the time of sampling. In the present study, no investigation was carried out to determine intraspecific aspects affecting prey density. Aulacorthum circumflexus is a relatively sedentary species in that it does not show a strong tendency to wander between plants. Mortality due to wandering off plants was assumed to be low. Apart from weekly prey manipulation it was assumed that most of the mortality of the aphid population was the result of attack by the predator.

The relationship between anthocorid density and aphid weekly population change is shown in Fig. 15. The k-value for aphid change was calculated as the difference between the log. density after manipulation in one week and the log. density prior to manipulation the following week. Negative k-values indicate that aphid population increase has occurred but do not necessarily imply that the aphid is increasing at its maximum rate. There



H. F. EVANS 783

15 E

10 _

5 - @

0

0 0 *E

V C I I

E cs

2v 5 - :

U) L

E 2@ I I1

V

0 0 1 0~

01

2*5 - I

0~~~~~~~~~~~

0 1 0 20

Expected change in aphid numbers (thousands)

FIG. 16. The relationships between the observed and expected changes in aphid numbers from week to week when different densities of fifth instar and adult anthocorids are present in the ecosystem. I. Low adult density (log 10 and less), fifth instar density minimum. y = 0 860x+0 80, 95% limits 0-170. II. Intermediate adult density (log 1-0-1-5), fifth instar absent or decreasing. y = 0 586x-0-366, 95% limits 0 084. III. Intermediate adult density (log. 1- 1-5), fifth instar present and increasing (log. 1 0-1 5). y = 0473x- 2-496, 95% limits 0 190. IV. Peak adult (log. 1-5-2-0) and fifth instar (log. 1-5-2-0) density,

y = 0 072x-0 974, 95% limits 0-060.

is a significant correlation between prey density change and fifth instar and adult anthocorid density (but not for any of the earlier predator instars). This is understandable in view of the fact that fifth instar Anthocoris confusus require twelve times as many first instar sycamore aphids as do first instar Anthocoris (Dixon & Russel 1972). Adult anthocorids appear to cause the greatest mortality but it must be realized that several of the anthocorid stages act simultaneously on aphid density and that the relationships shown in Fig. 15 are inter-related to a great extent. However, since adult anthocorids are present at all times and, with the fifth instar, are the stages most capable of killing a




large number of aphids, they can be regarded as having the greatest effect on the prey population.

The same information is plotted in a different way in Fig. 16 to show how expected changes in aphid density are only realized when predators are rather scarce. Figure 16 shows how the interaction in the cage can be split up into four fairly discrete stages of abundance of adult and fifth instar anthocorids (i.e. the two stages that have the most dramatic effect on the prey population). Stages I-IV, representing increasing abundance of the predator, do not necessarily act in sequence.

I. Low anthocorid adult density (log. 1 0 or less), fifth instar density minimum At those low densities anthocorids had very little effect on the aphid population which

increased at a rate near to its maximum observed value. The slope of 086 was not significantly different from a slope of b = 1 0 which would be observed in the absence of any predation.

II. Intermediate adult density (log. 1 0 to 1 5), fifth instar absent or decreasing The rate of increase of the aphid was reduced without actually lowering the total weekly

density level. This stage normally acted prior to I but followed III or IV.

III. Intermediate adult density (log. 1 0 to 1 5), fifth instar present and increasing (log. 1-0 to 15)

Total aphid density began to drop and the rate of aphid reproduction was considerably lowered.

IV. Peak adult andfifth instar densities (both log. 1 5 to 20) Aphid density dropped and the reproductive rate was insufficient to compensate for

the effects of predation.

DISCUSSION

This study has broadly outlined some of the factors affecting anthocorid populations. It has been especially useful in that several mortality factors not immediately apparent from isolated laboratory and field studies have been identified. These are important in that they may provide an explanation for the often remarkable stability of predator populations in the field and for the success of anthocorids in biological control pro- grammes.

Life table and key-factor analyses showed that ko, reduction in fecundity, was the key- factor governing changes in the predator population. This resulted from interference between anthocorid adults leading to a density-dependent decrease in the predator population. Interference has not been demonstrated previously in insect predators although Kuchlein (1966) showed that it did occur in predatory mites. In anthocorids it appears to be the result of physical contacts between searching adults resulting in some leaving the plants and flying to the walls of the cage. Such periods spent off the plant result in a reduction in the time available for feeding and oviposition, both of which lead to a decrease in fecundity. Hassell (1971a,b) has shown that interference in Nemeritis canescens (Grav.) occurs when contacts between searching females decreases the proportion of total time spent searching by each parasite as parasite density increases. Rogers & Hassell (1974) attempt to explain the effects of parasite interference in terms of an



H. F. EVANS 785

equilibrium between those parasites that are searching and those that abandon search as a result of encounters with other parasites.

It is arguable that the demonstration of interference in the laboratory bears little relationship to the situation in the field. Practical problems make it difficult to prove beyond doubt that it can apply in natural populations. However, figures given by Dixon & Russel (1972) for peak numbers of adults emerging in August on sycamore trees (by extrapolation approximately 26 per m2) are well within the range of densities observed in a similar area in the present study. A maximum figure of eighty-seven anthocorid adults per bush was given by Dempster (1968) in his study of insect populations on broom. Since the bushes were small (maximum height 1 85 m (Waloff 1968)) this represents a high density per unit area. Clearly, at these field densities, the probability of interference is high and could result in dispersal of adults from the site of encounter. Thus the observation of Dempster (1968) that emigration was high when psyllid populations were low could also be partially explained by encounters between high numbers of anthocorids searching for a limited resource. Further circumstantial evidence for interference in the field is provided by the fact that anthocorids tend to search on similar areas of a plant, such as the undersides of leaves (Dixon & Russel 1972) and, once prey is encountered, will remain in close proximity to that prey. Both behavioural traits increase the probability of encounters between searching adults. The time wasted following an encounter in the field will be greater thap in the confines of a cage where long distance dispersal is not possible. Thus a low encounter frequency in the field will be compensated for by a long period of wasted time.

The density-dependence of ko was sufficient to regulate the predator population within broad limits but, if it had acted alone, would have led to a gradual attainment of stability at a higher equilibrium level than that observed. However, k7 (fifth instar to adult mortality) was also density-dependent and acted as the final regulator keeping the population within narrower limits. Thus two density-dependent mortalities acting early and late in a generation were sufficient to regulate the pr?dator population. In contrast, Dixon (1958) in his study of coccinellid predators regArded the first instar larva as the most vulnerable stage, and hence the one on which population fluctuation would depend. The present study has shown that, at least in anthocorids, mortality factors affecting other stages may be more important in maintaining stability and that first instar mortality, although quite large, was independent of density.

The new results from this study, in conjunction with other work, have shown that anthocorids have many of the characteristics of an ideal biological control agent (Hassell & Rogers 1972), i.e. a high searching efficiency, an ability to increase more rapidly when prey is abundant (Dempster 1968), a density-dependent decrease in fecundity resulting from interference, and the ability to aggregate in regions of high prey density. This may explain the obvious success of anthocorids in biological control of insect pests. In the United States Orius insidiosus (Say) is an important predator in natural ecosystems (Hanison 1960; Bell & Whitcomb 1964), while experimentally introduced anthocorids resulted in partial control of pests such as the balsam woolly aphid (Mitchell & Wright 1967). Anthocorids are important predators of rice pests in Japan (Oho 1955; Chu 1969) and of crop pests in Hawaii (Weber 1953). A common feature of many of these successes was the relatively slow rate of increase of the prey which allowed predator populations to multiply sufficiently to prevent a destructive outbreak by the pest. How- ever, in the present study Anthocoris confusus was capable of regulating prey population levels only if a mortality was introduced to prevent maximum reproduction by its aphid




prey. Thus anthocorids may be successful against rapidly reproducing pests only in conjunction with other mortality factors, or if some degree of asynchrony results in high predator numbers when prey numbers are already declining. This is evidenced by Dixon & Russel (1972) who show that when sycamore aphids are relatively low in autumn late stage and adult anthocorids can have a marked influence on the rate of increase of the aphid population.

ACKNOWLEDGMENTS

I would like to thank Professor G. C. Varley who supervised the work and Dr D. J. Rogers for his helpful criticism of the manuscript. The work was supported by a grant from the Natural Environment Research Council to whom I am most grateful.

SUMMARY

(1) A study of the population dynamics of the predatory bug, Anthocoris confusus, is described. All predator interactions were carried out in an artificial cage ecosystem. Single and multiple generation interactions were studied.

(2) The mortality factors acting on the predator population are described and related to predator density. Interference between adult anthocorids resulted in a density- dependent decrease in egg production per female (ko). Egg mortality (k1) and early adult mortality (k7) were directly density-dependent and contributed strongly to stability of the predator populations.

(3) Changes in prey numbers were related to the densities of fifth instar and adult anthocorids present at any time.

(4) The role of anthocorids in biological control is discussed.

REFERENCES

Banks, C. J. (1957). The behaviour of individual coccinellid larvae on plants. Br. J. Anim. Behav. 5, 12-24.

Bell, K. D. & Whitcomb, W. H. (1964). Field studies on egg predators of the bollworm Heliothis zea (Boddie). Florida Ent. 47, 171-180.

Chu, Ya. I. (1969). On the bionomics of Lyctocoris beneficus (Huira) and Xylocoris galactinus (Fieber) (Anthocoridae, Heteroptera)., J. Fac. Agric., Kyushu Univ. 15,1-136.

Dempster, J. P. (1968). Intra-specific competition and dispersal as exemplified by a Psyllid and its anthocorid predator. Insect Abundance (Ed. by T. R. E. Southwood), pp. 8-187. R.E.S. Symposium No. 4.

Dixon, A. F. G. (1958). A study of the searching behaviour of certain insects feeding on aphids. Unpublished D.Phil. thesis, University of Oxford.

Dixon, A. F. G. & Russel, R. J. (1972). The effectiveness of Anthocoris nemorum and A. confusus as predators of the sycamore aphid, Drepanosiphum platanoides. II. Searching behaviour and the incidence of predation in the field. Ent. exp. appl. 15, 35-50.

Evans, H. F. (1976). The role of predator-prey size ratio in determining the efficiency of capture by Anthocoris nemnorum and the escape reactions of its prey, Acyrthosiphon pisum. Ecol. Ent. 1, 85- 90.

Hanison, F. P. (1960). Corn earworm oviposition and the effects of D.D.T. on the egg predator complex in corn silk. J. econi. Ent. 37, 660-6.

Hassell, M. P. (1966). Evaluation of parasite or predator responses. J. Anim. Ecol. 35, 65-75. Hassell, M. P. (1971a). Mutual interference between searching insect parasites. J. Anim. Ecol. 40,

473-86. Hassell, M. P. (1971b). Parasite behaviour as a factor contributing to the stability of insect host-parasite

interactions. Dynamics of Populations, pp. 366-79. Proc. Adv. Study Inst. (Oosterbeek, 1970). Centre for Agricultural Publishing and Documentation, Wageningen.



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Hassell, M. P. & Huffaker, C. B. (1969). Regulatory processes and population cyclicity in laboratory populations of Anagasta kuhniella (Zeller) (Lepidoptera: Phycitidae). III. The development of population models. Researches popul. Ecol. Kyoto Univ. 11, 186-210.

Hassell, M. P. & Rogers, D. J. (1972). Insect parasite responses in the development of population models. J. Anim. Ecol. 42, 661-76.

Hassell, M. P. & Varley, G. C. (1969). New inductive population model for insect parasites and its bearing on biological control. Nature, Lond. 223, 1133-7.

Holling, C. S. (1959). Some characteristics of simple types of predation and parasitism. ;Can. Ent. 91, 385-98.

Holling, C. S. (1966). The functional response of invertebrate predators to prey density. Mem. ent. Soc. Can. 48, 1-86.

Kuchlein, J. H. (1966). Mutual interference among the predacious mite, Typhlodromus longipilus Nesbitt (Acari, Phytoeseiidae). 1. Effects of predator density on oviposition rate and migration tendency. Meded. Rijksfac. Landb. Wet. Gent. 31, 740-6.

Mitchell, R. G. & Wright, K. H. (1967). Foreign predator introductions for control of the balsam woolly aphids in the Pacific Northwest. J. econ. Ent. 60, 140-7.

Morris, R. F. (1959). Single factor analysis in population dynamics. Ecology, 40, 580-8. Oho, N. (1955). On the Euspudaeus sp. as a predator of the rice stem borer. Proc. Assoc. PI. Prot.

Kyushu, 34, 222-4. Podoler, H. (1974). Analysis of life tables for a host and parasite (Plodia-Nemeritis) ecosystem. J.

Anim. Ecol. 43, 653-79. Richards, 0. W. (1959). The study of natural populations of insects. Proc. R. ent. Soc. Lond. C, 23, 75-9. Richards, 0. W. & Waloff, N. (1961). The study of the natural population of Phytodecta olivacea (Forster)

(Coleoptera: Chrysomeloidea). Phil. Trans. B, 244, 205-57. Richards, 0. W., Waloff, N. & Spradbery, J. P. (1960). The measurement of mortality in an insect

population in which recruitment and mortality widely overlap. Oikos, 11, 306-10. Rogers, D. J. & Hassell, M. P. (1974). General models for insect parasite and predator searching beha-

viour: interference. J. Anim. Ecol. 43, 239-53. Stradbery, J. P. (1969). The biology of Pseudorhyssa sternata Merrill (Hym., Ichneumonidae), a clepto-

parasite of siricid woodwasps. Bull. ent. Res. 59, 291-7. Spradbery, J. P. (1970). Host finding by Rhyssa persuasoria (L.), an ichneumonid parasite of Siricid

woodwasps. Anim. Behav. 18, 103-14. Takahashi, F. (1961). Studies on the fluctuation in the experimental population of the almond moth,

Ephestia ceutella, Walker. Jap. J. Ecol. 11, 239-45. Varley, G. C. & Gradwell, G. R. (1960). Key factors in population studies. J. Anim. Ecol. 29, 399-401. Varley, G. C. & Gradwell, G. R. (1968). Population models for the winter moth. Symp. R. ent. Soc.

Lond. 4, 132-42. Varley, G. C. & Gradwell, G. R. (1970). Recent advances in insect population dynamics. Ann. Rev.

Ent. 15, 1-24. Waloff, N. (1968). A comparison of factors affecting different insect species on the same host plant.

Insect Abundance (Ed. by T. R. E. Southwood), pp. 76-87. R.E.S. Symposium No. 4. Weber, P. W. (1953). Recent liberation of beneficial insects in Hawaii, JI. Proc. Hawaii Elit. Soc. for

1952, 15, 127-30.

(Received 6 November 1975)

APPENDIX

The relationship between the numbers offirst and second instar nymphs counted at the time of sampling and the total number present in the ecosystem

Detailed single generation experiments have shown that first instar nymphs often drop off the plant. This happens less often in the second instar and is rare in subsequent instars. Since some of the young nymphs regain the plant at a later stage, population estimates based only on the numbers of first and second instars on the plant will be inaccurate. It was therefore necessary to determine the proportion of nymphs off the plant at the time of sampling in order to increase the counts of first and second instar nymphs for use in Richards & Waloff's model. The derivation of the factors used is described below.

(a) An estimate of the number entering the fifth instar was obtained. With a developmental period of seven days and very little fifth instar mortality, the total number of




fifth instar nymphs counted in all samples can be taken as the number which entered the instar.

(b) The Richards & Waloff method for the number entering the stage was rewritten to solve the number entering the previous instar. Knowing the number in the fifth instar, the developmental period of the fourth instar and the number of fourth instars taken in all samples, an estimate of the survival probability in the fourth instar can be obtained

I. SalogeS = X.,+' (1) Sa_ I Nx

I = sampling interval; S = daily survival probability of instar X; a = duration of instar (days); Xx,+ 1 = number entering instar x + 1; Nx = total counted in all samples instar x.

Table Al. The predictions for the numbers entering the third instar using eqn (1) and the use of these estimates to give an expected k3 value in

the ecosystem

Number entering Number entering Number entering Generation fifth instar third instar first instar (k3 + k4)

number xv log.xv XIII log.XIII XI log.XI log.XI-log.XIII

2 48 1-681 116 2-064 350 2 544 0-480 3 147 2-167 238 2-376 611 2-786 0-410 4 168 2 225 290 2.462 1148 3-060 0.598 5 64 1-806 143 2-155 425 2-628 0 473 6 85 1-929 171 2-233 543 2 735 0 502 7 87 1.939 168 2 225 961 2-983 0 758 8 41 1.613 124 2-093 492 2-692 0.599

Total (k3 + k4) 3-820 Mean (k3+k4) 0 546 Mean k4 single generation experiment 0.112 Mean expected k3 in ecosystem 0.546-0-112

= 0 434

Table A2. The derivation of a factor by which to increase the number of first instar nymphs sampled

Generation 2 3 4 5 6 7 8

Daily survival probability (dsp) from model 0 344 0 352 0 337 0-347 0-342 0-297 0 343

Mean dsp from model 0-339 Expected dsp 0-717 Factor for increase of first 0-717

0339= 2.1

Equation (1) was also used to predict the numbers entering the third instar. By sub- tracting the log. number in the fifth instar from the log. number in the third instar, a value for (k5 +k6) was obtained for each generation of the interaction.

(c) Having predicted the numbers entering the third instar it was now possible to obtain figures for the k-values acting on the first and second instars (k3 +k4) in each generation. This has been done in Table Al, and the mean value for all generations was 0-546. The



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mean value for k4 in the single generation experiments was 04112 and this was assumed to be acting at the same level in the ecosystem. This value was then subtracted from the estimated (k3 +k4) values to give a final expected k3 value in the ecosystem of 0 434. This value was obtained without having to use sampling figures for the first instar and was therefore free from the errors assumed to be associated with sampling at this stage.

(d) The mean daily survival probability (dsp) of the first instar was calculated using actual figures (mean dsp = 0 334, Table A2). This was then compared with the dsp calculated using the expected k3 value of 0 434 (here dsp was 0i717, first instar developmental period- 30 days).

10- .0/ Q 0'8 _

:3~ ~ ~~S 00 ?+. E06-

0 o 02

0 0-2 0.4 0.6 0-8 1.0 Daily survival probability S

FIG. Al. The relationship between daily survival probability (S) and the proportion of the population sampled = NT/(NT when S = 10)

Formula used n = L NT loge S sa -1

a = 3 0 (instar duration), I = 70 (sampling interval), n = 100 (no. entering stage), S = variable 0-10 (daily survival), NT = number counted in all samples.

Table A3. The derivation of a factor by which to increase the number of second instar nymphs sampled

Generation 2 3 4 5 6 7 8

Number entering second instar 113 207 356 140 183 232 158 Daily survival probability

(dsp) from model 0-79 0-95 0-79 0 83 099 0 86 0 77 Mean dsp from model 0 85 Expected dsp 0 91 Factor for increase of second 091

instar nymphs sampled 085 =

(e) Using the unmodified Richards & Waloff equation with fixed values for a (3 0), I (7T0) and n (100) and varying S it was found that a simple relationship existed between the proportion of the total number taken in all samples and the daily survival probability (Fig. A1). When S is doubled the proportion sampled is approximately doubled. Thus the correction factors were derived from the ratio of the expected to the observed dsp for both the first and second instars. These are shown in Tables A2 and A3.

(f) Although the calculated factors derived by this method were 2 1 for the first instar and 1 1 for the second instar it was found by trial and error that factors of 2 0 and 1 2 gave better predictions of numbers entering the later stages, particularly the fifth instar. It was therefore decided to use factors of 2-0 and 1 2 for the -further analysis of the ecosystem.



The Population Dynamics of Anthocoris confusus in a Laboratory Cage Ecosystem

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