Quantifying Apple Maggot (Diptera: Tephritidae) Preference for Apples to Optimize the Distribution of Traps Among Trees

B. C. MURPHY,' L. T. WILSON,2 AND ROBERT V. DOWELL'

Environ. Entomol. 20(4): 981-987 (1991) ABSTRACT The spatial and temporal distribution pattern of apple maggot, Rhagoletis pomonella (Walsh), fly captures was monitored among trees within an unmanaged apple orchard. Each tree within the orchard was monitored weekly for the presence of flies using sticky traps. Fruit maturity was monitored weekly to determine percentage soluble solids. Significantly more apple maggot were captured on trees with mature fruit than on trees with immature fruit. A selective predation model was used to quantify the effect of fruit preference on apple maggot captures. Two hypotheses were evaluated. The first hypothesis was that fly capture among trees is a function of the relative sequence or phenology of fruit maturation (tree category hypothesis). The second hypothesis was that fly capture among trees is a function of apple maturity among trees, r6gardless of the phenology of fruit maturation (fruit maturity hypothesis). Both models explained the distribution of fly capture among trees early in the growing season, but the fruit maturity hypothesis best explained the entire season. The use of the model for predicting the distribution pattern of apple maggot captures and the optimum placement of traps for apple maggot detection are discussed.

KEY WORDS Insecta, Rhagoletis pomonella, fruit preference, distribution

WITH THE INTRODUCTION of the apple maggot, sequent elimination of apple maggot infestations, Rhagoletis pomonella (Walsh), to the western the state was advised that every host tree in the United States, increasing attention has been given infested region needed to be monitored with traps to the efficacy of detection procedures for deter- (Dowell 1985). The cost of labor and materials mining the presence and movement of apple mag- needed to monitor every host tree was prohibitive got populations. Apple maggot is detected through and became a contributing factor to ending the the use of various sticky traps which are placed eradication program (Dowell 1990). within host trees. These traps rely on visual and None of the traps used for detection of apple olfactory cues to attract flies (Still 1960, Prokopy maggot has been found to attract flies beyond the 1968, Kring 1970). Attempts to improve trap effi- canopy of the host tree in which it is placed (John- ciency in the western United States have concen- son 1983, Stanley et al. 1987). Therefore, fly cap- trated on evaluating the relative sensitivity of trap ture is dependent not only on the attractive char- types to the presence of flies within host trees (Davis acteristics of the trap but also on flies being within & Jones 1986, AliNiazee et al. 1987, Brunner 1987). the immediate vicinity of the trap. Studies exam- Although these comparisons were relevant for find- ining apple maggot dispersal have noted an asso- ing the most effective trap, they did not address ciation between the number of flies captured on the efficiency of areawide detection programs. Use sticky traps and the cultivar of the apple host (Max- of sticky traps for detecting and monitoring apple well & Parsons 1968, Neilson 1971, Dean & Chap- maggot populations over larger areas, such as large man 1973). Those cultivars with earlier ripening orchard blocks or apple-growing districts, is often fruit were found to capture a larger number of impractical because of the limited range of trap flies, and fruit were more susceptible to attack. The attraction to apple maggot adults, requiring a large present study examines the role of fruit maturity proportion of available host trees to be monitored in influencing the number of apple maggot flies to ensure fly detection (Johnson 1983, Stanley et caught on sticky traps, and addresses two questions al. 1987). The limited range of these traps became regarding apple maggot capture among trees: acutely apparent during California's eradication To what extent do relative differences in fruit program against the apple maggot, where the state maturity among trees determine the spatial pat- attempted to monitor fly numbers in an area of tern of fly capture within an apple orchard? 62,000 km2 in seven northern California counties How may information on fruit maturity be used (Dowell 1990). To ensure the detection and sub- to increase efficiency of fly detection among ap- ple hosts? ' Department of Entomology, University of California, Davis, In answering these questions, we examined the Calif. 95616. Current address: Department of Entomology, Texas A&M spatial and temporal distribution pattern of flies University, College Station, Tex. 77843. captured among orchard trees during the season

0046-225X/91/0981-0987$02.00/00 1991 Entomological Society of America 982 ENVIRONMENTAL ENTOMOLOGY Vol. 20, no. 4 and analyzed the association between these pat- sured twice for each apple sampled, once from the terns and the differences in the relative maturity blush side and once from the green side of the of fruit among the trees. Second, we quantified this fruit. The percentage BRIX values were averaged relationship by means of a fruit preference model among the fruit sampled and used as the estimate to explain and predict the distribution pattern of of fruit maturity for each tree for the sample date. fly captures among trees with different maturity The number of flies trapped and the average per- classes of fruit. centage BRIX of fruit for each tree were recorded weekly from 22 June through 5 October 1987. Distribution Pattern of Apple Maggot Captures. Materials and Methods The distribution of apple maggot captures among The study was conducted in an unmanaged ap- all trees during the season was tested against an ple orchard near Brookings, Oreg., 16 km north of expected uniform distribution, and the frequency the California border, during 1987. The orchard distribution of fly captures among trees was tested was infested with apple maggot and had not re- against expected random and negative binomial ceived insecticide treatments against the fly for the distributions using a xz goodness of fit test (Zar previous 2 yr. Of the 93 trees, 5 were nonapple 1984). We tested for temporal changes by com- species and 5 were apple trees that did not bear paring the frequency distribution of apple maggot fruit. The remaining 83 trees produced fruit and captures during each of the three 5-wk periods of consisted of the following cultivars: 14 ‘Red’ and the season against these same expected distribu- ‘Stripped Gravenstien’, 3 ‘Yellow Transpafent’, 7 tions, also using a x2 goodness of fit test. ‘Delicious’ (strain unknown), 4 ‘Johnathan’, and 11 The distribution of fly capture among trees whose ‘Winesap’. The cultivars of the remaining trees fruit matured at different times was compared by could not be identified with certainty and they grouping the trees into one of three categories based were classified according to the time to harvestable on the time fruit within each tree ripened to a maturity. stage of harvestable maturity (12% BRIX). The first Fly abundance was monitored using yellow sticky 28 trees to reach this stage were categorized as panels (Pherocon apple maggot trap, Zoecon, Palo early-maturing, the second group of 28 trees as Alto, Calif.) and 7.5-cm unbaited red sticky spheres. midseason-maturing, and the remaining 27 trees One trap of each type was hung toward the outside as late-maturing. Early-, mid-, and late-maturing of the tree canopy on a north-south axis 2.0-3.5 m trees had fruit which reached an average 12 per- from the ground. Compass direction of the trap cent BRIX by 3 August, 16 August, and 23 Sep- types was reversed for each tree trapped. The yel- tember, respectively. The seasonal differences in low sticky panels were replaced with new traps fruit maturation rates among the tree categories as every 2 wk and red spheres were replaced every defined by percentage BRIX were subjected to a 30 d. repeated measures analysis of variance. Linear con- The physiological maturity of fruit was moni- trasts giving the linear trend in percentage BRIX tored by measuring the total dissolved solids (per- for each tree were used for comparisons among the centage BRIX), using a hand-held refractometer tree categories (Gurevitch & Chester 1986). (American Optical, Buffalo, N.Y., Model 10430). We tested the relationship between the time of The percentage BRIX of the fruit is a measure of fruit maturity and the frequency of apple maggot maturity, where pest and disease pressures. At the be- a few trees within any one category. For both meth- ginning of the study, 40 fruit were sampled for ods, a two-factor analysis of variance was used, percentage BRIX from trees representing each of where tree category (corrected for unequal sample the known cultivars to determine sample locations size) was the first factor and sample date a blocking of the canopy that provided the maximum range factor. Fishers protected LSD test at P = 0.05 was of maturity. The ripest fruit were found on the used for pairwise comparisons between treatment south side of the tree toward the outside of the means. Replicates were generated by randomly di- canopy and the least mature fruit on the north side viding the trees within each category into two within the tree canopy. The average fruit maturity groups and taking the mean value of square root- was calculated by averaging the maximum and transformed apple maggot captures or trees with minimum maturities found at these locations. Late- captures for each group (Steele & Torrie 1980). bearing trees often had the least variability, and Quantifying Apple Maggot Preference for Fruit. average maturity could be estimated with as little The relationship between fly capture and fruit ma- as two samples; earlier-bearing trees required as turity was analyzed with respect to two hypotheses: many as six fruit samples. Fruit maturity was mea- (1) fly capture among trees is a function of the August 1991 MURPHY ET AL.: QUANTIFYING APPLE MAGGOT PREFERENCE 983 temporal sequence in which fruit within trees reach where ii,, equals the weighted number of trees in harvestable maturity (tree category hypothesis), and the ith maturity category in the kth 5-wk period, (2) fly capture among trees is a function of differ- nfPequals the number of trees in the ith maturity ences in the relative maturity of their fruit at the category during week j of the kth 5-wk period. fiik time of sampling, regardless of the phenology of equals the numbers of flies trapped in the ith ma- fruit maturation (fruit maturity hypothesis). Anal- turity category during week j of the kth 5-wk pe- yses were conducted to determine which hypoth- riod. This removed a potential bias due to differ- esis best explained apple maggot distribution pat- ences in the availability of trees in each fruit terns among orchard trees during the season. maturity category from one week of sampling to The analyses of both hypotheses made use of a the next. selective predation model (Wilson 1977, Wilson & The weighted tree values (filk) were then substi- Gutierrez 1980). As with similar multiprey pre- tuted for n, in equation 1 and used in estimating dation models (Murdoch 1969; Manly et al. 1972; preference coefficients, one set for each time pe- Chesson 1978, 1983), the model can be used to riod. For the tree category hypothesis, data within quantify the relative preference shown by a pop- each of the three 5-wk periods were collapsed with- ulation of predators toward different prey and for out introducing a bias because a tree “category” predicting the pattern of attack by the predator did not change through time. for prey of differing availability (Blood & Wilson Composite Estimates of Preference. Seasonal 1978, Wilson and Waite 1982, Pickett et al. 1989). average preference coefficients for the tree hy- In the present study, apple maggot flies were char- pothesis were estimated from a weighted average acterized as predators, whereas prey were either of the three 5-wk preference estimates discussed tree categories or fruit at different maturity levels. above. Because a tree type does not change through For the “tree category” hypothesis, preference the season, this technique was again applied to the coefficients were derived for four tree categories. tree category preference model without introduc- Three of these categories were the early-, mid- and ing a bias. For the fruit maturity hypothesis, a late-maturing trees as discussed previously. The weighted composite estimate of preference had to fourth category contained the five nonbearing ap- account for the changing availability of fruit ma- ple trees. For the fruit maturity hypothesis, on each turity categories through time. Differences in the sample date, trees were divided into fruit maturity availability of maturity categories during each time categories of 1%BRIX intervals; these ranged from period caused differences in the scale of the pref- 16%. erence coefficients for each period. Therefore, be- Derivation of Tree Category and Fruit Matu- fore deriving seasonal weighted preference esti- rit y Preference Coefficients. Preference coeffi- mates, the preference coefficients (S,) for the first cients were derived for each hypothesis using equa- and third time periods were scaled to the second tion l: time period because this period had the largest number of categories in common with the other two periods. The common scale for each time pe- riod was derived using equations 3 and 4: where r, equals the proportion or number of flies captured on ith tree category or ith fruit maturity category, n,equals the proportion or number of all (3) available ith tree or maturity categories and max (r,/n,)is the maximum ratio of r,/n,for the tree or maturity categories (Pickett et al. 1989). Prefer- (4) ence coefficients derived using the model range from 0.0 to 1.0. For our hypotheses, a value of 1.0 indicates that a category was most preferred com- where C,,, equals the scalar for converting the S, pared with all other available categories; a value values for the first time period to those for the of 0.0 indicates that a category has no attractiveness second time period and i, and i, are the youngest to the flies. and oldest common categories respectively for the The 15-wk sampling period was divided into first two 5-wk periods. C,,, equals the scaled S, three 5-wk periods to provide a sample size suffi- coefficients for the third time period scaled to the cient for estimating preference coefficients for the second time period and i, and i, are the youngest tree and fruit maturity hypotheses and to allow us and oldest common categories, respectively, for to test for changes in fly preference through time. these two time periods. For the fruit maturity hypothesis, the availability Fig. 1 A and 13 illustrate an example of the meth- of trees in each maturity category during each od used for estimating composite preference coef- 5-wk period was weighted by fly abundance during ficients for the fruit maturity hypothesis. In the each week using equation 2: example, there are four categories common to the two hypothetical data sets (Fig. IA). The S, values common to each set are summed and the ratio between the two sets is calculated. The five S, values 984 ENVIRONMENTAL ENTOMOLOGY Vol. 20, no. 4

Table 1. Chi squared goodness of fit test of apple mag- got captures to three theoretical distributions

Distribu- Sampling period, wk tion 0-5 df 6-10 df 11-15 df Uniform 213.1** 88 461.4** 88 173.9** 88 Poisson 18.7 7 132.3** 17 27.7** 6 Negative binomial 0.37 ns 7 2.92 ns 17 1.7611s 6

*, 0.05 5 P < 0.01; **, P 5 0.01; ns, not significantly different (P > 0.05).

a random distribution (x2= 374.9, df = 23, PA 0.001), indicating that the seasonal pattern of fly capture was aggregated. During each sample pe- riod, fly capture was aggregated and differed sig- nificantly from what would be expected with either a uniform or random distribution (Table 1). The aggregated spatial pattern suggested apple maggot flies show a preference for some trees. 0.0 J Categorizing trees with respect to the time when 16 fruit become harvestable resulted in 28 trees in the Maturity Category (%BRIX) early-maturing category, of which 14 were ‘Gra- Fig. 1. (A) Two hypothetical preference curves whose venstien’ and 3 were ‘Yellow Transparent’. The values are on different scales. -, preference curve midseason trees also contained 28 trees and in- 1; &, preference curve 2. (B) Same hypothetical cluded 7 ‘Delicious’ and 4 ‘Johnathan’, whereas the preference curves after scaling curve I to the values of late-season contained 27 trees, of which 11 were scale 2. ‘Winesap’. The maturity in percentage BRIX for the early, mid, and late categories was 13.04 k in the first data set are then adjusted to the same 0.51 (a f SEM), 12.2 -t 0.51, and 11.56 f 0.40, scale in the second data set by multiplying each of respectively. Linear contrasts against time among its coefficients by the ratio (Z Si,/Z Si,) (Fig. 1B). the tree categories found these differences to be Weighted mean preference estimates were then significant (F = 10.7; df = 2, 77; P < 0,001). calculated for each category. Maxwell & Parsons (1968), Neilson (1971) and A weighted mean preference coefficient was es- Dean & Chapman (1973) have noted higher num- timated for each category using equation 5: bers of apple maggot captured on sticky traps hung within early-maturing apple cultivars relative to late-maturing cultivars, suggesting trees with ripe fruit are more attractive to flies. We found that the number of flies caught also varied with the tree where nkequals the number of flies trapped during maturity category. The early-ripening trees caught time period k, s,k equals the scaled preference co- the largest proportion of flies (48.5%),whereas mid- efficient for maturity category i during time period and late-ripening trees caught 27.0 and 23.5% of k; and n equals the total flies captured during the the population, respectively. Fly capture differed 5-wk period. significantly among the tree categories (F = 12.2; Statistical Analysis. To test the robustness of the df = 2, 45; P < 0.001) and for each sample date composite estimates for the tree category and the of the season (F = 6.3; df = 14, 45; P < 0.001). fruit maturity hypotheses, expected fly distribu- There was no significant interaction between tree tions were generated from each model and com- category and time of season (F = 1.3;df = 28, 45; pared with the observed fly distribution during each P > 0.05). Pairwise comparisons of treatment means of the 5-wk periods using a x2 goodness of fit test. confirmed early tree types had significantly greater Departures between the expected and observed data fly catch (P < 0.05). Significant differences also were used as a partial validation for the preference were found when comparing the number of trees models developed for each hypothesis. in each category that captured flies (F = 13.0; df = 2, 45; P < 0.001) and when comparing sample dates (F = 9.1; df = 14, 45; P < 0.001) but with Results and Discussion no significant interaction between tree type and A total of 412 flies (a, 4.8/tree; S2 = 31.05) was sample date (F = 1.0; df = 28, 45; P > 0.05). captured during the 15-wk study. Fly capture Pairwise comparisons found significantly larger among trees differed significantly from a uniform numbers of early tree types with flies during the distribution (xz = 546.8, df = 88, P < 0.001) and season (P < 0.05). The results of these analyses August 1991 MURPHY ET AL.: QUANTIFYING APPLE MAGGOT PREFERENCE 985

Table 2. Results of the composite tree category pref- Table 3. Chi squared goodness of fit test between ex- erence model pected capture distribution generated from tree category preference model and observed distribution found during three sampling periods Tree category No. trees Total no. flies Preference captured coefficient (Si) Sample Early season 28 200 LOO0 period, wk Tree type expectedNo. No.served ob- Xk Midseason 28 109 0.545 Late season 27 95 0.493 0-5 Early 27.6 34 - No fruit 5 8 0.224 Mid 15.1 12 - Late 13.1 11 - No fruit 1.1 0 3.53 ns 6-10 Early 107.3 118 - further suggest that apple maggot capture is as- Mid 58.5 58 - sociated with the maturity of fruit within a tree Late 51.0 40 - and that the discrimination exhibited by apple No fruit 4.3 5 3.54 ns maggot for the maturity of apples may be a key 11-15 Early 65.0 48 - factor explaining the spatial pattern of captures Mid 35.5 39 - Late - among orchard trees. 31.0 44 No fruit 2.6 3 10.43* Tree Category Preference Model. The prefer- ence coefficients (Table 2) derived using equation *, P 5 0.05;ns, not significantly different (P > 0.05). 1 indicate that a trap in an early-maturing tree is approximately twice as likely to capture flies as one in a mid- or late-ripening tree, and four times as BRIX category were most preferred, whereas fruit likely as those in trees without fruit. A partial val- with 16 0.612 ficients (S,)in Table 4 indicate that fruit in the 15% 986 ENVIRONMENTAL ENTOMOLOGY VOl. 20, no. 4

Table 5. Chi squared goodness of fit test between the in those trees predicted to have the highest fre- expected capture distribution generated from the fruit ma- turity model and the observed distribution found during quency of fly activity. This method maximizes the the three sampling periods capture efficiency of each trap by placing them in trees likely to have the greatest fly activity and Sample % BRIX No. ex- No. ob- eliminates the placement of traps in trees with little period, wk category pected served x20 or no fly activity. The preference model provides 0-5 16 0 0 In this way, the labor and materials required for 6- 0 < 10 0 0 detection programs may be minimized while max- 10-10.99 5.3 7 11-11.99 60.6 52 imizing the probability of detection. 12-12.99 92.8 95 13-13.99 30.9 37 14-14.99 10.4 4 Acknowledgment 15-15.99 ' 6.6 8 > 16 9.3 13 We gratefully acknowledge the assistance of R. Ogier 11-15 < 10 0 0 in the collection of field data. This research was funded 10-10.99 0 0 in part by a contract (8616) to L.T.W. from the Cali- 11- 11.99 0 0 fornia Department of Food and Agriculture. 12-12.99 23.0 33 13-13.99 36.4 36 14-14.99 30.1 32 15-15.99 30.9 25 References Cited > 16 10.6 5 AliNiazee, M. T., A. B. Mohammad & S. R. Booth. (1 11s. riot significantly different (P > 0.05) 1987. Apple maggot (Diptera: Tephritidae) re- sponse to traps in an unsprayed orchard in Oregon. tionship between fly capture and fruit maturity was J. Econ. Entomol. 80: 1143-1148. Blood, P.R.B. & L. T. Wilson. 1978. Field validation not simply a function of the differences in the time of a crop/pest management descriptive model, pp. fruit began to ripen, as suggested by previous re- 91-94. In Proceedings of SIMSIG-78, Simulation searchers (Maxwell & Parsons 1968, Neilson 1971), Conference, Australian National University, Canber- but rather was best explained by the relative dif- ra. ferences in fruit maturity at any point in time Brunner, J. F. 1987. Apple maggot in Washington regardless of the type of cultivar. Our results con- state: a review with special reference to its status in cur with those of other workers who have suggested other western states. Melanderia 45: 34-51. the mechanism which attracts flies to fruit may be California Food and Agricultural Code. 1985. Reg- an olfaction response to volatile compounds ema- ister 85, no. 8. Carle, S. A., A. L. Averill, G. S. Rule, W. H. Reissig & nating from maturing fruit (Prokopy et al. 1973, W. L. Roelofs. 1987. Variation in host fruit vol- Reissig 1974). Changes in the composition of these atiles attractive to apple maggot fly, Rhagoletis po- volatiles as the fruit matures may also explain the monella. J. Chem. Ecol. 13: 795-805. degree of preference exhibited by the flies for ap- Chesson, J. 1978. Measuring preference in selective ples in different stages of maturity as measured by predation. Ecology 59: 21 1-215. percentage BRIX (Carle et al. 1987). 1983. The estimation and analysis of preference and The fruit maturity preference model not only its relationship to foraging models. Ecology 64: 1297- identifies apple maturity as a key factor associated 1304. with apple maggot capture among trees; it may Davis, D. W. & V. P. Jones. 1986. Understanding the apple maggot. Utah Sci. 47: 94-97. also be used to increase the efficiency of detecting Dean, R. W. & P. J. Chapman. 1973. Bionomics of the presence of apple maggot flies in apple trees. the apple maggot in eastern New York. Search Agric. Current trapping strategies in the West, and par- (Geneva, N.Y.) 3: 1-64. ticularly in California, have relied on randomly Dowell, R. V. 1985. Environmental assessment of the trapping a large proportion of host trees in a given apple maggot. Calif. Dept. Food Agric. Pub. area to ensure an accurate assessment of the pres- 1990. History of apple maggot in the western United ence and abundance of the 5y. As stated previously, States. In R. V. Dowell, L. T. Wilson & V. P. Jones traps used to detect apple maggot 5ies are generally [eds.], Apple maggot in the west: history, biology and control. Agric. Sci., University of California, Oakland, effective only within the canopy of the tree. Based Pub. 3341. on our findings, the presence of flies within a tree Gurevitch, J. & S. T. Chester. 1986. Analysis of re- is influenced by the maturity of its fruit. Thus, by peated measures experiments. Ecology 67( 1): 251- measuring the relative degree of fruit maturity 255. among trees to be monitored, the efficiency of de- Johnson, P. C. 1983. Response of adult apple maggot tecting flies may be increased by placing traps only (Diptera: Tephritidae) to pherocon A.M. traps and August 1991 MURPHY ET AL.: QUANTIFYING APPLE MAGGOT PREFERENCE 987

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