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Development of Time-Efficient Method for Estimating Aphids Density Using Yellow Sticky Traps in Cucumber Greenhouses
J. Asia-Pacific Entomol. 4 (2) : 143 ~ 148 (2001) www.cntomology.or.kr
Development of Time-Efficient Method for Estimating Aphids Density Using Yellow Sticky Traps in Cucumber Greenhouses Jung-Joon Park, Jong-Kwan Kim, Heungsun Park' and Kijong Cho* Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 136-70 I, Korea 'Department of Statistics, J-Iankuk University of Foreign Studies, Yongin, 449-791, Korea
Abstract The presence-absence model (PAM) was implemented to estimate the mean population density of Aphis gossypii Glover on grid-sticky traps in commercial cucumber greenhouses. The grid consisted of 4 by 6 cells (24cells per trap), each cell in size of -lcm" (2 by 2cm). The PAM described the relationship between the number of occupied cells and the number of aphids in a natural logarithmic scale reasonably well, and most trap cases were within 95% confidence limits of the predicted model. The distribution pattern of aphids on each trap was confirmed mostly nonrandom (75.3% of total traps) by Morisita's index, whereas the pattern among the traps was random according to PAM. The time cost for estimating the mean density of aphids by the PAM method was more efficient, compared to the whole trap counting method. This study demonstrated that the cell-occupied method based on the presence-absence model could be successfully implemented for estimating mean density of aphids in cucumber greenhouses. Key words Aphis gossypii, presence-absence model, yellow 'sticky trap, cucumber
Introduction Using sticky traps provides a very simple method of detecting early pest infestations and obtaining relative measurements of insect densities. The use of yellow sticky traps to monitor populations of flying pests becomes an essential component of pest management programs in greenhouses (Gillespie and Quiring, 1992; Heinz et al., 1992; Steiner et al., 1999). In spite of these advantages to using sticky traps, greenhouse pest control practices usually take place with little regard to pest populations at the time of "'Corresponding author. E-mail: [email protected] rei: 02-3290-3064; Fax: 02-925-1970
(Received September 6, 2001; Accepted October 17, 2001)
implementation. The reasons include the cost of regular replacement, the inconvenience of handling sticky material, lack of grower expertise or confidence in separating pest species from general ones, and the time involved in counting and recording the data (Steiner et al., 1999). The major cost of monitoring insect pests with the traps lies not in the capital outlay in traps, but with the time associated with identifying and counting the insect caught. The cost is usually not a problem where there are low-density levels of pest densities on the traps. However, even when traps are changed on a weekly basis, there are occasions when pests are numerous and counting becomes too time-consuming (Steiner et al., 1999). Therefore, development of a time-efficient method of counting yellow sticky traps catches is necessary to reduce the cost of monitoring pest populations in greenhouses. Heinz et al. (1992) reported that the densities of Diglyphus begini Ashmead, Frankliniella occidentalis Pergande, Liriomyza trifolii Burgess, and Trialeurodes vaporariorum Westwood on an entire trap could be accurately predicted by counting a 2.3-cm-wide continuous vertical strip on both trap sides in commercial greenhouses because these insect species tended to cluster along the vertical trap plane. Because this counting technique uses only 20% of a trap surface, it can be reduced the counting time by 80%. However, the reliability and accuracy of this method are strongly dependent on the distribution patterns of pests across the sticky trap and orientation of traps. Consequently, density estimates of insect pests on the traps might vary according to the pest species, trap orientation and greenhouse environment conditions. Steiner et at. (1999) devised an altemative method using presence-absence sampling, which minimized the influence of variation in the vertical or horizontal distribution of pests across the trap by examining the whole trap. Therefore, this method could accurately predict the number of insects on yellow traps regardless of placement, orientation and site. Sticky traps could become an integral part of Aphis gossypii Glover management monitoring program in
144 J. Asia-Pacific Entomol. Vol. 4 (2001)
Jung-Joon Park, Jong-Kwan Kim. Hcungsun Park and Kijong Cho
cucumber greenhouses. A. gossypil sucks the sap from the leaves of cucumber, weakening the plants and reducing both the quantity and quality of the cucumber fruits. Tn years of abundance, they kill the plants and ruin the cucumber crops over extensive areas of greenhouses. This has been considered the most destructive aphid occurring in Korean cucumber greenhouses. This study was conducted to develop a time-efficient trap monitoring system for A. gossypii in commercial cucumber greenhouses using the presence-absence modeling method proposed by Steiner et at. (1999). Density estimates and counting efficiencies from this method were compared with whole trap counting method.
was a 10 x 2 pattern. The distance between traps was ",3 m in a cross-row and ",4 rn in a down-row for both greenhouses. Traps were collected and replaced weekly. The collected traps were covered with a clearplastic wrap and brought into the laboratory for further analysis. The method used to estimate aphids numbers on traps was to overlay a 2.0 em X 2.0 em grid printed on clear acetate sheet over the trap, after coIIecting the traps (Fig. 1). The grids consisted of 6 by 4 cells. The numbers of winged adult of A. gossypii in each cell were counted and recorded. Distribution of Aphids on Sticky Trap. Morisita
10 index (Morisita, 1962) was examined for distribution Materials and Methods Surveyed Greenhouses. Population changes of A. gossypii were monitored with yellow sticky traps from two commercial cucumber greenhouses (HI and H2) located at Hwacheon, Kangwon Province, Korea, for three months (from the late August to late October) in 1999. Each greenhouse ranged in size of 1,000m' for HI and 340m 2 for H2 greenhouse. Three-week-old greenhouse grown cucumber plants (cv. Chuichung) were transplanted late July in each greenhouse at 1.5m apart between rows, and plants were spaced at O.Sm apart within row on soil beds covered with black polyethylene mulch. Cucumber plants were grown using the modi fled vertical cordon training system (Cho et al., 200 I). As the plant reached the top supporting wire, the grower removed the clips and released the reserved twine, leaving the plant '" 0.3m closer to the ground, with its lower section lying on the ground. Therefore, the youngest leaf always occurred at the same top canopy height. The greenhouses were managed with standard recommended practices, including the use of fertilizers and pesticides. Pesticides were used to suppress and prevent insect pests and plant disease at grower's discretion. Temperature in the greenhouse fluctuated between 20 - 3SoC during this study. Trap Design and Arrangement in Greenhouse. Winged adults of A. gossipy were sampled using a yellow sticky trap (8.4 X 13.8cm) (Panaplate'", Kossil Products, Seoul, Korea). The traps were rolled into a cylindrical shape and placed just above the plant canopy level throughout this study. Therefore, the trap was effective only one side. Within each greenhouse, a grid of pennanent sampling stations was established with one sticky trap per location. The sampling array for HI greenhouse consisted of 30 grids, laid out in a lOX 3 grid pattern. The array for H2 greenhouse
of adult aphids on each trap. The parameter of 0 is defined as the probability that individuals of a randomly drawn pair will come from the same cell. The 1 0 index can be defined as the ratio of 0 to its expected value assuming a random distribution. Letting N be the total number of individuals sampled n cells, 1 0 may be calculated as follows (Morisita, 1962): I =
n-"",2~x---,-,_(x-,----"-_1_) N(N _I)
o
(1)
Value of 1 0 can be used to classify dispersion patterns as random (10 = I), aggregated (1".> I), or uniform (/6'<1). To determine if the sampled population significantly differs from randomness, the following large sample test of significance Can be used (Hutcheson and Lyons, 1989): Z
=
(fb -I)/(2/nx')'I"
(2)
We compared value of Z with tabulated value for a normal distribution and rejected the hypothesis that the sampled population is dispersed randomly if /Z/ > z( a /2). Difference of the distribution between mean population densities was compared using the Fisher exact test (Sokal and Rohlf, 1981). Development of Presence-Absence Model. Steiner et al. (1999) proposed a presence-absence model (PAM) for the trap catch data: InlY) = ex + I1X,
(3)
where A, is number of occupied cells of the ith trap, and Y; is the number of aphids assumed to follow a negative binomial distribution with: Mean = I-li
=
Variance
Var(Y;)
=
exp(ex + 11K,) =
{t.(1 +
{lj /
rjJ)
=
u
2
(4) (S),
where ¢ is a constant index independent of f.L i (Me-
Development of Time-Efficient Sampling Method for Aphids
Table 1. Number of non-randomness trap cases", categorized by aphids density per trap in two commercial cucumber greenhouses No. of 11011Range of Greenhouse aphids No. of traps randomness trap (%) -----_._--- .del~~i!1:ltrap 9 (47.4) 19 2 - 10 10 (90.9) HI 11 - 20 11 22 (78.6) 28 >20 2 - 10 7 (70.0) 10 1-12 6 6 (100.0) II - 20 7 (100.0) >20 7 61 (75.3)
81
Total
"Departure from randomness was determined by the method developed by Hutcheson and Lyons (1989).
Table 2. Presence-absence model statistics' for winged adult aphids 011 yellow sticky traps in commercial cucumber greenhouses
Greenhouse
a ±SEM
/3 ± SEM
¢
Sample number
HI
O± 0.06.98
0±0.01.18
10.30
125
1-12
O± 0.08.81
53
O± 0.05.85
o± 0.01.23 o± 0.01.21
7_72
Total
8.55
178
-_ .. _---
'Equations 3 and 5.
Cullagh and NeIder, 1989). In negative binomial distribution, the mean and variance are; Mean = j.L "" kp and Variance=kp + kp' = J.l(1 + J.l/ k).l11(~refore, themeaning of r./J in equation 5 is same to that of k in negative binomial distribution as a clumping parameter, which is r./J>8 indicate random distribution, and as r./J approaches o the distribution become more aggregated (Davis, 1994; Elliott, 1983). All the parameters, a, j3, and r./J were estimated by using GENMOD procedure in SAS (SAS Institute, 1995) based on an over-dispersed Poisson distribution with a log link function (Park and Cho, 1999). Confidence limits (eL) of 95% for the predicted values were calculated using the formula: (6)
Efficiency of Presence-Absence modeling. Comparisons in efficiency of the PAM method and whole trap counting method were made by evaluating a time cost factor at various density ranges of aphids. The cost was consisted of time spent counting aphids on traps, expressed as hours of labor.
Results During this study, average density of aphid populations
145
was very low level « I aphid per trap) at the beginning of trapping and gradually increased to very high densities in both greenhouses. The highest number of aphids caught per week per trap was 138 for HI greenhouse and 205 for H2 greenhouse. Distribution of Aphids on Trap Surface. In total, 177 sticky traps from two greenhouses were examined to analyze the distribution patterns of aphids on traps using the Morisita I" index (Morisita, 1962). However, after elimination of the traps that contained aphids less than 2 per trap, only 81 traps were useful for the analysis (Table 1). According to equation 1, 10' index from each trap can be estimated where the density was higher than 1 per trap. Value of 18 index ranged from 0.66 to 24.0 in this study. The dispersion analysis showed significant non-random distribution (equation 2, P<0.05) in 75.3% of the 81 traps (Table 1). When the data were grouped by counts ranging from 2 to 10, 11 to 20, and > 20 adults per trap, the counts showed the non-random distribution at least 47.4% of aphids population between the cells in each trap. Although the occurrence of non-random distribution was 47.4% at population densities of 2 to 10 adults per trap in HI greenhouse, the Fisher exact test showed that the proportions of the occurrence of the nonrandom distribution across the various population densities were not significantly different.
Presence-Absence Model Parameter Estimation. Parameter estimates of the presence-absence model in equation 3 were listed in Table 2. The relationship was described as In(Y)=O.98+0.18X for HI greenhouse and In(Y)=O.81 +O.23X, where for H2 greenhouse where Y is the predicted number of aphids per trap, and X is the number of occupied cells. Data sets from each greenhouse were combined based on overlapping of 95% confidence limits and generated a common line for further analysis: In(Yl=O.85+0.21X (Fig. I). Values of r./J were 10.30 for HI greenhouse and 7.72 for H2 greenhouse, indicating random distribution of aphid population between the traps. The munber of occupied cells and estimated natural logarithmic numbers of aphids were described using the common line with 95% confidential limits (Fig. 2). A total of 92.7% in 178 trap cases was within 95% confidence limits of the common mean predicted line. 1110se cases that did not fall within the 95% confidence limits were obtained from the number of occupied cells ranged of 8 - 11 cells (predicted mean estimates ranged of 12.5 - 29.1 per trap). Actual means (independent variable) were regressed with the predicted means (dependent variable) with the 95% predicted limits (Fig. 3). Mean obtained from 170 of the 178 trap cases were within the 95% predicted limits of the estimate means from whole trap
146 J. Asia-Pacific Entornol. VoL 4 (2001)
q
Jung-Joon Park, Jong-Kwan Kim, Heungsun Park and Kijong Cho
i 8Acmt----------If----+--+--+----+-----j
D Each cell consists of 4 ern? (2 X 2 em)
Cylinder shaped in greenhouses
..
13.8em
Fig. 1. Diagrammatic representation or 24-cell-yellow sticky trap used to sample the winged adult aphids in cucumber greenhouses.
counting. However, in general, the PAM overestimated the mean within the density ranges of 20 - 40 per trap compared to the mean estimates from the whole trap counting.
Efficiency of Presence-Absence Modeling. Efficiency of the PAM method was graphically compared to the whole trap counting method by measuring the time cost for processing (Fig. 4). The time costs in whole trap counting increased linearly as the density of insects increased on traps. This was expected because the time counting the insects was the dominant factor. However, the costs in PAM method were unaffected by the density changes of aphids on traps, and maintained constant at low level. At high density of aphids, the PAM method was superior to the whole counting method in terms of the time cost.
Discussion Accurate estimates of pest and natural enemy population size are necessary to determine timing and rate of natural enemy release in greenhouse biological control programs. Information about greenhouse pest population dynamics could also lessen the need for preventive pesticide application and increase pesticide efficiency through timing applications (Heinz and Parrella, 1991). Therefore, development of accurate and time-efficient sampling methods becomes an essential part ofpest management monitoring program in greenhouses. In this respect, sticky traps can be an integral part of a greenhouse pest management program because using the traps provides an enormously simple and efficient method of detecting early pest infestations and obtaining relative measurements of insect densities in greenhouses. The presence-absence model (equation 3) described
the relationship between the number of occupied cells and the number of insects in a log scale reasonably well because most trap cases were within 95% confidence limits of the predicted model (Fig. 1). However, the method is not robust enough to generalize the relationship because a few outliers occurred at the number of cells occupied ranges over 8 - 11 cells which were an intermediate level of aphids densities (predicted mean estimates ranged of 12.5 - 29.1 per trap). Moreover, the presence-absence method overestimates the mean densities within this range (Fig. 2). This range was experienced frequently during aphids scouting in cucumber greenhouses. Accurate prediction of the mean density within this range is very critical for aphids management in cucwnber greenhouses. At low density « 10 per trap), the efficiency and performance of the cell counting was identical or similar to those of the whole trap counting. Steiner et al. (1999) noted that the time spent examining the trap should be appreciably less than counting individuals, as a maximum of 30 observations need to be made for each pest regardless of the number of individuals present. At high aphids densities (> 100 per trap), it is not important for estimating mean density accurately because this level might be over the economic threshold for aphids in cucumber greenhouses and chemical control should be applied immediately. Nature of the bias between the predicted and the observed mean densities may be explained by two factors. First, the model (equation 3) may contain a high level of heterogeneity in nature and, therefore, this is not robust enough to explain the relationship between the number of occupied cells and estimated natural logarithmic numbers of aphids. Because the estimation of population density based on empirical model such as equation 3 is inherently stochastic process, the bias observed in this study can occur with any density estimation procedures, regardless of the mathematical model selected to represent the
Development of Time-Efficient Sampling Method for Aphids
I::
.S! 1U E
0:6 ltI .l= ....
5
:;::::;
4-
Q)
~
60
::i
60
IJl
ltI 2
cD U l::
ci
.s I::
100
U
~3
...J
120
IJl
32
'0
'"0 - - - - - - - - - - - - - - - - - - - - , 140
W
0..
IJl
147
o o
<0
Q)
IJl
0
+---~--~--,------~--~-
o
4
8
12
16
20
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o
Q)
Q: 20
60
80
100
120
140
160
Whole Trap Count Estimation
No. of occupied cells Fig. 2. Relationship between the number of occupied cells and the (I n) number of aphids per trap for HI (0) and 112 (e) greenhouses. The grid consisted of 4 X 6 cells each 4cm' laid over the trap face. The dotted lines represent 95% confidence intervals around the predicted equation (solid line) of In(Y) = 0.85 + 0.21X where Y is the predicted number of aphids per trap, and X is the number of occupied cells.
40
Fig. 3. Relationship between the density estimates Irorn the presence-absence model and whole trap counting method Ill!" HI (U) and J·12 (e) greenhouses. The dotted lines represent 95% predicted intervals around the linear regression line (solid line) (y = bx).
80
relationship. In this study, the parameter ¢ from PAM indicated that winged adult aphids population had distributed in random manner between the traps in the greenhouses (Table 2), whereas the dispersion index, Morisita I J' value, showed the non-random distribution (Table 1) between the cells in the traps. These results showed that aphid populations on the cells in the traps are easy to confirm presence or absence (non-random betweenthe cells in the traps) and reduction of the number of the traps to density estimation (random distribution between the traps in the greenhouses). Second, trap size (cell numbers and size in trap) affects the relationship between the numbers of occupied cells and estimated natural logarithmic numbers of aphids. Steiner et al. (1999) mentioned that increasing the cell size would reduce the maximum population density that could be accurately predicted. In opposite case, decreasing the cell size would increase the likelihood of finding an empty cell, so theoretically one could predict more accurately at higher densities. This study demonstrated that the cell-occupiedmethod based on the presence-absence model could be successfully implemented for estimating mean density of aphids in cucumber greenhouses. Care must be exercised to interpret and estimate the mean density from the presence-absence model. To implement this method accurately and efficiently in Korean cucumber greenhouses, the relationship of aphids densities on yellow traps and on cucumber plant must be elucidated. Acknowledgments This research was supported in part by a grant from Rural Development Administration to Kijong Cho and Heungsun Park.
IJl
•
"C
I::
o
6D
•••
U
en
.=
-
40
IJl
o
U
•
20
E
i=
0:00
0
6b
o -I---_ _~ - - - ~ 50
100
150
200
250
300
No. of Aphids on Sticky Trap Fig. 4. A comparison of the efficiency for estimating mean density or aphids per trap using the presence-absence model (U) and the whole trap counting method (e)- The efficiency was evaluated based the time cost, expressed as labor of seconds.
Literature Cited Cho, K., J.-H. Lee, J.-J. Park, J.-K. Kim and K.-B. Uhm. 2001. Analysis of spatial pattern of Frnkliniella occidentalis (Thysanoptera: Thripidae) on greenhouse cucumbers using dispersion index and spatial autocorrelation. App1. Entomol. Zool. 36: 25-32_ Davis, P. M. 1994. Statistics for describing populations, in Handbook of sampling methods for arthropods in agriculture, Eds. L. P. Pedigo and G. D. Buntin, pp. 33-54. CRC Press, FL. Elliott, J. M. 1983. Some methods for the statistical analysis of samples of Benthic invertebrates. Freshwater Biological Assoc. Sci. Publ. 25. Ambleside, Cambria. Gillespie, D_ R. and D. J. M. Quiring. 1992. Flight behavior of the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) (Homoptera: Aleurodidac), in relation to yellow sticky traps. Can. Entomol. 124: 907-916. Heinz, K. M. and M. P. Parrella. 1991. A shortcut with sticky
148 1. Asia-Pacific Entomol. Vol. 4 (2001)
Jung-Joon Park, Jong-Kwan Kim, Heungsun Park and Kijong Cho
traps. GrowerTalk 8/12: 40-45. Heinz, K. M., M. P. Parrella and 1. P. Newman. 1992. Timeefficient use of yellow sticky traps in monitoring insect populations. J. Econ. Entomol. 85: 2263-2269. Hutcheson, K. and N. I. Lyons. 1989. A significance test for Morisitas index of dispersion and the moments when the population is negative binomial and Poisson, in Estimation and analysis of insect populations, Eds. L. McDonald, B. Manly, J. Lockwood and J. Logan, 335pp. SpringerVerlag, Berlin. McCullagh, P. and J. A. Nelder. 1989. Generalized linear mode!. 2nd ed., 511pp. Chapman and Hall, London. Morisita, M. 1962. J rindex, a measure of dispersion of
individuals. Res. Popu!. Eco!. 6: 1-7. Park, H. and K. Cho. 1999. Variance modeling estimation for sequential sampling in biological control. The 1999 Proceedings of the Section on Statistics and the Environment of the American Statistical Association. 50-52. SAS Institute. 1995. SAS/ST AT users guide for personal computers, version 6.11 ed. SAS Institude, Cary, NC. Sokal, R. R. and F_ J. Rholf. 1981. Biometry. 2nd ed. Freeman, N.V. Steiner, M. Y., L. J_ Spohr, L Barchia and S_ Goodwin. 1999. Rapid estimation of numbers of whiteflies (Hemiptera: Aleurodidae) and thrips (Thysanoptera: Thripidac) on sticky traps. Aust. J. Entomol, 38: 367-372.
Development of Time-Efficient Method for Estimating Aphids Density Using Yellow Sticky Traps in Cucumber Greenhouses Jung-Joon Park, Jong-Kwan Kim, Heungsun Park' and Kijong Cho* Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 136-70 I, Korea 'Department of Statistics, J-Iankuk University of Foreign Studies, Yongin, 449-791, Korea
Abstract The presence-absence model (PAM) was implemented to estimate the mean population density of Aphis gossypii Glover on grid-sticky traps in commercial cucumber greenhouses. The grid consisted of 4 by 6 cells (24cells per trap), each cell in size of -lcm" (2 by 2cm). The PAM described the relationship between the number of occupied cells and the number of aphids in a natural logarithmic scale reasonably well, and most trap cases were within 95% confidence limits of the predicted model. The distribution pattern of aphids on each trap was confirmed mostly nonrandom (75.3% of total traps) by Morisita's index, whereas the pattern among the traps was random according to PAM. The time cost for estimating the mean density of aphids by the PAM method was more efficient, compared to the whole trap counting method. This study demonstrated that the cell-occupied method based on the presence-absence model could be successfully implemented for estimating mean density of aphids in cucumber greenhouses. Key words Aphis gossypii, presence-absence model, yellow 'sticky trap, cucumber
Introduction Using sticky traps provides a very simple method of detecting early pest infestations and obtaining relative measurements of insect densities. The use of yellow sticky traps to monitor populations of flying pests becomes an essential component of pest management programs in greenhouses (Gillespie and Quiring, 1992; Heinz et al., 1992; Steiner et al., 1999). In spite of these advantages to using sticky traps, greenhouse pest control practices usually take place with little regard to pest populations at the time of "'Corresponding author. E-mail: [email protected] rei: 02-3290-3064; Fax: 02-925-1970
(Received September 6, 2001; Accepted October 17, 2001)
implementation. The reasons include the cost of regular replacement, the inconvenience of handling sticky material, lack of grower expertise or confidence in separating pest species from general ones, and the time involved in counting and recording the data (Steiner et al., 1999). The major cost of monitoring insect pests with the traps lies not in the capital outlay in traps, but with the time associated with identifying and counting the insect caught. The cost is usually not a problem where there are low-density levels of pest densities on the traps. However, even when traps are changed on a weekly basis, there are occasions when pests are numerous and counting becomes too time-consuming (Steiner et al., 1999). Therefore, development of a time-efficient method of counting yellow sticky traps catches is necessary to reduce the cost of monitoring pest populations in greenhouses. Heinz et al. (1992) reported that the densities of Diglyphus begini Ashmead, Frankliniella occidentalis Pergande, Liriomyza trifolii Burgess, and Trialeurodes vaporariorum Westwood on an entire trap could be accurately predicted by counting a 2.3-cm-wide continuous vertical strip on both trap sides in commercial greenhouses because these insect species tended to cluster along the vertical trap plane. Because this counting technique uses only 20% of a trap surface, it can be reduced the counting time by 80%. However, the reliability and accuracy of this method are strongly dependent on the distribution patterns of pests across the sticky trap and orientation of traps. Consequently, density estimates of insect pests on the traps might vary according to the pest species, trap orientation and greenhouse environment conditions. Steiner et at. (1999) devised an altemative method using presence-absence sampling, which minimized the influence of variation in the vertical or horizontal distribution of pests across the trap by examining the whole trap. Therefore, this method could accurately predict the number of insects on yellow traps regardless of placement, orientation and site. Sticky traps could become an integral part of Aphis gossypii Glover management monitoring program in
144 J. Asia-Pacific Entomol. Vol. 4 (2001)
Jung-Joon Park, Jong-Kwan Kim. Hcungsun Park and Kijong Cho
cucumber greenhouses. A. gossypil sucks the sap from the leaves of cucumber, weakening the plants and reducing both the quantity and quality of the cucumber fruits. Tn years of abundance, they kill the plants and ruin the cucumber crops over extensive areas of greenhouses. This has been considered the most destructive aphid occurring in Korean cucumber greenhouses. This study was conducted to develop a time-efficient trap monitoring system for A. gossypii in commercial cucumber greenhouses using the presence-absence modeling method proposed by Steiner et at. (1999). Density estimates and counting efficiencies from this method were compared with whole trap counting method.
was a 10 x 2 pattern. The distance between traps was ",3 m in a cross-row and ",4 rn in a down-row for both greenhouses. Traps were collected and replaced weekly. The collected traps were covered with a clearplastic wrap and brought into the laboratory for further analysis. The method used to estimate aphids numbers on traps was to overlay a 2.0 em X 2.0 em grid printed on clear acetate sheet over the trap, after coIIecting the traps (Fig. 1). The grids consisted of 6 by 4 cells. The numbers of winged adult of A. gossypii in each cell were counted and recorded. Distribution of Aphids on Sticky Trap. Morisita
10 index (Morisita, 1962) was examined for distribution Materials and Methods Surveyed Greenhouses. Population changes of A. gossypii were monitored with yellow sticky traps from two commercial cucumber greenhouses (HI and H2) located at Hwacheon, Kangwon Province, Korea, for three months (from the late August to late October) in 1999. Each greenhouse ranged in size of 1,000m' for HI and 340m 2 for H2 greenhouse. Three-week-old greenhouse grown cucumber plants (cv. Chuichung) were transplanted late July in each greenhouse at 1.5m apart between rows, and plants were spaced at O.Sm apart within row on soil beds covered with black polyethylene mulch. Cucumber plants were grown using the modi fled vertical cordon training system (Cho et al., 200 I). As the plant reached the top supporting wire, the grower removed the clips and released the reserved twine, leaving the plant '" 0.3m closer to the ground, with its lower section lying on the ground. Therefore, the youngest leaf always occurred at the same top canopy height. The greenhouses were managed with standard recommended practices, including the use of fertilizers and pesticides. Pesticides were used to suppress and prevent insect pests and plant disease at grower's discretion. Temperature in the greenhouse fluctuated between 20 - 3SoC during this study. Trap Design and Arrangement in Greenhouse. Winged adults of A. gossipy were sampled using a yellow sticky trap (8.4 X 13.8cm) (Panaplate'", Kossil Products, Seoul, Korea). The traps were rolled into a cylindrical shape and placed just above the plant canopy level throughout this study. Therefore, the trap was effective only one side. Within each greenhouse, a grid of pennanent sampling stations was established with one sticky trap per location. The sampling array for HI greenhouse consisted of 30 grids, laid out in a lOX 3 grid pattern. The array for H2 greenhouse
of adult aphids on each trap. The parameter of 0 is defined as the probability that individuals of a randomly drawn pair will come from the same cell. The 1 0 index can be defined as the ratio of 0 to its expected value assuming a random distribution. Letting N be the total number of individuals sampled n cells, 1 0 may be calculated as follows (Morisita, 1962): I =
n-"",2~x---,-,_(x-,----"-_1_) N(N _I)
o
(1)
Value of 1 0 can be used to classify dispersion patterns as random (10 = I), aggregated (1".> I), or uniform (/6'<1). To determine if the sampled population significantly differs from randomness, the following large sample test of significance Can be used (Hutcheson and Lyons, 1989): Z
=
(fb -I)/(2/nx')'I"
(2)
We compared value of Z with tabulated value for a normal distribution and rejected the hypothesis that the sampled population is dispersed randomly if /Z/ > z( a /2). Difference of the distribution between mean population densities was compared using the Fisher exact test (Sokal and Rohlf, 1981). Development of Presence-Absence Model. Steiner et al. (1999) proposed a presence-absence model (PAM) for the trap catch data: InlY) = ex + I1X,
(3)
where A, is number of occupied cells of the ith trap, and Y; is the number of aphids assumed to follow a negative binomial distribution with: Mean = I-li
=
Variance
Var(Y;)
=
exp(ex + 11K,) =
{t.(1 +
{lj /
rjJ)
=
u
2
(4) (S),
where ¢ is a constant index independent of f.L i (Me-
Development of Time-Efficient Sampling Method for Aphids
Table 1. Number of non-randomness trap cases", categorized by aphids density per trap in two commercial cucumber greenhouses No. of 11011Range of Greenhouse aphids No. of traps randomness trap (%) -----_._--- .del~~i!1:ltrap 9 (47.4) 19 2 - 10 10 (90.9) HI 11 - 20 11 22 (78.6) 28 >20 2 - 10 7 (70.0) 10 1-12 6 6 (100.0) II - 20 7 (100.0) >20 7 61 (75.3)
81
Total
"Departure from randomness was determined by the method developed by Hutcheson and Lyons (1989).
Table 2. Presence-absence model statistics' for winged adult aphids 011 yellow sticky traps in commercial cucumber greenhouses
Greenhouse
a ±SEM
/3 ± SEM
¢
Sample number
HI
O± 0.06.98
0±0.01.18
10.30
125
1-12
O± 0.08.81
53
O± 0.05.85
o± 0.01.23 o± 0.01.21
7_72
Total
8.55
178
-_ .. _---
'Equations 3 and 5.
Cullagh and NeIder, 1989). In negative binomial distribution, the mean and variance are; Mean = j.L "" kp and Variance=kp + kp' = J.l(1 + J.l/ k).l11(~refore, themeaning of r./J in equation 5 is same to that of k in negative binomial distribution as a clumping parameter, which is r./J>8 indicate random distribution, and as r./J approaches o the distribution become more aggregated (Davis, 1994; Elliott, 1983). All the parameters, a, j3, and r./J were estimated by using GENMOD procedure in SAS (SAS Institute, 1995) based on an over-dispersed Poisson distribution with a log link function (Park and Cho, 1999). Confidence limits (eL) of 95% for the predicted values were calculated using the formula: (6)
Efficiency of Presence-Absence modeling. Comparisons in efficiency of the PAM method and whole trap counting method were made by evaluating a time cost factor at various density ranges of aphids. The cost was consisted of time spent counting aphids on traps, expressed as hours of labor.
Results During this study, average density of aphid populations
145
was very low level « I aphid per trap) at the beginning of trapping and gradually increased to very high densities in both greenhouses. The highest number of aphids caught per week per trap was 138 for HI greenhouse and 205 for H2 greenhouse. Distribution of Aphids on Trap Surface. In total, 177 sticky traps from two greenhouses were examined to analyze the distribution patterns of aphids on traps using the Morisita I" index (Morisita, 1962). However, after elimination of the traps that contained aphids less than 2 per trap, only 81 traps were useful for the analysis (Table 1). According to equation 1, 10' index from each trap can be estimated where the density was higher than 1 per trap. Value of 18 index ranged from 0.66 to 24.0 in this study. The dispersion analysis showed significant non-random distribution (equation 2, P<0.05) in 75.3% of the 81 traps (Table 1). When the data were grouped by counts ranging from 2 to 10, 11 to 20, and > 20 adults per trap, the counts showed the non-random distribution at least 47.4% of aphids population between the cells in each trap. Although the occurrence of non-random distribution was 47.4% at population densities of 2 to 10 adults per trap in HI greenhouse, the Fisher exact test showed that the proportions of the occurrence of the nonrandom distribution across the various population densities were not significantly different.
Presence-Absence Model Parameter Estimation. Parameter estimates of the presence-absence model in equation 3 were listed in Table 2. The relationship was described as In(Y)=O.98+0.18X for HI greenhouse and In(Y)=O.81 +O.23X, where for H2 greenhouse where Y is the predicted number of aphids per trap, and X is the number of occupied cells. Data sets from each greenhouse were combined based on overlapping of 95% confidence limits and generated a common line for further analysis: In(Yl=O.85+0.21X (Fig. I). Values of r./J were 10.30 for HI greenhouse and 7.72 for H2 greenhouse, indicating random distribution of aphid population between the traps. The munber of occupied cells and estimated natural logarithmic numbers of aphids were described using the common line with 95% confidential limits (Fig. 2). A total of 92.7% in 178 trap cases was within 95% confidence limits of the common mean predicted line. 1110se cases that did not fall within the 95% confidence limits were obtained from the number of occupied cells ranged of 8 - 11 cells (predicted mean estimates ranged of 12.5 - 29.1 per trap). Actual means (independent variable) were regressed with the predicted means (dependent variable) with the 95% predicted limits (Fig. 3). Mean obtained from 170 of the 178 trap cases were within the 95% predicted limits of the estimate means from whole trap
146 J. Asia-Pacific Entornol. VoL 4 (2001)
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Jung-Joon Park, Jong-Kwan Kim, Heungsun Park and Kijong Cho
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Fig. 1. Diagrammatic representation or 24-cell-yellow sticky trap used to sample the winged adult aphids in cucumber greenhouses.
counting. However, in general, the PAM overestimated the mean within the density ranges of 20 - 40 per trap compared to the mean estimates from the whole trap counting.
Efficiency of Presence-Absence Modeling. Efficiency of the PAM method was graphically compared to the whole trap counting method by measuring the time cost for processing (Fig. 4). The time costs in whole trap counting increased linearly as the density of insects increased on traps. This was expected because the time counting the insects was the dominant factor. However, the costs in PAM method were unaffected by the density changes of aphids on traps, and maintained constant at low level. At high density of aphids, the PAM method was superior to the whole counting method in terms of the time cost.
Discussion Accurate estimates of pest and natural enemy population size are necessary to determine timing and rate of natural enemy release in greenhouse biological control programs. Information about greenhouse pest population dynamics could also lessen the need for preventive pesticide application and increase pesticide efficiency through timing applications (Heinz and Parrella, 1991). Therefore, development of accurate and time-efficient sampling methods becomes an essential part ofpest management monitoring program in greenhouses. In this respect, sticky traps can be an integral part of a greenhouse pest management program because using the traps provides an enormously simple and efficient method of detecting early pest infestations and obtaining relative measurements of insect densities in greenhouses. The presence-absence model (equation 3) described
the relationship between the number of occupied cells and the number of insects in a log scale reasonably well because most trap cases were within 95% confidence limits of the predicted model (Fig. 1). However, the method is not robust enough to generalize the relationship because a few outliers occurred at the number of cells occupied ranges over 8 - 11 cells which were an intermediate level of aphids densities (predicted mean estimates ranged of 12.5 - 29.1 per trap). Moreover, the presence-absence method overestimates the mean densities within this range (Fig. 2). This range was experienced frequently during aphids scouting in cucumber greenhouses. Accurate prediction of the mean density within this range is very critical for aphids management in cucwnber greenhouses. At low density « 10 per trap), the efficiency and performance of the cell counting was identical or similar to those of the whole trap counting. Steiner et al. (1999) noted that the time spent examining the trap should be appreciably less than counting individuals, as a maximum of 30 observations need to be made for each pest regardless of the number of individuals present. At high aphids densities (> 100 per trap), it is not important for estimating mean density accurately because this level might be over the economic threshold for aphids in cucumber greenhouses and chemical control should be applied immediately. Nature of the bias between the predicted and the observed mean densities may be explained by two factors. First, the model (equation 3) may contain a high level of heterogeneity in nature and, therefore, this is not robust enough to explain the relationship between the number of occupied cells and estimated natural logarithmic numbers of aphids. Because the estimation of population density based on empirical model such as equation 3 is inherently stochastic process, the bias observed in this study can occur with any density estimation procedures, regardless of the mathematical model selected to represent the
Development of Time-Efficient Sampling Method for Aphids
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Whole Trap Count Estimation
No. of occupied cells Fig. 2. Relationship between the number of occupied cells and the (I n) number of aphids per trap for HI (0) and 112 (e) greenhouses. The grid consisted of 4 X 6 cells each 4cm' laid over the trap face. The dotted lines represent 95% confidence intervals around the predicted equation (solid line) of In(Y) = 0.85 + 0.21X where Y is the predicted number of aphids per trap, and X is the number of occupied cells.
40
Fig. 3. Relationship between the density estimates Irorn the presence-absence model and whole trap counting method Ill!" HI (U) and J·12 (e) greenhouses. The dotted lines represent 95% predicted intervals around the linear regression line (solid line) (y = bx).
80
relationship. In this study, the parameter ¢ from PAM indicated that winged adult aphids population had distributed in random manner between the traps in the greenhouses (Table 2), whereas the dispersion index, Morisita I J' value, showed the non-random distribution (Table 1) between the cells in the traps. These results showed that aphid populations on the cells in the traps are easy to confirm presence or absence (non-random betweenthe cells in the traps) and reduction of the number of the traps to density estimation (random distribution between the traps in the greenhouses). Second, trap size (cell numbers and size in trap) affects the relationship between the numbers of occupied cells and estimated natural logarithmic numbers of aphids. Steiner et al. (1999) mentioned that increasing the cell size would reduce the maximum population density that could be accurately predicted. In opposite case, decreasing the cell size would increase the likelihood of finding an empty cell, so theoretically one could predict more accurately at higher densities. This study demonstrated that the cell-occupiedmethod based on the presence-absence model could be successfully implemented for estimating mean density of aphids in cucumber greenhouses. Care must be exercised to interpret and estimate the mean density from the presence-absence model. To implement this method accurately and efficiently in Korean cucumber greenhouses, the relationship of aphids densities on yellow traps and on cucumber plant must be elucidated. Acknowledgments This research was supported in part by a grant from Rural Development Administration to Kijong Cho and Heungsun Park.
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Literature Cited Cho, K., J.-H. Lee, J.-J. Park, J.-K. Kim and K.-B. Uhm. 2001. Analysis of spatial pattern of Frnkliniella occidentalis (Thysanoptera: Thripidae) on greenhouse cucumbers using dispersion index and spatial autocorrelation. App1. Entomol. Zool. 36: 25-32_ Davis, P. M. 1994. Statistics for describing populations, in Handbook of sampling methods for arthropods in agriculture, Eds. L. P. Pedigo and G. D. Buntin, pp. 33-54. CRC Press, FL. Elliott, J. M. 1983. Some methods for the statistical analysis of samples of Benthic invertebrates. Freshwater Biological Assoc. Sci. Publ. 25. Ambleside, Cambria. Gillespie, D_ R. and D. J. M. Quiring. 1992. Flight behavior of the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) (Homoptera: Aleurodidac), in relation to yellow sticky traps. Can. Entomol. 124: 907-916. Heinz, K. M. and M. P. Parrella. 1991. A shortcut with sticky
148 1. Asia-Pacific Entomol. Vol. 4 (2001)
Jung-Joon Park, Jong-Kwan Kim, Heungsun Park and Kijong Cho
traps. GrowerTalk 8/12: 40-45. Heinz, K. M., M. P. Parrella and 1. P. Newman. 1992. Timeefficient use of yellow sticky traps in monitoring insect populations. J. Econ. Entomol. 85: 2263-2269. Hutcheson, K. and N. I. Lyons. 1989. A significance test for Morisitas index of dispersion and the moments when the population is negative binomial and Poisson, in Estimation and analysis of insect populations, Eds. L. McDonald, B. Manly, J. Lockwood and J. Logan, 335pp. SpringerVerlag, Berlin. McCullagh, P. and J. A. Nelder. 1989. Generalized linear mode!. 2nd ed., 511pp. Chapman and Hall, London. Morisita, M. 1962. J rindex, a measure of dispersion of
individuals. Res. Popu!. Eco!. 6: 1-7. Park, H. and K. Cho. 1999. Variance modeling estimation for sequential sampling in biological control. The 1999 Proceedings of the Section on Statistics and the Environment of the American Statistical Association. 50-52. SAS Institute. 1995. SAS/ST AT users guide for personal computers, version 6.11 ed. SAS Institude, Cary, NC. Sokal, R. R. and F_ J. Rholf. 1981. Biometry. 2nd ed. Freeman, N.V. Steiner, M. Y., L. J_ Spohr, L Barchia and S_ Goodwin. 1999. Rapid estimation of numbers of whiteflies (Hemiptera: Aleurodidae) and thrips (Thysanoptera: Thripidac) on sticky traps. Aust. J. Entomol, 38: 367-372.