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Spatial Distribution of Thrips in Greenhouse Cucumber and Development of a Fixed-Precision Sampling Plan for Estimating Population Density
1. Asia-Pacific Entomol. 1(2): 163-170 (1998)
Spatial Distribution of Thrips in Greenhouse Cucumber and Development of a Fixed-Precision Sampling Plan for Estimating Population Density Kijong Cho*, Sang Hoon Kang! and Jeang Don LeeAbstract - Dispersion patterns of phytophagous thrips (Thysanoptera: Thripidae) were determined for greenhouse cucumber, Cucumis sativus L., in Cheju-do, Korea, during 1995 and 1996. Thrips populations were sampled using leaf sample, yellow sticky trap and visual estimate concurrently on each sampling date. Frankliniella occidentalis (Pergande) was the most dominant thrips species, accounting 92% of all specimens collected from leaf samples and yellow sticky traps. Dispersion indices generated by Taylor's power law and Iwao's patchiness regression were compared. Generally, Taylor's power law provided better description of mean-variance relationship than did Iwao's patchiness regression with exception of the data from the sticky trap. Slopes and intercepts of Taylor's power law from leaf sample and visual estimate did not differ among thrips species and surveyed greenhouses. A fixed-precision-level of sequential sampling plan was developed using Taylor's power law parameters generated from total number of thrips in leaf sample and visual estimate. This sampling plan for visual estimate was tested with sequential resampling simulation using 4 independent data sets. Resampling simulation analysis demonstrated that actual D values were always less than desired D values of 0.20, 0.25 and 0.30.
Key Words - Spatial distribution, Phytophagous thrips, Cucumber,Sequential sampling,Validation
Introduction Greenhouse cucumber, Cucumis sativus L., is one of the major greenhouse vegetable crops in Chejudo, Korea. Aphis gossypii Glove and Trialeurodes vaporariorum (Westwood) have been considered as the major insect pests of greenhouse cucumber. However, Frankliniella occidentalis (Pergande) and Thrips palmi Karny were recently introduced from the abroad and are now widespread in floricultural and vegetable crops in Korea. These species of thrips gained the status of major pests of greenhouse-grown vegetables because of lack of effective control methods. Thrips damage the plants both directly and indirectly (Steiner, 1990; Murai, 1994). Direct damage, caused by feeding punctures, results
in necrosis of leaves. Indirect damage, like fruit malformation and scarring, are of even greater economic importance. The primary control strategy is the application of insecticides and use of crop sanitation as a preventive measure because no effective biological control agents are currently available in Korea. There is little information related to sampling procedures and management guidelines for thrips on greenhouse cucumber. A sampling program is needed for thrips on greenhouse cucumber to improve the timing of control measures, to assist in assessing the effectiveness of these measures and to facilitate establishment of economic threshold values (Shipp and Zariffa, 1991). Binomial sampling program, however, existed for F. occidentalis on greenhouse cucumber in Canada (Steiner, 1990). Although a
* To whom
correspondence should be addressed. Department of Agricultural Biology, College of Natural Resources, Korea University, 1-5 Ka, Anam - Dong, Sungbuk- ku, Seoul 137- 70 I, Korea. I Department of Plant and Environment, Cheju RDA, Cheju Do, Korea. 2 Department of Agricultural Entomology, National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
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binomial sampling program often represents an increase in sampling efficiency compared with sampling plans relying on fixed sample size (Binns and Nyrop, 1992), the need for microscopic examination to differentiate thrips species which have different pest status minimizes the utility of a binomial sampling program (Cho et al., 1995). Sequential sampling plans designed to classify the population density below or above threshold (Wald, 1947) requires fewer resources to make decisions. However, such a plan is currently inappropriate for greenhouse cucumbers in Korea because economic thresholds have not been established for thrips. In this context, a fixed precision sampling plan is appropriate for estimating thrips population densities. The objectives of this study were (1) to determine the distribution characteristics of thrips on greenhouse cucumber, (2) to develop a fixed precision sequential sampling plan using Green's (1970) method and (3) to validate the performance of the developed sampling plan against independent field data sets using a resampling simulation method.
Materials and Methods Study Plot In 1995, monitoring was conducted from April to late July in three commercial greenhouses (greenhouse A, B, and C) owned by different growers but located in the same area, Aewol, Cheju -do, Korea. In 1996, another greenhouse (greenhouse D) was monitored at the same area. Greenhouse ranged in size from 500 to 1026 m-. Three-week-old greenhouse-grown cucumber plants (cv. Maneungchungjung) were transplanted early March in each greenhouse at 1.2 m apart between rows, and plants were spaced at 0.8 m apart within a row. To create conditions for maximum yield production of high-quality fruits, modified vertical cordon training system (Papadopoulos, 1994) was adopted in all greenhouses. Horizontal support wires were positioned directly over the row of plants at a height of 2.0-2.5 m. Initially each plant was trained vertically along and around the support plastic string. Once the plant reached the top supporting wire, the plant was pulled :::::: 0.3 m down regularly and the excessive bottom-stem was
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coiled up at the ground level. Vertical age distributions of cucumber leaves were uniform in the greenhouse because newly expanded leaves always occurred at the top canopy. Excessive leaves on the stem located at the ground level were pruned to promote flower and fruit production. Cucumber maintenance followed by local agronomic practices, including use of insecticides and fertilizers. Pesticides used regularly to suppress or prevent insect pests and plant diseases whenever necessary based on grower's decision.
Sampling Procedures Weekly sampling of thrips began when adult thrips were detected on cucumber leaves and continued until late July. Three sampling methods (leaf sample, sticky trap and visual estimate) were used concurrently to estimate thrips densities on each sampling date. For sampling purpose, thrips life stages were divided into immatures and adults. The division of thrips life stage is a common practice because thrips eggs were imbedded between leaf tissues and most pupal stage occurred in soil (Lewis, 1973). Leaf sampling. Cucumber leaf was taken from middle stratum of the plants because the highest number of thrips was observed at this position during this study (Cho, unpublished data). A whole leaf from the randomly selected plant was immediately placed in a sealable plastic bag (0.5 liter), which then was placed in an ice chest, and returned to laboratory. Each leaf was placed on a 100-mesh soil testing sieve and rinsed thoroughly with running tap water (Cho, 1993). Thrips accumulating in the 100mesh sieve were backwashed with 70% ethanol into a 20 ml scintillation vial and examined under a dissecting microscope. On each sampling date, 10 leaf samples were collected per greenhouse. The sample size was limited because leaf sampling was destructive (i.e., amount of photosynthesis can be reduced for fruits due to reduction of leaf areas). Adult thrips were counted and identified to species level. Because no keys for identifying immatures to species were available, all immatures were combined into a single group. Sticky Traps. Yellow sticky traps were used to monitor airborne thrips in cucumber greenhouses. Cylindrical shaped yellow traps were made by roll-
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ing cards (8.5 X 16 em) coated with Tanglefootv (The Tanglefoot Company, Grand Rapid, MI, USA). Because traps were cylindrical, movement onto the trap in horizontal plane was not influenced by orientation of the traps (Cho, 1993). Twelve of these traps were placed 15 em above the plant canopy throughout the growing season. Traps were replaced weekly and returned to laboratory to the count and identify the thrips. Visual Estimate. For visual estimates of thrips on cucumber leaves, each greenhouse was divided into 55-7~ subplots. Each subplot ranged 8.3-18.7 mand contained 42-55 cucumber plants. A· sample unit in each subplot consisted of one leaf located at the middle stratum of the one randomly selected plant because the highest number of thrips was observed at this position. On each sampling date, thrips densities of adults and immatures were counted from ventral and dorsal sides of the leaf. Identification of thrips species was not attempted for visual estimate because it is not feasible to identify to species level without aid of a dissecting microscope.
Sample Plant Development Mean and variance for each species of adults thrips and immature were calculated for each green house on each sample date for leaf and sticky trap samples. For visual estimates of thrips, mean and variance relationship for adult and immature was calculated separately. Following two methods were used to analyze the count data: Taylor's power law (Taylor, 1961) and Iwao's patchiness regression (Iwao, 1968). Taylor (1961) showed that variance (S2) is related to mean density (m) such that log S2 = a + b log m, where the slope (b) is a measure of aggregation and the intercept (log a) is a scaling factor related to the environment, sampling procedures and sampling unit employed (Southwood, 1978). Iwao's patchiness regression is based on the relationship of Lloyd's index of mean crowding (m*) (Lloyd, 1967) to mean (m) where m*=m+ [(s 2/m)1] and the regression model is m*=a + ~m. The intercept (a) is an index of basic contagion and the slope (~) has the same meaning as in the Taylor's power law. The general linear regression model procedure (PROC GLM) of SAS (SAS Institute, 1992) was
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used to estimate regression parameters. The goodness-of-fit of each linear model (Taylor's power law and Iwao's patchiness regression) was evaluated by estimates of r2 • Student t-tests were used to determine if the slopes of regression lines (b and ~) were significantly different from 1.0. Homogeneity of equalities of slopes and intercepts from Taylor and Iwao regressions for counts of each species and each field were tested with analysis of covariance (ANCOVA) (Sokal and Rholf, 1981). Where no significant differences were detected, data were pooled. . Coefficients from the Taylor's power law regression were used to develop constant-precision-level sampling plans for total number of thrips. The sampling stop line was calculated by the following formula (Green, 1970): log Tn =
log(D 2Ia)
b-2
b-1
+ b-2 log n
where a and b are from the Taylor's regression, Tn=cumulative number of thrips, n=sample size and D=the fixed level of precision in terms of SEMI mean. The levels of precision used in estimating stop lines were 0.20, 0.25, and 0.30. These levels generally represent an acceptable balance between high precision and impractically large sample size, and have been considered acceptable for most pest management applications (Southwood, 1978).
Sample Plan Validation Actual precision levels obtained from a sequential sampling program for visual estimates at specific levels of precision were evaluated by Resampling Validation for Sampling Plans (RVSP) software (Naranjo and Hutchison, 1997) performed on independently collected data sets which were not used in developing the sampling plan. For this purpose, two fields each were surveyed in 1995 and 1996 on 2 separate dates. Sample sizes in these fields varied from 31 to 73 per field per date. RVSP simulation program was used to evaluate the fixed precision stop line plan. Basically, the simulation randomly selected successive samples without replacement from a given data set until the stop line criteria were met. For each desired precision level, the simulation was repeated 500 times for each data set, after which distribution of preci-
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sion values was formulated, and mean precision, mean density and mean sample size value were calculated.
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values from Taylor power law were significantly ) 1 (P< 0.05), indicating that thrips exhibited aggregated dispersion pattern in greenhouse cucumber. Similar distribution for F. occidentalis were reported in greenhouse cucumber (Steiner, 1990) and greenhouse sweet pepper (Shipp and Zariffa, 1991) in Canada. ANCOV4\ indicated that there were no significant differencei between intercepts (a) (F=2.65, df=2, 45, p) 0.10) and slopes (b) (F=1.85, df=2, 45, P) 0.10) of Taylor's power law between thrips species and immatures. Hence, a common Taylor regression can be utilized (Table 1) and was used to model the functional relationship between mean and variance for use in the sequential estimation of Green's (1970) sampling plan. Sticky Trap. Unlike leaf sampling, Iwao's patchiness regression provided better fit than Taylor's power law (Table 2). Coefficients of determinant from Iwao's patchiness regression ranged from 0.94 to 0.99, whereas the values of Taylor's ranged from 0.61 to 0.88. However, both regression methods yielded the inconsistent parameters across all the greenhouses. Results of ANCOVA indicated that the slopes (~) (F=1O.95, df=2, 72, P< 0.01) and intercepts (ex) (F=8.32, df=2, 45, P< 0.001) of Iwao's patchiness regression differed among the thrips species and the greenhouses. Similar result was observed in Taylor's power law regression; the slopes (F=12.25, df=2, 45, P< 0.01) and intercepts (F=9.05, df=2, 45, P< 0.01). These implied that both regression methods were not appropriate to explain the spatial distribution patterns of thrips across thrips species and cucumber greenhouses. Thus, parameter estimates from combined data for each regression procedure were not attempted. This result may be due to the limited size of sticky trap
Result and Discussion Adult thrips accounted for 17.4 and 100% of total thrips collected from leaf samples and sticky traps, respectively, during this study. This low percentage of adult thrips from leaf samples was expected because most adults were likely habituated on the cucumber flowers rather than leaves (Lewis, 1973). Frankliniella occidentalis was the most dominant thrips species contributing 92% of total adult thrips collected from the both sample methods. Thrips tabaci Lindeman, Frankliniella intonsa (Trybom) and Thrips palmi were found in low-intermediate numbers.
Distribution Characteristics Because the numbers of F. intonsa and T. palmi were too low to calculate aggregation indices, only data for F. occidentalis, T. tabaci and immatures were used for dispersion analysis. Leaf Sampling. Taylor's power law generally provided a better description of spatial dispersion of counts of thrips than did Iwao's method (Table 1). The coefficients of determinant (r-) for the Taylor's power law ranged from 0.90 to 0.98, whereas r2 values for Iwao's patchiness regression ranged from 0.67 to 0.99. Also, values of parameters a and b from Taylor's power law were more consistent than those of the ex and ~ of the Iwao's pachiness regression. The b values from Taylor's power law ranged from 1.58 to 1.81, whereas ~ of the Iwao's patchiness regression ranged from 1.37 to 4.93. All b
Table 1. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from leaf samples of thrips from cucumber greenhouses at Aewol, Cheju -do, in 1995-1996 Species
na
F. occidentalis
13 9 13 15
T. tabaci Immature Total a
The number of
Iwao
Taylor loga±SEM
b±SEM
r2
0.39±0.20 0.60±0.18 0.33±0.18 0.04±0.25
1.58±0.21 1.81 ±0.29 1.81 ±0.14 1.80±0.24
0.90 0.98 0.96 0.88
x and S2 or m* pairs used to calculate the regression statistics
a±SEM 4.38±3.81 -0.88±0.46 3.36±6.29 2.83±3.51
~±SEM
r2
Density range per leaf
l.37±0.38 4.93±0.35 1.95±0.08 1.40±0.27
0.67 0.99 0.99 0.76
0.40- 12.67 0.20- 6.75 3.94-114.66 7.80-123.01
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Table 2. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from sticky trap catches of thrips from cucumber greenhouses at Aewol, Cheju-do, in 1995-1996 Green house A
B
C D
a
b
na
Species b
16 16 16 10 10 23 23 7 7
F.O T.T Total F.O Total EO Total F.O Total
Iwao
Taylor loga±SEM
b±SEM
r2
a±SEM
-0.30±0.85 0.66±0.68 1.27±0.87 -un ±3.17 -1.98±3.16 -3.14± 1.33 -3.47±1.23 -0.18± 1.60 -0.80±2.23
1.57±0.22 1.29±0.18 1.22±0.19 1.95±0.59 1.99±0.58 2.20±0.21 2.24±0.20 1.63±0.29 1.75 ±0.41
0.77 0.79 0.75 0.61 0.63 0.83 0.86 0.88 0.78
-4.53±5.87 7.24±3.94 13.53±6.41 -3.64±41.66 -6.07 ±51.52 -87.21 ±38.13 -96.84±39.86 19.41 ±29.71 31.06±28.06
~±SEM
r2
1.30±0.06 1.03 ±0.03 1.00±0.03 1.29±0.11 1.31 ±0.12 1.36±0.04 1.37±0.04 1.05±0.06 1.04±0.06
0.97 0.99 0.99 0.95 0.94 0.98 0.98 0.99 0.98
x
The number of and S2 or m* pairs used to calculate the regression statistics F. O. : Frankliniella occidentalis, T.T: Thrips tabaci.
Table 3. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from visual estimates of thrips from cucumber leaf located at middle stratum at Aewol, Cheju -do, in 1995-1996 Green house A B
C D Combined
a
na
Stage
5 5 7 7 9 9 7 7 28 28 28
The number of
x and
Taylor
Iwao a±SEM
~±SEM
r2
Density range per leaf
loga±SEM
b±SEM
r2
Adult Immature Adult Immature Adult Immature Adult Immature
0.41 ±0.16 1.07±0.22 0.84±0.13 1.l4±0.50 0.83±0.89 1.04±0.41 0.65±0.15 0.96±0.20
1.65±0.15 1.63±0.1O 1.63±0.13 1.64±0.19 1.38±0.50 1.59±0.1O 1.48±0.09 1.57±0.07
0.96 0.99 0.97 0.93 0.71 0.97 0.97 0.99
-0.19±0.67 2.14± 1.39 0.42±0.68 2.0~ ±6.91 5.48±7.1O 22.55±8.1O 0.75 ±0.51 4.96±3.58
1.68±0.23 1.77±O.lO 1.92±0.26 2.00±0.32 0.99± 1.04 1.l6±0.07 1.l6±0.16 1.34±0.12
0.95 0.99 0.91 0.88 0.11 0.97 0.91 0.96
0.51-5.16 0.25-30.61 2.12-9.76 1.50-42.04 0.29-1.91 23.74-262.67 0.03-3.21 2.10-30.61
Adult Immature Total
0.71 ±0.12 1.08±0.13 0.79±0.1O
1.39±0.08 1.59±0.04 1.62±0.04
0.92 0.98 0.96
0.95± 1.42 1.07±0.13 5.95± 1.64
1.54±0.31 1.59±0.04 1.28±0.03
0.48 0.98 0.96
0.03-9.76 0.25 - 262.67 0.75-272.38
S2
or m* pairs used to calculate the regression statistics
used in this study. During the peak populations of thrips in cucumber greenhouses, the sticky traps might not represent the thrips population level correctly because the traps were nearly saturated with thrips caught. The attractiveness of traps saturated with thrips seems to be reduced. Thrips were saturated on the traps on 6 out of 22 sampling dates during this study. These results did not imply that the importance of sticky traps should be neglected for thrips monitoring in cucumber greenhouse. Sticky traps have been sucessfully used to detect early population of thrips in greenhouse
(Robb, 1989). Robb (1989) reported the intercepts and slopes of Taylor's power law for F. occidentalis were not significantly different among ornamental greenhouses in California. Visual Estimate. Both Taylor's power law and Iwao's patchiness regression fit the data well (Table 3). However, the Taylor's power law generally provided better fit than Iwao's method. The coefficients of determinant for Taylor's power law ranged from 0.71 to 0.99, whereas the values of Iwao's method ranged from 0.11 to 0.99. The lowest r 2 (0.11) occurred with the Iwao's method for adult in green-
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house C where population density was low (0.291.91 per leaf). The inability of the Iwao's regression to satisfactorily describe the dispersal analysis has been reported for thrips (Cho et al., 1995) and other insects (Zehnder and Trumble, 1985; Pena and Duncan, 1992). All b values from Taylor power law were significantly) 1 (P< 0.05) indicating that both of adult and immature thrips exhibited aggregated dispersion in cucumber greenhouses. Results from Taylor's power law and Iwao's regression did not agree for adult and immature of greenhouse C and adult of greenhouse D (Table 3). Taylor's power law indicated the aggregated distributions, whereas Iwao's method indicated random distributions. The inconsistency of ~ values with Iwao's patchiness regression was reported for thrips in staked tomatoes (Cho et al., 1995). ANCOVA for Taylor power law regression indicated that slopes (F=5.23, df=3, 45, p) 0.10) and intercepts (F=1.26, df=3, 45, p) 0.10) for adult did not differ across the greenhouses. Results of ANCOVA for immature showed no significant differences of slopes (F=2.65, df=3, 45, p) 0.10) and intercepts (F=1.85, df=2, 45, P) 0.10) across the greenhouses. Further ANCOVA comparing the slopes and intercepts for adult and immature indicated no significant differences among slopes(F= 1.78, df =2, 45, p) 0.10) and intercepts (F=1.02, df=3, 45, p) 0.10). Thus, data from adult and immature were combined, and a common Taylor regression lines was generated across the greenhouses to develop sequential sampling stop lines.
Sequential Sample Plan Fixed-precision-level sampling stop lines were constructed using Green's (1970) plan and the parameters of Taylor's power law for leaf sample (Table 1) and visual estimate (Table 3) at D=0.20, 0.25 and 0.30 (Fig. 1). In a sample plan using these stop lines, samples are taken sequentially until cumulative number of thrips exceeds the stop line values for a given number of samples taken. The stop lines clearly reflect the higher cumulative thrips count (Tn) and corresponding sample size (n) required as the desired precision level increased from D=0.30 to D=0.20. For leaf samples, Green's plan for D=0.20, 0.25
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250 ,..-----1:----t---~,...----------,
(A) 200 ....... Do
~
100
(/)
50
~0.20
- D o ,,::0.25 - D o 0=0.30
150
~ 0.. 10
ci C
250
15
20
25
,..----t---'l----------:-------,
Q)
(B)
>
:i:i ~ :::I
E
30
200
150
:::I
U 100
50
10
15
20
25
30
No. samples (n)
Fig. 1. Sequential sampling stop lines for fixed-precisionlevels (D) of 0.20,0.25 and 0.30 for various thrips densities using leaf sample (A) and visual estimate (B).
and 0.30 would require maximally 17, 11 and 8 samples, respectively, within mean density ranges of thrips catches (7.8-123.0) in this study (Fig. 1). The required number of samples is quite reasonable to estimate thrips density for practical purposes. However, field application of this sampling plan should be limited because destructive leaf sampling method is difficult to be performed in commercial greenhouses. Any reduction in cucumber leaf area reduced the amount of the plant food manufactured, which in turn reduces growth of cucumber plants (Papadopoulos, 1994). The number of samples required for D=0.20 is too high for practical purpose in visual estimates of thrips. A minimum of 65 samples is required at densities < 1 with D=0.20, whereas a minimum of 35 and 25 is required with D=0.25 and D=0.30, respectively. When densities exceed 10, which is a low to moderate level of infestation level in Korea, 14 samples are required with D=0.25. This implies that a sample size of 14 with D=0.25 is sufficient to prevent direct damage because low densities of
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thrips rarely result in damage. An estimate of population density with D=0.25 is considered sufficiently accurate for damage assessment and control studies (Southwood, 1978).
Sample Plan Validation It is important that the developed sample plan should be evaluated in terms of expected performance in the field so that the limit of its utility can be better defined (Naranjo and Hutchison, 1997). To evaluate or validate a sampling plan, Monte Carlo tools and resampling techniques are frequently used in entomology fields (Nyrop and Binns, 1991; Hutchison et al., 1988). Monte Carlo approach is based on specific underlying statistical distributions (e.g., negative binomial and Poisson) that may not mimic the actual sampling distributions of individuals in all instances. In this approach, repeated samples were drawn from a theoretical distribution or some defined stochastic process. In contrast, resampling techniques such as bootstrap simulation are drawing repeated samples, all with a sample size equal to the original number of observation. This method has statistical power where the parametric methods cannot be met (Efron and Tibshirani, 1986). Naranjo and Hutchison (1997) developed RVSP simulation program based on resampling techniques. A sample plan developed for visual. estimates of thrips was examined for the accuracy in predicting variances observed in independent field collected data sets using RVSP simulation. With constraint that leaf sample is destructive, data set in leaf samples were excluded for the simulation because generally expected sequential sample size was higher than our maximum sample size 10 (Fig. 1) (Hutchison, 1994). Four data sets for visual estimates of thrips densities, which represented the ranges from 2.71 to 26.25 per sample, were used for the validation (Table 4). Actual average D values were lower than the desired D values across all density ranges in this study. Similar results were found in staked tomato fields where various thrips species occurred at the same time (Cho et al., 1995). These results indicated that the sampling plan is applicable to the fields where adult and immatures of thrips coexisted. When the densities were> 12, which are a low and moderate thrips infestation level, this sampling
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Table 4. RVSP simulation statistics of 500 sequential sampling simulation runs for visual estimates of thrips on . independent data sets collected in 1995 and 1996 Data set
Simulation
. 11ean Mean Data set" Estimate Desired Mean 1 n .. d . samp . (Year) density precision ensity t : e precision SIze
2111ay (1995)
26.25
56
17 June (1995)
3.93
73
18 April (1996)
11.81 31
911ay (1996)
2.71
31
0.20 0.25 0.30 0.20 0.25 0.30 0.20 0.25 0.30 0.20 0.25 0.30
27.00 26.57 26.56 4.01 3.96 4.11 12.22 12.50 12.24
16 11
10
0.19 0.23 0.24 0.19 0.24 0.30 0.21 0.25 0.29
25 18
0.25 0.30
10
33 22 15 22 14
b
2.71 2.74
"Data sets in 1995 and 1996 were collected from greenhouse Band D, respectively. b Results are not given at D=O.20 because expected sequential sample size was higher than the sample number from the data set.
plan required less than 14 samples at the desired precision level of 0.25. Between densities of 2.71 and 3.93 which are very low densities in cucumber greenhouses, this plan required 25 samples with D=0.25. For management application, the plan based on D=0.25 is recommended because the actual mean precision level was S;;0.25, which is reasonable for pest management purpose (Southwood, 1978). In conclusion, the consistent variance-mean relationship observed in this study showed that thrips were spatially aggregated in cucumber greenhouses. In leaf samples and visual estimates, aggregation indices were similar among thrips species and thrips stage across the surveyed greenhouses. The fixed precision sequential sampling plan developed in this study allows the efficient estimation of thrips population density in cucumber and proves useful for research and management purpose in Korea.
<
Acknowledgment - This research was financed in part by KOSEF Post-Doctoral Fellowship to K. Cho.
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Literature Cited Binns, M.R and J.P. Nyrop. 1992. Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37: 427-453. Cho, K 1993. Population dynamics of thrips (Thysanoptera: Thripidae) in relation to tomatoes in North Carolina. Ph.D. dissertation, North Carolina State University at Raleigh Cho, K, C.S. Eckel, J.F. Walgenbach and G.G. Kennedy. 1995. Spatial distribution and sampling procedures for Frankliniella spp.(Thysanoptera: Thripidae) in staked tomato. J. Econ. Entomol. 88: 1658-1665. Efron, B. and R Tibshirani. 1986. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1: 54-77. Green, RH. 1970. On fixed precision sequential sampling. Res. Popul. Ecol. (Kyoto) 12: 249-251. Hutchison, W.D. 1994. Sequential sampling to determine population density. pp.207-244. In Handbook of sampling methods for arthropods in agricultural pests. Eds. L. Pedigo and G. Buntin. CRC, Boca Raton, FL. Hutchison, W.D., D.B. Hoggs, M.A. Poswal, RC. Berberet and G.W. Cuperus. 1988. Implications of the stochastic nature of Kuno's and Green's fixed-precision stop lines: Sampling plans for the pea aphid (Homoptera: Aphididae) in alfalfa as an example. J. Econ. Entomol. 81: 749-758. Iwao, S. 1968. A new regression method for analyzing the aggregation pattern of animal population. Res. Popul. Ecol. (Kyoto) 10: 1-20. Lewis, T. 1973. Thrips: Their biology, ecology and economic importance. Academic Press, London and New York. Lloyd, M. 1967. Mean crowding. J. Anim. Ecol. 36: 1-30. Murai, T. 1994. Possibility of biological control of thrips by parasitoid, Ceranisus menes. Plant Protection 48: 418-422. Naranjo, S.E. and W.D. Hutchison. 1997. Validation of arthropod sampling plans using a resampling approach: Software and analysis. Am. Entomol. 43: 48-57.
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Nyrop, J.P. and M. Binns. 1991. Quantitative methods for designing and analyzing sampling program for use in pest management, pp. 67 -132. In Handbook of pest management in agriculture, Ed. D. Pimental vol. 2. CRC, Boca Raton, FL. Papdopoulos, A.P. 1994. Growing greenhouse seedless cucumber in soil and in soilless media. Agric. and Agri-Food Canada Pub. 1902/E. Pena, J.E. and R. Duncan. 1992. Sampling methods for Prodiplosis longifila (Diptera: Cecidomyiidae) in limes. Environ. Entomol. 21: 996-1001. Robb, KL. 1989. Analysis of Frankliniella occidentalis (Pergande) as a pest of floricultural crops in California greenhouse. Ph.D. Dissertation. Univ. California at Davis. SAS Institute. 1988. SAS user's guide, 6.03 ed. SAS Institute, Cary, NC. Shipp, J.L. and N. Zariffa. 1991. Spatial patterns and sampling methods for western flower thrips (Thysanoptera: Thripidae) on greenhouse sweet pepper. Can. Entomol. 123: 989-1000. Sokal, RR and F.J. Rohlf. 1981. Biometry. 2nd Ed. Freeman. New York. Southwood, T.RE. 1978. Ecological methods. 2nd Ed. Chapman & Hall, London. Steiner, M.Y. 1990. Determining population characteristics and sampling procedures for western flower thrips (Thysanoptera: Thripidae) and the predatory mite Amblyseiulus cucumeris (Acari: Phytoseiidae) on greenhouse cucumber. Environ. Entomol. 19: 1605-1613. Taylor, L.R 1961. Aggregation, variance and the mean. Nature 189: 732-755. Wald, A 1947. Sequential analysis. Wiley, New York. Zehnder, G.W. and J.T. Trumble. 1985. Sequential sampling plans with fixed levels of precision for Liriomyza (Diptera: Agromyzidae) in fresh. market tomatoes. J. Econ. Entomol. 78: 138-142. (Received March 16, 1998; Accepted July 20,1998)
Spatial Distribution of Thrips in Greenhouse Cucumber and Development of a Fixed-Precision Sampling Plan for Estimating Population Density Kijong Cho*, Sang Hoon Kang! and Jeang Don LeeAbstract - Dispersion patterns of phytophagous thrips (Thysanoptera: Thripidae) were determined for greenhouse cucumber, Cucumis sativus L., in Cheju-do, Korea, during 1995 and 1996. Thrips populations were sampled using leaf sample, yellow sticky trap and visual estimate concurrently on each sampling date. Frankliniella occidentalis (Pergande) was the most dominant thrips species, accounting 92% of all specimens collected from leaf samples and yellow sticky traps. Dispersion indices generated by Taylor's power law and Iwao's patchiness regression were compared. Generally, Taylor's power law provided better description of mean-variance relationship than did Iwao's patchiness regression with exception of the data from the sticky trap. Slopes and intercepts of Taylor's power law from leaf sample and visual estimate did not differ among thrips species and surveyed greenhouses. A fixed-precision-level of sequential sampling plan was developed using Taylor's power law parameters generated from total number of thrips in leaf sample and visual estimate. This sampling plan for visual estimate was tested with sequential resampling simulation using 4 independent data sets. Resampling simulation analysis demonstrated that actual D values were always less than desired D values of 0.20, 0.25 and 0.30.
Key Words - Spatial distribution, Phytophagous thrips, Cucumber,Sequential sampling,Validation
Introduction Greenhouse cucumber, Cucumis sativus L., is one of the major greenhouse vegetable crops in Chejudo, Korea. Aphis gossypii Glove and Trialeurodes vaporariorum (Westwood) have been considered as the major insect pests of greenhouse cucumber. However, Frankliniella occidentalis (Pergande) and Thrips palmi Karny were recently introduced from the abroad and are now widespread in floricultural and vegetable crops in Korea. These species of thrips gained the status of major pests of greenhouse-grown vegetables because of lack of effective control methods. Thrips damage the plants both directly and indirectly (Steiner, 1990; Murai, 1994). Direct damage, caused by feeding punctures, results
in necrosis of leaves. Indirect damage, like fruit malformation and scarring, are of even greater economic importance. The primary control strategy is the application of insecticides and use of crop sanitation as a preventive measure because no effective biological control agents are currently available in Korea. There is little information related to sampling procedures and management guidelines for thrips on greenhouse cucumber. A sampling program is needed for thrips on greenhouse cucumber to improve the timing of control measures, to assist in assessing the effectiveness of these measures and to facilitate establishment of economic threshold values (Shipp and Zariffa, 1991). Binomial sampling program, however, existed for F. occidentalis on greenhouse cucumber in Canada (Steiner, 1990). Although a
* To whom
correspondence should be addressed. Department of Agricultural Biology, College of Natural Resources, Korea University, 1-5 Ka, Anam - Dong, Sungbuk- ku, Seoul 137- 70 I, Korea. I Department of Plant and Environment, Cheju RDA, Cheju Do, Korea. 2 Department of Agricultural Entomology, National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
-163-
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binomial sampling program often represents an increase in sampling efficiency compared with sampling plans relying on fixed sample size (Binns and Nyrop, 1992), the need for microscopic examination to differentiate thrips species which have different pest status minimizes the utility of a binomial sampling program (Cho et al., 1995). Sequential sampling plans designed to classify the population density below or above threshold (Wald, 1947) requires fewer resources to make decisions. However, such a plan is currently inappropriate for greenhouse cucumbers in Korea because economic thresholds have not been established for thrips. In this context, a fixed precision sampling plan is appropriate for estimating thrips population densities. The objectives of this study were (1) to determine the distribution characteristics of thrips on greenhouse cucumber, (2) to develop a fixed precision sequential sampling plan using Green's (1970) method and (3) to validate the performance of the developed sampling plan against independent field data sets using a resampling simulation method.
Materials and Methods Study Plot In 1995, monitoring was conducted from April to late July in three commercial greenhouses (greenhouse A, B, and C) owned by different growers but located in the same area, Aewol, Cheju -do, Korea. In 1996, another greenhouse (greenhouse D) was monitored at the same area. Greenhouse ranged in size from 500 to 1026 m-. Three-week-old greenhouse-grown cucumber plants (cv. Maneungchungjung) were transplanted early March in each greenhouse at 1.2 m apart between rows, and plants were spaced at 0.8 m apart within a row. To create conditions for maximum yield production of high-quality fruits, modified vertical cordon training system (Papadopoulos, 1994) was adopted in all greenhouses. Horizontal support wires were positioned directly over the row of plants at a height of 2.0-2.5 m. Initially each plant was trained vertically along and around the support plastic string. Once the plant reached the top supporting wire, the plant was pulled :::::: 0.3 m down regularly and the excessive bottom-stem was
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coiled up at the ground level. Vertical age distributions of cucumber leaves were uniform in the greenhouse because newly expanded leaves always occurred at the top canopy. Excessive leaves on the stem located at the ground level were pruned to promote flower and fruit production. Cucumber maintenance followed by local agronomic practices, including use of insecticides and fertilizers. Pesticides used regularly to suppress or prevent insect pests and plant diseases whenever necessary based on grower's decision.
Sampling Procedures Weekly sampling of thrips began when adult thrips were detected on cucumber leaves and continued until late July. Three sampling methods (leaf sample, sticky trap and visual estimate) were used concurrently to estimate thrips densities on each sampling date. For sampling purpose, thrips life stages were divided into immatures and adults. The division of thrips life stage is a common practice because thrips eggs were imbedded between leaf tissues and most pupal stage occurred in soil (Lewis, 1973). Leaf sampling. Cucumber leaf was taken from middle stratum of the plants because the highest number of thrips was observed at this position during this study (Cho, unpublished data). A whole leaf from the randomly selected plant was immediately placed in a sealable plastic bag (0.5 liter), which then was placed in an ice chest, and returned to laboratory. Each leaf was placed on a 100-mesh soil testing sieve and rinsed thoroughly with running tap water (Cho, 1993). Thrips accumulating in the 100mesh sieve were backwashed with 70% ethanol into a 20 ml scintillation vial and examined under a dissecting microscope. On each sampling date, 10 leaf samples were collected per greenhouse. The sample size was limited because leaf sampling was destructive (i.e., amount of photosynthesis can be reduced for fruits due to reduction of leaf areas). Adult thrips were counted and identified to species level. Because no keys for identifying immatures to species were available, all immatures were combined into a single group. Sticky Traps. Yellow sticky traps were used to monitor airborne thrips in cucumber greenhouses. Cylindrical shaped yellow traps were made by roll-
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ing cards (8.5 X 16 em) coated with Tanglefootv (The Tanglefoot Company, Grand Rapid, MI, USA). Because traps were cylindrical, movement onto the trap in horizontal plane was not influenced by orientation of the traps (Cho, 1993). Twelve of these traps were placed 15 em above the plant canopy throughout the growing season. Traps were replaced weekly and returned to laboratory to the count and identify the thrips. Visual Estimate. For visual estimates of thrips on cucumber leaves, each greenhouse was divided into 55-7~ subplots. Each subplot ranged 8.3-18.7 mand contained 42-55 cucumber plants. A· sample unit in each subplot consisted of one leaf located at the middle stratum of the one randomly selected plant because the highest number of thrips was observed at this position. On each sampling date, thrips densities of adults and immatures were counted from ventral and dorsal sides of the leaf. Identification of thrips species was not attempted for visual estimate because it is not feasible to identify to species level without aid of a dissecting microscope.
Sample Plant Development Mean and variance for each species of adults thrips and immature were calculated for each green house on each sample date for leaf and sticky trap samples. For visual estimates of thrips, mean and variance relationship for adult and immature was calculated separately. Following two methods were used to analyze the count data: Taylor's power law (Taylor, 1961) and Iwao's patchiness regression (Iwao, 1968). Taylor (1961) showed that variance (S2) is related to mean density (m) such that log S2 = a + b log m, where the slope (b) is a measure of aggregation and the intercept (log a) is a scaling factor related to the environment, sampling procedures and sampling unit employed (Southwood, 1978). Iwao's patchiness regression is based on the relationship of Lloyd's index of mean crowding (m*) (Lloyd, 1967) to mean (m) where m*=m+ [(s 2/m)1] and the regression model is m*=a + ~m. The intercept (a) is an index of basic contagion and the slope (~) has the same meaning as in the Taylor's power law. The general linear regression model procedure (PROC GLM) of SAS (SAS Institute, 1992) was
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used to estimate regression parameters. The goodness-of-fit of each linear model (Taylor's power law and Iwao's patchiness regression) was evaluated by estimates of r2 • Student t-tests were used to determine if the slopes of regression lines (b and ~) were significantly different from 1.0. Homogeneity of equalities of slopes and intercepts from Taylor and Iwao regressions for counts of each species and each field were tested with analysis of covariance (ANCOVA) (Sokal and Rholf, 1981). Where no significant differences were detected, data were pooled. . Coefficients from the Taylor's power law regression were used to develop constant-precision-level sampling plans for total number of thrips. The sampling stop line was calculated by the following formula (Green, 1970): log Tn =
log(D 2Ia)
b-2
b-1
+ b-2 log n
where a and b are from the Taylor's regression, Tn=cumulative number of thrips, n=sample size and D=the fixed level of precision in terms of SEMI mean. The levels of precision used in estimating stop lines were 0.20, 0.25, and 0.30. These levels generally represent an acceptable balance between high precision and impractically large sample size, and have been considered acceptable for most pest management applications (Southwood, 1978).
Sample Plan Validation Actual precision levels obtained from a sequential sampling program for visual estimates at specific levels of precision were evaluated by Resampling Validation for Sampling Plans (RVSP) software (Naranjo and Hutchison, 1997) performed on independently collected data sets which were not used in developing the sampling plan. For this purpose, two fields each were surveyed in 1995 and 1996 on 2 separate dates. Sample sizes in these fields varied from 31 to 73 per field per date. RVSP simulation program was used to evaluate the fixed precision stop line plan. Basically, the simulation randomly selected successive samples without replacement from a given data set until the stop line criteria were met. For each desired precision level, the simulation was repeated 500 times for each data set, after which distribution of preci-
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sion values was formulated, and mean precision, mean density and mean sample size value were calculated.
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values from Taylor power law were significantly ) 1 (P< 0.05), indicating that thrips exhibited aggregated dispersion pattern in greenhouse cucumber. Similar distribution for F. occidentalis were reported in greenhouse cucumber (Steiner, 1990) and greenhouse sweet pepper (Shipp and Zariffa, 1991) in Canada. ANCOV4\ indicated that there were no significant differencei between intercepts (a) (F=2.65, df=2, 45, p) 0.10) and slopes (b) (F=1.85, df=2, 45, P) 0.10) of Taylor's power law between thrips species and immatures. Hence, a common Taylor regression can be utilized (Table 1) and was used to model the functional relationship between mean and variance for use in the sequential estimation of Green's (1970) sampling plan. Sticky Trap. Unlike leaf sampling, Iwao's patchiness regression provided better fit than Taylor's power law (Table 2). Coefficients of determinant from Iwao's patchiness regression ranged from 0.94 to 0.99, whereas the values of Taylor's ranged from 0.61 to 0.88. However, both regression methods yielded the inconsistent parameters across all the greenhouses. Results of ANCOVA indicated that the slopes (~) (F=1O.95, df=2, 72, P< 0.01) and intercepts (ex) (F=8.32, df=2, 45, P< 0.001) of Iwao's patchiness regression differed among the thrips species and the greenhouses. Similar result was observed in Taylor's power law regression; the slopes (F=12.25, df=2, 45, P< 0.01) and intercepts (F=9.05, df=2, 45, P< 0.01). These implied that both regression methods were not appropriate to explain the spatial distribution patterns of thrips across thrips species and cucumber greenhouses. Thus, parameter estimates from combined data for each regression procedure were not attempted. This result may be due to the limited size of sticky trap
Result and Discussion Adult thrips accounted for 17.4 and 100% of total thrips collected from leaf samples and sticky traps, respectively, during this study. This low percentage of adult thrips from leaf samples was expected because most adults were likely habituated on the cucumber flowers rather than leaves (Lewis, 1973). Frankliniella occidentalis was the most dominant thrips species contributing 92% of total adult thrips collected from the both sample methods. Thrips tabaci Lindeman, Frankliniella intonsa (Trybom) and Thrips palmi were found in low-intermediate numbers.
Distribution Characteristics Because the numbers of F. intonsa and T. palmi were too low to calculate aggregation indices, only data for F. occidentalis, T. tabaci and immatures were used for dispersion analysis. Leaf Sampling. Taylor's power law generally provided a better description of spatial dispersion of counts of thrips than did Iwao's method (Table 1). The coefficients of determinant (r-) for the Taylor's power law ranged from 0.90 to 0.98, whereas r2 values for Iwao's patchiness regression ranged from 0.67 to 0.99. Also, values of parameters a and b from Taylor's power law were more consistent than those of the ex and ~ of the Iwao's pachiness regression. The b values from Taylor's power law ranged from 1.58 to 1.81, whereas ~ of the Iwao's patchiness regression ranged from 1.37 to 4.93. All b
Table 1. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from leaf samples of thrips from cucumber greenhouses at Aewol, Cheju -do, in 1995-1996 Species
na
F. occidentalis
13 9 13 15
T. tabaci Immature Total a
The number of
Iwao
Taylor loga±SEM
b±SEM
r2
0.39±0.20 0.60±0.18 0.33±0.18 0.04±0.25
1.58±0.21 1.81 ±0.29 1.81 ±0.14 1.80±0.24
0.90 0.98 0.96 0.88
x and S2 or m* pairs used to calculate the regression statistics
a±SEM 4.38±3.81 -0.88±0.46 3.36±6.29 2.83±3.51
~±SEM
r2
Density range per leaf
l.37±0.38 4.93±0.35 1.95±0.08 1.40±0.27
0.67 0.99 0.99 0.76
0.40- 12.67 0.20- 6.75 3.94-114.66 7.80-123.01
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Table 2. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from sticky trap catches of thrips from cucumber greenhouses at Aewol, Cheju-do, in 1995-1996 Green house A
B
C D
a
b
na
Species b
16 16 16 10 10 23 23 7 7
F.O T.T Total F.O Total EO Total F.O Total
Iwao
Taylor loga±SEM
b±SEM
r2
a±SEM
-0.30±0.85 0.66±0.68 1.27±0.87 -un ±3.17 -1.98±3.16 -3.14± 1.33 -3.47±1.23 -0.18± 1.60 -0.80±2.23
1.57±0.22 1.29±0.18 1.22±0.19 1.95±0.59 1.99±0.58 2.20±0.21 2.24±0.20 1.63±0.29 1.75 ±0.41
0.77 0.79 0.75 0.61 0.63 0.83 0.86 0.88 0.78
-4.53±5.87 7.24±3.94 13.53±6.41 -3.64±41.66 -6.07 ±51.52 -87.21 ±38.13 -96.84±39.86 19.41 ±29.71 31.06±28.06
~±SEM
r2
1.30±0.06 1.03 ±0.03 1.00±0.03 1.29±0.11 1.31 ±0.12 1.36±0.04 1.37±0.04 1.05±0.06 1.04±0.06
0.97 0.99 0.99 0.95 0.94 0.98 0.98 0.99 0.98
x
The number of and S2 or m* pairs used to calculate the regression statistics F. O. : Frankliniella occidentalis, T.T: Thrips tabaci.
Table 3. Comparison of Taylor's power law and Iwao's patchiness regression statistics calculated from visual estimates of thrips from cucumber leaf located at middle stratum at Aewol, Cheju -do, in 1995-1996 Green house A B
C D Combined
a
na
Stage
5 5 7 7 9 9 7 7 28 28 28
The number of
x and
Taylor
Iwao a±SEM
~±SEM
r2
Density range per leaf
loga±SEM
b±SEM
r2
Adult Immature Adult Immature Adult Immature Adult Immature
0.41 ±0.16 1.07±0.22 0.84±0.13 1.l4±0.50 0.83±0.89 1.04±0.41 0.65±0.15 0.96±0.20
1.65±0.15 1.63±0.1O 1.63±0.13 1.64±0.19 1.38±0.50 1.59±0.1O 1.48±0.09 1.57±0.07
0.96 0.99 0.97 0.93 0.71 0.97 0.97 0.99
-0.19±0.67 2.14± 1.39 0.42±0.68 2.0~ ±6.91 5.48±7.1O 22.55±8.1O 0.75 ±0.51 4.96±3.58
1.68±0.23 1.77±O.lO 1.92±0.26 2.00±0.32 0.99± 1.04 1.l6±0.07 1.l6±0.16 1.34±0.12
0.95 0.99 0.91 0.88 0.11 0.97 0.91 0.96
0.51-5.16 0.25-30.61 2.12-9.76 1.50-42.04 0.29-1.91 23.74-262.67 0.03-3.21 2.10-30.61
Adult Immature Total
0.71 ±0.12 1.08±0.13 0.79±0.1O
1.39±0.08 1.59±0.04 1.62±0.04
0.92 0.98 0.96
0.95± 1.42 1.07±0.13 5.95± 1.64
1.54±0.31 1.59±0.04 1.28±0.03
0.48 0.98 0.96
0.03-9.76 0.25 - 262.67 0.75-272.38
S2
or m* pairs used to calculate the regression statistics
used in this study. During the peak populations of thrips in cucumber greenhouses, the sticky traps might not represent the thrips population level correctly because the traps were nearly saturated with thrips caught. The attractiveness of traps saturated with thrips seems to be reduced. Thrips were saturated on the traps on 6 out of 22 sampling dates during this study. These results did not imply that the importance of sticky traps should be neglected for thrips monitoring in cucumber greenhouse. Sticky traps have been sucessfully used to detect early population of thrips in greenhouse
(Robb, 1989). Robb (1989) reported the intercepts and slopes of Taylor's power law for F. occidentalis were not significantly different among ornamental greenhouses in California. Visual Estimate. Both Taylor's power law and Iwao's patchiness regression fit the data well (Table 3). However, the Taylor's power law generally provided better fit than Iwao's method. The coefficients of determinant for Taylor's power law ranged from 0.71 to 0.99, whereas the values of Iwao's method ranged from 0.11 to 0.99. The lowest r 2 (0.11) occurred with the Iwao's method for adult in green-
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house C where population density was low (0.291.91 per leaf). The inability of the Iwao's regression to satisfactorily describe the dispersal analysis has been reported for thrips (Cho et al., 1995) and other insects (Zehnder and Trumble, 1985; Pena and Duncan, 1992). All b values from Taylor power law were significantly) 1 (P< 0.05) indicating that both of adult and immature thrips exhibited aggregated dispersion in cucumber greenhouses. Results from Taylor's power law and Iwao's regression did not agree for adult and immature of greenhouse C and adult of greenhouse D (Table 3). Taylor's power law indicated the aggregated distributions, whereas Iwao's method indicated random distributions. The inconsistency of ~ values with Iwao's patchiness regression was reported for thrips in staked tomatoes (Cho et al., 1995). ANCOVA for Taylor power law regression indicated that slopes (F=5.23, df=3, 45, p) 0.10) and intercepts (F=1.26, df=3, 45, p) 0.10) for adult did not differ across the greenhouses. Results of ANCOVA for immature showed no significant differences of slopes (F=2.65, df=3, 45, p) 0.10) and intercepts (F=1.85, df=2, 45, P) 0.10) across the greenhouses. Further ANCOVA comparing the slopes and intercepts for adult and immature indicated no significant differences among slopes(F= 1.78, df =2, 45, p) 0.10) and intercepts (F=1.02, df=3, 45, p) 0.10). Thus, data from adult and immature were combined, and a common Taylor regression lines was generated across the greenhouses to develop sequential sampling stop lines.
Sequential Sample Plan Fixed-precision-level sampling stop lines were constructed using Green's (1970) plan and the parameters of Taylor's power law for leaf sample (Table 1) and visual estimate (Table 3) at D=0.20, 0.25 and 0.30 (Fig. 1). In a sample plan using these stop lines, samples are taken sequentially until cumulative number of thrips exceeds the stop line values for a given number of samples taken. The stop lines clearly reflect the higher cumulative thrips count (Tn) and corresponding sample size (n) required as the desired precision level increased from D=0.30 to D=0.20. For leaf samples, Green's plan for D=0.20, 0.25
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250 ,..-----1:----t---~,...----------,
(A) 200 ....... Do
~
100
(/)
50
~0.20
- D o ,,::0.25 - D o 0=0.30
150
~ 0.. 10
ci C
250
15
20
25
,..----t---'l----------:-------,
Q)
(B)
>
:i:i ~ :::I
E
30
200
150
:::I
U 100
50
10
15
20
25
30
No. samples (n)
Fig. 1. Sequential sampling stop lines for fixed-precisionlevels (D) of 0.20,0.25 and 0.30 for various thrips densities using leaf sample (A) and visual estimate (B).
and 0.30 would require maximally 17, 11 and 8 samples, respectively, within mean density ranges of thrips catches (7.8-123.0) in this study (Fig. 1). The required number of samples is quite reasonable to estimate thrips density for practical purposes. However, field application of this sampling plan should be limited because destructive leaf sampling method is difficult to be performed in commercial greenhouses. Any reduction in cucumber leaf area reduced the amount of the plant food manufactured, which in turn reduces growth of cucumber plants (Papadopoulos, 1994). The number of samples required for D=0.20 is too high for practical purpose in visual estimates of thrips. A minimum of 65 samples is required at densities < 1 with D=0.20, whereas a minimum of 35 and 25 is required with D=0.25 and D=0.30, respectively. When densities exceed 10, which is a low to moderate level of infestation level in Korea, 14 samples are required with D=0.25. This implies that a sample size of 14 with D=0.25 is sufficient to prevent direct damage because low densities of
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thrips rarely result in damage. An estimate of population density with D=0.25 is considered sufficiently accurate for damage assessment and control studies (Southwood, 1978).
Sample Plan Validation It is important that the developed sample plan should be evaluated in terms of expected performance in the field so that the limit of its utility can be better defined (Naranjo and Hutchison, 1997). To evaluate or validate a sampling plan, Monte Carlo tools and resampling techniques are frequently used in entomology fields (Nyrop and Binns, 1991; Hutchison et al., 1988). Monte Carlo approach is based on specific underlying statistical distributions (e.g., negative binomial and Poisson) that may not mimic the actual sampling distributions of individuals in all instances. In this approach, repeated samples were drawn from a theoretical distribution or some defined stochastic process. In contrast, resampling techniques such as bootstrap simulation are drawing repeated samples, all with a sample size equal to the original number of observation. This method has statistical power where the parametric methods cannot be met (Efron and Tibshirani, 1986). Naranjo and Hutchison (1997) developed RVSP simulation program based on resampling techniques. A sample plan developed for visual. estimates of thrips was examined for the accuracy in predicting variances observed in independent field collected data sets using RVSP simulation. With constraint that leaf sample is destructive, data set in leaf samples were excluded for the simulation because generally expected sequential sample size was higher than our maximum sample size 10 (Fig. 1) (Hutchison, 1994). Four data sets for visual estimates of thrips densities, which represented the ranges from 2.71 to 26.25 per sample, were used for the validation (Table 4). Actual average D values were lower than the desired D values across all density ranges in this study. Similar results were found in staked tomato fields where various thrips species occurred at the same time (Cho et al., 1995). These results indicated that the sampling plan is applicable to the fields where adult and immatures of thrips coexisted. When the densities were> 12, which are a low and moderate thrips infestation level, this sampling
169
Table 4. RVSP simulation statistics of 500 sequential sampling simulation runs for visual estimates of thrips on . independent data sets collected in 1995 and 1996 Data set
Simulation
. 11ean Mean Data set" Estimate Desired Mean 1 n .. d . samp . (Year) density precision ensity t : e precision SIze
2111ay (1995)
26.25
56
17 June (1995)
3.93
73
18 April (1996)
11.81 31
911ay (1996)
2.71
31
0.20 0.25 0.30 0.20 0.25 0.30 0.20 0.25 0.30 0.20 0.25 0.30
27.00 26.57 26.56 4.01 3.96 4.11 12.22 12.50 12.24
16 11
10
0.19 0.23 0.24 0.19 0.24 0.30 0.21 0.25 0.29
25 18
0.25 0.30
10
33 22 15 22 14
b
2.71 2.74
"Data sets in 1995 and 1996 were collected from greenhouse Band D, respectively. b Results are not given at D=O.20 because expected sequential sample size was higher than the sample number from the data set.
plan required less than 14 samples at the desired precision level of 0.25. Between densities of 2.71 and 3.93 which are very low densities in cucumber greenhouses, this plan required 25 samples with D=0.25. For management application, the plan based on D=0.25 is recommended because the actual mean precision level was S;;0.25, which is reasonable for pest management purpose (Southwood, 1978). In conclusion, the consistent variance-mean relationship observed in this study showed that thrips were spatially aggregated in cucumber greenhouses. In leaf samples and visual estimates, aggregation indices were similar among thrips species and thrips stage across the surveyed greenhouses. The fixed precision sequential sampling plan developed in this study allows the efficient estimation of thrips population density in cucumber and proves useful for research and management purpose in Korea.
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Acknowledgment - This research was financed in part by KOSEF Post-Doctoral Fellowship to K. Cho.
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