- Email: [email protected]
Predator Ratio Test to Tetranychus urticae and Phytoseiulus persimilis System in Greenhouse Roses
J. Asia-Pacific Entomol. 3(2) : 121-126 (2000)
Application of Sequential Classification of Prey I Predator Ratio Test to Tetranychus urticae and Phytoseiulus persimilis System in Greenhouse Roses Jung-Joon Park, Heungsun Park', Yong Heon Klm- and Kijong Cho* Abstract - A sequential sampling procedure for classifying the ratio of prey/predators developed by Nyrop (1988) was examined to implement for a biological control in the greenhouse roses. The procedure was combined with a sequential density classification procedure for use in monitoring a phytophagous mite, Tetranychus urticae Koch, and a phytoseiid predator, Phytoseiulus persimilis Athias-Henriot. This procedure was required four parameters: Means and variances for T. urticae and P persimilis, correlation coefficient between densities of prey and predator and critical ratio of prey and predator. The parameter values used in this study were 0.725 for the correlation coefficient and 10 for the critical ratio. The variances for each species were estimated using a Taylor's power law model. The procedure is proven to be successfully applicable to T. urticae and P persimilis system in greenhouse roses at two action threshold levels of 5 and 10 T. urticae per three-leaflet leave. The limitations and implementationof this procedure is also discussed.
Key Words - Acari, Sampling, Prey/predator ratio, Integrated mite control, Two-spotted spider mite
Introduction The development of insecticide-resistant populations of pests, and environmental concerns have led to increased research in the area of alternative strategies such as a biological control to pesticides for the control of damaging pest populations (Elzen and King, 1999). Augmentation of natural enemies has received considerable attention and has been successfully implemented to suppress pest populations in greenhouse vegetables and flowers (van Lenteren, 1986). A biological control agent alone in the augmentative release often fails to maintain pest density below a commercially acceptable level. In situation where natural enemy can not control pest density successfully, alternative control measures should be applied in time to prevent crop damage. Integrated pest management (IPM) for the green-
house crops should include the successful integration of chemical and biological controls, and also the development of an efficient monitoring program for both natural enemy and pest populations. In a biological control program, the decision of whether or not to apply the control measures will depend on the association of natural enemies with pests. The ratio of prey to natural enemies (R) is often used to determine whether natural enemies are sufficiently abundant to control prey population growth (Croft and Nelson, 1972; Nyrop, 1988). Consideration of R in relation to a critical ratio of prey to predators (CR), and prey density (mN) in relation to action threshold of prey (T), results in four regions into which the prey and predator populations can be jointly classified (see Nyrop, 1988) (Fig. 1). In region 1 where,R
*To whom correspondence should be addressed. E-mail. [email protected]; Tel. 02-3290-3064; Fax. 02-925-1970 Department of Agricultural Biology, College of Natural Resources, Korea University, 1-5 Ka, Anam-Dong, Sungbuk-ku, Seoul 136-701, Korea. 1 Department of Statistics, Hankuk University of Foreign Studies, Yongin, Korea. 2 Department of Agricultural Entomology, National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
-121-
122
-
g
Q
J. Asia-PacificEntomol.
T
CR
-., >.
'iii c: Ql
...o
'lU
-e ~
0..
Prey density (m N )
Fig. 1. Classification regions for biological control strategy sampling of phytophagous mite. T = action threshold for the prey; CR = critical ratio of prey / predators.
necessary. In region 2 where R::;;;, CR and mN::;;;' T, the predators may eventually regulate the prey populations' growth; however, significant plant damage will occur before the prey populations are controlled. In region 3 where and, the ratio of. prey Ipredators can be adjusted by using a selective pesticide so that R::;;;, CR. Finally, in region 4 where R::;;;' CR and mN::;;;' T, a full rate of pesticide is called for to reduce the prey density because the predators are not able to control the growth of prey population. This decision-making system would result in > 40% saving in sample number for a biological control of Panonchus ulmi (Koch) by Typhlodromus pyri (Scheuten) in New York apple orchard (Nyrop, 1988). Nyrop and Werf (1994) presented a FORTRAN computer program that can be used to design and analyze this sampling procedure. The two-spotted spider mite, Tetranychus urticae Koch, is one of the most serious pests of the greenhouse roses (Field and Hoy, 1986). For control of the greenhouse roses with T. urticae infestations, Phytoseiulus persimilis Athias-Henriot has been commonly used because it is highly specialized predator of T. urticae and so reduced pest density quickly (Grafton-Cardwell et al., 1997). Periodic monitoring of T. urticae and P persimilis after releasing P persimilis is necessary to evaluate the result of a biological control program. Because estimation of T. urticae and P persimilis densities can be very time consuming and therefore costly, the sequential
September 2000
sampling methods for use in biological control strategies of T. urticae are desirable in the greenhouse roses (Nyrop , 1988). Binomial sampling program has been developed to estimate population density of T. urticae in greenhouse roses in Korea (Cho et al. 1998). However, there is no information related sampling procedure and management guidelines for biological control of T. urticae on greenhouse roses in Korea. In this paper, we presented the application of a sequential classification ratio test developed by Nyrop (1988) for a biological control of T. urticae using P persimilis in rose greenhouses.
Materials and Methods Sequential classification for prey/predator ratio A computer program written by Nyrop and Werf (1994) was used to develop the sequential classification ratio test (SCRT) for T. urticae and P persimilis in greenhouse roses. Formulae for constructing SCRT have been described fully by Nyrop (1988) and will not be repeated in detail here. Stop limits for prey and predator sequential sampling plan are comprised of two sets of stop limits; one set with which the estimated prey density is compared and the other set with which prey I predator ratio is compared. Stop limits for the prey were developed using the method of Iwao (1975) based on the confidence limit about the action threshold. Stop limits for the ratio of prey to predators (R) are based on estimated confidence limits for R (Cochran, 1977):
,[(1- Z2a/2CND) ± Za/2f{CN+CD(l-z2 - 2CNd- z2 d CNCD- C2ND1] CD) a
a12
(1)
In this equation, the subscripts Nand D denote the numerator and denominator of the preyl predator ratio. The other values are defined as follows: CN = s2NI(nm2N) , CD =s2D/(nm 2D), CND = PNDsNsDI (nmNmD), S2 = the sample variance, mN and mi, are the estimated means for the prey and predator, PND is an estimated correlation coefficient between the numerator and denominator, r = mN/mD, and Zal2 is a standard normal deviate such that P(Z~Zal2) = a12. Equation 1 was used to account for skewness in estimates of R(r).
Vol. 3, NO.2
J. Asia-Pacific Entomol.
Calculation of the stop limits of the ratio using equation 1 requires that PND, SN, and SD are known or can be written in terms of m», mn, and n: The variance of T. urticae (S2N) and P. persimilis (S2 D) were obtained from sample means and Taylor's power law (TPL) parameters. Taylor (1961) showed that sample variance (S2) is related to mean density (m) such that: S2 = am" and solve for the coefficient parame-ters with linear regression if log transformation is used: log s2=log a+b log m
(2)
The parameter a has been described as a scaling factor related to sample size (Southwood, 1978), and b describes aggregation pattern that b > 1 is aggregated distribution. With PND known and SN, and SD modeled using equation 2, stop lines can be determined for any value of n, the sample size and mN, the prey densities. The sample size and mn are used to determine the upper (L u ) and lower stop limits (LI). Classification decisions are made by comparing the predator density with L; and LI according to the following rules: If ms«. L;., stop samplingand classify R as > CR. If m»> Lu, stop sampling and classify R as < CR. If neither of these conditions is met, take another sample of n observations, calculate the means based on all the observations, and repeat the comparison. When both the ratio and prey density are being classified, stop boundaries for both parameters must be crossed before sampling is terminated.
Monitoring of T. urticae and R persimilis The T. urticae colony was mass-reared on the lima bean seedlings, Phaseolus lunatus L., in the laboratory. The P persimilis colony was obtained from the National Institute of Agricultural Science and Technology, Suwon, Korea. The miniature roses (Rosa hybrida 'Live Wire') had been grown under commercial conditions in greenhouses without any insecticide treatments. Forty plants were laid out a 5 x 8 pattern 'in the greenhouse, which was 5 m 2 (2.5 x 2.0 m) in size. Each plant consisted of ::::10 main stems with a :::: 0.35 m in diameter and ::::0.30 m in height. To allow a free migration of both mite species (T. urticae and P persimilis) between plants, the plants were adjoined each other, spaced 0.1 m apart. Temperature
123
and light source were controlled as 23-28°C and 16 (L) : 8(D), respectively. Humidity was not controlled, but maintained between 45-60% RH throughout the experiment. Ten adult females of T. urticae were placed at the middle stratum, ::::0.2 m height from the ground level, of each plant. The T. urticae populations were allowed to disperse and increase for 3 weeks. Therefore, all stages of T. urticae were existed on the plants. Changes of T. urticae densities (motile stages) were monitored at 2 days intervals from the middle stratum of each plant using a hand-free dual magnifier (x 3.5, Gempler's Inc., USA). On each sampling date, T urticae densities were counted on the underside of 3 three-leaflet leafs by carefully turning the leaf over by rotating the petiole. Consequently the habitat structures for T. urticae were not destructed throughout the experiment. For biological control experiment, five adult P. persimilis were released on the middle stratum of each plant, 3 weeks after infestation of T urticae. Motile stages of P persimilis and T. urticae were counted 10 days after P persimilis were released. Sampling procedures for P persimilis and T. urticae were the same as described above. This study was repeated twice on different dates. In the first experiment, P persimilis was released once, but in the second experiment P. persimilis were released twice 4 days after the first release. Data were recorded as number of motile stage. mites per three-leaflet leaf; mites included both P. persimilis and T. urticae.
Coefficients using in sequential classification ratio test The procedure of SCRT can be illustrated using data for T. urticae and P. persimilis to describe and model PND, SN, SD, and CR. The correlation coefficient between T. urticae and P. persimilis (PND) was set equal to 0.725. This coefficient value was the most observed during this study. Stop limits for classifying R were computed using a z value of 1.28, which approximate 80% of confidence limit. Stop limits for prey densities were computed also using a value of 1.28. The values of SN and SD were estimated using equation 2. The value of CR was set equal to 10.0 because commercial suppliers of P. persimilis recommended the ratio for biological control of T. urticae (Biotactics Inc., CA., USA).
September 2000
J. Asia-Pacific Entomol.
124
Action threshold (T) for TSSM on miniature roses has not been determined. However, in conventional roses grown by pinching method, previous studies have indicated that action thresholds of about 0.5 mite per leaflet to about 10 mites per leaflet should prevent losses in rose quality (Jesiotr, 1978) and yield (Boys and Burbutis, 1972), respectively. We consider these thresholds tentative yet useful until modem thresholds are developed. However, by reason of assumption that roses infested with various T. urticae densities, we used T= 5 and 10 as action thresholds for SCRT per 3-leaflet-Ieaf of miniature roses.
20 16 12 8
t ....lQ
-~
0 0.8
30 25
;;:: lQ
.!! I >.
'iii e
20
Sequential classification for prey/predator ratio The TPL parameters used for the predator/prey ratio classification are a = 9.87 and b = 1.10 for T. urticae, and a =2.11 and b = 1.14 for P. persimilis, respectively. Stop limits of the prey densities and prey/predator ratios at T= 5 and T= 10 are shown in Fig. 3. Vertical lines are compared with the estimated T. urticae density using particular sample sizes, and sloping lines are compared with the estimated predator density using the same sample sizes and the estimated T. urticae density. Each set corresponds to sample size of 40, 60, and 80, with the interval between lines becoming narrower as the sample size increases. The thick lines in the center of both sets of stop limits are used if the maximum
litV
--
tV 1lI .~
0
>.
'iii
0.2
tV
I-:
-
.!!
""i
10 5
S
.IU...
- (")
0.4
15
e
tV
Q
.!!!
Q
Change of T. urticae and P. persimilis densities The density of T. urticae changed significantly over time in the absence and presence of P. persimilis, In the experiments where P. persimilis released, P. persimilis densities increased rapidly and declined following the decreased of T. urticae (Fig. 2). Regardless of the initial density of P. persimilis, T. urticae densities were declined before reaching 16 mites per 3-leaflet leaf. This result demonstrated that P. persimilis could control T. urticae at a certain level of density. There are ample evidences that P. persimilis population could control T urticae population below some specific density levels (ca. economic level) in greenhouses (Laing and Huffaker, 1969; Takafuji and Chant, 1976; Nachman, 1981).
t tV
0.6
tV
(")
. Results and Discussion
4
0.0 4
25
::::: ,§ ~
tV
Q.
20
3
0.:
15 2 10 5 0 2
4
6
8
10 12 14 16 18 20 22
Day After Monitoring Fig. 2. Mean densities of Tetranychus urticae and Phytoseiulus persimilis per 3-leaflet leaf on miniature rose plants: (A) absence of P. persimilis; (B) and (C) presence of P. persimilis.
sample size is reached by comparing the estimated mean to it. The value of correlation coefficient used in this study was 0.725 because this value was most observed. Positive coefficient value showed that same rate of increase and/or decrease prey and predator densities and this positive coefficient in this study. Nyrop (1988) referred that maximum negative correlation coefficient produce the most conservative stop limits. However, Nachman (1981) showed that various correlation coefficients between T urticae and P. persimilis in the glasshouse cucumbers, which were mostly positive value. Thus, the value of 0.725 used in this study was
Vol. 3, No.2 2.0
J. Asia-Pacific Entomol.
~---. .-'-----,r-----;--r-r--------,
Samplesi:le ---40 - - --60
T=5 1.5
...... : ... 80
1.0
>t 0.5
::= U)
s:::: cP 0.0 Q
2
.!!? :::::
.§
4
6
8
10
3.0 ,---------,-,r;---r---:n-----------,
~
cP Q. 2.5
a.:
-'----------r--'-L..,----l--,..-!-\---,--_
T=10
2.0 1.5 1.0 0.5
0.0
L--_~--'...l------f-..ll..-_____._--_
5
10
15
20
T. urticae Density Fig. 3. Stop lines for Tetranychus urticae and Phytoseiulus persimilis density and prey/predator ratio with two action thresholds (vertical thick line) with a critical ratio of 10 (sloping thick line).
appropriate for T. urticae and P. persimilis system in greenhouse roses. Suppose a sample of 40 sampling units provides T. urticae and P. persimilis density estimates of 3.0 and 0.4, respectively (Fig. 3, T= 5, black circle 1). The T. urticae density is less than the lower stop limit for the threshold-decision; however, P persimilis density lies between the stop limits for the ratio decision. As a result, a decision to stop sampling cannot be made and another sample is taken. With a sample size of 60, suppose T. urticae and P. persimilis density estimates are now 3.8 and 0.7 (Fig. 3, T= 5, black circle 2). The T. urticae density
125
is again < the vertical lower limit for n = 60, and the estimated P. persimilis density is > the diagonal upper stop limit. Sampling is then terminated with the classifications that the T. urticae density is < T and the prey/predator ratio is S CR (region 1 in Fig. 1). This study indicated that the SCRT procedure was proven to be successfully applicable to T. urticae and P. persimilis system in greenhouse roses. The sample sizes simulated in this study were set empirically based on the sampling costs. Although a precise decision making in the SCRT procedure can be obtained with large sample sizes (Fig. 3), it will probably not be of practical used in commercial rose production. If the decision can not be made in a minimal sample of 40, sampling is continued until a conclusion is drawn. In practice, the sampling plan should be truncated after sampling more than 50; if the density relationship of T. urticae and P. persimilis falls in the indecisive zone after 50 samples, a biological control should be considered as unsuccessful and alternative control measures should be applied immediately. Nyrop and Werp (1994) referred that the weakest point of this procedure is that complete enumeration of counts necessary. For many small insect pests such as mites, this is nearly hard work under field situations. Thus SCRT procedure should be improved to the binomial count on sample units (Nyrop and Werp, 1994). However, Nyrop and Werp (1994) said that it is not clear how random variables that correctly model all sources of variation should be generated during simulations to determine performance of SCRT based on binomial count. In addition, the SCRT procedure should be needed critical action thresholds of insect pests and critical prey/ predators ratio. For the implementation of the SCRT procedure in the fields, it should be needed that realistic estimates of the critical density thresholds for target population on various crops. The SCRT procedure reported here can be adjusted using more sound action threshold and the ratio, whenever available in greenhouse roses. The SCRT procedure presented here provides guidelines that can improve the efficacy of the biological control program for T. urticae by P. persimilis in rose greenhouses. In general, biological control practitioners typically have a limited num-
126
J. Asia-Pacific Entomol.
ber of individual agents with which they hope to found populations in as many places as possible as quickly as possible (Mammott et al., 1996). Prior to application of the SCRT procedure for: greenhouse biological control programs, number of releasing sites and number of population per site should be determined reasonably to maximize the overall chance of success. There is evidence that larger releases in a small area can be achieved the local success of biological control (Hopper and Roush, 1993), but releasing all available biological control agents at few sites often failed to lower the pest populations below the commercially acceptable levels for greenhouse crops. This important dilemma in the biological control programs has rarely been addressed in literatures.
Acknowledgement - This research was supported in part by a grant from The Rural Development Administration to Kijong Cho and Heungsun Park.
Literature Cited Boys, F.E. and P.P. Burbutis. 1972. Influence of Phytoseiulus persimilis on populations of Tetranychus turkestani at economic threshold on roses. J. Econ. Entomol. 65: 114116. Cho, K, 1.1. Park, H. Park and Y.H. Kim. 1998. Binomial sampling plan for estimating Tetranychus urticae (Acari: Tetranychidae) populations in glasshouse rose grown by arching method. Korean 1. Appl. Entomol. 37: 151-157 Cochran, W.G. 1977. Sampling techniques. Wiley, New York. Croft, B.A. and E.E. Nelson. 1972. An index to predict efficient interaction of Typholodromus occidentalis in control of Tetranychus meclanieli in Southern California apple trees. 1. Econ. Entomol. 64: 845-850. Elzen, G.W. and E.G. King. 1999. Periodic release and manipulation of natural enemies. pp. 253-270, in Handbook of biological control, Eds. T.S. Bellow and T.W. Fisher. 1046 pp. Academic Press, San Diego. Field, RP. and M.A. Hoy, 1986. Evaluation of genetically improved strains of Metaseiulus occidentalis (Nesbitt) (Acarina: Phytoseiidae) for integrated control of spider mites on roses in greenhouse. Hilgardia 54: 1-31.
September 2000
Grafton-Cardwell, E.E., Y. Ouyang and RA. Striggow. 1997. Predacious mites (Acari: Phytoseiidae) for control of spider mites (Acari: Tetranychidae) in nursery citrus. Environ. Entomol. 26: 121-130. Hopper, KR and R.T. Roush. 1993. Mate finding, dispersal, number released, success of biological control introductions. Ecol. Entomol. 18: 321-331. Iwao, S. 1975. A new method of sequential sampling to classify populations relative to a critical density. Res. Popul. Ecol. 16: 281-288. Jesiotr, LJ. 1978. The injurious effects of the two-spotted spider mite (Tetranychus urticae Koch) on greenhouse roses. Ekol. Pol. 26: 311-318. Laing, J.E. and C.B. Huffaker. 1969. Comparative studies on predation by Phytoseiulus persimilis Athias-Henriot and Metaseiulus occidentais (Nesbitt) (Acari: Phytoseiidae) on populations of Tetranychus urticae Koch (Acari: Tetranychidae). Res. Popul, Ecol. 11: 105-126. Mammott, J., S.V. Fowler, H.M. Harman and L.M. Hayes. 1996. How best to release a biological control agent. pp. 291-296, in Proceedings of the IX International Symposium on Biological Control of Weeds, Eds. V.C. Moranand and J.H. Hoffman, University of Cape Town, Cape Town, South Africa. Nachman, G. 1981. Temporal and spatial dynamics of an acarine predator-prey system. 1. Anim. Ecol. 50: 435-451. Nyrop, J.P. 1988. Sequential classification of prey/predator ratio with application to European red mite (Acari: Tetranychidae) and Typhlodromus pyri (Acari: Phytoseiidae) in New York apple orchards. 1. Econ. Entomol. 81: 14-21. Nyrop, J.P. and W. van der Werf. 1994. Sampling to predict or monitoring biological control, pp. 245-336. in Handbook of sampling methods for arthropods in agriculture, Eds. L.P. Pedigo and G.D. Buntin, CRC press, Boca Raton, FL. Southwood, T.R.E. 1978. Ecological Methods. 2nd ed., Chapman and Hall, London. Takafuji, A. and D.A. Chant. 1976. Comparative studies of the two species of predaceous phytoseiid mites (Acari: Phytoseiidae), with special reference to their responses to the density of their prey. Res. Popul. Ecol. 17: 255-310. Taylor, L.R 1961. Aggregation, variance and the mean. Nature 189: 732-735. van Lenteren, J.e. 1986. Parasitoids in greenhouse: Successes with seasonal inoculative release systems. pp. 341-374, in Insect parasitoids, Eds. J.K Waage and D.J. Greathead, Academic Press, New York.
(Received July 15, 2000; Accepted August 14, 2000)
Application of Sequential Classification of Prey I Predator Ratio Test to Tetranychus urticae and Phytoseiulus persimilis System in Greenhouse Roses Jung-Joon Park, Heungsun Park', Yong Heon Klm- and Kijong Cho* Abstract - A sequential sampling procedure for classifying the ratio of prey/predators developed by Nyrop (1988) was examined to implement for a biological control in the greenhouse roses. The procedure was combined with a sequential density classification procedure for use in monitoring a phytophagous mite, Tetranychus urticae Koch, and a phytoseiid predator, Phytoseiulus persimilis Athias-Henriot. This procedure was required four parameters: Means and variances for T. urticae and P persimilis, correlation coefficient between densities of prey and predator and critical ratio of prey and predator. The parameter values used in this study were 0.725 for the correlation coefficient and 10 for the critical ratio. The variances for each species were estimated using a Taylor's power law model. The procedure is proven to be successfully applicable to T. urticae and P persimilis system in greenhouse roses at two action threshold levels of 5 and 10 T. urticae per three-leaflet leave. The limitations and implementationof this procedure is also discussed.
Key Words - Acari, Sampling, Prey/predator ratio, Integrated mite control, Two-spotted spider mite
Introduction The development of insecticide-resistant populations of pests, and environmental concerns have led to increased research in the area of alternative strategies such as a biological control to pesticides for the control of damaging pest populations (Elzen and King, 1999). Augmentation of natural enemies has received considerable attention and has been successfully implemented to suppress pest populations in greenhouse vegetables and flowers (van Lenteren, 1986). A biological control agent alone in the augmentative release often fails to maintain pest density below a commercially acceptable level. In situation where natural enemy can not control pest density successfully, alternative control measures should be applied in time to prevent crop damage. Integrated pest management (IPM) for the green-
house crops should include the successful integration of chemical and biological controls, and also the development of an efficient monitoring program for both natural enemy and pest populations. In a biological control program, the decision of whether or not to apply the control measures will depend on the association of natural enemies with pests. The ratio of prey to natural enemies (R) is often used to determine whether natural enemies are sufficiently abundant to control prey population growth (Croft and Nelson, 1972; Nyrop, 1988). Consideration of R in relation to a critical ratio of prey to predators (CR), and prey density (mN) in relation to action threshold of prey (T), results in four regions into which the prey and predator populations can be jointly classified (see Nyrop, 1988) (Fig. 1). In region 1 where,R
*To whom correspondence should be addressed. E-mail. [email protected]; Tel. 02-3290-3064; Fax. 02-925-1970 Department of Agricultural Biology, College of Natural Resources, Korea University, 1-5 Ka, Anam-Dong, Sungbuk-ku, Seoul 136-701, Korea. 1 Department of Statistics, Hankuk University of Foreign Studies, Yongin, Korea. 2 Department of Agricultural Entomology, National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
-121-
122
-
g
Q
J. Asia-PacificEntomol.
T
CR
-., >.
'iii c: Ql
...o
'lU
-e ~
0..
Prey density (m N )
Fig. 1. Classification regions for biological control strategy sampling of phytophagous mite. T = action threshold for the prey; CR = critical ratio of prey / predators.
necessary. In region 2 where R::;;;, CR and mN::;;;' T, the predators may eventually regulate the prey populations' growth; however, significant plant damage will occur before the prey populations are controlled. In region 3 where and, the ratio of. prey Ipredators can be adjusted by using a selective pesticide so that R::;;;, CR. Finally, in region 4 where R::;;;' CR and mN::;;;' T, a full rate of pesticide is called for to reduce the prey density because the predators are not able to control the growth of prey population. This decision-making system would result in > 40% saving in sample number for a biological control of Panonchus ulmi (Koch) by Typhlodromus pyri (Scheuten) in New York apple orchard (Nyrop, 1988). Nyrop and Werf (1994) presented a FORTRAN computer program that can be used to design and analyze this sampling procedure. The two-spotted spider mite, Tetranychus urticae Koch, is one of the most serious pests of the greenhouse roses (Field and Hoy, 1986). For control of the greenhouse roses with T. urticae infestations, Phytoseiulus persimilis Athias-Henriot has been commonly used because it is highly specialized predator of T. urticae and so reduced pest density quickly (Grafton-Cardwell et al., 1997). Periodic monitoring of T. urticae and P persimilis after releasing P persimilis is necessary to evaluate the result of a biological control program. Because estimation of T. urticae and P persimilis densities can be very time consuming and therefore costly, the sequential
September 2000
sampling methods for use in biological control strategies of T. urticae are desirable in the greenhouse roses (Nyrop , 1988). Binomial sampling program has been developed to estimate population density of T. urticae in greenhouse roses in Korea (Cho et al. 1998). However, there is no information related sampling procedure and management guidelines for biological control of T. urticae on greenhouse roses in Korea. In this paper, we presented the application of a sequential classification ratio test developed by Nyrop (1988) for a biological control of T. urticae using P persimilis in rose greenhouses.
Materials and Methods Sequential classification for prey/predator ratio A computer program written by Nyrop and Werf (1994) was used to develop the sequential classification ratio test (SCRT) for T. urticae and P persimilis in greenhouse roses. Formulae for constructing SCRT have been described fully by Nyrop (1988) and will not be repeated in detail here. Stop limits for prey and predator sequential sampling plan are comprised of two sets of stop limits; one set with which the estimated prey density is compared and the other set with which prey I predator ratio is compared. Stop limits for the prey were developed using the method of Iwao (1975) based on the confidence limit about the action threshold. Stop limits for the ratio of prey to predators (R) are based on estimated confidence limits for R (Cochran, 1977):
,[(1- Z2a/2CND) ± Za/2f{CN+CD(l-z2 - 2CNd- z2 d CNCD- C2ND1] CD) a
a12
(1)
In this equation, the subscripts Nand D denote the numerator and denominator of the preyl predator ratio. The other values are defined as follows: CN = s2NI(nm2N) , CD =s2D/(nm 2D), CND = PNDsNsDI (nmNmD), S2 = the sample variance, mN and mi, are the estimated means for the prey and predator, PND is an estimated correlation coefficient between the numerator and denominator, r = mN/mD, and Zal2 is a standard normal deviate such that P(Z~Zal2) = a12. Equation 1 was used to account for skewness in estimates of R(r).
Vol. 3, NO.2
J. Asia-Pacific Entomol.
Calculation of the stop limits of the ratio using equation 1 requires that PND, SN, and SD are known or can be written in terms of m», mn, and n: The variance of T. urticae (S2N) and P. persimilis (S2 D) were obtained from sample means and Taylor's power law (TPL) parameters. Taylor (1961) showed that sample variance (S2) is related to mean density (m) such that: S2 = am" and solve for the coefficient parame-ters with linear regression if log transformation is used: log s2=log a+b log m
(2)
The parameter a has been described as a scaling factor related to sample size (Southwood, 1978), and b describes aggregation pattern that b > 1 is aggregated distribution. With PND known and SN, and SD modeled using equation 2, stop lines can be determined for any value of n, the sample size and mN, the prey densities. The sample size and mn are used to determine the upper (L u ) and lower stop limits (LI). Classification decisions are made by comparing the predator density with L; and LI according to the following rules: If ms«. L;., stop samplingand classify R as > CR. If m»> Lu, stop sampling and classify R as < CR. If neither of these conditions is met, take another sample of n observations, calculate the means based on all the observations, and repeat the comparison. When both the ratio and prey density are being classified, stop boundaries for both parameters must be crossed before sampling is terminated.
Monitoring of T. urticae and R persimilis The T. urticae colony was mass-reared on the lima bean seedlings, Phaseolus lunatus L., in the laboratory. The P persimilis colony was obtained from the National Institute of Agricultural Science and Technology, Suwon, Korea. The miniature roses (Rosa hybrida 'Live Wire') had been grown under commercial conditions in greenhouses without any insecticide treatments. Forty plants were laid out a 5 x 8 pattern 'in the greenhouse, which was 5 m 2 (2.5 x 2.0 m) in size. Each plant consisted of ::::10 main stems with a :::: 0.35 m in diameter and ::::0.30 m in height. To allow a free migration of both mite species (T. urticae and P persimilis) between plants, the plants were adjoined each other, spaced 0.1 m apart. Temperature
123
and light source were controlled as 23-28°C and 16 (L) : 8(D), respectively. Humidity was not controlled, but maintained between 45-60% RH throughout the experiment. Ten adult females of T. urticae were placed at the middle stratum, ::::0.2 m height from the ground level, of each plant. The T. urticae populations were allowed to disperse and increase for 3 weeks. Therefore, all stages of T. urticae were existed on the plants. Changes of T. urticae densities (motile stages) were monitored at 2 days intervals from the middle stratum of each plant using a hand-free dual magnifier (x 3.5, Gempler's Inc., USA). On each sampling date, T urticae densities were counted on the underside of 3 three-leaflet leafs by carefully turning the leaf over by rotating the petiole. Consequently the habitat structures for T. urticae were not destructed throughout the experiment. For biological control experiment, five adult P. persimilis were released on the middle stratum of each plant, 3 weeks after infestation of T urticae. Motile stages of P persimilis and T. urticae were counted 10 days after P persimilis were released. Sampling procedures for P persimilis and T. urticae were the same as described above. This study was repeated twice on different dates. In the first experiment, P persimilis was released once, but in the second experiment P. persimilis were released twice 4 days after the first release. Data were recorded as number of motile stage. mites per three-leaflet leaf; mites included both P. persimilis and T. urticae.
Coefficients using in sequential classification ratio test The procedure of SCRT can be illustrated using data for T. urticae and P. persimilis to describe and model PND, SN, SD, and CR. The correlation coefficient between T. urticae and P. persimilis (PND) was set equal to 0.725. This coefficient value was the most observed during this study. Stop limits for classifying R were computed using a z value of 1.28, which approximate 80% of confidence limit. Stop limits for prey densities were computed also using a value of 1.28. The values of SN and SD were estimated using equation 2. The value of CR was set equal to 10.0 because commercial suppliers of P. persimilis recommended the ratio for biological control of T. urticae (Biotactics Inc., CA., USA).
September 2000
J. Asia-Pacific Entomol.
124
Action threshold (T) for TSSM on miniature roses has not been determined. However, in conventional roses grown by pinching method, previous studies have indicated that action thresholds of about 0.5 mite per leaflet to about 10 mites per leaflet should prevent losses in rose quality (Jesiotr, 1978) and yield (Boys and Burbutis, 1972), respectively. We consider these thresholds tentative yet useful until modem thresholds are developed. However, by reason of assumption that roses infested with various T. urticae densities, we used T= 5 and 10 as action thresholds for SCRT per 3-leaflet-Ieaf of miniature roses.
20 16 12 8
t ....lQ
-~
0 0.8
30 25
;;:: lQ
.!! I >.
'iii e
20
Sequential classification for prey/predator ratio The TPL parameters used for the predator/prey ratio classification are a = 9.87 and b = 1.10 for T. urticae, and a =2.11 and b = 1.14 for P. persimilis, respectively. Stop limits of the prey densities and prey/predator ratios at T= 5 and T= 10 are shown in Fig. 3. Vertical lines are compared with the estimated T. urticae density using particular sample sizes, and sloping lines are compared with the estimated predator density using the same sample sizes and the estimated T. urticae density. Each set corresponds to sample size of 40, 60, and 80, with the interval between lines becoming narrower as the sample size increases. The thick lines in the center of both sets of stop limits are used if the maximum
litV
--
tV 1lI .~
0
>.
'iii
0.2
tV
I-:
-
.!!
""i
10 5
S
.IU...
- (")
0.4
15
e
tV
Q
.!!!
Q
Change of T. urticae and P. persimilis densities The density of T. urticae changed significantly over time in the absence and presence of P. persimilis, In the experiments where P. persimilis released, P. persimilis densities increased rapidly and declined following the decreased of T. urticae (Fig. 2). Regardless of the initial density of P. persimilis, T. urticae densities were declined before reaching 16 mites per 3-leaflet leaf. This result demonstrated that P. persimilis could control T. urticae at a certain level of density. There are ample evidences that P. persimilis population could control T urticae population below some specific density levels (ca. economic level) in greenhouses (Laing and Huffaker, 1969; Takafuji and Chant, 1976; Nachman, 1981).
t tV
0.6
tV
(")
. Results and Discussion
4
0.0 4
25
::::: ,§ ~
tV
Q.
20
3
0.:
15 2 10 5 0 2
4
6
8
10 12 14 16 18 20 22
Day After Monitoring Fig. 2. Mean densities of Tetranychus urticae and Phytoseiulus persimilis per 3-leaflet leaf on miniature rose plants: (A) absence of P. persimilis; (B) and (C) presence of P. persimilis.
sample size is reached by comparing the estimated mean to it. The value of correlation coefficient used in this study was 0.725 because this value was most observed. Positive coefficient value showed that same rate of increase and/or decrease prey and predator densities and this positive coefficient in this study. Nyrop (1988) referred that maximum negative correlation coefficient produce the most conservative stop limits. However, Nachman (1981) showed that various correlation coefficients between T urticae and P. persimilis in the glasshouse cucumbers, which were mostly positive value. Thus, the value of 0.725 used in this study was
Vol. 3, No.2 2.0
J. Asia-Pacific Entomol.
~---. .-'-----,r-----;--r-r--------,
Samplesi:le ---40 - - --60
T=5 1.5
...... : ... 80
1.0
>t 0.5
::= U)
s:::: cP 0.0 Q
2
.!!? :::::
.§
4
6
8
10
3.0 ,---------,-,r;---r---:n-----------,
~
cP Q. 2.5
a.:
-'----------r--'-L..,----l--,..-!-\---,--_
T=10
2.0 1.5 1.0 0.5
0.0
L--_~--'...l------f-..ll..-_____._--_
5
10
15
20
T. urticae Density Fig. 3. Stop lines for Tetranychus urticae and Phytoseiulus persimilis density and prey/predator ratio with two action thresholds (vertical thick line) with a critical ratio of 10 (sloping thick line).
appropriate for T. urticae and P. persimilis system in greenhouse roses. Suppose a sample of 40 sampling units provides T. urticae and P. persimilis density estimates of 3.0 and 0.4, respectively (Fig. 3, T= 5, black circle 1). The T. urticae density is less than the lower stop limit for the threshold-decision; however, P persimilis density lies between the stop limits for the ratio decision. As a result, a decision to stop sampling cannot be made and another sample is taken. With a sample size of 60, suppose T. urticae and P. persimilis density estimates are now 3.8 and 0.7 (Fig. 3, T= 5, black circle 2). The T. urticae density
125
is again < the vertical lower limit for n = 60, and the estimated P. persimilis density is > the diagonal upper stop limit. Sampling is then terminated with the classifications that the T. urticae density is < T and the prey/predator ratio is S CR (region 1 in Fig. 1). This study indicated that the SCRT procedure was proven to be successfully applicable to T. urticae and P. persimilis system in greenhouse roses. The sample sizes simulated in this study were set empirically based on the sampling costs. Although a precise decision making in the SCRT procedure can be obtained with large sample sizes (Fig. 3), it will probably not be of practical used in commercial rose production. If the decision can not be made in a minimal sample of 40, sampling is continued until a conclusion is drawn. In practice, the sampling plan should be truncated after sampling more than 50; if the density relationship of T. urticae and P. persimilis falls in the indecisive zone after 50 samples, a biological control should be considered as unsuccessful and alternative control measures should be applied immediately. Nyrop and Werp (1994) referred that the weakest point of this procedure is that complete enumeration of counts necessary. For many small insect pests such as mites, this is nearly hard work under field situations. Thus SCRT procedure should be improved to the binomial count on sample units (Nyrop and Werp, 1994). However, Nyrop and Werp (1994) said that it is not clear how random variables that correctly model all sources of variation should be generated during simulations to determine performance of SCRT based on binomial count. In addition, the SCRT procedure should be needed critical action thresholds of insect pests and critical prey/ predators ratio. For the implementation of the SCRT procedure in the fields, it should be needed that realistic estimates of the critical density thresholds for target population on various crops. The SCRT procedure reported here can be adjusted using more sound action threshold and the ratio, whenever available in greenhouse roses. The SCRT procedure presented here provides guidelines that can improve the efficacy of the biological control program for T. urticae by P. persimilis in rose greenhouses. In general, biological control practitioners typically have a limited num-
126
J. Asia-Pacific Entomol.
ber of individual agents with which they hope to found populations in as many places as possible as quickly as possible (Mammott et al., 1996). Prior to application of the SCRT procedure for: greenhouse biological control programs, number of releasing sites and number of population per site should be determined reasonably to maximize the overall chance of success. There is evidence that larger releases in a small area can be achieved the local success of biological control (Hopper and Roush, 1993), but releasing all available biological control agents at few sites often failed to lower the pest populations below the commercially acceptable levels for greenhouse crops. This important dilemma in the biological control programs has rarely been addressed in literatures.
Acknowledgement - This research was supported in part by a grant from The Rural Development Administration to Kijong Cho and Heungsun Park.
Literature Cited Boys, F.E. and P.P. Burbutis. 1972. Influence of Phytoseiulus persimilis on populations of Tetranychus turkestani at economic threshold on roses. J. Econ. Entomol. 65: 114116. Cho, K, 1.1. Park, H. Park and Y.H. Kim. 1998. Binomial sampling plan for estimating Tetranychus urticae (Acari: Tetranychidae) populations in glasshouse rose grown by arching method. Korean 1. Appl. Entomol. 37: 151-157 Cochran, W.G. 1977. Sampling techniques. Wiley, New York. Croft, B.A. and E.E. Nelson. 1972. An index to predict efficient interaction of Typholodromus occidentalis in control of Tetranychus meclanieli in Southern California apple trees. 1. Econ. Entomol. 64: 845-850. Elzen, G.W. and E.G. King. 1999. Periodic release and manipulation of natural enemies. pp. 253-270, in Handbook of biological control, Eds. T.S. Bellow and T.W. Fisher. 1046 pp. Academic Press, San Diego. Field, RP. and M.A. Hoy, 1986. Evaluation of genetically improved strains of Metaseiulus occidentalis (Nesbitt) (Acarina: Phytoseiidae) for integrated control of spider mites on roses in greenhouse. Hilgardia 54: 1-31.
September 2000
Grafton-Cardwell, E.E., Y. Ouyang and RA. Striggow. 1997. Predacious mites (Acari: Phytoseiidae) for control of spider mites (Acari: Tetranychidae) in nursery citrus. Environ. Entomol. 26: 121-130. Hopper, KR and R.T. Roush. 1993. Mate finding, dispersal, number released, success of biological control introductions. Ecol. Entomol. 18: 321-331. Iwao, S. 1975. A new method of sequential sampling to classify populations relative to a critical density. Res. Popul. Ecol. 16: 281-288. Jesiotr, LJ. 1978. The injurious effects of the two-spotted spider mite (Tetranychus urticae Koch) on greenhouse roses. Ekol. Pol. 26: 311-318. Laing, J.E. and C.B. Huffaker. 1969. Comparative studies on predation by Phytoseiulus persimilis Athias-Henriot and Metaseiulus occidentais (Nesbitt) (Acari: Phytoseiidae) on populations of Tetranychus urticae Koch (Acari: Tetranychidae). Res. Popul, Ecol. 11: 105-126. Mammott, J., S.V. Fowler, H.M. Harman and L.M. Hayes. 1996. How best to release a biological control agent. pp. 291-296, in Proceedings of the IX International Symposium on Biological Control of Weeds, Eds. V.C. Moranand and J.H. Hoffman, University of Cape Town, Cape Town, South Africa. Nachman, G. 1981. Temporal and spatial dynamics of an acarine predator-prey system. 1. Anim. Ecol. 50: 435-451. Nyrop, J.P. 1988. Sequential classification of prey/predator ratio with application to European red mite (Acari: Tetranychidae) and Typhlodromus pyri (Acari: Phytoseiidae) in New York apple orchards. 1. Econ. Entomol. 81: 14-21. Nyrop, J.P. and W. van der Werf. 1994. Sampling to predict or monitoring biological control, pp. 245-336. in Handbook of sampling methods for arthropods in agriculture, Eds. L.P. Pedigo and G.D. Buntin, CRC press, Boca Raton, FL. Southwood, T.R.E. 1978. Ecological Methods. 2nd ed., Chapman and Hall, London. Takafuji, A. and D.A. Chant. 1976. Comparative studies of the two species of predaceous phytoseiid mites (Acari: Phytoseiidae), with special reference to their responses to the density of their prey. Res. Popul. Ecol. 17: 255-310. Taylor, L.R 1961. Aggregation, variance and the mean. Nature 189: 732-735. van Lenteren, J.e. 1986. Parasitoids in greenhouse: Successes with seasonal inoculative release systems. pp. 341-374, in Insect parasitoids, Eds. J.K Waage and D.J. Greathead, Academic Press, New York.
(Received July 15, 2000; Accepted August 14, 2000)