Within-Plant Distribution of Spider Mites (Acari: Tetranychidae) on. Cotton: A Developing Implementable Monitoring Program'. L. T. WILSON.' D. GONZALEZ.
This article is the copyright property of the Entomological Society of America and may not be used for any commercial or other private purpose without specific written permission of the Entomological Society of America.
Within-Plant Distribution of Spider Mites (Acari: Tetranychidae) on Cotton: A Developing Implementable Monitoring Program' L. T. WILSON.' D.GONZALEZ.' T. F. LEIGH.' V . MAGGI.' C. FORISTIERE.'
AND
P. GOODELL'
ABSTRACT Environ. Entoinol. 12: 12-134 (IYX.3)
The vertical distribution of spider mites, 'Fe/rtrr~~c~lrir.s spp.. on cotton is largely deterniincd by the phenological stage o f crop growth. Early i n the season, plants have few leaves a n d thc mites are located close to the mainstem terminal: during the rapid phase of vegetative Frowth. the mites arc located increasingly Farther from the terniinal: as vegetative growth decreases latc i n the se;ison. the mites are again found closer to the terniinal. The small size a n d potcntially large numbcrs of mites found o n cotton nialie conventional monitoring proccdurcs economically impractical. A presence-absence monitoring procedure is presented which relates the proportion of mite-infested niainstcni node leiives at the most infested node to the nuniber of niites per leaf which can in tiirii be converted to an area estimate. An optimized sampling prograni was dcvclopcd by incorporating the spider mite distribution inforination into a presence-absence sequential sampling plan. Sonic monitoring shortcut5 that result in incorrect treatment decisions arc discussed.
The spider mite complex, T e t r c r n y h s spp., consisting of T . pncific~usMcGregor, T . turkesttrtri Ugarov and Nikolski. and T . urticcw Koch, is recognized as a major pest of cotton in the San Joaquin Valley of California. This pest group receives niore pesticide applications than all other cotton arthropod pests combined (Wilson et al. 1981). Mites are~foundin cotton as early as the seedling stage, and when the conditions arc favorable, population density will increase sufficiently to cause a reduction in both yield and lint quality (Canerday and Arant 1964ab. Furr and Pfrimmer 1968. Mistric 1969, Roussel et al. 1951). The potential for mites to cause yield reductions is documented by these authors; however, information is limited on the numerical relationship between mite numbers and yield reduction. The lack of quantitative information is largely due to the absence of an adequate monitoring procedure. Fieldimplementable, quantitative monitoring procedures are available for most of the major insect pests and predators on cotton in California (Allen et al. 1972. Sevacherian and Stern 1972, Wilson and Guticrrez 1980. Wilson et al. 1980, 1981, 1982). Mites, however, can reach high densities (tens of thousands per plant). which combined with their small size makes counting using conventional procedures economically impractical. As a natural consequence of the counting problem and a respectful fear of mite damage, spider mite control in cotton is primarily preventive in nature, and acaricides are often applied at first mite appearance. This unfortunate tactic carries with it increased spraying costs, as well as the serious problem of resurgence and induction of other pests due to predator suppression and an increased potential for pesticide resistance. This paper presents the results of 2 years of field experiments designed to develop a field-implementable monitoring procedure for mite pests of cotton. A concise and easy to use monitoring procedure is prcsentcd. and
optimal sample sizcs are estimated. The major drawbac'k of conventional monitoring procedures for mites, both research and commercial. is the tedium. inaccuracy, and time involved with counting mites. Presence-absence sampling. however. is ideally suited for mites becausc. instead of counting the individual mitcs. the number of units (leaves) with mites is recorded. The proportion of "infested" [P(I)Jleaves can be related back to the corresponding density and a control decision reached (see below). This method has proven useful for a range of cotton arthropods (Hearn et al. 1981, Sterling 1975, 1976. Wilson and Room, in press).
Materials and Methods Information required to develop a quantitative presence-absence sampling plan for mites on cotton is the following: ( I ) structural (leaves, fruits, etc.) distribution and seasonal abundance of mites; (2) seasonal vertical distribution of mites; (3) proportion infested-mean density [P(I)/TI] relationship for mites on leaves, and (4) cost efficiency of the field sampling component. Carey (1980) reported that ca. 6 0 9 of the mites are found on mainstem node leaves. Wilson et al. (1980. 1982) reported similar results for two Lepidoptera pests of cotton, Heliothis zeci (Boddie) and Spodoptercr exigutr (Hübner). and found that the proportion found on mainstem node leaves is closely related to plant density. As a result. sampling for our experiments was limited to mainstem node leaves. The 1980 experiment was conducted to determine the vertical distribution of mites on niainstem leaves. and also to provide information o n the P( IVX relationship. Four replicates of 25 plants were sampled on each of five dates during the season. Ench successive mainstem leaf was placed in a separate bag with replicates scparated. starting with the first partially unfurled niainstem node leaf at thc top of the plant. This involved sampling iis many as 1.500 leaves at a tinic period. Although counting mites is not economically feasible as part of a field monitoring program. these mites were counted to develop the data set from which t o develop the monitoring program. Since counting mites on individual leaves may require in excess of 1 h for each leaf at high dcnI28
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February I983
WIL.SON Lr‘ AI.
SPlDtK
MIT^
sities, a time-saving alternative was developed. The primary problems with counting mites on leaves are the presence of webbing and soil, the presence of veins and folds on the leaves, and mite mobility. A “mitc rinse” machine was developed to overcome these problems. The mite rinse machine circulated the mites in a hypochlorite solution, dissolved the silk, separated the mites from the leaves and silt, and allowed for their rapid counting (all age classes) on a filter paper, using a binocular scope (Leigh. Maggi, and Wilson, unpublished data). The 1981 expcriment was conducted to verify some of the 1980 vertical distribution results, to better define the P(I)/F relationship and to identify potential sources for field sampler error. The first two parts involved weekly sampling of 25 leaves from each of two mainstem nodes in each of several plots. These leaves were examined individually for mites using a binocular scope with females, males, and immatures (not eggs) recorded. In the last part of the expcrimcnt, 20 individual leaves from two or more nodes in each of 20 field plots (0.12 ha each) were examined weekly in the field, using an eyeglass (10 to 15 X ) and recording the presence (or absence) of mites and predators. The plots ranged from untreated to one or two treatments of dicofol or methyl n,-,..it[p’, m . All experiments were conducted at the USDA Cottcr *?-arch Station, Shafter, Calif.
DlSTRlHUTlON O\
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E~AGENCE O D
> 1Z0C FROM PLANTING
F IG. I .-Production of niainstem nodes and the niainstem node distribution of spider mites on cotton:
O 95 O 9C O 7C O 50
Analyses Equation I (Wilson and Room, in press) relates P(1) to ?I by use of a variance-mean density (SVTZ) relationship which incorporates Taylor’s S’/SI equation (S’ = axh) (Taylor 1961, 1965, 1971). p(I) = 1 - e -xlopc ( a b - I)/(uxb- I - 1 ) (1) where a and b are coefficients which describe the relationship between S2 and K and between P(1) and TI. Wilson and Room (in press) illustrated the reliability of equation 1, where the average r’ value for 20 categories of arthropods and plant parts was 0.92 (average of 278 data points, 96 visually inspected whole plants per point). Taylor’s S’/X equation can also be used to estimate confidence intervals given the sample size. Estimates of Taylor’s coefficients are required to develop the functional relationship as indicated by equation. T h e standard procedure for estimating these coefficients is to log transform the S’/X data. log, (S2) = log, (a)
+ b log, (x)
(2)
where the antilog of the resulting intercept is the a coefficient and the slope is the b coefficient. This method has some limitations in that it can overestimate S’ at low densities. To overconic this problem, a and b coefficients were estimated by using an interactive regression program. Results and Discussion Vertictrl Distribution
Figure I illustrates a typical pattern of niainstem node production and the average node of distribution for spi-
FIG. 2.-Phenology
of spider mite density through a season.
der mites. Mites disperse up the plant and as the crop matures the mites are found closer to the terminal. This late-season effect appears to be caused by the rate of mite dispersal exceeding rate of node production. Figure I also indicates that the average distance in nodes of mites from the cotyledon increases with time in a sigmoidal fashion. As mite density begins to increase (Fig. 2) the within-plant, spider mite dispersal rate increases corresponding to the period of rapid boll growth (Fig. 3). decreased leaf nitrogen and increased leaf senescence. The decreased rate of within-plant dispersal late in the season corresponds to a rapid decrease in the mite density and a rapid decrease in vegetative growth. It is likely that mites actively seek out new habitats, particularly when the food source becomes less desirable, such as when crowding causes a nutrient depletion, or due to leaf and crop aging and the concomitant drop in nitrogen (Thompson et al. 1976). Development of a quantitative, field-implementable monitoring program requires a5 a first step that the sampling unit be defined. Sampling all mainstem leaves is impractical, even when only recording the presence of mites and not counting their numbers. Figure I indicates that if the number of mainstem nodes is known then the node most likely to have mites can be predicted. Equation 3 modified from Stinner et al. (1975) and incorporating the rates of node production and mite dispersal among nodes, allows for this node to be identified.
130
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JUNE 30-JuLy I
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Vol. 12. no. I
ENTOMOi-OGY
-
-
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IOkO 1250 14kO 16k Physiological Time (oD)120C from planting)
-
FIG. 3.-The phenology of cotton square and boll production through the season. - I ) * I ( I- / ) W h P -
- I)@I'~"'I
+
r-
JULY 2 9 - 3 0
t
-
AUGUST I I - 14
(3) where NODE = the mainstem node leaf counting from the terminal with the first partially opened leaf as position l . f rch AUGUST 25-26 MSN,,,,, = The number of mainstem nodes at the end of season; z = (1950 - degree days ("D])/1950; and +,, (0.8301), +", (1.8975), k,, (0.8317), and k,,, (0.5041) are empirical constants describing the rate of node pron duction for the plants (p) and the rate of mite (m) dis" I 5 IO 15 20 25 persal among nodes. tterminal tnode of last leaf The "D time scale (threshold of 12°C) accounts for MAINSTEM NODE LEAVES FROM TERMINAL the effect of temperature on developmental rate and withinFIG. 4.-The frequency distribution of spider mites on mainplant dispersal. The major assumption implicit with stem nodes through the season. equation 3 is that the production of nodes stops by 1,950 OD. An appropriate value less than 1,950 OD might be necessary for calculating z, for fields having stress con- 1 more rapidly at higher densities (Fig. SB). Significant ditions promoting premature cessation of vegetative ((Y < 0.05) differences were not found comparing the growth, and the maximum number of mainstem nodes pattern of clumping for mites on the second, sixth, and would be less. Likewise, where excess nitrogen, water, eighth nodes. This consistency was supported by prelimor season length promotes greater than normal growth, inary analyses of the 1980 data which indicated a close these values would be greater. Cotton cultivar may also similarity between the P(I)/x data for all nodes. If deaffect these values. The system has some flexibility, sired, a confidence belt about the estimated curves can since the mites are fairly widespread in their vertical be easily obtained, using standard procedures (Steel and mainstem node distribution (Fig. 4). indicating that being Torrie 1960). off by one or two nodes in location when sampling is Equations 4 and 5 show the P(l)/x relationship for the not that critical. Table I , derived from equation 3, pre- mites before and after the population peak. The preposents the estimated mainstem node having the most mites pulation peak is: sampling at 100-"D intervals through the season from emergence of the seedling for plants having 20 to 30 P(I) = I - e-" Il,pe I6 If, x O SJIil6 I12°C From emergence
~~~~
~
High (High irrigation water or nitrogen)
Normal
20
21
22
23
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25
26
27
28
29
30
3 4 5 6
3 4 5 6
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200 300 400 500 600 700 800 900 I.OOO 1,100 1,200 1.300 1,400
I.500 1,600 1,100 1,800
1,900
7 7 7 6 5 5 4 3 2 2 I
8
Estimation cf Population Densic
Equation 6 modified from Wilson et al. (1982) is the general form of the equation for estimating mite density per m’ from a leaf sample. mite density/m’ = m,-P ‘*y I-ph I (6) where m, = the estimated density of miteshampled mainstem node leaf (node estimated from equation 3); f3 = the proportion of mites on mainstem node leaves which were on the sampled leaves; y = the proportion of total mites which are on mainstem node leaves; p = plants/m-row; and A = spacing between rows (rows per m). In the San Joaquin Valley, a large majority of the cotton is planted on single-row beds at 0.965 or 1.016 rows per m with plant densities ranging from 5 to 15 plants per m-row. Carey (1980) showed that, during a large part of the season, ca. 60% of mites are found on mainstem node leaves. This value is undoubtedly a function of plant density as shown for other cotton pests (Wilson et al. 1980, 1981), but should suffice. The proportion of mites on the mainstem node leaf with the most mites decreases from 7.9% for the 30 June to I July sample to ca. 6% for the last three samples. Very early in the season, when mainstem nodes are scarce, the node with the most mites must have a greater percentage of mites. An extreme case would be that when only cotyledons are present, 100% of the mites which are on leaves must be on cotyledons. Optimal Sample Size
An optimal sample size is the number of sample units which gives a population density estimate with a given
4
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II II II 10
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level of reliability. Wilson and Room (in press) used equation 7 , modified from an equation by Karandinos (1976). =
t 2
d-2
axh-2
(7)
where IZ = sample size, t, is a standard normal variate, d is a proportion of the mean (K), where j7 is expressed in terms of the number of mites on the mainstem node leaf having most mites, and a and b are Taylor’s coefficients (see equhtions 4 and 5 ) . Unfortunately, equation 7 is inappropriate when using presence-absence sampling, but it can be converted into the following form: n = t?,, d-’ 4 P-I (8) where p = I - q [P(l) in equation I ] , but in this case d is a proportion of p. In general, as density increases the presence-absence sample size estimate (equation 8) becomes increasingly greater than the “numerical” sample estimate (equation 7), although for species having a clumped distribution, the sample size curves diverge less rapidly (Wilson and Room, 1983). The error about the population estimate is relevant from a pest management perspective when ( I ) the estimate is used for predictive purposes (population trends), and (2) the population is being evaluated relative to a control decision threshold. In the first case, the reliability of the estimate is less when the number of samples through time being used to make a prediction is smaller. In the second case, the required reliability of the estimate is determined by the difference between the population density and the control decision threshold. This second case falls within the realm of sequential sampling
E N V IR O N M E N T A L ENTOMOI.OGY
132
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1981 COTTON P ( I ) vs Pre 5 August 81
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MEAN NUMBER OF MITES PER LEAF
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020 l-
a:
v
I
ooodOO
1, U
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1L00 2600 32‘00 40100 48100 56100
MEAN NUMBER OF MITES PER LEAF
FIG. 5.-The relationship between the proportion of leaves with mites, sampling the second, sixth, and eighth mainstem nodes, and the number of mites per leaf (a) before and (b) after the population peaked.
(Wald 1945). When the density is far above or below the threshold, fewer sample units are required to determine on which side of the threshold the population lies; conversely, as the density approaches a threshold, a greater number of samples is required. The approach taken in this paper, like conventional sequential sampling, involves the use of a and @ error rates. a = the probability of treating when the population density is below the control decision threshold, and p = the probability of not treating when the population density is above the threshold. In theory, a and @ represent a complex function of the mite density-yield relationship, the market value of the commodity (cotton lint and seed), the cost of the acaricides, and the effect of treatment on secondary pests. When lint prices drop, or damage per a given mite density decreases, or control costs increase, a drops and @ increases. Results are scarce on the relationship between mite density and cotton yield. For use as an example, however, we have chosen a = @ = 0.10. Instead of using two thresholds, as is done with conventional sequential sampling, where one of the thresholds is usually arbitrarily chosen as a percentage of the “real” threshold (see Wald 1945). we used equation 8. which requires only one threshold; and the a and @ error rates, which are used, respectively, to generate the two
Vol. 12, no. I
control decision lincs (scc below). Figure 6 illustrates a hypothetical population of mites through a season. Four cases arc highlighted: ( A ) the population density is far below the threshold. (B) the density is close to but below the threshold. (D) thc density is close to but above the threshold. and (C) the density is Far above the threshold. The four curves are each the probability of a sample estimate differing from the real density by varying amounts. For C and D. thc tail end of the curve which drops below the threshold is the f3 error rate (not treat when treatablc). whereas for A and B the tail end of the curve which rises above the threshold is the a error rate (treat when not treatablc). For the a error ratcs for both A and B to be equal requires that a greater number of samples be taken for B which is closer to thc threshold. The difference between the shape of the A and B curves (A is less peaked) is due to ( 1 ) a difference between the population density at the two times and therefore a different variance-mean ratio. and ( 2 ) the larger number of sample units per sample for B. A similar relationship exists for C and D. The distance between the population density and the threshold (T,) is relevant and is equal to d in equation 7 or 8 when using nunierical or presence-absence sampI ing . respectively . When using sequential sampling for spider mites cotton, the number of samples and the number of mites or leaves infested with mites (equation 7 or 8) are required to make a control decision. Figure 7 shows tentative sequential sampling control decision lines for numerical and presence-absence sampling. respectively, using ca. 5,000 mites per m? as a threshold. This level is equivalent to about 40 mites per leaf. 92% infested when the plants have about 12 or more mainstem nodes. Compared with conventional sequential sampling control decision lines which are parallel, these lines diverge and are curved. The difference is due to our using one threshold instead of two, and using a dynamic instead of a static variance-mean relationship. These lines will be adjusted as we obtain data to incorporate predator impact on mite population growth potential.
PHYSIOLOGICAL TIME
(OD
) 1 2 O C from planting)
Fic;. 6.-The relationship between mite density, expressed as Z o r P(I). the cuntrol decision threshold, and CY and p error rates.
W ILSON
February 1983
ET AL.: S PIDER
M ITE DISTKIBUTION O N CO.I..I.ON
Field Implementation The utility of presence-absence sequential sampling for spider mites is amply illustrated by comparing the number of mites which must be counted to reach a decision (Fig. 7A) with the number of infested leaves which must be counted (Fig. 7B). The only alternatives to presence-absence sampling of spider mites at this stage are ( I ) counting the mites, which is too labor intensive to allow a reliable sample to be taken and (2) preventative spraying, which carries with it problems of resistance, resurgence, and secondary outbreaks of normally subeconomic-level pests. The amount of time required to take a sequential sample depends bti the control decision threshold, the degree of error (aand p) that is built into the decision process, and the mite density. A sample which is terminated after sampling onc leaf on each of 3000 NUMERICAL SAMPLING n
2500
-
BELOW THRESHOLD)
0
20
IO
30
50
40
~
60
~~
PRESENCE - ABSENCE SAMPLING
3
/
REFERENCES CITED
2
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TREAT (POPULATION EXCEEDS THRESHOL
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> 30
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We thank Rex Friesen, Edie Warkentin, and Andres and Jon Luis Gonzalez for their valuable assistance, and the USDA Cotton Research Station, Shafter, Calif., for the use of land and supporting facilities. This study was funded in part by a contract grant from the UC/IPM Systemwide Program, the EPA/Consortium for Integrated Pest Management Cotton Project, and by the California Planting Cotton Seed Distributors.
50
t
k5
20 plants takes bebyeen IO and 20 min whcn recording mites and predators but only 5 to I O min whcn recording only mites. Predators were commonly recordcd in our program. since they appear to be extrcmcly important in preventing mites from increasing to damaging levels. Sampler error caused by excess haste can reduce the reliability of an estimate. As an example. one of us consistently finished sampling in about half the time as the others. Correspondingly, this person recorded only 42 and 65% of the leaves infested with mites and predators. respectively. (averaged for the season) as compared with those recorded by the other samplers who took more time to examine leaves. The differences were actually greater. since this person appeared to overestimate infested Icaves for mites in the early season by not taking more time to examine the leaves to determine if mites were present on leaves with damage. Our experience shows that mite colonies arc often completely exterminated by predators, especially early in the season. Recording damaged leaves as being infested will inevitably lead to overuse of acaricides. By the end of the season. our "fast sampler" was underestimating infestations by not examining the leaves sufficiently. The use of a monitoring program requires standardization of samplers. A consequence of standardization may in some cases be a slowing of the sampler's speed, due to a more careful examination of the leaves. The advantages of careful sampling (niore reliable treatment status estimates), as well as the decreased time required with the presence-absence mite sampling method, however, far outweigh the disadvantages.
Acknowledgment
NUMBER OF LEAVES SAMPLED
60
I33
20
NO TREAT
(POPULATION BELOW THRESHOLD)
IO
0 0
I
IO
1
I
I
40 50 NUMBER OF LEAVES SAMPLED . 20
30
60
F IG . 7.-Sequential sampling decision rules for mites using (a) numerical and (b) presence-absence sampling.
Allen, J . , D. Gonzalez. and D. V. Gokhale. 1972. Sequential sampling for the bollworm, Heliothis ~ ( 7 . Environ. Entomol. I : 771-780. Canerday, T. D., and F. S. Arant. 1964a. The effect of spider mite populations on yield and quality of cotton. J. Econ. Entomol. 57: 553-556. 1964b. The effect of late season infestations of the strawberry c7tlroiricit.s. on cotton production. spider mite. Trrrrrti~ch~rs Ibid. 57: 931-933. Carey, J. R. 1980. Ecological investigations of the tetranychid mites on cotton. Ph.D. thesis, University of California. Berkeley. 160 pp. Furr, R. E., and T. R . Pfrimmer. 1968. Effects of early-. mid-. and late-season infestations of two-spotted spidermites on the yield of cotton. J . Econ. Entotnol. 61: 14461447, Hearn, A. B., P. 11. Ives, P. M. Room, N. .I. Thomson, and L. T. Wilson. 1981. Computer-based cotton pest management in Aubtralia. Field Crops Res. 4: 321-332.
I34
ENVIRONMENTAL ENTOMOl.OGY
Karandinos, M. G. 1976. Optimum sample size and comments on some published formulae. Entomol. Soc. Am. Bull. 22: 417-421. Mistric, W. J. 1969. Damage by the strawberry spider mite on cotton when infestations commenced at the beginning, middle, and end of the flowering period. J . Econ. Entomol. 62: 192-195. Roussel, J. S., J. C. Weber, L. D. Newsom, and C. E. Smith. 1951. The effect of infestation by the spider mite Sepptanychus rumidus on growth and yield of cotton. Ibid. 44: 523-527. Sevacherian, V., and V. M. Stern. 1972. Sequential sampling plans for lygus bugs in California cotton fields. Environ. Entomol. 1: 704-710. Steel, R. G . D., and J. H. Torrie. 1960. Principles and procedures of statistics. McGraw-Hill Book Co., New York. pp. 481. Sterling, W. L. 1975. Sequential sampling of cotton insect populations. Proc. 1975 Beltwide Cotton Prod. Res. Conf. Natl. Cotton Counc., New Orleans. 1976. Sequential decision plans for the management of cotton arthropods in Southeast Queensland. Aust. J. Ecol. I : 265274. Stinner, R. E., G. D. Butler, J. S. Bacheler, and C. Tuttle. 1975. Simulation of temperature-dependent development in population dynamics models. Can. Entomol. 107: 11671174. Taylor, L. R. 1961. Aggregation, variance and the mean. Nature (London) 189: 732-735.
Vol. 12, no. 1
1965. A natural law for the spatial disposition of insects. Proc. Xllth Int. Cong. Entomol. pp. 396-397. 1971. Aggregation as a species characteristic, pp. 357-377. In G . P. Patil, E. C. Pielou, and W. E. Waters [eds.]. Statistical ecology. Vol. I . Penn. State Univ. Press, Univ. Park. Thompson, A. C., H. C. Lane, J. W. Jones, and J. D. Hesketh. 1976. Nitrogen concentrations of cotton leaves, buds, and bolls in relation to age and nitrogen fertilization. Agron. J. 68: 617-621. Wald, A. 1945. Sequential analysis in inspection and experimentation. Columbia University Press, New York. Wllson, L. T., and A. P. Gutierrez. 1980. Within-plant distribution of predators on cotton: Comments on sampling and predator efficiencies. Hilgardia 48: 1-1 I . Wilson, L. T., and P. M. Room. 1983. Clumping patterns of fruit and arthropods in cotton with implications for binomial sampling. Environ. Entomol. 12: 50-54. Wilson, L. T., A. P. Gutierrez, and T. F. Leigh. 1980. Within-plant distribution of the immatures of Hcliorhis zea (Boddie) on cotton. Hilgardia 48: 12-23. Wilson, L. T., T. F. Leigh, and V. Maggi. 1981. Presenceabsence sampling of spider mite densities on cotton. Calif. Agric. 35: IO. Wilson, L. T., A. P. Gutierrez, and D. B. Hogg. 1982. Within-plant distribution of cabbage looper (Trichoplusia ni Hubner) on cotton: Development of a sampling plan for eggs. Environ. Entomol. I I : 251-254.
