Transcript Within-Plant Distribution of Twospotted Spider Mite, Tetranychus

Within-Plant Distribution of Twospotted Spider Mite, Tetranychus urticae Koch (Acari: Tetranychidae), on Impatiens:
Development of a Presence-Absence Sampling Plan
F. J. ALATAWI, G. P. OPIT, D. C. MARGOLIES, AND J. R. NECHOLS
Department of Entomology, Kansas State University, Manhattan, Kansas, 66506-4004, [email protected]
ABSTRACT The twospotted spider mite, Tetranychus urticae Koch, is an important pest of impatiens, a floricultural crop of increasing economic importance in the USA. Because of the large amount of foliage on individual impatiens plants,
the small size of twospotted spider mites and the mite’s ability to build high populations quickly on impatiens, a reliable sampling method for T. urticae is required to develop a management program. We were particularly interested in spider
mite counts as the basis for release of biological control agents. Within-plant mite distribution data from greenhouse experiments were used to identify the sampling unit that should be used. Leaves were divided into three categories; inner,
intermediate, and other. On average, 40, 33, and 27% of the leaves belonged to the inner, intermediate, and other leaves categories, respectively. We found that 60% of the mites on a plant were on the intermediate leaves. These results lead
to development of a presence-absence (=binomial) sampling method for T. urticae using generic Taylor coefficients for this pest. By determining numerical or binomial sample sizes for accurately estimating twospotted spider mite
populations, growers will be able to estimate the number of predatory mites that should be released to control twospotted spider mite on impatiens.
INTRODUCTION
STEPS 2
Binomial sampling model
 Taylor’s Power Law describes the distribution of TSM in
the INT category (Fig 2).
• Taylor’s Power Law (Taylor 1961): S2 = amb, where S2 = variance, m = mean,
and “a” and “b” are coefficients;
“a” is largely a sampling factor and “b” an
.
index of aggregation.
• If a regression of ln S2 on ln m yields a significant p-value and a high
coefficient of determination, Taylor’s Power Law can be used to describe the
distribution of TSM in a sample unit category.
• The relationship between the mean number of TSM per INT leaf and the
variance, predicted by Taylor’s power law was highly significant (F = 1027.3;
df = 1, 22; P < 0.0001) (Fig. 2).
ln S2 = 1.21 + 1.32 ln m (R2 = 0.94)
• The value of “b”, the slope , is 1.32 and is significantly different from the
mean value of 1.49 found by Jones (1990)
• This difference may be attributed to the small sample size used to derive the
value of “b” in this study
OBJECTIVES
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5
EXPERIMENTAL DESIGN
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ln(variance)
• Determine the within-plant distribution of TSM on impatiens.
• Use numerical relationships to develop a binomial sampling plan that could
substitute for the more laborious and difficult direct counting method.
Optimal sample size
 Binomial samples are highly recommend to be used for
accurately estimating TSM populations (Fig. 4)
• Optimal numerical sample size, when using presence-absence sampling is
represented by:
n = Za/2 D-2p-1q
{2}
where n = sample size, Za/2 is the upper a/2 of the standard normal
distribution, D is a proportion of m, m is expressed in terms of the number of
TSM on the leaves, p is the proportion of the sampling units infested, q is the
proportion not infested, D = CI/2p is the level of precision, CI is the
confidence interval, and "a" and "b" are Taylor coefficients (Gutierrez 1996,
Karandinos 1976).
• From results above, we can now estimate binomial and numerical sample
sizes for accurately estimating TSM populations (Fig. 4 and Fig. 5)
• At a threshold TSM density of 0, when binomial sampling is used, only 23
leaves would have to be checked ( Fig. 3), while 60 leaves should be
observed when numerical sample is applied .
• However, because the maximum number of TSM per leaf that would have to
be counted is 1 and only 23 leaves are required to estimate density,
• Binomial samples would save growers considerable time and could
increase adoption.
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35
2
30
1
25
Sample size (leaves)
• Developing integrated control of twospotted spider mite, Tetranychus urticae
Koch (TSM), on any crop involves assessing pest distribution and developing
a sampling program for the crop in question.
• Major drawbacks of conventional monitoring procedures for mites are the
tedium, inaccuracy, and time involved with counting tiny individual mites.
• Sampling methods that are easy to use and provide estimates of reasonable
accuracy within a short time are likely to be more successful.
• This particularly is important on impatiens, which have a large amount of
foliage.
• Presence-absence sampling is ideally suited for mites because instead of
counting the individual mites, the number of units (leaves) with mite is
recorded. This is a relatively simple approach and, therefore, one that growers
may be willing to adopt.
• Developing a presence-absence sampling plan for TSM on impatiens
requires: 1) knowing the within-plant distribution of TSM, 2) specifying the
sampling unit, as well as 3) the relationship between the proportion of TSMinfested sampling units and the mean number of TSM on each sampling unit.
STEPS 3
0
STEPS 1
Within-plant TSM distribution
 Intermediate (INT) leaves were chosen as the sampling unit
(Fig. 1).
• At each sampling time, the leaves on each plant were divided into three
categories: Inner (IN), Intermediate (INT), and Other (OT) (Fig. 1)
• On each leaf, TSM in all stages of development but eggs were counted.
• The average percentage of total leaves in IN, INT and OT categories
was 40, 33, and 27%, respectively.
• The average percentage of total TSM was 31, 60, and 9%, respectively.
• For each week, the scatter plot of the total number of mites on each plant
against the mean number of mites on the INT category showed a linear
relationship and the value of R2 was 0.72. 0.88, 0.91, and 0.87.
• This conclusion is supported by the finding that the total number of mites on
a plant can be predicted using mean TSM on the INT category.
• Using the INT leaf as the sampling unit improves the: 1) efficiency and ease
of sampling because the sampling units are similar and easily recognized,
and 2) detection of TSM at low population densities because INT leaves are
the most infested.
Inner
Other
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ln(mean T. urticae)
Fig.2. Relationship between variance and the mean for TSM on impatiens leaves.
 At a tally threshold of 0 mites/leaf, the relationship
between mean number of pests per sampling unit m and
pest-infested sampling units PI was well described by the
binomial model and showed that generic Taylor coefficients
provide good prediction of mean TSM population levels in
impatiens (Fig. 3) .
• A binomial model developed by Wilson and Room (1983) shows the
relationship between the proportion of pest-infested sampling units (PI) and
the mean number of pests per sampling unit (m). It uses the variance-mean
relationship that incorporates Taylor's equation which, in simplified form, can
be expressed as:
In (1-PI) = -m ln (amb-1)/ (amb-1 – 1) {1}
• The fit of the binomial model was evaluated by regressing the right hand side
of equation 1 against the left using our data (Wilson and Morton 1993).
• Jones (1990) verified generic values of a = 3.3 and b = 1.49 for spider mites
under many situations. If the binomial model, with Jones’ generic values of a
and b, describes the relationship between mean number of TSM on INT
leaves and the proportion of these leaves that are infested, we can use the
binomial model to develop a sampling method.
• Tally thresholds of 0, 2 and 5 mites/leaf were tested to determine which
resulted in the best fit. If the model fits well, the intercept should be zero and
the slope equal to one.
• At a tally threshold of 0 mites/leaf, the relationship between m and PI was
described by the binomial model (F =280.8, df = 1, 27; P < 0.0001)
• The intercept was not significantly different from zero (t = 1.41; 27 df; SEM
=0.98; P =0.17).
• However, the slope was significantly different from 1 (t = 16.7; 27 df; SEM
=0.07; P < 0.0001), indicating that the model (equation 1) could be improved
(Fig. 3) by incorporation of only the regression slope.
1.7 ln (1 - PI) = -m ln (amb-1)/ (amb-1 - 1)
Which can be rewritten as :
PI = 1 - e ^ {(-m ln (3.3m0.49)/ (3.3m0.49 - 1)/1.7} (R2 = 0.91)
• Tally thresholds of 2 and 5 also had high R2 values, 0.89 and 0.84,
respectively, indicating they could be used as well.
Proportion of leaves infested (PI)
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Mean number of spider mites/leaf
Fig 4 . Optimal binomial sample size for TSM on impatiens; D = 0.5; α = 0.05
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0
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5
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Mean number of spider mites/leaf
Fig 5. Optimal numerical sample size for TSM on impatiens; D = 0.25 (broken
line) and D = 0.5 (continuous line); α = 0.05.
ACKNOWLEDGMENT
We thank the following individuals from Kansas State University for their
contributions: Kimberly Williams, Kiffnie Holt, Yan Chen, and Xiaoli Wu.
REFERENCES
Gutierrez, A. P. 1996. Sampling in applied population ecology. Pp. 9-26 In
Applied Population Ecology: A Supply-Demand Approach. John Wiley and
Sons, Inc. New York.
Jones, V. P. 1990. Developing sampling plans for spider mites (Acari:
Tetranychidae): Those who don’t remember the past may have to repeat it.
J. Econ. Entomol. 83:1656-1664.
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Karandinos, M. G. 1976. Optimum sample size and comments on some
published formulae. Entomol. Soc. Am. Bull. 22: 417-421.
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.
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Fig. 1. The three categories of leaves: inner, intermediate, and other
10
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Intermediate
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Col 1 vs Col 2
Plot 1 Regr
Sample size (leaves)
• Impatiens cultivar ‘Impulse Orange’.
• Randomized complete block design (RCBD) with two treatments – low and
high TSM densities.
• Four-week-old plants were inoculated with 7 or 13 adult female TSM.
• Weekly after inoculation, 8 plants were destructively sampled.
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Mean number of spider mites per leaf
Fig. 3. Mean number TSM/leaf vs. proportion of leaves infested with TSM
(points). Solid line represents predicted values from modified model of Wilson
et al. (1983) using generic Taylor’s coefficients .
Wilson, L. T. and R. Morton. 1993. Seasonal abundance and distribution of
Tetranychus urticae (Acari: Tetranychidae), the twospotted spider mite, on
cotton in Australia and implications for management. Bull. Entomol. Res.
83:291-303.