WithinPlant Distribution of Twospotted Spider Mite, Tetranychus urticae Koch Acari: Tetranychidae, o

1
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,
fkhalid98_at_hotmail.com
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
mites 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
• 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 TSM-infested sampling
  units and the mean number of TSM on each sampling
  unit.

• STEPS 2
• Binomial sampling model
• Taylors Power Law describes the distribution of
  TSM in the INT category (Fig 2).
• Taylors 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, Taylors 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
  Taylors power law was highly significant (F
  1027.3 df 1, 22 P lt 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

• STEPS 3
• 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.

.

• OBJECTIVES
• 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.

• EXPERIMENTAL DESIGN
• 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.

Fig.2. Relationship between variance and the mean
for TSM on impatiens leaves.

• 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.

Fig 4 . Optimal binomial sample size for TSM on
impatiens D 0.5 a 0.05
Fig 5. Optimal numerical sample size for TSM on
impatiens D 0.25 (broken line) and D 0.5
(continuous line) a 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 dont
remember the past may have to repeat it. J. Econ.
Entomol. 831656-1664. 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. 1250-54. 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. 83291-303.
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 Taylors
coefficients .
Fig. 1. The three categories of leaves inner,
intermediate, and other
2
(No Transcript)