Full Length Research Paper
Discrimination of the Palomena prasina L. (Heteroptera:
Pentatomidae) nymph stages and sex using some morphological
parameters by the multiple regression analysis
Department of Plant Protection, Faculty of Agriculture,
Ondokuz Mayıs University, 55139 Samsun, Turkey.
Nymph stages and sex.
Accepted 9 December, 2011
Discrimination of different nymphal stages and sex (male and
female) of insects is important in the morphological,
physiological and toxicological studies under laboratory and
field conditions. The morphometric study of different parts
of an insects body is needed to obtain an index to
distinguish between different nymphal stages and sex. In the
hazelnut production area of the Black Sea region, amongst
the sucking type bugs the green shield bug (Palomena
prasina L.) is the most important specie encountered due
to its intensity and economical damage threshold. The study
was aimed to develop modeling of the P. prasina nymph
stages and sex (NSS) using body length and prothorax width.
Eight regression equations were compared for accuracy and
adaptability. The best model developed was as follows: NSS =
0.344 + 0.235W + 0.309L (R² = 0.9882), where NSS is nymph
stages and sex, W
prothorax width (cm) and L is
body length (cm). For validation of the model, estimated
values for NSS showed strong agreement with the measured
values. Therefore, it
concluded that models presented herein may be useful for the
estimation of the individual NSS with a high degree of
Modeling, body length, prothorax width, Palomena prasina,
is in a
Materials and Methods
This study was conducted in the
laboratory of the Plant Protection
Department of Ondokuz Mayıs University,
Faculty of Agriculture in 2010. Eggs
were collected by beating sheet method
from different hazelnut orchards grown
in Samsun province, Turkey.
The nymphs hatched were reared on fresh
seeds of bean (Phaseolus vulgaris
L.). Petri dishes, 9 cm in diameter were
used in the experiments. Distilled
water-saturated filter paper was put in
the bottom of Petri dishes to regulate
the humidity. Fresh bean fruits opened
longitudinally and seeds were provided
for insects to meet nutrition needs of
nymphs in Petri dishes (Çetin and
Karsavuran, 2000). Seeds of common bean
in Petri dishes were changed once every
two days. Petri dishes were checked
every day until the nymphs reached adult
stages. Moulting of nymphs was checked
were used to measure the body
prothorax width of each
stages. A total of 50 individuals from
each biological stage were exposed to
Multiple regression analysis of the data
to develop nymph stages and sex.
The general purpose of multiple
regressions is to learn more about the
relationship between several independent
or predictor variables and a dependent
or criterion variable. Given a data set of n
statistical units, a linear regression
model assumes that the relationship
between the dependent variable yi
and the p-vector of regressors
xi is linear. Thus,
the model takes form
Where ′ denotes the transpose, so that
xi′β is the
inner product between vectors xi
and β. often these n
equations are stacked together and
written in vector form as where
Some remarks on terminology and general
the dependent variable. The
decision as to which variable in a data
set is modelled as the dependent
variable and which are modelled as the
independent variables may be based on a
presumption that the value of one of the
variables is caused by, or directly
influenced by the other variables. are called
independent variables. Usually a
constant is included as one of the
regressors. For example,
we can take for. The
corresponding element of β is
called the intercept. The regressors
xi may be viewed either
as random variables, which we simply
observe, or they can be considered as
predetermined fixed values which we can
be choose. Both interpretations may be
appropriate in different cases, and they
generally lead to the same estimation
procedures; however different approaches
to asymptotic analysis are used in the
two situations. is
a p-dimensional parameter
vector. Its elements are also called
effects, or regression coefficients.
This variable captures all other factors
which influence the dependent variable
yi other than the
regressors . The
relationship between the error term and
the regressors, for example
whether they are correlated as a crucial
step in formulating a linear regression
model, will determine the method to use
for estimation (Erper et al., 2011).The
most common regression equations used to
develop NSS models were evaluated for
accuracy and adaptability. All
equations were composed of various
subsets of independent variables, such
as body length (L) and prothorax width
(W). Eight models were determined and
selected as the most suitable for
estimating NSS of P. prasina. All
variables in the models below were
significant at P = 0.05 level.
is the nymph stages and sex;
the body length (cm);
the prothoax width (cm)
and a, b, and c are the co-efficiencies.
All data was analyzed using the
R-program. Slopes, intercepts and
regression coefficients of the models
were compared using the R-program.
Correlation coefficients were calculated
between measured and estimated data (Cho
et al., 2007; Caliskan et al., 2010a,
b; Celik and Odabas, 2009). SlideWrite
program was used for 3-D graphic.
Table 1. Regression models for the
estimation of nymph stages and sex of
NSS = 0.344 + 0.235 W +
NSS = 0.477 + 0.554 W +
NSS = 0.548 + 0.425 L +
NSS = 1.670 + 0.044 LW
NSS = 2.231 + 0.003 WL2
NSS = 2.119 + 0.005 W2L
NSS = 1.520 + 0.003 L2
+ 0.067 W2
NSS = 2.460 + 0.0003 L2W2
All variables in the models above are
significant at P = 0.05. NSS is growth
stages and sex; L is length; W is width.
Asteriks denote that P<0.0001.
Results and Discussion
Of the all models, body length (L) and prothorax width (W) were selected for estimation of the NSS of P. prasina (Table 1). Equation 1 had a higher R2 value than other equations tested. Table 1 shows that the R² values are ranging between values 0.9882 to 0.8298. Equation 1 (NSS = 0.344 + 0.235W + 0.309L) was found to have the highest R² value (R²= 0.9882) and the lowest R² value (R²= 0.8298) was found Equation 8 (NSS = 2.460 + 0.0003L2W2). The other models can also be used to predicted stages and sex of P. prasina. That is why the researchers can prefer the model whichever they want. Figure 1 shows that both body length and prothorax width were highly related to NSS of P. prasina. Equations with P > 0.05 and lower R2 values were eliminated at the beginning of this study. To estimate the NSS of P. prasina, 8 models using L and W were selected (Table 1). Of the eight models, Equation 1 showed the highest relationship. According to obtained results, body length and prothorax width contribute to accurately discrimi-nation of NSS by the developed software herein. The using of software is very simple. When the researcher entered the data, the program shows the NSS as a number. The numbers are from 1 to 7. The numbers show us the NSS of P. prasina. Number 1 is first instar, number 2 is second instar, number 3 is third instar, number 4 is fourth instar, number 5 is fifth instar, number 6 is male and number 7 is female (Figure 1).
As a result of multi-regression analysis of NSS of P. prasina, the effects of body length and prothorax width were found to be significant. When the prothorax width and body length are increased from 1 to 7 cm, the NSS increase also (Figure 2). Increasing prothorax width and body length effected positively. Prothorax width is more effective than body length for determining NSS (Figure 2). The estimation models that aim to predict the NSS of P. prasina can provide more accurate data to researches in biological studies on heteropteran insect species. Moreover, these kinds of models enable researchers to carry out NSS on the same metamorphoses studies. P. prasina of 1 to 2 and 3, 4, 5 nymphal stages are very diffi-cult to distinguish morphologically. However, their stages can be understood by following the developments in the laboratory. Furthermore, estimation of NSS saves times. There are no published reports related to NSS prediction model for P. prasina. In this study, NSS are well cor-related with body length and prothorax width, with high R2 values (Tables 1). The body size of P. prasina was significant factor in the estimation of NSS. This method was rapid and was relatively accurate. According to the results of the current study, NSS of P. prasina may be estimated by nonlinear regression models including body length and prothorax width.
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