Influence of Different Precipitation Periods on Dendrolimus superans Occurrence: A Biostatistical
Analysis
Zhiru Li1,2, Zhenkun Miao1,2, Xiaofeng Wu1,2*,
Beihang Zhang1,2, Quangang Li1,2, Lizhi Han1,2 and
Jun Wang3
1Harbin
Research Institute of Forestry Machinery, the State Forestry and Grassland Administration,
Harbin, 150086, P.R. China
2Research
Institute of Forestry New Technology, Beijing, 100091, P.R. China
3Forest Pest Control and Quarantine Station of Yichun
City, Yichun 153000, P.R.
China
*For correspondence:
xiaofengwu_hlj@126.com
Received 28 July 2020; Accepted 10 September 2020; Published 10 December
2020
Precipitation is one of the
most important abiotic factors that affect Dendrolimus superans occurrence. In this study, a grey slope-correlation
model was used, and a
simplified grey slope-correlation model was constructed to uncover the most
crucial periods of precipitation that pest occurrence. Results revealed that
the two models were similar;
however, the simplified grey slope-correlation model required less calculative
steps and was easier to operate. The calculation results revealed that the most
crucial period occurred during the
first 10 days of July (γ13
= 0.67, γ`13 = 0.69). The other precipitation periods
associated with pest occurrence
included the first 10 days of August (γ16 = 0.62, γ`16 =
0.61), the third 10 days of May (γ09
= 0.59, γ`09 = 0.62), the sec 10 days of May (γ08 = 0.58, γ`08 =
0.60), and the third 10 days of August (γ18 =
0.58, γ`18 = 0.60). The less associated precipitation
periods included the first 10 days of March (γ01 =
0.54, γ`01 = 0.47), the sec 10 days of March (γ02 = 0.50, γ`02 =
0.49), the third 10 days of April (γ06
= 0.47, γ`06 = 0.48), the sec 10 days of June (γ11 = 0.51, γ`11 =
0.48), and the third 10 days of June (γ12 =
0.51, γ`12 = 0.51). Precipitation in May (γ07 + γ08
+ γ09 = 1.74, γ`07 + γ`08 + γ`09
= 1.79) and July (γ13 + γ14
+ γ15 = 1.74, γ`13 + γ`14 + γ`15
= 1.79) was mostly associated with D. superans occurrence.
The findings of this study provided a simple operative model for determining
the most crucial precipitation periods
of pest occurrence, and these analytical methods can serve as a theoretical
reference for pest forecasting and early warning, which contributes to
ecological protection. © 2021 Friends Science
Publishers
Keywords: Dendrolimus
superans; Grey slope correlation; Occurrence; Precipitation; Simplified model
The occurrence of forest pests, which
is known as the no-smoke forest fire are likely to cause tree die-out,
ecological destruction, and subsequently reduce forest carbon sequestration (Xu
2015). Dendrolimus superans (Butler) is the main leaf-eating insect
found in the northeastern forests of China, which turns tree branches bleak
when its larvae gnaw the leaves (Dang et al. 2018). D. superans can also be found
in other regions under similar latitude and climatic conditions (Kang 2005;
Tomin et al. 2011; Myong et al. 2012). The pests can outbreak
depending on the environment and climatic conditions, and the degree of damage,
spreading direction, and duration of different stages can be forecast by
studying the growth ratio of larvae (Natalia et al. 2009).
The
occurrence of forest pests is a result of many factors, including biological
characteristics, natural enemies, meteorological conditions, site conditions,
and stand structure (Chen et al. 2017). The relationship between
meteorological factors and the occurrence of forest pests is a system
consisting of many mathematical inputs. These inputs have an interactive effect
with one another, including evaporation capacity, precipitation, average
temperature, and accumulated temperature (Tang and Niu 2010). However, it is difficult to formulate the relationship between a designated meteorological factor and the occurrence of forest pests (Feng
et al. 2013). Most of the existing
research has obtained an approximate relationship between these two factors
through data integration, analysis, and exploration, and most of these results
were non-linear (Zhang et al. 2012;
Abdul et al. 2014).
Previous studies on
the relationship between the occurrence of
forest pests and meteorological factors in Northern China have revealed
that temperature and precipitation during the spring and summer were the most
critical factors influencing pest population (Tang and Niu 2010; Chen and Zhang 2011). This influence was greater at
the larval stage, while the annual accumulated temperature (The sum, counted in
degrees, by which the actual air temperature rises above or falls below a datum
level over a year), annual precipitation, and dryness had the greatest Pearson
correlation coefficients with pest area (Yang et al. 2014; Nie et al. 2017). A similar
study concluded that extreme heat or cold had little effect on annual catches
of Ips typographus, while growth rate had a linear relationship with
temperatures between 15 and 25°C (Bakke 1992; Wermelinger and Seifert 1998). In
a separate study, Diprion hercyniae outbreak was induced by the hot and dry climate and severe low moisture (Marchisio et al. 1994). Moreover, a stepwise
regression analysis revealed that the daily average temperature during the
winter and precipitation during the breeding season was a key factor
influencing population fluctuations of D. superans, while the larval stage and breeding season
were the most critical periods (Yu et al. 2016).
More and more novel
algorithms are being used for pest control and forecasting by utilizing big
data and information on climate globalization (Kumar et al. 2015). The artificial neural network, multilayer feedforward
neural network (MLFN), generalized regression neural network (GRNN), support
vector machine (SVM), and other algorithms have been used to forecast the
occurrence of pest, and these machine learning measures have been more accurate
than multiple linear regression predictions (Chon et al. 2000; Zhang et al. 2017; Rathee
and Kashyap 2018). However, like other systemic analysis methods in machine
learning measures, regression analyses require mass data and expect much of the
data to take on a typical probability distribution (e.g., linear, exponential,
logarithmic, and so on). Multiplication, division, and power
operations are often involved in the computational process, but small errors
can result in serious errors, which lead to discrepancies between the
quantitative results and qualitative analysis. This may also lead to a
relationship between systems that cannot be objectively expressed (Cao 2007;
Liu and Xie 2013). Additionally, due to
the complexity of computational models, they are not widely used by forest
workers or researchers. Therefore, when the grey correlation analysis is used
to study the relationship between meteorological variation and pest occurrence,
the vector set was easy to divided and call for no more others variables, the
model needs to possess less calculative complexity and
easy to operate.
In this study, the selected meteorological index was easy to calculate
and the system did not affect the simplicity or functioning of the model. Thus, this analytical method and the findings of
this study can serve as a theoretical reference for pest forecasting and early
warning. For example, this analytical method and the findings can be widely
used for forest workers, when precipitation during the first 10 days of July
(the Breeding season of D. superans) was less than others year, more
attention should be paid the D. superans outbreak next year.
Location and status of the studied habitats
This study was conducted in the southeast of
Xiaoxinganling Mountains located in Tieli of Yichun City, Heilongjiang
Province, China. Regional vegetation mainly includes Pinus koraiensis, Larix
gmelinii, and Picea jezoensis. As for the climate, the winters are
long and the summer is short. The maximum air temperature may exceed 35°C,
while the minimum air temperature can drop below -41°C. Meteorological data
were collected from the Tieli weather station (128°01′E and
46°59′N). The altitude of the observation site was 210.5 m, and the
altitude of the senor of the barometer was 213.4 m. The height of the wind
speed sensor to the platform was 9.36 m, while the height of the observation
platform to the ground was 11.76 m.
Statistics and data compilation
A grey correlation analysis was used to study the
relationship between meteorological variation and pest occurrence. The
selection of characteristic data is key for the foundation of this analysis. D. superans occurrence from 1997 to 2017 was used
as the main time period response sequence: X0 and X0
= x0(k) (k = 1, 2,
n), then Xi
= xi(k) (i = 1, 2,
m, k =
1, 2,
n) (n = 21), where xi was the ith
influencing factor of the system and Xi (i = 1, 2,
n)
was the characteristic time response data sequence. The analysis in same sample
plot did not consider the soil composition, stand structure, or human
disturbance, which made D. superans occurrence
of Tieli the main data sequence, while the temperature, precipitation, average
wind speed, and other time node meteorological data were used as the
characteristic data sequences. The grey correlation degree was acquired by the
grey correlation analysis, and the degree revealed that precipitation during
the spring and summer had the greatest effect on D.
superans occurrence (Li et al. 2019).
The influence of different
precipitation parameters on D. superans occurrence was investigated further.
In this analysis, D. superans occurrence
from 1997 to 2017 was the main time response sequence: X0,
and Xi = xi
(k) (i = 1, 2
m, k = 1, 2,
n) (m
= 18, n = 21), where Xi was the different
precipitation periods from March to August. Each month was
divided into 3 parts, x1, x2, and x3, which
represented the first, sec, and third 10 days of March, while x4, x5, and x6 represented
the first, sec, and third 10 days of April; this pattern spanned through August until
x18 (Table 1).
Then, the correlative
relationship between precipitation and D.
superans occurrence was investigated.
Data processing and analysis
The goal of the grey correlation analysis was to explore the similarity
between data sequence trends, where higher similarity indicates a higher degree
of correlation in the system. While comparing the similarity between these two
data sequences, both the numerical values and the dimensions were considered.
If the data sequence was incomparable, data transformation was conducted in
order to eliminate dimensions.
When Xi = (xi (1), xi (2),
, xi
(n)) was the characteristic time response data sequence of the system, D1 was the operator of the
sequence, such that:
(Eq.
1),
(Eq. 2),
Where D1
represents the average operator of the sequence and XiD1 represents the average image. Then, the
characteristic average image sequence data table was acquired (Table 2).
Grey
relational degree calculation
The calculation for the grey relational degree was conducted as
follows: after data transformation, the characteristic average image sequence
was obtained, then the grey degree was calculated. In addition to the general
relation degree, the mathematical model according to the characteristics of
this system was explored, including B, C, and T types of grey relation
degrees, as well as the degree of grey slope-correlation (Liu and Xie 2013).
The grey slope-correlation expresses the average change over time response
sequence, system factors, and the main sequence (Wekan et al. 2011). If the change tended closer,
then the grey slope-correlation was larger (Zhang et al. 2019). When investigating the influence of different
precipitation periods on D. superans occurrence, these periods
during different months exhibited a time
response; this
grey slope-correlation was selected for further analysis. When ξ (k) was
the correlation coefficient:
(i = 1, 2,
m; k = 1, 2,
n) (Eq. 3).
When calculating the
last year, k + 1was empty. Therefore, it was decided to stop at the k
- 1 year. This did not affect the trend of data changes. In this
calculation, i = 1, 2,
18 and k =
1, 2,
21. Then, the correlation coefficient sequence data was obtained (Table
3).
The grey slope-correlation relation degree of X0 and Xi
were donated as γ(X0, Xi), which was calculated as
follows:
(i = 1, 2,
m; k = 1, 2,
n) (Eq. 4).
Simplified grey slope-correlation model
The grey slope-correlation was used to compare the
correlational degree of factors over time. However, the calculating process of
Eq. 3 (ξ (k)) required many
calculative steps. Therefore, the computational model was simplified as
follows:
(i = 1, 2,
m,
k = 1, 2,
n) (Eq. 5),
Where the simplified correlation coefficient, ξ (k), was affected by the
denominator coefficient. When the numerical values of and were similar, the data sequence curves were
parallel and the variation tendencies of X0 and Xi
were closer, thereby simplifying the calculation of the correlation coefficient
data (Table 4).
Then, the simplified grey
slope-correlation relation degree of X0
and Xi were marked as γ`(X0, Xi),
where
(i = 1, 2,
m;
k = 1, 2,
n) (Eq. 6),
The grey slope-correlation relation degree of X0
and Xi
It could be derived from Eq. 4 that the results of the
grey slope-correlation relation degree were γ01 = 0.54, γ02
= 0.50, γ03 = 0.63, γ04 =
0.50, γ05 = 0.54, γ06 = 0.47, γ07
= 0.57, γ08 = 0.58, γ09 =
0.59, γ010 = 0.53, γ011 = 0.51, γ012
= 0.51, γ013 = 0.67, γ014 =
0.59, γ015 = 0.48, γ016 = 0.62, γ017
= 0.53, and γ018 = 0.58, which represented the
time responses of precipitation during different months and D. superans occurrence. The results revealed that
precipitation during different seasons had different degrees of correlation
with D. superans occurrence, when the value of γ (X0,
Xi) was higher, the correlation degree of X0 and
Xi was greater. Specifically, as it turns out, precipitation
during the first 10 days of July had the largest correlation (γ013
= 0.67), and the third 10 days of March (γ03 =
0.63) and first 10 days of August (γ016 = 0.62) had
better correlation with D. superans occurrence. Meanwhile, the
precipitation during the third 10 days of April (γ06 =
0.47) and the third 10 days of July (γ015 = 0.48)
correlated less with D. superans occurrence (Table 3).
The simplified grey slope-correlation relation degree of
X0 and Xi
From Eq. 6, the following results could be
obtained: γ`01 =
0.47, γ`02 = 0.49, γ`03 = 0.56, γ`04 = 0.50, γ`05 = 0.56, γ`06 = 0.48, γ`07 = 0.57, γ`08 = 0.60, γ`09 = 0.62, γ`10 = 0.58, γ`11 = 0.48, γ`12 = 0.51, γ`13 = 0.69, γ`14 = 0.58, γ`15 = 0.52, γ`16 = 0.61, γ`17 = 0.55, and γ`18 = 0.60.
Table 1: The
characteristic sequence data table
Area and precipitation |
1997 |
1998 |
1999 |
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
X0/Χ105hm2) |
4.07 |
7.73 |
5.05 |
3.25 |
3.07 |
2.75 |
4.13 |
2.87 |
1.67 |
1.24 |
1.31 |
0.95 |
0.9 |
0.98 |
0.8 |
0.75 |
0.35 |
0.35 |
0.28 |
0.34 |
0.48 |
X1/(0.1mm) |
5 |
0 |
6 |
25 |
73 |
0 |
28 |
0 |
32 |
99 |
21 |
147 |
62 |
0 |
18 |
41 |
7 |
37 |
122 |
40 |
63 |
X2/(0.1mm) |
22 |
204 |
51 |
68 |
36 |
0 |
1 |
67 |
51 |
30 |
4 |
63 |
44 |
193 |
75 |
3 |
0 |
30 |
127 |
10 |
38 |
X3/(0.1mm) |
67 |
61 |
88 |
16 |
79 |
0 |
120 |
46 |
64 |
113 |
144 |
220 |
94 |
85 |
0 |
91 |
95 |
0 |
73 |
0 |
0 |
X4/(0.1mm) |
0 |
51 |
71 |
31 |
89 |
164 |
0 |
50 |
99 |
11 |
125 |
89 |
3 |
163 |
7 |
0 |
9 |
4 |
122 |
115 |
20 |
X5/(0.1mm) |
76 |
38 |
85 |
48 |
11 |
305 |
216 |
123 |
103 |
1 |
85 |
0 |
161 |
91 |
2 |
77 |
33 |
18 |
11 |
53 |
71 |
X6/(0.1mm) |
77 |
90 |
150 |
225 |
64 |
175 |
92 |
58 |
306 |
84 |
1 |
281 |
5 |
79 |
30 |
252 |
3 |
110 |
32 |
10 |
40 |
X7/(0.1mm) |
13 |
75 |
37 |
127 |
145 |
71 |
0 |
385 |
144 |
32 |
124 |
393 |
54 |
459 |
302 |
72 |
249 |
266 |
247 |
368 |
201 |
X8/(0.1mm) |
183 |
263 |
1 |
183 |
195 |
286 |
175 |
189 |
163 |
0 |
329 |
129 |
9 |
354 |
56 |
67 |
206 |
391 |
467 |
364 |
205 |
X9/(0.1mm) |
436 |
365 |
134 |
161 |
26 |
139 |
444 |
212 |
111 |
34 |
520 |
309 |
133 |
151 |
367 |
244 |
156 |
545 |
146 |
228 |
166 |
X10/(0.1mm) |
616 |
571 |
51 |
91 |
50 |
504 |
215 |
3 |
600 |
284 |
222 |
336 |
180 |
156 |
536 |
1504 |
432 |
115 |
368 |
456 |
126 |
X11/(0.1mm) |
175 |
699 |
275 |
62 |
223 |
421 |
137 |
352 |
211 |
863 |
15 |
233 |
760 |
80 |
197 |
340 |
401 |
145 |
320 |
275 |
785 |
X12/(0.1mm) |
106 |
732 |
568 |
316 |
33 |
320 |
342 |
349 |
58 |
861 |
395 |
192 |
1278 |
126 |
15 |
115 |
391 |
1535 |
1313 |
826 |
398 |
X13/(0.1mm) |
447 |
1646 |
969 |
253 |
426 |
230 |
688 |
477 |
441 |
343 |
280 |
608 |
668 |
351 |
586 |
1388 |
970 |
943 |
923 |
1021 |
100 |
X14/(0.1mm) |
88 |
12 |
54 |
1217 |
347 |
351 |
565 |
75 |
342 |
371 |
222 |
170 |
447 |
561 |
346 |
297 |
209 |
749 |
32 |
67 |
960 |
X15/(0.1mm) |
961 |
97 |
505 |
992 |
514 |
39 |
521 |
180 |
1468 |
794 |
378 |
2 |
304 |
312 |
62 |
157 |
786 |
1518 |
892 |
670 |
88 |
X16/(0.1mm) |
1119 |
631 |
449 |
741 |
393 |
300 |
625 |
372 |
173 |
355 |
226 |
273 |
479 |
1066 |
790 |
528 |
1562 |
110 |
787 |
353 |
1669 |
X17/(0.1mm) |
194 |
550 |
193 |
327 |
533 |
218 |
494 |
33 |
137 |
161 |
148 |
58 |
804 |
245 |
528 |
110 |
393 |
1108 |
203 |
262 |
0 |
X18/(0.1mm) |
621 |
327 |
273 |
533 |
221 |
424 |
1206 |
443 |
41 |
121 |
669 |
591 |
289 |
812 |
110 |
790 |
292 |
188 |
187 |
238 |
247 |
Table 2: The characteristic average image sequence data table
Average image |
1997 |
1998 |
1999 |
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
X0D1 |
1.98 |
3.75 |
2.45 |
1.58 |
1.49 |
1.33 |
2 |
1.39 |
0.81 |
0.6 |
0.64 |
0.46 |
0.44 |
0.48 |
0.39 |
0.36 |
0.17 |
0.17 |
0.14 |
0.17 |
0.23 |
X1D1 |
0.13 |
0 |
0.15 |
0.64 |
1.86 |
0 |
0.71 |
0 |
0.81 |
2.52 |
0.53 |
3.74 |
1.58 |
0 |
0.46 |
1.04 |
0.18 |
0.94 |
3.1 |
1.02 |
1.6 |
X2D1 |
0.41 |
3.84 |
0.96 |
1.28 |
0.68 |
0 |
0.02 |
1.26 |
0.96 |
0.56 |
0.08 |
1.18 |
0.83 |
3.63 |
1.41 |
0.06 |
0 |
0.56 |
2.39 |
0.19 |
0.71 |
X3D1 |
0.97 |
0.88 |
1.27 |
0.23 |
1.14 |
0 |
1.73 |
0.66 |
0.92 |
1.63 |
2.08 |
3.17 |
1.36 |
1.23 |
0 |
1.31 |
1.37 |
0 |
1.05 |
0 |
0 |
X4D1 |
0 |
0.88 |
1.22 |
0.53 |
1.53 |
2.82 |
0 |
0.86 |
1.7 |
0.19 |
2.15 |
1.53 |
0.05 |
2.8 |
0.12 |
0 |
0.15 |
0.07 |
2.09 |
1.97 |
0.34 |
X5D1 |
0.99 |
0.5 |
1.11 |
0.63 |
0.14 |
3.98 |
2.82 |
1.61 |
1.35 |
0.01 |
1.11 |
0 |
2.1 |
1.19 |
0.03 |
1 |
0.43 |
0.24 |
0.14 |
0.69 |
0.93 |
X6D1 |
0.75 |
0.87 |
1.45 |
2.18 |
0.62 |
1.7 |
0.89 |
0.56 |
2.97 |
0.81 |
0 |
2.73 |
0.05 |
0.74 |
0.29 |
2.44 |
0.03 |
1.07 |
0.31 |
0.1 |
0.39 |
X7D1 |
0.07 |
0.42 |
0.21 |
0.71 |
0.81 |
0.4 |
0 |
2.15 |
0.8 |
0.18 |
0.69 |
2.2 |
0.3 |
2.56 |
1.69 |
0.4 |
1.39 |
1.49 |
1.38 |
2.06 |
1.12 |
X8D1 |
0.91 |
1.31 |
0 |
0.91 |
0.97 |
1.42 |
0.87 |
0.94 |
0.81 |
0 |
1.64 |
0.64 |
0.04 |
1.76 |
0.28 |
0.33 |
1.02 |
1.95 |
2.32 |
1.81 |
1.02 |
X9D1 |
1.82 |
1.53 |
0.56 |
0.67 |
0.11 |
0.58 |
1.86 |
0.89 |
0.46 |
0.14 |
2.18 |
1.29 |
0.56 |
0.63 |
1.54 |
1.02 |
0.65 |
2.28 |
0.61 |
0.95 |
0.69 |
X10D1 |
1.75 |
1.62 |
0.14 |
0.26 |
0.14 |
1.43 |
0.61 |
0 |
1.7 |
0.8 |
0.63 |
0.95 |
0.51 |
0.44 |
1.52 |
4.26 |
1.22 |
0.33 |
1.04 |
1.29 |
0.36 |
X11D1 |
0.53 |
2.11 |
0.83 |
0.19 |
0.67 |
1.27 |
0.41 |
1.06 |
0.64 |
2.6 |
0.05 |
0.7 |
2.29 |
0.24 |
0.59 |
1.02 |
1.21 |
0.44 |
0.96 |
0.83 |
2.36 |
X12D1 |
0.22 |
1.5 |
1.16 |
0.65 |
0.07 |
0.65 |
0.7 |
0.71 |
0.12 |
1.76 |
0.81 |
0.39 |
2.61 |
0.26 |
0.03 |
0.24 |
0.8 |
3.14 |
2.69 |
1.69 |
0.81 |
X13D1 |
0.68 |
2.51 |
1.48 |
0.39 |
0.65 |
0.35 |
1.05 |
0.73 |
0.67 |
0.52 |
4.23 |
0.93 |
1.02 |
0.54 |
0.89 |
2.12 |
1.48 |
1.44 |
1.41 |
1.56 |
0.15 |
X14D1 |
0.24 |
0.03 |
0.15 |
3.33 |
0.95 |
0.96 |
1.54 |
0.2 |
0.93 |
1.01 |
0.61 |
0.46 |
1.22 |
1.53 |
0.95 |
0.81 |
0.57 |
2.05 |
0.09 |
0.18 |
2.62 |
X15D1 |
1.8 |
0.18 |
0.94 |
1.85 |
0.96 |
0.07 |
0.97 |
0.34 |
2.74 |
1.48 |
0.71 |
0 |
0.57 |
0.58 |
0.12 |
0.29 |
1.47 |
2.84 |
1.67 |
1.25 |
0.16 |
X16D1 |
1.81 |
1.02 |
0.73 |
1.2 |
0.63 |
0.48 |
1 |
0.6 |
0.28 |
0.57 |
0.37 |
0.44 |
0.77 |
1.72 |
1.28 |
0.85 |
2.52 |
0.18 |
1.27 |
0.57 |
2.7 |
X17D1 |
0.61 |
1.72 |
0.61 |
1.03 |
1.67 |
0.68 |
1.55 |
0.1 |
0.43 |
0.5 |
0.46 |
0.18 |
2.52 |
0.77 |
1.66 |
0.34 |
1.23 |
3.47 |
0.64 |
0.82 |
0 |
X18D1 |
1.51 |
0.8 |
0.66 |
1.3 |
0.54 |
0.69 |
2.93 |
0.72 |
0.1 |
0.29 |
1.63 |
1.44 |
0.7 |
1.98 |
0.27 |
1.92 |
0.71 |
0.46 |
0.45 |
0.58 |
0.6 |
The simplified grey slope-correlation
of X0 and Xi revealed that the
precipitation during the first 10 days of July had the best correlation (γ`13 = 0.69), which
reflected the results of the classical model. In the simplified model, the top
5 groups of precipitation that had better associations with D.
superans occurrence included the precipitation during the first 10 days of July (γ`13 =
0.69), the third 10 days of May (γ`09
= 0.62), the first 10 days of August (γ`16 = 0.61), the sec
10 days of May (γ`08 =
0.60), and the third 10 days of August (γ`18
= 0.60). However, according to the classical model, the top 5 group
included the first 10 days of July (γ13
= 0.67), the third 10 days of March (γ03 = 0.63), the first 10 days of August (γ16 = 0.62), the sec 10
days of May (γ08 = 0.58),
and the third 10 days of August (γ18
= 0.58). The results showed
that, the top 5 groups of precipitation that had better associations
with D. superans occurrence between the two models was very
similar. In the simplified model, the 5 groups of precipitation that
were less associated with D. superans occurrence included the
precipitation during the first 10 days of March (γ`01 = 0.47), the third 10 days of April (γ`06 = 0.48), the sec 10
days of June (γ`11 =
0.48), the sec 10 days of March (γ`02
= 0.49), and the third 10 days of June (γ`12 = 0.51). In the classical
model, the 5 groups that were less associated with D. superans occurrence
included the third 10 days of April (γ06
= 0.47), the third 10 days of July (γ15 = 0.48), the sec 10 days of March (γ02 = 0.50), the sec 10
days of June (γ11 =
0.51), and the third 10 days of June (γ12
= 0.51). The results also
proved that the two models were very similar (concluded from Eq. 4 and Eq. 6).
Comparative and analysis
Considering that many complex computing models are not
and cannot be widely used by forest workers or researchers, the simple grey
slope-correlation model was developed to analyze the relationship between
precipitation and D. superans occurrence. The grey
slope-correlation model is able to express the average changes in many factors
over a time response sequence.
Table 3: Correlation coefficient sequence data table
Correlation coefficient |
1997 |
1998 |
1999 |
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
ξ0k |
0.68 |
0.40 |
0.43 |
0.58 |
0.89 |
0.60 |
0.69 |
0.37 |
0.49 |
0.21 |
0.44 |
0.42 |
0.92 |
0.45 |
0.61 |
0.21 |
0.55 |
0.52 |
0.31 |
0.91 |
ξ1k |
0.70 |
0.29 |
0.56 |
0.55 |
0.89 |
0.60 |
0.41 |
0.71 |
0.73 |
0.14 |
0.43 |
0.70 |
0.59 |
0.43 |
0.04 |
0.47 |
0.50 |
0.51 |
0.08 |
0.68 |
ξ2k |
0.64 |
0.55 |
0.20 |
0.54 |
0.89 |
0.60 |
0.46 |
0.50 |
0.56 |
0.87 |
0.58 |
0.43 |
0.84 |
0.81 |
0.48 |
0.46 |
1.00 |
0.45 |
0.85 |
0.79 |
ξ3k |
0.65 |
0.56 |
0.57 |
0.58 |
0.63 |
0.75 |
0.41 |
0.45 |
0.12 |
0.54 |
0.98 |
0.03 |
0.53 |
0.04 |
0.93 |
0.32 |
0.47 |
0.46 |
0.81 |
0.17 |
ξ4k |
0.41 |
0.49 |
0.83 |
0.23 |
0.48 |
0.57 |
0.76 |
0.65 |
0.01 |
0.52 |
0.72 |
0.50 |
0.54 |
0.03 |
0.49 |
0.83 |
0.56 |
0.66 |
0.62 |
1.00 |
ξ5k |
0.75 |
0.53 |
0.53 |
0.29 |
0.57 |
0.45 |
0.87 |
0.40 |
0.30 |
0.94 |
0.42 |
0.02 |
0.54 |
0.43 |
0.51 |
0.01 |
0.51 |
0.31 |
0.31 |
0.67 |
ξ6k |
0.73 |
0.67 |
0.44 |
0.84 |
0.52 |
0.75 |
0.41 |
0.51 |
0.24 |
0.60 |
0.48 |
0.14 |
0.56 |
0.78 |
0.24 |
0.35 |
0.94 |
0.88 |
0.87 |
0.48 |
ξ7k |
0.86 |
0.67 |
0.39 |
0.89 |
0.70 |
0.51 |
0.66 |
0.64 |
0.74 |
0.52 |
0.46 |
0.06 |
0.53 |
0.17 |
0.81 |
0.36 |
0.68 |
0.73 |
0.69 |
0.49 |
ξ8k |
0.60 |
0.45 |
0.58 |
0.17 |
0.52 |
0.74 |
0.61 |
0.82 |
0.34 |
0.53 |
0.77 |
0.43 |
0.97 |
0.55 |
0.70 |
0.64 |
0.58 |
0.28 |
0.85 |
0.61 |
ξ9k |
0.65 |
0.09 |
0.50 |
0.56 |
0.49 |
0.37 |
0.69 |
0.37 |
0.56 |
0.75 |
0.58 |
0.54 |
0.81 |
0.52 |
0.58 |
0.42 |
0.27 |
0.53 |
0.98 |
0.26 |
ξ10k |
0.78 |
0.49 |
0.26 |
0.56 |
0.63 |
0.29 |
0.49 |
0.94 |
0.48 |
0.02 |
0.43 |
0.59 |
0.10 |
0.55 |
0.67 |
0.44 |
0.36 |
0.57 |
0.75 |
0.72 |
ξ11k |
0.72 |
0.83 |
0.81 |
0.11 |
0.50 |
0.79 |
0.69 |
0.19 |
0.44 |
0.45 |
0.59 |
0.54 |
0.10 |
0.12 |
0.51 |
0.35 |
0.57 |
0.96 |
0.57 |
0.43 |
ξ12k |
0.79 |
0.84 |
0.31 |
0.68 |
0.58 |
0.75 |
1.00 |
0.61 |
0.94 |
0.55 |
0.24 |
0.92 |
0.51 |
0.62 |
0.60 |
0.59 |
0.97 |
0.84 |
0.93 |
0.09 |
ξ13k |
0.12 |
0.43 |
0.40 |
0.29 |
0.88 |
0.96 |
0.14 |
0.40 |
0.70 |
0.58 |
0.94 |
0.62 |
0.89 |
0.72 |
0.92 |
0.59 |
0.58 |
0.04 |
0.76 |
0.60 |
ξ14k |
0.10 |
0.43 |
0.49 |
0.54 |
0.07 |
0.63 |
0.41 |
0.39 |
0.67 |
0.47 |
0.72 |
0.50 |
0.94 |
0.22 |
0.60 |
0.34 |
0.67 |
0.67 |
0.66 |
0.12 |
ξ15k |
0.45 |
0.91 |
0.52 |
0.54 |
0.84 |
0.84 |
0.82 |
0.70 |
0.54 |
0.62 |
0.65 |
0.70 |
0.68 |
0.90 |
0.70 |
0.36 |
0.07 |
0.48 |
0.42 |
0.65 |
ξ16k |
0.85 |
0.43 |
0.51 |
0.69 |
0.43 |
0.82 |
0.07 |
0.40 |
0.67 |
0.87 |
0.46 |
0.52 |
0.30 |
0.57 |
0.21 |
0.35 |
0.61 |
0.19 |
0.96 |
0.79 |
ξ17k |
0.42 |
0.78 |
0.49 |
0.43 |
0.75 |
0.70 |
0.28 |
0.15 |
0.50 |
0.57 |
0.79 |
0.49 |
0.64 |
0.14 |
0.52 |
0.63 |
0.65 |
0.84 |
0.95 |
0.81 |
ξ18k |
0.68 |
0.40 |
0.43 |
0.58 |
0.89 |
0.60 |
0.69 |
0.37 |
0.49 |
0.21 |
0.44 |
0.42 |
0.92 |
0.45 |
0.61 |
0.21 |
0.55 |
0.52 |
0.31 |
0.91 |
Table 4: The simplified
correlation coefficient sequence data table
Simplified correlation coefficient |
1997 |
1998 |
1999 |
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
ξ`1k |
0.35 |
0.60 |
0.22 |
0.34 |
0.53 |
0.40 |
0.59 |
0.63 |
0.30 |
0.54 |
0.14 |
0.65 |
0.48 |
0.55 |
0.43 |
0.77 |
0.19 |
0.29 |
0.53 |
0.82 |
ξ`2k |
0.12 |
0.71 |
0.59 |
0.71 |
0.53 |
0.40 |
0.02 |
0.85 |
0.86 |
0.52 |
0.07 |
0.80 |
0.23 |
0.70 |
0.53 |
0.68 |
0.50 |
0.23 |
0.47 |
0.30 |
ξ`3k |
0.50 |
0.56 |
0.68 |
0.20 |
0.53 |
0.40 |
0.76 |
0.55 |
0.49 |
0.83 |
0.55 |
0.65 |
0.84 |
0.55 |
0.52 |
0.64 |
0.50 |
0.55 |
0.45 |
0.43 |
ξ`4k |
0.35 |
0.58 |
0.83 |
0.34 |
0.51 |
0.40 |
0.59 |
0.42 |
0.61 |
0.09 |
0.99 |
0.52 |
0.02 |
0.57 |
0.52 |
0.68 |
0.65 |
0.03 |
0.79 |
0.46 |
ξ`5k |
0.42 |
0.39 |
0.93 |
0.58 |
0.04 |
0.56 |
0.89 |
0.80 |
0.58 |
0.01 |
0.58 |
0.51 |
0.66 |
0.56 |
0.03 |
0.96 |
0.69 |
0.81 |
0.21 |
0.99 |
ξ`6k |
0.58 |
0.50 |
0.54 |
0.60 |
0.35 |
0.50 |
0.94 |
0.17 |
0.68 |
0.48 |
0.58 |
0.52 |
0.07 |
0.70 |
0.12 |
0.68 |
0.03 |
0.65 |
0.53 |
0.28 |
ξ`7k |
0.20 |
0.87 |
0.27 |
0.83 |
0.71 |
0.40 |
0.59 |
0.83 |
0.66 |
0.27 |
0.29 |
0.55 |
0.12 |
0.87 |
0.59 |
0.25 |
0.93 |
0.91 |
0.78 |
0.55 |
ξ`8k |
0.69 |
0.60 |
0.61 |
0.89 |
0.64 |
0.53 |
0.72 |
0.78 |
0.57 |
0.48 |
0.75 |
0.53 |
0.02 |
0.60 |
0.80 |
0.28 |
0.52 |
0.73 |
0.70 |
0.56 |
ξ`9k |
0.49 |
0.78 |
0.64 |
0.56 |
0.19 |
0.37 |
0.82 |
0.94 |
0.70 |
0.06 |
0.89 |
0.66 |
0.97 |
0.38 |
0.79 |
0.86 |
0.29 |
0.64 |
0.74 |
0.61 |
ξ`10k |
0.51 |
0.64 |
0.45 |
0.71 |
0.10 |
0.48 |
0.59 |
0.63 |
0.79 |
0.78 |
0.56 |
0.70 |
0.81 |
0.27 |
0.35 |
0.84 |
0.58 |
0.30 |
0.97 |
0.48 |
ξ`11k |
0.32 |
0.79 |
0.71 |
0.28 |
0.50 |
0.46 |
0.35 |
0.98 |
0.23 |
0.49 |
0.07 |
0.30 |
0.50 |
0.38 |
0.55 |
0.58 |
0.61 |
0.42 |
0.74 |
0.40 |
ξ`12k |
0.17 |
0.89 |
0.92 |
0.54 |
0.11 |
0.70 |
0.76 |
0.71 |
0.07 |
0.62 |
0.81 |
0.15 |
0.50 |
0.59 |
0.12 |
0.26 |
0.25 |
0.97 |
0.63 |
0.53 |
ξ`13k |
0.36 |
0.94 |
0.72 |
0.58 |
0.74 |
0.40 |
1.00 |
0.75 |
0.97 |
0.12 |
0.67 |
0.88 |
0.64 |
0.54 |
0.41 |
0.82 |
0.97 |
0.87 |
0.90 |
0.44 |
ξ`14k |
0.36 |
0.19 |
0.04 |
0.60 |
0.89 |
0.91 |
0.64 |
0.20 |
0.74 |
0.68 |
0.97 |
0.37 |
0.86 |
0.84 |
0.93 |
0.81 |
0.28 |
0.56 |
0.56 |
0.07 |
ξ`15k |
0.36 |
0.18 |
0.43 |
0.70 |
0.55 |
0.07 |
0.74 |
0.12 |
0.83 |
0.63 |
0.58 |
0.51 |
0.93 |
0.62 |
0.40 |
0.18 |
0.52 |
0.81 |
0.68 |
0.45 |
ξ`16k |
0.43 |
0.94 |
0.50 |
0.71 |
0.88 |
0.63 |
0.91 |
0.90 |
0.44 |
0.71 |
0.68 |
0.56 |
0.47 |
0.94 |
0.79 |
0.29 |
0.52 |
0.14 |
0.57 |
0.23 |
ξ`17k |
0.52 |
0.77 |
0.49 |
0.60 |
0.67 |
0.56 |
0.61 |
0.21 |
0.70 |
0.87 |
0.75 |
0.07 |
0.56 |
0.43 |
0.58 |
0.24 |
0.35 |
0.61 |
0.94 |
0.43 |
ξ`18k |
0.42 |
0.85 |
0.43 |
0.65 |
0.72 |
0.27 |
0.69 |
0.69 |
0.32 |
0.18 |
0.86 |
0.68 |
0.37 |
0.60 |
0.14 |
0.91 |
0.74 |
0.87 |
0.93 |
0.76 |
Table 5: The
correlation coefficient accumulation table
March |
April |
May |
June |
July |
August |
|
classical |
γ01+γ02+γ03 |
γ04+γ05+γ06 |
γ07+γ08+γ09 |
γ10+γ11+γ12 |
γ13+γ14+γ15 |
γ16+γ17+γ18 |
1.67 |
1.51 |
1.74 |
1.55 |
1.74 |
1.73 |
|
simplified |
γ`01+γ`02+γ`03 |
γ`04+γ`05+γ`06 |
γ`07+γ`08+γ`09 |
γ`10+γ`11+γ`12 |
γ`13+γ`14+γ`15 |
γ`16+γ`17+γ`18 |
1.52 |
1.54 |
1.79 |
1.57 |
1.79 |
1.76 |
In this study, the
grey slope-correlation model of precipitation and D. superans occurrence in
the Xiaoxinganling Mountain Tieli forest region was constructed and the grey
slope-correlation relation degree was calculated. According to the calculations
and analysis of the grey slope-correlation classical model, the simplified grey
slope-correlation model required fewer steps and was easier to operate. After incorporating the correlation
coefficient of each month in the classical and simplified models, the correlation coefficient accumulation was
obtained (Table 5).
The correlation
coefficient accumulation during May and July had good
measure in both models (Table 5). This indicated that the precipitation during May and July
were the greatest contributing precipitation
factors on D. superans occurrence compared to other
periods. Although both models exhibited different correlation coefficient
accumulations, both models indicated that the precipitation period that contributed the least was during the
spring. Moreover, the results of the models were similar, where the precipitation during May and July had
the greatest associations with D.
superans occurrence, but the simplified model required fewer calculative steps.
According to the results, precipitation during May (γ07
+ γ08 + γ09
= 1.74, γ`07 + γ`08
+ γ`09 = 1.79) and July (γ13 + γ14
+ γ15 = 1.74, γ`13 + γ`14 + γ`15
= 1.79) had the
greatest associations with D.
superans occurrence. In previous studies, the larval stage and breeding
season were found to be the critical periods for D. superans (Yang et al. 2014; Yu et
al. 2016). The life cycle of D.
superans can be divided into the
larval, larger larval, pupa, eclosion, adult, and spawning stages. D.
superans produces 1 generation a
year and overwinters as larvae in the Tieli forest region. The larval stage
lasts for 3 seasons in Northeast China. Larvae hatch in autumn, stay in the
litter layer during the winter, and finally climb up the trees during the
spring of the next year. Precipitation has a great effect on D.
superans occurrence. Aside from precipitation,
temperature is also a critical factor that affects the larvaes ability to
climb trees, while warm and dry climatic conditions benefit larval growth (Liu
1994; Tiit et al. 2010).
Precipitation during the third 10 days of March (γ03 =
0.63, γ`03 = 0.56) was found to be the most
important time period affecting D. superans occurrence compared to the other periods in March (Li et al.
2019). Because more precipitation tends to increase humidity, the additional
humidity disturbs the water balance in
insects, leading to epidemics of pathogenic microorganisms. Precipitation during the late spring
promotes tree growth and provides food for larvae. When larvae experience high humidity for long
periods of time, the body water loss balance can cause developmental
delays or abnormalities (Chen and
Zhang 2011; Abdul et al. 2014).
In the D. superans
breeding season (July, August), precipitation during the first 10 days of
July (γ13 = 0.67, γ`13 =
0.69) was found to be the most important factor affecting D.
superans occurrence (Chen and
Zhang 2011). This finding was
similar to the results of a previous study on climate change and the occurrence
of crop insects, where occurrence and precipitation exhibited a positive correlation (Zhang et
al. 2012). When the average annual
precipitation and heavy rainfall increased by 1 mm, pest occurrence rates
increased by 0.004 and 0.008 and pest occurrence increased by 59.5 Χ 104 and
11.89 Χ 104 hm2, respectively. Thus, precipitation
clearly facilitated migratory pest decent and increased the base number of
insects (Zhang et al. 2012). Moreover, precipitation influences pest food sources (i.e., trees and
others plant), their natural enemies (e.g., Trichogramma), and other
biotic factors (Vladimir et al.
2016)
The application of grey system theory requires less data input
than the multiple linear regression analysis method, and the calculations
are simple and easy to operate (Abdul et al. 2019; Zhang et al. 2019). Specifically, the selected
meteorological index was easy to calculate and the system had no force
requirement due to its simple capacity and regularity (Cao et al. 2007). Moreover, grey slope-correlation can reveal the
correlational degree of factors over time (Wekan et al. 2011).
The simplified correlation coefficient was affected by the denominator
coefficient, while the simplified model was easy to calculate, required fewer
steps, and the results was very similar with the classical model (concluded from Eq. 4, 6 and Table 5).
These findings provide a theoretical reference for pest forecasting and early
warning. This simple method was used to uncover the most critical periods of precipitation affecting pest occurrence,
which was found to be the first 10 days of July. Thus, the grey system theory
can be widely used by forest workers and researchers. Monitoring
precipitation during the first 10 days in July should be a focus, and when the
precipitation of the first 10 days in July increases while other factors are
consistent, precautions should be implemented the next spring.
Different precipitation periods from
March to August were measured by the weather service
department. While D. superans was prone to sprawling, occurrence was the main time response
sequence data that was difficult to calculate. In this study, occurrence data
was obtained from actual measurements and empirical prediction, but its
scientific foundation and precision needs to be improved. The period of
precipitation with the greatest effect on pest occurrence was the first 10 days of July, which corresponds with
the breeding season of D. superans. However, the life stages of this pest overlap in time and determining the
specific pest stage (i.e., feathering,
spawning, or hatching periods) was
affected by which precipitation period was difficult to discern. Therefore, refinement
of this scientific model requires further investigation.
Both grey slope-correlation and simplified models
revealed that precipitation during the
first 10 days of July had the greatest correlation (γ13 =
0.67, γ`13 = 0.69) with D. superans occurrence,
while the first 10 days of August (γ16 = 0.62, γ`16 =
0.61), the sec 10 days of May (γ08
= 0.58, γ`08 = 0.60), and the third 10 days of August (γ18=0.58, γ`18=0.60)
also had large correlations with D. superans occurrence. However, the
least-correlated time periods of
precipitation affecting D. superans occurrence were quite
different between the two models. The classical model showed that third 10 days of April (γ06 = 0.47) were the least correlated, while the simplified
model showed that the first 10 days of
March (γ`01 = 0.47) were the least correlated with D. superans occurrence. When adding the correlation
coefficients of each month in the classical and simplified models, the correlation coefficient in May and July
had good measures in both models. Thus, precipitation
during May and July was clearly the most important precipitation factor affecting D.
superans occurrence, while precipitation during the spring was the least
important precipitation factor.
This study was supported by the Fundamental Research
Funds for the Central Non-Profit Research Institution of the Chinese Academy of
Forestry (No. CAFYBB2018QA011).
Zhiru Li and Zhenkun Miao conceived and designed the
models; Xiaofeng Wu, Beihang Zhang and Quangang Li performed the experiments;
Lizhi Han and Jun Wang contributed the insect data of forestry, and Zhiru Li
wrote the paper.
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