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Predicting the potential
distribution of Dendrolimus
punctatus and its host Pinus
massoniana in China under
climate change conditions
Yijie Wang, Youjie Zhao*, Guangting Miao , Xiaotao Zhou ,
Chunjiang Yu and Yong Cao*
College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
Introduction: Dendrolimus punctatus, a major pest endemic to the native Pinus
massoniana forests in China, displays major outbreak characteristics and causes
severe destructiveness. In the context of global climate change, this study aims to
investigate the effects of climatic variations on the distribution of D. punctatus
and its host, P. massoniana.
Methods: We predict their potential suitable distribution areas in the future,
thereby offering a theoretical basis for monitoring and controlling D. punctatus,
as well as conserving P. massoniana forest resources. By utilizing existing
distribution data on D. punctatus and P. massoniana, coupled with relevant
climatic variables, this study employs an optimized maximum entropy (MaxEnt)
model for predictions. With feature combinations set as linear and product (LP)
and the regularization multiplier at 0.1, the model strikes an optimal balance
between complexity and accuracy.
Results: The results indicate that the primary climatic factors influencing the
distribution of D. punctatus and P. massoniana include the minimum temperature
of the coldest month, annual temperature range, and annual precipitation. Under the
influence of climate change, the distribution areas of P. massoniana and its pests
exhibit a high degree of similarity, primarily concentrated in the region south of the
Qinling−Huaihe line in China. In various climate scenarios, the suitable habitat areas
for these two species may expand to varying degrees, exhibiting a tendency to shift
toward higher latitude regions. Particularly under the high emission scenario (SSP5-
8.5), D. punctatus is projected to expand northwards at the fastest rate.
Discussion: By 2050, its migration direction is expected to closely align with that
of P. massoniana, indicating that the pine forests will continue to be affected by
the pest. These findings provide crucial empirical references for region-specific
prevention of D. punctatus infestations and for the rational utilization and
management of P. massoniana resources.
KEYWORDS
climate change, Dendrolimus punctatus,Pinus massoniana, species distribution
modeling, MaxEnt model
Frontiers in Plant Science frontiersin.org01
OPEN ACCESS
EDITED BY
Xiao Ming Zhang,
Yunnan Agricultural University, China
REVIEWED BY
Feng Chen,
Yunnan University, China
Yusheng Wang,
Hunan Agricultural University, China
Muhammad Waheed,
University of Okara, Pakistan
*CORRESPONDENCE
Youjie Zhao
zhaoyoujie@163.com
Yong Cao
cncaoyong@swfu.edu.cn
RECEIVED 27 December 2023
ACCEPTED 07 May 2024
PUBLISHED 24 May 2024
CITATION
Wang Y, Zhao Y, Miao G, Zhou X, Yu C and
Cao Y (2024) Predicting the potential
distribution of Dendrolimus punctatus and its
host Pinus massoniana in China under
climate change conditions.
Front. Plant Sci. 15:1362020.
doi: 10.3389/fpls.2024.1362020
COPYRIGHT
© 2024 Wang, Zhao, Miao, Zhou, Yu and Cao.
This is an open-access article distributed under
the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 24 May 2024
DOI 10.3389/fpls.2024.1362020
1 Introduction
The Dendrolimus punctatus, a member of the Lasiocampidae
family within the Lepidoptera order, is a widespread coniferous tree
leaf-eating pest in the forest ecosystems of southern China. It affects
an area of over one million acres and is one of the most broadly
distributed and severely damaging pests in the country (Chen, 2013;
Li et al., 2019;Zhang et al., 2020). As a longstanding pest in Chinese
forest ecosystems, its primary target is Pinus massoniana (Chai,
1995;Zhang et al., 2017;Niu and Fan, 2023), P. massoniana,a
native Chinese tree species, is distinguished by its robust
adaptability, rapid growth rate, and drought resistance. It stands
as one of the primary species used for afforestation on barren hills
and plays a crucial role in ecological construction projects (Liu et al.,
2015;Yang et al., 2016;Meng et al., 2018). However, during
outbreaks, D. punctatus can rapidly devastate pine forests, leading
to widespread death of P. massoniana within a matter of days. These
outbreaks cause ecological imbalances and substantial economic
losses. Consequently, they pose a severe threat to the safety of forest
ecosystems and the sustainability of forestry.
Global climate change, particularly alterations in temperature
and precipitation, is a pivotal factor affecting the geographical
distribution of species (Guo et al., 2014). The Intergovernmental
Panel on Climate Change (IPCC)’s Sixth Assessment Report
indicates that the global surface temperature from 2011 to 2020
was 1.1°C higher than the average from 1850 to 1900, with
projections of a continual rise in temperature over the coming
decades (Calvin et al., 2023). This warming trend is anticipated to
increase precipitation levels and lead to alterations in river basins
and adjustments in forest community structures (Shu et al., 2022;
Chen et al., 2023;Zhao et al., 2024). As a poikilothermic organism,
D. punctatus is highly sensitive to changes in environmental
temperature and precipitation. These factors are decisive in the
distribution and outbreak patterns of D. punctatus; temperature
influences its developmental rate, and precipitation contributes to
the survival of eggs and larvae (Lian et al., 2022). Future climate
changes may exacerbate the spread of forest pests and significantly
impact plant growth (Tang et al., 2021;Johnson and Haynes, 2023).
Therefore, in the context of global climate change, studying the
adaptability of D. punctatus and P. massoniana to future climatic
conditions and their distributional changes is crucial for developing
effective control strategies, protecting forest resources, and
maintaining ecological balance.
Species distribution models are mathematical models based on
species presence or abundance data, as well as environmental
factors, utilized to assess and predict the potential impact of
climate change on species (Guisan and Zimmermann, 2000).
Common species distribution models include bioclimatic
modeling (BIOCLIM), the genetic algorithm for rule-set
prediction, generalized linear models, random forests, and the
maximum entropy (MaxEnt) model (Phillips et al., 2006;Elith
and Leathwick, 2009;Belgiu and Dragut, 2016;Yang et al., 2020).
The MaxEnt model is considered one of the best-performing non-
ensemble methods in niche modeling. Compared with other
models, MaxEnt offers several advantages, such as the ability to
utilize continuous and categorical data, and accounts for
interactions among different variables. Moreover, MaxEnt can
provide high accuracy even in scenarios where distribution data
are scarce or incomplete (Elith and Leathwick, 2009;Warren and
Seifert, 2011;Elith et al., 2015). Consequently, this model has
extensive applications in various fields such as biodiversity
conservation, the assessment of risks associated with invasive
species, endangered species protection, impact assessments of
climate change, and the prediction of quarantine pests (Cao et al.,
2020;Li et al., 2020;Wan et al., 2020;Shi et al., 2023).
In this study, we used the MaxEnt model to simulate the impact
of climate change on the suitable distribution of D. punctatus and P.
massoniana, aiming to provide a scientific basis for the protection of
P. massoniana forest resources and effective control of D. punctatus.
The specific objectives of the study include: (1) predicting the
potential distribution areas of these two species in China; (2)
analyzing the impact of major environmental factors on their
distribution; (3) forecasting and comparing the suitable habitats
and trends of change in 2050 and 2070 under different climate
scenarios; and (4) systematically analyzing the spatiotemporal
changes in their distribution centroids caused by climate change.
2 Data sources and preprocessing
2.1 Species occurrence data
Data for D. punctatus and P. massoniana were obtained from
the Center for Agriculture and Bioscience International (CABI:
https://www.cabi.org), the Global Biodiversity Information Facility
(GBIF: https://www.gbif.org/), the Chinese Virtual Herbarium
(CVH: https://www.cvh.ac.cn/), and relevant published literature.
To avoid autocorrelation among samples, we further processed
the distribution information of the target species. Initially, the Baidu
Maps coordinate picker system was used to acquire precise
longitudes and latitudes for collection points missing this
information. The distribution point data in degrees, minutes, and
seconds format were then converted into decimal (floating-point)
numbers. Finally, to reduce spatial autocorrelation between sample
points and decrease the errors in the model’soutcomes,we
established a 5 km × 5 km grid (corresponding to the 2.5 arc-
minute environmental data detailed in Section 2.2), retaining only
one sampling point per grid and eliminating duplicate and sea-
based points. The final number of sample points used for building
the species distribution model was 429 for D. punctatus and 236 for
P. massoniana (Figure 1).
2.2 Environmental data
We selected 19 bioclimatic variables (BIO01-BIO19, Table 1)
from the WorldClim database (version 2.1, spanning 1970–2000)
(http://www.worldclim.org/) as the current climate data, with a spatial
resolution of 2.5 arc minutes. To reduce multicollinearity among
variables and prevent model overfitting, it was necessary to filter these
19 environmental variables (Morales et al., 2017). Initially, we
imported the distribution data of the target species and the 19
Wang et al. 10.3389/fpls.2024.1362020
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environmental factors into MaxEnt, utilizing default parameters for
pre-training, and a jackknife method to determine the contribution
rate of each environmental factor (Table 1). We subsequently
conducted a correlation analysis of the 19 environmental factors
using ArcGIS (Esri) (Figure 2). If two or more environmental
variables exhibited a high correlation (|r| > 0.8), we retained the
variable with a higher contribution to the model and excluded those
with a contribution rate of zero. Ultimately, six climatic variables were
selected for modeling the potential distribution of the pine caterpillar
and pine, namely, isothermality (BIO03), the minimum temperature
of the coldest month (BIO06), the annual temperature range (BIO07),
the mean temperature of the coldest quarter (BIO11), annual
precipitation (BIO12), and the precipitation of driest month (BIO14).
Three global circulation models (GCMs) from the Sixth
Coupled Model Intercomparison Project (CMIP6), namely, BCC-
CSM2-MR (Beijing Climate Center Climate System Model),
MIROC6 (Model for Interdisciplinary Research on Climate), and
CMCC-ESM2 (Centro Euro-Mediterraneo sui Cambiamenti
Climatici Earth System Model 2) were selected as the future
climate models. To mitigate the uncertainty associated with
reliance on a single GCM, we averaged the occurrence
probabilities of these three models. Based on this, we employed
the Shared Socioeconomic Pathways (SSPs) SSP1-2.6 and SSP5-8.5,
representing sustainable and business-as-usual development
scenarios (Riahi et al., 2017;Liu et al., 2021), respectively, applied
to the years 2041–2060 (2050s) and 2061–2080 (2070s). These
choices of models and SSPs aimed to explore the potential
distribution changes of the pine caterpillar and pine under
different future development pathways.
3 Research methods
3.1 Research framework
Aligned with our research objectives and the foundational
principles of the MaxEnt model, we developed a framework to
simulate the potential distribution of D. punctatus and P.
massoniana and evaluate the spatiotemporal variations in their
future suitable habitats under the influence of climate change. The
framework is segmented into four key sections: (1) data collection; (2)
data preprocessing; (3) optimization of the MaxEnt model; and (4)
mapping of suitable habitats and analysis of the results (Figure 3). In
the first section, we compiled occurrence records for D. punctatus and
P. massoniana, along with current and future environmental
variables that influence their distribution. The second section
involved filtering the occurrence records to remove duplicates and
samples that did not fit within the range of environmental variables.
We also reduced redundancy in the environmental data by analyzing
the contributions and correlations of environmental variables. In the
third section, two critical parameters of the MaxEnt model were
optimized to enhance the accuracy of the predictions. Finally, in the
fourth section, using the processed data and the optimized MaxEnt
model, we simulated the potential distribution areas of both species
and predicted their future distributions. We then conducted an
analysis and discussion of the prediction results and key
environmental factors.
3.2 MaxEnt model
3.2.1 Model description
The MaxEnt model was employed to predict the potential
geographic distribution range of D. punctatus and P. massoniana.
This model adopts the principle of maximum entropy, which is a
statistical axiom asserting that the probability distribution with the
highest entropy represents the best estimate of an unknown
probability distribution when limited information is available
(Phillips et al., 2006). This principle suggests that a system, in the
absence of external constraints, will naturally gravitate toward a
state of maximum entropy. Under specific conditions, the state of
maximum entropy is most likely to resemble the system’s true state
(Jaynes, 1957). Two types of data are essential when applying
MaxEnt to species distribution modeling. The first type is the
known geographical distribution of the species, represented by
latitude and longitude data. The second type of data encompasses
BA
FIGURE 1
Occurrence records of D. punctatus and P. massoniana in China. (A) D. punctatus;(B) P. massoniana.
Wang et al. 10.3389/fpls.2024.1362020
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the environmental variables within the predictive spatial range. In
deploying the maximum entropy principle for species distribution
prediction, we utilize environmental variables and species presence
data to establish constraints for the model. These constraints are
based on the congruence of two mathematical expectations: one
considers the mathematical expectation of each environmental
variable under an unknown probability distribution; and the
other focuses on the mathematical expectation of each
environmental variable within the species presence data under a
uniform distribution. The optimal prediction should adhere to these
constraints while possessing the maximum information entropy.
The MaxEnt model is essentially a constrained optimization
algorithm. In this model, given an input xresulting in an output
y, and for a specific training dataset and feature functions fi(x,y),
where i=1,2,…,n, MaxEnt determines the optimal solution by
solving a series of equations. These equations aim to maximize the
information entropy of the overall system while fulfilling all known
constraints. The MaxEnt equation-solving process is described as
follows:
maxp∈cHPðÞ=o
x,y
~
PXðÞP(y∣x)logP(y∣x),
s:tEP(fi)=E~
P(fi), i=1,2,3…,n,
o
y
P(y∣x)=1
where H(P) represents the conditional entropy; P(y|x)isthe
assumption of the conditional probability distribution;
~
Pdenotes
the empirical distribution; and E
p
(f
i
) is the expected value of the
feature function relative to the empirical distribution. The equation
is solved using the Lagrangian multiplier method, which effectively
converts the original constrained optimization problem into an
unconstrained dual problem, thereby streamlining the optimization
process within the framework of the MaxEnt model.
3.2.2 Model optimization
Research indicates that using default parameters in MaxEnt
modeling can lead to excessive complexity and the poor portability
of the model (Morales et al., 2017;Wiltshire and Tanner, 2020). This
phenomenon is closely associated with two critical parameters in the
MaxEnt model: feature combination (FC) and the regularization
multiplier (RM) (Akaike, 1974;Zhu and Qiao, 2016). We utilized the
‘Kuenma’package in R version 3.6.3 to optimize the RM and feature
categories in the MaxEnt model. During the modeling process, all
occurrence records were randomly divided into a training (75%) and
test (25%) set. We created 1160 candidate models, covering 40
different RM settings ranging from 0.1 to 4 (in increments of 0.1)
and 29 different combinations of feature categories. The selection of
candidate models was based on three criteria: (1) statistical
significance; (2) an omission rate less than 5%; and (3) a model
complexity (the minimum information criterion AICc value,
delta.AICc) less than 2 (Cobos et al., 2019). We first filtered out
statistically significant models and then retained those within the
models that met the omission rate criterion (E< 5%). Finally, we
selected the model that performed best in terms of significance,
omission rate, and complexity. Reliable models typically have a delta
AICc< 2, with delta AICc = 0 considered as the optimal model (Zhan
et al., 2022). Based on the AICc values, we identified the optimal
combination of FC and RM parameters (Table 2).
3.3 Model construction and validation
During construction of the model, 75% of the geographic
distribution data was used for model training, and the remaining
25% was used for model validation. To enhance reliability of the
TABLE 1 Description of climate variables and their pre-training
contributions used for simulating the potential distribution of
Dendrolimus punctatus and Pinus massoniana.
Variable Climate variable Contribution of
D. punctatus (%)
Contribution of
P.
massoniana (%)
bio01 Annual
mean temperature 1.9 0.6
bio02 Mean diurnal range 1.1 1.1
bio03 Isothermality 0.6 3.6
bio04 Temperature
seasonality 5.5 3.0
bio05 Maximum
temperature of the
warmest month 4.6 0.8
bio06 Minimum
temperature of the
coldest month 1.2 1.7
bio07 Annual
temperature range 2.6 1.3
bio08 Mean temperature of
the wettest quarter 0.6 0.5
bio09 Mean temperature of
the driest quarter 0.2 0.3
bio10 Mean temperature of
the warmest quarter 1.6 1.5
bio11 Mean temperature of
the coldest quarter 1 0.4
bio12 Annual precipitation 4.2 10.8
bio13 Precipitation of the
wettest month 0.5 0.2
bio14 Precipitation of the
driest month 70 72.2
bio15 Precipitation
seasonality 1.8 0.6
bio16 Precipitation of the
wettest quarter 0.3 0.2
bio17 Precipitation of the
driest quarter 0.5 0.4
Bio18 Precipitation of the
warmest quarter 0.2 0.1
Bio19 Precipitation of the
coldest quarter 1.5 0.7
Selected climate variables are highlighted in bold and gray.
Wang et al. 10.3389/fpls.2024.1362020
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results, we established 10 replicates and employed bootstrapping as
the sampling method. The Logistic function was used as the output
format, and the final results represent the average of the 10
replicates. We subsequently adopted the jackknife method to
assess the relative importance of each climatic factor within the
suitable areas for D. punctatus and P. massoniana, thus identifying
the key limiting factors affecting the distribution of these two
species (Phillips et al., 2006). The accuracy of the model was
evaluated by determining the area under the curve (AUC) of the
receiver operating characteristic (ROC) curve. The AUC value is
not influenced by specific thresholds, hence is widely used to assess
the accuracy of predictive models. The AUC ranges from 0 to 1 and
is directly proportional to the accuracy of a model. In general, AUC
values less than 0.7 indicate poor model performance; values
between 0.7 and 0.8 denote moderate performance; values
between 0.8 and 0.9 indicate good performance; and values
greater than 0.9 suggest excellent performance. The closer the
AUC value is to 1, the better the predictive performance of the
model (Zhao et al., 2021).
3.4 Variations in the spatial pattern of the
suitable distribution area
The MaxEnt model outputs the probability of species presence
(p) in each grid cell in ASCII format, with values ranging from 0 to
1. We visualized the model’s output in ArcGIS and employed the
reclassification tool to categorize suitability levels, calculating the
corresponding area for each category. Based on the suitability index,
the suitable areas were divided into four levels: unsuitable (p< 0.1),
low suitability (0.1< p< 0.3), moderate suitability (0.3< p< 0.5), and
high suitability (p > 0.5) (Yan et al., 2021).
We defined the suitable areas (assigned a value of 1) for D.
punctatus and P. massoniana as spatial units with p > 0.1 and areas
with p< 0.1 as unsuitable (assigned a value of 0) (Zhao et al., 2021).
Based on this principle, we established potential distribution
matrices for these species under current and future climate
change scenarios, where 0 indicates absence and 1 indicates
presence. This approach allowed us to analyze the spatial pattern
changes in suitable distribution areas under future climate
scenarios. Changes in areas for 2050 and 2070 were calculated
based on current and projected distributions for 2050. Moreover,
we defined four types of changes: newly suitable areas (matrix value
changing from 0 to 1); lost suitable areas (from 1 to 0); retained
suitable areas (1 remaining constant); and consistently unsuitable
areas (0 remaining constant).
To further analyze the dynamics of species distribution, we
simplified the distribution of D. punctatus and P. massoniana to a
single centroid point and created a vector file to describe the
magnitude and direction of changes in the suitable areas over
time. By tracking the centroid changes under different climate
scenarios, we could explore the dynamics of species distribution.
Furthermore, we calculated the cosine similarity of the changes in
the distribution centroids of D. punctatus and P. massoniana under
the same scenarios to determine whether the migration directions of
these two species were similar.
FIGURE 2
Correlation analysis results of the environmental variables. Red indicates a positive correlation, blue indicates a negative correlation, and highly
correlated environmental variables (|r| > 0.8) are highlighted with yellow dots.
Wang et al. 10.3389/fpls.2024.1362020
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B
C
D
A
FIGURE 3
Research framework: (A) data collection, (B) data preprocessing, (C) optimization of the MaxEnt model, and (D) habitat suitability mapping and
analysis of the results.
TABLE 2 Performance of the MaxEnt model under the optimal parameters.
Species Type RM FC delta AICc
D. punctatus
Default 1 LQPH 127.313
Optimization 0.1 LP 0
P. massoniana
Default 1 LQPH 30.6034
Optimization 0.1 QP 0
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4 Results
4.1 Model accuracy evaluation
The MaxEnt model employs the ROC curve to assess the accuracy
of the analytical results. The ROC curve is a tool for evaluating the
performance of classification models, plotting thefalsepositiverateon
the x-axis against the true positive rate on the y-axis. The area between
the curve and the x-axis, denoted as the AUC, is used to quantify the
overall performance of the model. The average AUC values (obtained
through 10 replicate runs of the distribution models) for D. punctatus
and P. massoniana were 0.931 and 0.923, respectively, indicating that
both models exhibit excellent performance, and their predictive
accuracy is reliable. Furthermore, the high AUC values indicate the
efficiency of the models in differentiating between suitable and
unsuitable areas, thereby providing robust scientific support for
our research.
4.2 Environmental variable analysis
We determined the key environmental variables influencing the
species distribution by analyzing the percentage contributions of
various environmental variables within the predictive model. Six
environmental variables were selected for analysis (Figure 4).
Among these variables, the minimum temperature of the coldest
month (bio06), annual temperature range (bio07), and annual
precipitation (bio12) collectively contributed to 81.4% and 84.6%
of the overall impact of D. punctatus and P. massoniana,
respectively. This highlights the significance of these variables as
the three main environmental driving factors in the distribution of
D. punctatus and P. massoniana. The remaining contribution,
accounting for less than 20%, was jointly provided by
isothermality (bio03), the mean temperature of the coldest
quarter (bio11), and the precipitation of the driest month (bio14).
4.3 Current distribution of D. punctatus
and P. massoniana
Figure 5 reveals that D. punctatus and P. massoniana are
primarily distributed in regions south of 35°N latitude in China,
consistent with existing literature. The distribution areas of both
species exhibit significant similarity, with their northern boundaries
aligning with the Qinling Mountains and the Huai River, and their
southern boundaries extending to Hainan Island. The total area of
their distribution is approximately 193.98 × 10
4
km
2
for D.
punctatus and 191.43 × 10
4
km
2
for P. massoniana, respectively.
The high-suitability areas for D. punctatus are mainly concentrated
in Hunan and Jiangxi Provinces, followed by Chongqing, Hubei,
Henan, Anhui, Jiangsu, Zhejiang, Fujian, and Guangdong Provinces
and Guangxi Zhuang Autonomous Region. In contrast, the high-
suitability areas for P. massoniana are more extensive, stretching
eastward to the coastal regions of China, covering the central part of
Guangdong Province and the southwestern region of Guangxi
Zhuang Autonomous Region, as well as the eastern part of
Guizhou Province.
4.4 Projected distribution of D. punctatus
and P. massoniana under future
climate scenarios
The impact of climate change on the distribution of D.
punctatus and P. massoniana may increase the risk of pest
outbreaks. Figure 6 presents the projected distribution of suitable
habitats for these species under different future climate scenarios.
Predictions indicate that the distribution range of both species will
continue to expand in the coming decades. Particularly under the
future SSP1-2.6 scenario (Figures 6A,C), the suitable habitat of D.
punctatus is expected to expand north-eastward to northern
Shandong, with the suitable habitat area reaching 224.68 × 10
4
FIGURE 4
Percentage contributions of environmental variables used in the final MaxEnt model. On the left is D. punctatus and on the right is P. massoniana.
Wang et al. 10.3389/fpls.2024.1362020
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km
2
in 2050 and 235.75 × 10
4
km
2
in 2070. Under the SSP5-8.5
scenario (Figures 6B,D), the north-eastward expansion trend of D.
punctatus is even more pronounced, with the suitable habitat area
estimated at 240.02 × 10
4
km
2
in 2050 and 267.78 × 10
4
km
2
in
2070. Moreover, under both climate scenarios, the high-suitability
area for D. punctatus markedly increases, suggesting that future
climatic conditions may be more conducive to the survival of this
species and could increase the risk of outbreaks. In contrast, the
northward expansion of P. massoniana is slower (Figures 6E–H),
but the high-suitability area for this species continues to increase
under both climate scenarios, indicating that southern regions of
China will become more suitable for the growth of P. massoniana.
However, the suitable habitat of D. punctatus almost entirely
overlaps with the suitable area for P. massoniana, suggesting that
P. massoniana may continue to suffer from the pest in the future.
5 Discussion
5.1 Impact of environmental variables on
the distribution of D. punctatus and
P. massoniana
Environmental variables are widely acknowledged as key factors
affecting species distribution patterns. These variables impact the
growth, development, and interspecies interactions of species (Raza
et al., 2015). Future climate change is anticipated to markedly affect
the distribution of D. punctatus and P. massoniana. In our study,
the primary environmental variables influencing the distribution of
D. punctatus and P. massoniana are identified as the minimum
temperature of the coldest month (bio06), annual temperature
range (bio07), and annual precipitation (bio12). Among these
factors, those related to temperature contribute more significantly,
indicating a higher sensitivity of D. punctatus and P. massoniana to
temperature variations. Concurrently, annual precipitation is also
instrumental in the spatial distribution modeling of these two
species. This finding aligns with conclusions drawn from
physiological and ecological studies of D. punctatus and P.
massoniana (Zeng et al., 2010;Lei and Wang, 2024). The
development of D. punctatus requires a certain amount of
accumulated temperature. In the distribution areas of this species,
from north to south, the number of generations completed per year
increases with the rise in average annual temperature. Precipitation
influences the occurrence of pest outbreaks by altering air humidity
and through the washing effect on the larvae of D. punctatus.P.
massoniana, preferring a light-abundant and deep-rooted
environment, thrives in warm and moist climates and is typically
found in regions with distinct seasons and concurrent periods of
heat and rainfall (Fei et al., 2014;Wang et al., 2016;Yan et al., 2019).
The distribution area of D. punctatus lies within China’s subtropical
monsoon and tropical monsoon climate zones, characterized by hot
summers, mild winters, minimum average temperatures above 0°C
in the coldest months, and annual rainfall ranging from 1000 to
2000 mm. This region predominantly features P. massoniana as its
representative vegetation. Suitable climatic conditions and extensive
P. massoniana forests provide favorable conditions for outbreaks of
D. punctatus. In addition to temperature and precipitation,
environmental factors such as elevation, solar radiation intensity,
predation competition, and extent of vegetation cover significantly
influence the distribution of insects and plants (Li et al., 2023). For
example, during the hatching period of the D. punctatus, larvae
often disperse with the wind, with their direction of spread being
influenced by the wind direction. The dispersal process is affected
by wind force, wind speed, and topography (Chen, 1990). Soil pH
values can impact the distribution of P. massoniana, a species that
prefers acidic soils and is intolerant to saline conditions.
Appropriate climatic and soil environments are essential for the
formation of P. massoniana forests. Changes in P. massoniana
forests can also affect the distribution patterns of the D. punctatus.
However, integrating all influencing factors into a single model to
simulate the potential distribution of species is a challenging task.
Moreover, introducing too many variables may lead to increased
multicollinearity issues, diminishing the impact of key variables.
Nonetheless, the predictions of future suitable habitat migration
changes in this study are consistent with the growth habits of D.
punctatus and P. massoniana, and thus our results are a valuable
reference for potential suitability forecasts of these two species
under the context of climate change.
BA
FIGURE 5
Current distribution of D. punctatus and P. massoniana in China. (A) D. punctatus;(B) P. massoniana.
Wang et al. 10.3389/fpls.2024.1362020
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5.2 Changes in the distribution areas of D.
punctatus and P. massoniana under future
climate scenarios
Under all future climate scenarios, the total distribution areas of
D. punctatus and P. massoniana are projected to increase to varying
degrees compared to the present, generally showing a trend of
northward expansion (Figures 7,8). By 2070, under the SSP5-8.5
scenario, predictions indicate that the suitable habitat areas for these
two species will reach their maximum. Numerous studies have
highlighted that climate change may cause significant shifts in
species distribution patterns. Under the SSP1-2.6 scenario, by 2050,
the new areas for D. punctatus are expected to include central
Shandong, Henan, parts of Yunnan, and some areas of Liaoning
(Figure 8A). By 2070, although the suitable area for D. punctatus
continues to expand northward, the increase is relatively modest, and
the suitable areas in the Yunnan region will have decreased slightly
compared to 2050 (Figure 8C). Under the SSP5-8.5 scenario, the
B
CD
EF
GH
A
FIGURE 6
Suitable habitat maps for D. punctatus and P. massoniana under two different future scenarios. (A) D. punctatus SSP1-2.6-2050; (B) D. punctatus
SSP5-8.5-2050; (C) D. punctatus SSP1-2.6-2070; (D) D. punctatus SSP5-8.5-2070; (E) P. massoniana SSP1-2.6-2050; (F) P. massoniana SSP5-8.5-
2050; (G) P. massoniana SSP1-2.6-2070; (H) P. massoniana SSP5-8.5-2070.
Wang et al. 10.3389/fpls.2024.1362020
Frontiers in Plant Science frontiersin.org09
changes in suitable habitat for D. punctatus are more significant,
expanding overall toward the northeastern region, covering areas
from southern Gansu through Shaanxi, Shanxi, Hebei, Beijing, and
Tianjin, and extending to Liaoning and parts of Jilin (Figures 8B,D).
In future periods, the expansion area of the suitable distribution of D.
punctatus generally exceeds its contraction area, indicating that future
climatic conditions will be more favorable for the survival of this
species. In contrast, the changes in suitable habitat for P. massoniana
are relatively small but also exhibit a trend of northward expansion
(Figure 8). In both future climate scenarios, the changes in suitable
habitat for P. massoniana are broadly consistent, showing a trend of
northward expansion by 2050 (Figures 8E,F), with the expansion
area mainly including Henan and Shandong, as well as central Shanxi
and parts of Gansu in the SSP5-8.5 scenario. By 2070, the suitable
habitat for P. massoniana remains relatively stable under both climate
scenarios, with only a small part of the area experiencing expansion
(Figures 8G,H). In these future climate scenarios, the stable areas of
suitable habitat for D. punctatus and P. massoniana are essentially
consistent, implying that P. massoniana will continue to face pest
risks in the future.
5.3 Changes in the habitat centroids of D.
p unctatus and P. massoniana under future
climate scenarios
Climate change is likely to cause significant changes in species
distribution patterns, prompting their northward migration
(Chen et al., 2022). Long-term climate observations indicate that,
with the global warming trend, China’s annual average temperature
is expected to rise by approximately 2.6°C, particularly under the
high greenhouse gas emission scenario SSP5-8.5, where the
temperature increase will be more pronounced. Concurrently, the
annual average precipitation is projected to increase by 5.2%,
particularly in North China and the Northwest region (Fu and
Jiang, 2011). These changes are anticipated to further promote the
northward migration of many species. Under current climatic
conditions, the centroids of suitable areas for D. punctatus and P.
massoniana are situated in Hunan Province (Figure 9). Model
predictions for the SSP1-2.6 scenario suggest that from the
present to 2050, the centroid of the suitable habitat for D.
punctatus will move 59.37 km to the northwest, and from 2050 to
2070, it will further move 31.52 km to the northeast., respectively,
resulting in a total northward shift of 90.48 km. Under the SSP5-8.5
scenario, from the present to 2050, the centroid of the suitable
habitat for D. punctatus is projected to move 84.85 km northwest,
and from 2050 to 2070, it is expected to shift 76.54 km northeast,
totaling a northward movement of 156.43 km. The suitable habitat
centroid of P. massoniana also shifts northward, although the extent
of migration is smaller, moving northward by 37.20 km and
73.09 km under the two future climate scenarios by 2070. This
may be associated with the ecological characteristics of P.
massoniana, whose needle-like leaves effectively prevent water
evaporation, thus reducing its sensitivity to temperature changes.
These results indicate that the suitable areas for D. punctatus and P.
massoniana are expected to expand and migrate toward higher
latitude regions with increasing temperature and precipitation,
aligning with their preference for warm and humid environments.
Global warming facilitates the spread of insects limited by low
temperatures to higher latitude areas. Future trends of surface
warming in China intensifying toward higher latitudes and the
Tibetan Plateau, coupled with increased winter precipitation in the
north, provide favorable conditions for the northward expansion of
D. punctatus. Under different future climate scenarios, the
FIGURE 7
Suitable habitat areas of D. punctatus and P. massoniana under different climate scenarios.
Wang et al. 10.3389/fpls.2024.1362020
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movement directions and ranges of D. punctatus and P. massoniana
show similarities, indicating that P. massoniana may face greater
pest risks in the future.
Therefore, in the context of climate change, it is vital to
effectively manage D. punctatus in various suitable habitats and to
protect P. massoniana resources. Strengthening pest detection and
adopting more proactive management practices are essential.
Enhanced pest control measures in high-suitability areas for D.
punctatus are critical, whereas in low-suitability areas, considering
ecosystem conservation, reducing the use of chemical agents in
favor of natural control methods is recommended. For P.
massoniana, trunk injection technology can potentially be
employed as an effective method to control pest infestations.
5.4 Similarity in centroid shift changes
between D. punctatus and P. massoniana
In this study, we employed the statistical method of cosine
similarity to assess the similarity in the direction of centroid
B
CD
EF
GH
A
FIGURE 8
Geographic distribution changes of D. punctatus and P. massoniana under different future climate scenarios. (A) D. punctatus SSP1-2.6-2050; (B) D.
punctatus SSP5-8.5-2050; (C) D. punctatus SSP1-2.6-2070; (D) D. punctatus SSP5-8.5-2070; (E) P. massoniana SSP1-2.6-2050; (F) P. massoniana
SSP5-8.5-2050; (G) P. massoniana SSP1-2.6-2070; (H) P. massoniana SSP5-8.5-2070.
Wang et al. 10.3389/fpls.2024.1362020
Frontiers in Plant Science frontiersin.org11
migration for D. punctatus and P. massoniana under different
future climate scenarios and at two time points. Cosine similarity
measures the similarity between two vectors by calculating the
cosine of the angle between them. A cosine similarity value of 1
indicates that the two vectors are in the same direction, while a
value of −1 indicates they are in opposite directions. Under the
scenarios SSP1-2.6 and SSP5-8.5, from the present to 2050, the shift
in distribution centroids for D. punctatus and P. massoniana
showed a high degree of similarity, with a similarity index of 0.99.
This indicates that the migration directions of the two species are
almost identical. Furthermore, under the SSP5-8.5 scenario by 2050,
their distribution centroids of D. punctatus and P. massoniana are
closest, at a mere distance of 25.18 km. These findings reveal a
critical insight: the period from the present to 2050 could be a high-
risk phase for P. massoniana forests due to D. punctatus infestation,
making this interval crucial for the control and management of D.
punctatus in pine forests. This discovery holds significant
implications for formulating future pest control strategies and
management measures. The similarity in the shift directions of
the distribution centroids for the two species between 2050 and
2070 under both scenarios also remains high, at 0.97 and 0.85,
respectively. However, if proactive control measures against D.
punctatus are implemented in the first phase, pest issues may be
mitigated in the latter half of the century.
In this study, we focused on analyzing the potential distribution
changes of D. punctatus and P. massoniana under future climate
change scenarios. However, the model does not encompass all key
factors that could influence the distribution of these two species, such
as geographical barriers, natural enemies, human activities, and land
use. Despite this, currently, no comprehensive model exists that can
integrate all these factors for species distribution prediction (Guan
et al., 2022). The future suitable habitats predicted in this study for the
two species align with their growth habits, providing a valuable
reference for understanding the habitat changes and migration
directions of D. punctatus and P. massoniana in the context of
climate change. Future research should consider a broader range of
influencing factors to develop more comprehensive species
distribution prediction models, which would enable more accurate
predictions of the distribution of these two species.
5.5 Management and
control recommendations
The impact of climate change on ecosystems has become
increasingly evident, leading to the northward spread of forest
pests including Monochamus alternatus in China and the general
trend of the potato pest Schrankia costaestrigalis moving toward the
northeast and higher latitudes (Xu et al., 2020;Xian et al., 2023). In
our study, the increases in two primary factors of climate change—
temperature and precipitation—are key in influencing the spread of
D. punctatus. As global climate change intensifies, global warming
has significantly accelerated the northward expansion of D.
punctatus. This phenomenon is concerning in that, in the not-
too-distant future, D. punctatus may continue to migrate northward
in search of new habitats, posing a greater threat to tree species in
the northern regions. In addition, our research found that D.
punctatus and P. massoniana (its primary host) share similar
BA
FIGURE 9
Spatial changes of the geometric centroids of suitable habitat areas by 2050 and 2070 under two different climate change scenarios: (A) D.
punctatus;(B) P. massoniana.
Wang et al. 10.3389/fpls.2024.1362020
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geographic distributions and migration trends. This finding
suggests that in the future, with the further intensification of
climate change, P. massoniana may face more severe pest risks.
Therefore, our study underscores the importance of monitoring and
managing these pest migration trends to mitigate their potential
impact on ecosystems.
This study outlines three pivotal strategies for managing D.
punctatus and conserving P. massoniana: (1) Given the projected
suitable habitats in the current climatechangescenario,enhancing
surveillance and early warning systems for D. punctatus is critical. This
encompasses promptly identifying and managing pest outbreaks to
ensure swift and effective containment; (2) Considering the anticipated
distribution patterns of D. punctatus, particularly in newly affected
regions in northern China, reinforcing preventative measures is
imperative. Forestry practices, in establishment and regeneration,
should aim to modify habitats to create conditions less conducive to
the proliferation of D. punctatus. This holistic approach is vital for
curbing the spread of D. punctatus and mitigating its detrimental effects
on ecosystems; (3) In P. massoniana forests already experiencing D.
punctatus infestation, introducing broadleaf species and creating mixed
coniferous-broadleaf forests are recommended. This strategy curbs the
likelihood of extensive, high-density D. punctatus outbreaks and
bolsters the overall resilience and stability of forest ecosystems.
Additionally, utilizing the natural predators of D. punctatus (e.g.,
Trichogramma) for biological control is advisable. Cultivating and
periodically releasing Trichogrammatid in suitable regions could
serve as an effective natural control mechanism, preventing
widespread D. punctatus infestations and thus safeguarding P.
massoniana resources.
6 Conclusion
This study analyzed the distribution patterns of D. punctatus and
its host P. massoniana based on occurrence records and current and
future climate data. The MaxEnt model, parameterized through
optimization, was employed to predict the distribution of both
species under current and future conditions. The results indicate
that under current climate conditions, D. punctatus and P.
massoniana are primarily distributed in the region south of China’s
Qinling–Huaihe line. The main environmental variables influencing
the distribution of both species are related to temperature and
precipitation, including the lowest temperature of the coldest
month, the annual temperature range, and annual precipitation.
Under future climate conditions, the suitable habitat area for D.
punctatus is expected to increase and shift toward higher latitudes. In
the SSP5-8.5 climate scenario, characterized by increased greenhouse
gas emissions and intensified global warming, D. punctatus is
projected to expand further toward higher latitudes. The similarity
in the migration direction between the two species is remarkably
high, reaching 0.99 in the SSP5-8.5 scenario by 2050. Meanwhile, the
distance between the distribution centroids of D. punctatus and P.
massoniana is only 25.18 km during this period, signifying a critical
phase for preventing and managing D. punctatus infestations in P.
massoniana forests. Although the future suitable habitat and
migration direction of P. massoniana are highly similar to those of
D. punctatus, the changes are relatively slow, indicating that P.
massoniana will continue to be affected by D. punctatus
infestations in the long term. This study on the distribution of D.
punctatus and P. massoniana provides valuable theoretical insights
for the future prevention of D. punctatus infestations and the
conservation of P. massoniana resources.
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material. Further inquiries can be
directed to the corresponding authors.
Author contributions
YW: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Project administration, Software,
Visualization, Writing –original draft, Writing –review & editing.
ZY: Project administration, Writing –review & editing. GM: Writing
–review & editing, Conceptualization, Funding acquisition,
Methodology, Supervision. XZ: Project administration, Writing –
review & editing. CY: Writing –review & editing, Project
administration. YC: Funding acquisition, Methodology,
Supervision, Validation, Writing –review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This
research was funded by the National Natural Science Foundation
of China (grant numbers 61962055 and 31960142).
Acknowledgments
We thank the editors and reviewers for their time and effort in
reviewing and improving this work. We also would like to thank
The Charlesworth Group (www.cwauthors.com)forlinguistic
assistance during the preparation of this manuscript.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
Wang et al. 10.3389/fpls.2024.1362020
Frontiers in Plant Science frontiersin.org13
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