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Article

Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China

by
Linfeng Yu
1,
Zhongyi Zhan
2,
Quan Zhou
2,
Bingtao Gao
2,
Lili Ren
2,
Huaguo Huang
3 and
Youqing Luo
2,*
1
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
2
Key Laboratory for Forest Pest Control, College for Forestry, Beijing Forestry University, Beijing 100083, China
3
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(12), 2728; https://doi.org/10.3390/rs14122728
Submission received: 21 April 2022 / Revised: 31 May 2022 / Accepted: 5 June 2022 / Published: 7 June 2022
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Abstract

:
Outbreaks of pine shoot beetles (Tomicus spp.) have caused widespread tree mortality in Southwest China. However, the understanding of the role of climatic drivers in pine shoot beetle outbreaks is limited. This study aimed to characterize the relationships between climate variables and pine shoot beetle outbreaks in the forests of Yunnan pine (Pinus yunnanensis Franch) in Southwest China. The pine shoot beetle-infested total area from 2000 to 2017 was extracted from multi-data Landsat images and obtained from field survey plots. A temporal prediction model was developed by partial least squares regression. The results indicated that multi consecutive year droughts was the strongest predictor, as such a condition greatly reduced the tree resistance to the beetles. The beetle-infested total area increased with spring temperature, associated with a higher success rate of trunk colonization and accelerated larval development. Warmer temperatures and longer solar radiation duration promoted flight activity during the trunk transfer to the shoot period and allowed the completion of sister broods. Multi consecutive year droughts combined with the warmer temperatures and higher solar radiation duration could provide favorable conditions for shoot beetle outbreaks. Generally, identifying the climate variables that drive pine shoot beetle outbreaks could help improve current strategies for outbreak control.

1. Introduction

Pine shoot beetles (Tomicus yunnanensis Kirkendall and Faccoli, and Tomicus minor Hartig) are considered the most destructive insects of coniferous forests in Southwest China. Their primary host is the Yunnan pine (Pinus yunnanensis Franch), a keystone species in Southwest China that plays an important role in water and soil protection. The Yunnan pine covers approximately five million hectares of the land surface in Yunnan Province, accounting for 52% of the forest area [1,2,3]. Millions of trees were killed during pine shoot beetle outbreaks in Southwest China during the 2000s. In addition, pine shoot beetles continued to spread over nearby districts in recent years, resulting in serious economic losses [4,5,6].
The life cycle of pine shoot beetles in Southwest China has been well characterized by several bioecological studies [4,7,8,9,10,11,12,13]. In principle, pine shoot beetles cause two different kinds of damage in different periods: shoot feeding and trunk attacks. During shoot damage periods, the newly adults emerged from trunks and fly to the crowns of nearby pines in early May, and feed inside the shoots from May to November to become sexually mature. Shoots damaged by the beetles eventually die. In the shoot transfer to trunk periods, mature pine shoot beetles prefer hosts with high shoot damage ratios to lay eggs, as extensive shoot feeding by adult beetles can make the trees susceptible to trunk attacks [14]. Some pine shoot beetles have the habit of sister breeding. Sexually mature adults move towards the trunk to lay eggs in early November. Then, adult beetles re-emerge in late December and fly to the pine crowns to feed in the shoots, followed by the initiation of a sister brood in March of the following year. The life cycle from egg hatching to adult emergent are all under bark during trunk damage periods. In Yunnan, the two Tomicus species can co-occur in the same pine tree [12,13]. Gallery construction and larval feeding disrupt the flow of nutrients within the phloem tissue and weakens or kills the hosts.
Beetles are among the most important biotic disturbance agents affecting forest ecosystems [15,16,17,18,19,20]. Although the factors driving recent outbreaks of beetles are complex, climate change is a major driver of beetle outbreaks [21,22,23,24,25,26,27]. Climate change facilitates the temporal and spatial dynamics of beetles in several ways. Temperature can directly affect the development time, metabolism, emergence synchronicity, and mortality of beetles [28,29,30,31,32,33,34,35]. Warmer temperatures in the growing season affect the reproduction and dispersal of beetles [36,37], enabling beetles to complete the first generation earlier and potentially a second generation [38]. The increase in winter temperature will also reduce the winter beetle mortality [39,40,41]. Many studies have shown that drought has driven historical bark beetle outbreaks [42,43,44,45]. The impacts of drought on beetle dynamics are reflected in the host tree, because the defense mechanism of trees may be damaged under drought conditions, especially extreme drought conditions. Drought stress may make host trees more susceptible to beetle infestation in several ways. For instance, by preventing pine trees from producing enough resin to resist the attack of beetles [46,47,48,49,50]. Drought stress also reduced the phloem thickness of trees, which has been shown to be positively correlated with beetle spawning [51]. The warmer and drier conditions associated with future climate change are thus expected to increase the severity of beetle outbreaks in coniferous forests [45,52].
The scientific literature on the relationship between the beetle and climate mainly focused on bark beetles, such as Dendroctonus ponderosae, Ips typographus, Orthotomicus erosus, etc. (e.g., [23,53,54,55,56]). Although Tomicus species (Coleoptera, Scolytidae) are important pests of pine forests in Europe [57,58,59], the United States [60,61,62], Asia [4], etc., little is known about how different climatic factors affect outbreaks of Tomicus species. In Europe, Horn et al. [63] found that T. destruens prefers warmer temperatures, whereas T. piniperda prefers colder climates. A few researchers have studied the effect of temperature on the development of Tomicus spp. [4,57,64,65,66]. To our knowledge, no study to date has examined the relationships between climate changes and outbreaks of T. yunnanensis and T. minor in China based on time-series data. Given that forest damage induced by pine shoot beetles has increased markedly during previous decades throughout Southwest China, there is a pressing need to understand the relative importance of various climate variables in driving outbreaks of these significant biotic disturbance agents.
Here, we characterized pine shoot beetle outbreak dynamics by analyzing data of beetle-infested total area extracted from multi-Landsat satellite images in Southwest China from 2000 to 2017. The objective of this study is to quantify the importance of key climate variables in driving pine shoot beetle outbreaks at a county level using partial least squares (PLS) regression. We hypothesized that (1) severe droughts, especially multi consecutive year droughts conditions, are an important predictor triggering the pine shoot beetle outbreak because of reduced host defenses, and (2) warmer temperatures and solar radiation would accelerate beetle development and promote flight activity. Detailed quantitative analyses that link the damages to Yunnan pines induced by pine shoot beetles to multiple climate variables, which will enhance our understanding of the causes of recent outbreaks and improve currently used strategies for outbreak control.

2. Materials and Methods

2.1. Study Area

Our study area was centered on Xiangyun County, Dali City, Yunnan Province, China (Figure 1a). According to the historical survey data of the local forestry bureau, there was an outbreak of pine shoot beetles in this area since 2010. The area has two seasons: the dry season (i.e., November–May) and the wet season (i.e., June–October). The average annual precipitation is 998 mm, and the average yearly temperature is 13.7 °C. The average monthly maximum and minimum temperatures are 27 °C and 3 °C (http://data.cma.cn/) (accessed on 1 January 2019). The boundary of Yunnan pine forest was extracted based on a subcompartment factor vector diagram of Xiangyun County (Figure 1c). Due to the poor soil and limited irrigation conditions, the main afforestation method of Yunnan pine is direct seeding afforestation. The forests in the study area are dominated by pure planted Yunnan pine forests and covers an area of approximately 2000 km2. A flowchart of the variable selection processes and the development of a prediction model is presented in Figure 2 below.

2.2. Response Variable

The response variable was the pine shoot beetle-infested total area, which was used as an indicator of beetle populations. The time trajectory of the beetle-infested total area was established from 2000 to 2017 via following steps.

2.2.1. Landsat Data

We used yearly Landsat imagery data from 2000 to 2017 to predict percent shoot damage ratios within a Landsat pixel. A total of 18 Landsat images (Table 1) were downloaded from the USGS GLOVIS website (http://glovis.usgs.gov) (accessed on 1 January 2019) and radiometrically normalized. The selection date and pretreatment method of Landsat images were described in a previous study [67]. Moisture stress index, MSI was chosen because previous research has shown that it is effective in detecting pine shoot beetle damage using satellite data [67]. MSI was calculated by the ratio of SWIR 1 and NIR bands. Compared with the “healthy pine” classes, the “non-healthy pine” classes have higher reflectance values in SWIR1 band and lower reflectance values in NIR band (Figure 3). We calculated the MSI anomalies, e.g., MSI’, the spectral anomaly for a given year is the difference between the multi-temporal undisturbed average (determined by the iterative process of eliminating the interference year) and the pixel value in a given year.

2.2.2. Field Data

Forty field plots (30 m × 30 m) established in November 2016 in the southeastern portion of the study area (Figure 1d) were used for model development and evaluation. We measured each tree’s shoot damage ratio (SDR) in each plot. SDR of each tree is the ratio of damaged shoots to total shoots. Plot SDR was the average SDR of all trees in the plot. The SDR was defined as the degree of damage sustained by beetle-infested forests. Plots included different degrees of damage to beetle-infested forests, and the SDR ranged from 0 to 90%. We used a Trimble GeoExplorer 3 GPS to record the geographical coordinates of each plot.

2.2.3. Statistical Modeling of Percent Shoot Damage Ratios

We developed and evaluated a regression model based on the MSI’ (independent variable) and SDR of field plots in 2016 (dependent variable) to predict the percentage of SDR induced by pine shoot beetles within a grid cell (30 m). The inversion model was detailed described in our previous study [68]. The overall mean R2 for the inversion model for the reference data (i.e., from field sample plots) vs. model predictions (i.e., from Landsat images) of SDR was 0.837. The slopes and intercepts of observed vs. predicted values (Figure 4) indicated that the model tended to slightly overestimate the SDR of healthy and slightly damaged forests (SDR range: 0–20%) and slightly underestimate the SDR of severely damaged forests (SDR range: 50–100%). Estimates of SDR for moderately damaged forests (SDR range: 20–50%) were relatively accurate. Based on the performance of the inversion model, we defined pixels with SDR >20% as beetle-infested areas and built a time series of the pine shoot beetle-infested total area from 2000 to 2017. The SDR map for 2010 was also validated by comparison with the field survey data collected by the Xiangyun County Forestry. Investigators conducted a comprehensive ground survey of all Yunnan pine forests in study area in 2010. The damaged shoots were sampled to determine whether they were damaged by pine shoot beetles. Moreover, SDR was used as an index to determine the infested area of pine forests. Finally, taking the stand class as the unit, the investigators drew the distribution map of the forests damaged by pine shoot beetles in 2010.
Field sample surveys conducted by local foresters in the study area indicated that the infested area of pine shoot beetles in Pinus yunnanensis accounts for above 95% of the total infested area of pine forest pests; the density of pine shoot beetles was much higher in beetle-infested forests than other beetle species, such as Shirahoshizo patruelis Voss and Monochamus alternatus Hope. We assumed that pine shoot beetles primarily drove recent forest disturbance. The error associated with the prediction of the pine shoot beetle-infested area will not substantially affect the results.

2.3. Explanatory Variables

We used climatic variables as independent predictors to make inferences about pine shoot beetle infestation. Daily weather data from five local weather stations (Lijiang, Dali, Jingdong, Chuxiong, and Huaping) within the study area from 1970 to 2017 were interpolated via second-order inverse distance weighting. The spatial resolution of the interpolated raster was 30 m. Daily climate data were downloaded from the China Meteorological Data Sharing Service System (http://data.cma.cn) (accessed on 10 November 2018). We averaged the values of all pixels in the study area to obtain climate variables for each day, month, and year. Climate explanatory variables were selected to analyze the relative importance of different climate drivers in affecting beetle outbreaks.
Temperature is believed to be one of the limiting factors affecting the pine shoot beetle population [7,64,65,69]. In this study, we tried to capture the effects of various temperature factors on the different life stages of pine shoot beetles. We used the following temperature variables (Table 2): (1) annual mean temperature; (2) monthly mean temperature; (3) the minimum temperature of the coldest month; (4) the minimum daily temperature of the coldest month; and (5) quarter temperature. We measured Tspring, Tsummer, Tautumn, and Twinter. According to previous studies [64,65], light intensity plays a role in the flight activities of pine shoot beetles. To explore the relationship between solar radiation and flight activity of pine shoot beetles, we also measured (1) monthly cumulative solar radiation duration; (2) quarter cumulative solar radiation duration (Sspring, Ssummer, Sautumn, and Swinter); and (3) cumulative solar radiation duration in the current year.
Drought events are thought to be an important factor triggering beetle outbreaks [42,47,70]. The effects of precipitation might be long-lasting. In this study, precipitation variables were divided into three groups (Table 2), including (1) quarter precipitation (Pspring, Psummer, Pautumn, and Pwinter); (2) annual cumulative precipitation (current-year precipitation was denoted as “P0”, previous-year precipitation was denoted as “P1” to determine whether the previous year’s precipitation was related to the current infested area); and (3) cumulative precipitation for the previous two or three years (P12 and P123, respectively), which were determined by adding the previous years together and represented multi consecutive year droughts-related host tree stress. Temperature-based drought indexes were not used to avoid correlations with thermal sums. These variables were used because they describe variation in temperature, solar radiation, and precipitation related to the pine shoot beetle phenology and physiological stress experienced by host trees [71,72]. The climate variables considered are shown in Table 2.

2.4. Statistical Analysis

We characterized the relationships between climate variables and beetle-infested total area by building a temporal prediction model for the entire study area using PLS regression analysis. This analysis was proven to be an effective method in assessing the relationship [73]. When relating the pine shoot beetle-infested total area with multiple climate variables, we will encounter the problem that climate variables are always highly correlated and it is difficult to model with traditional technology. PLS method is a regression technique that can be used effectively in these situations, where the number of independent variables greatly exceeds the number of observations and when independent variables exhibit high multicollinearity [74,75]. The problem of overfitting in the PLS model is avoided by conducting principal component analysis of several potential factors and then using the principal components to fit the regression model. PLS models were fitted using climate variables as the independent variables and beetle-infested total area as the dependent variable. The most important results of the PLS model were the variable importance projection (VIP) scores and the standardized model coefficients. The VIP values indicate the importance of independent variables in explaining changes in the dependent variable, and 1 is usually used as the threshold for identifying important variables [76]. The standardized model coefficients indicate the intensity and direction of the effect of independent variables on changes in the dependent variable [73,77]. The interpretation of the standardized model coefficients is based on the premise that warm temperatures and high solar radiation duration are positively correlated with the beetle-infested total area, and high precipitation is negatively correlated with the beetle-infested total area.
Model prediction accuracy was determined by the coefficient of determination (R2) and the mean absolute error (MAE). R2 and MAE were calculated by Equations (1) and (2), respectively:
R 2 = 1 − ∑ i = 1 n ( y i ^ − y i ) 2 ∑ i = 1 n ( y i ¯ − y i ) 2
MAE = 1 n ∑ i = 1 n | y i − y i ^ |
where n represents the number of samples; y i represents the predicted value of sample i; y i ^ represents the actual value of sample i; and y i ¯ represents the average of i samples.
All statistical analyses were performed in Ref. [78], and the packages pls, ggplot2, and CarData were used.

3. Results

3.1. Changes in Beetle-Infested Total Forest Area from 2000 to 2017

The area of pine shoot beetle-infested forests from 2000 to 2017 showed that the outbreak of pine shoot beetle changed from local and small attack patches to large-area infestation (Figure 5). The percent similarity between the beetle-infested forest prediction map for 2010 with the observed map of the beetle-infested area generated by the Xiangyun County Forestry Bureau reached 76.72% (Figure 6), the predicted infested area and observed infested area were all approximately 10,000 hectares, which verified the reliability of our SDR prediction model. The beetle-infested total area from 2000 to 2017 was plotted against time (Figure 7A). The result showed that the beetle-infested total area had been slowly increasing since ca 2000, and the initial mostly scattered damage turned to large-scale damage after 2010. In the following years, the damage extent continued to increase, and in 2014 reached 25,000 ha, which makes up approximately 37% of the total Yunnan pine forest area in the region, and decreased in 2015.
We characterized changes in climate variable anomalies (differences from mean values calculated from 1971 to 2000) from 2000 to 2017 (Figure 7B–L). The analysis of the annual precipitation values (Figure 7D) shows the study area experienced multiple consecutive years of extreme to exceptional drought from 2009 to 2014, with 100–200 mm precipitation reduction, nearly 20% of the average annual cumulative precipitation (998 mm per year). A more detailed analysis of Pspring (Figure 7B) shows that Pspring anomalies were −76 mm and −50 mm in 2014 and 2015, compared with 100 mm of the average Pspring, indicating that exceptional spring drought may coincide with large-scale pine shoot beetle outbreaks. Substantially increased temperatures and solar radiation duration were accompanied by severe drought conditions occurring throughout the study area. The analysis of the temperature series reveals that an increasing trend has been evident throughout 2000–2017, approximately 1 °C above average for the annual temperature from 2009 to 2014 (Figure 7L). The Tspring has also warmed more than the yearly average, with an approximate 0.5 °C to 1 °C increase in drought years (Figure 7J). It is worth noting that Tspring increased by 2 °C in 2014. In addition, above-average solar radiation duration often coincided with drought and high temperature (Figure 7F–H).

3.2. Responses of Pine Shoot Beetle Outbreaks to Climate Variation from 2000 to 2017

The PLS model built to describe pine shoot beetle infestation provided significant components that together explained 75.7% of the original variance, revealing that climatic suitability was significantly related to the infestation success. The PLS regression model provided clear insights into the responses of pine shoot beetle outbreaks to climate variables (Figure 8). The VIP values of MTMay and MTJune exceeded one (the threshold value for variable importance). Tspring, Tsummer, and Twinter were strongly related to pine shoot beetle outbreaks, whereas Coldt and Tmin, which related to winter mortality, were unimportant. The annual mean temperature had the largest VIP value among the temperature variables (1.748). Model coefficients of important temperature variables were all positive, indicating that warmer temperatures had a strong effect on pine shoot beetle outbreaks. Solar radiation duration showed similar results; MSMay, MSJune, Sspring, and Smean were positively correlated with beetle-infested total area.
For quarter precipitation variables, only Pspring was strongly related to pine shoot beetle outbreaks. The VIP of P1 (1.423) was greater than the VIP of P0 (0.886), indicating that precipitation had a lagged effect on pine shoot beetle outbreaks; P123 had the largest VIP value (2.016) among all climate variables, indicating that multi consecutive year droughts was the strongest predictor of pine shoot beetle outbreaks. Model coefficients indicated that precipitation variables were negatively related to the beetle-infested total area.
Pine shoot beetle-infested total area was the dependent variable, and the 15 important climate variables selected by the VIP were the independent variables in the PLS model (Table 3). There was a linear relationship between the measured value and the predicted value of the beetle-infested total area (Figure 9). The adjusted R2 value was 0.748, with the MAE of 2681.05 hectares, indicating that the model could be used to predict the pine shoot beetle-infested total area in the study region.

4. Discussion

The lack of historical pine shoot beetle monitoring data makes it difficult to explore the relationship between beetle outbreaks and climate factors. It is mainly due to the shortcomings of traditional field investigation methods, such as a high cost, poor timeliness, a low efficiency, and the unsuitability for a large-scale investigation. This research successfully predicted the infested forests from 2000 to 2017 using multi-date Landsat images. According to the long-term historical data of pine shoot beetle-infested total area, our study provided the first systematic assessment of the effects of precipitation, temperature, and solar radiation duration on infestations of Tomicus spp. at the county level. Many studies have predicted yearly infestations of beetles at the county level [79,80,81]. The county level is important for forecasting because management recommendations are typically implemented at the county level in China. Furthermore, the findings at this scale provide more comprehensive insights into the determinants of pine shoot beetle infestations in Southwest China.

4.1. Influence of Drought on Pine Shoot Beetle Outbreaks

We found that the large-scale outbreak was observed subsequently during or after a period of reduced precipitation spanning from 2009–2014. We compared the effect of precipitation conditions of the current year with different sets of previous years (t − 3) and found that precipitation in the previous year (P1) had a lagged effect on pine shoot beetle outbreaks. Cumulative precipitation in the previous 1–3 years (P123) was the best predictor of pine shoot beetle outbreaks, which supported our first hypothesis, and more infections were likely to develop during or after a prolonged drought, which was consistent with previous studies [56,82,83,84,85]. Sufficiently severe drought can reduce the amount of the resin ducts in pines [86,87,88], which is an important physical barrier to prevent pine shoot beetles to lay eggs and hatch larvae [4,89,90,91,92]. Pine trees under drought stress are more susceptible to pine beetles (Figure 10), because pine beetles are secondary pine pests, mainly attacking weak pine trees [57,93].
Another interesting finding is that Spring drought was strongly related to pine shoot beetle infestations in Southwest China, which is consistent with some studies [94,95] that spring drought may result in large scale outbreak of pine shoot beetles. Pine shoot beetles in Southwest China are more aggressive than those in Europe. The drought period from October to May may be one of the main factors for their reproduction in living trunks [4]. Water is the main limiting factor for the radial growth of conifers in the Yunnan and Guizhou region [96], especially affected by water conditions in spring [97,98,99]. Spring drought that contributes to water deficits would strongly reduce tree growth [100], making Yunnan pines more vulnerable to pine shoot beetle infestation.

4.2. Influence of Warmer Temperature and Higher Solar Radiation Duration on Pine Shoot Beetle Outbreaks

We selected several temperature and solar radiation variables as predictors in the regression model to explore their effects on pine shoot beetle phenology. Our results verified our second hypothesis that spring climate variables, including Tspring and Sspring, were positively and significantly associated with the beetle-infested total area. These results can be explained as follows: The main development period of pine shoot beetles from egg to adult was in spring (February–May). The oviposition rate and the rate of egg gallery construction were positively correlated with the temperature [7,64,65,69]. Furthermore, the breeding success ratio has a strong positive relation to warm spring temperature [10]; the development time of egg, larval, and pupa stages of pine shoot beetles decreased drastically with warm spring temperatures, and improved their survival rate [7,65], which strongly affected the yearly population dynamics of pine shoot beetles.
Colonization of the shoots following emergence from the trunk, which corresponds to the beginning of the shoot damage period, is a key dispersal process in pine shoot beetles. In this study, the positive effect of mean temperature on pine shoot beetle occurrence prevailed during May and June, as these months correspond to the peak flight period (Figure 10). These findings were consistent with the previous studies showing that temperature and light intensity were critically important factors affecting the flight activity and peak period of pine shoot beetles [64,65,101,102,103,104]. Most pine shoot beetles fly in the afternoon and avoid flying at night. The higher the temperature at the beginning of the flight period, the larger the number of beetles flying on that day [57,69,102,105]. In addition, phototaxis plays an important role in guiding the newly emerged adults to feed shoots during the trunk transfer to shoot period [64,102,106]. The results of our study indicated that increased solar radiation duration in May accelerated the flight behavior of pine shoot beetles.
Winter temperature was an important factor associated with pine shoot beetle mortality [65,107,108]. In our study, ColdT and Tmin had no significant effect on pine shoot beetle outbreaks, this result can be explained because there were no extreme climate changes in the winters of 2000 to 2017, such as storms or excessive low temperatures, the ColdT and Tmin were greater than −1 °C and 3 °C, respectively in our study area. The supercooling points of adults of T. yunnanensis (−15.39 ± 4.53 °C) and T. minor (−13.81 ± 3.38 °C) in the study area, indicated that adults had a sufficient cold tolerance to withstand the lowest temperatures. On the other hand, Twinter has a strong positive relation to pine shoot beetle outbreaks. Due to the warm Twinter (greater than 10 °C) in our study area, Tomicus spp. did not overwinter and can thus continue to feed on the shoots [4,93], which was different from the overwinter in the bark at the base of living pine trees in Northern China and some European countries, e.g., [57,64,65]. In addition, the reemergence rate has a positive response to temperature [69], the warm Twinter also provides an opportunity for sister broods to reduce their risk of death and complete their development during winter [4]. The propagation of pine shoot beetles can be enhanced substantially by sister broods [108,109,110], sister broods were considered to be a mechanism to avoid intraspecific competition in large-scale attacks and increased the viability of future generations [111]. Sister broods would significantly increase pine shoot beetle populations, resulting in an increased attack pressure in subsequent seasons, especially during outbreaks.

5. Conclusions

This study reveals that the climatic suitability of both host trees and pine shoot beetles explains the pine shoot beetle outbreaks in Southwest China. Our results evidence that Yunnan pine forests are more vulnerable to pine shoot beetle infestation after drought conditions prevailed in preceding years, particularly when these sites are also under warmer spring temperature and a higher solar radiation in the current year at the county level. A prediction model that incorporates multiple climate variables might be helpful for predicting infested forests damaged by pine shoot beetles. This in-depth study of climate–beetle relationships enhances our understanding of the factors affecting the success of pine shoot beetle populations at the county level. This work would help forestry managers take appropriate measures to prevent the spread of pine shoot beetles in the Yunnan pine forest.

Author Contributions

Conceptualization, L.Y. and Y.L.; methodology, L.Y. and Z.Z.; software, Q.Z. and B.G.; writing—original draft preparation, L.Y.; writing—review and editing, L.R., H.H. and Y.L. All authors wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Beijing’s Science and Technology Planning Project (Z201100008020001) and Major emergency science and Technology Project of National Forestry and Grassland Administration (ZD202001).

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank the Yunnan Forestry Inspection Bureau and Forestry Bureau of Xiangyun County for supporting the field experiments and data collection. We appreciate the linguistic assistance provided by TopEdit during preparation of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Yunnan province in China; (b) Landsat image of Xiangyun County; and (c) Yunnan pine forests in the study area. (d) Distribution of the 40 field plots in the study area.
Figure 1. (a) Yunnan province in China; (b) Landsat image of Xiangyun County; and (c) Yunnan pine forests in the study area. (d) Distribution of the 40 field plots in the study area.
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Figure 2. Flowchart of the variable selection processes and the development of a prediction model.
Figure 2. Flowchart of the variable selection processes and the development of a prediction model.
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Figure 3. Reflectance values of the non-healthy pine and healthy pine based on the Landsat image.
Figure 3. Reflectance values of the non-healthy pine and healthy pine based on the Landsat image.
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Figure 4. Comparison between reference (i.e., from field plots) and Landsat-predicted percent shoot damage ratios within 30-m grid cells using linear regression models. Solid line is the regression line; gray dashed line is the 1:1 line; RMSE, root mean square error. SDR: shoot damage ratio.
Figure 4. Comparison between reference (i.e., from field plots) and Landsat-predicted percent shoot damage ratios within 30-m grid cells using linear regression models. Solid line is the regression line; gray dashed line is the 1:1 line; RMSE, root mean square error. SDR: shoot damage ratio.
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Figure 5. Time series of Yunnan pine forests with different shoot damage ratios in the study area from 2000 to 2017.
Figure 5. Time series of Yunnan pine forests with different shoot damage ratios in the study area from 2000 to 2017.
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Figure 6. Comparison of the predicted area (left) and observed area (right) of beetle infestation in 2010.
Figure 6. Comparison of the predicted area (left) and observed area (right) of beetle infestation in 2010.
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Figure 7. Temporal changes of infested area and climate variables anomalies (differences from mean values calculated from 1971 to 2000) from 2000 to 2017. (A): Beetle-infested total area; (B): Pspring (Cumulative precipitation between March to May) anomaly; (C): Psummer (Cumulative precipitation between June to August) anomaly; (D): P0 (Cumulative precipitation in the current year (0)) anomaly; (E): P123 (Cumulative precipitation in previous 1–3 years) anomaly; (F): MSMay (Mean solar radiation duration in May) anomaly; (G): Sspring (Cumulative solar radiation duration between March to May) anomaly; (H): Smean (Annual cumulative solar radiation duration) anomaly; (I): MTMay (Mean temperature in May); MTJune (Mean temperature in June) anomaly; (J): Tspring (Mean temperature between March to May) anomaly; (K): Tsummer (Mean temperature between June to August) anomaly; and (L): Tmean (Annual mean temperature) anomaly.
Figure 7. Temporal changes of infested area and climate variables anomalies (differences from mean values calculated from 1971 to 2000) from 2000 to 2017. (A): Beetle-infested total area; (B): Pspring (Cumulative precipitation between March to May) anomaly; (C): Psummer (Cumulative precipitation between June to August) anomaly; (D): P0 (Cumulative precipitation in the current year (0)) anomaly; (E): P123 (Cumulative precipitation in previous 1–3 years) anomaly; (F): MSMay (Mean solar radiation duration in May) anomaly; (G): Sspring (Cumulative solar radiation duration between March to May) anomaly; (H): Smean (Annual cumulative solar radiation duration) anomaly; (I): MTMay (Mean temperature in May); MTJune (Mean temperature in June) anomaly; (J): Tspring (Mean temperature between March to May) anomaly; (K): Tsummer (Mean temperature between June to August) anomaly; and (L): Tmean (Annual mean temperature) anomaly.
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Figure 8. Results of the partial least squares regression analysis. Blue bars (figure above) indicate that the variable importance projection (VIP) score exceeds the 1 threshold. Yellow bars (figure below) indicate positive (and important) model coefficients, whereas red bars indicate negative (and important) model coefficients, and gray bars indicate unimportant model coefficients.
Figure 8. Results of the partial least squares regression analysis. Blue bars (figure above) indicate that the variable importance projection (VIP) score exceeds the 1 threshold. Yellow bars (figure below) indicate positive (and important) model coefficients, whereas red bars indicate negative (and important) model coefficients, and gray bars indicate unimportant model coefficients.
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Figure 9. Comparison between the measured and predicted beetle-infested total area using linear regression models. Dashed line is the regression line; RMSE, root mean square error.
Figure 9. Comparison between the measured and predicted beetle-infested total area using linear regression models. Dashed line is the regression line; RMSE, root mean square error.
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Figure 10. Influence of important climate variables on pine shoot beetle outbreak; The life cycle of Tomicus spp. Red indicates the trunk damage period, and blue indicates the shoot damage period.
Figure 10. Influence of important climate variables on pine shoot beetle outbreak; The life cycle of Tomicus spp. Red indicates the trunk damage period, and blue indicates the shoot damage period.
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Table
Table 1. Landsat TM, ETM+, OLI images used in time series analysis.
Table 1. Landsat TM, ETM+, OLI images used in time series analysis.
YearMonth/DayJulian Day of YearSensorCloud Cover
12000November/16321ETM+6%
22001December/05339ETM+1%
32002December/24358ETM+0%
42003November/17321TM50%
52004November/11316ETM+0%
62005November/6310TM50%
72006November/17321ETM+0%
82007November/20324ETM+0%
92008November/14319TM53%
102009November/09313ETM+0%
112010December/14348ETM+4%
122011November/15319ETM+14%
132012November/01306ETM+13%
142013November/12316OLI1.65%
152014November/23327ETM+3%
162015December/12346ETM+12%
172016November/20325OLI0%
182017November/15319ETM+4%
Table
Table 2. Description of the explanatory variables used in the PLS model of the probability of pine shoot beetle infestation of forests in Southwest China from 2000 to 2017.
Table 2. Description of the explanatory variables used in the PLS model of the probability of pine shoot beetle infestation of forests in Southwest China from 2000 to 2017.
Climate VariablesDescription
PrecipitationPspringCumulative precipitation between March to May.
PsummerCumulative precipitation between June to August.
PautumnCumulative precipitation between September to November.
PwinterCumulative precipitation between December to February.
P0Cumulative precipitation in the current year (0).
P1Cumulative precipitation in the previous year (1).
P12Cumulative precipitation in previous 1–2 years.
P123Cumulative precipitation in previous 1–3 years.
TemperatureMTMonthly mean temperature.
TspringMean temperature between March to May.
TsummerMean temperature between June to August.
TautumnMean temperature between September to November.
TwinterMean temperature between December to February.
TminThe minimum temperature of the coldest month.
ColdtThe minimum daily temperature of the coldest month.
TmeanAnnual mean temperature.
Solar radiation durationMSMonthly solar radiation duration.
SspringCumulative solar radiation duration between March to May.
SsummerCumulative solar radiation duration between June to August.
SautumnCumulative solar radiation duration between September to November.
SwinterCumulative solar radiation duration between December to February.
SmeanAnnual cumulative solar radiation duration.
Table
Table 3. Coefficients (SE) of important climate variables based on the PLS models.
Table 3. Coefficients (SE) of important climate variables based on the PLS models.
EstimateSEt.ValuePr (>|t|)Sig
MTMay0.0840.0155.6023.1 × 10−05***
MTJune0.0830.0263.2165.1 × 10−03**
Tspring0.0850.0214.0837.7 × 10−04***
Tsummer0.0740.0135.7532.3 × 10−05***
Twinter0.0480.0291.6681.1 × 10−01
Tmean0.0850.0127.1381.6 × 10−06***
MSMay0.0750.0203.7621.5 × 10−03***
MSNov0.0630.0222.8221.2 × 10−02*
Sspring0.0680.0262.5901.9 × 10−02*
Sautumn0.0530.0281.8737.8 × 10−02
Smean0.0540.0222.4442.5 × 10−02*
Pspring−0.0940.016−5.8272.0 × 10−05***
P1−0.0730.018−3.9611.0 × 10−03**
P12−0.0910.019−4.9311.2 × 10−04***
P123−0.1030.015−7.1061.7 × 10−06***
Note *: Significant; **: Very significant; ***: Extremely significant.
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MDPI and ACS Style

Yu, L.; Zhan, Z.; Zhou, Q.; Gao, B.; Ren, L.; Huang, H.; Luo, Y. Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China. Remote Sens. 2022, 14, 2728. https://doi.org/10.3390/rs14122728

AMA Style

Yu L, Zhan Z, Zhou Q, Gao B, Ren L, Huang H, Luo Y. Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China. Remote Sensing. 2022; 14(12):2728. https://doi.org/10.3390/rs14122728

Chicago/Turabian Style

Yu, Linfeng, Zhongyi Zhan, Quan Zhou, Bingtao Gao, Lili Ren, Huaguo Huang, and Youqing Luo. 2022. "Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China" Remote Sensing 14, no. 12: 2728. https://doi.org/10.3390/rs14122728

APA Style

Yu, L., Zhan, Z., Zhou, Q., Gao, B., Ren, L., Huang, H., & Luo, Y. (2022). Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China. Remote Sensing, 14(12), 2728. https://doi.org/10.3390/rs14122728

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