Next Article in Journal
Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Trees and Multi-Layer Perceptron
Previous Article in Journal
Study on the Limit of Moisture Content of Smoldering Humus during Sub-Surface Fires in the Boreal Forests of China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 2
10.3390/f14020253
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario

by
Umer Hayat
1,†,
Muhammad Akram
1,†,
Sumeet Kour
2,†,
Tahreem Arif
3 and
Juan Shi
1,*
1
Sino-France Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University, Beijing 100083, China
2
School of Agriculture, Geography, Environment, Ocean and Natural Sciences, The University of the South Pacific, Laucala Campus, Suva 999210, Fiji
3
Department of Silviculture, School of Forestry, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(2), 253; https://doi.org/10.3390/f14020253
Submission received: 27 December 2022 / Revised: 15 January 2023 / Accepted: 21 January 2023 / Published: 29 January 2023
(This article belongs to the Topic Plant Invasion)
Browse Figures
Review Reports Versions Notes

Abstract

:
Aeolesthes sarta (Solsky 1871) (Coleoptera: Cerambycidae) is a polyphagous longhorned beetle species that primarily damages broadleaved tree species. This pest is distributed in the western and northern regions of Pakistan, where it caused serious damage to Populus spp. plantations. However, the growth and dispersal patterns of insects and pests are changing due to climate change. Modeling the range expansion or contraction of A. sarta development regions in Pakistan was the goal of the current study, assuming climate change might influence the geographical distribution of A. sarta in Pakistan. Under historical and future climatic conditions, A. sarta distribution areas were estimated using the CLIMEX model. Three time periods, 2030 (early century), 2070 (late century), and 2100 (end century), were forecasted for habitat suitability using the two climate change scenarios (CCSs) A1B and A2. Under the historic climatic condition (HCC), A. sarta was distributed in most areas of Pakistan, and its optimum habitat accounted for 71.67% of its total potential distribution. In the early-century period, optimum habitat dropped to 50.60% and 52.22% under A1B and A2 scenarios in the suitable condition. In the late-century period, optimum habitat further reduced to 31.76% and 30.60% under A1B and A2 scenarios. Moreover, at the end-century period, severe range shrinkage was predicted in the optimum habitat (19.99% under both CSSs). The model predicted a shift in the suitable habitat areas for A. sarta to the west and north. Furthermore, most climatically suitable areas under historic conditions became unsuitable during the end-century period. These projected results will assist in identifying the impacts of global warming on the possible distribution of A. sarta, thereby offering vital information for developing early forecasting and pest-prevention techniques to prevent further loss of forest and woodland trees.

1. Introduction

Approximately 30% of the Earth’s land area is covered by forests [1]. Natural forests comprise about 5% of Pakistan’s total land area, located mainly in the country’s north and northwest mountainous regions [2]. Forests are essential to mitigate climate change as they serve as a carbon sink [2,3,4]. However, pathogens and pests each year kill millions of trees across commercial woodlands and natural forests [5]. The loss of trees resulting from major pest invasions can be catastrophic for total net productivity and carbon sequestration.
A hostile biotic potential threat to trees is pest infestations. The Coleoptera and Lepidoptera orders are home to the most frequently reported pest species [5]. In the Coleoptera order, Cerambycidae is one of the world’s most prolific, diverse, ecologically and commercially significant families, which includes longhorn beetles [6]. Aeolesthes sarta (City Longhorned Beetle) is one of the dominant polyphagous pest species of the Cerambycidae family, chiefly invading broadleaved tree species such as Acer spp., Juglans spp., Malus spp., Platanus spp., Populus spp., Salix spp., and Ulmus spp. [7]. Larvae of this family are internal feeders of living or dead tree tissues [8]. This behavior of internal feeding causes structural damage, which interjects the flow of water and nutrition, resulting in the loss of branches and, ultimately, whole tree death [9,10,11,12].
With a significant spread into Afghanistan, Iran, and other countries in central Asia, it is believed that A. sarta emerged in Pakistan and the western part of India [13,14]. Invasive pests thrive in areas with warm temperatures and their preferred host tree species [15]. This species may be highly hazardous in regions with hot and dry weather [16]. The longhorn beetle is a common pest in central Asia that attacks most broadleaved tree species. A. sarta can attack and proliferate on the main stem and major branches [17]. Infestations are particularly evident in highland forests, which may contribute to the decrease in poplar tree forests, which are a dynamic source of wood for the wood industry/market [18]. A severe infestation (high population) may cause the afflicted trees’ canopy to decline and their leaves to dry off within two to three years [19].
Aeolesthes sarta has gained notoriety in Quetta and throughout Balochistan because it has harmed poplar, willow, and elm trees; for example, in the region (1900–1907), over 3000 trees were damaged by A. sarta [20]. A. sarta is one of the most destructive poplar borers [21] and has seriously harmed the number of Populus plantations across Pakistan [18,21,22].
The scientific foundation for assessing the hazards that plant pests cause to both cultivated and wild flora is supplied by pest risk assessments [23]. By calculating the potential for the introduction (entrance, transfer, and development) and spreading of plant pests and the ensuing effects on crops and plants in the larger environment, they include the rigorous synthesis of information and characterization of hazards [24].
Over the past century, global warming has been a major climate change concern [25]. The patterns of species dispersion have changed, affecting the areas where various species may survive and decreasing biodiversity [26]. Due to their ectothermic nature and wide variation in population size across time and geography, insects can acclimate to climate change well [27]. Many pests that widely disperse will also be able to expand their range more quickly than more stationary or habitat-specific species [28]. We can acquire vital information to control the spread of invasive pests by simulating how climate change impacts these pests [29].
Ecological niche models, including CLIMEX, CLIMATE, MaxEnt, DOMAIN, GARP, HABITAT, and ANUCLIM/BIOCLIM have all proven effective in evaluating the possible geographic dispersion of various invasive species [30]. Among these models, CLIMEX is a specialized toolset designed to investigate how climate affects invasive species and assess how such species might proliferate in the future [31]. Biological data can be frequently adjusted and simulated in CLIMEX, improving the findings. The key benefit of CLIMEX is that it can be theoretically tuned using knowledge of population demographics and phenological evidence [32].
Research on the precise potential distribution of A. sarta in Pakistan under projected climatic conditions is lacking. We modified parameter values following the literature published [7,18,21,22] to better match the known distribution in Pakistan. We hypothesized that a changing climate would affect the A. sarta range and the region in Pakistan where it thrives. Using the CLIMEX model, we looked into the potential distribution of A. sarta in Pakistan and assessed high-risk areas for its invasion. We assessed the vulnerability of different regions in Pakistan for A. sarta establishment under historical and future climates (A1B and A2 Climate Change Scenarios (CCSs)).

2. Materials and Methods

2.1. Study Species

Pest species known as Aeolesthes sarta (City Longhorned Beetle) grow in temperate locations with warm, dry weather [7]. This pest species is considered harmful since it can harm and destroy healthy and weak trees [21]. On a regional level, climate change is anticipated to modify the region’s ideal for growth and development. Numerous studies in Pakistan have described the damage [18,21,22] and mitigation measures [33]; however, ecological niche models have never been used to predict the state of A. sarta development areas in a semi-arid nation such as Pakistan under a future climate change scenario. Therefore, this species was chosen for the current modeling research. We divided Pakistan into six administrative parts (Punjab (PB), Khyber Pakhtunkhwa (KPK), Azad Jammu and Kashmir (AJK), Balochistan (BLC), Sindh (SD), and Gilgit Baltistan (GB)) so that we could illustrate the A. sarta distribution well (Figure 1).

2.2. CLIMEX Model

In order to provide a set of yearly indices, the CLIMEX model incorporates generic morphological and physiological data and weekly interactions of the species to climate. The CLIMEX model takes on species distribution increases and decreases in response to suitable and unsuitable climatic conditions. The species’ annual growth (GA) rate is used to compute the temperature index (TI) and moisture index (MI) and is further used to calculate the species’ weekly growth [34]. Four different stresses (cold, dry, hot, and wet) impact the GA and add to stress indices (SI). Based on all these variables, CLIMEX generates an ecoclimatic index (EI) range of 0 to 100 [35]. EI = 0 represents that no growth is possible and EI = 100 indicates ideal growth. Geographical areas in this study that have an EI > 15, 0 < EI = 15, and EI = 0 indicate climatic conditions that are optimum, suitable, and unsuitable habitats for the establishment, survival, and proliferation of A. sarta. The CLIMEX uses a set of variables to determine the EI for a specific species [36]. The species’ responsiveness determines these attributed to the environmental stresses stated above and its preferred temperature and moisture levels. These parameters’ values might be inferred from the current distribution range or determined empirically. Therefore, the parameter fitting operation requires a thorough understanding of species biology and the current distribution spectrum [15]. The variables are modified until a good fit is attained. Following the fitting of a baseline model (under historical/current climatic conditions), predictions for future climatic conditions under various climate change scenarios could be established [37].

2.3. Climatic Data

Historical and future climatic data downloaded from CliMond (https://www.climond.org/ (accessed on 16 August 2022)). The 10’ gridded data had 26,323 location points for Pakistan and monthly averaged data on maximum and minimum temperature, rainfall, and daily relative humidity at 0900 h and 1500 h for each location. Historical climatic data averaged from 1961 to 1990 and finally averaged in 1975 [38].
Future climatic data are available based on two global circulation models (GCMs) and two separate climate change scenarios. These two GCMs were chosen for data generation because they performed better than the other available GCMs [38]. Data were acquired and utilized from the CliMond website for the 2030 (early-century), 2070 (late-century), and 2100 (end-century) time periods. Only data from the A1B and A2 SRES scenarios from CSIRO-Mk3.0 were employed to model the distribution of A. sarta in future climate change scenarios [39].

2.4. Collection of A. sarta Distribution Data in Pakistan

Aeolesthes sarta known distribution data in Pakistan collected from the Centre of Agriculture and Bioscience International (CABI →https://www.cabi.org/ (accessed on 15 November 2022)), Global Biodiversity Information Facility (GBIF →https://www.gbif.org/ (accessed on 15 November 2022)), European and Mediterranean Plant Protection Organization (EPPO →https://gd.eppo.int/ (accessed on 15 November 2022)), and from literature review [7,18,21,22,31] were used. Except in challenging environments, A. sarta has disseminated throughout Pakistan’s western and northern mountainous regions.

2.5. Parameters Fitting

2.5.1. Temperature Index (TI) and Population Degree Days (PDD)

Aeolesthessarta is more common in temperate climates with dry and cold weather [7]; thus, while generating the "species parameter file," the Temperate template included with the CLIMEX software was used. Physiological tolerance criteria specific to each species were used to calibrate the model. According to Ahmad et al. [21], oviposition does not occur at <11 °C or >35 °C, whereas at 40 °C, species survive without laying viable eggs. However, Vanhanen et al. [40] used 20 °C and 37 °C as DV1 and DV2 values. Initially, we ran the model following Vanhanen et al. [40] and Ahmad et al. [21] using 11 °C, 20 °C, 35 °C, and 40 °C as DV0, DV1, DV2, and DV3 values, but we did not get all the known occurrence points covered, i.e., Kalam. Then, we adjusted the values to 10 °C (DV0), 15 °C (DV1), 37 °C (DV2), and 40 °C (DV3) and reran the model; it was able to predict the currently known occurrence. Therefore, we finalized these values as temperature indices to forecast the potential global distribution.
A. sarta completes its life cycle in almost two years [9,13,21]; therefore, we set the population day degree (PDD) value as 700, according to Vanhanen et al. [40].

2.5.2. Moisture Index (MI)

Another variable of the CLIMEX model is the soil moisture index (MI), which provides information about the amount of precipitation. The lower soil moisture threshold (SM0), lower optimal soil moisture (SM1), upper optimum soil moisture (SM2), and upper soil moisture threshold (SM3) are the four additional characteristics that the CLIMEX model uses to classify the MI [34]. SM0 was adjusted to 0 to allow for normal species development [40]. Soil moisture values for optimal growth (SM1 and SM2) were established at 0.001 and 1.5 to suit the observed occurrence of species in known distribution. Following a similar procedure, an upper soil moisture level (SM3) of 2.5 was adjusted [40].

2.5.3. Diapause Index

Diapause duration was reported to be around 90 days [9,13], so we set the DPD (diapause development days) as 90 days. DPD0 (diapause induction day length), DPT0 (diapause induction temperature), DPT1 (diapause termination temperature), and DPSW (summer or winter diapause) were set to be 12 days, 13 °C, 10 °C, and 0 days, respectively, according to [40].

2.5.4. Stress Index (SI)

Cold stress (CS), heat stress (HS), dry stress (DS), and wet stress (WS) are the four environmental stress indices in the CLIMEX modeling system that are reflective of hostile circumstances that restrict the population development of a species [34]. We only took CS and HS into consideration for this research study. Stress starts to restrict a species’ development when the temperature falls below that species’ cold stress threshold temperature (TTCS) at a particular rate (THCS). In the current study, 9 °C was considered the TTCS for A. sarta since A. sarta development is inhibited below this temperature [21].
Based on the TTCS result, the THCS was set to −0.00001. Similarly, heat stress affects a species as soon as the temperature surpasses its heat stress temperature threshold (TTHS) at a specific rate (THHS). In the present research, we set the TTHS to 41 °C and the THHS to 0.005 since A. sarta did not survive over 40 °C [21]. The parametric values used to run the CLIMEX model are shown in Table 1.

3. Results

3.1. Potential Distribution of A. sarta in Pakistan under Historic Climatic Conditions (HCC)

The model calculation results illustrate that the known distributions gathered from CABI, EPPO, GBIF, and the literature review were within the potential distribution area projected by CLIMEX under historic climate conditions (HCC) (Figure 2). This demonstrates that the finalized parameters can accurately predict Pakistan’s A. sarta distribution pattern. Using the map developed by ArcMap, we estimated the potential area of A. sarta distribution in Pakistan to be 7.32 HT-Km2 (100,000 square kilometers), or 83.43% of Pakistan’s total area (Figure 3).
The area covering optimum habitat (EI > 15) was 6.26 HT-Km2, which is 71.67% of the total area of Pakistan, accounting for 85.91% of the total potential distribution area. This potential distribution comprises 100% of the area of Punjab (PB), 78.23% of Khyber Pakhtunkhwa (KPK), 67.48% of Azad Jammu and Kashmir (AJK), 80.58% of Balochistan (BLC), 37.45% of Sindh (SD), and 3.53% of Gilgit Baltistan (GB).
The area covering suitable habitat (0 < EI = 15) was 1.03HT-Km2, 11.76% of the total area of Pakistan, accounting for 14.09% of the total potential distribution area. This covers 15.60% of KPK, 13.35% of AJK, 12.45% of BLC, 19.47% of SD, and 22.48% of GB. However, only 1.45 HT-Km2, which is 16.57% of the total area of Pakistan, covers unsuitable habitats (EI = 0).

3.2. Potential Distribution of A. sarta in Pakistan under Future Climatic Condition (FCC)

Under the future climatic condition (FCC), the model projected a gradual upsurge in the suitable habitat for A. sarta development areas for the early-century period (Figure 3 and Figure 4). The unsuitable areas’ percentage increased compared with HCC. Under A1B and A2 CCSs for the early-century period, the optimum habitat area for A. sarta development decreased to 50.60% (4.44 HT Km2) and 52.22% (4.58 HT Km2) compared with HCC 71.67% (6.28 HT Km2), while the suitable habitat area increased to 19.02% (1.67 HT Km2) and 18.71% (1.64 HT Km2) compared with HCC 11.76% (1.03 HT Km2). The unsuitable habitat area increased to 30.38% (2.66 HT Km2) and 29.08% (2.55 HT Km2) compared with historic climatic conditions and 16.57% (1.45 HT Km2), respectively.
For the late-century period under A1B and A2 CCSs, the optimum habitat area for A. sarta development further decreased to 31.76% (2.78 HT Km2) and 30.60% (2.68 HT Km2) compared with historic climatic conditions, 71.67% (6.28 HT Km2). In comparison, the suitable habitat area also decreased to 10.73% (0.94 HT Km2) and 11.13% (0.98 HT Km2) compared with HCC 11.76% (1.03 HT Km2), and the unsuitable habitat area further increased to 57.51% (5.04 HT Km2) and 58.26% (5.11 HT Km2) compared with the historic climatic conditions of 16.57% (1.45 HT Km2), respectively.
However, during the end-century period, the optimum habitat area for A. sarta development further decreased to 19.99% (1.75 HT Km2) under A1B and A2 CCSs compared with HCC 71.67% (6.28 HT Km2); the suitable habitat area increased to 14.60% (1.28 HT Km2) under A1B and A2 CCSs compared with the late-century period but decreased compared with HCC 11.76% (1.03 HT Km2); and the unsuitable habitat area further increased to 65.41% (5.74 HT Km2) under A1B and A2 CCSs compared with HCC 16.57% (1.45 HT Km2), respectively.

3.3. Administrative Area-Wise Distribution under Historic and Future Climatic Conditions

The optimum habitat area distribution was forecasted to decrease in five administrative areas except for GB (Table 2; Figure 5).
In Punjab (PB), 100% (20.63 TT Km2) of the area under HCC was optimum for A. sarta development, but this trend decreased under FCC. In the early-century period, optimum habitat area decreased to 64.69% (13.34 TT Km2) and 68.78% (14.18 TT Km2) under A1B and A2 CCSs. In the late-century period, optimum habitat area decreased to 14.71% (3.03 TT Km2) and 13.07% (2.69 TT Km2) under both CCSs, and in the end-century period, the optimum habitat area further decreased to 1.29% (0.27 TT Km2), whereas the unsuitable habitat area increased to 95.88% (19.77 TT Km2) under both CCSs.
In Khyber Pakhtunkhwa (KPK) under HCC, 78.23% (7.85 TT Km2) of the area comprised optimum habitat, and suitable habitat area accounted for 15.60% (1.57 TT Km2). In the early-century period, the optimum habitat area shrunk to 78.79% (8.01 TT Km2) and 79.99% (8.03 TT Km2); in the late-century period, the optimum habitat area further reduced to 67.57% (6.78 TT Km2) and 66.18% (6.64 TT Km2) and in the end-century period, the optimum habitat area further reduced to 51.18% (5.14 TT Km2) under both CCSs. However, suitable habitats decreased in the early and late centuries but increased in the end-century period to 27.73% (2.28 TT Km2) under both CSSs.
In Azad Jammu and Kashmir (AJK), under HCC, optimum and suitable habitat areas comprise 67.48% (0.93 TT Km2) and 13.35% (0.18 TT Km2). In the early-century period, the optimum habitat area increased to 68.93% (0.95 TT Km2) and 68.45% (0.94 TT Km2), and suitable habitat decreased to 12.62% (0.17 TT Km2) and 12.38% (0.17 TT Km2) under both CCSs. However, in the late- and end-century periods, the optimum habitat decreased, and suitable habitat increased under both CCSs.
In Balochistan (BLC), for the early-century period, optimum habitat area reduced to 60.94% (20.91 TT Km2) and 62.58% (21.47 TT Km2), while the suitable habitat area increased to 21.07% (7.23 TT Km2) and 20.99% (7.20 TT Km2) under both CCSs compared with HCC 80.58% (27.65 TT Km2) and 12.45% (4.27 TT Km2). However, in the late- and end-century periods, optimum and suitable habitats decreased under both CCSs. In the end-century period, optimum habitat covers 25.55% (8.77 TT Km2) while suitable habitat covers 18.65% (6.4 TT Km2) under both CCSs.
In Sindh (SD), under HCC, the optimum and suitable habitat areas covered 37.35% (5.21 TT Km2) and 19.47% (2.71 TT Km2). In the early, late, and end centuries, optimum and suitable habitat areas were reduced to the minimum, while unsuitable habitat areas increased to the maximum under both CCSs.
In Gilgit Baltistan (GB) under HCC, optimum and suitable habitat areas covered 3.53% (0.24 TT Km2) and 22.48% (1.53 TT Km2). In the early-century period, optimum habitat area increased to 7.95% (0.54 TT Km2) and 7.51% (0.51 TT Km2), while suitable habitat also increased to 23.47% (1.59 TT Km2) and 23.66% (1.61 TT Km2) under both CCSs. However, in the late and end centuries, optimum and suitable habitat areas gradually increased with a decrease in unsuitable areas. At the end of the century, the optimum habitat area increased to 36.03% (2.45 TT Km2) under both CCSs.
Based on the assessment of the model’s findings, predictive maps on a regional basis of Pakistan show that the potential climate zone for A. sarta is confined in different areas by cold and heat stress (Figure 6). The northern region, such as the GB potential climatic zone boundaries, is restricted by cold stress under historic and early-century climatic conditions, which reduce under FCC at the end of the century. In contrast, the potential climatic zone limits of the southern, central, and eastern regions such as SD, PB, and south BLC are constrained by heat stress. However, the cold stress severity decreased under A1B and A2 CCSs, whereas heat stress increased over time.

4. Discussion

We aimed to provide a robust method for investigating the geographical distribution of A. sarta in Pakistan under the historic and future climates using CLIMEX. The results of this study demonstrated that, compared with the historic climate, future climatic conditions under A1B and A2 CCSs strongly influence the distribution pattern of A. sarta in Pakistan.
The habitat range of many species is significantly impacted by climate change, particularly cold-blooded species such as insects [41]. With the improvement of tools and techniques for climate modeling, it is now convenient to forecast the geographical range of a specified pest species under projected climate conditions, making timely monitoring and management strategies even more feasible [32,42]. CLIMEX can simulate a species’ seasonal environmental adaptability [43] and numerous insect species’ habitats have been predicted using it [15,43,44]. The notion that A. sarta exhibits polyphagous traits and has wreaked havoc on the plantations and forests in its native environment [7] suggests that it has the potential to damage trees seriously. As a result, we forecasted its possible distribution in Pakistan under the historic and future A1B and A2 climatic scenarios. All the cities in Pakistan where the distribution of A. sarta is documented fall within our projected range, demonstrating our model’s accuracy.
Our CLIMEX models predicted that most of the regions in Pakistan, such as PB (EI > 15 = 100%), KPK (EI > 15 = 78.23), AJK (EI > 15 = 67.48%), BLC (EI > 15 = 80.58%), and SD (EI > 15 = 37.58%) under HCC were optimum for A. sarta development; the models also forecasted reduced development of the species at the end-century period under A1B and A2 CCSs. With some variations in climate change scenarios, the suitable regions predicted to be suitable under HCC would become unsuitable by the end of the century. The variations in the expected regions are caused by their various GHG emission scenarios [45]. Numerous researchers have stated that global warming will affect the possible insect/pest dispersion pattern [15,42,43,44].
Model predictions revealed that at the end-century period, the optimum habitat area reduced to northwest BLC, northwest KPK, central AJK, and eastern GB areas. Areas within the current known distribution of A. sarta are likely to remain in optimum habitat range for its development under FCC.
CLB is a diapause species [7]; adults undergo a suspended development phase during cold winter and require optimum temperatures such as 20 °C [46] to break dormancy and come out from the pupal chamber in spring [9,21]; therefore, its growth and mobility, as well as its dispersal, can be impacted by temperature.
We observed the shift of EI values from eastern to western regions and southern to northern regions of Pakistan. Central, eastern, and southern regions of Pakistan mainly comprise plain areas with dominancy of agricultural lands, while western and northern regions comprise hills and mountain ranges reaching up to 6600 m msl [15]. Moreover, due to the diversity of elevation, the surface temperatures of the central, eastern, and southern regions are higher than the western and northern mountain ranges. Almost 86% of natural forests in Pakistan are present in the country’s northern regions (AJK, GB, and KPK) [47], with a diverse distribution of A. sarta preferred host tree species such as Juglans spp., Malus spp., Platanus spp., Populus spp., Prunus spp., Pyrus spp., Salix spp., Ulmus spp. [7,18,21,22,31], and Quercus spp. [7,48]. Therefore, the availability of preferred host species and suitable environmental conditions (temperature and elevation) makes these regions optimum for A. sarta development.
Pakistan is a region that is highly susceptible to climate change [49]. The optimum temperature range for A. sarta survival is between 10 and 40 °C. As a result, the temperature changes and generally higher temperatures in central and southern Pakistan would not be conducive to A. sarta habitation, which would probably cause a shift in the A. sarta distribution under FCC. Furthermore, the development of A. sarta in the PB, SD, and south BLC was observed to be constrained by increasing heat stress at the end of the 21st century period, but the GB region became more suitable due to a potential decline in cold stress (Figure 6). In addition, these regions have semi-arid to dry climates, making them more sensitive to climate change than Pakistan’s northern region because even slight temperature changes can result in high heat in such climates. Over the past 50 years, extreme temperature variations and increased frequency of heavy rainstorms have been seen globally [39]. The frequency of cold days, cold nights, and frost occurrences has also decreased owing to changes in the climate, while the frequency of hot days/nights and heat waves has climbed [50,51].
The CLIMEX model predictions are mainly based on climatic variables, although non-climatic variables such as biological linkages, genetic evolution, visual barriers, and human activities might impact studies of species dispersion [52]. Recent research suggests invasive pest range changes under future climates may be predicted using genetic variation and climatic models [53]. The impact of non-climatic variables on the potential spread of A. sarta will be further investigated with the conception and advancement of species distribution models. In the following research, we will continue investigating A. sarta distribution in Pakistan, analyze the precise impacts of changes in the A. sarta external environment on its biological characteristics, and set up a method for evaluating model performance.
According to the study’s model, in the next 78 years, most of Pakistan’s western and northern regions will be a potentially suitable distribution area for A. sarta. Since the commencement of the 21st century, cultivation of trees on farmland and woodland in northern Pakistan has amplified considerably and has been utilized in furniture, wood, and sawmill industries and shipped to other parts of the country to fulfil the demand for timber and fuelwood. Additionally, A. sarta spends its egg, larvae, pupae, and young adult stages inside a tree trunk [7], so it easily spreads in the host for a long time. If efficient control measures are not implemented, A. sarta will continue to expand in Pakistan. A. sarta advanced surveillance and early warning technology can precisely predict its temporal dynamics and provide a firm assurance for halting it from spreading and causing more damage.
Once a tree is detected to be infected by A. sarta, it is recommended to fall and burn the contaminated tree [18,54]. Furthermore, it is recommended to follow sanitary/phytosanitary procedures before using the wood for trading, such as stem debarking and kiln drying [48]. Biological control techniques can be more economical, practical, ecologically safe, and long lasting [55]; therefore, we highly recommend the use of biological control agents such as the use of click beetle Alaus larvae (a predator of A. sarta grubs) [22], Proctolaelaps spp. (Acarina) (a predator of A. sarta grubs) [22], parasitoid Sclerodermus turkmenicus (a parasite of A. sarta larvae) [56], and Beauveria bassiana white muscardine fungus (a parasite of A. sarta adults) [18,56,57]. All these are very effective biological agents in stem borer control. We highly advise avoiding excessive chemical use for A. sarta control as these are not environmentally friendly and costly [5,58].

5. Conclusions

In this model-based research study, the CLIMEX model was used, which is the first study of its kind, leveraging accurate parametric variables to forecast the probable distribution of A. sarta in Pakistan under historic and future climatic conditions (A1B and A2 climate change scenarios). This work illustrated a helpful technique for mapping A. sarta habitat adaptability. The results show that in the next 78 years, most of Pakistan’s western and northern regions will be potential distribution areas for A. sarta. With the warming of the climate, the optimum habitats are mainly located in the western and northern parts of Pakistan, while the central, eastern, and southern parts of Pakistan appear likely to become unsuitable areas. Four of the six administrative areas will remain the optimum habitat under FCC, such as BLC, KPK, AJK, and GB. To avoid the further spread of A. sarta in Pakistan and to guarantee the forest’s security and the forest industry, Pakistan should continue to undertake stringent monitoring, preventive, and control measures.

Author Contributions

Conceptualization, U.H., M.A., S.K. and J.S.; investigation and methodology, U.H., M.A. and S.K.; formal analysis, U.H., M.A. and S.K.; investigation, U.H.; resources, U.H., M.A., S.K. and T.A.; data curation, UH; writing—original draft preparation, U.H. and T.A.; writing—review and editing, U.H., M.A., S.K., T.A. and J.S.; supervision and project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by (1) National Key R&D Program of China (Grant No. 2021YFC2600400); (2) National Natural Science Foundation of China (NSFC) (Grant No. 32171794); and (3) Forestry Science and Technology Innovation Special of Jiangxi Forestry Department (Grant No. 201912).

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank our lab mates at the Sino-France Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University, for administrative and technical support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

CCSClimate change scenario
HCCHistoric climatic condition
FCCFuture climatic condition

References

  1. Keenan, R.J.; Reams, G.A.; Achard, F.; de Freitas, J.V.; Grainger, A.; Lindquist, E. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. Forest Ecol. Manag. 2015, 352, 9–20. [Google Scholar] [CrossRef]
  2. Khan, I.; Hayat, U.; Mujahid, A.; Majid, A.; Chaudhary, A.; Badshah, M.T.; Huang, J. Evaluation of growing stock, biomass and soil carbons and their association with a diameter: A case study from planted chir pine (Pinus roxburghii) forest. Appl. Ecol. Environ. Res. 2021, 19, 1457–1472. [Google Scholar] [CrossRef]
  3. Hayat, U.; Khan, I.; Roman, M. A response of tropical tree Terminalia arjuna (Arjun) [Combretaceae-Myrtales] seeds and seedlings towards the presowing seed treatment in forest nursery of Sargodha, Pakistan. Int. J. Biosci. 2020, 16, 495–505. [Google Scholar] [CrossRef]
  4. Farooqi, T.J.A.; Hayat, U.; Roman, M.; Abbas, H.; Hussain, S. Comparative study determining the impacts of broadleaved and Needle leaved forest harvesting on hydrology and water yield: State of knowledge and research outlook. Int. J. Biosci. 2020, 16, 231–240. [Google Scholar] [CrossRef]
  5. Balla, A.; Silini, A.; Cherif-Silini, H.; ChenariBouket, A.; Moser, W.K.; Nowakowska, J.A.; Oszako, T.; Benia, F.; Belbahri, L. TheThreat of Pests and Pathogens andthe Potential for Biological Control in Forest Ecosystems. Forests 2021, 12, 1579. [Google Scholar] [CrossRef]
  6. Rossa, R.; Goczał, J. Global diversity and distribution of longhorn beetles (Coleoptera: Cerambycidae). Eur. Zool. J. 2021, 88, 289–302. [Google Scholar] [CrossRef]
  7. Hayat, U. City longhorn beetle (Aeolesthes sarta): A review of the species, its morphology, distribution, damage, prevention and control. J. For. Sci. 2022, 68, 199–212. [Google Scholar] [CrossRef]
  8. Ślipiński, A.; Escalona, H. Australian Longhorn Beetles (Coleoptera: Cerambycidae). Vol. 2, Subfamily Cerambycinae; CSIRO Publishing: Clayton, CA, USA, 2016; p. 640. [Google Scholar] [CrossRef]
  9. Khan, S.A.; Bhatia, S.; Tripathi, N. Entomological investigation on Aeolesthes sarta (Solsky), a major pest on walnut trees (Juglans regia L.) in Kashmir valley. J. Acad. Ind. Res. 2013, 2, 325. [Google Scholar]
  10. Mazaheri, A.; Khajehali, J.; Marzieh, K.; Hatami, B. Laboratory and field evaluation of insecticides for the control of Aeolesthes sarta Solsky (Col.: Cerambycidae). J. Crop Prot. 2015, 4, 257–266. [Google Scholar]
  11. Morewood, W.D.; Hoover, K.; Neiner, P.R.; McNeil, J.R.; Sellmer, J.C. Host tree resistance against the polyphagous wood-boring beetle Anoplophora glabripennis. Entomol. Et Appl. 2004, 110, 79–86. [Google Scholar] [CrossRef]
  12. Poland, T.M.; Haack, R.A.; Petrice, T.R.; Miller, D.L.; Bauer, L.S.; Gao, R. Field evaluations of systemic insecticides for control of Anoplophora glabripennis (Coleoptera: Cerambycidae) in China. J. Econ. Entomol. 2006, 99, 383–392. [Google Scholar] [CrossRef]
  13. Farashiani, M.E.; Shamohammadi, D.; Sadeghi, S.E. Biological study of Sart long horn beetle, Aeolesthes sarta Solsky (Coleoptera: Cerambycidae) in the laboratory. J. Entomol. Soc. Iran 2000, 20, 77–90. [Google Scholar]
  14. Orlinskii, A.D. Outcomes of the EPPO project on quarantine pests for forestry 1. Eppo. Bull. 2006, 36, 497–511. [Google Scholar] [CrossRef]
  15. Hayat, U.; Qin, H.; Zhao, J.; Akram, M.; Shi, J.; Ya, Z. Variation in the potential distribution of Agrotis ipsilon (Hufnagel) globally and in Pakistan under current and future climatic conditions. Plant Prot. Sci. 2021, 57, 148–158. [Google Scholar] [CrossRef]
  16. CABI. Forestry Compendium. 2006. Available online: https://www.cabi.org/fc (accessed on 15 November 2022).
  17. Farashiani, M.E.; Sadeghi, S.E.; Abaii, M. Geographic distribution and hosts of Sart longhorn beetle, Aeolesthes sarta Solsky (Col.: Cerambycidae) in Iran. J. Entomol. Soc. Iran 2001, 20, 81–96. [Google Scholar]
  18. Arshad, M.; Hafiz, I.A. Microbial trials of a pathogenic fungus, Beauveria bassiana (Bals.) Vuill. against the adults of Aeolesthes sartus Solsky (Cerambycidae: Coleoptera). Pak. J. Zool. 1983, 15, 213–215. [Google Scholar]
  19. Krivosheina, N.P.; Tokgaev, T.B. The formation of trunk-insect complexes on irrigated areas in the Kopet-dag foothills. Izv. Akad. Nauk. Turkm. SSR Biol. Nauk. 1985, 5, 34–40. [Google Scholar]
  20. Stebbing, E.P. Indian Forest Insects of Economic Importance: Coleoptera; Eyre & Spottiswoode: London, UK, 1914; p. 648. [Google Scholar]
  21. Ahmad, M.I.; Hafiz, I.A.; Chaudhry, M.I. Biological studies on Aeolesthes sarta Solsky attacking poplars in Pakistan. Pak. J. For. 1977, 27, 123–129. [Google Scholar]
  22. Gul, H.; Chaudhry, M.I. Some observations on natural enemies of poplar borers in Pakistan. Pak. J. For. 1992, 42, 214–222. [Google Scholar]
  23. EFSA Panel on Plant Health (PLH); Jeger, M.; Bragard, C.; Caffier, D.; Candresse, T.; Chatzivassiliou, E.; Dehnen-Schmutz, K.; Grégoire, J.C.; Jaques Miret, J.A.; MacLeod, A.; et al. Guidance on quantitative pest risk assessment. Efsa J. 2018, 16, e05350. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. FAO (Food and Agriculture Organization of the United Nations). ISPM (International Standards for Phytosanitary Measures) No. 11. Pest Risk Analysis for Quarantine Pests; FAO: Rome, Italy, 2017; 36p, Available online: https://www.ippc.int/en/publications/639/ (accessed on 28 November 2022).
  25. Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar] [CrossRef] [Green Version]
  26. Gao, X.; Zhao, Q.; Wei, J.; Zhang, H. Study on the Potential Distribution of Leptinotarsa decemlineata and Its Natural Enemy Picromerusbidens Under Climate Change. Front. Ecol. Evol. 2022, 9, 786436. [Google Scholar] [CrossRef]
  27. Bale, J.S.; Masters, G.J.; Hodkinson, I.D.; Awmack, C.; Bezemer, T.M.; Brown, V.K.; Butterfield, J.; Buse, A.; Coulson, J.C.; Farrar, J.; et al. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Change Biol. 2002, 8, 1–16. [Google Scholar] [CrossRef]
  28. Pöyry, J.; Luoto, M.; Heikkinen, R.K.; Kuussaari, M.; Saarinen, K. Species traits explain recent range shifts of Finnish butterflies. Glob. Change Biol. 2009, 15, 732–743. [Google Scholar] [CrossRef]
  29. Wei, J.; Peng, L.; He, Z.; Lu, Y.; Wang, F. Potential distribution of two invasive pineapple pests under climate change. Pest Manag. Sci. 2020, 76, 1652–1663. [Google Scholar] [CrossRef]
  30. Ge, X.Z.; He, S.Y.; Zhu, C.Y.; Wang, T.; Xu, Z.C.; Zong, S.X. Projecting the current and future potential global distribution of Hyphantriacunea (Lepidoptera: Arctiidae) using CLIMEX. Pest Manag. Sci. 2019, 75, 160–169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Kriticos, D.J.; Maywald, G.F.; Yonow, T.; Zurcher, E.J.; Herrmann, N.I.; Sutherst, R. Exploring the effects of climate on plants, animals and diseases. CLIMEX Version 2015, 4, 184. [Google Scholar]
  32. Early, R.; Rwomushana, I.; Chipabika, G.; Day, R. Comparing, evaluating and combining statistical species distribution models and CLIMEX to forecast the distributions of emerging crop pests. Pest Manag. Sci. 2022, 78, 671–683. [Google Scholar] [CrossRef]
  33. Kamran, K.; Kakar, A.; Arif, S.; Iqbal, A. Evaluation of insect repellent and insecticide implantation techniques against Aeolesthes sarta Solsky in Quetta district of Baluchistan province, Pakistan. Pak. J. Entomol. Zool. Stud. 2017, 5, 273–276. [Google Scholar]
  34. Sutherst, R.W.; Maywald, G.F.; Kriticos, D.J. CLIMEX Version 3: User’s Guide; Hearne Scientific Software: South Yarra, Australia, 2007; p. 47. [Google Scholar]
  35. Sutherst, R.W.; Maywald, G.F.; Bourne, A.S. Including species interactions in risk assessments for global change. Glob. Change Biol. 2007, 13, 1843–1859. [Google Scholar] [CrossRef]
  36. Shabani, F.; Kumar, L.; Solhjouy-fard, S. Variances in the projections, resultingfrom CLIMEX, Boosted Regression Trees and Random Forests techniques. Theor. Appl. Climatol. 2017, 129, 801–814. [Google Scholar] [CrossRef]
  37. Kriticos, D.J.; Watt, M.S.; Potter, K.J.B.; Manning, L.K.; Alexander, N.S.; Tallent-Halsell, N. Managing invasive weeds under climate change: Considering the current and potential future distribution of Buddlejadavidii. Weed Res. 2011, 51, 85–96. [Google Scholar] [CrossRef]
  38. Kriticos, D.J.; Webber, B.L.; Leriche, A.; Ota, N.; Macadam, I.; Bathols, J.; Scott, J.K. CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 2012, 3, 53–64. [Google Scholar] [CrossRef]
  39. IPCC. Climate Change 2007: Synthesis Report. In Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Reisinger, A., Eds.; IPCC: Geneva, Switzerland, 2007; p. 104. Available online: https://www.ipcc.ch/site/assets/uploads/2018/02/ar4_syr_full_report.pdf (accessed on 10 August 2022).
  40. Vanhanen, H.; Veteli, T.O.; Niemelä, P. Potential distribution ranges in Europe for Aeolesthes sarta, Tetropiumgracilicorne and Xylotrechusaltaicus, a CLIMEX analysis. EPPO Bull. 2008, 38, 239–248. [Google Scholar] [CrossRef]
  41. Aljaryian, R.; Kumar, L.; Taylor, S. Modelling the current and potential future distributions of the sunn pest Eurygasterintegriceps (Hemiptera: Scutelleridae) using CLIMEX. Pest Manag. Sci. 2016, 72, 1989–2000. [Google Scholar] [CrossRef] [PubMed]
  42. Wei, J.F.; Zhao, Q.; Zhao, W.Q.; Zhang, H.F. Predicting the potential distributions of the invasive cycad scale Aulacaspisyasumatsui (Hemiptera: Diaspididae) under different climate change scenarios and the implications for management. PeerJ 2018, 6, e4832. [Google Scholar] [CrossRef] [Green Version]
  43. Zhao, J.; Song, C.; Ma, L.; Yan, X.; Shi, J.; Hao, C. The Impacts of Climate Change on the Potential Distribution of Plodia interpunctella (Hübner)(Lepidoptera: Pyralidae) in China. Insects 2022, 13, 636. [Google Scholar] [CrossRef]
  44. Ding, W.C.; Li, H.Y.; Wen, J.B. Climate Change Impacts on the Potential Distribution of Apocheimacinerarius (Erschoff) (Lepidoptera:Geometridae). Insects 2022, 13, 59. [Google Scholar] [CrossRef]
  45. Farooq, S.; Maqbool, M.M.; Bashir, M.A.; Ullah, M.I.; Shah, R.U.; Ali, H.M.; Al Farraj, D.A.; Elshikh, M.S.; Hatamleh, A.A.; Bashir, S.; et al. Production suitability of date palm under changing climate in a semi-arid region predicted by CLIMEX model. J. King Saud Univ. Sci. 2021, 33, 101394. [Google Scholar] [CrossRef]
  46. Mazaheri, A.; Khajehali, J.; Hatami, B. Oviposition preference and larval performance of Aeolesthes sarta (Coleoptera: Cerambycidae) in six hardwood tree species. J. Pest Sci. 2011, 84, 355–361. [Google Scholar] [CrossRef]
  47. Bhatti, S.I. An over View of Forests in Pakistan. World Environmental Day 16th June 2011. pp. 88–97. Available online: https://pecongress.org.pk/images/upload/books/(9)%20An%20Overview%20of%20Forests%20in%20Pakistan%20By%20Engr.%20Saeed%20Iqbal%20.pdf (accessed on 18 August 2022).
  48. EPPO. Aeolesthessarta. Datasheets on quarantine pests. EPPO Bull. 2005, 35, 387–389. [Google Scholar]
  49. Chaudhry, Q.; Rasul, G.; Kamal, A.; Ahmad, M.; Mahmood, S. Technical Report on Karachi Heat Wave June 2015. Government of Pakistan Ministry of Climate Change 2015. Available online: http://www.ndma.gov.pk/files/heatwave.pdf (accessed on 18 August 2022).
  50. Caesar, J.; Alexander, L.; Vose, R. Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res. Atmos. 2006, 111, D05101. [Google Scholar] [CrossRef]
  51. Tebaldi, C.; Hayhoe, K.; Arblaster, J.M.; Meehl, G.A. Going to the extremes. Clim. Change 2006, 79, 185–211. [Google Scholar] [CrossRef]
  52. Guisan, A.; Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef] [PubMed]
  53. Chen, Y.; Liu, Z.; Régnière, J.; Vasseur, L.; Lin, J.; Huang, S.; Ke, F.; Chen, S.; Li, J.; Huang, J.; et al. Large-scale genome-wide study reveals climate adaptive variability in a cosmopolitan pest. Nat. Commun. 2021, 12, 7206. [Google Scholar] [CrossRef]
  54. Naves, P.; de Sousa, E. Threshold temperatures and degree-day estimates for development of post-dormancy larvae of Monochamusgalloprovincialis (Coleoptera: Cerambycidae). J. Pest Sci. 2009, 82, 1–6. [Google Scholar] [CrossRef]
  55. Trumbore, S.; Brando, P.; Hartmann, H. Forest health and global change. For. Health 2015, 349, 814–818. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. CABI. Trirachyssartus—Invasive Species Compendium 2022. Available online: https://www.cabi.org/isc/datasheet/3430 (accessed on 15 August 2022).
  57. Khan, A.A.; Kundoo, A.A. Pests of walnut. In Pests and Their Management; Springer: Singapore, 2018; pp. 605–647. [Google Scholar] [CrossRef]
  58. Hussain, A.; Muhammad, A.; Hayat, U.; Ahmad, B.; Murtaza, A.M.; Khalid, K.M.B.; Ullah, S. Response of rice cultivars and insecticides against Rice stem borer (Scirpophagaincertulus) in Pakistan (Swat). J. Biodivers. Environ. Sci. 2019, 15, 88–94. [Google Scholar]
Forests 14 00253 g001 550
Figure 1. Map of Pakistan with six administrative areas (highlighted in six colors). Black dots represent the present known distribution of Aeolesthes sarta in Pakistan.
Figure 1. Map of Pakistan with six administrative areas (highlighted in six colors). Black dots represent the present known distribution of Aeolesthes sarta in Pakistan.
Forests 14 00253 g001
Forests 14 00253 g002 550
Figure 2. Potential distribution of Aeolesthes sarta in Pakistan under historic climatic conditions. Blue color indicates optimum habitat (EI > 15), orange color indicates suitable habitat (0 < EI = 15), white color indicates unsuitable habitat (EI = 0), and green circles indicate the current known distribution of A. sarta in Pakistan.
Figure 2. Potential distribution of Aeolesthes sarta in Pakistan under historic climatic conditions. Blue color indicates optimum habitat (EI > 15), orange color indicates suitable habitat (0 < EI = 15), white color indicates unsuitable habitat (EI = 0), and green circles indicate the current known distribution of A. sarta in Pakistan.
Forests 14 00253 g002
Forests 14 00253 g003 550
Figure 3. The global land area percentage of Aelosthes sarta within the three ecoclimatic index ranges under the historic and future climatic conditions under A1B and A2 climate change scenarios. Optimum habitat (EI > 15); suitable habitat (0 < EI = 15); and unsuitable habitat (EI = 0). HS = historic; CSS = climate change scenario.
Figure 3. The global land area percentage of Aelosthes sarta within the three ecoclimatic index ranges under the historic and future climatic conditions under A1B and A2 climate change scenarios. Optimum habitat (EI > 15); suitable habitat (0 < EI = 15); and unsuitable habitat (EI = 0). HS = historic; CSS = climate change scenario.
Forests 14 00253 g003
Forests 14 00253 g004 550
Figure 4. Forecast potential distribution of Aelosthes sarta in Pakistan under future climatic conditions for time periods of 2030, 2070, and 2100 under A1B and A2 climate change scenarios. The projected global distributions (three colors) narrate the ecoclimatic index (EI) from the CLIMEX model: blue →optimum (EI > 15); orange →suitable (0 < EI = 15), and white →unsuitable (EI = 0).
Figure 4. Forecast potential distribution of Aelosthes sarta in Pakistan under future climatic conditions for time periods of 2030, 2070, and 2100 under A1B and A2 climate change scenarios. The projected global distributions (three colors) narrate the ecoclimatic index (EI) from the CLIMEX model: blue →optimum (EI > 15); orange →suitable (0 < EI = 15), and white →unsuitable (EI = 0).
Forests 14 00253 g004
Forests 14 00253 g005 550
Figure 5. Predicted administrative area-wise potential distribution of Aelosthes sarta in Pakistan under historic and future climatic conditions in the time periods of 2030, 2070, and 2100 under A1B and A2 scenarios. Predicted regional distributions (three colors) narrate the ecoclimatic index (EI) from the CLIMEX model: blue = optimum habitat (EI > 15); orange = suitable habitat (0 < EI = 15); white = unsuitable habitat (EI = 0). PB = Punjab; KPK = Khyber Pakhtunkhwa; AJK = Azad Jammu and Kashmir; BLC = Balochistan; SD = Sindh; GB = Gilgit Baltistan.
Figure 5. Predicted administrative area-wise potential distribution of Aelosthes sarta in Pakistan under historic and future climatic conditions in the time periods of 2030, 2070, and 2100 under A1B and A2 scenarios. Predicted regional distributions (three colors) narrate the ecoclimatic index (EI) from the CLIMEX model: blue = optimum habitat (EI > 15); orange = suitable habitat (0 < EI = 15); white = unsuitable habitat (EI = 0). PB = Punjab; KPK = Khyber Pakhtunkhwa; AJK = Azad Jammu and Kashmir; BLC = Balochistan; SD = Sindh; GB = Gilgit Baltistan.
Forests 14 00253 g005
Forests 14 00253 g006 550
Figure 6. The predicted region in Pakistan where Aelosthes sarta survival is affected by cold and heat stress for different periods under the scenarios of A1B and A2 climate change.
Figure 6. The predicted region in Pakistan where Aelosthes sarta survival is affected by cold and heat stress for different periods under the scenarios of A1B and A2 climate change.
Forests 14 00253 g006
Table
Table 1. Aeolesthes sarta parameter values used in the CLIMEX model.
Table 1. Aeolesthes sarta parameter values used in the CLIMEX model.
ParametersCodeSettled Values
Temperature
Limiting low temperature (°C)DV010
Lower optimal temperature (°C)DV115
Upper optimal temperature (°C)DV237
Limiting high temperature (°C)DV340
Population degree dayPDD700
Moisture Index
Limiting low soil moistureSM00
Lower optimal soil moistureSM10.001
Upper optimal soil moistureSM21.5
Limiting high soil moistureSM32.5
Diapause Index
Diapause induction day lengthDPD012
Diapause induction temperature (°C)DPT013
Diapause termination temperature (°C)DPT110
Diapause development daysDPD90
Summer or winter diapauseDPSW0
Cold Stress
CS temperature threshold (°C)TTCS9
CS temperature rateTHCS−0.00001
Heat Stress
HS temperature threshold (°C)TTHS41
HS temperature rateTHHS0.005
Table
Table 2. Administrative area-wise land area percentage of Aelosthes sarta within the three ecoclimatic index ranges under the historic and future climatic conditions under A1B and A2 scenarios.
Table 2. Administrative area-wise land area percentage of Aelosthes sarta within the three ecoclimatic index ranges under the historic and future climatic conditions under A1B and A2 scenarios.
LocationArea (10,000 Km2)
EI = 00 < EI = 15EI > 15
1990 2030207021001990 2030207021001990 203020702100
PB0A1B scenario1.7015.9819.770A1B scenario5.591.610.5820.62A1B scenario13.343.030.27
KPK0.620.581.762.611.571.451.502.287.858.016.785.14
AJK0.260.250.250.280.180.170.280.300.930.950.850.78
BLC2.396.1814.4119.154.277.234.076.3927.6520.9115.838.77
SD5.9912.9513.6313.742.710.610.240.175.210.350.040
GB5.024.653.961.371.531.591.692.970.240.541.142.45
PB0A2 scenario0.9816.5619.770A2 scenario5.461.370.5820.62A2 scenario14.182.690.27
KPK0.620.691.782.621.571.321.622.287.858.036.645.14
AJK0.260.260.300.280.180.170.280.310.930.940.800.78
BLC2.395.6414.6419.154.277.204.366.4027.6521.4715.328.77
SD5.9912.9413.6113.742.710.600.270.175.210.370.040
GB5.024.673.771.371.531.611.842.970.240.511.182.45
EI = ecoclimatic index; PB = Punjab; KPK = Khyber Pakhtunkhwa; AJK = Azad Jammu and Kashmir; BLC = Balochistan; SD = Sindh; GB = Gilgit Baltistan.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hayat, U.; Akram, M.; Kour, S.; Arif, T.; Shi, J. Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario. Forests 2023, 14, 253. https://doi.org/10.3390/f14020253

AMA Style

Hayat U, Akram M, Kour S, Arif T, Shi J. Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario. Forests. 2023; 14(2):253. https://doi.org/10.3390/f14020253

Chicago/Turabian Style

Hayat, Umer, Muhammad Akram, Sumeet Kour, Tahreem Arif, and Juan Shi. 2023. "Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario" Forests 14, no. 2: 253. https://doi.org/10.3390/f14020253

APA Style

Hayat, U., Akram, M., Kour, S., Arif, T., & Shi, J. (2023). Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario. Forests, 14(2), 253. https://doi.org/10.3390/f14020253

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop