Temperature-Related Bioclimatic Variables Play a Greater Role in the Spatial Distribution of Bumblebee Species in Northern Pakistan
by
, , ,
Muhammad Naeem
1,2,†
,
Arzoo Rani
2,†,
Weiyao Lyu
3,
Huaibo Zhao
3,
Maryam Riasat
2,
Saail Abbas
2,
Sabir Hussain
4,
Nawaz Haider Bashir
1,
Qiang Li
1,* and
Huanhuan Chen
1,* 1
College of Biological Resource and Food Engineering, Qujing Normal University, Qujing 655011, China
2
Department of Zoology, Faculty of Engineering and Applied Sciences, Riphah International University, Faisalabad Campus, Faisalabad 38000, Pakistan
3
Qilin District Livestock Improvement Station, Qujing 655000, China
4
Insect Pest Management Program, Institute of Plant & Environmental Protection, National Agricultural Research Centre, Islamabad 45500, Pakistan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Insects 2025, 16(1), 1; https://doi.org/10.3390/insects16010001
Submission received: 15 November 2024
/
Revised: 3 December 2024
/
Accepted: 18 December 2024
/
Published: 24 December 2024
(This article belongs to the Special Issue Bumblebee Biology and Ecology)
Simple Summary
Over 250 bumblebee species have been reported worldwide, making them some of the most important wild pollinators, providing essential pollination services for many wild flowers and crops, including fruits and vegetables. However, their populations are declining due to various factors, including climate change. In Pakistan, only the northern areas provide more suitable habitats for these bumblebee species, but their populations are under threat in this region due to global warming and climate change. In this study, we identified the most suitable areas for bumblebee conservation and examined the contribution of bioclimatic factors to their spatial distribution.
Abstract
Bumblebee species are vital wild pollinators, providing essential pollination services for various crops, fruits, and vegetables. However, their biodiversity is vulnerable to decline due to climate change, particularly in regions like northern Pakistan. Despite this, no research has yet been conducted on the distribution patterns of bumblebee species in this region. The current study aimed to model the spatial distribution of three important bumblebee species: Bombus haemorrhoidalis, B. rufofasciatus, and B. subtypicus in northern Pakistan. Habitat suitability and the contribution of bioclimatic variables to the spatial distribution of species were assessed using the MaxEnt approach. Current and future bioclimatic variables, along with presence-only records of three bumblebee species, were incorporated into the species distribution model. The results indicated that nearly 96% of the area (43 out of 45 cities in northern Pakistan) showed habitat suitability for all three species in the current scenario. Among these 43 cities, five exhibited a 100% overlap in suitable areas for the three species. However, this overlap area is expected to decrease in the future, particularly by the middle of the 21st century, highlighting these regions as prime candidates for conservation. In terms of bioclimatic factors influencing spatial distribution, the study found that temperature-related variables played a more significant role than precipitation-related ones in current and future scenarios. Specifically, bio3 (isothermality) contributed 48% to B. haemorrhoidalis and 43% to B. rufofasciatus, while bio2 (mean diurnal range) was the most influential factor for B. subtypicus. Temperature-related variables accounted for more than 80%, 69.4%, and 78.3% of the spatial variation in B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, respectively. This study demonstrates the critical influence of temperature on the spatial distribution of bumblebee species in northern Pakistan, underscoring the need for climate-focused conservation strategies to protect these important wild pollinators.
1. Introduction
Bumblebees, belonging to the genus Bombus (a group of large social insects), are among the most effective pollinators, providing essential pollination services that support both biodiversity and agricultural productivity worldwide [1,2]. They contribute to the pollination of wild plants and agricultural crops alike [3]. In Pakistan, most bumblebee species are concentrated in the northern regions, where the abundance of flowers and favorable climate provide suitable habitats [4]. In these mountainous regions, bumblebees contribute to diverse ecological systems. However, local bioclimatic factors significantly influence the distribution range of these species, highlighting the importance of understanding how these factors shape bumblebee habitats [5].
Temperature, precipitation, humidity, and seasonality are all bioclimatic and environmental factors that shape species distributions by defining the ecological conditions suitable for their survival [6]. Bumblebee species are particularly sensitive to these bioclimatic factors, and their distribution is closely linked to these environmental conditions [7]. Therefore, studying bioclimatic factors is essential for understanding the ecological range and habitat preferences of bumblebee species within a particular region.
Previous studies have also focused on the distribution patterns of bumblebee species in relation to bioclimatic factors. For example, in the Tibetan region of China, various groups of bumblebee species exhibit distinct distribution patterns across different areas, with each group evolving in association with specific bioclimatic factors [8]. Bumblebee species establish their ecological boundaries by responding to changes in temperature and precipitation [9]. These distribution patterns and responses to bioclimatic conditions are essential to study at a local scale for each species [10,11]. Despite evidence that bioclimatic factors play a key role in the distribution patterns of bumblebee species in different regions, our knowledge remains limited regarding bumblebee species in northern Pakistan. Although over ten bumblebee species have been reported in Pakistan, three species are particularly abundant: Bombus haemorrhoidalis Smith, 1852; B. rufofaciatus Smith, 1852; and B. subtypicus (Skorikov, 1914) [12]. However, their local distribution patterns remain largely unknown.
Furthermore, global climate change is a major driver of biodiversity loss [13]. In various regions worldwide, climate change has led to shifts in bumblebee ranges [14], and widespread declines in bumblebee species have also been documented [15]. In North America and Europe, long-term observation data spanning approximately 110 years have been used to assess the movement or range shift in bumblebee species due to climate change, with range losses observed in southern regions [10]. Similarly, a climate change study conducted in China showed that more than 70% of bumblebee species are at risk. Furthermore, climate change increases the risk of alien species invasion [7,16,17]. At the local scale, such studies are important for regions where no previous research has been conducted.
The limited research on the role of bioclimatic factors in shaping the distribution of bumblebee species in the northern landscapes of Pakistan highlights a significant gap in understanding how these factors influence bumblebee distribution. Although studies from other regions provide a valuable foundation [16,17,18], the unique climatic and topographic conditions of northern Pakistan may lead to distinct distribution patterns or ecological requirements for these species, underscoring the need for focused investigation. The aim of this study is to assess the influence of current bioclimatic factors on the spatial distribution of the three key bumblebee species, B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, in northern Pakistan. The objectives are to (1) map the spatial distribution of these species in northern Pakistan, (2) identify the most influential factors shaping the specific distribution patterns of these bumblebee species in this region, and (3) identify the area of prime conservation potential for these three bumblebee species in northern Pakistan.
2. Materials and Methods
2.1. Site Selection and Data Gathering
To assess the local distribution patterns and the contribution of bioclimatic factors, we gathered records for three important bumblebee species: B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus. The data on the geographic coordinates of these species were obtained from the Global Biodiversity Information Facility (GBIF, http://www.gbif.org) (access on 5 November 2024) in November 2024. For the local data we also focused on literature [12,19,20,21,22,23]. The total number of collection sites was 272. All collection sites were separated by a distance of more than 10 km from each other; therefore, there was no need to apply spatial filtering. The study area encompasses the northern region of Pakistan, which includes 45 cities across Azad Kashmir, FATA, Khyber Pakhtunkhwa, Gilgit-Baltistan, and the Punjab provinces. The total area selected for the study of distribution patterns was 178,372 km2. The distribution sites of these three bumblebee species were located within this study region (Figure 1).
2.2. Modeling Procedure
For the assessment of spatial distribution modeling and to determine the contribution of the most important bioclimatic variables, we utilized 19 bioclimatic layers from www.worldclim.org (access on 15 October 2024) with a spatial resolution of approximately 1 km2 (Table 1). To reduce multicollinearity among these bioclimatic variables, we used the Species Distribution Modeling Toolbox of ArcGIS v10.0 and only included those bioclimatic variables that showed Pearson correlation coefficient values less than 0.8 [16,24,25].
To assess the future distribution of these three bumblebee species, we used bioclimatic data for two future time periods (2021–2040 and 2041–2060). The future climatic projections were based on four Shared Socio-economic Pathways (SSPs); however, we focused on two scenarios from the global climate model (GCM) ACCESS-CM2: SSP1 and SSP4, for both time periods (2021–2040 and 2041–2060). These scenarios represent contrasting radiative forcing levels, with SSP1 indicating low radiative forcing (2.6 W/m2) and SSP4 indicating high radiative forcing (8.5 W/m2), corresponding to minimum and maximum levels of greenhouse gas emissions and socioeconomic changes, respectively.
The maximum entropy (MaxEnt) model was selected to assess the spatial distribution of the three most important bumblebee species in the northern areas of Pakistan (Figure 1). The default feature types and regularization settings in MaxEnt were used. Since we only have presence data, MaxEnt is an ideal choice as it performs well with such data by applying the principles of maximum entropy to assess the potential distribution of species using bioclimatic variables [25]. MaxEnt used a total of 10,000 background points, which is the default setting in MaxEnt and is considered a standard practice for ensuring robustness and producing precise modeling outputs for species. The bioclimatic variables, which are temperature- and precipitation-related factors, are known to have a significant impact on bumblebee distribution [26]. All 12 bioclimatic raster layers (Table 1) for the current and future scenarios were converted into ASCII format using ArcGIS v10.0 for use in MaxEnt v3.3.3k software for species distribution modeling. Similarly, all occurrence records of three bumblebee species were converted into CSV (comma-delimited) format for input into MaxEnt v3.3.3k. For MaxEnt, we created a bias file based on the occurrence records of the three species to guide background point selection and ensure a more accurate prediction of spatial distribution. This bias file was created using ArcGIS v10.0. We ran the MaxEnt model for all regions of Asia where bumblebee samples were collected for the current and future scenarios. After obtaining the species distribution output, the focus was narrowed to northern Pakistan [27].
The output of the MaxEnt model, based on the relationship between occurrence records and bioclimatic variables, is a logistic probability with values ranging from 0 (unsuitable habitat) to 1 (highly suitable habitat). The accuracy of the MaxEnt model was evaluated using AUC (Area Under the Curve) values from ROC (Receiver Operating Characteristic) curves and TSS (True Skill Statistics) values. AUC values range from 0 to 1, with higher values indicating better model accuracy, while TSS values range from −1 to 1, with values above 0.5 suggesting good model performance [25,28].
Using ArcGIS software, the predictive model outputs were processed and used to determine habitat suitability for bumblebees. This process allowed the application of spatial analysis to produce thematic maps depicting the predicted distribution patterns of bumblebees in the study area. This evaluation provides valuable insights into the relationships between bioclimatic factors and bumblebee distribution in the research region.
3. Results
3.1. Modeling Accuracy
For the accuracy of our analysis, AUC values for the training data ranged from 0.97 to 0.99, and similarly, AUC values for the test data ranged from 0.97 to 0.99 across all three bumblebee species: B. haemorrhoidalis, B. rufofaciatus, and B. subtypicus. The 10th percentile training presence logistic threshold values varied from 0.147 to 0.411, with fractional predicted areas ranging from 0.021 to 0.068. Additionally, TSS values ranged from 0.55 to 0.86, indicating robust model performance (Table 2).
3.2. Spatial Distribution of Bumblebee Species
Distribution maps of current and future scenarios for all three species were converted into binary maps based on the 10th percentile logistic threshold values, and overlapping areas were identified. The overlapping area represents regions with the highest habitat suitability for all three species in northern Pakistan (Figure 2). Suitable habitats shared by all three species were found across 43 cities, with the percentage of suitable overlap area ranging from 0.04% to 100%. This overlap is predicted to decrease in the future (Table 3 and Figure 2).
In the current scenario, the five cities, Islamabad, Mardan, Poonch, Sudhnati, and Swabi, showed 100% overlap in suitable habitat areas for all three species. The areas of these five cities have been identified as the most important regions for implementing conservation strategies. Under the SSP1 scenario, the overlap is predicted to decrease substantially, with values ranging from 0% in Islamabad, Swabi, and Mardan to a maximum of 4.71% in Poonch and 6.25% in Sudhnati in the future (Table 3). Similarly, under the SSP4 scenario, the overlap in suitable areas is projected to decline further, ranging from 0% in Islamabad and Swabi to 32.09% in Poonch by 2060 (Table 2). These results indicate a substantial reduction in habitat overlap under future climate scenarios, with notable variations between cities (Table 3).
For the individual species, B. haemorrhoidalis had very high suitable areas in the 16 cities across northern Pakistan, with suitable area ranges between 44 km2 and 2671 km2 under the current scenario (Figure 2). These cities, listed in decreasing order of suitable area, include Swat, Dir, Kurram, Mansehra, Abbottabad, Muzaffarabad, Bagh, Kohistan, Shangla, Attock, Khyber, Battagram, Neelum, Kohat, Karak, and Mohmand (Table 4). For the future, this species is projected to have highly suitable areas in 12 to 14 cities. Further details about future prediction across different cities are present in Supplementary Materials (Tables S1–S12).
For B. rufofasciatus, very high suitability was found in 15 cities, with suitable areas ranging from 21 km2 to 4737 km2 (Figure 2 and Table 5). The largest areas of very high suitability for this species were in Swat, followed by Neelum, Kohistan, Mansehra, Khyber, Dir, Battagram, Chilas, Shangla, Kupwara (Gilgit Wazarat), Kargil, Attock, Gilgit (Tribal Territory), Chitral, and Gilgit (Table 5). For the future, this species is projected to have highly suitable areas in 13 to 18 cities under the SSP1 and SSP2 scenarios (see Supplementary Materials).
Similarly, B. subtypicus showed very high suitability in 14 cities, with areas ranging from 45 km2 to 4509 km2 (Figure 2). The highest areas of very high suitability for this species were in Mansehra, followed by Swat, Dir, Shangla, Kargil, Neelum, Kohistan, Battagram, Gilgit, Kupwara (Gilgit Wazarat), Chilas, Ladakh (Leh), Chitral, and Gilgit (Tribal Territory) (Table 6). The highest suitability for this species was limited to one to three cities under both SSP1 and SSP4 future scenarios (see Supplementary Materials).
Each of the three species, B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, exhibits habitat suitability distributed across five classes: very low, low, medium, high, and very high (Figure 3).
The area covered by each habitat suitability class varies among these species, reflecting differences in the extent and concentration of suitable habitats across the northern region of Pakistan (Figure 2). For B. haemorrhoid––alis, the largest area is covered by the very low suitability class, followed by decreasing extents in the high, very high, low, and medium suitability classes. In contrast, B. rufofasciatus occupies the largest area in the very low suitability class, followed by low, medium, high, and very high suitability classes (Figure 3). B. subtypicus, on the other hand, is predominantly found in the very high habitat suitability class, followed by very low, high, low, and medium suitability classes (Figure 3).
Under future climate scenarios (SSP1 and SSP4), a significant reduction in the high and very high habitat suitability classes was detected for all three bumblebee species. In B. haemorrhoidalis, the very high suitability habitat class showed a range reduction of up to 62% under the SSP4 scenario during the years 2041–2060 compared to current baseline values. The very high suitability range of B. rufofasciatus decreased by up to 24% under the SSP4 scenario for the period 2021–2040. Lastly, B. subtypicus exhibited the most substantial loss, with the very high suitability range declining by up to 72% under the SSP4 scenario during 2041–2060 (Figure 4).
3.3. Contribution of Most Important Variables in the Spatial Distribution of Most Important Bumblebee Species in Northern Areas of Pakistan
Overall, temperature-related bioclimatic variables contributed more than precipitation-related variables to the spatial distribution of the three bumblebee species (Figure 5). For B. haemorrhoidalis, the temperature-related variable bio3 made the highest contribution (48%), followed by bio1 (18.2%). In the case of B. rufofasciatus, bio3 contributed the most (43%), while for B. subtypicus, bio2 had the highest contribution (37%). Temperature-related bioclimatic variables (bio1–bio11) contributed more than 80%, 69.4%, and 78.3% to the spatial distribution of B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, respectively. In contrast, precipitation-related bioclimatic variables contributed 18.8%, 30.6%, and 21.7% to the spatial distribution of B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, respectively (Figure 5). Furthermore, the future contribution of bioclimatic factors also showed that temperature-related variables played a larger role in the spatial distribution of the three bumblebee species across all future scenarios (Figure S1).
4. Discussion
Our findings indicate that temperature-related bioclimatic factors play a more significant role in the spatial distribution of three bumblebee species: B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus (Figure 5). Temperature-related variables contributed up to 80% for B. haemorrhoidalis, followed by 65.3% for B. rufofasciatus and 65.1% for B. subtypicus. These results are consistent with previous studies emphasizing the critical role of temperature in shaping the spatial distribution of bumblebee species [29]. Notably, bio3 (isothermality) contributed over 50% to the spatial distribution of B. haemorrhoidalis, 43% for B. rufofasciatus, and bio2 contributed 37% to the distribution of B. subtypicus, underscoring that temperature-related variables are key determinants in shaping the geographic range of these species. These temperature-related factors are also considered the most influential in affecting the foraging and reproductive behavior of bumblebees [30].
Although precipitation-related variables also contribute to the spatial distribution of these three bumblebee species, their contribution is less compared to that of temperature-related factors (Figure 5). For example, the overall contribution of precipitation-related variables was 20%, 34.6%, and 34.9% for B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, respectively. This suggests that water availability is important, but secondary, in determining the distribution of these species in northern Pakistan (Figure 2). These findings highlight the importance of water content in regulating the habitat suitability for bumblebee species, as discussed by a previous study [31].
We identified overlap areas of high suitability for all three bumblebee species in the northern regions of Pakistan. Almost 96% of the area (43 out of 45 cities in northern Pakistan) showed habitat suitability for all three species. Among these 43 cities, five cities exhibited 100% overlap in suitable areas for the three species, indicating that these areas are of prime importance for conservation (Figure 2). Our findings also revealed that the three species have diverse ecological preferences. B. haemorrhoidalis and B. rufofasciatus were associated with a broader range of habitats, primarily in the “very low” suitability class, whereas B. subtypicus predominantly occupied areas within the “very high” suitability class (Figure 3).
The bumblebee species B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus thrive in the climate of Northern Pakistan, which has the perfect temperature, humidity, and precipitation levels. Other regions may not have the same conditions [32]. The flora and habitat structures in Northern Pakistan are crucial for the foraging and nesting needs of these bumblebee species, which are not found in other regions [33].The higher altitudes and varied terrain of Northern Pakistan make it more suitable for these species compared to the flatter and hotter southern regions [34]. Each species has developed to take advantage of different ecological niches, resulting in differences in how they react to bioclimatic factors like temperature, precipitation, and seasonality [35].
MaxEnt achieved the best results compared to other modeling methods, which aligns with past research [36]. The findings of the research are crucial for developing successful conservation plans. By recognizing the important bioclimatic elements that impact the distribution of B. haemorrhoidalis, B. rufofaciatus, and B. subtypicus, we can better anticipate changes in bumblebee habitats and make appropriate preparations [26]. Conservationists can determine and prioritize important habitats for protection and restoration by studying how species are distributed in relation to temperature, precipitation, and elevation [37].
Future climate change scenarios indicate a substantial decline in habitat suitability for all three bumblebee species in northern Pakistan. The overlap areas of suitable habitats are also decreasing. For example, cities such as Islamabad, Mardan, and Swabi, which currently exhibit 100% overlap in suitable habitat areas, are predicted to experience a complete loss of overlap under future scenarios. Similarly, two other cities, Poonch and Sudhnati, that presently show a 100% overlap are projected to see a reduction to 4.71% and 6.25%, respectively (Table 2). This reduction is attributed to shifts in bioclimatic conditions in the future [38]. The decline in the very high suitability class for B. haemorrhoidalis and B. subtypicus by up to 62% and 72%, respectively, under the SSP4 scenario underscores the vulnerability of these species in northern Pakistan to warming temperatures and changing climatic patterns.
Our findings of this study are consistent with previous studies indicating that habitat suitability is critical to species distribution [30]. The results of this study enhance our understanding of the spatial distribution of these species and provide insight into their habitat requirements, which are essential for implementing effective conservation strategies [39,40]. The distinct habitat suitability patterns observed for each species suggest that conservation strategies should consider species-specific habitat preferences, particularly in regions where overlapping suitable habitats were identified and predicted to decline in the future in northern Pakistan.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/insects16010001/s1, Table S1: Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis during SSP1 (2021–2040); Table S2: Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus during SSP1 (2021–2040); Table S3: Distribution of habitat suitability areas across different suitability classes for Bombus subtypicus during SSP1 (2021–2040); Table S4: Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis during SSP1 (2041–2060); Table S5: Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus during SSP1 (2041–2060); Table S6: Distribution of habitat suitability areas across different suitability classes for Bombus subtypicus during SSP1 (2041–2060); Table S7: Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis during SSP4 (2021–2040); Table S8: Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus during SSP4 (2021–2040); Table S9: Distribution of habitat suitability areas across different suitability classes for Bombus subtypicus during SSP4 (2021–2040); Table S10: Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis during SSP4 (2041–2060); Table S11: Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus during SSP4 (2041–2060); Table S12: Distribution of habitat suitability areas across different suitability classes for Bombus typicus during SSP4 (2041–2060); Figure S1: Contribution of bioclimatic factors in the spatial distribution of the three most important bumblebee species in northern areas of Pakistan under future scenarios.
Author Contributions
Conceptualization, M.N., H.C. and N.H.B.; methodology, M.N. and H.C.; software, M.N., S.A., S.H. and A.R.; validation, M.N., W.L., H.Z., Q.L., S.H., S.A. and H.C.; formal analysis, M.N.; investigation, H.C.; resources, H.C.; data curation, M.R., N.H.B., S.A., S.H. and A.R.; writing—original draft preparation, M.N., M.R. and H.C.; writing—review and editing, H.C., N.H.B., Q.L., S.H., S.A., W.L. and H.Z.; visualization, M.N. and H.C.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities [202101BA070001-209] and the Scientific Research Fund project of the Education Department of Yunnan Province [2024J0938]. General Programs of the Provincial Department of Science and Technology [202101BA070001-076]. Yunnan Provincial Strawberry Science and Technology Backyard in Huize. The Identification of International Science and Technology Commissioners (Huanhuan Chen) of Yunnan Province in 2024 [202403AK140027] and the Yunnan Provincial Department of Science and Technology “Yunnan Talent Program” plan [202403AM140021], and Supported by the Special Basic Cooperative Research Innovation Programs of Qujing Science and Technology Bureau & Qujing Normal University [KJLH2024ZD05].
Data Availability Statement
All the data generated or analyzed during this study are included in this published article and its Supplementary Materials.
Acknowledgments
We are thankful for the support from the Program for Innovative Research Teams at Qujing Normal University. The authors are grateful for the High-Level Talent Recruitment Plan of Yunnan Province (‘High-End Foreign Experts’ Program).
Conflicts of Interest
The authors declare no conflicts of interest.
Correction Statement
This article has been republished with a minor correction to the readability of figure 1. This change does not affect the scientific content of the article.
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Figure 1.
Bumblebees’ collection sites (green and red bolls) in north part of Pakistan and its surrounding regions.
Figure 1.
Bumblebees’ collection sites (green and red bolls) in north part of Pakistan and its surrounding regions.
Figure 2.
Spatial distribution of habitat suitability areas and overlap areas of the three most important bumblebee species in north part of Pakistan at current and future scenarios.
Figure 2.
Spatial distribution of habitat suitability areas and overlap areas of the three most important bumblebee species in north part of Pakistan at current and future scenarios.
Figure 3.
The distribution of area (km2) by different habitat suitability class of all three bumblebee species in north part of Pakistan.
Figure 3.
The distribution of area (km2) by different habitat suitability class of all three bumblebee species in north part of Pakistan.
Figure 4.
Percentage range changes in habitat suitability ranges in three bumblebee species during the future time periods in north part of Pakistan.
Figure 4.
Percentage range changes in habitat suitability ranges in three bumblebee species during the future time periods in north part of Pakistan.
Figure 5.
Contribution of bioclimatic factors in the spatial distribution of the three most important bumblebee species in north part of Pakistan.
Figure 5.
Contribution of bioclimatic factors in the spatial distribution of the three most important bumblebee species in north part of Pakistan.
Table 1.
Bioclimatic variables used to assess the spatial distribution of the three most important bumblebee species, Bombus haemorrhoidalis, B. rufofasciatus, and B. subtypicus, in north part of Pakistan. Bioclimatic variables with an asterisk (*) were included in our modeling because their Pearson’s correlation coefficients (r) were <0.8.
Table 1.
Bioclimatic variables used to assess the spatial distribution of the three most important bumblebee species, Bombus haemorrhoidalis, B. rufofasciatus, and B. subtypicus, in north part of Pakistan. Bioclimatic variables with an asterisk (*) were included in our modeling because their Pearson’s correlation coefficients (r) were <0.8.
| Sr. No. | Variable Names | Abbreviations |
|---|---|---|
| 1 | * Annual Mean Temperature | * bio1 |
| 2 | * Mean Diurnal Range (Mean of monthly (Max Temp–Min Temp)) | * bio2 |
| 3 | * Isothermality (BIO2/BIO7) (×100) | * bio3 |
| 4 | * Temperature Seasonality (Standard Deviation ×100) | * bio4 |
| 5 | Max Temperature of Warmest Month | bio5 |
| 6 | * Min Temperature of Coldest Month | * bio6 |
| 7 | * Temperature Annual Range (BIO5-BIO6) | * bio7 |
| 8 | * Mean Temperature of Wettest Quarter | * bio8 |
| 9 | * Mean Temperature of Driest Quarter | * bio9 |
| 10 | Mean Temperature of Warmest Quarter | bio10 |
| 11 | Mean Temperature of Coldest Quarter | bio11 |
| 12 | Annual Precipitation | bio12 |
| 13 | Precipitation of Wettest Month | bio13 |
| 14 | Precipitation of Driest Month | bio14 |
| 15 | * Precipitation Seasonality (Coefficient of Variation) | * bio15 |
| 16 | Precipitation of Wettest Quarter | bio16 |
| 17 | * Precipitation of Driest Quarter | * bio17 |
| 18 | * Precipitation of Warmest Quarter | * bio18 |
| 19 | * Precipitation of Coldest Quarter | * bio19 |
Table 2.
Area Under the Curve (AUC) values for training and test data, along with the 10th percentile training presence threshold, fractional predicted area, and True Skill Statistics (TSS) values for three bumblebee species.
Table 2.
Area Under the Curve (AUC) values for training and test data, along with the 10th percentile training presence threshold, fractional predicted area, and True Skill Statistics (TSS) values for three bumblebee species.
| Species | AUC Training Data | AUC Test Data | 10th Percentile Value | Fractional Predicted Area | TSS |
|---|---|---|---|---|---|
| B. haemorrhoidalis | 0.99 | 0.97 | 0.282 | 0.021 | 0.55 |
| B. rufofaciatus | 0.98 | 0.99 | 0.147 | 0.037 | 0.64 |
| B. subtypicus | 0.97 | 0.99 | 0.411 | 0.068 | 0.86 |
Table 3.
Percentage overlap of suitable areas for all three bumblebee species in north part of Pakistan at current and future scenarios.
Table 3.
Percentage overlap of suitable areas for all three bumblebee species in north part of Pakistan at current and future scenarios.
| Sr. No. | Cities | Percentage of Suitable Area | ||||
|---|---|---|---|---|---|---|
| Current | SSP1 (2021–2040) | SSP1 (2041–2060) | SSP4 (2021–2040) | SSP4 (2041–2060) | ||
| 1 | Islamabad | 100 | 0 | 0 | 0 | 0 |
| 2 | Mardan | 100 | 0.04 | 0 | 0.09 | 0 |
| 3 | Poonch | 100 | 4.71 | 5 | 20.71 | 32.09 |
| 4 | Sudhnati | 100 | 0.29 | 0 | 7.37 | 10.69 |
| 5 | Swabi | 100 | 0 | 0 | 0 | 0 |
| 6 | Haripur | 99.89 | 0 | 6.25 | 0 | 0 |
| 7 | Buner | 99.48 | 1.36 | 0 | 2.08 | 1.28 |
| 8 | Orakzai | 98.87 | 25.68 | 0 | 14.19 | 4.40 |
| 9 | Rawalpindi | 98.33 | 0.00 | 0.88 | 0.00 | 0 |
| 10 | Malakand P.A. | 98.19 | 0.74 | 0 | 1.14 | 0.20 |
| 11 | Kotli | 95.26 | 0 | 0 | 0.30 | 0.22 |
| 12 | Charsadda | 94.97 | 0 | 0 | 0 | 0 |
| 13 | Bagh | 93.48 | 19.18 | 20.69 | 31.13 | 47.97 |
| 14 | Abbottabad | 91.69 | 0 | 60.98 | 0 | 2.63 |
| 15 | Kurram | 89.39 | 16.50 | 0 | 17.32 | 4.67 |
| 16 | Shangla | 88.59 | 25.92 | 2.44 | 29.56 | 15.17 |
| 17 | Muzaffarabad | 87.90 | 22.17 | 52.94 | 36.61 | 44.88 |
| 18 | Peshawar | 87.32 | 0 | 0 | 0 | 0 |
| 19 | Nowshera | 85.42 | 0 | 0 | 0 | 0 |
| 20 | Hangu | 81.56 | 0 | 0 | 0 | 0 |
| 21 | Attok | 73.36 | 0 | 0 | 0 | 0 |
| 22 | Battagram | 65.78 | 3.6 | 15.15 | 10.49 | 1.80 |
| 23 | Adam Khel | 62.48 | 0 | 0 | 0 | 0 |
| 24 | Khyber | 59.45 | 24.84 | 1.72 | 16.39 | 1.51 |
| 25 | Mansehra | 58.98 | 4.43 | 28.57 | 6.95 | 3.52 |
| 26 | Dir | 57.53 | 4.04 | 3.92 | 6.60 | 3.60 |
| 27 | Chakwal | 52.46 | 0 | 0 | 0 | 0 |
| 28 | Bajaur | 52.09 | 0 | 0 | 0 | 0.71 |
| 29 | Swat | 46.76 | 23.06 | 3.74 | 31.44 | 20.75 |
| 30 | Neelum | 27.97 | 6.05 | 3.30 | 8.40 | 3.43 |
| 31 | Bhimber | 23.82 | 0 | 0 | 0 | 0 |
| 32 | Kohistan | 19.19 | 9.1 | 0 | 10.26 | 0.04 |
| 33 | Mirpur | 18.76 | 0 | 0 | 0 | 0 |
| 34 | Kohat | 17.95 | 0 | 0 | 0 | 0 |
| 35 | Karak | 13.81 | 0 | 0 | 0 | 0 |
| 36 | Jhelum | 8.89 | 0 | 0 | 0 | 0 |
| 37 | Mohmand | 8.15 | 0 | 0 | 0 | 0.27 |
| 38 | Kupwara (Gilgit Wazarat) | 4.55 | 0 | 0 | 0 | 0 |
| 39 | Chilas | 3.07 | 0 | 0 | 0 | 0 |
| 40 | Gilgit (Tribal Territory) | 0.88 | 0 | 0 | 0 | 0 |
| 41 | Kargil | 0.45 | 0 | 0 | 0 | 0 |
| 42 | Gilgit | 0.24 | 0 | 0 | 0 | 0 |
| 43 | Chitral | 0.04 | 0 | 1.73 | 0 | 0 |
Table 4.
Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis in north part of Pakistan.
Table 4.
Distribution of habitat suitability areas across different suitability classes for Bombus haemorrhoidalis in north part of Pakistan.
| Cities | Area (km2) Distribution in Different Habitat Suitability Classes | ||||
|---|---|---|---|---|---|
| Very Low | Low | Medium | High | Very High | |
| Abbottabad | 10 | 295 | 373 | 1082 | 1822 |
| Adam Khel | 510 | 551 | 369 | 59 | 0 |
| Attok | 10 | 3201 | 3919 | 5065 | 413 |
| Bagh | 120 | 401 | 318 | 557 | 1405 |
| Bajaur | 130 | 820 | 1359 | 352 | 0 |
| Bannu | 2036 | 393 | 0 | 0 | 0 |
| Battagram | 920 | 424 | 602 | 813 | 165 |
| Bhimber | 4 | 2130 | 500 | 0 | 0 |
| Buner | 205 | 972 | 2148 | 0 | 0 |
| Chakwal | 2904 | 4460 | 5354 | 724 | 0 |
| Charsadda | 225 | 572 | 1078 | 0 | 0 |
| Chilas | 8715 | 2271 | 48 | 0 | 0 |
| Chitral | 28,451 | 569 | 16 | 0 | 0 |
| Dir | 2470 | 1057 | 2234 | 2210 | 2624 |
| Gilgit | 58,134 | 312 | 8 | 0 | 0 |
| Gilgit (Tribal Territory) | 6816 | 789 | 81 | 0 | 0 |
| Hangu | 160 | 386 | 526 | 1515 | 0 |
| Haripur | 23 | 461 | 3242 | 0 | 0 |
| Islamabad | 7 | 1660 | 0 | 0 | 0 |
| Jhelum | 1806 | 2318 | 2333 | 358 | 0 |
| Karak | 83 | 4001 | 1290 | 530 | 69 |
| Kargil | 29,764 | 127 | 0 | 0 | 0 |
| Khyber | 424 | 1177 | 1463 | 2014 | 397 |
| Kohat | 3 | 4475 | 482 | 182 | 81 |
| Kohistan | 9055 | 2161 | 1197 | 910 | 837 |
| Kotli | 1966 | 1694 | 0 | 0 | 0 |
| Kupwara (Gilgit Wazarat) | 7648 | 397 | 49 | 0 | 0 |
| Kurram | 336 | 478 | 988 | 2315 | 2325 |
| Ladakh (Leh) | 22,369 | 0 | 0 | 0 | 0 |
| Malakand P.A. | 58 | 733 | 1153 | 0 | 0 |
| Mansehra | 3821 | 910 | 774 | 2053 | 2244 |
| Mardan | 603 | 1615 | 902 | 0 | 0 |
| Mirpur | 276 | 1310 | 209 | 0 | 0 |
| Mohmand | 112 | 2860 | 1169 | 288 | 44 |
| Muzaffarabad | 148 | 541 | 442 | 870 | 1792 |
| Neelum | 7416 | 1768 | 608 | 321 | 160 |
| Nowshera | 632 | 2542 | 420 | 72 | 0 |
| Orakzai | 7 | 144 | 1458 | 1060 | 0 |
| Peshawar | 348 | 380 | 317 | 1475 | 0 |
| Poonch | 1 | 391 | 1181 | 0 | 0 |
| Rawalpindi | 8 | 430 | 3502 | 6373 | 0 |
| Shangla | 153 | 170 | 733 | 1499 | 639 |
| Sudhnati | 365 | 497 | 0 | 0 | 0 |
| Swabi | 225 | 2137 | 722 | 0 | 0 |
| Swat | 5066 | 769 | 716 | 1272 | 2671 |
Table 5.
Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus in north part of Pakistan.
Table 5.
Distribution of habitat suitability areas across different suitability classes for Bombus rufofasciatus in north part of Pakistan.
| Cities | Area (km2) Distribution in Different Habitat Suitability Classes | ||||
|---|---|---|---|---|---|
| Very Low | Low | Medium | High | Very High | |
| Abbottabad | 400 | 3182 | 0 | 0 | 0 |
| Adam Khel | 278 | 943 | 268 | 0 | 0 |
| Attok | 20 | 3389 | 6396 | 2771 | 31 |
| Bagh | 290 | 1115 | 1396 | 0 | 0 |
| Bajaur | 61 | 1427 | 1033 | 140 | 0 |
| Bannu | 171 | 2258 | 0 | 0 | 0 |
| Battagram | 42 | 83 | 131 | 1577 | 1090 |
| Bhimber | 2213 | 421 | 0 | 0 | 0 |
| Buner | 72 | 1956 | 1297 | 0 | 0 |
| Chakwal | 6797 | 6645 | 0 | 0 | 0 |
| Charsadda | 222 | 188 | 198 | 1268 | 0 |
| Chilas | 3725 | 1749 | 2350 | 2264 | 945 |
| Chitral | 23,950 | 3984 | 870 | 209 | 23 |
| Dir | 743 | 820 | 2795 | 4327 | 1911 |
| Gilgit | 51,088 | 5494 | 1424 | 427 | 21 |
| Gilgit (Tribal Territory) | 3943 | 1537 | 1530 | 644 | 30 |
| Hangu | 271 | 718 | 1553 | 44 | 0 |
| Haripur | 4 | 1871 | 1850 | 0 | 0 |
| Islamabad | 1491 | 177 | 0 | 0 | 0 |
| Jhelum | 6322 | 492 | 0 | 0 | 0 |
| Karak | 86 | 5031 | 808 | 48 | 0 |
| Kargil | 24,857 | 3382 | 1231 | 324 | 98 |
| Khyber | 1087 | 892 | 1005 | 486 | 2005 |
| Kohat | 4324 | 700 | 160 | 40 | 0 |
| Kohistan | 4101 | 1612 | 1710 | 4013 | 2723 |
| Kotli | 184 | 3010 | 456 | 10 | 0 |
| Kupwara (Gilgit Wazarat) | 5135 | 1041 | 892 | 619 | 407 |
| Kurram | 136 | 294 | 1659 | 4352 | 0 |
| Ladakh (Leh) | 22,222 | 147 | 0 | 0 | 0 |
| Malakand P.A. | 79 | 985 | 603 | 277 | 0 |
| Mansehra | 1331 | 1057 | 534 | 4545 | 2336 |
| Mardan | 1 | 808 | 2310 | 0 | 0 |
| Mirpur | 1 | 1480 | 314 | 0 | 0 |
| Mohmand | 3187 | 1111 | 154 | 21 | 0 |
| Muzaffarabad | 4 | 83 | 2178 | 1529 | 0 |
| Neelum | 3442 | 1487 | 640 | 608 | 4095 |
| Nowshera | 430 | 1680 | 762 | 794 | 0 |
| Orakzai | 1 | 89 | 804 | 1775 | 0 |
| Peshawar | 229 | 818 | 626 | 846 | 0 |
| Poonch | 343 | 808 | 423 | 0 | 0 |
| Rawalpindi | 146 | 9202 | 965 | 0 | 0 |
| Shangla | 11 | 33 | 133 | 2428 | 589 |
| Sudhnati | 1 | 396 | 442 | 23 | 0 |
| Swabi | 471 | 1460 | 1153 | 0 | 0 |
| Swat | 2652 | 855 | 554 | 1696 | 4737 |
Table 6.
Distribution of habitat suitability areas across different suitability classes for Bombus subtypicus in north part of Pakistan.
Table 6.
Distribution of habitat suitability areas across different suitability classes for Bombus subtypicus in north part of Pakistan.
| Cities | Area (km2) Distribution in Different Habitat Suitability Classes | ||||
|---|---|---|---|---|---|
| Very Low | Low | Medium | High | Very High | |
| Abbottabad | 14 | 1005 | 2563 | 0 | 0 |
| Adam Khel | 574 | 916 | 0 | 0 | 0 |
| Attok | 3 | 2313 | 10,291 | 0 | 0 |
| Bagh | 11 | 1063 | 1727 | 0 | 0 |
| Bajaur | 1 | 230 | 1352 | 1077 | 0 |
| Bannu | 2083 | 346 | 0 | 0 | 0 |
| Battagram | 25 | 179 | 663 | 1147 | 909 |
| Bhimber | 1088 | 1543 | 3 | 0 | 0 |
| Buner | 274 | 3051 | 0 | 0 | 0 |
| Chakwal | 21 | 5142 | 8278 | 0 | 0 |
| Charsadda | 1875 | 0 | 0 | 0 | 0 |
| Chilas | 1764 | 2725 | 4740 | 1610 | 196 |
| Chitral | 17,324 | 6636 | 4020 | 969 | 86 |
| Dir | 961 | 1071 | 1574 | 3763 | 3226 |
| Gilgit | 32,252 | 14,490 | 9380 | 1872 | 459 |
| Gilgit (Tribal Territory) | 2722 | 3185 | 1340 | 393 | 45 |
| Hangu | 813 | 1773 | 0 | 0 | 0 |
| Haripur | 396 | 3329 | 0 | 0 | 0 |
| Islamabad | 188 | 1480 | 0 | 0 | 0 |
| Jhelum | 266 | 6483 | 65 | 0 | 0 |
| Karak | 575 | 4442 | 957 | 0 | 0 |
| Kargil | 10,211 | 8191 | 7781 | 2504 | 1204 |
| Khyber | 55 | 605 | 3734 | 1081 | 0 |
| Kohat | 3165 | 2058 | 0 | 0 | 0 |
| Kohistan | 3880 | 3646 | 3979 | 1713 | 941 |
| Kotli | 725 | 2935 | 0 | 0 | 0 |
| Kupwara (Gilgit Wazarat) | 1946 | 2579 | 1864 | 1294 | 411 |
| Kurram | 7 | 151 | 3201 | 3082 | 0 |
| Ladakh (Leh) | 15,440 | 2903 | 3126 | 769 | 131 |
| Malakand P.A. | 141 | 1803 | 0 | 0 | 0 |
| Mansehra | 783 | 1600 | 1080 | 1831 | 4509 |
| Mardan | 1 | 3119 | 0 | 0 | 0 |
| Mirpur | 196 | 1574 | 24 | 0 | 0 |
| Mohmand | 7 | 1142 | 2476 | 848 | 0 |
| Muzaffarabad | 10 | 164 | 1443 | 2178 | 0 |
| Neelum | 1157 | 2846 | 2084 | 3051 | 1133 |
| Nowshera | 252 | 3414 | 0 | 0 | 0 |
| Orakzai | 6 | 1498 | 1166 | 0 | 0 |
| Peshawar | 213 | 2306 | 0 | 0 | 0 |
| Poonch | 349 | 1225 | 0 | 0 | 0 |
| Rawalpindi | 6 | 1901 | 8407 | 0 | 0 |
| Shangla | 16 | 85 | 208 | 971 | 1915 |
| Sudhnati | 287 | 575 | 0 | 0 | 0 |
| Swabi | 25 | 3058 | 0 | 0 | 0 |
| Swat | 2715 | 1571 | 1287 | 1641 | 3280 |
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Naeem, M.; Rani, A.; Lyu, W.; Zhao, H.; Riasat, M.; Abbas, S.; Hussain, S.; Bashir, N.H.; Li, Q.; Chen, H. Temperature-Related Bioclimatic Variables Play a Greater Role in the Spatial Distribution of Bumblebee Species in Northern Pakistan. Insects 2025, 16, 1. https://doi.org/10.3390/insects16010001
AMA Style
Naeem M, Rani A, Lyu W, Zhao H, Riasat M, Abbas S, Hussain S, Bashir NH, Li Q, Chen H. Temperature-Related Bioclimatic Variables Play a Greater Role in the Spatial Distribution of Bumblebee Species in Northern Pakistan. Insects. 2025; 16(1):1. https://doi.org/10.3390/insects16010001
Chicago/Turabian StyleNaeem, Muhammad, Arzoo Rani, Weiyao Lyu, Huaibo Zhao, Maryam Riasat, Saail Abbas, Sabir Hussain, Nawaz Haider Bashir, Qiang Li, and Huanhuan Chen. 2025. "Temperature-Related Bioclimatic Variables Play a Greater Role in the Spatial Distribution of Bumblebee Species in Northern Pakistan" Insects 16, no. 1: 1. https://doi.org/10.3390/insects16010001
APA StyleNaeem, M., Rani, A., Lyu, W., Zhao, H., Riasat, M., Abbas, S., Hussain, S., Bashir, N. H., Li, Q., & Chen, H. (2025). Temperature-Related Bioclimatic Variables Play a Greater Role in the Spatial Distribution of Bumblebee Species in Northern Pakistan. Insects, 16(1), 1. https://doi.org/10.3390/insects16010001
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