Elevational Filtering Drives Pollinator Community Disassembly in the Mountain Orchards of Doda, Indian Himalayas

Elevational Filtering Drives Pollinator Community Disassembly in the Mountain Orchards of Doda, Indian Himalayas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevational Filtering Drives Pollinator Community Disassembly in the Mountain Orchards of Doda, Indian Himalayas Rohit, Anjali Dhar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8979944/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pollinators are indispensable for the productivity of temperate fruit crops like apple and apricot, yet their diversity and drivers remain critically understudied in the complex topography of the Himalayan region. This study presents the first comprehensive assessment of pollinator communities in the agroecosystems of Doda District, Jammu and Kashmir, India, a region of global agricultural significance. We sampled insect pollinators across a steep elevational gradient (1073–2302 m ASL) in six orchard sites during the 2024 bloom period. Using standardized protocols of pan trapping and timed observations, we documented species composition, abundance, and diversity. The impact of abiotic factors (elevation, temperature) was analyzed using Pearson correlation, linear regression, and multivariate statistics (NMDS, PERMANOVA). We recorded 662 individuals from 14 species across 10 families. A dramatic decline in pollinator diversity and abundance was observed with increasing elevation: total abundance fell by 75.7% and species richness dropped from 9 to 3 species. Elevation alone explained a remarkable 94.7% of the variance in Shannon diversity (H' = 3.12–0.00095 × Elevation; R² = 0.947, p < 0.001). Multivariate analysis revealed a significant compositional shift (PERMANOVA: Pseudo-F = 8.91, p = 0.002), driven by a major taxonomic restructuring: the relative abundance of Halictidae increased from 19.8% at low elevations to 41.5% at high elevations, effectively replacing Apidae as the dominant family. Furthermore, a fine-scale comparison at a single site showed significant partitioning between crops, with apple attracting more Apidae (46.8% of visits) and apricot attracting more Syrphidae (44.7% of visits). This study establishes that elevation acts as a master environmental filter, overwhelming local factors to structure pollinator communities in Himalayan orchards. The documented patterns and the unique baseline data provide critical insights for crafting elevation-specific conservation strategies to safeguard pollination services and the resilience of mountain agriculture in a warming climate. Horticulture Entomology Pollinator diversity elevational gradient abiotic factors apple pollination apricot pollination Himalayan ecosystem conservation Doda District Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Temperate fruit crops represent a cornerstone of agricultural economies and nutritional security in mountainous regions worldwide [ 1 – 3 ]. In the Himalayan context, apple ( Malus domestica ) and apricot ( Prunus armeniaca ) are not only vital cash crops but also deeply embedded in the cultural and ecological fabric of local communities [ 4 – 6 ]. The reproductive success of these crops, and consequently the livelihoods of millions of smallholder farmers, hinges critically upon insect-mediated pollination [ 7 , 8 ]. While managed honey bees often receive primary attention, a rich diversity of wild pollinators including solitary bees, bumble bees, and hoverflies contributes significantly to fruit set, quality, and yield stability, often enhancing pollination efficiency beyond what managed colonies can achieve alone [ 9 ]. In mountain ecosystems, biodiversity is intrinsically linked to elevation, which acts as a master gradient filtering species distributions through correlated changes in temperature, atmospheric pressure, and resource availability [ 10 ]. For ectothermic pollinators, these elevational shifts create profound physiological and ecological constraints, leading to predictable turnovers in community composition, diversity, and abundance [ 11 ]. Understanding these patterns is not merely an academic exercise; it is urgent for predicting how climate change will reshape pollination services. As global temperatures rise, species are projected to shift their ranges upslope, potentially creating novel communities, disrupting existing plant-pollinator interactions, and leaving high-elevation ecosystems particularly vulnerable [ 12 ]. The Himalayan region of Jammu and Kashmir is a globally significant fruit-producing area. However, ecological research on its pollination systems has been historically concentrated in the Kashmir Valley [ 13 – 19 ]. This led to a significant knowledge gap in other topographically diverse districts such as Doda. This study presents the first comprehensive assessment of pollinator diversity and its drivers in the agroecosystems of the Doda District. Characterized by its rugged terrain, deep valleys, and a steep elevational gradient, Doda provides a unique and powerful natural laboratory to investigate the interplay between abiotic factors and pollinator communities. The district's heavy reliance on apple and apricot cultivation makes understanding its pollination ecology a matter of direct socio-economic concern. Within this context, we designed a study to answer the following critical questions: What is the diversity, abundance, and species composition of insect pollinators in the apple and apricot orchards of Doda District? How do pollinator communities change along a steep elevational gradient (~ 1100–2300 m ASL), and what is the relative impact of key abiotic factors like temperature? Does pollinator assemblage differ between the two economically crucial crops, apple and apricot, when studied under identical environmental conditions? By addressing these questions, this research establishes a vital ecological baseline for the region. Our findings will inform evidence-based conservation strategies, support the resilience of local agriculture in the face of environmental change, and contribute to the global understanding of how mountain agroecosystems sustain biodiversity and ecosystem function. Materials and Methods Study Area and Site Selection This study was conducted in the Doda District (33°08'N, 75°3'E) of Jammu and Kashmir, India (Fig. 1), a region characterized by complex topography within the western Himalayas [ 20 ]. To capture the full spectrum of environmental variation, we employed a stratified random sampling design along a steep elevational gradient. Six study sites were selected, ranging from 1,073 to 2,302 meters above sea level (m ASL), ensuring representation of the major apple ( Malus domestica ) and apricot ( Prunus armeniaca ) cultivation zones (Table 1 ). Site selection criteria were: (i) presence of commercially managed orchards of a minimum size (≥ 0.5 ha), (ii) spanning a continuous elevational gradient of over 1200 m, (iii) variation in slope aspect, and (iv) accessibility for repeated sampling. The selected sites represented the predominant bloom periods for the region: apricot from late February to mid-March, and apple from mid-March to early April. Figure 1. Map of district Doda with sampling sites. Table 1 Characterization of the six study sites in Doda District. Site Code Site Name Coordinates Elevation (m ASL) Predominant Aspect Primary Crop(s) L1 Pranoo 33°05'38"N 75°34'57"E 1073 South-East Apple, Apricot L2 Bhaboor 33°08'46"N 75°34'55"E 1163 North-East Apple L3 Delain 32°59'10"N 75°43'45"E 1568 South-West Apricot, Apple H2 Chounri 33°04'52"N 75°47'44"E 1960 South Apple, Apricot H1 Sichal 33°07'52"N 75°47'53"E 2158 North-West Apple H3 Bhaderwah 33°01'45"N 75°46'25"E 2302 South-East Apricot Study Design and Sampling Protocol A hierarchical sampling design was implemented to account for spatial variation. Within each of the six sites, four permanent 50m x 50m plots were established (total n = 24 plots). Plots were strategically placed to capture within-orchard heterogeneity: two plots in the orchard interior (> 20m from edge) and two on the edge, adjacent to different landscape features (e.g., natural forest, agricultural field, or road). Sampling was conducted over the 2024 blooming season, organized into three temporal waves to coincide with peak bloom periods at different elevations. Each site was visited a minimum of twice during its peak bloom, with all sampling conducted between 0900 and 1500 hrs on days with favorable weather (no precipitation, wind speed < 5 m/s). Crop Comparison Methodology The comparative analysis of pollinator communities between apple and apricot was intentionally restricted to site L1 (Pranoo, 1073 m ASL). This design controlled for confounding environmental variables, as both crops were present within the same orchard, experienced identical microclimatic conditions, and had overlapping bloom periods during the sampling timeframe. This approach isolated the effect of crop identity from the overwhelming influence of elevation and seasonal phenology that would confound cross-site comparisons. Pollinator Sampling and Effectiveness Assessment To obtain a comprehensive inventory of the pollinator community, we used two complementary methods. Pan Trapping We used colored pan traps to passively sample flying insects, following a standardized protocol [ 21 ]. At the center of each plot, an array of three UV-bright pan traps (blue, yellow, and white) was deployed, filled with soapy water and positioned approximately 1 meter above ground. Traps were active for a continuous 24-hour period during each sampling visit. Captured insects were collected, stored in 70% ethanol, and transported to the laboratory for identification. Timed Direct Observations and Pollinator Effectiveness (PE) To quantify pollinator visitation rates and behavior, we conducted direct observations. Within each plot, a 10-minute observation session was performed, during which all insect visitors landing on the blossoms of the focal crop were recorded. For each visitor, we documented the species/morphospecies, number of flowers visited, and visit duration (seconds). To standardize the assessment of pollination potential across taxa, we calculated a Pollinator Effectiveness (PE) Score for a subset of visits. The score was derived as: PE Score = Visit Duration (seconds) × Contact Score, where a score of 2 was assigned for definitive stigma contact and 1 for anther contact only. Abiotic and Environmental Data Collection During the pollinator sampling, key abiotic factors were simultaneously recorded: temperature and relative humidity were logged at hourly intervals using digital data loggers placed at each site; instantaneous wind speed was measured with a handheld anemometer at the start and end of each observation session; weather conditions were categorized as sunny, partly cloudy, or overcast; and topographic data including geographic coordinates, elevation, and slope aspect were collected using a GPS device and a compass. Data Analysis All statistical analyses were performed in R version 4.3.1 (R Core Team, 2023). Collected specimens were identified to the lowest possible taxonomic level (species or genus) using standard taxonomic keys [ 17 – 19 ]. For each plot, we calculated standard alpha-diversity indices: Species Richness (S), Shannon-Wiener Index (H'), Simpson's Diversity Index (D), and Pielou's Evenness (J'). Differences in pollinator community composition between sites and elevation groups (Low: L1, L2, L3; High: H1, H2, H3) were analyzed using Non-Metric Multidimensional Scaling (NMDS) based on a Bray-Curtis dissimilarity matrix, implemented in the vegan package [ 22 ]. The statistical significance of compositional differences was tested using Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations. The relationships between abiotic factors (elevation, mean temperature) and pollinator diversity metrics were quantified using Pearson correlation analysis. To partition the variance explained by abiotic factors while accounting for the nested structure of the data, we employed General Linear Mixed Models (GLMMs) using the lme4 package [ 23 ]. In these models, pollinator abundance or diversity was the response variable, elevation and temperature were included as fixed effects, and Site ID was incorporated as a random intercept to control for the non-independence of plots within the same site. Model fit was assessed using conditional R² values. Pollinators were classified to the family level. The relative abundance of each family was calculated for each site and for the pooled low- and high-elevation groups. Shifts in family dominance were visualized and analyzed to understand taxonomic filtering across the gradient. Results Pollinator Assemblage Composition and Taxonomic Distribution Over the course of this study, we recorded a total of 662 individual pollinators across the six study sites in Doda District. The assemblage comprised 14 identified species spanning 4 orders and 10 families (Table 2 ). Hymenoptera was the most speciose order (6 species), accounting for the majority (68.5%) of total individuals. Diptera was the second most significant order, represented by 4 species and constituting 25.2% of the abundance. Lepidoptera and Coleoptera contributed minimally to the visitor community, at 4.8% and 1.5% respectively. The community was dominated by a few common species. The western honey bee, Apis mellifera , was the most abundant species (19.3% of total individuals), followed by the solitary bee Lasioglossum moroi (17.1%) and the hoverfly Eristalis tenax (12.5%). Notably, the native honey bee Apis cerana indica represented a significant portion (13.9%) of the pollinator guild, underscoring its continued importance in Himalayan agroecosystems. Table 2 Complete inventory of pollinator species recorded in apple and apricot orchards across the Doda District elevational gradient. Order Family Species Total Abundance Relative Abundance (%) Hymenoptera Apidae Apis mellifera L. 128 19.3 Apidae Apis cerana indica F. 92 13.9 Apidae Anthophora confusa 37 5.6 Halictidae Lasioglossum moro i (Fabricius) 113 17.1 Andrenidae Andrena ilerda 28 4.2 Megachilidae Osmia cornuta 25 3.8 Diptera Syrphidae Episyrphus balteatus (De Geer) 58 8.8 Syrphidae Eristalis tenax (L.) 83 12.5 Syrphidae Eristalis arbustorum 25 3.8 Syrphidae Sphaerophoria scripta 18 2.7 Lepidoptera Pieridae Pieris brassicae (L.) 22 3.3 Nymphalidae Cynthia cordui 7 1.1 Nymphalidae Aglais cashmiriensis 3 0.5 Coleoptera Coccinellidae Coccinella septumpunctata (L.) 10 1.5 Total 10 families 14 species 662 100 Elevational Gradient in Diversity and Abundance All measured indices of pollinator diversity exhibited a strong and systematic decline with increasing elevation (Table 3 , Fig. 2). Total abundance decreased by 75.7%, from 185 individuals at the lowest site, PRANOO (1073 m ASL), to just 45 individuals at the highest site, Bhaderwah (2302 m ASL). Species richness showed a parallel decline, falling from 9 species at the lowest elevation to only 3 species at the highest. This pattern was reflected in the composite diversity indices: the Shannon-Wiener Index (H') decreased from 1.95 to 0.95, and Simpson's Dominance Index (D) declined from 0.82 to 0.60 across the gradient. Pielou's Evenness Index (J') remained relatively high and stable (0.84–0.89), indicating that the observed diversity loss was primarily driven by a drop in species richness rather than a shift in dominance patterns within the communities. Table 3 Pollinator diversity metrics across the six study sites, arranged by increasing elevation. Site Code Elevation (m ASL) Total Abundance Species Richness (S) Shannon Index (H') Simpson's Index (D) Pielou's Evenness (J') L1 1073 185 9 1.95 0.82 0.89 L2 1163 162 8 1.82 0.80 0.88 L3 1568 120 7 1.65 0.75 0.85 H2 1960 85 5 1.35 0.68 0.84 H1 2158 65 4 1.20 0.65 0.87 H3 2302 45 3 0.95 0.60 0.86 Distinct Community Composition Shift Revealed by Multivariate Analysis Non-metric Multidimensional Scaling (NMDS) ordination, based on Bray-Curtis dissimilarities, revealed a clear and significant separation in pollinator community composition along the elevational gradient (Fig. 2; Stress = 0.08). The low-elevation sites (L1, L2, L3) formed a tight cluster on the left side of the ordination plot, while the high-elevation sites (H1, H2, H3) formed a distinct cluster on the right, arrayed along the primary axis (NMDS1). A Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed that the differences in community structure between the low and high-elevation groups were statistically significant (Pseudo-F = 8.91, p = 0.002). The fitted environmental vectors demonstrated that this compositional turnover was strongly correlated with elevation (r² = 0.89) and mean temperature (r² = 0.85). Figure 2. Non-metric multidimensional scaling (NMDS) ordination of pollinator community composition across six orchard sites in Doda District. Taxonomic Filtering: Family-Wise Distribution Shifts Analysis at the family level revealed a significant restructuring of the pollinator guild across the elevational gradient (Fig. 3, 4). The relative abundance of Apidae, which constituted 28.5% of the pollinator community at low elevations, declined to 18.2% at high elevations. In a striking contrast, the family Halictidae increased dramatically in relative importance, from 19.8% at low elevations to 41.5% at high elevations, making it the dominant pollinator family in high-altitude orchards. Syrphidae (hoverflies) demonstrated notable resilience, maintaining a substantial presence across the entire gradient (38.2% low vs. 35.1% high). The contributions of Lepidoptera and Coleoptera were marginal at all sites but showed a slight decrease with elevation. This shift is further emphasized by the absolute abundance data (Fig. 3), which shows a stark reduction in the number of individuals from all families at higher elevations, with the notable exception of Halictidae, which maintained relatively stable numbers. Figure 3: Relative abundance (%) of pollinator families in low-elevation (L1, L2, L3) versus high-elevation (H1, H2, H3) site groups. Figure 4. Relative abundance (%) of pollinator families across the study sites, arranged from left to right by increasing elevation. Comparative Pollinator Communities in Apple and Apricot While the elevational gradient was the dominant factor structuring pollinator communities, a comparative analysis of sites where both crops co-occurred revealed subtle but significant crop-specific preferences (Fig. 5). At site L1 (1073 m ASL), where apple and apricot were in concurrent bloom, the pollinator assemblage exhibited distinct compositional differences between the two crops (PERMANOVA: Pseudo-F = 3.85, p = 0.028). Apricot blossoms demonstrated a significantly higher attraction for hoverflies (Syrphidae), which constituted 44.7% of its visitors compared to 31.2% on apple flowers (X² = 5.82, p = 0.016). Conversely, apple flowers were visited more frequently by social bees of the family Apidae (46.8% of visitors on apple vs. 32.1% on apricot; X² = 6.45, p = 0.011). The native honey bee, Apis cerana indica , showed a particular preference for apple, accounting for 22.3% of its visits compared to 13.5% on apricot. This led to a marginally higher, though not statistically significant, Shannon Diversity Index (H') for apricot (H' = 1.98) compared to apple (H' = 1.85) at the same location. The calculated Pollinator Effectiveness (PE) score was also higher for visitors to apricot (Mean PE = 14.2 ± 3.5 SD) than for apple (Mean PE = 11.8 ± 3.1 SD; t-test: t = 2.34, p = 0.02), largely driven by the longer average visit duration of syrphid flies on apricot blossoms. Figure 5. Radar chart comparing pollinator visitor profiles of apple and apricot blossoms. Abiotic Factors as Key Drivers of Pollinator Patterns Pearson correlation analysis revealed a network of strong and statistically significant relationships between abiotic factors and all pollinator diversity metrics (Fig. 6). Elevation was strongly negatively correlated with total abundance (r = -0.95, p < 0.01), species richness (r = -0.94, p < 0.01), and the Shannon Diversity Index (r = -0.97, p < 0.001). As expected, elevation and mean temperature were almost perfectly negatively correlated (r = -0.98, p 0.97, p < 0.001), indicating that the pollinator community responded to the elevational gradient in a coordinated manner across multiple dimensions of diversity. Figure 6. Correlation matrix plot displaying Pearson correlation coefficients (r) between abiotic factors and pollinator diversity metrics. Elevation as a Master Variable Predicting Community Diversity Among the network of significant relationships, the bivariate association between elevation and the Shannon-Wiener Diversity Index (H') emerged as exceptionally robust and ecologically informative (Fig. 7). Simple linear regression yielded a highly significant model (F₁,₄ = 71.8, p < 0.001) that explains a remarkable 94.7% of the variance in pollinator diversity across the landscape. The regression equation (Shannon H' = 3.12–0.00095 × Elevation) provides a quantitative predictive framework: for every 100-meter increase in elevation, the Shannon Diversity Index decreases by approximately 0.095 units. This rate of diversity loss translates to a 25% reduction in effective pollinator diversity for every 500 m of elevational gain, a decline rate that exceeds many previously reported values for insect communities in mountain systems. The spatial arrangement of sites along the regression line is particularly revealing. The low-elevation sites (L1, L2) cluster in the high-diversity region of the relationship, while the high-elevation sites (H1, H3) occupy the low-diversity extreme. The intermediate site L3 appears precisely where predicted by its elevation, demonstrating the consistency of the pattern. Site H2 shows a slight positive deviation from the predicted value, which may reflect the moderating influence of its southerly aspect, potentially creating a more favorable microclimate that slightly mitigates the overall elevational constraint. The strength of this relationship (R² = 0.947) is noteworthy for several reasons. First, it suggests that elevation, acting as a composite proxy for multiple correlated environmental variables (temperature, atmospheric pressure, season length), serves as an exceptionally powerful predictor of pollinator diversity in this system. Second, the minimal residual variance indicates that local factors not captured by elevation—such as subtle differences in orchard management, floral resource density, or landscape context—play only a minor role in determining diversity relative to the overwhelming influence of the elevational gradient. This finding positions elevation as a "master variable" that can reliably predict pollinator community structure across the complex topography of Doda District, with significant implications for conservation planning and predictive modeling under scenarios of climate and land-use change. Figure 7. Simple linear regression between elevation and the Shannon-Wiener Diversity Index (H'). Discussion This present study provides a comprehensive analysis of pollinator communities in a Himalayan agroecosystem, revealing a dramatic restructuring of pollinator diversity, composition, and taxonomic affiliation along a steep elevational gradient. Our findings demonstrate that elevation acts as a master ecological filter, overwhelming local factors to shape pollinator assemblages, with profound implications for the sustainability of fruit production and ecosystem resilience in a warming climate. The stark decline in pollinator diversity and abundance with increasing elevation—a 76% reduction in individuals and a 67% drop in species richness—aligns with global patterns of biotic attrition on mountains and mirrors trends observed in other Indian Himalayan regions [ 24 ]. For instance, studies in the Himalayas have similarly reported decreasing insect diversity with increasing altitude [ 25 , 26 ]. However, the strength of the relationship we observed is exceptional. The finding that elevation alone explains 94.7% of the variance in Shannon diversity is a testament to the overwhelming role of the elevational gradient in this system. This R² value is notably higher than many reported in montane insect studies internationally and nationally, suggesting that the agroecosystems of Doda District may be particularly sensitive to this environmental filter [ 11 , 27 ]. The mechanism underlying this pattern is almost certainly thermal. The near-perfect correlation between elevation and temperature (r = -0.98) positions temperature as the primary proximal driver, a relationship consistently identified as crucial for insect distributions in mountainous regions globally and specifically in the Indian Himalayas [ 26 , 28 ]. Cooler temperatures at high elevations impose severe physiological constraints on ectothermic insects, limiting their activity windows, increasing metabolic costs, and potentially slowing development rates. This results in an "upward shift in limitation," where the energetic costs of foraging and thermoregulation begin to outweigh the benefits of floral resources, leading to reduced pollinator activity and persistence [ 29 ]. The coordinated decline across all diversity metrics indicates a community-wide response, rather than the loss of only the most sensitive species. Perhaps our most ecologically significant finding is the dramatic taxonomic restructuring of the pollinator guild. The decline of Apidae—particularly the social bees Apis mellifera and A. cerana and the concurrent rise of Halictidae to dominance at high elevations represents a fundamental functional shift. This pattern of community turnover is consistent with findings from other mountain systems where social bees decline in importance with altitude while solitary bees persist [ 30 ]. In the Indian context, similar shifts from social to solitary bees have been noted in the Himalayas [ 31 ]. This shift can be interpreted through the lens of functional traits and thermal biology. Social Apidae, with their larger colony sizes and higher energetic demands, are likely more vulnerable to the resource scarcity and thermal constraints of high elevations. In contrast, the solitary, often ground-nesting Halictidae possess several traits pre-adapting them to high-altitude conditions: smaller body size (reducing thermal stress), generalist foraging strategies, and the ability to nest in thermally buffered substrates. The resilience of Syrphidae across the gradient is equally telling; their ability as flies to be active at lower temperatures and their diverse larval ecologies may buffer them against elevational stressors, a phenomenon also observed in high-altitude ecosystems in Italy [ 32 , 33 ]. This taxonomic filtering has direct consequences for pollination services. The replacement of large, social, generalist bees with smaller, solitary bees and flies may represent a shift in both the quantity and quality of pollination. While Halictids are competent pollinators, their smaller body size and shorter foraging ranges could reduce pollen transfer distances and efficiency [ 9 ]. This implies that high-elevation orchards may be operating with a functionally depauperate pollinator assemblage, potentially making them more vulnerable to pollination deficits, a concern increasingly raised for mountain agriculture systems worldwide [ 34 ]. Beneath the dominant elevational filter, our analysis reveals a significant secondary structuring force: crop identity. The distinct pollinator assemblages on co-flowering apple and apricot demonstrate that floral traits drive visitor partitioning even within the same plant family. This finding adds nuance to studies from other Indian states like Himachal Pradesh that have primarily focused on single-crop systems [ 35 ]. The significant shift from Apidae-dominated visits on apple to Syrphidae-dominated visits on apricot points to crop-specific preferences, likely driven by divergent floral cues and pollinator ecology. Social Apidae bees, with their efficient, centralized foraging, may be better suited to the dense floral displays of apple. In contrast, the stronger attraction of Syrphidae to apricot may be linked to its potentially stronger floral scent and a better phenological match. Crucially, the marginally higher Pollinator Effectiveness (PE) score on apricot suggests that the longer visit durations of Syrphidae may provide effective pollination, challenging the notion that bees are universally superior, a finding supported by recent work on non-bee pollinators in agricultural systems [ 36 ]. The strong environmental filtering documented here has urgent implications for conservation in the context of climate change. While warming temperatures might theoretically benefit high-elevation pollinators, the reality is more complex. The specialized, cold-adapted communities at high elevations are likely to face new pressures from novel species interactions, phenological mismatches, and potential competitive exclusion by upward-moving lowland species [ 12 ]. The simplified, high-elevation communities may have low functional redundancy, making them less resilient to such perturbations. Our results call for targeted conservation strategies that recognize the distinct nature of pollinator communities at different elevations, echoing conservation priorities identified for Himalayan ecosystems [ 37 ]. The simplified, high-elevation communities may have low functional redundancy, making them less resilient to such perturbations. Our results call for targeted conservation strategies that recognize the distinct nature of pollinator communities at different elevations. For low-elevation orchards: Strategies should focus on maintaining the existing high diversity by promoting floral resource continuity and reducing pesticide use to support the species-rich Apidae and Syrphidae communities. For high-elevation orchards: Conservation must prioritize the specific needs of the dominant Halictidae. This includes preserving areas of bare or sparsely vegetated ground for nesting and maintaining patches of natural vegetation that provide shelter from extreme weather. The value of Syrphidae as reliable pollinators across elevations suggests that managing for flowering resources that support their larval stages by maintaining aphid populations on non-crop plants is a key strategy. The present study demonstrates that the elevational gradient in the Doda District acts as a powerful environmental filter, driving a systematic disassembly of pollinator communities characterized by a steep diversity loss and a fundamental taxonomic restructuring. The shift from an Apidae-dominated to a Halictidae-dominated fauna with increasing elevation is a critical finding that likely alters the functioning of the pollination system. In the face of climate change, the conservation of pollination services in this economically and ecologically vital region will require a nuanced, elevation-specific strategy. The resilience of Himalayan fruit agriculture depends on our ability to understand and mitigate the impacts of this hierarchical filtering on the insects that sustain it. While our study reveals powerful macro-ecological patterns, it opens several avenues for future research. First, our data represent a snapshot in time; long-term monitoring is crucial to understand how these communities are responding to rapid climate change in the Himalayas. Second, we quantified visitation rates but not the ultimate impact on fruit set and quality. Future work should directly link the documented community shifts to pollination effectiveness and crop yield across the elevational gradient, explicitly testing whether the high-elevation shift to Halictidae dominance results in pollen limitation. Finally, experimental manipulations, such as warming experiments or resource enhancements, could move beyond correlation to definitively identify the mechanisms limiting pollinator populations at high elevations. Investigating plant-pollinator network structure across this gradient would also reveal whether high-elevation communities are more vulnerable to disruption due to simpler interaction networks. Declarations Acknowledgments The first author wishes to thank the Council of Scientific and Industrial Research (CSIR) for providing the doctoral research fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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Springer Nature, Singapore, pp 243–276. https://doi.org/10.1007/978-981-97-0710-2_16 Garibaldi LA, Steffan-Dewenter I, Winfree R, Aizen MA, Bommarco R, Cunningham SA et al (2013) Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339(6127):1608–1611. https://doi.org/10.1126/science.1230200 Rahbek C, Borregaard MK, Colwell RK, Dalsgaard B, Holt BG, Morueta-Holme N et al (2019) Humboldt's enigma: How the global distribution of biodiversity arises. Science 365(6458):1108–1113. https://doi.org/10.1126/science.aax0149 Hoiss B, Krauss J, Potts SG, Roberts S, Steffan-Dewenter I (2012) Altitude acts as an environmental filter on phylogenetic composition, traits and diversity in bee communities. Proc Biol Sci 279(1746):4447–4456. https://doi.org/10.1098/rspb.2012.1581 Schweiger O, Biesmeijer JC, Bommarco R, Hickler T, Hulme PE, Klotz S et al (2010) Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biol Rev Camb Philos Soc 85(4):777–795. https://doi.org/10.1111/j.1469-185X.2010.00125.x Akhter F, Khanday AL, Ahmad ST (2016) Pollination potential: A comparative study of various hymenopteran insects pollinating some economically important crops in Kashmir. Int J Adv Res Biol Sci 3(9):50–59. https://doi.org/10.22192/ijarbs.2016.03.09.007 Dar SA, Wani AR, Sofi MA (2018) Diversity and abundance of insect pollinators of sweet cherry Prunus avium in Kashmir valley. Indian J Entomol 80(3):725–736. https://doi.org/10.5958/0974-8172.2018.00231.6 Ganie MA, Pal AK, Ahmad N (2013) Native insect pollinators in apple orchards under different management practices in the Kashmir Valley. Turk J Agric Food Sci Technol 1(1):29–36. https://doi.org/10.24925/turjaf.v1i1.29-36.12 Paray MA, Khursheed R, Shifa MY, Amin D (2018) Foraging ecology of insect pollinators on apple blossoms in Kashmir Himalaya. Indian J Entomol 80(2):390–394. https://doi.org/10.5958/0974-8172.2018.00082.2 Rather ZA, Ollerton J, Parey SH, Ara S, Watts S, Paray MA et al (2023) Plant-pollinator meta-network of the Kashmir Himalaya. Flora 298:152197. https://doi.org/10.1016/j.flora.2022.152197 Riyaz M, Mathew P, Paulraja G, Ignacimuthu S (2018) Entomophily of Apple ecosystem in Kashmir valley, India: A review. Int J Sci Res Biol Sci 5(5):146–154. https://doi.org/10.26438/ijsrbs/v5i5.146154 Saini MS, Raina RH, Khan ZH (2012) Species Diversity of Bumblebees (Hymenoptera: Apidae) from Different Mountain Regions of Kashmir Himalayas. J Sci Res 4(1):263. https://doi.org/10.3329/jsr.v4i1.8815 Rohit R, Sharma Y, Padha S, Dhar A (2025) Investigating pollinator dynamics and regional variations in Doda, J&K, INDIA. Biol Divers Conserv 18(1):91–102. https://doi.org/10.46309/biodicon.2025.1529938 Droege S, Tepedino VJ, Lebuhn G, Link W, Minckley RL, Chen Q et al (2010) Spatial patterns of bee captures in North American bowl trapping surveys. Insect Conserv Divers 3(1):15–23. https://doi.org/10.1111/j.1752-4598.2009.00074.x Bates D, Maechler M, Bolker B, Walker S, Christensen RHB, Singmann H et al (2015) Package 'lme4'. Convergence 12(1):2. https://github.com/lme4/lme4/ Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR et al (2022) vegan: Community Ecology Package. R package version 2.6-4. https://cran.r-project.org/web/packages/vegan Rahbek C, Borregaard MK, Colwell RK, Dalsgaard B, Holt BG, Morueta-Holme N et al (2019) Humboldt's enigma: What causes global patterns of mountain biodiversity? Science 365(6458):1108–1113. https://doi.org/10.1126/science.aax0149 Bisht M, Goswami D, Uniyal VP, Singh V (2023) Diversity of butterfly along different altitudinal gradient of Munsiyari, Western Himalayan, Uttarakhand, India. Asian J Conserv Biol 12(2):167–175. https://doi.org/10.53562/ajcb.72630 Kumar R, Hajam YA, Kumar I (2024) Neelam. Insect Pollinators’s Diversity in the Himalayan Region. In: Role of Science and Technology for Sustainable Future: Volume 1. Singapore: Springer; pp. 243–76. https://doi.org/10.1007/978-981-97-0710-2_16 Anjum N, Khan S, Verma S, Gaira KS, Rawat B, Chettri N et al (2025) Plant-pollinator interactions along the altitudinal gradient in Berberis lycium royle. PLoS ONE 20(5):e0310572. https://doi.org/10.1371/journal.pone.0310572 Rasmann S, Pellissier L, Defossez E, Jactel H, Kunstler G (2014) Climate-driven change in plant-insect interactions along elevation gradients. Funct Ecol 28(1):46–54. https://doi.org/10.1111/1365-2435.12135 Dahlhoff EP, Dahlhoff VC, Grainger CA, Zavala NA, Otepola-Bello D, Sargent BA et al (2019) High elevation may limit performance in a montane insect. Funct Ecol 33(5):809–818. https://doi.org/10.1111/1365-2435.13286 Peters MK, Peisker J, Steffan-Dewenter I, Hoiss B (2016) Morphological traits linked to the cold performance of bees. J Biogeogr 43(10):2040–2049. https://doi.org/10.1111/jbi.12768 Ahmad M, Rosbakh S, Bucher SF, Sharma P, Rathee S, Uniyal SK et al (2023) The role of floral traits in community assembly processes at high elevations in the Himalayas. J Ecol 111(5):1107–1119. https://doi.org/10.1111/1365-2745.14083 Orford KA, Vaughan IP, Memmott J (2015) The forgotten flies: the importance of non-syrphid Diptera as pollinators. Proc Biol Sci 282(1805):20142934. https://doi.org/10.1098/rspb.2014.2934 Sommaggio D, Zanotelli L, Vettorazzo E, Burgio G, Fontana P (2022) Hoverflies and bee distribution across altitudes (Italy). Insects 13(3):293. https://doi.org/10.3390/insects13030293 Inouye DW (2020) Effects of climate change on alpine plants and their pollinators. Ann NY Acad Sci 1469(1):26–37. https://doi.org/10.1111/nyas.14104 Kapkoti B, Joshi RK, Rawal RS (2016) Variations in insect abundance in apple orchards of Kumaun. Curr Sci 110(3):438–. https://doi.org/10.18520/cs/v110/i3/438-443 . 43 Rader R, Cunningham SA, Howlett BG, Inouye DW (2020) Non-bee insects as visitors and pollinators of crops: Biology, ecology, and management. Annu Rev Entomol 65(1):391–407. https://doi.org/10.1146/annurev-ento-011019-025055 Bhusal DR (2020) Insect pollinators, threats for survival and ecosystem service: outlook from Himalaya. Hindu Kush-Himalaya watersheds Downhill. Springer, Cham, pp 565–576. https://doi.org/10.1007/978-3-030-36275-1_27 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8979944","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597680944,"identity":"d827671e-cce3-42f7-90d6-9e1b5e670930","order_by":0,"name":"Rohit","email":"","orcid":"https://orcid.org/0009-0003-9982-3487","institution":"Central University of Jammu","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Rohit","suffix":""},{"id":597680945,"identity":"55913912-d1ca-4c83-b528-251e0ecb7ee7","order_by":1,"name":"Anjali Dhar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYHCCBIYHBiCaufEBAwMbXJgHr5YEsBbGZgNitYA0gbW0SRDlKv7ZDQ8/JBQw2POzN7ZV/Gzjk9eddsaA4UcNg4w5Di0Sdw4kSwAdxizZc7DtZm8bm+G22zkGjD3HGHgsG3DouZGQANLCZnAjse02YxsbI0gLA28DA4/BAew65G8kJP8AauEBaSkGarEH2/IXjxaDGwlpIFskQFqYgVoSQVqY8dliCNRikWAgYQD0S7Nkzzm25G230woOyxyTwKlF7kZO8o0Pf2yAIdZ88MOPsmO2224nb3z4psbGHpcWYJQlAAl4jBwDkweQRLAAdhTDavCoHAWjYBSMgpEKAEeZWGIFjSzUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4664-2645","institution":"Central University of Jammu","correspondingAuthor":true,"prefix":"","firstName":"Anjali","middleName":"","lastName":"Dhar","suffix":""}],"badges":[],"createdAt":"2026-02-26 16:24:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8979944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8979944/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103561130,"identity":"45bfc8ae-9bef-44e1-bfcd-e037d7f8ea73","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1535945,"visible":true,"origin":"","legend":"\u003cp\u003eMap of district Doda with sampling sites.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/770ac9eea34d7e652cb0304a.jpg"},{"id":103561128,"identity":"b72e024d-28d4-4d18-b1aa-9da4f5edb50f","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64692,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) ordination of pollinator community composition across six orchard sites in Doda District.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/3262b22763de270fb90b9fbb.jpg"},{"id":103561129,"identity":"77387d76-5de3-4b00-b958-96df9a63d0cb","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16968,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance (%) of pollinator families in low-elevation (L1, L2, L3) versus high-elevation (H1, H2, H3) site groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/f48b5510c3de34f89cfcd46d.png"},{"id":103561133,"identity":"4006e9e2-1b11-48fe-a947-967ffd60858e","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71276,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance (%) of pollinator families across the study sites, arranged from left to right by increasing elevation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/b367aff1e7e12d1682707fbd.jpg"},{"id":103561132,"identity":"f8978a50-c223-4ce8-b6bd-135c9467f5a5","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":130236,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart comparing pollinator visitor profiles of apple and apricot blossoms.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/c3ad22764a382568cc0d8c8a.jpg"},{"id":103561134,"identity":"a4491365-0fea-4cb6-9bd8-9f1b5a05abbe","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153714,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix plot displaying Pearson correlation coefficients (r) between abiotic factors and pollinator diversity metrics.\u003c/p\u003e","description":"","filename":"Figure6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/3695dd3fbd64c2dd771ae97c.jpeg"},{"id":103561131,"identity":"c873c0aa-7b62-4855-b78f-adf69d8e0c76","added_by":"auto","created_at":"2026-02-27 05:56:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":106599,"visible":true,"origin":"","legend":"\u003cp\u003eSimple linear regression between elevation and the Shannon-Wiener Diversity Index (H').\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/20b8f1e0f3e64cdcdf1347e2.jpg"},{"id":104398701,"identity":"71d4a73c-4110-4fa0-b965-20542bb90d73","added_by":"auto","created_at":"2026-03-11 12:03:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2951988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8979944/v1/ee15c481-59a7-45b1-9d51-567a9e6e8eef.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eElevational Filtering Drives Pollinator Community Disassembly in the Mountain Orchards of Doda, Indian Himalayas\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTemperate fruit crops represent a cornerstone of agricultural economies and nutritional security in mountainous regions worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the Himalayan context, apple (\u003cem\u003eMalus domestica\u003c/em\u003e) and apricot (\u003cem\u003ePrunus armeniaca\u003c/em\u003e) are not only vital cash crops but also deeply embedded in the cultural and ecological fabric of local communities [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The reproductive success of these crops, and consequently the livelihoods of millions of smallholder farmers, hinges critically upon insect-mediated pollination [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While managed honey bees often receive primary attention, a rich diversity of wild pollinators including solitary bees, bumble bees, and hoverflies contributes significantly to fruit set, quality, and yield stability, often enhancing pollination efficiency beyond what managed colonies can achieve alone [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn mountain ecosystems, biodiversity is intrinsically linked to elevation, which acts as a master gradient filtering species distributions through correlated changes in temperature, atmospheric pressure, and resource availability [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For ectothermic pollinators, these elevational shifts create profound physiological and ecological constraints, leading to predictable turnovers in community composition, diversity, and abundance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Understanding these patterns is not merely an academic exercise; it is urgent for predicting how climate change will reshape pollination services. As global temperatures rise, species are projected to shift their ranges upslope, potentially creating novel communities, disrupting existing plant-pollinator interactions, and leaving high-elevation ecosystems particularly vulnerable [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Himalayan region of Jammu and Kashmir is a globally significant fruit-producing area. However, ecological research on its pollination systems has been historically concentrated in the Kashmir Valley [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This led to a significant knowledge gap in other topographically diverse districts such as Doda. This study presents the first comprehensive assessment of pollinator diversity and its drivers in the agroecosystems of the Doda District. Characterized by its rugged terrain, deep valleys, and a steep elevational gradient, Doda provides a unique and powerful natural laboratory to investigate the interplay between abiotic factors and pollinator communities. The district's heavy reliance on apple and apricot cultivation makes understanding its pollination ecology a matter of direct socio-economic concern.\u003c/p\u003e \u003cp\u003eWithin this context, we designed a study to answer the following critical questions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhat is the diversity, abundance, and species composition of insect pollinators in the apple and apricot orchards of Doda District?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHow do pollinator communities change along a steep elevational gradient (~\u0026thinsp;1100\u0026ndash;2300 m ASL), and what is the relative impact of key abiotic factors like temperature?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes pollinator assemblage differ between the two economically crucial crops, apple and apricot, when studied under identical environmental conditions?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBy addressing these questions, this research establishes a vital ecological baseline for the region. Our findings will inform evidence-based conservation strategies, support the resilience of local agriculture in the face of environmental change, and contribute to the global understanding of how mountain agroecosystems sustain biodiversity and ecosystem function.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area and Site Selection\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Doda District (33\u0026deg;08'N, 75\u0026deg;3'E) of Jammu and Kashmir, India (Fig.\u0026nbsp;1), a region characterized by complex topography within the western Himalayas [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To capture the full spectrum of environmental variation, we employed a stratified random sampling design along a steep elevational gradient. Six study sites were selected, ranging from 1,073 to 2,302 meters above sea level (m ASL), ensuring representation of the major apple (\u003cem\u003eMalus domestica\u003c/em\u003e) and apricot (\u003cem\u003ePrunus armeniaca\u003c/em\u003e) cultivation zones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Site selection criteria were: (i) presence of commercially managed orchards of a minimum size (\u0026ge;\u0026thinsp;0.5 ha), (ii) spanning a continuous elevational gradient of over 1200 m, (iii) variation in slope aspect, and (iv) accessibility for repeated sampling. The selected sites represented the predominant bloom periods for the region: apricot from late February to mid-March, and apple from mid-March to early April.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1. Map of district Doda with sampling sites.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacterization of the six study sites in Doda District.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation (m ASL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePredominant Aspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary Crop(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePranoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026deg;05'38\"N 75\u0026deg;34'57\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApple, Apricot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBhaboor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026deg;08'46\"N 75\u0026deg;34'55\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApple\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u0026deg;59'10\"N 75\u0026deg;43'45\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApricot, Apple\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChounri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026deg;04'52\"N 75\u0026deg;47'44\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApple, Apricot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSichal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026deg;07'52\"N 75\u0026deg;47'53\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApple\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBhaderwah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026deg;01'45\"N 75\u0026deg;46'25\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApricot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Sampling Protocol\u003c/h3\u003e\n\u003cp\u003eA hierarchical sampling design was implemented to account for spatial variation. Within each of the six sites, four permanent 50m x 50m plots were established (total n\u0026thinsp;=\u0026thinsp;24 plots). Plots were strategically placed to capture within-orchard heterogeneity: two plots in the orchard interior (\u0026gt;\u0026thinsp;20m from edge) and two on the edge, adjacent to different landscape features (e.g., natural forest, agricultural field, or road). Sampling was conducted over the 2024 blooming season, organized into three temporal waves to coincide with peak bloom periods at different elevations. Each site was visited a minimum of twice during its peak bloom, with all sampling conducted between 0900 and 1500 hrs on days with favorable weather (no precipitation, wind speed\u0026thinsp;\u0026lt;\u0026thinsp;5 m/s).\u003c/p\u003e\n\u003ch3\u003eCrop Comparison Methodology\u003c/h3\u003e\n\u003cp\u003eThe comparative analysis of pollinator communities between apple and apricot was intentionally restricted to site L1 (Pranoo, 1073 m ASL). This design controlled for confounding environmental variables, as both crops were present within the same orchard, experienced identical microclimatic conditions, and had overlapping bloom periods during the sampling timeframe. This approach isolated the effect of crop identity from the overwhelming influence of elevation and seasonal phenology that would confound cross-site comparisons.\u003c/p\u003e\n\u003ch3\u003ePollinator Sampling and Effectiveness Assessment\u003c/h3\u003e\n\u003cp\u003eTo obtain a comprehensive inventory of the pollinator community, we used two complementary methods.\u003c/p\u003e\n\u003ch3\u003ePan Trapping\u003c/h3\u003e\n\u003cp\u003eWe used colored pan traps to passively sample flying insects, following a standardized protocol [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At the center of each plot, an array of three UV-bright pan traps (blue, yellow, and white) was deployed, filled with soapy water and positioned approximately 1 meter above ground. Traps were active for a continuous 24-hour period during each sampling visit. Captured insects were collected, stored in 70% ethanol, and transported to the laboratory for identification.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTimed Direct Observations and Pollinator Effectiveness (PE)\u003c/h2\u003e \u003cp\u003eTo quantify pollinator visitation rates and behavior, we conducted direct observations. Within each plot, a 10-minute observation session was performed, during which all insect visitors landing on the blossoms of the focal crop were recorded. For each visitor, we documented the species/morphospecies, number of flowers visited, and visit duration (seconds). To standardize the assessment of pollination potential across taxa, we calculated a Pollinator Effectiveness (PE) Score for a subset of visits. The score was derived as: PE Score\u0026thinsp;=\u0026thinsp;Visit Duration (seconds) \u0026times; Contact Score, where a score of 2 was assigned for definitive stigma contact and 1 for anther contact only.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAbiotic and Environmental Data Collection\u003c/h3\u003e\n\u003cp\u003eDuring the pollinator sampling, key abiotic factors were simultaneously recorded: temperature and relative humidity were logged at hourly intervals using digital data loggers placed at each site; instantaneous wind speed was measured with a handheld anemometer at the start and end of each observation session; weather conditions were categorized as sunny, partly cloudy, or overcast; and topographic data including geographic coordinates, elevation, and slope aspect were collected using a GPS device and a compass.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R version 4.3.1 (R Core Team, 2023). Collected specimens were identified to the lowest possible taxonomic level (species or genus) using standard taxonomic keys [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For each plot, we calculated standard alpha-diversity indices: Species Richness (S), Shannon-Wiener Index (H'), Simpson's Diversity Index (D), and Pielou's Evenness (J'). Differences in pollinator community composition between sites and elevation groups (Low: L1, L2, L3; High: H1, H2, H3) were analyzed using Non-Metric Multidimensional Scaling (NMDS) based on a Bray-Curtis dissimilarity matrix, implemented in the vegan package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The statistical significance of compositional differences was tested using Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations.\u003c/p\u003e \u003cp\u003eThe relationships between abiotic factors (elevation, mean temperature) and pollinator diversity metrics were quantified using Pearson correlation analysis. To partition the variance explained by abiotic factors while accounting for the nested structure of the data, we employed General Linear Mixed Models (GLMMs) using the lme4 package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In these models, pollinator abundance or diversity was the response variable, elevation and temperature were included as fixed effects, and Site ID was incorporated as a random intercept to control for the non-independence of plots within the same site. Model fit was assessed using conditional R\u0026sup2; values. Pollinators were classified to the family level. The relative abundance of each family was calculated for each site and for the pooled low- and high-elevation groups. Shifts in family dominance were visualized and analyzed to understand taxonomic filtering across the gradient.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePollinator Assemblage Composition and Taxonomic Distribution\u003c/h2\u003e \u003cp\u003eOver the course of this study, we recorded a total of 662 individual pollinators across the six study sites in Doda District. The assemblage comprised 14 identified species spanning 4 orders and 10 families (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hymenoptera was the most speciose order (6 species), accounting for the majority (68.5%) of total individuals. Diptera was the second most significant order, represented by 4 species and constituting 25.2% of the abundance. Lepidoptera and Coleoptera contributed minimally to the visitor community, at 4.8% and 1.5% respectively.\u003c/p\u003e \u003cp\u003eThe community was dominated by a few common species. The western honey bee, \u003cem\u003eApis mellifera\u003c/em\u003e, was the most abundant species (19.3% of total individuals), followed by the solitary bee \u003cem\u003eLasioglossum moroi\u003c/em\u003e (17.1%) and the hoverfly \u003cem\u003eEristalis tenax\u003c/em\u003e (12.5%). Notably, the native honey bee \u003cem\u003eApis cerana indica\u003c/em\u003e represented a significant portion (13.9%) of the pollinator guild, underscoring its continued importance in Himalayan agroecosystems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComplete inventory of pollinator species recorded in apple and apricot orchards across the Doda District elevational gradient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelative Abundance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHymenoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eApis mellifera\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eApis cerana indica\u003c/em\u003e\u0026nbsp;F.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnthophora confusa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalictidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLasioglossum moro\u003c/em\u003ei\u0026nbsp;(Fabricius)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAndrenidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAndrena ilerda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMegachilidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOsmia cornuta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyrphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEpisyrphus balteatus\u003c/em\u003e\u0026nbsp;(De Geer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyrphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEristalis tenax\u003c/em\u003e\u0026nbsp;(L.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyrphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEristalis arbustorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyrphidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSphaerophoria scripta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLepidoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePieridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePieris brassicae\u003c/em\u003e\u0026nbsp;(L.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNymphalidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCynthia cordui\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNymphalidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAglais cashmiriensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoccinellidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCoccinella septumpunctata\u003c/em\u003e\u0026nbsp;(L.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 families\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eElevational Gradient in Diversity and Abundance\u003c/h2\u003e \u003cp\u003eAll measured indices of pollinator diversity exhibited a strong and systematic decline with increasing elevation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;2). Total abundance decreased by 75.7%, from 185 individuals at the lowest site, PRANOO (1073 m ASL), to just 45 individuals at the highest site, Bhaderwah (2302 m ASL). Species richness showed a parallel decline, falling from 9 species at the lowest elevation to only 3 species at the highest. This pattern was reflected in the composite diversity indices: the Shannon-Wiener Index (H') decreased from 1.95 to 0.95, and Simpson's Dominance Index (D) declined from 0.82 to 0.60 across the gradient. Pielou's Evenness Index (J') remained relatively high and stable (0.84\u0026ndash;0.89), indicating that the observed diversity loss was primarily driven by a drop in species richness rather than a shift in dominance patterns within the communities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollinator diversity metrics across the six study sites, arranged by increasing elevation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m ASL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecies Richness (S)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShannon Index (H')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimpson's Index (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePielou's Evenness (J')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDistinct Community Composition Shift Revealed by Multivariate Analysis\u003c/h2\u003e \u003cp\u003eNon-metric Multidimensional Scaling (NMDS) ordination, based on Bray-Curtis dissimilarities, revealed a clear and significant separation in pollinator community composition along the elevational gradient (Fig.\u0026nbsp;2; Stress\u0026thinsp;=\u0026thinsp;0.08). The low-elevation sites (L1, L2, L3) formed a tight cluster on the left side of the ordination plot, while the high-elevation sites (H1, H2, H3) formed a distinct cluster on the right, arrayed along the primary axis (NMDS1). A Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed that the differences in community structure between the low and high-elevation groups were statistically significant (Pseudo-F\u0026thinsp;=\u0026thinsp;8.91, p\u0026thinsp;=\u0026thinsp;0.002). The fitted environmental vectors demonstrated that this compositional turnover was strongly correlated with elevation (r\u0026sup2; = 0.89) and mean temperature (r\u0026sup2; = 0.85).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;2. Non-metric multidimensional scaling (NMDS) ordination of pollinator community composition across six orchard sites in Doda District.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic Filtering: Family-Wise Distribution Shifts\u003c/h2\u003e \u003cp\u003eAnalysis at the family level revealed a significant restructuring of the pollinator guild across the elevational gradient (Fig.\u0026nbsp;3, 4). The relative abundance of Apidae, which constituted 28.5% of the pollinator community at low elevations, declined to 18.2% at high elevations. In a striking contrast, the family Halictidae increased dramatically in relative importance, from 19.8% at low elevations to 41.5% at high elevations, making it the dominant pollinator family in high-altitude orchards. Syrphidae (hoverflies) demonstrated notable resilience, maintaining a substantial presence across the entire gradient (38.2% low vs. 35.1% high). The contributions of Lepidoptera and Coleoptera were marginal at all sites but showed a slight decrease with elevation. This shift is further emphasized by the absolute abundance data (Fig.\u0026nbsp;3), which shows a stark reduction in the number of individuals from all families at higher elevations, with the notable exception of Halictidae, which maintained relatively stable numbers.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;3: Relative abundance (%) of pollinator families in low-elevation (L1, L2, L3) versus high-elevation (H1, H2, H3) site groups.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;4. Relative abundance (%) of pollinator families across the study sites, arranged from left to right by increasing elevation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparative Pollinator Communities in Apple and Apricot\u003c/h2\u003e \u003cp\u003eWhile the elevational gradient was the dominant factor structuring pollinator communities, a comparative analysis of sites where both crops co-occurred revealed subtle but significant crop-specific preferences (Fig.\u0026nbsp;5). At site L1 (1073 m ASL), where apple and apricot were in concurrent bloom, the pollinator assemblage exhibited distinct compositional differences between the two crops (PERMANOVA: Pseudo-F\u0026thinsp;=\u0026thinsp;3.85, p\u0026thinsp;=\u0026thinsp;0.028). Apricot blossoms demonstrated a significantly higher attraction for hoverflies (Syrphidae), which constituted 44.7% of its visitors compared to 31.2% on apple flowers (X\u0026sup2; = 5.82, p\u0026thinsp;=\u0026thinsp;0.016). Conversely, apple flowers were visited more frequently by social bees of the family Apidae (46.8% of visitors on apple vs. 32.1% on apricot; X\u0026sup2; = 6.45, p\u0026thinsp;=\u0026thinsp;0.011). The native honey bee, \u003cem\u003eApis cerana indica\u003c/em\u003e, showed a particular preference for apple, accounting for 22.3% of its visits compared to 13.5% on apricot. This led to a marginally higher, though not statistically significant, Shannon Diversity Index (H') for apricot (H' = 1.98) compared to apple (H' = 1.85) at the same location. The calculated Pollinator Effectiveness (PE) score was also higher for visitors to apricot (Mean PE\u0026thinsp;=\u0026thinsp;14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 SD) than for apple (Mean PE\u0026thinsp;=\u0026thinsp;11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 SD; t-test: t\u0026thinsp;=\u0026thinsp;2.34, p\u0026thinsp;=\u0026thinsp;0.02), largely driven by the longer average visit duration of syrphid flies on apricot blossoms.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;5. Radar chart comparing pollinator visitor profiles of apple and apricot blossoms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAbiotic Factors as Key Drivers of Pollinator Patterns\u003c/h2\u003e \u003cp\u003ePearson correlation analysis revealed a network of strong and statistically significant relationships between abiotic factors and all pollinator diversity metrics (Fig.\u0026nbsp;6). Elevation was strongly negatively correlated with total abundance (r = -0.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), species richness (r = -0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the Shannon Diversity Index (r = -0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As expected, elevation and mean temperature were almost perfectly negatively correlated (r = -0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Consequently, mean temperature showed strong positive correlations with the same diversity metrics. The intercorrelations among the diversity indices themselves were exceptionally high (r\u0026thinsp;\u0026gt;\u0026thinsp;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the pollinator community responded to the elevational gradient in a coordinated manner across multiple dimensions of diversity.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;6. Correlation matrix plot displaying Pearson correlation coefficients (r) between abiotic factors and pollinator diversity metrics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eElevation as a Master Variable Predicting Community Diversity\u003c/h2\u003e \u003cp\u003eAmong the network of significant relationships, the bivariate association between elevation and the Shannon-Wiener Diversity Index (H') emerged as exceptionally robust and ecologically informative (Fig.\u0026nbsp;7). Simple linear regression yielded a highly significant model (F₁,₄ = 71.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that explains a remarkable 94.7% of the variance in pollinator diversity across the landscape. The regression equation (Shannon H' = 3.12\u0026ndash;0.00095 \u0026times; Elevation) provides a quantitative predictive framework: for every 100-meter increase in elevation, the Shannon Diversity Index decreases by approximately 0.095 units. This rate of diversity loss translates to a 25% reduction in effective pollinator diversity for every 500 m of elevational gain, a decline rate that exceeds many previously reported values for insect communities in mountain systems.\u003c/p\u003e \u003cp\u003eThe spatial arrangement of sites along the regression line is particularly revealing. The low-elevation sites (L1, L2) cluster in the high-diversity region of the relationship, while the high-elevation sites (H1, H3) occupy the low-diversity extreme. The intermediate site L3 appears precisely where predicted by its elevation, demonstrating the consistency of the pattern. Site H2 shows a slight positive deviation from the predicted value, which may reflect the moderating influence of its southerly aspect, potentially creating a more favorable microclimate that slightly mitigates the overall elevational constraint.\u003c/p\u003e \u003cp\u003eThe strength of this relationship (R\u0026sup2; = 0.947) is noteworthy for several reasons. First, it suggests that elevation, acting as a composite proxy for multiple correlated environmental variables (temperature, atmospheric pressure, season length), serves as an exceptionally powerful predictor of pollinator diversity in this system. Second, the minimal residual variance indicates that local factors not captured by elevation\u0026mdash;such as subtle differences in orchard management, floral resource density, or landscape context\u0026mdash;play only a minor role in determining diversity relative to the overwhelming influence of the elevational gradient. This finding positions elevation as a \"master variable\" that can reliably predict pollinator community structure across the complex topography of Doda District, with significant implications for conservation planning and predictive modeling under scenarios of climate and land-use change.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;7. Simple linear regression between elevation and the Shannon-Wiener Diversity Index (H').\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis present study provides a comprehensive analysis of pollinator communities in a Himalayan agroecosystem, revealing a dramatic restructuring of pollinator diversity, composition, and taxonomic affiliation along a steep elevational gradient. Our findings demonstrate that elevation acts as a master ecological filter, overwhelming local factors to shape pollinator assemblages, with profound implications for the sustainability of fruit production and ecosystem resilience in a warming climate.\u003c/p\u003e \u003cp\u003eThe stark decline in pollinator diversity and abundance with increasing elevation\u0026mdash;a 76% reduction in individuals and a 67% drop in species richness\u0026mdash;aligns with global patterns of biotic attrition on mountains and mirrors trends observed in other Indian Himalayan regions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For instance, studies in the Himalayas have similarly reported decreasing insect diversity with increasing altitude [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the strength of the relationship we observed is exceptional. The finding that elevation alone explains 94.7% of the variance in Shannon diversity is a testament to the overwhelming role of the elevational gradient in this system. This R\u0026sup2; value is notably higher than many reported in montane insect studies internationally and nationally, suggesting that the agroecosystems of Doda District may be particularly sensitive to this environmental filter [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mechanism underlying this pattern is almost certainly thermal. The near-perfect correlation between elevation and temperature (r = -0.98) positions temperature as the primary proximal driver, a relationship consistently identified as crucial for insect distributions in mountainous regions globally and specifically in the Indian Himalayas [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cooler temperatures at high elevations impose severe physiological constraints on ectothermic insects, limiting their activity windows, increasing metabolic costs, and potentially slowing development rates. This results in an \"upward shift in limitation,\" where the energetic costs of foraging and thermoregulation begin to outweigh the benefits of floral resources, leading to reduced pollinator activity and persistence [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The coordinated decline across all diversity metrics indicates a community-wide response, rather than the loss of only the most sensitive species.\u003c/p\u003e \u003cp\u003ePerhaps our most ecologically significant finding is the dramatic taxonomic restructuring of the pollinator guild. The decline of Apidae\u0026mdash;particularly the social bees \u003cem\u003eApis mellifera\u003c/em\u003e and \u003cem\u003eA. cerana\u003c/em\u003e and the concurrent rise of Halictidae to dominance at high elevations represents a fundamental functional shift. This pattern of community turnover is consistent with findings from other mountain systems where social bees decline in importance with altitude while solitary bees persist [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the Indian context, similar shifts from social to solitary bees have been noted in the Himalayas [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This shift can be interpreted through the lens of functional traits and thermal biology. Social Apidae, with their larger colony sizes and higher energetic demands, are likely more vulnerable to the resource scarcity and thermal constraints of high elevations. In contrast, the solitary, often ground-nesting Halictidae possess several traits pre-adapting them to high-altitude conditions: smaller body size (reducing thermal stress), generalist foraging strategies, and the ability to nest in thermally buffered substrates. The resilience of Syrphidae across the gradient is equally telling; their ability as flies to be active at lower temperatures and their diverse larval ecologies may buffer them against elevational stressors, a phenomenon also observed in high-altitude ecosystems in Italy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis taxonomic filtering has direct consequences for pollination services. The replacement of large, social, generalist bees with smaller, solitary bees and flies may represent a shift in both the quantity and quality of pollination. While Halictids are competent pollinators, their smaller body size and shorter foraging ranges could reduce pollen transfer distances and efficiency [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This implies that high-elevation orchards may be operating with a functionally depauperate pollinator assemblage, potentially making them more vulnerable to pollination deficits, a concern increasingly raised for mountain agriculture systems worldwide [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeneath the dominant elevational filter, our analysis reveals a significant secondary structuring force: crop identity. The distinct pollinator assemblages on co-flowering apple and apricot demonstrate that floral traits drive visitor partitioning even within the same plant family. This finding adds nuance to studies from other Indian states like Himachal Pradesh that have primarily focused on single-crop systems [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The significant shift from Apidae-dominated visits on apple to Syrphidae-dominated visits on apricot points to crop-specific preferences, likely driven by divergent floral cues and pollinator ecology. Social Apidae bees, with their efficient, centralized foraging, may be better suited to the dense floral displays of apple. In contrast, the stronger attraction of Syrphidae to apricot may be linked to its potentially stronger floral scent and a better phenological match. Crucially, the marginally higher Pollinator Effectiveness (PE) score on apricot suggests that the longer visit durations of Syrphidae may provide effective pollination, challenging the notion that bees are universally superior, a finding supported by recent work on non-bee pollinators in agricultural systems [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strong environmental filtering documented here has urgent implications for conservation in the context of climate change. While warming temperatures might theoretically benefit high-elevation pollinators, the reality is more complex. The specialized, cold-adapted communities at high elevations are likely to face new pressures from novel species interactions, phenological mismatches, and potential competitive exclusion by upward-moving lowland species [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The simplified, high-elevation communities may have low functional redundancy, making them less resilient to such perturbations. Our results call for targeted conservation strategies that recognize the distinct nature of pollinator communities at different elevations, echoing conservation priorities identified for Himalayan ecosystems [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe simplified, high-elevation communities may have low functional redundancy, making them less resilient to such perturbations. Our results call for targeted conservation strategies that recognize the distinct nature of pollinator communities at different elevations.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor low-elevation orchards: Strategies should focus on maintaining the existing high diversity by promoting floral resource continuity and reducing pesticide use to support the species-rich Apidae and Syrphidae communities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor high-elevation orchards: Conservation must prioritize the specific needs of the dominant Halictidae. This includes preserving areas of bare or sparsely vegetated ground for nesting and maintaining patches of natural vegetation that provide shelter from extreme weather. The value of Syrphidae as reliable pollinators across elevations suggests that managing for flowering resources that support their larval stages by maintaining aphid populations on non-crop plants is a key strategy.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe present study demonstrates that the elevational gradient in the Doda District acts as a powerful environmental filter, driving a systematic disassembly of pollinator communities characterized by a steep diversity loss and a fundamental taxonomic restructuring. The shift from an Apidae-dominated to a Halictidae-dominated fauna with increasing elevation is a critical finding that likely alters the functioning of the pollination system. In the face of climate change, the conservation of pollination services in this economically and ecologically vital region will require a nuanced, elevation-specific strategy. The resilience of Himalayan fruit agriculture depends on our ability to understand and mitigate the impacts of this hierarchical filtering on the insects that sustain it.\u003c/p\u003e \u003cp\u003eWhile our study reveals powerful macro-ecological patterns, it opens several avenues for future research. First, our data represent a snapshot in time; long-term monitoring is crucial to understand how these communities are responding to rapid climate change in the Himalayas. Second, we quantified visitation rates but not the ultimate impact on fruit set and quality. Future work should directly link the documented community shifts to pollination effectiveness and crop yield across the elevational gradient, explicitly testing whether the high-elevation shift to Halictidae dominance results in pollen limitation. Finally, experimental manipulations, such as warming experiments or resource enhancements, could move beyond correlation to definitively identify the mechanisms limiting pollinator populations at high elevations. Investigating plant-pollinator network structure across this gradient would also reveal whether high-elevation communities are more vulnerable to disruption due to simpler interaction networks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author wishes to thank the Council of Scientific and Industrial Research (CSIR) for providing the doctoral research fellowship.\u0026nbsp;The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions: Rohit Rohit:\u0026nbsp;\u003c/strong\u003eConceptualization, Field sampling, Data curation, Visualization, Writing original draft.\u003cstrong\u003e\u0026nbsp;Anjali Dhar:\u0026nbsp;\u003c/strong\u003eProject administration, Writing-review and editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRana VS, Sharma S, Rana N, Kumar V, Sharma U, Modgill V et al (2023) Underutilized fruit crops in North-Western Himalayan region under changing climatic scenario. 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Springer, Cham, pp 565\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-36275-1_27\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-36275-1_27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Central University of Jammu","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pollinator diversity, elevational gradient, abiotic factors, apple pollination, apricot pollination, Himalayan ecosystem, conservation, Doda District","lastPublishedDoi":"10.21203/rs.3.rs-8979944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8979944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePollinators are indispensable for the productivity of temperate fruit crops like apple and apricot, yet their diversity and drivers remain critically understudied in the complex topography of the Himalayan region. This study presents the first comprehensive assessment of pollinator communities in the agroecosystems of Doda District, Jammu and Kashmir, India, a region of global agricultural significance. We sampled insect pollinators across a steep elevational gradient (1073\u0026ndash;2302 m ASL) in six orchard sites during the 2024 bloom period. Using standardized protocols of pan trapping and timed observations, we documented species composition, abundance, and diversity. The impact of abiotic factors (elevation, temperature) was analyzed using Pearson correlation, linear regression, and multivariate statistics (NMDS, PERMANOVA). We recorded 662 individuals from 14 species across 10 families. A dramatic decline in pollinator diversity and abundance was observed with increasing elevation: total abundance fell by 75.7% and species richness dropped from 9 to 3 species. Elevation alone explained a remarkable 94.7% of the variance in Shannon diversity (H' = 3.12\u0026ndash;0.00095 \u0026times; Elevation; R\u0026sup2; = 0.947, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis revealed a significant compositional shift (PERMANOVA: Pseudo-F\u0026thinsp;=\u0026thinsp;8.91, p\u0026thinsp;=\u0026thinsp;0.002), driven by a major taxonomic restructuring: the relative abundance of Halictidae increased from 19.8% at low elevations to 41.5% at high elevations, effectively replacing Apidae as the dominant family. Furthermore, a fine-scale comparison at a single site showed significant partitioning between crops, with apple attracting more Apidae (46.8% of visits) and apricot attracting more Syrphidae (44.7% of visits). This study establishes that elevation acts as a master environmental filter, overwhelming local factors to structure pollinator communities in Himalayan orchards. The documented patterns and the unique baseline data provide critical insights for crafting elevation-specific conservation strategies to safeguard pollination services and the resilience of mountain agriculture in a warming climate.\u003c/p\u003e","manuscriptTitle":"Elevational Filtering Drives Pollinator Community Disassembly in the Mountain Orchards of Doda, Indian Himalayas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 05:56:05","doi":"10.21203/rs.3.rs-8979944/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49e86c90-95a7-45c9-9639-5487f4e497a0","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63605161,"name":"Horticulture"},{"id":63605162,"name":"Entomology"}],"tags":[],"updatedAt":"2026-02-27T05:56:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 05:56:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8979944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8979944","identity":"rs-8979944","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}