Balancing agriculture and conservation in the Ramsar-listed Kole paddy wetlands: The bee diversity and role of non- crop vegetation amid pesticide use

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Bee populations were sampled over two years, in six pesticide treated and six non-treated control fields, along with adjacent bunds as non-crop habitats. A total of 173 bees representing 10 species across two families—Halictidae and Apidae—were collected. Species richness and Shannon diversity, were consistently lower in pesticide-treated fields compared to control fields. Non-Metric Multidimensional Scaling (NMDS) showed a distinct clustering of treated sites, indicating homogenized bee communities dominated by species such as Tetragonula sp. and Apis florea . In contrast, species like Halictus sp. were less common in treated fields. The bunds with dense vegetation, adjacent to treated fields showed a positive correlation with bee diversity, suggesting these areas act as refugia against pesticide exposure. Pearson correlation analysis revealed a significant positive relationship (r = 0.8389, p = 0.0369) between the diversity of treated fields and their adjacent non-crop habitats. Our findings signify the need for integrated pest management strategies that reduce pesticide use and promote the conservation of non-crop habitats, such as bunds to support pollinator populations, thereby ensuring the overall health and functioning of Kole paddy wetlands. Kole wetlands bee diversity pollinator decline pesticide impact non-crop habitats Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Wetlands are crucial ecosystems providing essential services including water regulation, nutrient cycling, carbon sequestration, and biodiversity conservation (Eric et al. 2022 ). The ability of wetlands to remove excess nutrients and degrade pesticides plays a critical role in maintaining the water quality, especially in agricultural wetlands subjected to periodic pesticide treatments (Vymazal 2017 ). India represents 13.67 million hectares of natural and man-made wetlands, with 85 designated as Ramsar sites (Ministry of Environment, Forest & Climate Change 2024). The Kole/Kol paddy wetlands, part of the Vembanad-Kole system, are one of the three Ramsar sites in Kerala state, located south-west coast of India. These natural coastal lowlands are deeply integrated with Kerala’s agricultural heritage, primarily supporting paddy cultivation. The paddy fields are divided by 1–2 metre wide mud embankments, known locally as bunds. The bunds may be barren or inhabited by native flowering plants and weeds. Kole paddy wetlands sustain local livelihoods through rice and fish farming (Kumar and Kunhamu 2021 ) along with occasional cultivation of aquatic plants like Nymphaea and Nelumbo species. The Kole paddy wetlands are also paramount in biodiversity conservation (Remani et al. 2010 ). Despite its cultural, economic and ecological importance, Kole wetlands are underrepresented in the international literature. Similar to the global scenario (Vasumthi et al. 2023 ; Vincent et al. 2021), Kole paddy wetlands are threatened by habitat degradation. The agricultural intensification catalyzed by non-judicious use of pesticides including insecticides, weedicides, herbicides and fungicides lead to biodiversity loss and ecological imbalances. Pesticides like neonicotinoids and organophosphates are extensively used in the Kole paddy wetlands that are reported to cause lethal and sub-lethal effects on non-target organisms, including pollinators and a range of other taxa from microbes to vertebrates (Pisa et al. 2021 ). Apart from unscientific agricultural practices, anthropogenic stressors such as mining, construction, sewage disposal and habitat fragmentation further amplify the Kole wetland deterioration (Jenin and Bhaskara 2017 ; Raj and Azeez 2018; Zainulabdeen and Nagaraj 2022 ). The subtle environmental changes trigger the loss of unique biodiversity associated with it, and conserving biodiversity is crucial for the long-term productivity of agroecosystems (Kremen and Niles 2012; Seppelt et al. 2020 ). With the recognition of wetlands as waterfowl habitats by Ramsar Convention, avifauna dominates the Kole biodiversity research (Sivaperuman and Jayson 2009 ; Narayanan and Thomas 2011; Pournami et al. 2023 ) along with few other groups- odonates (Chandran and Jose 2021) and butterflies (Sarath et al. 2017 ; Johny S 2023 ). Although birds and odonates are key indicators of wetland health, pollinators received limited consideration for their equally critical roles in promoting a wetland agroecosystem. However, recent study by Cohen et al. ( 2024 ), have demonstrated that pollinator diversity, particularly bumblebees, are reliable indicators of wetland health. Furthermore, periodic monitoring of pollinators is essential to assess changes in diversity and abundance with respect to space, time and focal crop (Senapathi et al. 2021 ). While rice is mainly self-pollinating, research has shown that insect pollinators, particularly bees, can enhance cross-pollination, leading to genetic diversity and potentially increasing yields (Pu et al. 2014 ; Rader et al. 2020 ). Of the 510 insect species found out to pollinate rice, bees are the most effective, outperforming hoverflies and butterflies (Chauhan et al. 2021 ; Khalifa et al. 2021 ; Muhammad et al. 2022 ; Patel et al. 2020). Beyond rice, bees contribute to the broader agroecosystem by pollinating wildflowers, enriching floral diversity and thereby maintains ecosystem services such as natural pest control (Carvalheiro et al. 2011 & Kremen et al. 2007 ). Despite their roles, bee populations are dwindling globally due to multiple anthropogenic factors, including agricultural intensification, habitat loss, climate change, pests and diseases and influence of invasive alien species (Dar et al. 2021 .; Hristov et al. 2020 ; Lima et al. 2022 ). The expansion of monoculture farming and the widespread pesticide use are critical drivers of bee decline (Abudulai et al. 2022 ; Langlois et al. 2020 ; Shi et al. 2024 ). Several studies have examined the impacts of pesticides bee populations in various agricultural settings (Arena et al. 2024; Nicholson et al. 2024 ; Raine et al. 2024; Uhl et al. 2019; Woodcock et al. 2017 ) Pesticides, especially broad-spectrum insecticides like organophosphates, pyrethroids, and neonicotinoids, threaten bees through both acute toxicity and sub-lethal effects, impairing foraging, reproduction and immune function (Siviter et al. 2018 ; Schuhmann et al. 2022 ). Understanding how non-crop habitats, especially the bund vegetation in the Kole wetlands can mitigate pesticide impacts on bee diversity can provide insights for sustainable agriculture. Research experiment that semi-natural and non-crop habitats, such as field margins, hedgerows, and strips of wild flowering plants, within a dominant crop field play a crucial role in supporting bee populations (Gaspar et al. 2022 ; Kowalska et al. 2022 ; Maccagnani et al. 2020 ) by providing essential resources, including nectar, pollen, and nesting sites, which are often missing in monoculture fields like a paddy crop field (Carvell et al. 2022 ; Cole et al. 2017 ; Hass et al. 2018 ; Gurr et al. 2003 ). In intensively farmed landscapes, such habitats can act as refugia, helping bees evade pesticide exposure (Duff et al. 2024 ; Kujawa et al. 2020 ; Winfree et al; 2009 ). However, their effectiveness depends on the proximity of non-crop habitat to pesticide-treated fields and the extent of pesticide drift (Teysserie et al. 2021). To the best our knowledge, the potential refugia effect of bunds in Kole wetlands are not explored. Despite the ecological-economic importance of Kole wetlands as well as the importance of pollinators as wetland health indicators, to the best of our knowledge, no study has evaluated the aspect. Addressing these gaps, this study intends to explore the following research questions: (1) How does conventional pesticide use impact pollinator diversity in the Ramsar-listed Kole paddy wetlands? (2) What role does non-crop vegetation (specifically bund vegetation) play in supporting pollinator populations in pesticide-impacted areas? Methodology Study Area The South-West Indian state of Kerala (10.1632° N, 76.6413° E) is geographically rich with varied range of wetlands, including rivers, streams, backwaters, estuaries, paddy wetlands, mangroves, lakes and ponds, along with the artificial structures such as reservoirs, canals and tanks. The state has a wetland area of 160,590 hectares with three Ramsar sites: Vembanad-Kole wetland, Ashtamudi and Shasthamkotta lake. The Vembanad- Kole wetland system, the largest (151,250 ha) of the three consists of two subtypes-Vembanad lake and Kole paddy wetlands. The Kole paddy lands are known as the rice granaries of Kerala, that spans the Thrissur and Malappuram districts. It follows a distinct pattern of cultivation, known as “Kole puncha”, from December through May. During the monsoon season, Kole experience heavy flooding, followed by salinity intrusion during the post-monsoon. The rice cultivation begins with dewatering of the flooded fields and storing the water in canals for irrigation. Sowing takes place in December using short-duration rice varieties, referred to as ‘hraswa’ (e.g., Jyothi, Uma and Jaya) which are harvested by May. The cropping season is generally limited to one per year, except in rare cases, two (referred to as ‘Mundakan’), if flooding does not affect the fields (Johnkutty & Venugopal 1993). The Kole soils are fertile due to alluvial deposits with textures ranging from sandy loam to clay and a pH range of 2.6 to 6.3, influenced by organic matter and waterlogging conditions. For this study, we selected Kole paddy fields in Malappuram district as they consistently follow one cropping season (puncha) per year pattern. Sampling Design Bee sampling was conducted over two years (2021–2023), during two Puncha seasons, one per year. We selected six pesticide-treated fields (T1, T2, T3, T4, T5 and T6) and six non-treated fields (C1, C2, C3, C4, C5 and C6) as control sites (Fig. 1 ), based on the similarity in type of pesticides used and their application frequency. The treated fields received regular pesticide applications at same frequency and intervals, while control fields were managed without pesticides, although fungicides, herbicides and fertilizers were occasionally used. The commonly used pesticides belonged to the chemical classes: carbamates, diamides, organophosphates, neonicotinoids and pyrethroids. Two random plots were sampled from each field, resulting in a total of 24 plots; 12 plots per treatment type (treated and control). In addition to paddy fields, six adjacent non-crop habitats (NC1, NC2, NC3, NC4, NC5 and NC6), located within a 200-meter radius of the treated fields were also sampled during the second year to evaluate the potential refugia effect. The selected non-crop habitats varied in vegetation type, NC1 : Non-crop habitat of T1 : Field margins with thick patches of weeds NC2 : Non-crop habitat of T2 : Field margins of thin assemblages of small native flowering plants and intermittently, weeds NC3 & NC4 : Non-crop habitat of T3&T4 : Barren field margin NC5 : Non-crop habitat of T5 : Field margins with dense patches of flowering plants, weeds and the field margin separated from non-crop by small water canals used for Nelumbo sp. cultivation NC6 : Non-crop habitat of T6 : Scarce patches of native wild plants Sampling was carried out every 20 days after transplantation (DAT), specifically at 20,40,60,80 and 100 DAT, using a combination of sweep nets and pan traps, which are recommended as the effective sampling techniques for estimating species richness and abundance of bees (Prado et al. 2017 ). In order to standardize the effect of different paddy field sizes and bunds, equal sampling efforts was applied across all paddy and bund habitats. The sweep netting was standardized by time, allocating 20 minutes per plot. Sweep netting involved actively sweeping through both the paddy field types and adjacent non-crop vegetation during the morning hours between 9.00 am and 11.00 am when bee activity is typically high. The specimens collected were aspirated and transferred to 70% alcohol. Pan traps, consisting of yellow and blue pans filled with soapy water, were set to passively capture bees that get attracted to the color. The number of traps were uniform across all locations and left for 24 hours. At the end of sampling period, the traps are emptied through a sieve from which the bees are carefully transferred to 70% alcohol. All wet preserved specimens were later pinned to dry preservation. Species identification was carried out to the species level using standard taxonomic keys and reference collections. Data Analysis All statistical analyses were conducted using R Studio (R core Team 2024). Species accumulation curves were generated for both treated and control fields to assess the sufficiency of sampling efforts. Alpha diversity indices, including species richness and the Shannon diversity index, were calculated for each field type to evaluate within-field diversity. Differences in diversity indices between treated and control fields were tested for statistical significance using non-parametric Mann-Whitney U tests, due to non-normal distributions indicated by Shapiro-Wilk normality tests. To examine community composition differences between treated and control fields, Bray-Curtis dissimilarity indices were calculated for all pairs of fields. These indices measure the dissimilarity between two communities based on species abundance and presence/absence data. The significance of observed differences in community composition was tested using Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations. Non-Metric Multidimensional Scaling (NMDS) was used to visualize these differences in species composition between field types over the two-year period. To evaluate the impact of pesticide treatment and year on alpha diversity indices (species richness and Shannon diversity index), linear mixed-effects models (LMMs) were fitted. LMMs were chosen to account for both fixed effects (treatment and year) and random effects (field-specific variability), for robust handling of repeated measures data. For binary outcomes (species presence/absence), generalized linear mixed models (GLMMs) with a binomial distribution were constructed to model the effects of treatment, year, species identity, and their interactions, while also considering random field effects. The GLMM is checked for over dispersion. The use of both LMMs and GLMMs allowed for comprehensive modelling of continuous and binary response variables, thus improving accuracy and credibility of the results. To assess the influence of non-crop habitats on bee diversity in treated fields, Shannon diversity indices were calculated. Due to small sample size of non-crop habitats (sampling carried out only in the second year), bootstrap resampling is done to increase the validity of data. To assess the variability of species diversity (Shannon diversity index), bootstrap resampling was applied for each of the 12 sites (R = 999). Pearson correlation analysis was performed with the resampled data to evaluate the relationship between diversity in treated fields and adjacent non-crop habitats. A hierarchical clustering dendrogram based on Bray-Curtis dissimilarity was also constructed to precisely explore similarities in species composition between treated fields and their adjacent non-crop habitats. In manuscript preparation, we have used LLM, specifically edit GPT for language editing assistance. All the content generated are reviewed thoroughly for their accuracy. (The R packages and functions used in the statistical analysis are provided in Online Resource, Table 1 ). Results Species Diversity in Treated and Control Fields: A total of 173 bees representing ten species from two families, Halictidae (six species) and Apidae (four species), were collected from both paddy fields and adjacent non-crop habitats over the two-year study period (Fig. 2 ). The species accumulation curves for both treated and control fields plateaued, suggesting that the sampling effort was sufficient to capture the majority of bee species present in the study area (Fig. 3 ). The alpha diversity indices, including species richness and the Shannon diversity index, were consistently higher in control fields compared to treated fields across both years (Table 1 ). In Year 1, control fields (C1-C6) exhibited Shannon diversity indices ranging from 1.242 to 1.559, while treated fields (T1-T6) ranged from 0.00 to 1.560. In Year 2, a further decline in diversity was observed in treated fields, with several fields (T2, T3, T4, T6) showing little to no diversity (Shannon diversity indices between 0.00 and 0.500), whereas control fields maintained stable diversity levels (1.213 to 1.641). Mann-Whitney U tests revealed significant differences in both species richness (W = 116, p = 0.008) and Shannon diversity indices (W = 109.5, p = 0.031) between treated and control fields, indicating a negative impact of pesticide use on bee diversity (Fig. 4 ). Table 1 Shannon diversity indices for treated and control paddy fields across two years. No Paddy field Shannon Diversity (H) Year 1 Year 2 1. T1 1.560 1.386 2. T2 0.00 0.500 3. T3 0.00 0.00 4. T4 0.00 0.00 5. T5 1.560 1.494 6. T6 1.332 0.00 7. C1 1.540 1.497 8. C2 1.494 1.641 9. C3 1.332 1.560 10. C4 1.427 1.414 11. C5 1.242 1.332 12. C6 1.559 1.213 Community Composition and Pesticide Impact: Bray-Curtis dissimilarity indices (Online Resource, Table 2 ) were calculated to quantify differences in species composition between treated and control fields. These indices ranged from 0.0 to 1.0, with higher values indicating greater dissimilarity between fields. The PERMANOVA analysis showed a significant effect of pesticide treatment on community composition (Df = 23, F = 6.569, p < 0.001), explaining approximately 23% of the observed variation in species composition. The NMDS plot (Fig. 4 ) illustrated distinct clustering, with closely grouped treated fields are dispersed control groups. Effect of Year and Field Variability on Bee Diversity: The LMM analysis showed significant variability in species richness among fields (variance = 2.500, SD = 1.5811). Although a trend towards reduced species richness in treated fields was observed, this effect was not statistically significant (p = 0.257) However, significant temporal changes in species richness were noted across both field types (F = 25.000, p = 0.03775), indicating a general decline in bee diversity over time, potentially due to broader environmental changes or cumulative effects of pesticide exposure. Similarly, while some variability in baseline Shannon diversity index (H) among fields was observed (variance = 0.01750, SD = 0.13229), differences in the Shannon diversity index between treated and control fields were not statistically significant. Interaction plots (Fig. 5 ) summarizing trends in species richness and Shannon diversity over time, showed a more pronounced decline in treated fields, particularly in the second year, emphasizing the temporal dynamics of pesticide impacts on bee communities. Species-Specific Responses to Pesticide Treatments in Kole paddy fields: GLMMs were used to model species presence across different fields, incorporating fixed effects (treatment, year, species identity) and random effects (field-specific variability). The over dispersion ratio for the model is 0.84, which implies that the model is fitting the data The analysis revealed substantial variability (Online Resource, Table 4) in species presence probabilities among fields (random effect variance = 0.7601, SD = 0.8718). However, large standard errors for fixed effects (treatment and year) suggest weak evidence for significant effects. Species-specific response plots (Fig. 6 a & 6 b) showed that species like Halictus sp. and Braunsapis sp. had higher probabilities of presencein control fields, while others, such as Tetragonula sp. and Apis florea , exhibited minimal differences between treated and control fields, indicating varying sensitivities to pesticide exposure. Influence of wetland-adjacent non-crop habitats on bee diversity: The non-crop habitats (bunds) showed a range of Shannon diversity index values from 0 to 1.40 (Table 2 ), corresponding to the vegetation characteristics of bunds. As expected, the non-crop habitats showing higher indices are the one with comparatively dense vegetation. Pearson correlation using the bootstrapped diversity (Online Resource, Table 6) analysis revealed a significant positive correlation between bee diversity in treated fields and adjacent non-crop habitats (r = 0.8389, p = 0.0369), suggesting that these habitats may act as refuges, partially mitigating the adverse effects of pesticide exposure on bee populations. The hierarchical clustering dendrogram based on Bray-Curtis dissimilarity (Fig. 7 ) (Online Resource, Table 7)indicated that treated fields adjacent to more diverse non-crop habitats (T1 & T5) had species compositions more similar to their respective non-crop habitats (NC1 & NC5). Whereas the fields adjacent to less diverse or barren non-crop habitats (T3, T4, T6) had a dissimilar species composition with respective non-crop habitats (NC3, NC4, NC6). As demonstrated in Fig. 8 , non-crop fields tend to have a more varied species composition, with species like Tetragonula sp., Braunsapis sp., and Ceratina sp. present in higher abundance and the treated fields are typically dominated by Halictus sp. These results indicate that non-crop field margins in the wetland ecosystem are likely to buffer the impacts of pesticide exposure. Table 2 Shannon diversity of treated fields and adjacent non-crop habitat during the second year of sampling. Treated Field Shannon_H Non-Crop Habitat Shannon_H T1 1.3862944 NC1 1.2770343 T2 0.5004024 NC2 1.0114043 T3 0.0000000 NC3 0.6931472 T4 0.0000000 NC4 0.00000 T5 1.2770343 NC5 1.3972048 T6 0.0000000 NC6 0.6931472 Discussion This study advocates that pesticide use has a significant negative impact on bee diversity in the Kole wetlands, reflecting the global trends of pollinator decline in agricultural landscapes. This finding is consistent with previous highlighting the detrimental effects of pesticide exposure on pollinator communities, particularly bees, in agroecosystems (Dicks et al. 2021 ; Knauer et al. 2024 ; Sahayaraj and Hassan 2023 ). Impact of Pesticide Use on Bee Diversity: The observed reduction in bee diversity in pesticide-treated fields, as indicated by lower species richness and Shannon diversity indices, suggests that pesticides are a major driver of bee decline in these agroecosystems. Bees are exposed to pesticides in various ways, such as through direct contact during application, exposure to residues, or ingestion of contaminated pollen, nectar, or guttation fluid (Ellis 2012 ; Hrynko et al. 2021 ). Owing to the use of systemic insecticides in the Kole paddy wetlands, the bees are susceptible to the chemical remnants present in plant tissues, including pollen and nectar, throughout the blooming period (Lundin et al. 2015 ). In addition, the life history traits such as foraging traits could influence the differential response of bees to pesticide exposure, with extensive foragers being most vulnerable to pesticides than limited foragers (Knapp et al. 2023 ; Tuell and Isaac 2010). The distinct clustering of treated fields shown by NMDS, implies a more homogenized bee composition within them. Moreover, the species response plot shows that the treated fields were dominated by a few species such as Tetragonula sp. and Apis florea while there is a reduction in diversity of species such as Halictus sp. and Braunsapis sp. This pattern of shifting community structure reflects biotic homogenization, which refers to the increase in genetic, taxonomic, or functional similarity between two or more locations, as a result of species invasions and extinctions over time (Olden et al. 2008). This loss of functional biodiversity by biotic homogenization affect the wetland flora and eventually destabilizing the resilience of wetland ecosystems through altered pollinator dynamics. Even though biotic homogenization is documented in pollinator communities (Fourcade et al. 2021 ; White and Kerr 2007 ; Gossner et al. 2023 ), this study is inconclusive to confirm the trend in Kole wetlands and require long-term monitoring. However, our study strongly supports the hypothesis that intensive pesticide application can lead to a loss of species diversity within bee communities. The significant temporal changes in bee diversity observed across both field types as well as the comparatively higher diversity in certain fields (T1 and T5) indicates the possibility of multiple factors beyond pesticide exposure, affecting bee populations. The could include aspects such as climate variability, field-specific characters like vegetation type and floral resource availability, or cumulative effects of pesticide exposure over time (Goulson et al. 2015 ; Raderschall et al. 2021 ). With the circumstances in consideration, the influence of field specific characteristics, i.e., bund vegetation to serves as reservoirs of biodiversity has paramount importance. While the agricultural practices intensify and climate become more variable, the ability of these habitats to support diverse bee communities is increasingly important for maintaining ecosystem resilience and function (Winfree et al. 2009 ). Role of Non-Crop Habitats in Mitigating Pesticide Effects: In the Kole wetlands, treated fields adjacent to bunds covered with dense native vegetation, were positively correlated with higher bee diversity, suggesting these areas may serve as refugia mitigating some of the adverse effects of pesticide exposure. The bunds provide critical resources such as nectar, pollen, and nesting sites that are often limited in monoculture-dominated landscapes (Forrest et al. 2015 ; Carvell et al. 2022 ). The hierarchical clustering dendrogram revealed that treated fields near diverse bunds shared a more similar species composition than the treated fields near to less diverse and barren bunds. This finding suggests that maintaining or enhancing the quality and diversity of non-crop habitats could help support more resilient bee communities by providing refugia against pesticide exposure. There is a noticeable similarity in species composition in T1-NC1 and T5-NC5 pair which could possibly be because of spill over effects from non-crop to treated field (Blitzer et al. 2012 ; Rague et al. 2022) However, non-crop fields exhibit higher overall diversity compared to their adjacent crop fields In Kole paddy wetlands, where large non-crop areas are absent or limited, small habitat patches such as bunds and hedge grows play a vital role. These patches support ecosystem services by providing foraging opportunities and shelter for pollinators within a limited range, typically within 1 km of their nests (Rands and Whitney 2011 ). This finding aligns with the broader understanding that semi-natural habitats within agricultural systems, such as the bunds studied here, act as refugia and mitigate the adverse effects of intensive farming practices (Duelli and Obrist 2003 ; Lüscher et al. 2016 ). Not alone pollinators, the bund vegetation are sheltering and feeding sites for the migratory birds and unique water birds of wetlands. Nonetheless, the effectiveness of non-crop habitats as refuges is context-dependent. Undoubtedly, the presence of diverse non-crop habitats alleviates the negative effects of pesticide exposure to some degree, but the risk of pesticide drift from crop fields to wild habitats could potentially reduce their effectiveness as shelter for pollinators (Graham et al. 2024 ). This highlights the need for better management practices to minimize pesticide drift into non-crop habitats, such as applying pesticides during times when bees are less active and educating farm workers regarding targeted pesticide application (Karbassion and Stanley 2023, Mu et al. 2022 ). Additionally, enhancing habitat connectivity between agricultural and non-crop areas may further bolster bee populations by facilitating movement and resource access across landscapes (Buhk et al. 2018 ; Maurer et al. 2022 ; Westphal et al. 2015 ). Implications for pollinator conservation and wetland management strategies For responsible wetland management, it is crucial to integrate pest management strategies with pollinator conservation. Strategies such as reducing pesticide use, adopting less harmful alternatives (e.g., biopesticides), and enhancing the quality and connectivity of non-crop habitats could help mitigate the negative impacts of agricultural practices on bee populations (Singh et al. 2022 ; Inoka 2005). The observed positive relationship between bund flora diversity and bee diversity in adjacent treated fields suggests that promoting diverse, flower-rich habitats within agricultural landscapes could enhance pollinator support and contribute to more sustainable agricultural systems (Wratten et al. 2012 ). The studyfound out that unmanaged field bunds facilitated higher bee diversity and this call for actions at local level, to prioritize the preservation and restoration of these bunds that render ecosystem services at zero cost input. The efforts could include establishing wildflower strips, hedgerows, and other semi-natural features that provide critical resources and refuges for pollinators (Carvell et al. 2022 ; Hevia et al. 2021 ). Field margins with weeds are proved as pollinator friendly regions (Balfour and Ratnieks 2022 ; Bambaradeniya et al. 2004 ; Kleiman et al. 2020 ) and adopting this strategy could be a practical approach in Kole paddy wetlands where weeds are in abundance. The findings of Deeksha et al. ( 2022 ) reaffirms the utility of weeds as resource for pollinators who recorded 14 weed species supporting 25 insect pollinator interactions in the scrubland weeds of Northwestern Indian Himalayas Additionally, farmer participatory approaches can be implemented, to educate of the ecological consequences of over dependency of pesticides and why pollinators are pivotal in sustaining their livelihood through wetlands. Farmer participatory approaches, as suggested by Garcia-Vega et al. (2024), should be prioritized to highlight how sustainable agriculture and pollinator conservation are essential for both ecosystem health and farmer livelihoods. Current practices followed by the Kole paddy farmers, such as burning and plowing the bund vegetation and other immediate non-crop habitats surrounding the crop fields, manual and machinery assisted weed pulling, contribute to biodiversity loss (Barros-Rodríguez et al. 2021 ). Raising awareness among farmers about the ecosystem services provided by bund vegetation and discouraging its mislabelling as a nuisance or 'undesirable,' could lead to foster better conservation outcomes. The cumulative efforts could help reconcile bee conservation with the human-induced menaces. Although this study is envisaged in specific geographical area, there is a need for regional and local-level research to tailor field-specific conservation and management strategies. While this study attempted to provide insights on pesticide effects and impact of non-crop habitats as major objectives, it is limited primarily, by the short sampling period for assessing non-crop habitats and the lack of in-depth analysis of non-crop vegetation. To address the limitations, future studies should consider longer-term monitoring of bee communities and analyse a broader range of environmental conditions and landscape contents. This approach would provide insights into the temporal and spatial variations affecting bee communities and help to better understand the complex interactions between agricultural practices, habitat diversity, and pollinator communities. Conclusion This study underscores the significant impact of pesticide use on bee diversity in the Kole wetlands of Kerala, with treated fields showing reduced species richness and diversity. Our results stress the potential of non-crop habitats, bund vegetation, to mitigate these negative effects by offering refugia for pollinators. The findings of the study prioritize the need for integrated pest management strategies that not only reduce pesticide exposure but also enhance habitat quality to support pollinator populations. By adopting sustainable agricultural practices that balance productivity with biodiversity conservation, the resilience of pollinator communities can be enhanced ensuring their ecosystem services in wetland agroecosystems worldwide. However, given the temporal limitations of this study, future studies should involve long term monitoring and varied landscape contexts. Declarations Acknowledgement The first author duly acknowledges Council of Scientific and Industrial Research (CSIR), India for funding the research and University of Calicut, Kerala for the resources. We express our sincere gratitude to the farmers and landholders of paddy fields for cooperating with us during the specimen collection and sharing information. Data availability statement The data is available in the supplementary information. Funding This study is funded by Council of Scientific and Industrial Research (CSIR), India, Grants no: 08/706(0004)/2019-EMR-I Competing interests The authors have no relevant financial or non-financial interests to disclose. Author contributions Conceptualization: Rabeea Habeeb & Muhammed Abdul Rafeeq Karuvally Ummer; Methodology, formal analysis, investigation and writing-original draft preparation: Rabeea Habeeb; Writing-reviewing and editing: Muhammed Abdul Rafeeq Karuvally Ummer& Jobiraj Thayyullathil; Resources: Jobiraj Thayyullathil; Supervision: Muhammed Abdul Rafeeq Karuvally Ummer. References Abudulai M, Nboyine JA, Quandahor P, Seidu A, Traore F (2022) Agricultural intensification causes decline in insect biodiversity. 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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-5266316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366972474,"identity":"334950a2-11d5-413f-8857-cb5b5e6a7caf","order_by":0,"name":"Rabeea Habeeb","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACHghlwN4AIi1I0cJzAERKkKJFIgFEEaHFvP3swQcfau4kbpd8fnXDjwIJBv727gS8WmTO5CUbzjj2LHHn7Jyymz1Ah0mcObsBrxYJhhwzaR62w8YGt3PSbvAAtRhI5BLQwv/G/Peff0AtN8+k3fxDlBaJHDNmxrbDcgY32I/dJs4WiXfJkr19z+QMzuSw3ZYxkOAh7Bf+3IMffny7w2Nw/Pizm2/+2Mjxt/fi1wIFB4CYxwDE4iFGOUwL+wNiVY+CUTAKRsEIAwBKdUo/8SKtfwAAAABJRU5ErkJggg==","orcid":"","institution":"MES Mampad College (Autonomous), Affiliated to University of Calicut","correspondingAuthor":true,"prefix":"","firstName":"Rabeea","middleName":"","lastName":"Habeeb","suffix":""},{"id":366972478,"identity":"e70d210d-9e5b-44f1-b900-127faaf6ace3","order_by":1,"name":"Muhammed Abdul Rafeeq Karuvally Ummer","email":"","orcid":"","institution":"MES Mampad College (Autonomous), Affiliated to University of Calicut","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Abdul Rafeeq Karuvally","lastName":"Ummer","suffix":""},{"id":366972480,"identity":"546a6f8f-5632-4f3c-8a94-7c45179d75f1","order_by":2,"name":"Jobiraj Thayyullathil","email":"","orcid":"","institution":"Government College Kodenchery, Affiliated to University of Calicut","correspondingAuthor":false,"prefix":"","firstName":"Jobiraj","middleName":"","lastName":"Thayyullathil","suffix":""}],"badges":[],"createdAt":"2024-10-15 07:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5266316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5266316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66928096,"identity":"89eab96b-51cf-4437-a34d-be3f78f6fe16","added_by":"auto","created_at":"2024-10-18 06:26:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72502,"visible":true,"origin":"","legend":"\u003cp\u003eMalappuram Kole paddy fields, the study area indicating the treated and control fields selected for the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/cf81d4f4986301e1fe4b31af.png"},{"id":66928103,"identity":"74a661df-fb2b-4bd9-a82c-2e7a9aaadc56","added_by":"auto","created_at":"2024-10-18 06:26:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32387,"visible":true,"origin":"","legend":"\u003cp\u003eBee species collected from all the fields during the survey and their respective families. Halictidae exhibited the highest number of species.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/411ef3b6ac503998295c1819.png"},{"id":66928095,"identity":"ff7ff4c3-7ada-4744-b3e4-a231de8cdb66","added_by":"auto","created_at":"2024-10-18 06:26:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59520,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies Accumulation Curves for Treated and Control Fields. The curves illustrate the cumulative species richness as a function of the number of samples for both treated (blue) and control (red) fields. Control fields show higher species richness compared to treated fields, suggesting greater biodiversity in untreated areas. The shaded regions represent the variability in species richness estimates.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/584ab1db4b3ed87c1dd2a4c7.png"},{"id":66928099,"identity":"ff404a9f-7105-414e-8beb-e6fcf9f8ece8","added_by":"auto","created_at":"2024-10-18 06:26:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51054,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS plot illustrating the clustering of control and treated fields, showing clear separation between the two groups, reflecting the effects of pesticide treatments. Points represent individual field samples, with shapes indicating the year of sampling. The ellipses represent 95% confidence intervals for each group, showing the variability in species composition within control and treated field.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/5a66b7d3823785d74a90b2e4.png"},{"id":66928509,"identity":"a1d894e6-c435-47a1-b2ba-188cc41b9b7f","added_by":"auto","created_at":"2024-10-18 06:34:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54142,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Species Richness and Shannon Diversity Index Over Time for Control and Treated Fields. The plot illustrates the changes in species richness and Shannon diversity index of bee communities in control and pesticide-treated fields over two years. Points represent the mean values for each treatment group, and error bars denote 95% confidence intervals around these means. The lines connecting the points show temporal trends in diversity metrics, with control fields consistently maintaining higher species richness and Shannon diversity compared to treated fields. The observed differences highlight the negative impact of pesticide application on bee diversity over time.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/fad7bd21efe01759236df98d.png"},{"id":66928104,"identity":"b3d45786-b4e9-48c1-9d0f-45432f7335b7","added_by":"auto","created_at":"2024-10-18 06:26:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Predicted Probability of Species Presence by Year. \u003cstrong\u003eb\u003c/strong\u003e Predicted Probability of Species Presence by Treatment. The plots highlight species-specific responses to year and treatment effects, illustrating variations in species presence probabilities between control and treated fields and across years. (Error bars denote 95% confidence intervals around the predicted probabilities)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/dfeb818cb682c6541c17973a.png"},{"id":66928507,"identity":"a70dc085-5f3a-4075-8926-64028191ea05","added_by":"auto","created_at":"2024-10-18 06:34:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":20090,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering dendrogram showing the grouping of treated and non-crop habitat in accordance with respective species composition.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/e74f81a966149f59a7f732e7.png"},{"id":66928508,"identity":"40d6a9ab-48c3-4454-a300-d703061e7de2","added_by":"auto","created_at":"2024-10-18 06:34:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":44930,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of relative species abundance by site type, respective treated-noncrop pair.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/69f8eb0a951763aedcb60098.png"},{"id":82299176,"identity":"9b022d3b-05b1-4b4c-93d8-a2d6370bd0c2","added_by":"auto","created_at":"2025-05-08 20:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":979075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/3912db2d-b6dc-4078-8bc7-6a233f92c1bd.pdf"},{"id":66928097,"identity":"afcc4b14-d084-4447-8465-7212c2ecfe7c","added_by":"auto","created_at":"2024-10-18 06:26:41","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":228849,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/44983167dbaf6f9513f62a57.png"},{"id":66928101,"identity":"c1be99f0-b3b2-486a-9d3d-3146540b2e0f","added_by":"auto","created_at":"2024-10-18 06:26:42","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":513196,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5266316/v1/a647b50c2682cf11a847a454.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Balancing agriculture and conservation in the Ramsar-listed Kole paddy wetlands: The bee diversity and role of non- crop vegetation amid pesticide use","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands are crucial ecosystems providing essential services including water regulation, nutrient cycling, carbon sequestration, and biodiversity conservation (Eric et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ability of wetlands to remove excess nutrients and degrade pesticides plays a critical role in maintaining the water quality, especially in agricultural wetlands subjected to periodic pesticide treatments (Vymazal \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). India represents 13.67\u0026nbsp;million hectares of natural and man-made wetlands, with 85 designated as Ramsar sites (Ministry of Environment, Forest \u0026amp; Climate Change 2024). The Kole/Kol paddy wetlands, part of the Vembanad-Kole system, are one of the three Ramsar sites in Kerala state, located south-west coast of India. These natural coastal lowlands are deeply integrated with Kerala\u0026rsquo;s agricultural heritage, primarily supporting paddy cultivation. The paddy fields are divided by 1\u0026ndash;2 metre wide mud embankments, known locally as bunds. The bunds may be barren or inhabited by native flowering plants and weeds. Kole paddy wetlands sustain local livelihoods through rice and fish farming (Kumar and Kunhamu \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) along with occasional cultivation of aquatic plants like \u003cem\u003eNymphaea\u003c/em\u003e and \u003cem\u003eNelumbo\u003c/em\u003e species. The Kole paddy wetlands are also paramount in biodiversity conservation (Remani et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Despite its cultural, economic and ecological importance, Kole wetlands are underrepresented in the international literature.\u003c/p\u003e \u003cp\u003eSimilar to the global scenario (Vasumthi et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vincent et al. 2021), Kole paddy wetlands are threatened by habitat degradation. The agricultural intensification catalyzed by non-judicious use of pesticides including insecticides, weedicides, herbicides and fungicides lead to biodiversity loss and ecological imbalances. Pesticides like neonicotinoids and organophosphates are extensively used in the Kole paddy wetlands that are reported to cause lethal and sub-lethal effects on non-target organisms, including pollinators and a range of other taxa from microbes to vertebrates (Pisa et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Apart from unscientific agricultural practices, anthropogenic stressors such as mining, construction, sewage disposal and habitat fragmentation further amplify the Kole wetland deterioration (Jenin and Bhaskara \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Raj and Azeez 2018; Zainulabdeen and Nagaraj \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The subtle environmental changes trigger the loss of unique biodiversity associated with it, and conserving biodiversity is crucial for the long-term productivity of agroecosystems (Kremen and Niles 2012; Seppelt et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the recognition of wetlands as waterfowl habitats by Ramsar Convention, avifauna dominates the Kole biodiversity research (Sivaperuman and Jayson \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Narayanan and Thomas 2011; Pournami et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) along with few other groups- odonates (Chandran and Jose 2021) and butterflies (Sarath et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Johny S \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough birds and odonates are key indicators of wetland health, pollinators received limited consideration for their equally critical roles in promoting a wetland agroecosystem. However, recent study by Cohen et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), have demonstrated that pollinator diversity, particularly bumblebees, are reliable indicators of wetland health. Furthermore, periodic monitoring of pollinators is essential to assess changes in diversity and abundance with respect to space, time and focal crop (Senapathi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While rice is mainly self-pollinating, research has shown that insect pollinators, particularly bees, can enhance cross-pollination, leading to genetic diversity and potentially increasing yields (Pu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rader et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Of the 510 insect species found out to pollinate rice, bees are the most effective, outperforming hoverflies and butterflies (Chauhan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khalifa et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Muhammad et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Patel et al. 2020). Beyond rice, bees contribute to the broader agroecosystem by pollinating wildflowers, enriching floral diversity and thereby maintains ecosystem services such as natural pest control (Carvalheiro et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e \u0026amp; Kremen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Despite their roles, bee populations are dwindling globally due to multiple anthropogenic factors, including agricultural intensification, habitat loss, climate change, pests and diseases and influence of invasive alien species (Dar et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.; Hristov et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lima et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The expansion of monoculture farming and the widespread pesticide use are critical drivers of bee decline (Abudulai et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Langlois et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several studies have examined the impacts of pesticides bee populations in various agricultural settings (Arena et al. 2024; Nicholson et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raine et al. 2024; Uhl et al. 2019; Woodcock et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Pesticides, especially broad-spectrum insecticides like organophosphates, pyrethroids, and neonicotinoids, threaten bees through both acute toxicity and sub-lethal effects, impairing foraging, reproduction and immune function (Siviter et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schuhmann et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding how non-crop habitats, especially the bund vegetation in the Kole wetlands can mitigate pesticide impacts on bee diversity can provide insights for sustainable agriculture. Research experiment that semi-natural and non-crop habitats, such as field margins, hedgerows, and strips of wild flowering plants, within a dominant crop field play a crucial role in supporting bee populations (Gaspar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kowalska et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maccagnani et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) by providing essential resources, including nectar, pollen, and nesting sites, which are often missing in monoculture fields like a paddy crop field (Carvell et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cole et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hass et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gurr et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In intensively farmed landscapes, such habitats can act as refugia, helping bees evade pesticide exposure (Duff et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kujawa et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Winfree et al; \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, their effectiveness depends on the proximity of non-crop habitat to pesticide-treated fields and the extent of pesticide drift (Teysserie et al. 2021). To the best our knowledge, the potential refugia effect of bunds in Kole wetlands are not explored.\u003c/p\u003e \u003cp\u003eDespite the ecological-economic importance of Kole wetlands as well as the importance of pollinators as wetland health indicators, to the best of our knowledge, no study has evaluated the aspect. Addressing these gaps, this study intends to explore the following research questions:\u003c/p\u003e \u003cp\u003e(1) How does conventional pesticide use impact pollinator diversity in the Ramsar-listed Kole paddy wetlands?\u003c/p\u003e \u003cp\u003e(2) What role does non-crop vegetation (specifically bund vegetation) play in supporting pollinator populations in pesticide-impacted areas?\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eStudy Area\u003c/p\u003e \u003cp\u003eThe South-West Indian state of Kerala (10.1632\u0026deg; N, 76.6413\u0026deg; E) is geographically rich with varied range of wetlands, including rivers, streams, backwaters, estuaries, paddy wetlands, mangroves, lakes and ponds, along with the artificial structures such as reservoirs, canals and tanks. The state has a wetland area of 160,590 hectares with three Ramsar sites: Vembanad-Kole wetland, Ashtamudi and Shasthamkotta lake. The Vembanad- Kole wetland system, the largest (151,250 ha) of the three consists of two subtypes-Vembanad lake and Kole paddy wetlands. The Kole paddy lands are known as the rice granaries of Kerala, that spans the Thrissur and Malappuram districts. It follows a distinct pattern of cultivation, known as \u0026ldquo;Kole puncha\u0026rdquo;, from December through May. During the monsoon season, Kole experience heavy flooding, followed by salinity intrusion during the post-monsoon. The rice cultivation begins with dewatering of the flooded fields and storing the water in canals for irrigation. Sowing takes place in December using short-duration rice varieties, referred to as \u0026lsquo;hraswa\u0026rsquo; (e.g., Jyothi, Uma and Jaya) which are harvested by May. The cropping season is generally limited to one per year, except in rare cases, two (referred to as \u0026lsquo;Mundakan\u0026rsquo;), if flooding does not affect the fields (Johnkutty \u0026amp; Venugopal 1993). The Kole soils are fertile due to alluvial deposits with textures ranging from sandy loam to clay and a pH range of 2.6 to 6.3, influenced by organic matter and waterlogging conditions.\u003c/p\u003e \u003cp\u003eFor this study, we selected Kole paddy fields in Malappuram district as they consistently follow one cropping season (puncha) per year pattern.\u003c/p\u003e \u003cp\u003eSampling Design\u003c/p\u003e \u003cp\u003eBee sampling was conducted over two years (2021\u0026ndash;2023), during two Puncha seasons, one per year. We selected six pesticide-treated fields (T1, T2, T3, T4, T5 and T6) and six non-treated fields (C1, C2, C3, C4, C5 and C6) as control sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), based on the similarity in type of pesticides used and their application frequency. The treated fields received regular pesticide applications at same frequency and intervals, while control fields were managed without pesticides, although fungicides, herbicides and fertilizers were occasionally used. The commonly used pesticides belonged to the chemical classes: carbamates, diamides, organophosphates, neonicotinoids and pyrethroids.\u003c/p\u003e \u003cp\u003eTwo random plots were sampled from each field, resulting in a total of 24 plots; 12 plots per treatment type (treated and control). In addition to paddy fields, six adjacent non-crop habitats (NC1, NC2, NC3, NC4, NC5 and NC6), located within a 200-meter radius of the treated fields were also sampled during the second year to evaluate the potential refugia effect. The selected non-crop habitats varied in vegetation type,\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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=\"left\" 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\u003eNC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-crop habitat of T1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eField margins with thick patches of weeds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-crop habitat of T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eField margins of thin assemblages of small native flowering plants and intermittently, weeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC3 \u0026amp; NC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-crop habitat of T3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarren field margin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-crop habitat of T5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eField margins with dense patches of flowering plants, weeds and the field margin separated from non-crop by small water canals used for \u003cem\u003eNelumbo\u003c/em\u003e sp. cultivation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-crop habitat of T6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScarce patches of native wild plants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSampling was carried out every 20 days after transplantation (DAT), specifically at 20,40,60,80 and 100 DAT, using a combination of sweep nets and pan traps, which are recommended as the effective sampling techniques for estimating species richness and abundance of bees (Prado et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In order to standardize the effect of different paddy field sizes and bunds, equal sampling efforts was applied across all paddy and bund habitats. The sweep netting was standardized by time, allocating 20 minutes per plot. Sweep netting involved actively sweeping through both the paddy field types and adjacent non-crop vegetation during the morning hours between 9.00 am and 11.00 am when bee activity is typically high. The specimens collected were aspirated and transferred to 70% alcohol. Pan traps, consisting of yellow and blue pans filled with soapy water, were set to passively capture bees that get attracted to the color. The number of traps were uniform across all locations and left for 24 hours. At the end of sampling period, the traps are emptied through a sieve from which the bees are carefully transferred to 70% alcohol. All wet preserved specimens were later pinned to dry preservation. Species identification was carried out to the species level using standard taxonomic keys and reference collections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R Studio (R core Team 2024). Species accumulation curves were generated for both treated and control fields to assess the sufficiency of sampling efforts. Alpha diversity indices, including species richness and the Shannon diversity index, were calculated for each field type to evaluate within-field diversity. Differences in diversity indices between treated and control fields were tested for statistical significance using non-parametric Mann-Whitney U tests, due to non-normal distributions indicated by Shapiro-Wilk normality tests. To examine community composition differences between treated and control fields, Bray-Curtis dissimilarity indices were calculated for all pairs of fields. These indices measure the dissimilarity between two communities based on species abundance and presence/absence data. The significance of observed differences in community composition was tested using Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations. Non-Metric Multidimensional Scaling (NMDS) was used to visualize these differences in species composition between field types over the two-year period. To evaluate the impact of pesticide treatment and year on alpha diversity indices (species richness and Shannon diversity index), linear mixed-effects models (LMMs) were fitted. LMMs were chosen to account for both fixed effects (treatment and year) and random effects (field-specific variability), for robust handling of repeated measures data. For binary outcomes (species presence/absence), generalized linear mixed models (GLMMs) with a binomial distribution were constructed to model the effects of treatment, year, species identity, and their interactions, while also considering random field effects. The GLMM is checked for over dispersion. The use of both LMMs and GLMMs allowed for comprehensive modelling of continuous and binary response variables, thus improving accuracy and credibility of the results.\u003c/p\u003e \u003cp\u003eTo assess the influence of non-crop habitats on bee diversity in treated fields, Shannon diversity indices were calculated. Due to small sample size of non-crop habitats (sampling carried out only in the second year), bootstrap resampling is done to increase the validity of data. To assess the variability of species diversity (Shannon diversity index), bootstrap resampling was applied for each of the 12 sites (R\u0026thinsp;=\u0026thinsp;999). Pearson correlation analysis was performed with the resampled data to evaluate the relationship between diversity in treated fields and adjacent non-crop habitats. A hierarchical clustering dendrogram based on Bray-Curtis dissimilarity was also constructed to precisely explore similarities in species composition between treated fields and their adjacent non-crop habitats. In manuscript preparation, we have used LLM, specifically edit GPT for language editing assistance. All the content generated are reviewed thoroughly for their accuracy. (The R packages and functions used in the statistical analysis are provided in Online Resource, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSpecies Diversity in Treated and Control Fields: A total of 173 bees representing ten species from two families, Halictidae (six species) and Apidae (four species), were collected from both paddy fields and adjacent non-crop habitats over the two-year study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The species accumulation curves for both treated and control fields plateaued, suggesting that the sampling effort was sufficient to capture the majority of bee species present in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The alpha diversity indices, including species richness and the Shannon diversity index, were consistently higher in control fields compared to treated fields across both years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Year 1, control fields (C1-C6) exhibited Shannon diversity indices ranging from 1.242 to 1.559, while treated fields (T1-T6) ranged from 0.00 to 1.560. In Year 2, a further decline in diversity was observed in treated fields, with several fields (T2, T3, T4, T6) showing little to no diversity (Shannon diversity indices between 0.00 and 0.500), whereas control fields maintained stable diversity levels (1.213 to 1.641). Mann-Whitney U tests revealed significant differences in both species richness (W\u0026thinsp;=\u0026thinsp;116, p\u0026thinsp;=\u0026thinsp;0.008) and Shannon diversity indices (W\u0026thinsp;=\u0026thinsp;109.5, p\u0026thinsp;=\u0026thinsp;0.031) between treated and control fields, indicating a negative impact of pesticide use on bee diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eShannon diversity indices for treated and control paddy fields across two years.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePaddy field\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eShannon Diversity (H)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCommunity Composition and Pesticide Impact: Bray-Curtis dissimilarity indices (Online Resource, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were calculated to quantify differences in species composition between treated and control fields. These indices ranged from 0.0 to 1.0, with higher values indicating greater dissimilarity between fields. The PERMANOVA analysis showed a significant effect of pesticide treatment on community composition (Df\u0026thinsp;=\u0026thinsp;23, F\u0026thinsp;=\u0026thinsp;6.569, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining approximately 23% of the observed variation in species composition. The NMDS plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) illustrated distinct clustering, with closely grouped treated fields are dispersed control groups.\u003c/p\u003e \u003cp\u003eEffect of Year and Field Variability on Bee Diversity: The LMM analysis showed significant variability in species richness among fields (variance\u0026thinsp;=\u0026thinsp;2.500, SD\u0026thinsp;=\u0026thinsp;1.5811). Although a trend towards reduced species richness in treated fields was observed, this effect was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.257) However, significant temporal changes in species richness were noted across both field types (F\u0026thinsp;=\u0026thinsp;25.000, p\u0026thinsp;=\u0026thinsp;0.03775), indicating a general decline in bee diversity over time, potentially due to broader environmental changes or cumulative effects of pesticide exposure. Similarly, while some variability in baseline Shannon diversity index (H) among fields was observed (variance\u0026thinsp;=\u0026thinsp;0.01750, SD\u0026thinsp;=\u0026thinsp;0.13229), differences in the Shannon diversity index between treated and control fields were not statistically significant. Interaction plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) summarizing trends in species richness and Shannon diversity over time, showed a more pronounced decline in treated fields, particularly in the second year, emphasizing the temporal dynamics of pesticide impacts on bee communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies-Specific Responses to Pesticide Treatments in Kole paddy fields: GLMMs were used to model species presence across different fields, incorporating fixed effects (treatment, year, species identity) and random effects (field-specific variability). The over dispersion ratio for the model is 0.84, which implies that the model is fitting the data The analysis revealed substantial variability (Online Resource, Table\u0026nbsp;4) in species presence probabilities among fields (random effect variance\u0026thinsp;=\u0026thinsp;0.7601, SD\u0026thinsp;=\u0026thinsp;0.8718). However, large standard errors for fixed effects (treatment and year) suggest weak evidence for significant effects. Species-specific response plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) showed that species like \u003cem\u003eHalictus\u003c/em\u003e sp. and \u003cem\u003eBraunsapis\u003c/em\u003e sp. had higher probabilities of presencein control fields, while others, such as \u003cem\u003eTetragonula\u003c/em\u003e sp. and \u003cem\u003eApis florea\u003c/em\u003e, exhibited minimal differences between treated and control fields, indicating varying sensitivities to pesticide exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInfluence of wetland-adjacent non-crop habitats on bee diversity: The non-crop habitats (bunds) showed a range of Shannon diversity index values from 0 to 1.40 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), corresponding to the vegetation characteristics of bunds. As expected, the non-crop habitats showing higher indices are the one with comparatively dense vegetation. Pearson correlation using the bootstrapped diversity (Online Resource, Table\u0026nbsp;6) analysis revealed a significant positive correlation between bee diversity in treated fields and adjacent non-crop habitats (r\u0026thinsp;=\u0026thinsp;0.8389, p\u0026thinsp;=\u0026thinsp;0.0369), suggesting that these habitats may act as refuges, partially mitigating the adverse effects of pesticide exposure on bee populations. The hierarchical clustering dendrogram based on Bray-Curtis dissimilarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) (Online Resource, Table\u0026nbsp;7)indicated that treated fields adjacent to more diverse non-crop habitats (T1 \u0026amp; T5) had species compositions more similar to their respective non-crop habitats (NC1 \u0026amp; NC5). Whereas the fields adjacent to less diverse or barren non-crop habitats (T3, T4, T6) had a dissimilar species composition with respective non-crop habitats (NC3, NC4, NC6). As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, non-crop fields tend to have a more varied species composition, with species like \u003cem\u003eTetragonula\u003c/em\u003e sp., \u003cem\u003eBraunsapis\u003c/em\u003e sp., and \u003cem\u003eCeratina\u003c/em\u003e sp. present in higher abundance and the treated fields are typically dominated by \u003cem\u003eHalictus\u003c/em\u003e sp. These results indicate that non-crop field margins in the wetland ecosystem are likely to buffer the impacts of pesticide exposure.\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\u003eShannon diversity of treated fields and adjacent non-crop habitat during the second year of sampling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreated Field\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon_H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Crop Habitat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon_H\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.3862944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2770343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5004024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0114043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6931472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2770343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3972048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6931472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study advocates that pesticide use has a significant negative impact on bee diversity in the Kole wetlands, reflecting the global trends of pollinator decline in agricultural landscapes. This finding is consistent with previous highlighting the detrimental effects of pesticide exposure on pollinator communities, particularly bees, in agroecosystems (Dicks et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Knauer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sahayaraj and Hassan \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImpact of Pesticide Use on Bee Diversity:\u003c/p\u003e \u003cp\u003eThe observed reduction in bee diversity in pesticide-treated fields, as indicated by lower species richness and Shannon diversity indices, suggests that pesticides are a major driver of bee decline in these agroecosystems. Bees are exposed to pesticides in various ways, such as through direct contact during application, exposure to residues, or ingestion of contaminated pollen, nectar, or guttation fluid (Ellis \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hrynko et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Owing to the use of systemic insecticides in the Kole paddy wetlands, the bees are susceptible to the chemical remnants present in plant tissues, including pollen and nectar, throughout the blooming period (Lundin et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, the life history traits such as foraging traits could influence the differential response of bees to pesticide exposure, with extensive foragers being most vulnerable to pesticides than limited foragers (Knapp et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tuell and Isaac 2010).\u003c/p\u003e \u003cp\u003eThe distinct clustering of treated fields shown by NMDS, implies a more homogenized bee composition within them. Moreover, the species response plot shows that the treated fields were dominated by a few species such as \u003cem\u003eTetragonula\u003c/em\u003e sp. and \u003cem\u003eApis florea\u003c/em\u003e while there is a reduction in diversity of species such as \u003cem\u003eHalictus\u003c/em\u003e sp. and \u003cem\u003eBraunsapis\u003c/em\u003e sp. This pattern of shifting community structure reflects biotic homogenization, which refers to the increase in genetic, taxonomic, or functional similarity between two or more locations, as a result of species invasions and extinctions over time (Olden et al. 2008). This loss of functional biodiversity by biotic homogenization affect the wetland flora and eventually destabilizing the resilience of wetland ecosystems through altered pollinator dynamics. Even though biotic homogenization is documented in pollinator communities (Fourcade et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; White and Kerr \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gossner et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study is inconclusive to confirm the trend in Kole wetlands and require long-term monitoring. However, our study strongly supports the hypothesis that intensive pesticide application can lead to a loss of species diversity within bee communities.\u003c/p\u003e \u003cp\u003eThe significant temporal changes in bee diversity observed across both field types as well as the comparatively higher diversity in certain fields (T1 and T5) indicates the possibility of multiple factors beyond pesticide exposure, affecting bee populations. The could include aspects such as climate variability, field-specific characters like vegetation type and floral resource availability, or cumulative effects of pesticide exposure over time (Goulson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Raderschall et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the circumstances in consideration, the influence of field specific characteristics, i.e., bund vegetation to serves as reservoirs of biodiversity has paramount importance. While the agricultural practices intensify and climate become more variable, the ability of these habitats to support diverse bee communities is increasingly important for maintaining ecosystem resilience and function (Winfree et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRole of Non-Crop Habitats in Mitigating Pesticide Effects:\u003c/p\u003e \u003cp\u003eIn the Kole wetlands, treated fields adjacent to bunds covered with dense native vegetation, were positively correlated with higher bee diversity, suggesting these areas may serve as refugia mitigating some of the adverse effects of pesticide exposure. The bunds provide critical resources such as nectar, pollen, and nesting sites that are often limited in monoculture-dominated landscapes (Forrest et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carvell et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The hierarchical clustering dendrogram revealed that treated fields near diverse bunds shared a more similar species composition than the treated fields near to less diverse and barren bunds. This finding suggests that maintaining or enhancing the quality and diversity of non-crop habitats could help support more resilient bee communities by providing refugia against pesticide exposure. There is a noticeable similarity in species composition in T1-NC1 and T5-NC5 pair which could possibly be because of spill over effects from non-crop to treated field (Blitzer et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rague et al. 2022) However, non-crop fields exhibit higher overall diversity compared to their adjacent crop fields\u003c/p\u003e \u003cp\u003eIn Kole paddy wetlands, where large non-crop areas are absent or limited, small habitat patches such as bunds and hedge grows play a vital role. These patches support ecosystem services by providing foraging opportunities and shelter for pollinators within a limited range, typically within 1 km of their nests (Rands and Whitney \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This finding aligns with the broader understanding that semi-natural habitats within agricultural systems, such as the bunds studied here, act as refugia and mitigate the adverse effects of intensive farming practices (Duelli and Obrist \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; L\u0026uuml;scher et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Not alone pollinators, the bund vegetation are sheltering and feeding sites for the migratory birds and unique water birds of wetlands. Nonetheless, the effectiveness of non-crop habitats as refuges is context-dependent. Undoubtedly, the presence of diverse non-crop habitats alleviates the negative effects of pesticide exposure to some degree, but the risk of pesticide drift from crop fields to wild habitats could potentially reduce their effectiveness as shelter for pollinators (Graham et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highlights the need for better management practices to minimize pesticide drift into non-crop habitats, such as applying pesticides during times when bees are less active and educating farm workers regarding targeted pesticide application (Karbassion and Stanley 2023, Mu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, enhancing habitat connectivity between agricultural and non-crop areas may further bolster bee populations by facilitating movement and resource access across landscapes (Buhk et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maurer et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Westphal et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImplications for pollinator conservation and wetland management strategies\u003c/p\u003e \u003cp\u003eFor responsible wetland management, it is crucial to integrate pest management strategies with pollinator conservation. Strategies such as reducing pesticide use, adopting less harmful alternatives (e.g., biopesticides), and enhancing the quality and connectivity of non-crop habitats could help mitigate the negative impacts of agricultural practices on bee populations (Singh et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Inoka 2005). The observed positive relationship between bund flora diversity and bee diversity in adjacent treated fields suggests that promoting diverse, flower-rich habitats within agricultural landscapes could enhance pollinator support and contribute to more sustainable agricultural systems (Wratten et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The studyfound out that unmanaged field bunds facilitated higher bee diversity and this call for actions at local level, to prioritize the preservation and restoration of these bunds that render ecosystem services at zero cost input. The efforts could include establishing wildflower strips, hedgerows, and other semi-natural features that provide critical resources and refuges for pollinators (Carvell et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hevia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Field margins with weeds are proved as pollinator friendly regions (Balfour and Ratnieks \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bambaradeniya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kleiman et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and adopting this strategy could be a practical approach in Kole paddy wetlands where weeds are in abundance. The findings of Deeksha et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reaffirms the utility of weeds as resource for pollinators who recorded 14 weed species supporting 25 insect pollinator interactions in the scrubland weeds of Northwestern Indian Himalayas\u003c/p\u003e \u003cp\u003eAdditionally, farmer participatory approaches can be implemented, to educate of the ecological consequences of over dependency of pesticides and why pollinators are pivotal in sustaining their livelihood through wetlands. Farmer participatory approaches, as suggested by Garcia-Vega et al. (2024), should be prioritized to highlight how sustainable agriculture and pollinator conservation are essential for both ecosystem health and farmer livelihoods. Current practices followed by the Kole paddy farmers, such as burning and plowing the bund vegetation and other immediate non-crop habitats surrounding the crop fields, manual and machinery assisted weed pulling, contribute to biodiversity loss (Barros-Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Raising awareness among farmers about the ecosystem services provided by bund vegetation and discouraging its mislabelling as a nuisance or 'undesirable,' could lead to foster better conservation outcomes. The cumulative efforts could help reconcile bee conservation with the human-induced menaces. Although this study is envisaged in specific geographical area, there is a need for regional and local-level research to tailor field-specific conservation and management strategies.\u003c/p\u003e \u003cp\u003eWhile this study attempted to provide insights on pesticide effects and impact of non-crop habitats as major objectives, it is limited primarily, by the short sampling period for assessing non-crop habitats and the lack of in-depth analysis of non-crop vegetation. To address the limitations, future studies should consider longer-term monitoring of bee communities and analyse a broader range of environmental conditions and landscape contents. This approach would provide insights into the temporal and spatial variations affecting bee communities and help to better understand the complex interactions between agricultural practices, habitat diversity, and pollinator communities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the significant impact of pesticide use on bee diversity in the Kole wetlands of Kerala, with treated fields showing reduced species richness and diversity. Our results stress the potential of non-crop habitats, bund vegetation, to mitigate these negative effects by offering refugia for pollinators. The findings of the study prioritize the need for integrated pest management strategies that not only reduce pesticide exposure but also enhance habitat quality to support pollinator populations. By adopting sustainable agricultural practices that balance productivity with biodiversity conservation, the resilience of pollinator communities can be enhanced ensuring their ecosystem services in wetland agroecosystems worldwide. However, given the temporal limitations of this study, future studies should involve long term monitoring and varied landscape contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author duly acknowledges Council of Scientific and Industrial Research (CSIR), India for funding the research and University of Calicut, Kerala for the resources. We express our sincere gratitude to the farmers and landholders of paddy fields for cooperating with us during the specimen collection and sharing information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is available in the supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is funded by Council of Scientific and Industrial Research (CSIR), India, Grants no: 08/706(0004)/2019-EMR-I\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Rabeea Habeeb \u0026amp; Muhammed Abdul Rafeeq Karuvally Ummer; Methodology, formal analysis, investigation and writing-original draft preparation: Rabeea Habeeb; Writing-reviewing and editing: Muhammed Abdul Rafeeq Karuvally Ummer\u0026amp; Jobiraj Thayyullathil; Resources: Jobiraj Thayyullathil; Supervision: Muhammed Abdul Rafeeq Karuvally Ummer.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbudulai M, Nboyine JA, Quandahor P, Seidu A, Traore F (2022) Agricultural intensification causes decline in insect biodiversity. In: Hamadttu Abdel Farag El-Shafie (ed) Global Decline of Insects. https://doi.org/10.5772/intechopen.101360\u003c/li\u003e\n\u003cli\u003eArena M, Sgolastra F (2014) A meta-analysis comparing the sensitivity of bees to pesticides. Ecotoxicology 23(3): 324\u0026ndash;334. https://doi.org/10.1007/s10646-014-1190-1\u003c/li\u003e\n\u003cli\u003eBalfour NJ, Ratnieks FLW (2022) The disproportionate value of \u0026lsquo;weeds\u0026rsquo; to pollinators and biodiversity. Journal of Applied Ecology 59(5): 1209\u0026ndash;1218. https://doi.org/10.1111/1365-2664.14132\u003c/li\u003e\n\u003cli\u003eBambaradeniya CNB, Edirisinghe JP, de Silva DN, Gunatilleke CVS, Ranawana KB, Wijekoon S (2004) Biodiversity associated with an irrigated rice agro-ecosystem in Sri Lanka. 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Journal of Geography, Environment and Earth Science International, 26(6), 28\u0026ndash;38. https://doi.org/10.9734/jgeesi/2022/v26i630355\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Kole wetlands, bee diversity, pollinator decline, pesticide impact, non-crop habitats","lastPublishedDoi":"10.21203/rs.3.rs-5266316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5266316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of pesticide use on bee diversity and the potential role of non-crop habitats in mitigating these effects in the Kole paddy wetlands, a Ramsar site in Kerala, South-West India. Bee populations were sampled over two years, in six pesticide treated and six non-treated control fields, along with adjacent bunds as non-crop habitats. A total of 173 bees representing 10 species across two families\u0026mdash;Halictidae and Apidae\u0026mdash;were collected. Species richness and Shannon diversity, were consistently lower in pesticide-treated fields compared to control fields. Non-Metric Multidimensional Scaling (NMDS) showed a distinct clustering of treated sites, indicating homogenized bee communities dominated by species such as \u003cem\u003eTetragonula\u003c/em\u003e sp. and \u003cem\u003eApis florea\u003c/em\u003e. In contrast, species like \u003cem\u003eHalictus\u003c/em\u003e sp. were less common in treated fields. The bunds with dense vegetation, adjacent to treated fields showed a positive correlation with bee diversity, suggesting these areas act as refugia against pesticide exposure. Pearson correlation analysis revealed a significant positive relationship (r\u0026thinsp;=\u0026thinsp;0.8389, p\u0026thinsp;=\u0026thinsp;0.0369) between the diversity of treated fields and their adjacent non-crop habitats. Our findings signify the need for integrated pest management strategies that reduce pesticide use and promote the conservation of non-crop habitats, such as bunds to support pollinator populations, thereby ensuring the overall health and functioning of Kole paddy wetlands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Balancing agriculture and conservation in the Ramsar-listed Kole paddy wetlands: The bee diversity and role of non- crop vegetation amid pesticide use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 06:26:37","doi":"10.21203/rs.3.rs-5266316/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":"dc0c0ef2-b711-4fad-871f-ea973a9a19c0","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T20:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 06:26:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5266316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5266316","identity":"rs-5266316","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}