Variation in local communities of insect pollinators in different land-use types in Northeastern Thailand

Variation in local communities of insect pollinators in different land-use types in Northeastern Thailand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Variation in local communities of insect pollinators in different land-use types in Northeastern Thailand Kornkanok Wongwila, Thotsapol Chaianunporn, Nakorn Pradit, Wangworn Sankamethawee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6494624/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Variations in land-use types reflect different levels of human activity, which can affect local biodiversity. We examined how land-use types influenced the composition of local insect communities. We analyzed the spatial and temporal patterns of four key pollinating insect groups: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (CDHL) across four land-use types: agricultural land, abandoned land, urban area, and a forest patch in Khon Kaen, northeastern Thailand, over three seasons. The forest patch supported the highest diversity and species richness of CDHL, whereas the abandoned land supported the highest insect abundance. Species turnover was more pronounced between land-use types than seasons and the urban area had the highest seasonal variation. Lepidoptera were the most abundant group in both space and time, contributing to 76% of all CDHL records. Hymenoptera, Coleoptera, and Diptera comprised 15.75%, 5.74% and 2.52% of the records, respectively. Apis florea was the most abundant bee, but A. cerana and A. dorsata showed considerably low numbers. Diversity and abundance of ground flowers were significantly correlated with insect abundance. Air temperature, humidity, and species richness of trees appeared to affect the overall abundance and distribution at the family level of CDHL. Implications for insect conservation The data provide a comprehensive understanding of the dynamics of pollinator communities in human-dominated landscapes. The results suggest a negative effect of urbanization on pollinating insect communities and highlight the importance of conserving both natural and human-modified green mosaics that maintain ecological connectivity across landscape matrices. Agroecology Conservation Biology Terrestrial Ecology biodiversity land-use gradient spatiotemporal variation tropical insects urbanization Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The global decline of pollinating insects has emerged as a significant environmental crisis (Kearns et al. 1998 ; Pott et al. 2010; Tangtorwongsakul et al. 2018 ; Wagner 2020 ). This decline, marked by reductions in both species richness and abundance, is driven by multiple factors, including agrochemical use (Kevan and Viana 2003 ; Ghazoul 2005 ; Newbold et al. 2015 ; Ganuza et al. 2022 ), climate change (Hostetler and McIntyre 2001 ; Pott et al. 2010; Herrera 2019 ), habitat degradation and fragmentation (Sánchez-Bayo and Wyckhuys 2019 ), and the spread of invasive species (Bartomeus et al. 2008 ; Chitchak et al. 2024 ). Changes in land use patterns have also played a significant role in biodiversity loss (Schulze et al. 2004 ; McKinney 2008 ; Newbold et al. 2015 ). Numerous studies highlight the negative impacts of land-use changes and urbanization on the diversity, abundance, and community structure of pollinating insects (e.g., Ahrné et al. 2009; Rader et al. 2014; Lagucki et al. 2017 ; Ganuza et al. 2022 ). These impacts are often characterized by the dominance of generalist species, which are better adapted to a broader range of habitat conditions and tend to replace more specialized, less common species (Bates et al. 2011 ; Sánchez-Bayo and Wyckhuys 2019 ). This shift in community composition reflects a process of biotic homogenization, where ecological communities become increasingly similar due to the widespread replacement of specialist species by generalists (Clavel et al. 2011 ). However, insufficient data on the spatial and temporal dynamics of natural pollinator communities hinder the effective management of landscapes and the restoration of degraded ecosystems (Kevan and Viana 2003 ). Insect pollinators and their responses to land-use changes have been widely studied in Western countries (Newbold et al. 2015 ; Sánchez-Bayo and Wyckhuys 2019 ; Wagner 2020 ; Millard et al. 2021 ), while studies in tropical regions have been more focused on Neotropical ecosystems (Newbold et al. 2015 ; Millard et al. 2021 ). In Thailand, however, little research has been conducted on the impacts of land-use change on insect pollinator communities, despite the country being one of the largest users of agrochemicals globally (Walter-Echols and Yongfan 2005 ; Tawatsin et al. 2015 ). Agrochemical exposure, especially in agricultural areas, is presumed to have a negative impact on bee populations, as evidence from other regions suggests that pesticide use significantly affects pollinator abundance and biodiversity (Klein et al. 2007 ; Tangtorwongsakul et al. 2018 ; Millard et al. 2021 ; Ganuza et al. 2022 ). However, the specific effects in Thailand’s tropical landscapes remain largely unexplored. To address this knowledge gap in Northeastern Thailand, we aimed to investigate the diversity, abundance, and community composition of four key pollinating insect groups: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (hereafter CDHL) across different land-use types and seasons. We examined how these insect communities vary across varied land-use conditions and seasonal shifts, with a focus on uncovering the spatiotemporal dynamics driving pollinator distributions in human-modified landscapes. Given the central ecological role of bees (Kearns et al. 1998 ; Lee et al. 2001 ; Kevan and Viana 2003 ; Millard et al. 2021 ; Huang et al. 2022 ), especially native honeybee species ( Apis spp.) which are in global decline, we paid attention to their responses to land-use changes. In addition, we examined how the CDHL pollinators responded to flower abundance and broader environmental gradients to gain deeper insight into the ecological mechanisms influencing pollinator persistence. By establishing baseline data for these communities, our study offers a valuable reference for evaluating the impacts of anthropogenic pressures on ecosystem health. Importantly, we also emphasize the often-overlooked value of green wastelands as potential refuges for pollinators, highlighting their role in maintaining biodiversity in increasingly urbanized environments. MATERIAL AND METHODS Study areas The study was conducted in Khon Kaen Province, Northeastern Thailand. We focused on four different land-use types that were accessible 1) agricultural land, 2) abandoned land, 3) urban area, and 4) a forest patch. The urban area is located inside Khon Kaen City and is separated from the other three areas by the city ring road (Route 260). The agricultural and abandoned lands were located beyond a 2-km radius from the city ring road (buffer zone) to avoid the possible movement of insects between zones. Both agricultural and abandoned lands were located within a 7-km radius of the buffer zone (Fig. 1 ). The agricultural lands consisted primarily of active rice farming (83.3%) during July-November for rain-fed farming and February-May for irrigated farming while cassava plantations (16.6%) were active throughout the year. Most farmlands were bordered by field-edge vegetation or hedgerows, while the rice paddies were enclosed by walking dikes covered with diverse short ground flora. The abandoned lands (either public or private) were designated as wastelands that had been deserted for human use and dominated by meadows, overgrown grass, climbers, shrubs and scattered shrubby trees. The forest patch is a small wooded area (1.13 km 2 ) called Khok Phutaka located in Wiang Kao District (16° 38' N, 102° 17' E) which is 79.6 km away from Khon Kaen City. This forest patch is under the protection of the Plant Genetics Conservation Area under the Royal Initiative of Her Royal Highness Princess Maha Chakri Sirindhon. This forest consists of secondary growth deciduous dipterocarp forest on sandstone bedrock. It is mixed with small mosaics of seasonal evergreen vegetation along a seasonally running stream. The forest is dominated by dense, small regenerating trees (1,007 trees/ha with a 10–15 m canopy height), and had the lowest human activity and least disturbance among the four land-use types studied. Transects and insect sampling Prior to field data sampling, we located 100, 100, and 80 accessible points in the abandoned lands, agricultural lands, and urban area, respectively. Thirty of the 100 x 5 m belt transects were randomly selected from these points in each of these three land-use types with each transects placed at least 200 m apart (Fig. 1 ). In the forest patch, 30 belt transects were systematically placed at least 50 m apart due to the limited size of the forest area (Fig. 1 ). The large rock outcrops (light blue line in the lower left map) and the very dense thorny vegetation area on the right-hand side of the rocks were eliminated. We applied the Pollard walk method (Pollard 1977 ) for insect sampling within the 5-m width and 5-m height along the 100 m belt transects. We spent approximately 15–20 minutes per transect between 09:00–16:00 on non-rainy days. The same sampling protocols were repeated over three seasons: the late rainy season (August-October 2020), the dry season (January-March 2021), and the early rainy season (May-June 2021) resulting in a total of 360 transects in four land-use types. The cumulative rainfall of each season (only during the sampling months) provided by the Upper Northeastern Meteorological Center (Thai Meteorological Department, 2024 ) was 724.2 mm, 124.1 mm, and 264.1 mm for the late rainy, dry, and early rainy seasons, respectively (Figure S1). We collected data on four important orders of insect pollinators, namely Coleoptera, Diptera, Hymenoptera, and Lepidoptera (Kevan and Baker 1983 ; Herrera 2019 ; Millard et al. 2021 ). Common and well-known species were counted and identified into morphotypes during the field surveys without trapping, while the unknown species were collected with sweep nets and preserved in 70% ethanol for identification to the lowest possible taxonomic level, following specialized literature on local insects (Suwannapak 2016 ), bees and wasps (Barthelemy 2021 ; Michener 2000 ), or butterflies (Ek-Amnuay 2012; Kimura et al. 2011 ; 2014 ; 2016 ). Environmental variables Four physical and five biological variables were measured during insect sampling (Table S1). The physical variables were air temperature (Temp), air humidity (HD), light intensity (LG), and canopy cover (CC) which were measured at distances of 0, 50, and 100 m along each transect. The CC of each transect was estimated using photographs taken from a vertical camera position. Three images per transect were analyzed for percentage of canopy cover using the ImageJ software (version 1.53i) (Rasband 1997 ). Biological variables were classified into two main groups: (1) Ground flora (forbs, herbaceous vines, and shrubs) with only species that were in flower were recorded (GFsp). Flower cover (GFL) was visually assessed using an ordinal score from 1 to 4, where 1 = < 25%, 2 = 25–50%, 3 = 51–75%, and 4 = 76–100%. (2) Trees with a diameter at breast height (DBH) of at least 10 cm within the belt transect were identified to determine species richness (Tsp), and tree density (Tden) was recorded as the total number of trees per transect. The flower score of flowering trees (TFL) was assessed using the same ordinal scores as GFL, within the transect area (5 x 100 m 2 ). Data analysis The diversity indices of insects were calculated using the Shannon diversity index ( H’ ) for each land-use type (α diversity), Buzas and Gibson's evenness (E) of each community, gamma diversity (γ), calculated across the four combined land-use types, to represent the overall species diversity of the studied landscape. Global Whittaker beta-diversity (global-β) was calculated to access community turnover across all land-use types and three seasons. Pairwise comparisons of beta-diversities were conducted to evaluate dissimilarity between insect communities across land-use types and seasons. Shannon diversity t-tests were performed for pairs of H’ values to compare if the species diversity of insects differed between land-use types and the same test was done between seasons. All diversity indices, beta-diversity comparisons, and t-tests were also conducted for flowering plant communities. Rényi diversity profiles were calculated, where α = 0 gives the total number of species present, α = 1 gives an index to the relative abundance of species (evenness), α = 2 gives the Simpson index, and higher α values indicate increasing dominance, with α approaching infinity corresponding to the Berger-Parker index. These diversity index analyses were performed using PAST software version 4.03 (Hammer et al. 2001 ). To examine whether insect abundance varied between different land-use types and across seasons, Kruskal-Wallis tests were applied. The same method was applied to assess flower abundance. If the Kruskal-Wallis tests indicated significant differences, pairwise comparisons were conducted using the Wilcoxon rank-sum test. The same tests were made for abundance data of three Apis bee species from all land-use types. Additionally, a Chi-square test was employed to determine whether the distribution of three Apis species differed across land-use types and seasons. All these analyses were performed using R software, version 4.0.2 (R Core Team 2020 ). We used ordinary least squares regression method in bivariate linear models to test the relationships between each of nine environmental variables (abundance of ground flora, species richness of ground flora, species richness of trees, tree density, flower abundance of trees, % canopy cover, temperature, humidity, and light intensity) (Table S1) and the abundance of CDHL. We first evaluated the effect of each environmental predictor across all land-use types combined, totaling nine models (Table S5). Then, we examined these relationships within each land-use type separately, resulting in 36 models (Table S6). These linear models were tested in PAST software version 4.03 (Hammer et al. 2001 ). To visualize the associations between CDHL families and nine environmental variables, we used canonical correspondence analysis (CCA) in program R version 4.0.2 (R Core Team 2020 ) using package ‘vegan’ (Oksanen et al. 2018 ). These nine environmental variables were tested with the abundance of each insect family. We used function ‘ordistep’ for stepwise model selection using a permutation test with 999 permutations, applying AIC as the criterion for model selection to identify the environmental variables that most explained insect distribution and removed irrelevant variables. Environmental variables with p-values > 0.05 were eliminated, and the significantly related variables with p-value < 0.05 (Fig. 5 ), were further tested using the ‘anova.cca’ function with 999 permutation tests to assess the overall model and each explanatory predictor (Oksanen, 2015 ). The signs and relative magnitudes of the canonical coefficients indicate the relative contribution of each predictor to the ordination axes (Palmer 1993 ; ter Braak 1986 ). RESULTS Pollinating insect communities From August 2020 to June 2021, a total of 11,063 individual insects of 108 morphospecies (23% unknown species) were recorded across all four land-use types (Table S2). These insects belonged to five families of Coleoptera, four families of Diptera, five families of Hymenoptera, and nine families of Lepidoptera (Table S2). The gamma diversity ( γ ) of CDHL across our four habitats at the landscape scale was 3.54 and the evenness index was 0.29. The highest abundance of CDHL was found in abandoned land, followed by agricultural land, forest and urban area (Figure S2). Species richness was highest in the forest, followed by urban areas, agricultural land, and abandoned lands. Community evenness indices ( E ’) were relatively similar for all land-use types (Table 1 ). The species diversity (Shannon H’ diversity) was highest in the forest, followed by agricultural land, abandoned land, and urban area. Pairwise comparisons (diversity t-test) showed that the forest was significantly more diverse than the urban area ( t = 5.79, p < 0.001) and abandoned land ( t = -6.67, p < 0.001), but not significantly different from the agricultural land. The beta-diversity indices showed that the insect communities in the urban area and the forest were the most distinctive (β = 0.34), while agricultural and abandoned lands were relatively similar (β = 0.15) (Table 1 ). The forest hosted the highest number of unique species that were not found in other land-use types (Table S2). Table 1 Summary of diversity parameters, pairwise comparisons between land-use types with diversity t-test (t) and Whitaker beta-diversity (β). Community and Land-use type Number of Species H’ E ’ Comparison of diversity t-test ( t ) and beta diversity index (β) between land-use types AB AG FO UR CDHL AB 65 3.14 0.35 - t = -6.46* t = -6.67* t = -0.13 AG 69 3.30 0.39 β = 0.15 - t = -0.79 t = 5.70* FO 83 3.35 0.34 β = 0.28 β = 0.27 - t = 5.79* UR 71 3.12 0.32 β = 0.20 β = 0.18 β = 0.34 - Total 108 γ = 3.53 0.29 Plant in flower AB 58 3.19 0.42 - t = -4.77** t = -2.76** t = -2.49* AG 76 3.50 0.43 β = 0.35 - t = 1.39 t = 1.95 FO 60 3.40 0.49 β = 0.70 β = 0.70 - t = 0.41 UR 55 3.37 0.52 β = 0.41 β = 0.49 β = 0.79 - Total 143 γ = 3.93 0.35 AB abandoned land, AG agricultural land, FO forest patch, UR urban area H' Shanon-Weiner diversity index as alpha diversity, E' Buzas & Gibson's evenness index, γ gramma diversity for all land-use types. t Shannon diversity t-test, β Whitaker beta-diversity. ** indicates significant differences at p < 0.01, and * indicates p < 0.05 Lepidoptera were the most abundant group across all land-use types and all seasons (Figure S2), making up 76% of all CDHL records. Hymenoptera, Coleoptera, and Diptera contributed 15.75%, 5.74% and 2.52%, respectively. The Common Emigrant ( Catopsilia pomona ) was the most common butterfly accounting for 23.14% of all lepidopterans. Hymenopterans showed a higher abundance in the urban and abandoned areas, but were least abundant in the forest (Table S2). Coleopterans and dipterans were most abundant in agricultural lands (Table S2; Figure S2). Coleopterans were more diverse in the urban area but less diverse in the forest (Fig. 2 a). The composition of dipterans, hymenopterans, and lepidopterans was relatively similar across all the land-use types (Fig. 2 b, c, d). The hymenopteran community had the highest evenness in the forest and lowest in the urban area (Fig. 2 c). The lepidopteran community in the forest had the highest species richness (61 morphospecies), but exhibited the lowest evenness index (Fig. 2 d). Flowering plant communities We recorded a total of 143 ground flora species in flower (Table 1 ) and 21 tree species in flower across all four land-use types (Table S3 & S4). The species richness of ground flowering plants was highest in agricultural land, followed by forest, abandoned land, and urban areas. The H ’ diversity index was highest in agricultural land, followed by forest, urban areas, and abandoned land. Abandoned lands had the significantly lowest species diversity (Table 1 ). The beta-diversity showed that plant communities in flower in abandoned land, agricultural land, and urban area were relatively similar (β = 0.35–0.49), but the forest had a distinctly different species composition (β = 0.70–0.79). The most diverse flower families were Fabaceae (34 species), Malvaceae (15 species), Asteraceae (13 species), Convolvulaceae (9 species), Euphorbiaceae (9 species), and Rubiaceae (8 species). Asteraceae contributed the highest flower score in all areas (28.34%) while Fabaceae contributed 15.33%. The most common flowering species were Praxelis clematidea, Gomphrena celosioides, Tridax procumbens , and Mimosa pudica , contributing 14.22%, 7.24%, 6.71%, and 5.11% to the total flower score (GFL), respectively. The invasive plant P. clematidea also contributed the highest flower score on the forest floor, while other invasive flowers were rare. Spatiotemporal variation of CDHL During the late rainy season (August-October), when the rainfall was highest, the CDHL exhibited the highest species richness (99 morphospecies) across all land-use types. However, abundance was higher during the early rainy season (May–June). The H ’ diversity of the CDHL during the late rainy season, dry season, and early rainy season was 3.46, 3.20, and 3.34, respectively (Table 2 ). These H ’ diversity indices differed significantly between seasons, with all pairwise comparisons in diversity t-tests showing p < 0.001 for all pairs. The beta-diversity analysis showed that species turnover of CDHL between seasons was highest in the urban area (global β = 0.44), while the late rainy season showed the highest species turnover among land-use types (β = 0.78) (Table 2 A). Pairwise comparisons of beta-diversity between seasons (Table 2 B) for each land-use type showed that the urban area had the highest species turnover between the late rainy season and the dry season (β = 0.35), while the abandoned area, agricultural area, and forest had relatively low species turnover between seasons (Table 2 B). On the other hand, species turnover was highest between the forest and urban areas in the late rainy season (β = 0.46) and lowest between agricultural area and the urban area (β = 0.16) during the early rainy season (Table 2 C). Table 2 (A): Summary of spatiotemporal variation in CDHL communities based on α-diversity (Shannon H ’ diversity index) among land-use types and three seasons. Numbers in parentheses indicate the number of taxa. The global β-diversity values (italicized) are provided for species turnover between land-use types (last column) and among seasons (last row). (B): Pairwise β-diversity comparisons between seasons for each land-use type (β in the last column); and (C): pairwise β-diversity comparisons between land-use types for each season. (A) H’ index Global β diversities (among habitats) AB AG FO UR Late rainy 3.09 (50) 3.09 (47) 3.31 (75) 3.13 (51) 0.78 Dry 2.74 (39) 2.95 (41) 3.02 (51) 2.77 (45) 0.65 Early rainy 3.02 (54) 3.02 (58) 3.05 (52) 2.95 (52) 0.50 Global β diversities (among seasons) 0.36 0.42 0.39 0.44 (B) AB AG FO UR Late rainy - Dry 0.24 0.25 0.25 0.35 Dry - Early rainy 0.25 0.28 0.28 0.32 Early rainy - Late rainy 0.23 0.27 0.26 0.20 (C) Late rainy Dry Early rainy AB - AG 0.20 0.20 0.19 AB - FO 0.37 0.38 0.38 AB - UR 0.35 0.29 0.21 AG- FO 0.43 0.37 0.36 AG - UR 0.33 0.30 0.16 UR - FO 0.46 0.38 0.42 AB abandoned land, AG agricultural land, FO forest patch, UR urban area H' Shanon diversity index for alpha diversity (α) of each community, γ gramma diversity index for four land-use types. t Shannon diversity t-test, β Whitaker beta-diversity. The Wilcoxon rank-sum test showed that CDHL insect communities were more varied between seasons in most areas, except for agricultural land, which had relatively similar abundance levels among seasons (Fig. 3 ). Flower abundance varied significantly among seasons in the forest, while the urban area showed no difference between seasons. A similar pattern was observed in both abandoned and agricultural lands, where the dry and early rainy seasons had comparable levels, but both were higher in the late rainy season (Fig. 3 ). Honeybees composition There were three species of honeybees observed in this study: the dwarf honeybee ( Apis florea ), the giant honeybee ( A. dorsata ), and the Asian honeybee ( A. cerana ). The most abundant species was A. florea (37.8% of all hymenopterans) contributing 51% of hymenopteran detections in the urban area (Figure S2). Chi-square tests showed that the proportion of the three Apis bees differed among land-use types (χ 2 = 33.69, p < 0.001) and seasons (χ 2 = 37.43, p < 0.0001) (Figure S3). Apis bees also responded differently to land-use types across seasons. The urban area and abandoned lands supported the highest abundance of all Apis bees in the early rainy season, especially A. florea. However, during the dry season, they were more abundant in agricultural land. The forest had the lowest number of Apis bees, but A. florea and A. dorsata were detected more frequently during the dry season. A. cerana was not detected in the forest at all. CDHL abundance and environmental variables There were two biotic variables that positively influenced the overall pooled insect abundance from all habitats, including: 1) ground flowers score (coefficient = 2.16 ± 0.41, p < 0.001) that explains about 19% of the variance, and 2) species richness of ground flowers (coefficient = 3.35 ± 0.87, p < 0.001) that explains about 11% of the variance (Fig. 4 ; Table S5). Two abiotic variables moderately influenced the CDHL abundance, including: 1) percent canopy cover that had a negative relationship (coefficient = -0.43 ± 0.14, p < 0.01) that explains approximately 7% of the variance, and 2) the log of light intensity had a significantly positive effect (coefficient = 39.00 ± 13.58, p < 0.01) and explain about 6.5% of the variance (Fig. 4 ; Table S5). When the relationships between those nine environmental variables and insect abundance were tested separately for each land-use type, the same two biotic variables were significantly correlated in three land-use types, except the urban area (Table S6). The influence of ground flower score (coefficient = 2.67 ± 0.85, p < 0.01) and species richness of ground flowers (coefficient = 3.71 ± 1.23, p < 0.01) were strongest in the forest area, explaining about 26% and 24% of the variance, respectively. None of the physical variables showed a significant impact on insect abundance when land-use types were analyzed separately (Table S6). CDHL distribution and environmental variables When environmental variables were tested with insect families, the non-significant variables (p > 0.05: GFsp, GFL, TFL, CC, Tden, and LG) were removed from the final canonical correspondence analysis (CCA). The final CCA showed that the distribution patterns of insect families were explained by the species richness of trees (Tsp) (p = 0.001), air humidity (HD) (p = 0.029), and air temperature (Temp) (p = 0.096) and these variables together explained only 7.51% of the variance in the distribution of each insect family (sum of all constrained eigenvalues = 0.103, total inertia = 1.364; pseudo-F = 3.140, p = 0.001 for the whole model) (Fig. 5 ). The biplot (Fig. 5 a) shows that these four land-use types shared relatively similar environmental variables, except that some locations in agricultural land had relatively high temperatures. Moreover, the four land-use types shared most of the insect families (Fig. 5 b). The long-legged flies (Dolichopodidae) were more likely to be found in warmer areas, while the beetle families Epilachnidae, Buprestidae and Curculionidae occurred in habitats with lower temperatures. Flies in the family Muscidae, skippers (Hesperiidae), and moths (Uraniidae and Sphingidae) were found in areas of higher tree species richness and greater air humidity (Fig. 5 b), where these two variables were significantly correlated (r 2 = 0.162, p < 0.01). DISCUSSION Pollinating insect communities This study investigated the variations in local insect communities, focusing on the major orders recognized as the most common global pollinators: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (CDHL) across four land-use types in Northeastern Thailand: agricultural land, abandoned land, urban area, and a forest patch. The results show that CDHL communities were more diverse and had higher species richness in the forest area which also had the highest vegetation density and the lowest disturbance level. The lowest insect abundance, as well as the lowest species diversity and lowest evenness, found in the urban area, suggests a negative effect of urbanization on the species diversity and community of pollinating insects (McKinney 2008 ; Ahrne et al. 2009 ; Bates et al. 2011 ; Ganuza et al. 2022 ). Our results (Table 2 A) show that beta-diversities varied between land-use types but were quite similar among seasons. This was probably due to the relatively similar climate conditions in the tropics that result in a slow turnover of insect communities (Novotny et al. 2007 ), while vegetation structure varied widely among disturbed and undisturbed areas, especially in the forest, which had a significantly higher tree density and a more heterogeneous structure. Regarding habitat specialization, there seemed to be more forest-specialist butterflies (21 species; Table S2), while most of the CDHL species were generalists. Lepidopterans are the best-studied insects in Southeast Asia (Corlett 2004 ), and we found them to be the most abundant group, with the highest species richness in all land-use types across all seasons. This was most obvious in the forest, which hosted the most diverse assemblages of butterflies and moths, suggesting that higher plant diversity and greater structural complexity of the forest support both specialist and generalist butterflies (Takkis et al. 2018 ). Although they seem to be less important pollinators in tropical Asia compared to the Neotropics (Corlett 2004 ), this statement may be biased due to the limited number of ecological reports from the Asian tropics (Newbold et al. 2015 ). Coleopterans and Dipterans contributed very little to insect abundance in our study. Corlett ( 2004 ) noted that they were less common as pollinators compared to Hymenopterans and Lepidopterans in Asian dry tropical habitats. For Coleopterans, we found that the Coccinellidae family was the most common beetle family in rice fields, particularly the species Micraspis discolor . They are generally reported as key aphid predators in rice fields and as occasional flower pollinators (Shanker et al. 2012), but probably play a less important role as pollinators. Dipterans were significantly less common (only 2.52% of all CDHL abundance) in our study. We found only four species of important pollinating hoverflies (family Syrphidae), and only 12% were detected in the forest, while 80% were in agricultural and abandoned lands. Direct studies on the importance and population trends of hoverflies in Asia are very limited, but existing information from temperate regions highlights their important role in pollination services while also documenting serious population declines (Hallmann et al. 2021 ; Barendregt et al. 2022 ). It would be worthwhile to investigate how these insects contribute to pollination services (Chitchak et al. 2024 ) and to monitor population trends in various habitats in this under-studied Asian region. In this study, we expected that overall hymenopterans would be more abundant, as reported in many studies (e.g. Lee et al. 2001 ; Stewart et al. 2018 ; Ganuza et al. 2022 ; Wongwila and Sankamethawee 2023), but we found that they were more common in urban areas and abandoned lands. Lower detection of bees in the understory of the forest may be biased due to the complexity of vegetation structure, as mentioned by Lee et al. ( 2001 ) and Ulyshen et al. ( 2010 ). In the forest, we observed numerous bees and wasps, visiting flowering trees Bridelia retusa , Millettia brandisiana , and the scandent shrub Ziziphus oenopolia at heights above 6 m, which was beyond our survey limit of 5 m. Therefore, the lower flower abundance in the understory layer of the forest could have influenced foraging bees, causing them to concentrate on canopy flowers (e.g. Ulyshen et al. 2010 ; Stewart et al. 2018 ; Tommasi et al. 2021 ). Thus, including higher strata in surveys could fully characterize local bees and other pollinator communities in forests (Cunningham-Minnick et al. 2024 ). Although the abundance of Hymenopterans in our forest area was lower than in the urban area, the H ' index for Hymenopterans clearly indicates that the forest sustains the highest species diversity among all habitats (Table S2). This suggests that the forest patch in our study generally supports the greatest diversity of the most important pollinator group. Another limitation of our sampling effort in the forest area was the small forest size, which could result in relatively similar environmental variables among transects. It would be valuable to investigate how forest size influences pollinator communities, providing key insights into the impact of forest patch size on insect populations in surrounding agricultural areas. Apis bees are important pollinators in non-forest habitats on a global scale (Klein et al. 2007 ; Millard et al. 2021 ) and in the Indomalayan region (Corlett 2004 ). A. florea was the most abundant bee in all land-use types, especially in urban and abandoned areas, which is consistent with other studies in urban-related landscapes (Tangtorwongsakul et al. 2018 ; Simla et al. 2022 ; Wongwila and Sankamethawee 2023). This suggests their resilience in human-dominated environments (Stewart et al. 2018 ; Hsu et al. 2022 ). On the other hand, abandoned and urban areas often provide continuous floral resources that can support foraging bees (McFrederick and LeBuhn, 2006 ; Prendergast et al. 2022 ). Our transects in open, abandoned lands, which had a higher abundance of bees, were mostly covered with Waltheria indica , and Sida acuta (both Malvaceae), suggesting that social bees benefit from flower abundance, and that there may be fewer predators (Lagucki et al. 2017 ), which may reduce competition for resources (Cunningham-Minnick et al. 2020). We observed many A. florea and Xylocopa aestuans on a transect that passed through a Crotalaria juncea plantation in the rice fields during the dry season. This plant has been grown widely in Thailand after rice harvesting as a nitrogen-fixing legume (Wanapat et al. 2021 ) and for weed control (Bundit et al. 2021 ). This species could be an important food source for foraging bees during seasonal resource shortages in farmlands (Meagher et al. 2019 ). Promoting the cultivation of C. juncea by local farmers not only enhances soil quality but also boosts ecotourism by attracting visitors to cafés and restaurants near the flower plantations (Sankamethawee, pers. comm.). This practice can bring agricultural, economic, and ecological benefits. Our study reveals an alarming trend of extremely low numbers of A. cerana and A. dorsata which aligns with previous reports indicating a serious decline in the populations of Asian honeybees (Theisen-Jones and Bienefeld 2017). Tracking population fluctuations of pollinating bees across diverse landscapes is essential for developing effective conservation and management strategies (Schindler et al. 2013 ; Samraj and Agnihotri 2021 ). This is particularly important when considering potential interactions with non-native species like the Western honeybee and numerous invasive flowering plants (Chitchak et al. 2024 ). Pooling data from similar vegetation structures (such as abandoned agricultural lands and urban wastelands) is recommended to enable more robust comparisons with forested and active cultivated areas. This could provide a better picture of how the communities of pollinating insects respond to disturbance gradients including factors like agrochemical use (Hahn et al. 2015 ), the abundance of invasive species (Cunningham-Minnick et al. 2024 ), and the intensity of urbanization (Lagucki et al. 2017 ). Seasonality and environmental variables The results from CCA revealed that the distribution of each CDHL family had no significant relationship with most of the measured environmental variables. One possible explanation is that the environmental conditions across our studied habitats, particularly in abandoned lands and urban areas, were relatively similar. Additionally, the forest patch was surrounded by farmland which could potentially limit environmental contrast. It is also possible that other unmeasured landscape structures (Rahimi et al 2021), vegetation composition (Lajos et al 2021 ), historical land-use intensity (Cusser et al 2018 ) may also play an important role in shaping insect communities. The findings demonstrate that the communities of CDHL varied spatially and seasonally (Fig. 3 ). The early rainy season (May-June) supported a higher abundance of CDHL in all areas except the forest. The peak of CDHL abundance in the forest was during the late rainy season, while a higher number of hymenopterans were found in the dry season (January-March). This pattern can probably be explained by the flowering period of trees in the dry deciduous forests, which produce flowers in the dry season, such as Xylia xylocarpa , Pentacme siamensis, Millettia brandisiana , and Ellipanthus tomentosus. Rainy periods and seasonality have been reported to be the main environmental factors shaping the insect communities (Fontanarrosa et al. 2009 ; Castro and Espinosa 2015 ). Understanding the seasonal community structure of insects in the tropics is necessary for conservation efforts (Castro and Espinosa 2015 ). This knowledge can provide information on insect communities that respond to changing environments over time, and can help land managers target appropriate species in the right habitats at the right time of year. Flowering plant community and the impacts of invasive ground flora The community of ground flowering plants in the forest showed more variation between dry and wet seasons compared to the relatively stable flower availability in urban areas. Notably, the invasive species Praxelis clematidea (Gardner and Williges 2015 ; Ullah et al. 2024 ; Salgado et al. 2024 ) was the most abundant ground-flowering plant across all surveyed habitats, including the forest floor. Its dominance in both disturbed and undisturbed areas underscores its aggressive spread and ecological competitiveness, positioning it as a significant invasive threat that may alter native plant communities and ecosystem dynamics, if left unmanaged. The high abundance of invasive flowering species generally attracts more generalist pollinators (Bartomeus et al. 2008 ; Ojija et al. 2019 ), and they are considered a major cause of pollinator diversity loss (Kearns et al. 1998 ). In particular, P. clematidea indirectly influenced the structure of pollinator communities by acting as a peripheral species within pollination networks (Simla et al. 2022 ), which may attract generalist pollinators (Wongwila and Sankamethawee 2024) and impact the turnover of pollinating insect species (Bartomeus et al. 2008 ). Invasive plants may also compete with native flora, potentially leading to ecological shifts (Traveset and Richardson 2006 ; Vilà et al. 2011 ; Stout et al. 2017). Controlling invasive plants in more disturbed areas can be undertaken with less damage to local ecosystems, but this could be challenging in forest areas where ecological balance is more fragile (Foxcroft et al. 2017 ). Many findings showed that invasive plant species have an effect on a wide range of scales from individual organisms to populations and communities (Stout and Tiedeken 2017 ; Kovács-Hostyánszki et al. 2022 ). Monitoring the distribution and abundance of invasive plants is needed for a better understanding of how they alter ecosystem dynamics (e.g. Vilà et al. 2011 ) particularly their impacts on native plants (Kearns et al. 1998 ) and pollinating insect communities (Bartomeus et al. 2008 ; Chitchak et al. 2024 ). However, when certain pollinators benefit from invasive plants, managing human-dominated habitats for biodiversity conservation requires a careful evaluation of when, where, and how to control invasive plants that support the diversity of pollinating insects (Traveset and Richardson 2006 ; Stout and Tiedeken 2017 ). The importance of green wastelands and agricultural field-edges This study shows that wastelands, abandoned meadows, and field-edge vegetation, either in agricultural or urban areas play a significant role as refuges for pollinating insects (McFrederick and LeBuhn, 2006 ; Schwartz et al., 2013 ; Twerd et al. 2021 ). Preserving small forest remnants and growing field-edge flowers adjacent to agricultural landscapes can facilitate pollination services and help maintain crop yields (De Marco and Coelho 2004 ; Ricketts et al. 2008 ; Schindler et al. 2013 ). In 2020, Thailand implemented a Land and Building Tax Act, imposing significantly higher rates on unused land. This has led to the rapid clearance of wastelands as landowners sought to demonstrate productive land use, resulting in the destruction of natural habitats to alleviate the tax burdens (Phosri et al. 2021 ; Sridith 2022 ; Angkaew et al. 2023 ). A similar trend is ongoing with the conversion of wastelands into carbon-offset plantations of non-native trees, which may have resulted in negative impacts on natural ecosystems (Aguirre-Gutiérrez et al. 2023 ). Lowering wasteland taxes, along with promoting biodiversity credits, could help mitigate the clearance of abandoned green areas in urban and suburban landscapes. Thus, land management policies should prioritize protecting biodiversity conservation by preserving green areas (McFrederick and LeBuhn 2006 ; Schwartz et al. 2013 ; Twerd et al. 2021 ; Prendergast et al. 2022 ; Morpurgo et al. 2024 ). Our findings highlight the critical role that forest patches and abandoned green areas play in supporting biodiversity within increasingly fragmented and human-dominated landscapes. Rather than viewing these spaces as neglected or taxable burdens, land management policies should recognize their ecological value, particularly for pollinating insects. By improving our understanding of how these insects respond to different land-use gradients, we can better inform strategies that maintain and enhance biodiversity. Promoting the diversity of flowering plants within these green spaces can strengthen ecological connectivity between urban developments and natural ecosystems, supporting robust insect communities and contributing to overall ecosystem resilience. Therefore, conserving these green remnants in urban and surrounding landscapes should be a key component of sustainable land-use planning. Declarations Acknowledgements This work was supported by the Research Capability Enhancement Program through graduate student scholarship (K. Wongwila), Faculty of Science, Khon Kaen University. We sincerely thank the Division of Land Use Planning and Policy, Land Development Department for providing the land-use data, and the Upper Northeastern Meteorological Center (Thai Meteorological Department) for providing rainfall data. We gratefully thank to all those who helped with the field surveys. We thank Dr. Sawai Mattapha for helping with identification of Fabaceae and Pongsakorn Bouloy for insect identification. Finally, we thank Andrew Pierce for constructive comments and English proofreading on several versions of this manuscript. References Ahrne K, Bengtsson J, Elmqvist T (2009) Bumble bees ( Bombus spp) along a gradient of increasing urbanization. PLoS ONE 4(5):e5574. https://doi.org/10.1371/journal.pone.0005574 Angkaew R, Ngoprasert D, Powell LA, Round PD, Gale GA (2023) Variation in avian responses to rice intensification in the central plains of Thailand guides conservation opportunities. Biol Cons 286:110307. https://doi.org/10.1016/j.biocon.2023.110307 Aguirre-Gutiérrez J, Stevens N, Berenguer E (2023) Valuing the functionality of tropical ecosystems beyond carbon. Trends Ecol Evol 38 (12): 1109-1111 Barthelemy C (2021) Identification guide to the social wasps of Hong Kong (Hymenoptera; Vespidae). Hong Kong Bates AJ, Sadler JP, Fairbrass AJ, Falk SJ, Hale JD, Matthews TJ (2011) Changing bee and hoverfly pollinator assemblages along an urban-rural gradient. PLoS ONE 6(8):e23459. https://doi.org/10.1371/journal.pone.0023459 Barendregt A, Zeegers T, van Steenis W, Jongejans E (2022) Forest hoverfly community collapse: Abundance and species richness drop over four decades. Insect Conserv Divers 15(5):510–21. https://doi.org/10.1111/icad.12577 Bartomeus I, Vilà M, Santamaría L (2008) Contrasting effects of invasive plants in plant–pollinator networks. Oecologia 155:761–770. https://doi.org/10.1007/s00442-007-0946-1 Bundit A, Ostlie M, Prom-U-Thai C (2021) Sunn hemp ( Crotalaria juncea ) weed suppression and allelopathy at different timings. Biocontrol Sci Technol 31(7):694–704. https://doi.org/10.1080/09583157.2021.1881446 Castro A, Espinosa CI (2015) Seasonal diversity of butterflies and its relationship with woody-plant resources availability in an Ecuadorian tropical dry forest. Trop Conserv Sci 8(2):333–351. https://doi.org/10.1177/194008291500800205 Chitchak N, Hassa P, Traiperm P, Stewart AB (2024) Who pollinates exotic plants? A global assessment across native and exotic ranges. Glob Ecol Conserv 11:e03185. https://doi.org/10.1016/j.gecco.2024.e03185 Clavel J, Julliard R, Devictor V (2011) Worldwide decline of specialist species: toward a global functional homogenization?. Front. Ecol Environ 9(4):222-228. Corlett RT (2004) Flower visitors and pollination in the Oriental (Indomalayan) Region. Biol Rev Camb Philos Soc 79(3):497–532. https://doi.org/10.1017/S1464793103006341 Cunningham-Minnick MJ, Roberts HP, Milam J, King DI (2024) Sampling the understory, midstory, and canopy is necessary to fully characterize native bee communities of temperate forests and their dynamic environmental relationships. Front. Ecol. Evol 12:1352266. Cusser S, Neff JL, Jha S (2018) Land-use history drives contemporary pollinator community similarity. Landsc Ecol 33:1335-1351. De Marco P, Coelho FM (2004) Services performed by the ecosystem: forest remnants influence agricultural cultures' pollination and production. Biodivers Conserv 13:1245–1255. Ek-Ammuay P (2012) Butterflies of Thailand, 2nd edn. Baan Lae Suan, Bangkok Fontanarrosa MS, Collantes MB, Bachmann AO (2009) Seasonal patterns of the insect community structure in urban rain pools of temperate Argentina. J Insect Sci 9:10. https://doi.org/10.1673/031.009.1001 Foxcroft LC, Pyšek P, Richardson DM, Genovesi P, MacFadyen S (2017) Plant invasion science in protected areas: progress and priorities. Biol Invas 19(5):1353–1378. https://doi.org/10.1007/s10530-016-1367-z Ganuza C, Redlich S, Uhler J, Tobisch C, Rojas-Botero S, Peters MK et al (2022) Interactive effects of climate and land use on pollinator diversity differ among taxa and scales. Sci Adv 8(18):eabm9359. https://doi.org/10.1126/sciadv.abm9359 Gardner AG, Williges KA (2015) Praxelis clematidea (Asteraceae): a new plant invader of Florida. Southeast Nat 14: 21-27. https://doi.org/10.1656/058.014.0111 Ghazoul J (2005) Buzziness as usual? Questioning the global pollination crisis. Trends Ecol Evol 20(7):367–373. https://doi.org/10.1016/j.tree.2005.04.026 Hallmann CA, Ssymank A, Sorg M, de Kroon H, Jongejans E (2021) Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community. PNAS 118(2):e2002554117. https://doi.org/10.1073/pnas.2002554117 Hammer Ø, Harper DA, Ryan PD (2001) PAST: Paleontological statistics software package for education and data analysis. Palaeontol Electron 4(1):1–9 Hahn M, Schotthӧfer, A, Schmitz J, Franke L, Brȕhl, C (2015) The effects of agrochemicals on Lepidoptera, with a focus on moths, and their pollination service in field margin habitats. Agr Ecosyst Environ 207:153–162. https://doi.org/10.1016/j.agee.2015.04.002 Herrera CM (2019) Increasing Pollinator Abundance in Undisturbed Mediterranean Montane Habitats over Recent Decades. Bull Ecol Soc Am 100(1): e01480. https://doi.org/10.1002/bes2.1480 Hostetler NE, McIntyre ME (2001) Effects of urban land use on pollinator (Hymenoptera: Apoidea) communities in a desert metropolis. Basic Appl Ecol 2(3):209–218. https://doi.org/10.1078/1439-1791-00051 Hsu PS, Wu TH, Tian JX, Sung IH (2022) Origins and invasion characteristics of the recently introduced dwarf honeybee Apis florea Fabricius, 1787 (Hymenoptera, Apidae) in Taiwan. Bioinvasions Rec 11(1): 124–135. https://doi.org/10.3391/bir.2022.11.1.13 Huang MJ, Hughes AC, Xu CY, Miao BG, Gao J, Peng YQ (2022) Mapping the changing distribution of two important pollinating giant honeybees across 21000 years. Global Ecol Conserv 39:e02282. https://doi.org/10.1016/j.gecco.2022.e02282 Kearns CA, Inouye DW, Waser NM (1998) Endangered Mutualisms: The Conservation of Plant-Pollinator Interactions. Annu Rev Ecol Syst 29:83–112. https://doi.org/10.1146/annurev.ecolsys.29.1.83 Kevan PG, Baker HG (1983) Insects as Flower Visitors and Pollinators. Ann Rev Entomol 28:407–453. https://doi.org/10.1146/annurev.en.28.010183.002203 Kevan PG, Viana BF (2003) The global decline of pollination services. Biodiversity 4(4):3–8. https://doi.org/10.1080/14888386.2003.9712703 Kimura Y, Aoki T, Yamaguchi S, et al (2011) The Butterflies of Thailand Vol 1: Hesperiidae, Papilionidae and Pieridae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan Kimura Y, Aoki T, Yamaguchi S, et al (2014) The Butterflies of Thailand Vol 2: Lycaenidae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan. Kimura Y, Aoki T, Yamaguchi S, et al (2016) The Butterflies of Thailand Vol 3: Nymphalidae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan Klein AM, Vaissière BE, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T (2007) Importance of pollinators in changing landscapes for world crops. P R Soc B 274:303–313. http://doi.org/10.1098/rspb.2006.3721 Kovács-Hostyánszki A, Szigeti V, Miholcsa Z, Sándor D, Soltész Z, Török E, Fenesi A (2022) Threats and benefits of invasive alien plant species on pollinators. Basic Appl Ecol 64:89–102. https://doi.org/10.1016/j.baae.2022.07.003 Lagucki E, Burdine JD, McCluney KE (2017) Urbanization alters communities of flying arthropods in parks and gardens of a medium-sized city.PeerJ 5:e3620. https://doi.org/10.7717/peerj.3620 Lajos K, Samu F, Bihaly ÁD, Fülöp D, Sárospataki M (2021) Landscape structure affects the sunflower visiting frequency of insect pollinators. Sci Rep 11(1):8147. Lee HL, Sodhi NS, Elmqvist T (2001) Bee diversity along a disturbance gradient in tropical lowland forests of south-east Asia. J Appl Ecol 38:180–192. https://doi.org/10.1046/j.1365-2664.2001.00582.x McFrederick QS, LeBuhn G (2006) Are urban parks refuges for bumble bees Bombus spp. (Hymenoptera: Apidae)? Biol Conserv 129(3):372–382. https://doi.org/10.1016/j.biocon.2005.11.004 McKinney ML (2008) Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst 11:161–176. https://doi.org/10.1007/s11252-007-0045-4 Meagher RL, Watrous KM, Fleischer SJ, Nagoshi RN, Brown JT, Bowers K, Miller N, Hight SD, Legaspi JC, Westbrook JK (2019) Documenting potential sunn hemp ( Crotalaria juncea L.)(Fabaceae) pollinators in Florida. Environ Entomol 48(2):343-350. https://doi.org/10.1093/ee/nvy190 Michener CD (2000) The bees of the world. The Johns Hopkins University Press, Baltimore, Maryland Millard J, Outhwaite CL, Kinnersley R, et al (2021) Global effects of land-use intensity on local pollinator biodiversity. Nat Commun 12:2902. https://doi.org/10.1038/s41467-021-23228-3 Morpurgo J, Huurdeman MA, Oostermeijer JG, Remme RP (2024) Vegetation density is the main driver of insect species richness and diversity in small private urban front gardens. Urban For Urban Green 101:128531. https://doi.org/10.1016/j.ufug.2024.128531 Newbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA et al (2015) Global effects of land use on local terrestrial biodiversity. Nature 520:45–50. https://doi.org/10.1038/nature14324 Novotny V, Miller SE, Hulcr J et al (2007) Low beta diversity of herbivorous insects in tropical forests. Nature 448:692–695. https://doi.org/10.1038/nature06021 Ojija F, Arnold SE, Treydte AC (2019) Impacts of alien invasive Parthenium hysterophorus on flower visitation by insects to co-flowering plants. Arthropod-Plant Interactions 13:719–34. https://doi.org/10.1007/s11829-019-09701-3 Oksanen J (2015) Multivariate analysis of ecological communities in R: Vegan tutorial. Waikato, New Zealand. https://www.mooreecology.com/uploads/2/4/2/1/24213970/vegantutor.pdf. Accessed 17 April 2022 Oksanen J, Blanchet FG, Friendly M et al. 2018. Vegan: Community ecology package. Vienna, Austria. https://CRAN.R-project.org/package=vegan. Accessed 7 February 2019 Palmer MW (1993) Putting Things in Even Better Order: The Advantages of Canonical Correspondence Analysis. Ecology 74(8):2215–2230. https://doi.org/10.2307/1939575 Phosri, K., Tantipisanuh, N, Chutipong W, Gore ML, Giordano AJ, Ngoprasert D (2021) Fishing cats in an anthropogenic landscape: A multi-method assessment of local population status and threats. Glob Ecol Conserv 27: e01615. https://doi.org/10.1016/j.gecco.2021.e01615 Pollard E (1977) A method for assessing changes in the abundance of butterflies. Biol Conserv 12(2):115–134. https://doi.org/10.1016/0006-3207(77)90065-9 Potts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, Kunin WE (2010) Global pollinator declines: trends, impacts and drivers. Trends Ecol Evol 25(6):345–353. https://doi.org/10.1016/j.tree.2010.01.007 Prendergast KS, Tomlinson S, Dixon KW, Bateman PW, Menz MH (2022) Urban native vegetation remnants support more diverse native bee communities than residential gardens in Australia's southwest biodiversity hotspot. Biol Conserv 265:109408. https://doi.org/10.1016/j.biocon.2021.109408 R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org/ Rader R, Cunningham SA, Howlett BG, Inouye DW (2020) Non-bee insects as visitors and pollinators of crops: Biology, ecology, and management. Annu Rev Entomol 65:391–407. https://doi.org/10.1146/annurev-ento-011019-025055 Rasband WS (1997) ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA. Available at: https://imagej.net/ij/ Ricketts TH, Regetz J, Steffan‐Dewenter I, Cunningham SA, Kremen C, Bogdanski A et al (2008) Landscape effects on crop pollination services: are there general patterns? Ecol Lett 11(5):499–515. https://doi.org/10.1111/j.1461-0248.2008.01157.x Salgado VG, Viera Barreto JN, Rodríguez-Cravero JF, Grossi MA, Gutiérrez DG (2024) Factors influencing the global invasion of the South American weedy species Praxelis clematidea (Asteraceae): a niche shift and modelling-based approach. Bot J Linn Soc: boae079. https://doi.org/10.1093/botlinnean/boae079 Samraj JM, Agnihotri M (2021) Impact of land use pattern and seasonality on butterfly diversity in a sub-tropical Terai arc landscape (TAL), Pantnagar, Uttarakhand (India). Trop Ecol 62(2):301–310. https://doi.org/10.1007/s42965-021-00150-9 Sánchez-Bayo F, Wyckhuys KA (2019) Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv 232:8–927. https://doi.org/10.1016/j.biocon.2019.01.020 Schindler M, Diestelhorst O, Haertel S, Saure C, Scharnowski A, Schwenninger HR (2013) Monitoring agricultural ecosystems by using wild bees as environmental indicators. BioRisk 8:53–71. https://doi.org/10.3897/biorisk.8.3600 Schulze CH, Waltert M, Kessler PJA, Pitopang R, Veddeler D, Mühlenberg M et al (2004) Biodiversity indicator groups of tropical land-use systems: Comparing plants, birds, and insects. Ecol Appl 14(5):1321–1333. https://doi.org/10.1890/02-5409 Schwartz MW, Smith LM, Steel ZL (2013) Conservation investment for rare plants in urban environments. PLoS ONE 8(12):e83809. Shanker C, Mohan M, Sampathkumar M, Lydia C, Katti G (2013) Functional significance of Micraspis discolor (F.) (Coccinellidae: Coleoptera) in rice ecosystem. J Appl Entomol 137(8):601–609. https://doi.org/10.1111/jen.12035 Simla P, Chaianunporn T, Sankamethawee W, Hughes AC, Sritongchuay T (2022) Effect of Landscape Composition and Invasive Plants on Pollination Networks of Smallholder Orchards in Northeastern Thailand.Plants 11(15):1976. https://doi.org/10.3390/plants11151976 Sridith K (2022) Land tax loophole endangers our environment. Bangkok Post. https://www.bangkokpost.com/opinion/opinion/2471122. Accessed 15 February 2025 Stewart AB, Sritongchuay T, Teartisup P, Kaewsomboon S, Bumrungsri S (2018) Habitat and landscape factors influence pollinators in a tropical megacity, Bangkok, Thailand. PeerJ 6:e5335. https://doi.org/10.7717/peerj.5335 Stout JC, Tiedeken EJ (2017) Direct interactions between invasive plants and native pollinators: evidence, impacts and approaches. Funct Ecol 31: 38–46. https://doi.org/10.1111/1365-2435.12751 Suwannapak K (2016) Thailand Insect Guide. Sarakadee, Bangkok Takkis K, Kull T, Hallikma T, Jaksi P, Kaljund K, Kauer K, Kull T, Kurina O, Külvik M, Lanno K, Leht M (2018) Drivers of species richness and community integrity of small forest patches in an agricultural landscape. J Veg Sci 29(6):978–988. https://doi.org/10.1111/jvs.12689 Tangtorwongsakul P, Warrit N, Gale GA (2018) Effects of landscape cover and local habitat characteristics on visiting bees in tropical orchards. Agr Forest Entomol 20:28–40. https://doi.org/10.1111/afe.12226 Tawatsin A, Thavara U, Siriyasatien P (2015) Pesticides used in Thailand and toxic effects to human health. Med Res Arch 3:1–10. https://doi.org/10.18103/mra.v0i3.176 ter Braak CJ (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67(5):1167–1179. https://doi.org/10.2307/1938672 Thai Meteorological Department (2024) Daily weather reports of Khon Kaen Province (from 2020-2021). Theisen-Jones H, Bienefeld K (2016) The Asian honey bee ( Apis cerana ) is significantly in decline, Bee World 93(4):90–97. https://doi.org/10.1080/0005772X.2017.1284973 Traveset A, Richardson DM (2006) Biological invasions as disruptors of plant reproductive mutualisms. Trends Ecol Evol 21(4):208–216. https://doi:10.1016/j.tree.2006.01.006 Tommasi N, Biella P, Guzzetti L, Lasway JV, Njovu HK, Tapparo A, Agostinetto G, Peters MK, Steffan-Dewenter I, Labra M, Galimberti A (2021) Impact of land use intensification and local features on plants and pollinators in Sub-Saharan smallholder farms. Agric, Ecosyst Environ 319:107560. https://doi.org/10.1016/j.agee.2021.107560 Twerd L, Sobieraj-Betlińska A, Szefer P (2021) Roads, railways, and power lines: Are they crucial for bees in urban woodlands?. Urban For. Urban Green 61:127120. https://doi.org/10.1016/j.ufug.2021.127120 Ullah MH, Rahim MA, Khan SA, Sultana M, Naher N (2024) Praxelis clematidea (griseb.) Rm king & h. Rob.(asteraceae): a new allien invasive angiosperm record for bangladesh. Bangladesh J Plant Taxon 31(1):181-5. Ulyshen MD, Soon V, Hanula JL (2010) On the vertical distribution of bees in a temperate deciduous forest. Vilà M, Espinar JL, Hejda M, Hulme PE, Jarošík V, Maron JL et al (2011) Ecological impacts of invasive alien plants: a meta‐analysis of their effects on species, communities and ecosystems. Ecol Lett 14(7):702–708. https://doi.org/10.1111/j.1461-0248.2011.01628.x Wagner DL (2020) Insect declines in the Anthropocene. Annu Rev Entomol 65:457–480. https://doi.org/10.1038/s43017-023-00478-x Walter-Echols G, Yongfan P (2005) Regional Overview and Analysis of Country Reports. In Proceeding of Asia regional workshop on implementation, monitoring and observance international code of conduct on the distribution and use of pesticides. Bangkok: FAO regional office for Asia and the Pacific. Wanapat M, Totakul P, Viennasay B, Matra M (2021) Sunnhemp ( Crotalaria juncea L) silage can enrich rumen fermentation process, microbial protein synthesis, and nitrogen utilization efficiency in beef cattle crossbreds. Trop Anim Health Prod 53(1):187. https://doi.org/10.1007/s11250-021-02628-z Wongwila K, Sankamethawee W (2023) Floral visitors to an invasive plant Praxelis clematidea (Hieron. ex Kuntze) R.M. King & H. Rob. (Asteraceae) in Northeastern Thailand. Nat Hist Bull Siam Soc 65(1):1–14. Additional Declarations The authors declare no competing interests. Supplementary Files KWSupplymentarydata3ndrevision.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6494624","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445675602,"identity":"1b87a9cf-1ced-4ce2-8405-a011b303ceb8","order_by":0,"name":"Kornkanok Wongwila","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Kornkanok","middleName":"","lastName":"Wongwila","suffix":""},{"id":445675603,"identity":"85a7ef1a-e9c5-4453-896e-e1cd9514d157","order_by":1,"name":"Thotsapol Chaianunporn","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Thotsapol","middleName":"","lastName":"Chaianunporn","suffix":""},{"id":445675604,"identity":"8aec3446-ab07-44a5-afc8-e000ea448544","order_by":2,"name":"Nakorn Pradit","email":"","orcid":"","institution":"Mahasarakham University","correspondingAuthor":false,"prefix":"","firstName":"Nakorn","middleName":"","lastName":"Pradit","suffix":""},{"id":445675605,"identity":"bb762beb-128f-4555-acb7-8c90b04d20c6","order_by":3,"name":"Wangworn Sankamethawee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYPACGwMJKCuBWC1pMC0GRGs5TIIWg+NnD7/8UXPeWHJGAuOHHwx/8ghrOZOXZs1z7LaZtEQCs2QPg0ExQS1mB3LMjBnYbtvISSQwSAONSGwgqOX8GzPDH//OgbQw/yZOy40c4we8bQdADmMjzhb7G2/MmHn7ko0lex62WfYYGBPWItmfY/zxxzc7wxnHkw/f+FEhR1gLELBBo4QRqNiACPVAwPyBOHWjYBSMglEwYgEA3O85+x8g7JoAAAAASUVORK5CYII=","orcid":"","institution":"Khon Kaen University","correspondingAuthor":true,"prefix":"","firstName":"Wangworn","middleName":"","lastName":"Sankamethawee","suffix":""}],"badges":[],"createdAt":"2025-04-21 09:24:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6494624/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6494624/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81102069,"identity":"90c48d3c-1a61-46d8-b8db-7daaa9950d86","added_by":"auto","created_at":"2025-04-22 08:56:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25633180,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Khon Kaen Province (top left), modified from the Division of Land Use Planning and Policy, Land Development Department (2020). The red rectangle indicates the forest patch area, with transect locations shown in the lower left panel. The large circle highlights the urban and agricultural zones, with detailed site locations shown in the right panel. The large black arrow points to the ring road surrounding the urban zone.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/0e554673d797996b92573877.jpg"},{"id":81102070,"identity":"4d5382d4-069c-48c2-a70b-7c43c997ad09","added_by":"auto","created_at":"2025-04-22 08:56:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":702849,"visible":true,"origin":"","legend":"\u003cp\u003eRényi Diversity Profiles of Coleoptera (a), Diptera (b), Hymenoptera (c), and Lepidoptera (d) observed across four land-use types. Alpha values; α = 0 is the species richness; α = 1 is the Shannon diversity index; α = 2 is the Simpson index.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/7962605f44d14213342d1220.jpg"},{"id":81102071,"identity":"062b8b5f-474e-4d9f-ad68-f369e26d9120","added_by":"auto","created_at":"2025-04-22 08:56:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":778442,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal variations across four land-use types in abundance of CDHL insects (using insect abundance per transect) and average flowering score per transect.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/6d048674bbfbaeeb26f33b8e.jpg"},{"id":81102522,"identity":"c51ac121-9931-4125-b11f-3d83cd8b2e2a","added_by":"auto","created_at":"2025-04-22 09:04:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4007542,"visible":true,"origin":"","legend":"\u003cp\u003eOverall relationships between environmental variables derived from ordinary least squares linear model and CDHL insect abundance in all land-use types.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/dbf026a25f263638c4bc1851.jpg"},{"id":81103704,"identity":"83dae888-53fb-42d2-ac8f-2e934697d99d","added_by":"auto","created_at":"2025-04-22 09:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32053382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/05e4d472-0530-4c91-8fc2-5d0f2f7e4741.pdf"},{"id":81102065,"identity":"9ee01eb6-d62d-4a7f-b223-7f7c41a6c80c","added_by":"auto","created_at":"2025-04-22 08:56:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":442847,"visible":true,"origin":"","legend":"","description":"","filename":"KWSupplymentarydata3ndrevision.docx","url":"https://assets-eu.researchsquare.com/files/rs-6494624/v1/4bca916f75f0c02a3bf9700c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVariation in local communities of insect pollinators in different land-use types in Northeastern Thailand\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global decline of pollinating insects has emerged as a significant environmental crisis (Kearns et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Pott et al. 2010; Tangtorwongsakul et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wagner \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This decline, marked by reductions in both species richness and abundance, is driven by multiple factors, including agrochemical use (Kevan and Viana \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ghazoul \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Newbold et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ganuza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), climate change (Hostetler and McIntyre \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pott et al. 2010; Herrera \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), habitat degradation and fragmentation (S\u0026aacute;nchez-Bayo and Wyckhuys \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the spread of invasive species (Bartomeus et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chitchak et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Changes in land use patterns have also played a significant role in biodiversity loss (Schulze et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; McKinney \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Newbold et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Numerous studies highlight the negative impacts of land-use changes and urbanization on the diversity, abundance, and community structure of pollinating insects (e.g., Ahrn\u0026eacute; et al. 2009; Rader et al. 2014; Lagucki et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ganuza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These impacts are often characterized by the dominance of generalist species, which are better adapted to a broader range of habitat conditions and tend to replace more specialized, less common species (Bates et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; S\u0026aacute;nchez-Bayo and Wyckhuys \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This shift in community composition reflects a process of biotic homogenization, where ecological communities become increasingly similar due to the widespread replacement of specialist species by generalists (Clavel et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, insufficient data on the spatial and temporal dynamics of natural pollinator communities hinder the effective management of landscapes and the restoration of degraded ecosystems (Kevan and Viana \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsect pollinators and their responses to land-use changes have been widely studied in Western countries (Newbold et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; S\u0026aacute;nchez-Bayo and Wyckhuys \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wagner \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while studies in tropical regions have been more focused on Neotropical ecosystems (Newbold et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Thailand, however, little research has been conducted on the impacts of land-use change on insect pollinator communities, despite the country being one of the largest users of agrochemicals globally (Walter-Echols and Yongfan \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tawatsin et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Agrochemical exposure, especially in agricultural areas, is presumed to have a negative impact on bee populations, as evidence from other regions suggests that pesticide use significantly affects pollinator abundance and biodiversity (Klein et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tangtorwongsakul et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ganuza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the specific effects in Thailand\u0026rsquo;s tropical landscapes remain largely unexplored.\u003c/p\u003e \u003cp\u003eTo address this knowledge gap in Northeastern Thailand, we aimed to investigate the diversity, abundance, and community composition of four key pollinating insect groups: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (hereafter CDHL) across different land-use types and seasons. We examined how these insect communities vary across varied land-use conditions and seasonal shifts, with a focus on uncovering the spatiotemporal dynamics driving pollinator distributions in human-modified landscapes. Given the central ecological role of bees (Kearns et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kevan and Viana \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially native honeybee species (\u003cem\u003eApis\u003c/em\u003e spp.) which are in global decline, we paid attention to their responses to land-use changes. In addition, we examined how the CDHL pollinators responded to flower abundance and broader environmental gradients to gain deeper insight into the ecological mechanisms influencing pollinator persistence. By establishing baseline data for these communities, our study offers a valuable reference for evaluating the impacts of anthropogenic pressures on ecosystem health. Importantly, we also emphasize the often-overlooked value of green wastelands as potential refuges for pollinators, highlighting their role in maintaining biodiversity in increasingly urbanized environments.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy areas\u003c/h2\u003e \u003cp\u003eThe study was conducted in Khon Kaen Province, Northeastern Thailand. We focused on four different land-use types that were accessible 1) agricultural land, 2) abandoned land, 3) urban area, and 4) a forest patch. The urban area is located inside Khon Kaen City and is separated from the other three areas by the city ring road (Route 260). The agricultural and abandoned lands were located beyond a 2-km radius from the city ring road (buffer zone) to avoid the possible movement of insects between zones. Both agricultural and abandoned lands were located within a 7-km radius of the buffer zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The agricultural lands consisted primarily of active rice farming (83.3%) during July-November for rain-fed farming and February-May for irrigated farming while cassava plantations (16.6%) were active throughout the year. Most farmlands were bordered by field-edge vegetation or hedgerows, while the rice paddies were enclosed by walking dikes covered with diverse short ground flora. The abandoned lands (either public or private) were designated as wastelands that had been deserted for human use and dominated by meadows, overgrown grass, climbers, shrubs and scattered shrubby trees. The forest patch is a small wooded area (1.13 km\u003csup\u003e2\u003c/sup\u003e) called Khok Phutaka located in Wiang Kao District (16\u0026deg; 38' N, 102\u0026deg; 17' E) which is 79.6 km away from Khon Kaen City. This forest patch is under the protection of the Plant Genetics Conservation Area under the Royal Initiative of Her Royal Highness Princess Maha Chakri Sirindhon. This forest consists of secondary growth deciduous dipterocarp forest on sandstone bedrock. It is mixed with small mosaics of seasonal evergreen vegetation along a seasonally running stream. The forest is dominated by dense, small regenerating trees (1,007 trees/ha with a 10\u0026ndash;15 m canopy height), and had the lowest human activity and least disturbance among the four land-use types studied.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTransects and insect sampling\u003c/h3\u003e\n\u003cp\u003ePrior to field data sampling, we located 100, 100, and 80 accessible points in the abandoned lands, agricultural lands, and urban area, respectively. Thirty of the 100 x 5 m belt transects were randomly selected from these points in each of these three land-use types with each transects placed at least 200 m apart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the forest patch, 30 belt transects were systematically placed at least 50 m apart due to the limited size of the forest area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The large rock outcrops (light blue line in the lower left map) and the very dense thorny vegetation area on the right-hand side of the rocks were eliminated. We applied the Pollard walk method (Pollard \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) for insect sampling within the 5-m width and 5-m height along the 100 m belt transects. We spent approximately 15\u0026ndash;20 minutes per transect between 09:00\u0026ndash;16:00 on non-rainy days. The same sampling protocols were repeated over three seasons: the late rainy season (August-October 2020), the dry season (January-March 2021), and the early rainy season (May-June 2021) resulting in a total of 360 transects in four land-use types. The cumulative rainfall of each season (only during the sampling months) provided by the Upper Northeastern Meteorological Center (Thai Meteorological Department, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was 724.2 mm, 124.1 mm, and 264.1 mm for the late rainy, dry, and early rainy seasons, respectively (Figure S1).\u003c/p\u003e \u003cp\u003eWe collected data on four important orders of insect pollinators, namely Coleoptera, Diptera, Hymenoptera, and Lepidoptera (Kevan and Baker \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Herrera \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Common and well-known species were counted and identified into morphotypes during the field surveys without trapping, while the unknown species were collected with sweep nets and preserved in 70% ethanol for identification to the lowest possible taxonomic level, following specialized literature on local insects (Suwannapak \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), bees and wasps (Barthelemy \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Michener \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), or butterflies (Ek-Amnuay 2012; Kimura et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEnvironmental variables\u003c/h3\u003e\n\u003cp\u003eFour physical and five biological variables were measured during insect sampling (Table S1). The physical variables were air temperature (Temp), air humidity (HD), light intensity (LG), and canopy cover (CC) which were measured at distances of 0, 50, and 100 m along each transect. The CC of each transect was estimated using photographs taken from a vertical camera position. Three images per transect were analyzed for percentage of canopy cover using the ImageJ software (version 1.53i) (Rasband \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Biological variables were classified into two main groups: (1) Ground flora (forbs, herbaceous vines, and shrubs) with only species that were in flower were recorded (GFsp). Flower cover (GFL) was visually assessed using an ordinal score from 1 to 4, where 1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;25%, 2\u0026thinsp;=\u0026thinsp;25\u0026ndash;50%, 3\u0026thinsp;=\u0026thinsp;51\u0026ndash;75%, and 4\u0026thinsp;=\u0026thinsp;76\u0026ndash;100%. (2) Trees with a diameter at breast height (DBH) of at least 10 cm within the belt transect were identified to determine species richness (Tsp), and tree density (Tden) was recorded as the total number of trees per transect. The flower score of flowering trees (TFL) was assessed using the same ordinal scores as GFL, within the transect area (5 x 100 m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe diversity indices of insects were calculated using the Shannon diversity index (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e) for each land-use type (α diversity), Buzas and Gibson's evenness (E) of each community, gamma diversity (γ), calculated across the four combined land-use types, to represent the overall species diversity of the studied landscape. Global Whittaker beta-diversity (global-β) was calculated to access community turnover across all land-use types and three seasons. Pairwise comparisons of beta-diversities were conducted to evaluate dissimilarity between insect communities across land-use types and seasons. Shannon diversity t-tests were performed for pairs of \u003cem\u003eH\u0026rsquo;\u003c/em\u003e values to compare if the species diversity of insects differed between land-use types and the same test was done between seasons. All diversity indices, beta-diversity comparisons, and t-tests were also conducted for flowering plant communities. R\u0026eacute;nyi diversity profiles were calculated, where α\u0026thinsp;=\u0026thinsp;0 gives the total number of species present, α\u0026thinsp;=\u0026thinsp;1 gives an index to the relative abundance of species (evenness), α\u0026thinsp;=\u0026thinsp;2 gives the Simpson index, and higher α values indicate increasing dominance, with α approaching infinity corresponding to the Berger-Parker index. These diversity index analyses were performed using PAST software version 4.03 (Hammer et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo examine whether insect abundance varied between different land-use types and across seasons, Kruskal-Wallis tests were applied. The same method was applied to assess flower abundance. If the Kruskal-Wallis tests indicated significant differences, pairwise comparisons were conducted using the Wilcoxon rank-sum test. The same tests were made for abundance data of three \u003cem\u003eApis\u003c/em\u003e bee species from all land-use types. Additionally, a Chi-square test was employed to determine whether the distribution of three \u003cem\u003eApis\u003c/em\u003e species differed across land-use types and seasons. All these analyses were performed using R software, version 4.0.2 (R Core Team \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used ordinary least squares regression method in bivariate linear models to test the relationships between each of nine environmental variables (abundance of ground flora, species richness of ground flora, species richness of trees, tree density, flower abundance of trees, % canopy cover, temperature, humidity, and light intensity) (Table S1) and the abundance of CDHL. We first evaluated the effect of each environmental predictor across all land-use types combined, totaling nine models (Table S5). Then, we examined these relationships within each land-use type separately, resulting in 36 models (Table S6). These linear models were tested in PAST software version 4.03 (Hammer et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo visualize the associations between CDHL families and nine environmental variables, we used canonical correspondence analysis (CCA) in program R version 4.0.2 (R Core Team \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using package \u0026lsquo;vegan\u0026rsquo; (Oksanen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These nine environmental variables were tested with the abundance of each insect family. We used function \u0026lsquo;ordistep\u0026rsquo; for stepwise model selection using a permutation test with 999 permutations, applying AIC as the criterion for model selection to identify the environmental variables that most explained insect distribution and removed irrelevant variables. Environmental variables with p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were eliminated, and the significantly related variables with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), were further tested using the \u0026lsquo;anova.cca\u0026rsquo; function with 999 permutation tests to assess the overall model and each explanatory predictor (Oksanen, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The signs and relative magnitudes of the canonical coefficients indicate the relative contribution of each predictor to the ordination axes (Palmer \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; ter Braak \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePollinating insect communities\u003c/h2\u003e \u003cp\u003eFrom August 2020 to June 2021, a total of 11,063 individual insects of 108 morphospecies (23% unknown species) were recorded across all four land-use types (Table S2). These insects belonged to five families of Coleoptera, four families of Diptera, five families of Hymenoptera, and nine families of Lepidoptera (Table S2). The gamma diversity (\u003cb\u003eγ\u003c/b\u003e) of CDHL across our four habitats at the landscape scale was 3.54 and the evenness index was 0.29. The highest abundance of CDHL was found in abandoned land, followed by agricultural land, forest and urban area (Figure S2). Species richness was highest in the forest, followed by urban areas, agricultural land, and abandoned lands. Community evenness indices (\u003cem\u003eE\u003c/em\u003e\u0026rsquo;) were relatively similar for all land-use types (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The species diversity (Shannon \u003cem\u003eH\u0026rsquo;\u003c/em\u003e diversity) was highest in the forest, followed by agricultural land, abandoned land, and urban area. Pairwise comparisons (diversity t-test) showed that the forest was significantly more diverse than the urban area (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and abandoned land (\u003cem\u003et\u003c/em\u003e = -6.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not significantly different from the agricultural land. The beta-diversity indices showed that the insect communities in the urban area and the forest were the most distinctive (β\u0026thinsp;=\u0026thinsp;0.34), while agricultural and abandoned lands were relatively similar (β\u0026thinsp;=\u0026thinsp;0.15) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The forest hosted the highest number of unique species that were not found in other land-use types (Table S2).\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\u003eSummary of diversity parameters, pairwise comparisons between land-use types with diversity t-test (t) and Whitaker beta-diversity (β).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCommunity and\u003c/p\u003e \u003cp\u003eLand-use type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eH\u0026rsquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eComparison of diversity t-test (\u003cem\u003et\u003c/em\u003e) and beta diversity index (β) between land-use types\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCDHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -6.46*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -6.67*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.70*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.79*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e108\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eγ\u0026thinsp;=\u0026thinsp;3.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePlant in flower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -4.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -2.76**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e = -2.49*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e143\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eγ\u0026thinsp;=\u0026thinsp;3.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAB\u003c/em\u003e abandoned land, \u003cem\u003eAG\u003c/em\u003e agricultural land, \u003cem\u003eFO\u003c/em\u003e forest patch, \u003cem\u003eUR\u003c/em\u003e urban area\u003c/p\u003e \u003cp\u003e \u003cem\u003eH'\u003c/em\u003e Shanon-Weiner diversity index as alpha diversity, \u003cem\u003eE'\u003c/em\u003e Buzas \u0026amp; Gibson's evenness index, γ gramma diversity for all land-use types. \u003cem\u003et\u003c/em\u003e Shannon diversity t-test, β Whitaker beta-diversity.\u003c/p\u003e \u003cp\u003e** indicates significant differences at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and * indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003eLepidoptera were the most abundant group across all land-use types and all seasons (Figure S2), making up 76% of all CDHL records. Hymenoptera, Coleoptera, and Diptera contributed 15.75%, 5.74% and 2.52%, respectively. The Common Emigrant (\u003cem\u003eCatopsilia pomona\u003c/em\u003e) was the most common butterfly accounting for 23.14% of all lepidopterans. Hymenopterans showed a higher abundance in the urban and abandoned areas, but were least abundant in the forest (Table S2). Coleopterans and dipterans were most abundant in agricultural lands (Table S2; Figure S2). Coleopterans were more diverse in the urban area but less diverse in the forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The composition of dipterans, hymenopterans, and lepidopterans was relatively similar across all the land-use types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, d). The hymenopteran community had the highest evenness in the forest and lowest in the urban area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The lepidopteran community in the forest had the highest species richness (61 morphospecies), but exhibited the lowest evenness index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFlowering plant communities\u003c/h3\u003e\n\u003cp\u003eWe recorded a total of 143 ground flora species in flower (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 21 tree species in flower across all four land-use types (Table S3 \u0026amp; S4). The species richness of ground flowering plants was highest in agricultural land, followed by forest, abandoned land, and urban areas. The \u003cem\u003eH\u003c/em\u003e\u0026rsquo; diversity index was highest in agricultural land, followed by forest, urban areas, and abandoned land. Abandoned lands had the significantly lowest species diversity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The beta-diversity showed that plant communities in flower in abandoned land, agricultural land, and urban area were relatively similar (β\u0026thinsp;=\u0026thinsp;0.35\u0026ndash;0.49), but the forest had a distinctly different species composition (β\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.79). The most diverse flower families were Fabaceae (34 species), Malvaceae (15 species), Asteraceae (13 species), Convolvulaceae (9 species), Euphorbiaceae (9 species), and Rubiaceae (8 species). Asteraceae contributed the highest flower score in all areas (28.34%) while Fabaceae contributed 15.33%. The most common flowering species were \u003cem\u003ePraxelis clematidea, Gomphrena celosioides, Tridax procumbens\u003c/em\u003e, and \u003cem\u003eMimosa pudica\u003c/em\u003e, contributing 14.22%, 7.24%, 6.71%, and 5.11% to the total flower score (GFL), respectively. The invasive plant \u003cem\u003eP. clematidea\u003c/em\u003e also contributed the highest flower score on the forest floor, while other invasive flowers were rare.\u003c/p\u003e\n\u003ch3\u003eSpatiotemporal variation of CDHL\u003c/h3\u003e\n\u003cp\u003eDuring the late rainy season (August-October), when the rainfall was highest, the CDHL exhibited the highest species richness (99 morphospecies) across all land-use types. However, abundance was higher during the early rainy season (May\u0026ndash;June). The \u003cem\u003eH\u003c/em\u003e\u0026rsquo; diversity of the CDHL during the late rainy season, dry season, and early rainy season was 3.46, 3.20, and 3.34, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These \u003cem\u003eH\u003c/em\u003e\u0026rsquo; diversity indices differed significantly between seasons, with all pairwise comparisons in diversity t-tests showing p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all pairs.\u003c/p\u003e \u003cp\u003eThe beta-diversity analysis showed that species turnover of CDHL between seasons was highest in the urban area (global β\u0026thinsp;=\u0026thinsp;0.44), while the late rainy season showed the highest species turnover among land-use types (β\u0026thinsp;=\u0026thinsp;0.78) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Pairwise comparisons of beta-diversity between seasons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) for each land-use type showed that the urban area had the highest species turnover between the late rainy season and the dry season (β\u0026thinsp;=\u0026thinsp;0.35), while the abandoned area, agricultural area, and forest had relatively low species turnover between seasons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). On the other hand, species turnover was highest between the forest and urban areas in the late rainy season (β\u0026thinsp;=\u0026thinsp;0.46) and lowest between agricultural area and the urban area (β\u0026thinsp;=\u0026thinsp;0.16) during the early rainy season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\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\u003e(A): Summary of spatiotemporal variation in CDHL communities based on α-diversity (Shannon \u003cem\u003eH\u003c/em\u003e\u0026rsquo; diversity index) among land-use types and three seasons. Numbers in parentheses indicate the number of taxa. The global β-diversity values (italicized) are provided for species turnover between land-use types (last column) and among seasons (last row). (B): Pairwise β-diversity comparisons between seasons for each land-use type (β in the last column); and (C): pairwise β-diversity comparisons between land-use types for each season.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eH\u0026rsquo; index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlobal β diversities (among habitats)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.09 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.09 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.31 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.13 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.78\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.02 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.77 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.65\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.02 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.05 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.95 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.50\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal β diversities (among seasons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.36\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.42\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.39\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLate rainy - Dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDry - Early rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEarly rainy - Late rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLate rainy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEarly rainy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB - AG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB - FO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB - UR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG- FO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG - UR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUR - FO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.42\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 \u003cem\u003eAB\u003c/em\u003e abandoned land, \u003cem\u003eAG\u003c/em\u003e agricultural land, \u003cem\u003eFO\u003c/em\u003e forest patch, \u003cem\u003eUR\u003c/em\u003e urban area\u003c/p\u003e \u003cp\u003e \u003cem\u003eH'\u003c/em\u003e Shanon diversity index for alpha diversity (α) of each community, γ gramma diversity index for four land-use types. \u003cem\u003et\u003c/em\u003e Shannon diversity t-test, β Whitaker beta-diversity.\u003c/p\u003e \u003cp\u003eThe Wilcoxon rank-sum test showed that CDHL insect communities were more varied between seasons in most areas, except for agricultural land, which had relatively similar abundance levels among seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Flower abundance varied significantly among seasons in the forest, while the urban area showed no difference between seasons. A similar pattern was observed in both abandoned and agricultural lands, where the dry and early rainy seasons had comparable levels, but both were higher in the late rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHoneybees composition\u003c/h2\u003e \u003cp\u003eThere were three species of honeybees observed in this study: the dwarf honeybee (\u003cem\u003eApis florea\u003c/em\u003e), the giant honeybee (\u003cem\u003eA. dorsata\u003c/em\u003e), and the Asian honeybee (\u003cem\u003eA. cerana\u003c/em\u003e). The most abundant species was \u003cem\u003eA. florea\u003c/em\u003e (37.8% of all hymenopterans) contributing 51% of hymenopteran detections in the urban area (Figure S2). Chi-square tests showed that the proportion of the three \u003cem\u003eApis\u003c/em\u003e bees differed among land-use types (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;33.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and seasons (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;37.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Figure S3). \u003cem\u003eApis\u003c/em\u003e bees also responded differently to land-use types across seasons. The urban area and abandoned lands supported the highest abundance of all \u003cem\u003eApis\u003c/em\u003e bees in the early rainy season, especially \u003cem\u003eA. florea.\u003c/em\u003e However, during the dry season, they were more abundant in agricultural land. The forest had the lowest number of \u003cem\u003eApis\u003c/em\u003e bees, but \u003cem\u003eA. florea and A. dorsata\u003c/em\u003e were detected more frequently during the dry season. \u003cem\u003eA. cerana\u003c/em\u003e was not detected in the forest at all.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCDHL abundance and environmental variables\u003c/h2\u003e \u003cp\u003eThere were two biotic variables that positively influenced the overall pooled insect abundance from all habitats, including: 1) ground flowers score (coefficient\u0026thinsp;=\u0026thinsp;2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that explains about 19% of the variance, and 2) species richness of ground flowers (coefficient\u0026thinsp;=\u0026thinsp;3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that explains about 11% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table S5). Two abiotic variables moderately influenced the CDHL abundance, including: 1) percent canopy cover that had a negative relationship (coefficient = -0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) that explains approximately 7% of the variance, and 2) the log of light intensity had a significantly positive effect (coefficient\u0026thinsp;=\u0026thinsp;39.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and explain about 6.5% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table S5).\u003c/p\u003e \u003cp\u003eWhen the relationships between those nine environmental variables and insect abundance were tested separately for each land-use type, the same two biotic variables were significantly correlated in three land-use types, except the urban area (Table S6). The influence of ground flower score (coefficient\u0026thinsp;=\u0026thinsp;2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and species richness of ground flowers (coefficient\u0026thinsp;=\u0026thinsp;3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were strongest in the forest area, explaining about 26% and 24% of the variance, respectively. None of the physical variables showed a significant impact on insect abundance when land-use types were analyzed separately (Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCDHL distribution and environmental variables\u003c/h2\u003e \u003cp\u003eWhen environmental variables were tested with insect families, the non-significant variables (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05: GFsp, GFL, TFL, CC, Tden, and LG) were removed from the final canonical correspondence analysis (CCA). The final CCA showed that the distribution patterns of insect families were explained by the species richness of trees (Tsp) (p\u0026thinsp;=\u0026thinsp;0.001), air humidity (HD) (p\u0026thinsp;=\u0026thinsp;0.029), and air temperature (Temp) (p\u0026thinsp;=\u0026thinsp;0.096) and these variables together explained only 7.51% of the variance in the distribution of each insect family (sum of all constrained eigenvalues\u0026thinsp;=\u0026thinsp;0.103, total inertia\u0026thinsp;=\u0026thinsp;1.364; pseudo-F\u0026thinsp;=\u0026thinsp;3.140, p\u0026thinsp;=\u0026thinsp;0.001 for the whole model) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) shows that these four land-use types shared relatively similar environmental variables, except that some locations in agricultural land had relatively high temperatures. Moreover, the four land-use types shared most of the insect families (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The long-legged flies (Dolichopodidae) were more likely to be found in warmer areas, while the beetle families Epilachnidae, Buprestidae and Curculionidae occurred in habitats with lower temperatures. Flies in the family Muscidae, skippers (Hesperiidae), and moths (Uraniidae and Sphingidae) were found in areas of higher tree species richness and greater air humidity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), where these two variables were significantly correlated (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.162, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePollinating insect communities\u003c/h2\u003e \u003cp\u003eThis study investigated the variations in local insect communities, focusing on the major orders recognized as the most common global pollinators: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (CDHL) across four land-use types in Northeastern Thailand: agricultural land, abandoned land, urban area, and a forest patch. The results show that CDHL communities were more diverse and had higher species richness in the forest area which also had the highest vegetation density and the lowest disturbance level. The lowest insect abundance, as well as the lowest species diversity and lowest evenness, found in the urban area, suggests a negative effect of urbanization on the species diversity and community of pollinating insects (McKinney \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ahrne et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bates et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ganuza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) show that beta-diversities varied between land-use types but were quite similar among seasons. This was probably due to the relatively similar climate conditions in the tropics that result in a slow turnover of insect communities (Novotny et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while vegetation structure varied widely among disturbed and undisturbed areas, especially in the forest, which had a significantly higher tree density and a more heterogeneous structure. Regarding habitat specialization, there seemed to be more forest-specialist butterflies (21 species; Table S2), while most of the CDHL species were generalists.\u003c/p\u003e \u003cp\u003eLepidopterans are the best-studied insects in Southeast Asia (Corlett \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and we found them to be the most abundant group, with the highest species richness in all land-use types across all seasons. This was most obvious in the forest, which hosted the most diverse assemblages of butterflies and moths, suggesting that higher plant diversity and greater structural complexity of the forest support both specialist and generalist butterflies (Takkis et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although they seem to be less important pollinators in tropical Asia compared to the Neotropics (Corlett \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), this statement may be biased due to the limited number of ecological reports from the Asian tropics (Newbold et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eColeopterans and Dipterans contributed very little to insect abundance in our study. Corlett (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) noted that they were less common as pollinators compared to Hymenopterans and Lepidopterans in Asian dry tropical habitats. For Coleopterans, we found that the Coccinellidae family was the most common beetle family in rice fields, particularly the species \u003cem\u003eMicraspis discolor\u003c/em\u003e. They are generally reported as key aphid predators in rice fields and as occasional flower pollinators (Shanker et al. 2012), but probably play a less important role as pollinators.\u003c/p\u003e \u003cp\u003eDipterans were significantly less common (only 2.52% of all CDHL abundance) in our study. We found only four species of important pollinating hoverflies (family Syrphidae), and only 12% were detected in the forest, while 80% were in agricultural and abandoned lands. Direct studies on the importance and population trends of hoverflies in Asia are very limited, but existing information from temperate regions highlights their important role in pollination services while also documenting serious population declines (Hallmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Barendregt et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It would be worthwhile to investigate how these insects contribute to pollination services (Chitchak et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and to monitor population trends in various habitats in this under-studied Asian region.\u003c/p\u003e \u003cp\u003eIn this study, we expected that overall hymenopterans would be more abundant, as reported in many studies (e.g. Lee et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Stewart et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ganuza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wongwila and Sankamethawee 2023), but we found that they were more common in urban areas and abandoned lands. Lower detection of bees in the understory of the forest may be biased due to the complexity of vegetation structure, as mentioned by Lee et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Ulyshen et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the forest, we observed numerous bees and wasps, visiting flowering trees \u003cem\u003eBridelia retusa\u003c/em\u003e, \u003cem\u003eMillettia brandisiana\u003c/em\u003e, and the scandent shrub \u003cem\u003eZiziphus oenopolia\u003c/em\u003e at heights above 6 m, which was beyond our survey limit of 5 m. Therefore, the lower flower abundance in the understory layer of the forest could have influenced foraging bees, causing them to concentrate on canopy flowers (e.g. Ulyshen et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stewart et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tommasi et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, including higher strata in surveys could fully characterize local bees and other pollinator communities in forests (Cunningham-Minnick et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although the abundance of Hymenopterans in our forest area was lower than in the urban area, the \u003cem\u003eH\u003c/em\u003e' index for Hymenopterans clearly indicates that the forest sustains the highest species diversity among all habitats (Table S2). This suggests that the forest patch in our study generally supports the greatest diversity of the most important pollinator group. Another limitation of our sampling effort in the forest area was the small forest size, which could result in relatively similar environmental variables among transects. It would be valuable to investigate how forest size influences pollinator communities, providing key insights into the impact of forest patch size on insect populations in surrounding agricultural areas.\u003c/p\u003e \u003cp\u003e \u003cem\u003eApis\u003c/em\u003e bees are important pollinators in non-forest habitats on a global scale (Klein et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Millard et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in the Indomalayan region (Corlett \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). \u003cem\u003eA. florea\u003c/em\u003e was the most abundant bee in all land-use types, especially in urban and abandoned areas, which is consistent with other studies in urban-related landscapes (Tangtorwongsakul et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Simla et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wongwila and Sankamethawee 2023). This suggests their resilience in human-dominated environments (Stewart et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hsu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, abandoned and urban areas often provide continuous floral resources that can support foraging bees (McFrederick and LeBuhn, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Prendergast et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our transects in open, abandoned lands, which had a higher abundance of bees, were mostly covered with \u003cem\u003eWaltheria indica\u003c/em\u003e, and \u003cem\u003eSida acuta\u003c/em\u003e (both Malvaceae), suggesting that social bees benefit from flower abundance, and that there may be fewer predators (Lagucki et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which may reduce competition for resources (Cunningham-Minnick et al. 2020). We observed many \u003cem\u003eA. florea\u003c/em\u003e and \u003cem\u003eXylocopa aestuans\u003c/em\u003e on a transect that passed through a \u003cem\u003eCrotalaria juncea\u003c/em\u003e plantation in the rice fields during the dry season. This plant has been grown widely in Thailand after rice harvesting as a nitrogen-fixing legume (Wanapat et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and for weed control (Bundit et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This species could be an important food source for foraging bees during seasonal resource shortages in farmlands (Meagher et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Promoting the cultivation of \u003cem\u003eC. juncea\u003c/em\u003e by local farmers not only enhances soil quality but also boosts ecotourism by attracting visitors to caf\u0026eacute;s and restaurants near the flower plantations (Sankamethawee, pers. comm.). This practice can bring agricultural, economic, and ecological benefits.\u003c/p\u003e \u003cp\u003eOur study reveals an alarming trend of extremely low numbers of \u003cem\u003eA. cerana\u003c/em\u003e and \u003cem\u003eA. dorsata\u003c/em\u003e which aligns with previous reports indicating a serious decline in the populations of Asian honeybees (Theisen-Jones and Bienefeld 2017). Tracking population fluctuations of pollinating bees across diverse landscapes is essential for developing effective conservation and management strategies (Schindler et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Samraj and Agnihotri \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is particularly important when considering potential interactions with non-native species like the Western honeybee and numerous invasive flowering plants (Chitchak et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Pooling data from similar vegetation structures (such as abandoned agricultural lands and urban wastelands) is recommended to enable more robust comparisons with forested and active cultivated areas. This could provide a better picture of how the communities of pollinating insects respond to disturbance gradients including factors like agrochemical use (Hahn et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the abundance of invasive species (Cunningham-Minnick et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the intensity of urbanization (Lagucki et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSeasonality and environmental variables\u003c/h2\u003e \u003cp\u003eThe results from CCA revealed that the distribution of each CDHL family had no significant relationship with most of the measured environmental variables. One possible explanation is that the environmental conditions across our studied habitats, particularly in abandoned lands and urban areas, were relatively similar. Additionally, the forest patch was surrounded by farmland which could potentially limit environmental contrast. It is also possible that other unmeasured landscape structures (Rahimi et al 2021), vegetation composition (Lajos et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), historical land-use intensity (Cusser et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) may also play an important role in shaping insect communities.\u003c/p\u003e \u003cp\u003eThe findings demonstrate that the communities of CDHL varied spatially and seasonally (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The early rainy season (May-June) supported a higher abundance of CDHL in all areas except the forest. The peak of CDHL abundance in the forest was during the late rainy season, while a higher number of hymenopterans were found in the dry season (January-March). This pattern can probably be explained by the flowering period of trees in the dry deciduous forests, which produce flowers in the dry season, such as \u003cem\u003eXylia xylocarpa\u003c/em\u003e, \u003cem\u003ePentacme siamensis, Millettia brandisiana\u003c/em\u003e, and \u003cem\u003eEllipanthus tomentosus.\u003c/em\u003e Rainy periods and seasonality have been reported to be the main environmental factors shaping the insect communities (Fontanarrosa et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Castro and Espinosa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Understanding the seasonal community structure of insects in the tropics is necessary for conservation efforts (Castro and Espinosa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This knowledge can provide information on insect communities that respond to changing environments over time, and can help land managers target appropriate species in the right habitats at the right time of year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFlowering plant community and the impacts of invasive ground flora\u003c/h2\u003e \u003cp\u003eThe community of ground flowering plants in the forest showed more variation between dry and wet seasons compared to the relatively stable flower availability in urban areas. Notably, the invasive species \u003cem\u003ePraxelis clematidea\u003c/em\u003e (Gardner and Williges \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salgado et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was the most abundant ground-flowering plant across all surveyed habitats, including the forest floor. Its dominance in both disturbed and undisturbed areas underscores its aggressive spread and ecological competitiveness, positioning it as a significant invasive threat that may alter native plant communities and ecosystem dynamics, if left unmanaged. The high abundance of invasive flowering species generally attracts more generalist pollinators (Bartomeus et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ojija et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and they are considered a major cause of pollinator diversity loss (Kearns et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In particular, \u003cem\u003eP. clematidea\u003c/em\u003e indirectly influenced the structure of pollinator communities by acting as a peripheral species within pollination networks (Simla et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may attract generalist pollinators (Wongwila and Sankamethawee 2024) and impact the turnover of pollinating insect species (Bartomeus et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Invasive plants may also compete with native flora, potentially leading to ecological shifts (Traveset and Richardson \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vil\u0026agrave; et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stout et al. 2017). Controlling invasive plants in more disturbed areas can be undertaken with less damage to local ecosystems, but this could be challenging in forest areas where ecological balance is more fragile (Foxcroft et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany findings showed that invasive plant species have an effect on a wide range of scales from individual organisms to populations and communities (Stout and Tiedeken \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kov\u0026aacute;cs-Hosty\u0026aacute;nszki et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Monitoring the distribution and abundance of invasive plants is needed for a better understanding of how they alter ecosystem dynamics (e.g. Vil\u0026agrave; et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) particularly their impacts on native plants (Kearns et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and pollinating insect communities (Bartomeus et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chitchak et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, when certain pollinators benefit from invasive plants, managing human-dominated habitats for biodiversity conservation requires a careful evaluation of when, where, and how to control invasive plants that support the diversity of pollinating insects (Traveset and Richardson \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Stout and Tiedeken \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe importance of green wastelands and agricultural field-edges\u003c/h2\u003e \u003cp\u003eThis study shows that wastelands, abandoned meadows, and field-edge vegetation, either in agricultural or urban areas play a significant role as refuges for pollinating insects (McFrederick and LeBuhn, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schwartz et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Twerd et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Preserving small forest remnants and growing field-edge flowers adjacent to agricultural landscapes can facilitate pollination services and help maintain crop yields (De Marco and Coelho \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ricketts et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schindler et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2020, Thailand implemented a Land and Building Tax Act, imposing significantly higher rates on unused land. This has led to the rapid clearance of wastelands as landowners sought to demonstrate productive land use, resulting in the destruction of natural habitats to alleviate the tax burdens (Phosri et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sridith \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Angkaew et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A similar trend is ongoing with the conversion of wastelands into carbon-offset plantations of non-native trees, which may have resulted in negative impacts on natural ecosystems (Aguirre-Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lowering wasteland taxes, along with promoting biodiversity credits, could help mitigate the clearance of abandoned green areas in urban and suburban landscapes. Thus, land management policies should prioritize protecting biodiversity conservation by preserving green areas (McFrederick and LeBuhn \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schwartz et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Twerd et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Prendergast et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Morpurgo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings highlight the critical role that forest patches and abandoned green areas play in supporting biodiversity within increasingly fragmented and human-dominated landscapes. Rather than viewing these spaces as neglected or taxable burdens, land management policies should recognize their ecological value, particularly for pollinating insects. By improving our understanding of how these insects respond to different land-use gradients, we can better inform strategies that maintain and enhance biodiversity. Promoting the diversity of flowering plants within these green spaces can strengthen ecological connectivity between urban developments and natural ecosystems, supporting robust insect communities and contributing to overall ecosystem resilience. Therefore, conserving these green remnants in urban and surrounding landscapes should be a key component of sustainable land-use planning.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Research Capability Enhancement Program through graduate student scholarship (K. Wongwila), Faculty of Science, Khon Kaen University. We sincerely thank the Division of Land Use Planning and Policy, Land Development Department for providing the land-use data, and the Upper Northeastern Meteorological Center (Thai Meteorological Department) for providing rainfall data. We gratefully thank to all those who helped with the field surveys. We thank Dr. Sawai Mattapha for helping with identification of Fabaceae and Pongsakorn Bouloy for insect identification. Finally, we thank Andrew Pierce for constructive comments and English proofreading on several versions of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhrne K, Bengtsson J, Elmqvist T (2009) Bumble bees (\u003cem\u003eBombus\u003c/em\u003e spp) along a gradient of increasing urbanization. PLoS ONE 4(5):e5574. https://doi.org/10.1371/journal.pone.0005574\u003c/li\u003e\n\u003cli\u003eAngkaew R, Ngoprasert D, Powell LA, Round PD, Gale GA (2023) Variation in avian responses to rice intensification in the central plains of Thailand guides conservation opportunities. Biol Cons 286:110307. https://doi.org/10.1016/j.biocon.2023.110307\u003c/li\u003e\n\u003cli\u003eAguirre-Guti\u0026eacute;rrez J, Stevens N, Berenguer E (2023) Valuing the functionality of tropical ecosystems beyond carbon. Trends Ecol Evol 38 (12): 1109-1111 \u003c/li\u003e\n\u003cli\u003eBarthelemy C (2021) Identification guide to the social wasps of Hong Kong (Hymenoptera; Vespidae). Hong Kong\u003c/li\u003e\n\u003cli\u003eBates AJ, Sadler JP, Fairbrass AJ, Falk SJ, Hale JD, Matthews TJ (2011) Changing bee and hoverfly pollinator assemblages along an urban-rural gradient. PLoS ONE 6(8):e23459. https://doi.org/10.1371/journal.pone.0023459\u003c/li\u003e\n\u003cli\u003eBarendregt A, Zeegers T, van Steenis W, Jongejans E (2022) Forest hoverfly community collapse: Abundance and species richness drop over four decades. Insect Conserv Divers 15(5):510\u0026ndash;21. https://doi.org/10.1111/icad.12577\u003c/li\u003e\n\u003cli\u003eBartomeus I, Vil\u0026agrave; M, Santamar\u0026iacute;a L (2008) Contrasting effects of invasive plants in plant\u0026ndash;pollinator networks. Oecologia 155:761\u0026ndash;770. https://doi.org/10.1007/s00442-007-0946-1\u003c/li\u003e\n\u003cli\u003eBundit A, Ostlie M, Prom-U-Thai C (2021) Sunn hemp (\u003cem\u003eCrotalaria juncea\u003c/em\u003e) weed suppression and allelopathy at different timings. Biocontrol Sci Technol 31(7):694\u0026ndash;704. https://doi.org/10.1080/09583157.2021.1881446\u003c/li\u003e\n\u003cli\u003eCastro A, Espinosa CI (2015) Seasonal diversity of butterflies and its relationship with woody-plant resources availability in an Ecuadorian tropical dry forest. Trop Conserv Sci 8(2):333\u0026ndash;351. https://doi.org/10.1177/194008291500800205\u003c/li\u003e\n\u003cli\u003eChitchak N, Hassa P, Traiperm P, Stewart AB (2024) Who pollinates exotic plants? A global assessment across native and exotic ranges. Glob Ecol Conserv 11:e03185. https://doi.org/10.1016/j.gecco.2024.e03185\u003c/li\u003e\n\u003cli\u003eClavel J, Julliard R, Devictor V (2011) Worldwide decline of specialist species: toward a global functional homogenization?. Front. Ecol Environ 9(4):222-228.\u003c/li\u003e\n\u003cli\u003eCorlett RT (2004) Flower visitors and pollination in the Oriental (Indomalayan) Region. Biol Rev Camb Philos Soc 79(3):497\u0026ndash;532. https://doi.org/10.1017/S1464793103006341\u003c/li\u003e\n\u003cli\u003eCunningham-Minnick MJ, Roberts HP, Milam J, King DI (2024) Sampling the understory, midstory, and canopy is necessary to fully characterize native bee communities of temperate forests and their dynamic environmental relationships. Front. Ecol. Evol 12:1352266. \u003c/li\u003e\n\u003cli\u003eCusser S, Neff JL, Jha S (2018) Land-use history drives contemporary pollinator community similarity. Landsc Ecol 33:1335-1351.\u003c/li\u003e\n\u003cli\u003eDe Marco P, Coelho FM (2004) Services performed by the ecosystem: forest remnants influence agricultural cultures\u0026apos; pollination and production. Biodivers Conserv 13:1245\u0026ndash;1255. \u003c/li\u003e\n\u003cli\u003eEk-Ammuay P (2012) Butterflies of Thailand, 2nd edn. Baan Lae Suan, Bangkok\u003c/li\u003e\n\u003cli\u003eFontanarrosa MS, Collantes MB, Bachmann AO (2009) Seasonal patterns of the insect community structure in urban rain pools of temperate Argentina. J Insect Sci 9:10. https://doi.org/10.1673/031.009.1001\u003c/li\u003e\n\u003cli\u003eFoxcroft LC, Py\u0026scaron;ek P, Richardson DM, Genovesi P, MacFadyen S (2017) Plant invasion science in protected areas: progress and priorities. Biol Invas 19(5):1353\u0026ndash;1378. https://doi.org/10.1007/s10530-016-1367-z\u003c/li\u003e\n\u003cli\u003eGanuza C, Redlich S, Uhler J, Tobisch C, Rojas-Botero S, Peters MK et al (2022) Interactive effects of climate and land use on pollinator diversity differ among taxa and scales. Sci Adv 8(18):eabm9359. https://doi.org/10.1126/sciadv.abm9359\u003c/li\u003e\n\u003cli\u003eGardner AG, Williges KA (2015) Praxelis clematidea (Asteraceae): a new plant invader of Florida. Southeast Nat 14: 21-27. https://doi.org/10.1656/058.014.0111\u003c/li\u003e\n\u003cli\u003eGhazoul J (2005) Buzziness as usual? Questioning the global pollination crisis. Trends Ecol Evol 20(7):367\u0026ndash;373. https://doi.org/10.1016/j.tree.2005.04.026\u003c/li\u003e\n\u003cli\u003eHallmann CA, Ssymank A, Sorg M, de Kroon H, Jongejans E (2021) Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community. PNAS 118(2):e2002554117. https://doi.org/10.1073/pnas.2002554117\u003c/li\u003e\n\u003cli\u003eHammer \u0026Oslash;, Harper DA, Ryan PD (2001) PAST: Paleontological statistics software package for education and data analysis. Palaeontol Electron 4(1):1\u0026ndash;9\u003c/li\u003e\n\u003cli\u003eHahn M, Schotthӧfer, A, Schmitz J, Franke L, Brȕhl, C (2015) The effects of agrochemicals on Lepidoptera, with a focus on moths, and their pollination service in field margin habitats. Agr Ecosyst Environ 207:153\u0026ndash;162. https://doi.org/10.1016/j.agee.2015.04.002\u003c/li\u003e\n\u003cli\u003eHerrera CM (2019) Increasing Pollinator Abundance in Undisturbed Mediterranean Montane Habitats over Recent Decades. Bull Ecol Soc Am 100(1): e01480. https://doi.org/10.1002/bes2.1480\u003c/li\u003e\n\u003cli\u003eHostetler NE, McIntyre ME (2001) Effects of urban land use on pollinator (Hymenoptera: Apoidea) communities in a desert metropolis. Basic Appl Ecol 2(3):209\u0026ndash;218. https://doi.org/10.1078/1439-1791-00051\u003c/li\u003e\n\u003cli\u003eHsu PS, Wu TH, Tian JX, Sung IH (2022) Origins and invasion characteristics of the recently introduced dwarf honeybee \u003cem\u003eApis florea\u003c/em\u003e Fabricius, 1787 (Hymenoptera, Apidae) in Taiwan. Bioinvasions Rec 11(1): 124\u0026ndash;135. https://doi.org/10.3391/bir.2022.11.1.13\u003c/li\u003e\n\u003cli\u003eHuang MJ, Hughes AC, Xu CY, Miao BG, Gao J, Peng YQ (2022) Mapping the changing distribution of two important pollinating giant honeybees across 21000 years. Global Ecol Conserv 39:e02282. https://doi.org/10.1016/j.gecco.2022.e02282\u003c/li\u003e\n\u003cli\u003eKearns CA, Inouye DW, Waser NM (1998) Endangered Mutualisms: The Conservation of Plant-Pollinator Interactions. Annu Rev Ecol Syst 29:83\u0026ndash;112. https://doi.org/10.1146/annurev.ecolsys.29.1.83\u003c/li\u003e\n\u003cli\u003eKevan PG, Baker HG (1983) Insects as Flower Visitors and Pollinators. Ann Rev Entomol 28:407\u0026ndash;453. https://doi.org/10.1146/annurev.en.28.010183.002203\u003c/li\u003e\n\u003cli\u003eKevan PG, Viana BF (2003) The global decline of pollination services. Biodiversity 4(4):3\u0026ndash;8. https://doi.org/10.1080/14888386.2003.9712703\u003c/li\u003e\n\u003cli\u003eKimura Y, Aoki T, Yamaguchi S, et al (2011) The Butterflies of Thailand Vol 1: Hesperiidae, Papilionidae and Pieridae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan\u003c/li\u003e\n\u003cli\u003eKimura Y, Aoki T, Yamaguchi S, et al (2014) The Butterflies of Thailand Vol 2: Lycaenidae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan.\u003c/li\u003e\n\u003cli\u003eKimura Y, Aoki T, Yamaguchi S, et al (2016) The Butterflies of Thailand Vol 3: Nymphalidae based on Yunosuke Kimura Collection. Mokuyosha, Tokyo, Japan\u003c/li\u003e\n\u003cli\u003eKlein AM, Vaissi\u0026egrave;re BE, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T (2007) Importance of pollinators in changing landscapes for world crops. P R Soc B 274:303\u0026ndash;313. http://doi.org/10.1098/rspb.2006.3721\u003c/li\u003e\n\u003cli\u003eKov\u0026aacute;cs-Hosty\u0026aacute;nszki A, Szigeti V, Miholcsa Z, S\u0026aacute;ndor D, Solt\u0026eacute;sz Z, T\u0026ouml;r\u0026ouml;k E, Fenesi A (2022) Threats and benefits of invasive alien plant species on pollinators. Basic Appl Ecol 64:89\u0026ndash;102. https://doi.org/10.1016/j.baae.2022.07.003\u003c/li\u003e\n\u003cli\u003eLagucki E, Burdine JD, McCluney KE (2017) Urbanization alters communities of flying arthropods in parks and gardens of a medium-sized city.PeerJ 5:e3620. https://doi.org/10.7717/peerj.3620\u003c/li\u003e\n\u003cli\u003eLajos K, Samu F, Bihaly \u0026Aacute;D, F\u0026uuml;l\u0026ouml;p D, S\u0026aacute;rospataki M (2021) Landscape structure affects the sunflower visiting frequency of insect pollinators. Sci Rep 11(1):8147.\u003c/li\u003e\n\u003cli\u003eLee HL, Sodhi NS, Elmqvist T (2001) Bee diversity along a disturbance gradient in tropical lowland forests of south-east Asia. J Appl Ecol 38:180\u0026ndash;192. https://doi.org/10.1046/j.1365-2664.2001.00582.x\u003c/li\u003e\n\u003cli\u003eMcFrederick QS, LeBuhn G (2006) Are urban parks refuges for bumble bees \u003cem\u003eBombus\u003c/em\u003e spp. (Hymenoptera: Apidae)? Biol Conserv 129(3):372\u0026ndash;382. https://doi.org/10.1016/j.biocon.2005.11.004\u003c/li\u003e\n\u003cli\u003eMcKinney ML (2008) Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst 11:161\u0026ndash;176. https://doi.org/10.1007/s11252-007-0045-4\u003c/li\u003e\n\u003cli\u003eMeagher RL, Watrous KM, Fleischer SJ, Nagoshi RN, Brown JT, Bowers K, Miller N, Hight SD, Legaspi JC, Westbrook JK (2019) Documenting potential sunn hemp (\u003cem\u003eCrotalaria juncea\u003c/em\u003e L.)(Fabaceae) pollinators in Florida. Environ Entomol 48(2):343-350. https://doi.org/10.1093/ee/nvy190\u003c/li\u003e\n\u003cli\u003eMichener CD (2000) The bees of the world. The Johns Hopkins University Press, Baltimore, Maryland\u003c/li\u003e\n\u003cli\u003eMillard J, Outhwaite CL, Kinnersley R, et al (2021) Global effects of land-use intensity on local pollinator biodiversity. Nat Commun 12:2902. https://doi.org/10.1038/s41467-021-23228-3\u003c/li\u003e\n\u003cli\u003eMorpurgo J, Huurdeman MA, Oostermeijer JG, Remme RP (2024) Vegetation density is the main driver of insect species richness and diversity in small private urban front gardens. Urban For Urban Green 101:128531. https://doi.org/10.1016/j.ufug.2024.128531\u003c/li\u003e\n\u003cli\u003eNewbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA et al (2015) Global effects of land use on local terrestrial biodiversity. Nature 520:45\u0026ndash;50. https://doi.org/10.1038/nature14324\u003c/li\u003e\n\u003cli\u003eNovotny V, Miller SE, Hulcr J et al (2007) Low beta diversity of herbivorous insects in tropical forests. Nature 448:692\u0026ndash;695. https://doi.org/10.1038/nature06021\u003c/li\u003e\n\u003cli\u003eOjija F, Arnold SE, Treydte AC (2019) Impacts of alien invasive \u003cem\u003eParthenium hysterophorus\u003c/em\u003e on flower visitation by insects to co-flowering plants. Arthropod-Plant Interactions 13:719\u0026ndash;34. https://doi.org/10.1007/s11829-019-09701-3\u003c/li\u003e\n\u003cli\u003eOksanen J (2015) Multivariate analysis of ecological communities in R: Vegan tutorial. Waikato, New Zealand. https://www.mooreecology.com/uploads/2/4/2/1/24213970/vegantutor.pdf. Accessed 17 April 2022\u003c/li\u003e\n\u003cli\u003eOksanen J, Blanchet FG, Friendly M et al. 2018. Vegan: Community ecology package. Vienna, Austria. https://CRAN.R-project.org/package=vegan. Accessed 7 February 2019\u003c/li\u003e\n\u003cli\u003ePalmer MW (1993) Putting Things in Even Better Order: The Advantages of Canonical Correspondence Analysis. Ecology 74(8):2215\u0026ndash;2230. https://doi.org/10.2307/1939575\u003c/li\u003e\n\u003cli\u003ePhosri, K., Tantipisanuh, N, Chutipong W, Gore ML, Giordano AJ, Ngoprasert D (2021) Fishing cats in an anthropogenic landscape: A multi-method assessment of local population status and threats. Glob Ecol Conserv 27: e01615. https://doi.org/10.1016/j.gecco.2021.e01615\u003c/li\u003e\n\u003cli\u003ePollard E (1977) A method for assessing changes in the abundance of butterflies. Biol Conserv 12(2):115\u0026ndash;134. https://doi.org/10.1016/0006-3207(77)90065-9\u003c/li\u003e\n\u003cli\u003ePotts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, Kunin WE (2010) Global pollinator declines: trends, impacts and drivers. Trends Ecol Evol 25(6):345\u0026ndash;353. https://doi.org/10.1016/j.tree.2010.01.007\u003c/li\u003e\n\u003cli\u003ePrendergast KS, Tomlinson S, Dixon KW, Bateman PW, Menz MH (2022) Urban native vegetation remnants support more diverse native bee communities than residential gardens in Australia\u0026apos;s southwest biodiversity hotspot. Biol Conserv 265:109408. https://doi.org/10.1016/j.biocon.2021.109408\u003c/li\u003e\n\u003cli\u003eR Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eRader R, Cunningham SA, Howlett BG, Inouye DW (2020) Non-bee insects as visitors and pollinators of crops: \u003c/li\u003e\n\u003cli\u003eBiology, ecology, and management. Annu Rev Entomol 65:391\u0026ndash;407. https://doi.org/10.1146/annurev-ento-011019-025055\u003c/li\u003e\n\u003cli\u003eRasband WS (1997) ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA. Available at: https://imagej.net/ij/\u003c/li\u003e\n\u003cli\u003eRicketts TH, Regetz J, Steffan‐Dewenter I, Cunningham SA, Kremen C, Bogdanski A et al (2008) Landscape effects on crop pollination services: are there general patterns? Ecol Lett 11(5):499\u0026ndash;515. https://doi.org/10.1111/j.1461-0248.2008.01157.x\u003c/li\u003e\n\u003cli\u003eSalgado VG, Viera Barreto JN, Rodr\u0026iacute;guez-Cravero JF, Grossi MA, Guti\u0026eacute;rrez DG (2024) Factors influencing the global invasion of the South American weedy species Praxelis clematidea (Asteraceae): a niche shift and modelling-based approach. Bot J Linn Soc: boae079. https://doi.org/10.1093/botlinnean/boae079\u003c/li\u003e\n\u003cli\u003eSamraj JM, Agnihotri M (2021) Impact of land use pattern and seasonality on butterfly diversity in a sub-tropical Terai arc landscape (TAL), Pantnagar, Uttarakhand (India). Trop Ecol 62(2):301\u0026ndash;310. https://doi.org/10.1007/s42965-021-00150-9\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Bayo F, Wyckhuys KA (2019) Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv 232:8\u0026ndash;927. https://doi.org/10.1016/j.biocon.2019.01.020\u003c/li\u003e\n\u003cli\u003eSchindler M, Diestelhorst O, Haertel S, Saure C, Scharnowski A, Schwenninger HR (2013) Monitoring agricultural ecosystems by using wild bees as environmental indicators. BioRisk 8:53\u0026ndash;71. https://doi.org/10.3897/biorisk.8.3600\u003c/li\u003e\n\u003cli\u003eSchulze CH, Waltert M, Kessler PJA, Pitopang R, Veddeler D, M\u0026uuml;hlenberg M et al (2004) Biodiversity indicator groups of tropical land-use systems: Comparing plants, birds, and insects. Ecol Appl 14(5):1321\u0026ndash;1333. https://doi.org/10.1890/02-5409\u003c/li\u003e\n\u003cli\u003eSchwartz MW, Smith LM, Steel ZL (2013) Conservation investment for rare plants in urban environments. PLoS ONE 8(12):e83809.\u003c/li\u003e\n\u003cli\u003eShanker C, Mohan M, Sampathkumar M, Lydia C, Katti G (2013) Functional significance of \u003cem\u003eMicraspis discolor\u003c/em\u003e (F.) (Coccinellidae: Coleoptera) in rice ecosystem. J Appl Entomol 137(8):601\u0026ndash;609. https://doi.org/10.1111/jen.12035\u003c/li\u003e\n\u003cli\u003eSimla P, Chaianunporn T, Sankamethawee W, Hughes AC, Sritongchuay T (2022) Effect of Landscape Composition and Invasive Plants on Pollination Networks of Smallholder Orchards in Northeastern Thailand.Plants 11(15):1976. https://doi.org/10.3390/plants11151976\u003c/li\u003e\n\u003cli\u003eSridith K (2022) Land tax loophole endangers our environment. Bangkok Post. https://www.bangkokpost.com/opinion/opinion/2471122. Accessed 15 February 2025\u003c/li\u003e\n\u003cli\u003eStewart AB, Sritongchuay T, Teartisup P, Kaewsomboon S, Bumrungsri S (2018) Habitat and landscape factors influence pollinators in a tropical megacity, Bangkok, Thailand. PeerJ 6:e5335. https://doi.org/10.7717/peerj.5335\u003c/li\u003e\n\u003cli\u003eStout JC, Tiedeken EJ (2017) Direct interactions between invasive plants and native pollinators: evidence, impacts and approaches. Funct Ecol 31: 38\u0026ndash;46. https://doi.org/10.1111/1365-2435.12751\u003c/li\u003e\n\u003cli\u003eSuwannapak K (2016) Thailand Insect Guide. Sarakadee, Bangkok\u003c/li\u003e\n\u003cli\u003eTakkis K, Kull T, Hallikma T, Jaksi P, Kaljund K, Kauer K, Kull T, Kurina O, K\u0026uuml;lvik M, Lanno K, Leht M (2018) Drivers of species richness and community integrity of small forest patches in an agricultural landscape. J Veg Sci 29(6):978\u0026ndash;988. https://doi.org/10.1111/jvs.12689\u003c/li\u003e\n\u003cli\u003eTangtorwongsakul P, Warrit N, Gale GA (2018) Effects of landscape cover and local habitat characteristics on visiting bees in tropical orchards. Agr Forest Entomol 20:28\u0026ndash;40. https://doi.org/10.1111/afe.12226\u003c/li\u003e\n\u003cli\u003eTawatsin A, Thavara U, Siriyasatien P (2015) Pesticides used in Thailand and toxic effects to human health. Med Res Arch 3:1\u0026ndash;10. https://doi.org/10.18103/mra.v0i3.176\u003c/li\u003e\n\u003cli\u003eter Braak CJ (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67(5):1167\u0026ndash;1179. https://doi.org/10.2307/1938672\u003c/li\u003e\n\u003cli\u003eThai Meteorological Department (2024) Daily weather reports of Khon Kaen Province (from 2020-2021). \u003c/li\u003e\n\u003cli\u003eTheisen-Jones H, Bienefeld K (2016) The Asian honey bee (\u003cem\u003eApis cerana\u003c/em\u003e) is significantly in decline, Bee World 93(4):90\u0026ndash;97. https://doi.org/10.1080/0005772X.2017.1284973\u003c/li\u003e\n\u003cli\u003eTraveset A, Richardson DM (2006) Biological invasions as disruptors of plant reproductive mutualisms. Trends Ecol Evol 21(4):208\u0026ndash;216. https://doi:10.1016/j.tree.2006.01.006\u003c/li\u003e\n\u003cli\u003eTommasi N, Biella P, Guzzetti L, Lasway JV, Njovu HK, Tapparo A, Agostinetto G, Peters MK, Steffan-Dewenter I, Labra M, Galimberti A (2021) Impact of land use intensification and local features on plants and pollinators in Sub-Saharan smallholder farms. Agric, Ecosyst Environ 319:107560. https://doi.org/10.1016/j.agee.2021.107560\u003c/li\u003e\n\u003cli\u003eTwerd L, Sobieraj-Betlińska A, Szefer P (2021) Roads, railways, and power lines: Are they crucial for bees in urban woodlands?. Urban For. Urban Green 61:127120. https://doi.org/10.1016/j.ufug.2021.127120\u003c/li\u003e\n\u003cli\u003eUllah MH, Rahim MA, Khan SA, Sultana M, Naher N (2024) Praxelis clematidea (griseb.) Rm king \u0026amp; h. Rob.(asteraceae): a new allien invasive angiosperm record for bangladesh. Bangladesh J Plant Taxon 31(1):181-5.\u003c/li\u003e\n\u003cli\u003eUlyshen MD, Soon V, Hanula JL (2010) On the vertical distribution of bees in a temperate deciduous forest. \u003c/li\u003e\n\u003cli\u003eVil\u0026agrave; M, Espinar JL, Hejda M, Hulme PE, Jaro\u0026scaron;\u0026iacute;k V, Maron JL et al (2011) Ecological impacts of invasive alien plants: a meta‐analysis of their effects on species, communities and ecosystems. Ecol Lett 14(7):702\u0026ndash;708. https://doi.org/10.1111/j.1461-0248.2011.01628.x\u003c/li\u003e\n\u003cli\u003eWagner DL (2020) Insect declines in the Anthropocene. Annu Rev Entomol 65:457\u0026ndash;480. https://doi.org/10.1038/s43017-023-00478-x\u003c/li\u003e\n\u003cli\u003eWalter-Echols G, Yongfan P (2005) Regional Overview and Analysis of Country Reports. In Proceeding of Asia regional workshop on implementation, monitoring and observance international code of conduct on the distribution and use of pesticides. Bangkok: FAO regional office for Asia and the Pacific.\u003c/li\u003e\n\u003cli\u003eWanapat M, Totakul P, Viennasay B, Matra M (2021) Sunnhemp (\u003cem\u003eCrotalaria juncea\u003c/em\u003e L) silage can enrich rumen fermentation process, microbial protein synthesis, and nitrogen utilization efficiency in beef cattle crossbreds. Trop Anim Health Prod 53(1):187. https://doi.org/10.1007/s11250-021-02628-z\u003c/li\u003e\n\u003cli\u003eWongwila K, Sankamethawee W (2023) Floral visitors to an invasive plant \u003cem\u003ePraxelis clematidea\u003c/em\u003e (Hieron. ex Kuntze) R.M. King \u0026amp; H. Rob. (Asteraceae) in Northeastern Thailand. Nat Hist Bull Siam Soc 65(1):1\u0026ndash;14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Khon Kaen University","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":"biodiversity, land-use gradient, spatiotemporal variation, tropical insects, urbanization","lastPublishedDoi":"10.21203/rs.3.rs-6494624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6494624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVariations in land-use types reflect different levels of human activity, which can affect local biodiversity. We examined how land-use types influenced the composition of local insect communities. We analyzed the spatial and temporal patterns of four key pollinating insect groups: Coleoptera, Diptera, Hymenoptera, and Lepidoptera (CDHL) across four land-use types: agricultural land, abandoned land, urban area, and a forest patch in Khon Kaen, northeastern Thailand, over three seasons. The forest patch supported the highest diversity and species richness of CDHL, whereas the abandoned land supported the highest insect abundance. Species turnover was more pronounced between land-use types than seasons and the urban area had the highest seasonal variation. Lepidoptera were the most abundant group in both space and time, contributing to 76% of all CDHL records. Hymenoptera, Coleoptera, and Diptera comprised 15.75%, 5.74% and 2.52% of the records, respectively. \u003cem\u003eApis florea\u003c/em\u003e was the most abundant bee, but \u003cem\u003eA. cerana\u003c/em\u003e and \u003cem\u003eA. dorsata \u003c/em\u003eshowed considerably low numbers. Diversity and abundance of ground flowers were significantly correlated with insect abundance. Air temperature, humidity, and species richness of trees appeared to affect the overall abundance and distribution at the family level of CDHL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for insect conservation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data provide a comprehensive understanding of the dynamics of pollinator communities in human-dominated landscapes. The results suggest a negative effect of urbanization on pollinating insect communities and highlight the importance of conserving both natural and human-modified green mosaics that maintain ecological connectivity across landscape matrices.\u003c/p\u003e","manuscriptTitle":"Variation in local communities of insect pollinators in different land-use types in Northeastern Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 08:56:39","doi":"10.21203/rs.3.rs-6494624/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":"622c7bf4-46c8-4bb2-893f-db9cab11cbcf","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47441428,"name":"Agroecology"},{"id":47441429,"name":"Conservation Biology"},{"id":47441430,"name":"Terrestrial Ecology"}],"tags":[],"updatedAt":"2025-04-22T08:56:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 08:56:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6494624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6494624","identity":"rs-6494624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}