Can lowland plants shifting upwards overcompete mountain plants in terms of pollination efficiency?

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Karolina Jackwerth, Ondřej Mudrák, Jan Klečka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5957991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Oecologia → Version 1 posted 4 You are reading this latest preprint version Abstract Many plant species move to higher elevations due to global warming, but the effects of these elevational shifts on plant-pollinator interactions are not well understood. This study aimed to examine how flower visitation and seed set of lowland plants change after they shift uphill, and whether they compete for pollinators with plants native to the mountains. We conducted an experiment using two plant groups: lowland species pre-planted in a greenhouse and transplanted to both lowland and mountain sites, and mountain species. Pollinators were recorded at lowland sites for planted species and at mountain sites for both planted and native species. We also used pan traps in white, yellow, and blue colors to collect pollinators at both sites. Afterward, seed sets of the planted species were collected to compare reproductive success between elevations. Flower visitation rates on planted species were not significantly affected by elevation, though pollinator abundance in pan traps was higher in the mountains. The pollinator spectrum varied across elevations and plant species, influenced by flower and pan trap color. However, planted species produced more seeds at lowland sites, indicating higher pollinator efficiency there. Overall, we found no evidence of competitive advantage for range-shifting lowland species in terms of pollination. pollination floral color range-shifting species elevation pollination efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global warming is one of the main reasons why organisms tend to shift their distribution to more favorable conditions (Shah et al., 2020 ; Spence & Tingley, 2020 ; Vitasse et al., 2021 ). Distribution changes such as range shifts to increasing elevation can allow lowland species to incorporate into the new mountain sites and become a competitor in a novel locality. Range-shifting plant species and species originally growing at higher elevations can compete for resources (Nomoto & Alexander, 2021 ) and for pollinators (Hernández-Castellano et al., 2020 ). However, our knowledge about range-shifting species and their long-term ecological impacts in the original mountain community is still limited. Plant species are variable in their ability to shift to higher elevations. Some species can move for a few elevational meters (e.g. Lonicera nigra : + 4 m), but some of them can overcome the distance of 100 meters (e.g. Cephalanthera longifolia : + 495 m) over a few decades (Lenoir et al., 2008 ). However, not all plant species tend to shift upwards. Some of them are more adaptive to changed climate than others and persist on the original elevation (Lenoir et al., 2008 ). Hence, shifts in plant species distribution can create a new enriched combination of species in mountain communities (Walther et al., 2005 ; Van Der Putten, 2012 ; Steinbauer et al., 2018 ). Subsequently, some insect species can also shift to higher elevations (Shah et al., 2020 ) and even follow their host favorite plant (Marshall et al., 2020 ). Thus, insect community and plant species turnover occur. We have evidence that a shift to lower elevation can affect pollination. Richman et al. ( 2020 ) found out that the experimental shift of meadow turfs downslope can affect plant-pollinator interactions and also the reproduction of focal plant species. Hence, knowledge about the shifting of particular plants to increasing elevation and subsequently their pollination effectivity is rather limited and requires future research (Inouye, 2020 ). Therefore, it can raise the question whether newly arrived species may pose a competition for pollinators with native species as in the case of invasive plants (Hejda & Pyšek, 2006 ; Mitchell et al., 2009 ). Moreover, range-shifting plants often pose with different functional traits, which can be attractive for pollinators originating in mountain sites. It may result in competition among plants for pollinators as range-shifting plants may attract most pollinators occurring in mountains. Similar flower colors can confuse pollinators, which can visit beside usual plants also range-shifting plants (Van Der Kooi et al., 2016 ). The so-called “pollination syndrome” represents the adaptation of a particular pollinator group on a particular plant. As a result, pollination becomes more efficient (Fenster et al., 2004 ; Rosas-Guerrero et al., 2014 ). Thus, plant species with their specialized pollinators have a higher chance for successful pollination followed by successful seed set development (Rosas‐Guerrero et al., 2014). The color of flowers is a particularly important trait for attracting pollinators (Miller et al., 2011 ; Trunschke et al., 2021 ) and certain functional groups prefer certain colors. Bees as the largest group of pollinators (Chittka & Raine, 2006 ) prefer blue or yellow. Butterflies can see a broader spectrum of colors also with red, which is invisible to bees. Beetles visit often white or dull flowers, similarly as moths, which can easily find white flowers in the dark. Flowers attracted by flies have red (resembling carrion), brown (resembling dung) or white color (plants mostly from Apiaceae ) family (Miller et al., 2011 ). Consequently, it is important to focus on floral color as one of the most important functional traits for pollinators. Aim of our study was to observe the change in pollinator spectrum and abundance between lowland and mountains when we use experimentally planted lowland species and shift them to higher elevation. Our expectation was that range-shifting species placed upwards could be more attractive for pollinators originating in mountains and thus these species can over compete mountains species in terms of pollination. Therefore, we ask the following questions: Can range-shifting plant species threaten plants originally growing in mountains by distracting their pollinators? We expect that range-shifting plants can affect the pollination efficiency of local mountain plants. Will the abundance and spectrum of pollinators differ between lowland and mountains? We suggest that both pollinator spectrum and pollinator abundance will change with elevation, because pollinators will differ according to environmental conditions and different floral resources. Will species of plants with different flower color differ from each other in the abundance and spectrum of pollinators? We suppose that abundance and spectrum of pollinators will be affected by their color, which largely determines the pollination syndromes. What will be the reproductive success of lowland species in their native lowland sites compared to the sites in mountains? We suggest that reproductive success will differ between elevations due to different pollinator efficiency. Materials and methods Study sites The experiment was conducted in the southern part of Czech Republic in June 2022. We selected four lowland sites (531–582 m a.s.l.) near the city Český Krumlov and four mountain sites (956–1014 m a.s.l.) in the Šumava mountains (Table 1 ). Those sites were selected according to the presence of our focal plant species (detailed explanation below). Mountains sites were located above the highest determined occurrence of the target lowland species based on the Pladias database (Chytrý et al., 2021 ). We set up six mountain sites because two of them were mown too early for our experiment (details below). Sites from both elevations represent the place with relatively high plant species diversity with enough flowering entomogenous species. Certain sites were located inside the protected landscape areas with a low level of protection (Plánský les, Šumava), whereas the rest of the sites were part of private land. Table 1 The list of study sites in both lowland and mountain areas. Elevation and GPS coordinates of the center of each site are provided. Site ID Site name Elevation (m a.s.l.) GPS mountains 1 Horní Sněžná 1 994 48.8874083N, 13.9432683E mountains 2 Horní Sněžná 2 1014 48.8949333N, 13.9451050E mountains 3 Čtyři Domy 956 48.89779N, 13.95513E mountains 4 Strážný 1 910 48.92762N, 13.71185E mountains 5 Strážný 2 982 48.93432N, 13.71026E mountains 6 Strážný 3 985 48.9309747N, 13.7340800E lowland 1 Cvičák 566 48.8247917N, 14.3162778E lowland 2 Kalamandra 532 48.82179N, 14.27911E lowland 3 Staré Dobrkovice 531 48.8185411N, 14.2988031E lowland 4 Vyšný 582 48.8310425N, 14.2928086E Study plant species We used two groups of plant species in our experiment (Table 2 ). The first experimental group consists of three thermophilous species of Asteraceae ( Senecio jacobea , Centaurea jacea ) and Lamiaceae ( Origanum vulgare ) family, whose natural occurrence is at lowland sites. We refer to these plants as “pot” species. These three species were planted in the pots since 2021. We collected seeds of these plant species at the lowland sites in August – September 2020. We sowed the seeds into germination trays with a mixture of garden soil, compost, and sand (2:1:1 ratio) with the addition of a small quantity of limestone in March 2021. We transferred the seedlings individually into pots – each seedling per 1 pot (diameter 11 cm) about two weeks after germination and grew them in a greenhouse with windows and doors containing a fine mesh against insects. The plants were used for the experiments after a one year of growth, in June, during the blooming period. Table 2 The list of study plant species with family, origin, and position in the site. Origin distinguishes plants growing originally in lowland (pot) and plants naturally growing at both altitudes (natural). The position represents the location outside or inside of the experimental plot for naturally growing species and the experimental plot strictly for pot species. Species Family Origin Position Senecio jacobea Asteraceae pot experimental plot Centaurea jacea Asteraceae pot experimental plot Origanum vulgare Lamiaceae pot experimental plot Knautia arvensis Dipsacaceae natural outside, inside Hieracium sp Asteraceae natural outside, inside Ranunculus acris Ranunculaceae natural outside, inside Pimpinella major Apiaceae natural outside, inside Chaerophyllum aureum Apiaceae natural outside, inside Aegopodium podagraria Apiaceae natural outside, inside Hypericum maculatum Hypericaceae natural outside, inside The second group is represented by species naturally growing only at higher sites – these species were selected based on color, similar to experimental species, and secondly according to their sufficient abundance in the locality. Focal naturally growing species were from Dipsacaceae family – Knautia arvensis ; Asteraceae family – Hiracium sp .; Ranunculaceae family – Ranunculus acris ; Apiaceae family – Pimpinella major ; Chaerophyllum aureum , Aegopodium podagraria and Hypericaceae family – Hypericum maculatum . In the text, we use the abbreviation of plant species name. Experimental design Each study site at both lowland and mauntains (Table 1 ) contained one experimental plot. We placed the focal planted pot species (Table 2 ) into an experimental plot. We had two sets of pot species. One set was used exclusively for lower sites and the other set was used for higher sites. Each plant individual in the pot had its unique code to avoid random substitution. The timing of the exposure of the pot species in the experiment was dependent on their phenology of blossom. Firstly, we used S. jacobea , later O. vulgare , and then C. jacea . We arranged 20 pot individuals of one plant species in clusters per experimental plot according to the experimental design (Fig. 1 ). We did this setting simultaneously in lowland and mountain sites. The experimental plot was a square of 5 m × 5 m. The pot species were positioned there in a specific formation (Fig. 1 ). We arranged five clusters, each containing four plant pots of a currently blooming species, within one experimental plot. These pots were spaced 1 m apart from each other within each cluster, creating a small square. The distance between several squares was 2 m. Around each experimental plot was a 15 m wide strip where we observed naturally growing species and placed pan traps. The naturally growing species in the locality were observed not only outside (in the 15 m wide strip), but also inside (placed in the 5 m × 5 m experimental plot) (Table 2 ). We installed three clusters of three pan traps with blue, yellow, and white colors in a triangle shape, 1 m apart from each other, at a distance of 10 m from our experimental plot within the 15 m wide strip (Fig. 1 ). Observation of pollinators The pot plants stayed at lowland and mountain sites according to experimental design (Fig. 1 ) without any intervention for 4 days allowing the adaptation of pollinators to the novel source. We always recorded one site from lowlands and another from mountains at the same time. After observation of pollinators, the two sets of plants both from the lowland and mountains were shifted to another lowland and mountain site. The location of the clusters in the experimental plot was randomly shuffled at each site. We continued in the same way with the other two species. Besides pot species observation, we recorded pollinators on naturally growing species at mountain sites inside and outside of experimental plot. Naturally growing species were observed only at mountain sites for later comparison of pollination efficiency of pot plants in the lowland and in the mountains. Thus, we observed pot species and naturally growing species at the same time. We observed pollinators visually. We recorded only pollinators, which touched the reproductive organs of flowers. We created for this purpose protocols with functional groups of pollinators; namely, Syrphidae, other Diptera, Lepidoptera, Coleoptera, Apis mellifera , Bombus sp., and solitary bees. We observed a cluster of individuals with codes of the same flowering species for 30 minutes and then we shifted to the next group of species. Similarly to pot species, naturally growing species were also often observed in clusters (natural growth of more individuals of the same species). Thus, each group of species – in pots and naturally growing, inside and outside – was observed three times at a minimum per day. The period of observation was approximately from 9:00 am until 4:00 pm in time of the highest activity of pollinators. We recorded the color of observed species and number of flowers per one individuum per each pot and naturally growing species. We also recorded the weather type. Observations were made only under suitable weather conditions for pollinator activity. So, we did not observe pollinators during rain and cloudy weather with low temperatures. Pan traps We set pan traps at the same time during pollinator observation to increase the number of the potential pollinators on each site. Pan traps were exposed at all lowland and mountains sites. Three sets of pan traps were installed 10 meters from experimental plot (Fig. 1 ). We select blue, white, and yellow colors to attract as many potential pollinators as possible (Vrdoljak & Samways, 2012 ; Moreira et al., 2016 ). We laid pan traps at 9:00 am and filled them with a mixture of water and natural detergent without perfume (Campbell & Hanula, 2007 ; Dirrigl, 2012 ). After the pollinator observation, around 4:00 pm, we collected all potential pollinators from all pan traps. The samples were stored in plastic zip-lock with 96% ethanol. All samples of each color and study site were determined to functional groups in the lab. We used the following functional groups in protocol: Syrphidae, other Diptera, Bombus sp., solitary bees, Apis mellifera , Coleoptera, Lepidoptera, and Thysanoptera. Seed set counting To determine pollination efficiency, we counted the number of seeds after the end of the field experiment. We counted seeds of focal pot species once per week for a month period since 15. 8. until 15. 9. during season 2022. We focused pot only on C. jacea and S. jacobea. These species were in a greenhouse equipped with a fine mesh in the place where the windows and doors were preventing inappropriate pollination before and after the field experiment. We cut all inflorescences of C. jacea and a certain number of randomly selected inflorescences of S. jacobea after one month. However, we did not count the seeds of O. vulgare , because they are very small, and it was not feasible to count them properly. We counted C. jacea and S. jacobea seeds only in time of their maturity based on their shape, size, and color based on the Pladias database (Chytrý et al., 2021 ). Data analyses We conducted all analyses in the R software version 4.0.3 (R Core Team, 2022 ). We did analyses separately for insect observation, pan traps approach, and seed set counting. We used for visualization of the results package ( scales ) (Wickham & Seidel, 2022) and ( ggpot2 ) (Wickham & Chang, 2016 ). For statistical tests we used package ( lme4 ) (Bates et al., 2015 ). We fitted Generalized linear mixed model (GLMM) using glmer function with Poisson distribution for pollinator observation part. We tested the effect of listed explanatory variables: elevation (mountains and lowland – fixed effect), number of observed flowers – log transformed (fixed effect), weather (cloudy, partly cloudy, sunny – fixed effect) and locality (random effect) on a sum of visitors (response variable). We did this model separately for three planted species in the pots: S. jacobea , O. vulgare , and C. jacea . We utilize a similar model with all naturally growing species; we tested the effect of listed explanatory variables: plant species ( K. arvense , Hiracium sp, R. acris , P. major , Ch. aureum , A. podagraria , H. maculatum - random effect) nested in plant position (outside, inside – fixed effect), number of observed flowers (fixed effect), weather (cloudy, partly cloudy, sunny– fixed effect), and locality (random effect) on sum of visitors (response variable). In addition, we tested two models with naturally growing plant species separated based on color: white ( P. major , Ch. aureum , and A. podagraria ) and yellow ( Hiracium sp, R. acris , and H. maculatum ). Explanatory variables were the number of observed flowers (fixed effect), weather (cloudy, partly cloudy, sunny– fixed effect), plant position (outside, inside – fixed effect), and locality (random effect) on a sum of visitors (response variable). We analyzed the results of pan traps also with GLMM using Poisson distribution. We tested how explanatory variables: elevation, trap color (white, blue, yellow – fixed effect) and locality (random effect) affect the response variable – sum of pollinators. We also tested the interaction of trap color and elevation as in the second model. In conclusion, we used the second model with interaction, because it was significantly more parsimonious (having lower Akaike Information Criterion). Finally, we analyzed seed numbers for two planted species: C. jacea and S. jacobea . We used as previously GLMM with the Poisson distribution. We tested the effect of explanatory variables elevation (mountains and lowland – fixed effect) and plant number (random effect) on seed number (response variable). The effect of the individual explanatory variables (whether positive or negative) is indicated by estimated regression coefficient (abbreviated as "Regr. Coef"), and by its standard error (reflecting the precision of the regression coefficient estimate, abbreviated as "Std. error"), in all fitted models. The differences in abundances of individual insect groups we tested by Redundancy Analysis (RDA) in Canoco 5 statistical software (ter Braak & Šmilauer 2012 ). First, we tested the differences in visitation of flowers. Here the number of visits of the flower were explained by the position of the plant, plant species, altitude, locality, and weather. All variables were tested at first together in one model. Similarly, we tested the number of insect pollinators trapped in the pans of different color by RDA. Here the number of insect pollinators were at first explained by all variables together, then we conducted set of RDAs, where each variable was tested separately, with other variables as covariates. In all RDAs, significance was tested by Monte Carlo permutation test with 999 permutations. Results Observation of pollinators All three pot species together ( C. jacea, O. vulgare, S. jacobea) hosted a similar number of potential pollinators (Table 3 ) at both lowland and mountain sites. Separately, C. jacea and O. vulgare have more pollinators at original lowland sites, whereas S. jacobea had more pollinators in the mountains. The pollinator spectrum differed between lowlands and mountains and among plant species (Table 4 ; Fig. 2 ). Two pot species: C. jacea and O. vulgare had similar groups of potential pollinators. The dominant pollinator in lowland was Apis mellifera and hoverflies and butterflies prevailed in the mountains. Whereas S. jacobea hosted mostly Sirphidae , especially in mountains followed by solitary bees in lowland and bumblebees in mountains (Table 4 ). The number of pollinators of C. jacea was dependent on the number of observed flowers and sunny weather. The pollinator number of O. vulgare slightly differed between elevations, but the result was only marginally significant. The number of pollinators of O. vulgare was dependent on the number of observed flowers and on sunny weather. The number of pollinators of Senecio jacobea was positively dependent on the number of observed flowers. There was a significant difference between sunny and cloudy weather (Table 3 ). The number of pollinators of all naturally growing species was significantly affected by the number of observed flowers and by sunny and cloudy weather (Table 3 ). Table 3 The results of linear mixed effect model testing the impact of the number of open flowers, elevation, and weather on the number of pollinators of all three pot species and naturally growing species. Number of observed flowers was log-transformed before analysis. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results. Predictors (fixed effects) Centaurea jacea Regr. Coef Std. Error z value P Elevation (mountains) -0.53 0.47 -1.14 0.25 log (Observed flowers) 0.61 0.04 15.57 < 10 − 3 Weather partly cloudy 0.02 0.06 0.3 0.77 Weather sunny 0.14 0.06 2.39 0.02 Predictors (fixed effects) Origanum vulgare Regr. Coef Std. Error z value P Elevation (mountains) -0.63 0.35 -1.82 0.07 log (Observed flowers) 0.45 0.02 23.2 < 10 − 3 Weather partly cloudy 0.12 0.08 1.49 0.14 Weather sunny 0.13 0.06 2.14 0.03 Predictors (fixed effects) Senecio jacobea Regr. Coef Std. Error z value P Elevation (mountains) 0.51 0.44 1.15 0.25 log (Observed flowers) 0.44 0.02 26.4 < 10 − 3 Weather partly cloudy 0.12 0.03 4.83 < 10 − 3 Weather sunny 0.22 0.04 5.7 < 10 − 3 Predictors (fixed effects) Naturally growing species Regr. Coef Std. Error z value P log (Observed flowers) 0.30 0.01 21.45 < 10 − 3 Weather partly cloudy 0.25 0.03 7.94 < 10 − 3 Weather sunny 0.17 0.03 5.67 < 10 − 3 Table 4 Results of Redundancy Analysis (RDA) testing the abundances of individual insect groups on flowers (a) and abundances of individual insect groups trapped in the panes of different color (b) by individual variables. a) abundances of individual insect groups on flowers Tested variable Explained variability F P all together 41.3% 103 0.001 position of the plant 0.2% 3.8 0.004 plant species 26.0% 125 0.001 altitude 9.0% 318 0.001 locality 9.4% 42.3 0.001 weather 1.6% 27.3 0.001 b) abundances of individual insect groups in panes Tested variable Explained variability F P all together 31.24% 10.2 0.001 color 4.34% 5.9 0.001 altitude 14.23% 37.7 0.001 locality 17.55% 6.9 0.001 The sum of pollinators on naturally growing species was different within each color (white, and yellow) (Fig. 2 ). The sum of pollinators on plants with white and yellow color separately was significantly different based on weather and number of observed flowers (Table 5 ). White flowering species ( A. podagraria , Ch. aureum , P. major ) was significantly affected by the number of observed flowers (Table 5 ) and by sunny and cloudy weather. The number of pollinators was significantly higher when the species were observed inside of experimental plot. White flowers attracted mostly hoverflies and other dipterans. Ch. aureum and P. major host often also beetles (Table 4 ; Fig. 2 ). The yellow flowering species ( Hieracium sp ., H. maculatum and R. acris ) were significantly affected by the number of observed flowers (Table 5 ) and by sunny and cloudy weather. They were preferred mostly by Syrphidae and other Dipterans, the third group visited was solitary bees (Table 4 ; Fig. 2 ). Table 5 The results of linear mixed effect model testing the impact of the number of open flowers, weather, and plant position on the number of pollinators of naturally growing species according to flower color. The number of observed flowers was log-transformed before analysis. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results. Predictors (fixed effects) White flowers Yellow flowers Regr. Coef Std. Error z value P Regr. Coef Std. Error z value P log (Observed flowers) 0.21 0.02 12.66 < 10 − 3 0.29 0.03 10.95 < 10 − 3 Weather partly cloudy 0.37 0.04 10.20 < 10 − 3 0.40 0.06 6.34 < 10 − 3 Weather sunny -0.13 0.03 -4.06 < 10 − 3 -0.44 0.06 -6.81 < 10 − 3 Plant position (outside) -0.06 0.02 -2.64 0.008 0.04 0.03 1.26 0.209 Pan traps The number of pollinators collected from pan traps was significantly affected by elevation (difference between lowland and mountains). We collected more pollinator individuals in the mountains (2715) in comparison to the lowland (1343). Pan trap colors white and yellow significantly differed from blue in the number of pollinators. White color was visited mostly by dipterans at both elevations, but mainly at mountain sites, the second most abundant pollinators were solitary bees in the lowland followed by Coleoptera at both elevations. The yellow color was preferred by the majority of plant pollinators, mostly by flies except for syrphids in the lowland followed by solitary bees and Coleoptera (Fig. 3 ). The interaction of elevation and white color was significant similarly to the interaction of elevation and yellow color (Table 6 ). RDA revealed that pan traps significantly differed in the spectrum of pollinators depending on the elevation (Table 4 ; Fig. 3 ). Table 6 The results of linear mixed effect model testing the impact of elevation and pan trap color, separately and with interaction, on the number of pollinators. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results. Predictors (fixed effects) Regr. Coef Std. Error z value P Elevation (mountains) 0.94 0.18 5.22 < 10 − 3 Trap white 0.37 0.07 5.05 < 10 − 3 Trap yellow 0.62 0.07 8.89 < 10 − 3 Elevation x Trap white -0.36 0.09 -4.14 < 10 − 3 Elevation x Trap yellow -0.52 0.08 -6.13 < 10 − 3 Seed set counting Seeds were collected from C. jacea and S. jacobea . The number of seeds collected from C. jacea was significantly different across the elevation (lowland vs. mountains, Fig. 4 ). Also, the number of seeds collected from S. jacobea differed across the elevation (lowland vs. mountains, Table 7 , Fig. 4 ). Both plant species had more seeds in the lowland. The difference in number of C. jacea seeds between lowland and mountains was 17.3%. S. jacobea seeds differed by 14.7% between lowland and mountains. Table 7 The results of linear mixed effect model testing the impact of elevation on the number of seeds of C. jacea and S. jacobea . In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results. Species Predictors (fixed effects) Regr. Coef Std. Error z value P Centaurea jacea Elevation (mountains) -0.47 0.04 -11.62 < 10 − 3 Senecio jacobea Elevation (mountains) -0.08 0.01 -6.323 < 10 − 3 Discussion We have found no evidence that range-shifting plants, which may potentially migrate upward in the mountains, did not alter the pollinator network mountain ecosystems. We have found just minor changes in some interactions in the case of white flowering plants. This was likely because we found more pollinators in the mountains than in the lowlands. The spectrum of pollinators was largely dependent on flower color, suggesting a division of pollinators, likely based on different pollination syndromes. Flower visitation of range-shifting plants in pots was not significantly different between elevations. However, pollinator efficiency of range-shifting plants varied between lowland and mountain sites, with range-shifting plants in pots, especially C. jacea , showing higher pollinator efficiency, as indicated by a higher number of seeds at the original lowland sites. Observation of pollinators Pollinators of studied plant species strongly reacted to number of opened flowers per plant. The more flowers the plant offered; the more pollinators visited this plant. This trend was stable for all three pot species ( C. jacea, O. vulgare, S. jacobea ) and for all naturally growing species ( K. arvensis , A. podagraria , Ch. aureum , P. major, Hieracium sp ., H. maculatum and R. acris ) in mountains. Plant with more flowers is better visible from longer distance and may release more intensive odor offering potential visitors more rewards (Wright & Schiestl, 2009 ) it can also benefit from this by increasing pollen export to other plants from same species (Klinkhamer & De Jong, 1993 ). Increasing reproduction success with more flowers illustrates also Brys et al. (2004) on example with increasing fruit set in bigger populations of Primula vulgaris . Our investigation revealed no significant disparity in the pollinator numbers for lowland potted species ( C. jacea, O. vulgare, S. jacobea ) between lowland and mountain sites. Contrary to expectations, we did not observe a marked increase in pollinator visits in mountain regions, where the introduction of novel potted species from lowlands could potentially attract pollinators more than original mountain plant species. Higher pollinator abundance in the lowlands can be caused by the fact that lowland species in pots have their origin there, and local pollinators may have developed adaptations to them. Overall, the elevation difference is rather small (the distance between the lowest and the highest site was 683 m a.s.l). Thus, pollinators did not need to adapt to as demanding conditions as they are at more prominent high elevation, e.g., above tree line. Yet, the number of pollinator visits varied from species to species. Whereas C. jacea and O. vulgare had more pollinators at original lowland sites, S. jacobea hosted more pollinators in the mountains which relates to pollinator preference for certain color (Willmer, 2011 ). The pollinators differed in composition between lowlands and mountains and among plant species. Two pot species: C. jacea and O. vulgare had a similar pattern of pollinators. The dominant pollinator in the lowland was honeybee whereas in the mountains hoverflies and butterflies prevailed. We suggest that the color of flower, pink in both species, attracted the similar spectrum of pollen visitors (Trunschke et al., 2021 ). Also Szigeti et al. ( 2023 ) observed that pollinators of pink flowers are bees, hoverflies and butterflies. In contrast, Senecio jacobea had different pollinator spectrum; hoverflies were dominant at both elevations, mostly at mountains. They were followed by solitary bees in lowland and bumblebees in mountains. The importance of hoverflies and Apidae family as a main pollinators of S. jacobea found out also Vanparys et al., ( 2008 ). Naturally growing species, occurring only in the mountains, were classified according to sheared color, and thus separated into two groups (white, yellow). White flowering species ( Aegopodium podagraria , Chaerophylum aureum , Pimpinella major ) as a representatives of Apiaceae family, attracted mostly hoverflies and other dipterans. The fact that flies are main pollinators of Apiaceae family proved also Larson et al. ( 2001 ) who pointed out that flies select open flowers with nectar reward. The preference for white flowers for certain part of dipterans illustrated also Willmer, ( 2011 ) who demonstrated that white flowers were determined partly as a fly pollination syndrome. Moreover, white flowering plants had more pollinators inside of experimental plot (in close proximity to potted plants). We suggest that lowland potted plants can actually affect interactions of plants and pollinators in mountain community by attracting them (potted plants have more distinctive color of flowers). Chaerophylum aureum and Pimpinella major host often also beetles which prefer open flattened flowers with a distinctive odor (Gottsberger, 1977 ). In Addition, we found that there were more pollinators on naturally growing species from Apiaceae family placed inside of experimental plot close to the pot species. We hypothesized that pot lowland plants could attract attention of the majority of pollinators of a mountain site and pollinators thus visited besides pot plants also naturally growing plants in their vicinity. Moreover, higher pollinator activity inside of experimental plot can be also caused by a generally higher number of Diptera in the mountains, which prefer white color. Ultimately, the interplay between these two factors likely underlies the observed phenomenon. The group of yellow flowering species consisting of Hieracium sp ., H. maculatum , and R. acris was preferred mostly by hoverflies and other Diptera, third visited group was solitary bees. The preference for yellow color for certain type of flies confirmed also Inouye et al., ( 2015 ) and Campbell et al., ( 2010 ), while yellow attractivity for solitary bees was observed by Latha et al., ( 2020 ). Pollinators of both pot and naturally growing species were differently active in relation to different weather conditions. C. jacea and O. vulgare hosted significantly more pollinators during sunny weather. Generally, pollinators are more active during sunny and warmer weather (Goodwin et al., 2021 ). Insect pollinators are ectothermic and thus they rely on microclimate more than endotherms. Therefore, insect pollinator foraging behavior is more dependent on body temperature, which is determined by both ambient temperature and radiation, as well as on windspeed (Corbet, 1990 ). On the other hand, pollinator visitation of S. jacobea and all naturally growing plants were active in both types of weather. Surprisingly, species with white flowers ( Aegopodium podagraria , Chaerophylum aureum , Pimpinella major ) and yellow flowers ( Hieracium sp ., H. maculatum , and R.acris ) had more flowers visited during cloudy weather. Both groups of naturally growing species and also S. jacobea had dipterants including sirphids as a main pollinator. Dipterans are active also during cloudy days in comparison to other pollinators (Inouye et al., 2015 ). They are thus effective pollinators in the mountains where they can replace other pollinators (Ssymank et al., 2008 ). Pan traps Contrary to results for observation of pollinators on flowers, the number of pollinators collected in pan traps significantly differed between lowland and mountains. Interestingly, more pollinators were collected in the mountains. The same trend found out also Baumann et al., ( 2021 ), who observed pollinators on mountain hay meadows. We suggest that this fact could be influenced by increasing number of files with elevation (Ssymank et al., 2008 ). McCabe & Cobb, ( 2021 ) observed that bees are replaced by flies in case of increasing elevation and decreasing temperature. Increasing number of dipterans with elevation was confirmed also by study from Utah (Warren et al., 1988 ). Pollinator spectrum vary also with different colors of pan traps; especially with white and yellow color. White color was attractive for dipterans at both elevations, but mainly at mountains sites. We collected a high number of flies in white pan traps because there were many plants with white color, which are often visited by dipterans. It was mainly the plants from Apiaceae family (Larson et al., 2001 ); D. carota , P. major , A. podagraria , and Ch. aureum . Besides flies, solitary bees were attracted by white color. The third most frequent group of pollinators were beetles and hoverflies (hoverflies occurred mainly in mountains). In contrary to our results, study with blister beetle shown the preference for blue color (Lebesa et al., 2011 ). In lowland sites, there was quite common visitor also Apis mellifera as in study of Jaques et al., ( 2023 ). Yellow color was preferred by majority of plant pollinators. Harris et al., ( 2017 ) conducted experiments with different pan traps color, size and position and also confirmed the highest proportion of pollinators in yellow pan traps. In lowland, there was dominance of flies except of syrphids. The second mostly visited group was solitary bees; as in another case of observation experiment by Latha et al., ( 2020 ). Pan traps from mountains were mainly visited by dipterans, whereas other groups of pollinators were represented in small percentages. The majority of dipterans follow the general trend of flies as main pollinators in mountains (Warren et al.,1988). Additionally, the interaction of color (white and yellow) with elevation (lowland and mountains) significantly affected pollinator spectrum. It is well known that different colors are preferred by different types of pollinators, partly due to pollination syndrome (Van Der Pijl and Faegri, 1979 ; Willmer, 2011 ). Also, the pollinator spectrum is shifting with increasing elevation from bees to flies pollination (McCabe & Cobb, 2021 ). Bees are not so resilient to colder environment, so their ability to forage decrease with increasing elevation (Lázaro et al., 2008 ). For this reason, the interaction of both, color of flower together with elevation, might significantly affect pollinator spectrum turnover. Seed set counting The seed set of both planted species in the pots ( C. jacea and S. jacobea ) significantly differed with elevation. We collected seeds of selected flowers in the pots in two sets, from both lowland and mountains sites, to investigate the reproductive success (Rering et al., 2020 ). We counted seeds of C. jacea and S. jacobea . O. vulgare have tiny flowers and seeds; thus it was difficult to count it without common mistakes. Therefore, we did not count the seeds of this species. C. jacea had 17.3% seeds more in the lowland. Similarly, S. jacobea had 14.7% more seeds in lowland. This is the proof that the pollination was more effective in place where plant species originally grow. The pollinators of C. jacea were mainly honeybees in the lowland, which did not occur in the mountains. The second most abundant group of pollinators for C. jacea were solitary bees in the lowland, whose number dramatically decrease in mountains. The reason why S. jacobea had more pollinators in lowland was caused by two groups of pollinators – hoverflies and solitary bees, which together reach higher abundance in lowland. Although the abundance of hoverflies as such was increasing with elevation. Conclusion To sum up, all three pot species hosted a similar number of potential pollinators after observation. However, the pollinators differed in their pollinator spectrum, between lowland and mountains and among plant species resulting in higher pollination efficiency in lowlands. Also, naturally growing species differed in their pollinator spectrum. Interestingly, white flowering plants had more pollinators inside the experimental plot (in close proximity to potted plants), suggesting that potted plants influence the turnover of mountain pollinators. The novel presence of lowland plant species in mountain environments has the potential to alter community dynamics. However, our results do not support the idea that these plants will pose a significant threat to native mountain plant species through competitive displacement in their original habitat. Declarations Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Conflicts of interest We declare no conflict of interest. Consent to participate All patients included in this study gave written informed consent to participate in this research. Consent for publication All patients provided written informed consent to publish the data contained within this article. Funding This study was funded by GAČR project no.: 360 640 3317. Authors' contributions JK conceived the study; JK and KJ designed the methods; JK and KJ collected the data; JK and KJ and OM analysed the data; KJ and OM wrote the manuscript. Acknowledgement We are grateful to Andrea Tomešová, Aneta Pilíková, Daniel Jackwerth, Marie Egnerová, Dagmar Hucková, Pavla Kovářová, Lucie Sázavská, Klára Hájková and Iena Klečková for their help with experimental work in the field. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5957991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436778486,"identity":"b31ffee6-8387-43f5-adb0-9650be606197","order_by":0,"name":"Karolina Jackwerth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie2QMQrCMBSGXynEJeqaUtErKILibRIKnUQQFwXRguBUV+lkr6A3qAh16QEKWfQGdYsIajI4iUE3h3zTWz7+/38ABsMfQgAoQKJObJ1+VBC2m98q8FIQ+UpxAjgXIjvUu/EqnYjxbFBdJ9ZFaBQXwHPC/NCupRWf4wyNCKc20eXV5RYXCs4ihDvcWmIWcIpAt0oq3k0qc6UMr3fCYqVQfTHfhZxTIhUoB022VUqim78Avxdmj1aE+m0Xp5TtOFvIp3yGHEMvF6nfIHbWuojpjG24t9d+DGz81tvSZShKut4Gg8FgkDwBYHJLQ2VrzwMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1994-5242","institution":"University of South Bohemia: Jihoceska Univerzita v Ceskych Budejovicich","correspondingAuthor":true,"prefix":"","firstName":"Karolina","middleName":"","lastName":"Jackwerth","suffix":""},{"id":436778487,"identity":"38bb84d1-dbc7-4690-9332-26619b24cf52","order_by":1,"name":"Ondřej Mudrák","email":"","orcid":"","institution":"Charles University Faculty of Science: Univerzita Karlova Prirodovedecka fakulta","correspondingAuthor":false,"prefix":"","firstName":"Ondřej","middleName":"","lastName":"Mudrák","suffix":""},{"id":436778488,"identity":"25eae7f1-b866-4a0b-a98d-62dbba229fe3","order_by":2,"name":"Jan Klečka","email":"","orcid":"","institution":"Biologicke centrum Akademie ved Ceske republiky Entomologicky ustav","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Klečka","suffix":""}],"badges":[],"createdAt":"2025-02-04 12:13:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5957991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5957991/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00442-025-05787-0","type":"published","date":"2025-08-19T16:29:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81107793,"identity":"6ef9df73-c827-48c8-9afb-6e204a5732c6","added_by":"auto","created_at":"2025-04-22 09:54:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130577,"visible":true,"origin":"","legend":"\u003cp\u003eThe scheme of the experimental design with pot species, “inside” natural species within the experimental plot and “outside” natural species and pan traps within 15 m wide strip.\u003c/p\u003e","description":"","filename":"Fig.1..png","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/51a2711739a1775bc2cc3f86.png"},{"id":81108064,"identity":"6d5ef59b-5eae-44fc-8d79-1c9f3085cb65","added_by":"auto","created_at":"2025-04-22 10:02:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348312,"visible":true,"origin":"","legend":"\u003cp\u003eThe sum of potential pollinator groups a) on plant species in pots (\u003cem\u003eC. jacea\u003c/em\u003e, \u003cem\u003eO. vulgare\u003c/em\u003e, and \u003cem\u003eS. jacobea\u003c/em\u003e) located at lowland and mountain sites; b) on white flowers of naturally growing species (\u003cem\u003eA. podagraria\u003c/em\u003e, \u003cem\u003eCh. aureum\u003c/em\u003e, \u003cem\u003eP. major\u003c/em\u003e); c) on yellow flowers of naturally growing species (\u003cem\u003eHieracium sp\u003c/em\u003e., \u003cem\u003eH. maculatum\u003c/em\u003e and \u003cem\u003eR. acris\u003c/em\u003e) located at mountain sites.\u003c/p\u003e","description":"","filename":"Fig.2..png","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/9be14e0f146c6c5ebb8a3f6d.png"},{"id":81108065,"identity":"d9547003-0761-425d-815d-144466a6bd0e","added_by":"auto","created_at":"2025-04-22 10:02:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36741,"visible":true,"origin":"","legend":"\u003cp\u003eThe sum of pollinator groups is based on pan trap color and elevation.\u003c/p\u003e","description":"","filename":"Fig.3..png","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/0bfe65fa51f5490cf96f8402.png"},{"id":81107789,"identity":"35815068-4452-4b34-82fb-bc64402607bd","added_by":"auto","created_at":"2025-04-22 09:54:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35077,"visible":true,"origin":"","legend":"\u003cp\u003eThe seed set number of \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e along elevation.\u003c/p\u003e","description":"","filename":"Fig.4..png","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/0d2174428754729d70d02270.png"},{"id":89847278,"identity":"65de800f-45b0-4821-aad7-96695ee99aab","added_by":"auto","created_at":"2025-08-25 16:42:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/c5215961-8d73-4ed0-be65-9fb9a7d27276.pdf"},{"id":81107792,"identity":"17c559fd-fcd7-4d58-94c8-745eb76585b1","added_by":"auto","created_at":"2025-04-22 09:54:38","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":154787,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5957991/v1/52c4b51af78b1d0ac73c581c.png"}],"financialInterests":"","formattedTitle":"Can lowland plants shifting upwards overcompete mountain plants in terms of pollination efficiency?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal warming is one of the main reasons why organisms tend to shift their distribution to more favorable conditions (Shah et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spence \u0026amp; Tingley, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vitasse et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Distribution changes such as range shifts to increasing elevation can allow lowland species to incorporate into the new mountain sites and become a competitor in a novel locality. Range-shifting plant species and species originally growing at higher elevations can compete for resources (Nomoto \u0026amp; Alexander, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and for pollinators (Hern\u0026aacute;ndez-Castellano et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, our knowledge about range-shifting species and their long-term ecological impacts in the original mountain community is still limited.\u003c/p\u003e \u003cp\u003ePlant species are variable in their ability to shift to higher elevations. Some species can move for a few elevational meters (e.g. \u003cem\u003eLonicera nigra\u003c/em\u003e: + 4 m), but some of them can overcome the distance of 100 meters (e.g. \u003cem\u003eCephalanthera longifolia\u003c/em\u003e: + 495 m) over a few decades (Lenoir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, not all plant species tend to shift upwards. Some of them are more adaptive to changed climate than others and persist on the original elevation (Lenoir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Hence, shifts in plant species distribution can create a new enriched combination of species in mountain communities (Walther et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Van Der Putten, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Steinbauer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, some insect species can also shift to higher elevations (Shah et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and even follow their host favorite plant (Marshall et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, insect community and plant species turnover occur. We have evidence that a shift to lower elevation can affect pollination. Richman et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found out that the experimental shift of meadow turfs downslope can affect plant-pollinator interactions and also the reproduction of focal plant species. Hence, knowledge about the shifting of particular plants to increasing elevation and subsequently their pollination effectivity is rather limited and requires future research (Inouye, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it can raise the question whether newly arrived species may pose a competition for pollinators with native species as in the case of invasive plants (Hejda \u0026amp; Pyšek, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mitchell et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, range-shifting plants often pose with different functional traits, which can be attractive for pollinators originating in mountain sites. It may result in competition among plants for pollinators as range-shifting plants may attract most pollinators occurring in mountains. Similar flower colors can confuse pollinators, which can visit beside usual plants also range-shifting plants (Van Der Kooi et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe so-called \u0026ldquo;pollination syndrome\u0026rdquo; represents the adaptation of a particular pollinator group on a particular plant. As a result, pollination becomes more efficient (Fenster et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Rosas-Guerrero et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, plant species with their specialized pollinators have a higher chance for successful pollination followed by successful seed set development (Rosas‐Guerrero et al., 2014). The color of flowers is a particularly important trait for attracting pollinators (Miller et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Trunschke et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and certain functional groups prefer certain colors. Bees as the largest group of pollinators (Chittka \u0026amp; Raine, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) prefer blue or yellow. Butterflies can see a broader spectrum of colors also with red, which is invisible to bees. Beetles visit often white or dull flowers, similarly as moths, which can easily find white flowers in the dark. Flowers attracted by flies have red (resembling carrion), brown (resembling dung) or white color (plants mostly from \u003cem\u003eApiaceae\u003c/em\u003e) family (Miller et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, it is important to focus on floral color as one of the most important functional traits for pollinators.\u003c/p\u003e \u003cp\u003eAim of our study was to observe the change in pollinator spectrum and abundance between lowland and mountains when we use experimentally planted lowland species and shift them to higher elevation. Our expectation was that range-shifting species placed upwards could be more attractive for pollinators originating in mountains and thus these species can over compete mountains species in terms of pollination. Therefore, we ask the following questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCan range-shifting plant species threaten plants originally growing in mountains by distracting their pollinators? We expect that range-shifting plants can affect the pollination efficiency of local mountain plants.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWill the abundance and spectrum of pollinators differ between lowland and mountains? We suggest that both pollinator spectrum and pollinator abundance will change with elevation, because pollinators will differ according to environmental conditions and different floral resources.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWill species of plants with different flower color differ from each other in the abundance and spectrum of pollinators? We suppose that abundance and spectrum of pollinators will be affected by their color, which largely determines the pollination syndromes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat will be the reproductive success of lowland species in their native lowland sites compared to the sites in mountains? We suggest that reproductive success will differ between elevations due to different pollinator efficiency.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003eThe experiment was conducted in the southern part of Czech Republic in June 2022. We selected four lowland sites (531\u0026ndash;582 m a.s.l.) near the city Česk\u0026yacute; Krumlov and four mountain sites (956\u0026ndash;1014 m a.s.l.) in the Šumava mountains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Those sites were selected according to the presence of our focal plant species (detailed explanation below). Mountains sites were located above the highest determined occurrence of the target lowland species based on the Pladias database (Chytr\u0026yacute; et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We set up six mountain sites because two of them were mown too early for our experiment (details below). Sites from both elevations represent the place with relatively high plant species diversity with enough flowering entomogenous species. Certain sites were located inside the protected landscape areas with a low level of protection (Pl\u0026aacute;nsk\u0026yacute; les, Šumava), whereas the rest of the sites were part of private land.\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\u003eThe list of study sites in both lowland and mountain areas. Elevation and GPS coordinates of the center of each site are provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSite name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation (m a.s.l.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorn\u0026iacute; Sněžn\u0026aacute; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e48.8874083N, 13.9432683E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorn\u0026iacute; Sněžn\u0026aacute; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e48.8949333N, 13.9451050E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eČtyři Domy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.89779N, 13.95513E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStr\u0026aacute;žn\u0026yacute; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.92762N, 13.71185E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStr\u0026aacute;žn\u0026yacute; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.93432N, 13.71026E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emountains 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStr\u0026aacute;žn\u0026yacute; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e48.9309747N, 13.7340800E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003elowland 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCvič\u0026aacute;k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e48.8247917N, 14.3162778E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003elowland 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKalamandra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.82179N, 14.27911E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003elowland 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStar\u0026eacute; Dobrkovice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e48.8185411N, 14.2988031E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003elowland 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVyšn\u0026yacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.8310425N, 14.2928086E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy plant species\u003c/h3\u003e\n\u003cp\u003eWe used two groups of plant species in our experiment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first experimental group consists of three thermophilous species of Asteraceae (\u003cem\u003eSenecio jacobea\u003c/em\u003e, \u003cem\u003eCentaurea jacea\u003c/em\u003e) and Lamiaceae (\u003cem\u003eOriganum vulgare\u003c/em\u003e) family, whose natural occurrence is at lowland sites. We refer to these plants as \u0026ldquo;pot\u0026rdquo; species. These three species were planted in the pots since 2021. We collected seeds of these plant species at the lowland sites in August \u0026ndash; September 2020. We sowed the seeds into germination trays with a mixture of garden soil, compost, and sand (2:1:1 ratio) with the addition of a small quantity of limestone in March 2021. We transferred the seedlings individually into pots \u0026ndash; each seedling per 1 pot (diameter 11 cm) about two weeks after germination and grew them in a greenhouse with windows and doors containing a fine mesh against insects. The plants were used for the experiments after a one year of growth, in June, during the blooming period.\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\u003eThe list of study plant species with family, origin, and position in the site. Origin distinguishes plants growing originally in lowland (pot) and plants naturally growing at both altitudes (natural). The position represents the location outside or inside of the experimental plot for naturally growing species and the experimental plot strictly for pot species.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSenecio jacobea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eexperimental plot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCentaurea jacea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eexperimental plot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOriganum vulgare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLamiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eexperimental plot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKnautia arvensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDipsacaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHieracium sp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRanunculus acris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRanunculaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePimpinella major\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChaerophyllum aureum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAegopodium podagraria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHypericum maculatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypericaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoutside, inside\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\u003eThe second group is represented by species naturally growing only at higher sites \u0026ndash; these species were selected based on color, similar to experimental species, and secondly according to their sufficient abundance in the locality. Focal naturally growing species were from Dipsacaceae family \u0026ndash; \u003cem\u003eKnautia arvensis\u003c/em\u003e; Asteraceae family \u0026ndash; \u003cem\u003eHiracium sp\u003c/em\u003e.; Ranunculaceae family \u0026ndash; \u003cem\u003eRanunculus acris\u003c/em\u003e; Apiaceae family \u0026ndash; \u003cem\u003ePimpinella major\u003c/em\u003e; \u003cem\u003eChaerophyllum aureum\u003c/em\u003e, \u003cem\u003eAegopodium podagraria\u003c/em\u003e and Hypericaceae family \u0026ndash; \u003cem\u003eHypericum maculatum\u003c/em\u003e. In the text, we use the abbreviation of plant species name.\u003c/p\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eEach study site at both lowland and mauntains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) contained one experimental plot. We placed the focal planted pot species (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) into an experimental plot. We had two sets of pot species. One set was used exclusively for lower sites and the other set was used for higher sites. Each plant individual in the pot had its unique code to avoid random substitution. The timing of the exposure of the pot species in the experiment was dependent on their phenology of blossom. Firstly, we used \u003cem\u003eS. jacobea\u003c/em\u003e, later \u003cem\u003eO. vulgare\u003c/em\u003e, and then \u003cem\u003eC. jacea\u003c/em\u003e. We arranged 20 pot individuals of one plant species in clusters per experimental plot according to the experimental design (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We did this setting simultaneously in lowland and mountain sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe experimental plot was a square of 5 m \u0026times; 5 m. The pot species were positioned there in a specific formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We arranged five clusters, each containing four plant pots of a currently blooming species, within one experimental plot. These pots were spaced 1 m apart from each other within each cluster, creating a small square. The distance between several squares was 2 m. Around each experimental plot was a 15 m wide strip where we observed naturally growing species and placed pan traps. The naturally growing species in the locality were observed not only outside (in the 15 m wide strip), but also inside (placed in the 5 m \u0026times; 5 m experimental plot) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We installed three clusters of three pan traps with blue, yellow, and white colors in a triangle shape, 1 m apart from each other, at a distance of 10 m from our experimental plot within the 15 m wide strip (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eObservation of pollinators\u003c/h3\u003e\n\u003cp\u003e The pot plants stayed at lowland and mountain sites according to experimental design (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) without any intervention for 4 days allowing the adaptation of pollinators to the novel source. We always recorded one site from lowlands and another from mountains at the same time. After observation of pollinators, the two sets of plants both from the lowland and mountains were shifted to another lowland and mountain site. The location of the clusters in the experimental plot was randomly shuffled at each site. We continued in the same way with the other two species. Besides pot species observation, we recorded pollinators on naturally growing species at mountain sites inside and outside of experimental plot. Naturally growing species were observed only at mountain sites for later comparison of pollination efficiency of pot plants in the lowland and in the mountains. Thus, we observed pot species and naturally growing species at the same time.\u003c/p\u003e \u003cp\u003eWe observed pollinators visually. We recorded only pollinators, which touched the reproductive organs of flowers. We created for this purpose protocols with functional groups of pollinators; namely, Syrphidae, other Diptera, Lepidoptera, Coleoptera, \u003cem\u003eApis mellifera\u003c/em\u003e, Bombus sp., and solitary bees. We observed a cluster of individuals with codes of the same flowering species for 30 minutes and then we shifted to the next group of species. Similarly to pot species, naturally growing species were also often observed in clusters (natural growth of more individuals of the same species). Thus, each group of species \u0026ndash; in pots and naturally growing, inside and outside \u0026ndash; was observed three times at a minimum per day. The period of observation was approximately from 9:00 am until 4:00 pm in time of the highest activity of pollinators. We recorded the color of observed species and number of flowers per one individuum per each pot and naturally growing species. We also recorded the weather type. Observations were made only under suitable weather conditions for pollinator activity. So, we did not observe pollinators during rain and cloudy weather with low temperatures.\u003c/p\u003e\n\u003ch3\u003ePan traps\u003c/h3\u003e\n\u003cp\u003eWe set pan traps at the same time during pollinator observation to increase the number of the potential pollinators on each site. Pan traps were exposed at all lowland and mountains sites. Three sets of pan traps were installed 10 meters from experimental plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We select blue, white, and yellow colors to attract as many potential pollinators as possible (Vrdoljak \u0026amp; Samways, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Moreira et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We laid pan traps at 9:00 am and filled them with a mixture of water and natural detergent without perfume (Campbell \u0026amp; Hanula, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dirrigl, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). After the pollinator observation, around 4:00 pm, we collected all potential pollinators from all pan traps. The samples were stored in plastic zip-lock with 96% ethanol. All samples of each color and study site were determined to functional groups in the lab. We used the following functional groups in protocol: Syrphidae, other Diptera, \u003cem\u003eBombus\u003c/em\u003e sp., solitary bees, \u003cem\u003eApis mellifera\u003c/em\u003e, Coleoptera, Lepidoptera, and Thysanoptera.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSeed set counting\u003c/h2\u003e \u003cp\u003eTo determine pollination efficiency, we counted the number of seeds after the end of the field experiment. We counted seeds of focal pot species once per week for a month period since 15. 8. until 15. 9. during season 2022. We focused pot only on \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea.\u003c/em\u003e These species were in a greenhouse equipped with a fine mesh in the place where the windows and doors were preventing inappropriate pollination before and after the field experiment. We cut all inflorescences of \u003cem\u003eC. jacea\u003c/em\u003e and a certain number of randomly selected inflorescences of \u003cem\u003eS. jacobea\u003c/em\u003e after one month. However, we did not count the seeds of \u003cem\u003eO. vulgare\u003c/em\u003e, because they are very small, and it was not feasible to count them properly. We counted \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e seeds only in time of their maturity based on their shape, size, and color based on the Pladias database (Chytr\u0026yacute; et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData analyses\u003c/h3\u003e\n\u003cp\u003eWe conducted all analyses in the R software version 4.0.3 (R Core Team, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We did analyses separately for insect observation, pan traps approach, and seed set counting. We used for visualization of the results package (\u003cem\u003escales\u003c/em\u003e) (Wickham \u0026amp; Seidel, 2022) and (\u003cem\u003eggpot2\u003c/em\u003e) (Wickham \u0026amp; Chang, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For statistical tests we used package (\u003cem\u003elme4\u003c/em\u003e) (Bates et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe fitted Generalized linear mixed model (GLMM) using glmer function with Poisson distribution for pollinator observation part. We tested the effect of listed explanatory variables: elevation (mountains and lowland \u0026ndash; fixed effect), number of observed flowers \u0026ndash; log transformed (fixed effect), weather (cloudy, partly cloudy, sunny \u0026ndash; fixed effect) and locality (random effect) on a sum of visitors (response variable). We did this model separately for three planted species in the pots: \u003cem\u003eS. jacobea\u003c/em\u003e, \u003cem\u003eO. vulgare\u003c/em\u003e, and \u003cem\u003eC. jacea\u003c/em\u003e. We utilize a similar model with all naturally growing species; we tested the effect of listed explanatory variables: plant species (\u003cem\u003eK. arvense\u003c/em\u003e, \u003cem\u003eHiracium\u003c/em\u003e sp, \u003cem\u003eR. acris\u003c/em\u003e, \u003cem\u003eP. major\u003c/em\u003e, \u003cem\u003eCh. aureum\u003c/em\u003e, \u003cem\u003eA. podagraria\u003c/em\u003e, \u003cem\u003eH. maculatum\u003c/em\u003e - random effect) nested in plant position (outside, inside \u0026ndash; fixed effect), number of observed flowers (fixed effect), weather (cloudy, partly cloudy, sunny\u0026ndash; fixed effect), and locality (random effect) on sum of visitors (response variable). In addition, we tested two models with naturally growing plant species separated based on color: white (\u003cem\u003eP. major\u003c/em\u003e, \u003cem\u003eCh. aureum\u003c/em\u003e, and \u003cem\u003eA. podagraria\u003c/em\u003e) and yellow (\u003cem\u003eHiracium\u003c/em\u003e sp, \u003cem\u003eR. acris\u003c/em\u003e, and \u003cem\u003eH. maculatum\u003c/em\u003e). Explanatory variables were the number of observed flowers (fixed effect), weather (cloudy, partly cloudy, sunny\u0026ndash; fixed effect), plant position (outside, inside \u0026ndash; fixed effect), and locality (random effect) on a sum of visitors (response variable).\u003c/p\u003e \u003cp\u003eWe analyzed the results of pan traps also with GLMM using Poisson distribution. We tested how explanatory variables: elevation, trap color (white, blue, yellow \u0026ndash; fixed effect) and locality (random effect) affect the response variable \u0026ndash; sum of pollinators. We also tested the interaction of trap color and elevation as in the second model. In conclusion, we used the second model with interaction, because it was significantly more parsimonious (having lower Akaike Information Criterion).\u003c/p\u003e \u003cp\u003eFinally, we analyzed seed numbers for two planted species: \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e. We used as previously GLMM with the Poisson distribution. We tested the effect of explanatory variables elevation (mountains and lowland \u0026ndash; fixed effect) and plant number (random effect) on seed number (response variable).\u003c/p\u003e \u003cp\u003eThe effect of the individual explanatory variables (whether positive or negative) is indicated by estimated regression coefficient (abbreviated as \"Regr. Coef\"), and by its standard error (reflecting the precision of the regression coefficient estimate, abbreviated as \"Std. error\"), in all fitted models.\u003c/p\u003e \u003cp\u003eThe differences in abundances of individual insect groups we tested by Redundancy Analysis (RDA) in Canoco 5 statistical software (ter Braak \u0026amp; Šmilauer \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). First, we tested the differences in visitation of flowers. Here the number of visits of the flower were explained by the position of the plant, plant species, altitude, locality, and weather. All variables were tested at first together in one model. Similarly, we tested the number of insect pollinators trapped in the pans of different color by RDA. Here the number of insect pollinators were at first explained by all variables together, then we conducted set of RDAs, where each variable was tested separately, with other variables as covariates. In all RDAs, significance was tested by Monte Carlo permutation test with 999 permutations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eObservation of pollinators\u003c/h2\u003e \u003cp\u003eAll three pot species together (\u003cem\u003eC. jacea, O. vulgare, S. jacobea)\u003c/em\u003e hosted a similar number of potential pollinators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) at both lowland and mountain sites. Separately, \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eO. vulgare\u003c/em\u003e have more pollinators at original lowland sites, whereas \u003cem\u003eS. jacobea\u003c/em\u003e had more pollinators in the mountains. The pollinator spectrum differed between lowlands and mountains and among plant species (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Two pot species: \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eO. vulgare\u003c/em\u003e had similar groups of potential pollinators. The dominant pollinator in lowland was \u003cem\u003eApis mellifera\u003c/em\u003e and hoverflies and butterflies prevailed in the mountains. Whereas \u003cem\u003eS. jacobea\u003c/em\u003e hosted mostly \u003cem\u003eSirphidae\u003c/em\u003e, especially in mountains followed by solitary bees in lowland and bumblebees in mountains (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The number of pollinators of \u003cem\u003eC. jacea\u003c/em\u003e was dependent on the number of observed flowers and sunny weather. The pollinator number of \u003cem\u003eO. vulgare\u003c/em\u003e slightly differed between elevations, but the result was only marginally significant. The number of pollinators of \u003cem\u003eO. vulgare\u003c/em\u003e was dependent on the number of observed flowers and on sunny weather. The number of pollinators of \u003cem\u003eSenecio jacobea\u003c/em\u003e was positively dependent on the number of observed flowers. There was a significant difference between sunny and cloudy weather (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The number of pollinators of all naturally growing species was significantly affected by the number of observed flowers and by sunny and cloudy weather (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of linear mixed effect model testing the impact of the number of open flowers, elevation, and weather on the number of pollinators of all three pot species and naturally growing species. Number of observed flowers was log-transformed before analysis. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors (fixed effects)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCentaurea jacea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog (Observed flowers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather partly cloudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather sunny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictors (fixed effects)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOriganum vulgare\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog (Observed flowers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather partly cloudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather sunny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictors (fixed effects)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSenecio jacobea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog (Observed flowers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather partly cloudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather sunny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictors (fixed effects)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNaturally growing species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog (Observed flowers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e21.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather partly cloudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather sunny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Redundancy Analysis (RDA) testing the abundances of individual insect groups on flowers (a) and abundances of individual insect groups trapped in the panes of different color (b) by individual variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ea) abundances of individual insect groups on flowers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTested variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExplained variability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eall together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eposition of the plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplant species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealtitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eb) abundances of individual insect groups in panes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTested variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExplained variability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eall together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecolor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealtitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sum of pollinators on naturally growing species was different within each color (white, and yellow) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sum of pollinators on plants with white and yellow color separately was significantly different based on weather and number of observed flowers (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). White flowering species (\u003cem\u003eA. podagraria\u003c/em\u003e, \u003cem\u003eCh. aureum\u003c/em\u003e, \u003cem\u003eP. major\u003c/em\u003e) was significantly affected by the number of observed flowers (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and by sunny and cloudy weather. The number of pollinators was significantly higher when the species were observed inside of experimental plot. White flowers attracted mostly hoverflies and other dipterans.\u003cem\u003eCh. aureum\u003c/em\u003e and \u003cem\u003eP. major\u003c/em\u003e host often also beetles (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The yellow flowering species (\u003cem\u003eHieracium sp\u003c/em\u003e., \u003cem\u003eH. maculatum\u003c/em\u003e and \u003cem\u003eR. acris\u003c/em\u003e) were significantly affected by the number of observed flowers (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and by sunny and cloudy weather. They were preferred mostly by Syrphidae and other Dipterans, the third group visited was solitary bees (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of linear mixed effect model testing the impact of the number of open flowers, weather, and plant position on the number of pollinators of naturally growing species according to flower color. The number of observed flowers was log-transformed before analysis. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results.\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors (fixed effects)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWhite flowers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eYellow flowers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog (Observed flowers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather partly cloudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather sunny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant position (outside)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePan traps\u003c/h2\u003e \u003cp\u003eThe number of pollinators collected from pan traps was significantly affected by elevation (difference between lowland and mountains). We collected more pollinator individuals in the mountains (2715) in comparison to the lowland (1343). Pan trap colors white and yellow significantly differed from blue in the number of pollinators. White color was visited mostly by dipterans at both elevations, but mainly at mountain sites, the second most abundant pollinators were solitary bees in the lowland followed by Coleoptera at both elevations. The yellow color was preferred by the majority of plant pollinators, mostly by flies except for syrphids in the lowland followed by solitary bees and Coleoptera (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The interaction of elevation and white color was significant similarly to the interaction of elevation and yellow color (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). RDA revealed that pan traps significantly differed in the spectrum of pollinators depending on the elevation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of linear mixed effect model testing the impact of elevation and pan trap color, separately and with interaction, on the number of pollinators. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors (fixed effects)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrap white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrap yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation x Trap white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation x Trap yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSeed set counting\u003c/h2\u003e \u003cp\u003eSeeds were collected from \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e. The number of seeds collected from \u003cem\u003eC. jacea\u003c/em\u003e was significantly different across the elevation (lowland vs. mountains, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Also, the number of seeds collected from \u003cem\u003eS. jacobea\u003c/em\u003e differed across the elevation (lowland vs. mountains, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both plant species had more seeds in the lowland. The difference in number of \u003cem\u003eC. jacea\u003c/em\u003e seeds between lowland and mountains was 17.3%. \u003cem\u003eS. jacobea\u003c/em\u003e seeds differed by 14.7% between lowland and mountains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of linear mixed effect model testing the impact of elevation on the number of seeds of \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e. In addition to the test statistic values (z and P value), we included the regression coefficient (Regr. Coef) and standard error (Std. Error) in the results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors (fixed effects)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegr. Coef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCentaurea jacea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSenecio jacobea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (mountains)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have found no evidence that range-shifting plants, which may potentially migrate upward in the mountains, did not alter the pollinator network mountain ecosystems. We have found just minor changes in some interactions in the case of white flowering plants. This was likely because we found more pollinators in the mountains than in the lowlands. The spectrum of pollinators was largely dependent on flower color, suggesting a division of pollinators, likely based on different pollination syndromes. Flower visitation of range-shifting plants in pots was not significantly different between elevations. However, pollinator efficiency of range-shifting plants varied between lowland and mountain sites, with range-shifting plants in pots, especially \u003cem\u003eC. jacea\u003c/em\u003e, showing higher pollinator efficiency, as indicated by a higher number of seeds at the original lowland sites.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eObservation of pollinators\u003c/h2\u003e \u003cp\u003ePollinators of studied plant species strongly reacted to number of opened flowers per plant. The more flowers the plant offered; the more pollinators visited this plant. This trend was stable for all three pot species (\u003cem\u003eC. jacea, O. vulgare, S. jacobea\u003c/em\u003e) and for all naturally growing species (\u003cem\u003eK. arvensis\u003c/em\u003e, \u003cem\u003eA. podagraria\u003c/em\u003e, \u003cem\u003eCh. aureum\u003c/em\u003e, \u003cem\u003eP. major, Hieracium sp\u003c/em\u003e., \u003cem\u003eH. maculatum\u003c/em\u003e and \u003cem\u003eR. acris\u003c/em\u003e) in mountains. Plant with more flowers is better visible from longer distance and may release more intensive odor offering potential visitors more rewards (Wright \u0026amp; Schiestl, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) it can also benefit from this by increasing pollen export to other plants from same species (Klinkhamer \u0026amp; De Jong, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Increasing reproduction success with more flowers illustrates also Brys et al. (2004) on example with increasing fruit set in bigger populations of \u003cem\u003ePrimula vulgaris\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOur investigation revealed no significant disparity in the pollinator numbers for lowland potted species (\u003cem\u003eC. jacea, O. vulgare, S. jacobea\u003c/em\u003e) between lowland and mountain sites. Contrary to expectations, we did not observe a marked increase in pollinator visits in mountain regions, where the introduction of novel potted species from lowlands could potentially attract pollinators more than original mountain plant species. Higher pollinator abundance in the lowlands can be caused by the fact that lowland species in pots have their origin there, and local pollinators may have developed adaptations to them. Overall, the elevation difference is rather small (the distance between the lowest and the highest site was 683 m a.s.l). Thus, pollinators did not need to adapt to as demanding conditions as they are at more prominent high elevation, e.g., above tree line. Yet, the number of pollinator visits varied from species to species. Whereas \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eO. vulgare\u003c/em\u003e had more pollinators at original lowland sites, \u003cem\u003eS. jacobea\u003c/em\u003e hosted more pollinators in the mountains which relates to pollinator preference for certain color (Willmer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pollinators differed in composition between lowlands and mountains and among plant species. Two pot species: \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eO. vulgare\u003c/em\u003e had a similar pattern of pollinators. The dominant pollinator in the lowland was honeybee whereas in the mountains hoverflies and butterflies prevailed. We suggest that the color of flower, pink in both species, attracted the similar spectrum of pollen visitors (Trunschke et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also Szigeti et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that pollinators of pink flowers are bees, hoverflies and butterflies. In contrast, \u003cem\u003eSenecio jacobea\u003c/em\u003e had different pollinator spectrum; hoverflies were dominant at both elevations, mostly at mountains. They were followed by solitary bees in lowland and bumblebees in mountains. The importance of hoverflies and Apidae family as a main pollinators of \u003cem\u003eS. jacobea\u003c/em\u003e found out also Vanparys et al., (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNaturally growing species, occurring only in the mountains, were classified according to sheared color, and thus separated into two groups (white, yellow). White flowering species (\u003cem\u003eAegopodium podagraria\u003c/em\u003e, \u003cem\u003eChaerophylum aureum\u003c/em\u003e, \u003cem\u003ePimpinella major\u003c/em\u003e) as a representatives of Apiaceae family, attracted mostly hoverflies and other dipterans. The fact that flies are main pollinators of Apiaceae family proved also Larson et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) who pointed out that flies select open flowers with nectar reward. The preference for white flowers for certain part of dipterans illustrated also Willmer, (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) who demonstrated that white flowers were determined partly as a fly pollination syndrome. Moreover, white flowering plants had more pollinators inside of experimental plot (in close proximity to potted plants). We suggest that lowland potted plants can actually affect interactions of plants and pollinators in mountain community by attracting them (potted plants have more distinctive color of flowers). \u003cem\u003eChaerophylum aureum\u003c/em\u003e and \u003cem\u003ePimpinella major\u003c/em\u003e host often also beetles which prefer open flattened flowers with a distinctive odor (Gottsberger, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). In Addition, we found that there were more pollinators on naturally growing species from Apiaceae family placed inside of experimental plot close to the pot species. We hypothesized that pot lowland plants could attract attention of the majority of pollinators of a mountain site and pollinators thus visited besides pot plants also naturally growing plants in their vicinity. Moreover, higher pollinator activity inside of experimental plot can be also caused by a generally higher number of Diptera in the mountains, which prefer white color. Ultimately, the interplay between these two factors likely underlies the observed phenomenon. The group of yellow flowering species consisting of \u003cem\u003eHieracium sp\u003c/em\u003e., \u003cem\u003eH. maculatum\u003c/em\u003e, and \u003cem\u003eR. acris\u003c/em\u003e was preferred mostly by hoverflies and other Diptera, third visited group was solitary bees. The preference for yellow color for certain type of flies confirmed also Inouye et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Campbell et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while yellow attractivity for solitary bees was observed by Latha et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePollinators of both pot and naturally growing species were differently active in relation to different weather conditions. \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eO. vulgare\u003c/em\u003e hosted significantly more pollinators during sunny weather. Generally, pollinators are more active during sunny and warmer weather (Goodwin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Insect pollinators are ectothermic and thus they rely on microclimate more than endotherms. Therefore, insect pollinator foraging behavior is more dependent on body temperature, which is determined by both ambient temperature and radiation, as well as on windspeed (Corbet, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). On the other hand, pollinator visitation of \u003cem\u003eS. jacobea\u003c/em\u003e and all naturally growing plants were active in both types of weather. Surprisingly, species with white flowers (\u003cem\u003eAegopodium podagraria\u003c/em\u003e, \u003cem\u003eChaerophylum aureum\u003c/em\u003e, \u003cem\u003ePimpinella major\u003c/em\u003e) and yellow flowers (\u003cem\u003eHieracium sp\u003c/em\u003e., \u003cem\u003eH. maculatum\u003c/em\u003e, and \u003cem\u003eR.acris\u003c/em\u003e) had more flowers visited during cloudy weather. Both groups of naturally growing species and also \u003cem\u003eS. jacobea\u003c/em\u003e had dipterants including sirphids as a main pollinator. Dipterans are active also during cloudy days in comparison to other pollinators (Inouye et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They are thus effective pollinators in the mountains where they can replace other pollinators (Ssymank et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePan traps\u003c/h2\u003e \u003cp\u003eContrary to results for observation of pollinators on flowers, the number of pollinators collected in pan traps significantly differed between lowland and mountains. Interestingly, more pollinators were collected in the mountains. The same trend found out also Baumann et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who observed pollinators on mountain hay meadows. We suggest that this fact could be influenced by increasing number of files with elevation (Ssymank et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). McCabe \u0026amp; Cobb, (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed that bees are replaced by flies in case of increasing elevation and decreasing temperature. Increasing number of dipterans with elevation was confirmed also by study from Utah (Warren et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePollinator spectrum vary also with different colors of pan traps; especially with white and yellow color. White color was attractive for dipterans at both elevations, but mainly at mountains sites. We collected a high number of flies in white pan traps because there were many plants with white color, which are often visited by dipterans. It was mainly the plants from \u003cem\u003eApiaceae\u003c/em\u003e family (Larson et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); D. \u003cem\u003ecarota\u003c/em\u003e, \u003cem\u003eP. major\u003c/em\u003e, \u003cem\u003eA. podagraria\u003c/em\u003e, and \u003cem\u003eCh. aureum\u003c/em\u003e. Besides flies, solitary bees were attracted by white color. The third most frequent group of pollinators were beetles and hoverflies (hoverflies occurred mainly in mountains). In contrary to our results, study with blister beetle shown the preference for blue color (Lebesa et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In lowland sites, there was quite common visitor also \u003cem\u003eApis mellifera\u003c/em\u003e as in study of Jaques et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eYellow color was preferred by majority of plant pollinators. Harris et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) conducted experiments with different pan traps color, size and position and also confirmed the highest proportion of pollinators in yellow pan traps. In lowland, there was dominance of flies except of syrphids. The second mostly visited group was solitary bees; as in another case of observation experiment by Latha et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pan traps from mountains were mainly visited by dipterans, whereas other groups of pollinators were represented in small percentages. The majority of dipterans follow the general trend of flies as main pollinators in mountains (Warren et al.,1988).\u003c/p\u003e \u003cp\u003eAdditionally, the interaction of color (white and yellow) with elevation (lowland and mountains) significantly affected pollinator spectrum. It is well known that different colors are preferred by different types of pollinators, partly due to pollination syndrome (Van Der Pijl and Faegri, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Willmer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Also, the pollinator spectrum is shifting with increasing elevation from bees to flies pollination (McCabe \u0026amp; Cobb, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Bees are not so resilient to colder environment, so their ability to forage decrease with increasing elevation (L\u0026aacute;zaro et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For this reason, the interaction of both, color of flower together with elevation, might significantly affect pollinator spectrum turnover.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSeed set counting\u003c/h2\u003e \u003cp\u003eThe seed set of both planted species in the pots (\u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e) significantly differed with elevation. We collected seeds of selected flowers in the pots in two sets, from both lowland and mountains sites, to investigate the reproductive success (Rering et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We counted seeds of \u003cem\u003eC. jacea\u003c/em\u003e and \u003cem\u003eS. jacobea\u003c/em\u003e. \u003cem\u003eO. vulgare\u003c/em\u003e have tiny flowers and seeds; thus it was difficult to count it without common mistakes. Therefore, we did not count the seeds of this species. \u003cem\u003eC. jacea\u003c/em\u003e had 17.3% seeds more in the lowland. Similarly, \u003cem\u003eS. jacobea\u003c/em\u003e had 14.7% more seeds in lowland. This is the proof that the pollination was more effective in place where plant species originally grow. The pollinators of \u003cem\u003eC. jacea\u003c/em\u003e were mainly honeybees in the lowland, which did not occur in the mountains. The second most abundant group of pollinators for \u003cem\u003eC. jacea\u003c/em\u003e were solitary bees in the lowland, whose number dramatically decrease in mountains. The reason why \u003cem\u003eS. jacobea\u003c/em\u003e had more pollinators in lowland was caused by two groups of pollinators \u0026ndash; hoverflies and solitary bees, which together reach higher abundance in lowland. Although the abundance of hoverflies as such was increasing with elevation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, all three pot species hosted a similar number of potential pollinators after observation. However, the pollinators differed in their pollinator spectrum, between lowland and mountains and among plant species resulting in higher pollination efficiency in lowlands. Also, naturally growing species differed in their pollinator spectrum. Interestingly, white flowering plants had more pollinators inside the experimental plot (in close proximity to potted plants), suggesting that potted plants influence the turnover of mountain pollinators. The novel presence of lowland plant species in mountain environments has the potential to alter community dynamics. However, our results do not support the idea that these plants will pose a significant threat to native mountain plant species through competitive displacement in their original habitat.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eWe declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eAll patients included in this study gave written informed consent to participate in this research.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll patients provided written informed consent to publish the data contained within this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by GAČR project no.: 360 640 3317.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eJK conceived the study; JK and KJ designed the methods; JK and KJ collected the data; JK and KJ and OM analysed the data; KJ and OM wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eWe are grateful to Andrea Tomešov\u0026aacute;, Aneta Pil\u0026iacute;kov\u0026aacute;, Daniel Jackwerth, Marie Egnerov\u0026aacute;, Dagmar Huckov\u0026aacute;, Pavla Kov\u0026aacute;řov\u0026aacute;, Lucie S\u0026aacute;zavsk\u0026aacute;, Kl\u0026aacute;ra H\u0026aacute;jkov\u0026aacute; and Iena Klečkov\u0026aacute; for their help with experimental work in the field. We would like to thank Marie Egnerov\u0026aacute; and Kl\u0026aacute;ra H\u0026aacute;jkov\u0026aacute; for their help in the laboratory. We are also grateful to the management of the Blansk\u0026yacute; Les Protected Landscape Area, Šumava Protected Landscape Area and all landowners for their cooperation and permission to conduct the study.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eThe data will be available at the time of article acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e 67:1\u0026ndash;48. DOI: 10.18637/jss.v067.i01.\u003c/li\u003e\n\u003cli\u003eBaumann K, Keune J, Wolters V, Jauker F. 2021. 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DOI: 10.1111/j.1365-2435.2009.01627.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pollination, floral color, range-shifting species, elevation, pollination efficiency","lastPublishedDoi":"10.21203/rs.3.rs-5957991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5957991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany plant species move to higher elevations due to global warming, but the effects of these elevational shifts on plant-pollinator interactions are not well understood. This study aimed to examine how flower visitation and seed set of lowland plants change after they shift uphill, and whether they compete for pollinators with plants native to the mountains. We conducted an experiment using two plant groups: lowland species pre-planted in a greenhouse and transplanted to both lowland and mountain sites, and mountain species. Pollinators were recorded at lowland sites for planted species and at mountain sites for both planted and native species. We also used pan traps in white, yellow, and blue colors to collect pollinators at both sites. Afterward, seed sets of the planted species were collected to compare reproductive success between elevations. Flower visitation rates on planted species were not significantly affected by elevation, though pollinator abundance in pan traps was higher in the mountains. The pollinator spectrum varied across elevations and plant species, influenced by flower and pan trap color. However, planted species produced more seeds at lowland sites, indicating higher pollinator efficiency there. Overall, we found no evidence of competitive advantage for range-shifting lowland species in terms of pollination.\u003c/p\u003e","manuscriptTitle":"Can lowland plants shifting upwards overcompete mountain plants in terms of pollination efficiency?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 09:54:33","doi":"10.21203/rs.3.rs-5957991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-04T13:04:28+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T09:15:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-06T06:32:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oecologia","date":"2025-02-04T07:13:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8f7e403b-8681-4b50-904f-5e6c93d40cc0","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:35:10+00:00","versionOfRecord":{"articleIdentity":"rs-5957991","link":"https://doi.org/10.1007/s00442-025-05787-0","journal":{"identity":"oecologia","isVorOnly":false,"title":"Oecologia"},"publishedOn":"2025-08-19 16:29:25","publishedOnDateReadable":"August 19th, 2025"},"versionCreatedAt":"2025-04-22 09:54:33","video":"","vorDoi":"10.1007/s00442-025-05787-0","vorDoiUrl":"https://doi.org/10.1007/s00442-025-05787-0","workflowStages":[]},"version":"v1","identity":"rs-5957991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5957991","identity":"rs-5957991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}