bees 2016: คุณกำลังดูกระทู้
Conversely, after 48 h of extensive training (20 instances of string pulling), 11 of the 15 foragers solved the task without feedback from the moving blue flower ( S5 Video ). Latency to obtaining the reward (147 ± 23.44 s) was much higher than for normal blue flower training (22.1 ± 1.5 s; t test: t25 = 6.25, p < 0.0001). The subjects’ success differs significantly from their performance when they were relatively inexperienced (McNemar Test, χ 2 1 = 7.111, p = 0.008), thus indicating that the majority of highly experienced individuals may no longer require visual feedback to perform the necessary sequence of motor actions. In fact, experienced bees may not need the blue flower at all and perhaps have associated the string with the reward.
The success of bees learning such a behavior raises the question about the mechanisms by which the demonstrators learned to pull the string. One possibility is that demonstrators are stimulated to repeat the specific sequence of actions (moving the string with their legs) that induces the conditioned stimulus (i.e., the blue flower positioned under the table) to move a little closer. If so, we would expect bees not to move the string with their legs and fail at the task if the colored target stimulus is not present. To test this prediction, we challenged bees (Colony 2) to access the reward when a string was attached to only a colorless inverted Eppendorf cap containing sucrose solution (Materials and Methods) immediately after their initial stepwise training and then again after extensive experience with blue flowers and strings. Without a colored stimulus, only 2 of 15 bees tested obtained the reward after their initial training. We thus hypothesized that relatively inexperienced bees rely on visual feedback of the colored target moving closer while the string is being pulled. To explore this further, we examined the video material for the unsuccessful bees to see if they would attempt to pull the strings and then abort this action when visual feedback was not forthcoming. However, none of the unsuccessful bees demonstrated even an aborted pulling action on the colorless flower’s string. This suggests that most relatively inexperienced bees require the presence of the blue flower to even begin attempting to string pull. (However, there is also evidence for the importance of visual feedback during pulling from an experiment with coiled strings; see section The Mechanisms of Observational Learning in String Pulling.)
In comparison, we were able to train 23 of 40 individuals (Colony 1) through a stepwise training procedure to successfully pull a string to obtain reward ( Fig 1B horizontal black bar in column 4, S1 – S4 Videos). The stepwise training consisted of four steps of incremental difficulty within which flowers with strings were placed at progressively more distant positions under the transparent table (Steps 1–4, Fig 1A and 1B ). On average, successful training for an individual bee took 309 ± 18 min. Gaining access to the reward in the final step required grasping the string with the forelegs and/or mandibles and pulling it closer ( S4 Video ). The mean time required (latency) to obtain sucrose decreased significantly as a function of experience within each of the four successive training phases (Friedman test, Step 1: χ 2 4 = 59.1, p = <0.001; Step 2: χ 2 4 = 53.1, p = <0.001; Step 3: χ 2 4 = 52.1, p = <0.001; Step 4: χ 2 10 = 92.3, p < 0.001; Fig 1C and 1D ). Eight, three, one, and five individuals gave up at Steps 1, 2, 3, and 4, respectively, either because they ceased foraging activity or had irregular foraging activity (n = 11), or because they failed to obtain the reward (n = 6). Three of these successfully trained bees were later used as demonstrators in the social learning experiment.
(A) Arena set up for the observation of string pulling. (B) The various testing procedures. Tests 1 and 2 were identical and consisted of giving 5 min to individual bees to solve the string pulling task. After having been trained to forage from blue artificial flowers, bees were tested a first time (Test 1). Then, demonstrators were trained (see Fig 1 ) and used to display string pulling (two instances, straight strings) during each of five foraging bouts to individual observers (n = 52) placed in a transparent Plexiglas cage. After the observation phase, 25 observers were tested again with the straight-string task (Test 2) and 27 with the coiled-string task. Fifteen different bees observed the flower moving without visible actor so that a forager could then obtain the sucrose solution (“Ghost control”) and, where tested, with the straight-string task subsequently. Untrained bees (n = 25) were also tested a second time with string pulling. (C) Percentage of successful untrained, social, and nonsocial observer bees in Tests 1 and 2. Asterisk: Fisher’s exact test, p ≤ 0.0001. Double S: McNemar test, χ 2 1 = 13.067, p < 0.001. (D) Mean ± s.e. (s) latency in accessing the reward in untrained and observer bees. Observers’ latency was not different from that of the two “innovators” (Mann–Whitney U test, U 15 = 6, p = 0.205), (see S1 Data ).
(A) Stepwise string pulling training protocol. Successive steps: Step 0, pretraining on blue artificial flowers (note that all bees were trained on this step); Step 1, 50% of the flower covered by the transparent table; Step 2, 75% of the flower covered; Steps 3 and 4, 100% of the flower covered. The flower was positioned at the edge in Step 3 and 2 cm under the table in Step 4. (B) Percentage of successful bees in Steps 1 to 4 (n = 40, 32, 29, and 28, respectively). Black horizontal lines within bars indicate the percentage of bees of the original 40. (C) and (D), mean ± standard error (s.e.) (line and shaded area, s) latency to obtain the reward in Steps 1–3 and 4. (C) Mean latency for the five foraging bouts of Steps 1–3. Data points, from left to right, in (D) indicate the latency to reward in Step 4 for the bout with first occurrence of string pulling and the ten foraging bouts that followed. Bees needed 6.17 ± 1.2 foraging bouts before displaying string pulling in Step 4 (see S1 Data ).
To test bees’ capacity to learn the technique of string pulling, we first challenged untrained individuals with a stepwise training procedure (Materials and Methods; S1 – S4 Videos). We presented individual bees with three blue artificial flowers with a string attached to each flower and placed under a small transparent Plexiglas table (Materials and Methods). After learning to associate the reward with artificial flowers in a flight arena (Step 0, Fig 1A ), but prior to string pulling training, none of the bees from the eight colonies in which individuals were tested singly (n = 291) could solve the string pulling task on their first 5-min attempt (Test 1, Fig 2B ). Naïve to the string task but attracted to the artificial flowers, these bees tried to reach the reward from the top of the table through the Plexiglas.
We gave 50 individuals (Colony 1) the opportunity to solve the string pulling task spontaneously after having learnt that blue flowers are rewarding when they were openly accessible during pretraining (for a 5-min observation period). None of these individuals solved the task. When given a second 5-min opportunity, two of 25 untrained bees succeeded in obtaining the reward ( S6 Video ). However, they were more than ten times slower at obtaining the reward than experienced string pullers (22.1 ± 1.5 s, mean ± standard error [s.e.], Mann–Whitney U test, U 23 < 0.001, p = 0.024), requiring a relatively long latency of 245 ± 3.53 s. These two bees were exceptionally explorative, trying a wide variety of methods, and solved the task in several attempts by moving the string accidently while trying to reach the flower under the table (see S6 Video and legend for more information). This shows clearly that string pulling can be learned individually by some bumblebees, but this may be an exceptionally rare ability. Across experiments (see below), 291 naïve individuals were tested once, and a total 110 were tested twice, but no further “innovators” were found. In one experiment (the transmission chain experiment below, in which control colonies were not seeded with a skilled demonstrator), bees were given extensive opportunities. After 5 d of foraging, with a maximum number of 18 foraging bouts per individual, no single bee learned to pull the string. Of the 165 bees tested in this experiment in total, nine individuals were tested more than 10 times, and 26 more than 5 times, but all were invariably unsuccessful. Thus, solving a string pulling task spontaneously is a relatively rare occurrence in bumblebees and might either reflect an unusually explorative “personality” in these individuals or simple “luck” in the process of random exploration.
Finally, because smaller bees might be able to reach further under the table than larger bees, we examined whether body size influenced success in solving the task (Colony 1). Thorax width (as a proxy for body size) was not different between demonstrators (n = 40), observers (n = 25), and untrained bees (n = 25) (ANOVA, F 69 = 0.728, p = 0.486). Thorax width affected neither demonstrators’ (Student’s t test, t 26 = 0.659, p = 0.516) nor observers’ success rate (Mann–Whitney U test, U 23 = 79, p = 0.846). Similarly, the latency to obtain the reward was not affected by thorax width of demonstrators (Pearson correlation, r 23 = -0.086, p = 0.696) or observers (Pearson correlation, r 15 = 0.375, p = 0.169).
We also wished to disentangle the effects of demonstrator copying and object movement copying in how string pulling was learnt by observation. To this end, we used an experimental “ghost control” ([ 40 ], S8 Video ). We trained 15 nonsocial observers (Colony 3) in exactly the same manner as above with the modification that the flowers were moved without a visible actor: an experimenter pulled the flowers with thin nylon threads attached to the strings while the observers were locked inside the observation chamber (Materials and Methods). Once the string had been pulled, an untrained forager was released into the arena to feed from the now accessible flower. Without direct demonstration of string pulling by a bumblebee forager, none of the observers managed to solve the string pulling task. Nonsocial observers mostly tried to obtain the reward from the top of the table, indicating that the bees need to observe string pulling actions demonstrated by conspecifics to learn the technique. However, because no video material is available to show that observer bees directed their gaze towards the moving flower, it is also possible that in the absence of a conspecific demonstrator, observers simply failed to attend to the movement of the flower.
We explored whether uninformed bees (Colony 1) could learn this novel foraging technique via observation. After pretraining on blue flowers and Test 1 (Materials and Methods), an uninformed observer bee was placed in a transparent chamber ( Fig 2A ) where it could observe a demonstrator solve the string pulling task ten times. These observers (n = 25) were subsequently tested on the string pulling task alone (Test 2, Fig 2B ). In this experiment, observers never interacted directly with demonstrators in the flight arena and had access only to visual social information ( S7 Video ). Sixty percent of the individuals (15 of 25) that had the opportunity to observe a skilled demonstrator managed to pull the string and obtained the reward on the first trial after having observed the demonstration (Test 2, Fig 2C , S6 Video ). These bees, however, were initially almost as slow as the two individuals that solved the tasks without demonstration (181 ± 19 s; Fig 2D ). We speculate that the observers picked up the correct location to access the reward from observing skilled demonstrators but did not learn from them the actual technique of string pulling (further explored in the section beneath about the mechanisms of social learning).
Finally, trial-and-error learning was also evident in the learning process. Because individuals might only learn where to obtain the reward and then learn the string pulling by trial-and-error, observer bees (n = 27, Colony 5) were tested with a coiled-string paradigm where trial-and-error learning of actions causing the rewarding object moving closer is ineffective. After a standard demonstration of string pulling (Materials and Methods), a 14 cm string was attached to the flower and coiled under the table so that initial tugs on the string would provide no visual feedback of the flower moving closer to the bee. Such coiled-string tests have in the past been used to test whether animals can solve a string pulling puzzle by means-end comprehension, without the perceptual feedback of the reward coming closer [ 44 , 45 ]. Long-tailed macaques (Macaca fascicularis) [ 47 ] and wolves (Canis lupus) [ 48 ] have indeed been shown to solve the task even if the string is coiled. However, none of these observer bees were able to solve this task (n = 27, Fig 2B , S10 Video ), indicating that observers did not glean information about the string pulling technique itself by observing a demonstrator but instead were merely guided to the demonstrator’s previous location (by local enhancement) and the position of the string (stimulus enhancement). The actual act of string pulling relied on individual trial-and-error learning, which in turn necessitates the sensory feedback of tugging on the string, resulting in the target moving closer. We also tested eight experienced individuals (with an experience of more than 20 instances of string pulling) with the coiled-string test; three of these bees succeeded in pulling the coiled string to obtain the reward ( S11 Video ), indicating that highly experienced individuals do not necessarily require the feedback from seeing the flower move closer while they pull the string. In summary, these results suggest that observational learning of the string pulling task does not involve the “understanding” of the task (“insight”) but the combined use of several simple associative mechanisms and trial-and-error learning.
(A) Regions of interest used for the video analysis of bee behaviors (not true to scale): the original region (where the demonstrator pulled a string, solid dark grey), top region (on the table, solid light grey), the two regions where the string could be presented when it was at variance with the location during the observation phase in the stimulus enhancement tests (thin grey stripes on black) and the adjacent regions where no string was presented (thin black stripes on grey). When testing stimulus enhancement, bees were challenged with a string protruding on the opposite side of one of Plexiglas tables or at 90° compared to the location where it was seen during observational conditioning (dotted lines). Regions were all 16 cm 2 (adjacent areas: 8 x 2 cm; top region 4 x 4 cm). (B) Mean ± s.e. (s) time spent by unsuccessful observer (n = 10) and unsuccessful untrained bees in two of the four regions of interest in their first attempt to retrieve the reward (Test 1) the second attempt (Test 2). Light grey: top of table; dark grey: region where string protruded during observation. Asterisk: Friedman test, p < 0.01; letters and figures: post-hoc Tukey test. (C) Percentage of time spent by observer bees in the four regions of interest when the string was protruding in the region where bees had observed demonstrators (left bar, unsuccessful observers, n = 10) or the region of the table where the string protruded when it was incongruent with that seen from the observation chamber (right bar, bees tested for stimulus enhancement, n = 14). The shades in the various regions of the stacked bars correspond to the shades in Fig 3A (see S1 Data ).
To examine the local and stimulus enhancement possibilities, we analyzed the video footage to determine the time bees spent in four different regions of the arena (see Fig 3A , Materials and Methods). In Test 2, unsuccessful observers (n = 10, Colony 1) spent more time in the region where the demonstrator was observed (Friedman test, χ 2 3 = 14.160, p = 0.003, Fig 3B ), and untrained bees (n = 23, Colony 1) spent more time on top of the table closest to the flower (Friedman test, χ 2 3 = 35.162, p < 0.001, Fig 3B ) than in Test 1, indicating that local enhancement played a part in learning. None of the bees managed to obtain the reward when the string protruded in an area incongruent with that seen during demonstration. However, the string itself also played a role. If the string protruded from a different side of the table compared to the location during the observation period, observer bees (Test 2, n = 14, Colony 4; S9 Video ) spent more time exploring the region with the string than the region where the demonstrator had been observed (Mann–Whitney U test, U 22 = 105, p = 0.038, Fig 3C ), indicating that observers had noticed the string during the observation period and were thus attracted to it. In theory, however, these longer dwelling times in the string region might be explained by bees randomly exploring the edges of the table and simply stopping at a region that contains any protruding object. To explore this possibility, we also evaluated bees’ first approach flights after being released from the observation chamber before they had a chance of interacting with the string. If the string was in the same location as during observation, 92% of observers flew straight to the side of the string. When the location of the string was incongruent with demonstrator location, only 28.5% of observers first visited the region where the demonstrator had been observed (where chance expectation is 25%). The choice frequencies for the four sides of the table are significantly different depending on whether the string was in the correct location (Chi-square of fit, χ 2 4 = 206.857, p < 0.0001), indicating that bees were able to see the string from the observation chamber and responded differently when it was presented in an unexpected location. However, there was no appreciable attraction to the string when its location was at variance with that seen from the observation chamber (28.5%). Taken together, these results indicate a strong role for local enhancement (bees were attracted to the location where they had observed a demonstrator) and a subordinate role for stimulus enhancement (bees were attracted to the string when its location was concordant with that during prior observation) [ 25 , 46 ].
What mechanisms were the observers using to copy the behavior? To answer this question, we explored several associative mechanisms: local enhancement [ 30 , 41 , 42 ], whereby observers are attracted to the location of their conspecific; stimulus enhancement [ 30 , 43 ], an attraction to the item handled by the demonstrator; and perceptual feedback [ 44 , 45 ], a form of trial-and-error learning in which action causing movement of the rewarding object towards the animal produces positive feedback for continuing that action. We found that all three associative mechanisms were involved in the learning of the string pulling process.
The Spread of String Pulling in a Transmission Chain Experiment
Can the combination of multiple simple social learning mechanisms mediate the establishment of a culture-like phenomenon (e.g. group-specific behaviors, such as foraging techniques, that are transmitted via social learning and retained in the group over long periods)? We tracked the diffusion of an experimentally introduced string pulling behavior among foragers of test colonies (Colonies 6, 7, 8) to explore the speed of diffusion and also the retention of the technique in the group beyond the demonstration provided by the first knowledgeable individual. To seed the technique, we trained a single demonstrator per colony to pull the string. Subsequently, we allowed pairs of bees to engage with the string pulling task and tracked the diffusion of the technique among the foraging population (Materials and Methods, Fig 4). Pairs of bees were tested in the order in which they arrived in the corridor connecting the hive to the arena; pairs could be any combination of bees regardless of whether they were naïve, the seeded demonstrator, or a successful learner (S12 Video). As a control, foragers of three separate colonies were tested in the same manner without a seeded demonstrator (Colonies 9, 10, 11).
Cultural diffusion paradigm.
Bees were group-trained to feed from blue flowers in the foraging arena. Three bees were trained to pull a string to obtain an artificial flower from under a table where they would get reward (sucrose solution; see Fig 1A). These three demonstrators were placed in colonies 6, 7, and 8 (one each; seeded colonies), and bees that came out of the colony were paired up in order of exit from the hive to forage within the arena and tested with the string pulling task. Each bout was capped at 5 min, and we recorded 150 foraging bouts (150 bee pairs). In colonies 9, 10, and 11 (control colonies), no trained demonstrator was present. 150 foraging bouts were recorded (150 bee pairs) (see S2 Data).
After only 150 paired foraging bouts, a large proportion of each of the test colonies’ forager population (Colony 6: n = 25/47, Colony 7: n = 17/29, Colony 8: n = 12/28) learnt to string pull, whereas none of the control colony foragers (Colony 9, 10, 11: n = 51, 58, 57) learnt to pull the string (Fig 5, Materials and Methods, S13–S18 Videos). We conducted additional foraging bouts in two of the tested colonies and found that the technique continued to spread among the foragers for as long as we allowed the spread to progress (Colony 6: 34/47, Colony 8: 18/28, Fig 5, S13 and S15 Videos).
Diffusion of string pulling in bumblebee colonies.
(A–F) Nodes represent individual bees. Lines indicate that two bees interacted at least once. Thickness of lines represent total number of interactions between two individuals—one interaction equals one point line thickness and each interaction increases the line thickness by one point. See top insert for indication of line thickness and number of interactions. Size of nodes indicates number of interactions of that individual bee with any other bee—each interaction increases the size of a node by 15% of the original size (3% of the plot width). See middle insert for indication of node size and interactions. Color represents experience (learning “generation”) of that bee: prior to any experience, nodes are grey. After a bee interacts for the first time in the foraging arena, its node turns white. The “seeded” demonstrator (D1), pretrained to pull a string, is marked yellow and at the twelve o’clock position. Once a bee learns to string pull, its node turns from white to another color: orange for a first-order learner (D2, interacting with the seeded demonstrator and lower-order bees); pink for a second-order learner (D3, interacting with first-order and lower-order bees); blue for a third-order learner (D4, interacting with second-order and lower-order bees). See bottom insert for indication of node color and learning generation. Networks for the experiments (A–C) only show interactions within bouts where at least one bee pulled the string at least once. (A) Network for test colony 6 (bout n = 189). (B) Network for test colony 7 (bout n = 114). (C) Network for test colony 8 (bout n = 249). (D) Network for control colony 9 (bout n = 149). (E) Network for control colony 10 (bout n = 150). (F) Network for control colony 11 (bout n = 150) (see S2 Data).
We quantified the behavioral changes in learner bees over the time of the diffusion experiments. We first screened 81 of the total 419 available videos (~20%) of the paired bouts between demonstrators and learners and inventoried the repertoire of behavioral interactions. We listed 11 types of interactions (Table 1), the frequency of which changed with increasing experience of the learners (Fig 6). Behaviors went through a series of steps with increasing competence, which typically followed the following sequence. During an observer bee’s first few bouts, she would spend most of her time flying around the arena, occasionally landing on top of the table (NI, No Interaction) and spend little or no time near the table, strings, or the other bee. She would gradually start to land beside a bee who had already pulled a string for reward, thereby gaining reward without pulling a string (Sc, scrounging). The observer thus learns to associate the other bee with reward and typically begins following her around the table, keeping in close contact as they both walk (Fo, following). After one or more occurrences of scrounging, the observer bee would begin to reach under the table, sometimes extending her proboscis towards the flower, seemingly in an attempt to gain access to the flower without manipulating the string. While moving around the edge of the table and trying to reach under it, the observer bee might accidentally move a string, but make no subsequent effort to continue moving it (AMS, Accidentally Moving String). Often the observer bee would then position herself next to the bee already pulling a string. She would be in direct contact with the string pulling bee throughout the pull, usually not touching the string (A, Attending), although in some instances ineffectively manipulating the string (STA, String Touching while Attending), and ultimately gaining reward through the other bee’s efforts. Eventually, while in direct contact with a more knowledgeable bee, the observer bee would pull the string, but not enough to move the flower close enough to the edge of the table, extract it, and obtain the reward (PA, Pulling Action with demonstrator). In this phase, she would still rely on the efforts by the more experienced bee to obtain the reward (RP, Rewarded Pull). After more experience, the observer bee would attempt to pull the string on her own without interacting with the other bee, for example, while the demonstrator was flying around the arena. On the first few attempts to string pull on their own, the observer bees did not move the flower enough to be able to obtain the reward (PAa, Pulling Action alone). Finally, after few unrewarded attempts, and typically when paired with a less knowledgeable bee, the observer bee would learn to pull the string on her own to the point of extracting the flower from underneath the table and gaining reward (RPa, Rewarded Pulling alone) and become a trained observer.
These changes in behavior are reflected in the relative frequencies of behavior classes as a function of experience (Fig 6). Whilst nonsocial interactions such as NI and Sc represented more than 55% of the interactions at the onset of the diffusion experiment, they decreased rapidly to 0% over time (Fig 6). In comparison, the percentage of pulling actions displayed by the learners continuously increased with experience from 15% of the interactions at the onset to 60% after 11 bouts. Overall, no major change was observed for the other behavior classes. These results show that learners progressively changed their foraging behaviors from scroungers to competent string pullers.
In test colonies, on average 2 ± 0.06 string pulls were performed per foraging bout and 20 ± 3.9 pulls were displayed per individual over the whole diffusion experiment. Bees needed to be shown 5 ± 0.45 instances of string pulling by an experienced demonstrator before being able to pull the string themselves without demonstration and subsequently demonstrate the technique. Notably, 15 of 104 foragers (Colony 6, 7, 8: n = 10, 3, 2, respectively) picked up the technique very rapidly after only one or two observations. There was a significant variation between tested colonies in the average number of string pulls displayed per bee (Colony 6, 7, 8: n = 13 ± 4.7, 15.4 ± 9.2, 34.5 ± 7.6, respectively; Kruskal–Wallis test, H2 = 8.790, p = 0.012) and the number of observations necessary for a bee to learn the technique (Colony 6, 7, 8: n = 4.1 ± 0.4, 7.6 ± 1.1, 5.9 ± 0.9, respectively; Kruskal–Wallis test, H2 = 17.179, p ≤ 0.001). In addition, some bees did not manage to acquire the technique despite having been shown the same number of string pulling by other bees (5.6 ± 0.7; Mann–Whitney test, U93 = 1075.5, p = 0.261). These results suggest colony and individual variation in social learning ability.
To determine whether experience of the second bee influenced the observer bee’s choice of string to pull, we analyzed the pulling behavior of 25 randomly selected observer bees over the complete sequence of their foraging career during the diffusion experiment (282 paired foraging bouts). We found that observer bees more often pulled the same string as the other bee when paired with a more experienced observer bee or the seeded demonstrator (42 RP instances) than when paired with a less experienced bee (9 RP instances). In contrast, observer bees more often pulled a string alone when paired with a less experienced bee (72 RPa instances) than when paired with a more experienced observer bee or a seeded demonstrator (27 RPa instances).
To test whether bees might cooperate during string pulling, we needed to compare whether experienced bees performed more efficiently when paired with another experienced individual than when foraging alone. Because the diffusion experiment contained only trials with dyads of foragers, the only way to make a direct comparison was to use trials in which an experienced demonstrator was paired with a fully naïve individual that had not shown any pulling action (PA, PAa, RP, or RPa) and thus did not interact or interfere with the skilled forager, who pulled the string singly. Such pairings were compared with instances where both bees were experienced (had already displayed a pulling action). We hypothesized that if cooperation was occurring, strings would be pulled faster and reward obtained quicker in such dyads. However, when paired with an experienced bee, demonstrators (n = 16 randomly chosen individuals) took 2.5 times longer to pull the string and obtain the reward (39.9 ± 9 s) than when the same individual demonstrators were paired with an experienced observer who did not interact or interfere with them (15.6 ± 2.1 s; Wilcoxon test, Z30 = 3.409, p < 0.001). These results suggest that bees do not cooperate to pull the string but in fact hinder each other’s efforts to some degree.
Of particular interest for culture-like phenomena is the question of whether a socially learnt behavior routine persists in the population for longer than the original knowledgeable individual can serve as a demonstrator, so that former observers can themselves become demonstrators. If this is the case, then group-specific behavior routines can at least potentially be retained over biological generations. Our network analysis indeed indicates that the technique spread across sequential sets of learners, whereby some bees that learnt the technique never interacted with the seeded demonstrator. In fact, despite the death of the seeded demonstrator in one of the test colonies (Colony 6) after 58 paired foraging bouts, the technique continued to spread among foragers. Moreover we found that there were up to four sequential learning “generations” (as opposed to true biological generations) in two of the three colonies (Fig 5). Learners had string pulling demonstrated to them by up to eight different demonstrators (2.1 ± 0.13), and each demonstrator displayed the technique to 5.3 ± 0.93 learners. Overall, seeded demonstrators displayed eight times more string pulling (119.7 ± 26.5) than the other foragers (14.6 ± 3) (Mann–Whitney, U68 = 4, p = 0.004) and demonstrated the technique to five times more foragers (19 ± 2.8) than the other foragers (4.2 ± 0.7) (Mann–Whitney, U36 = 2, p = 0.006). This preponderance of the pretrained demonstrators could be a result of higher motivation simply because they obtained reward with every bout, whereas untrained bees often (in the beginning of the experiment) were unrewarded (i.e., unsuccessful until they were paired with a demonstrator or until they learned to pull the string themselves).
To test whether string pulling was diffused socially, we performed network-based diffusion analysis (NBDA). We used the time-based approach described by Hoppitt et al . The Aikake Information Criterion (AIC) was used to determine if string pulling was diffused socially by comparing a social and a nonsocial model for each of the diffusion experiments. We found that for all three experiments, social transmission was more likely than asocial transmission (Table 2).
Results of network-based diffusion analysis (NBDA).
The difference between the fit of the nonsocial model and the fit for the social model is denoted by the change in AIC (ΔAIC). Therefore, positive values indicate a better fit for the social model (p values indicate significance). The social transmission estimate reflects the degree to which social interactions between bees influence the diffusion of string pulling. Positive social transmission estimates that do not cross zero (intervals) indicate significant influence of social interactions.
We also analyzed the structure of the social networks using exponential-family random graph modeling  and found that for all diffusion experiments as well as the control experiments without a demonstrator bee, the structure of the networks was significantly different from random (see Table 3). This indicates that certain bees were more likely to forage together than other bees. Although this could be interpreted as certain individuals preferentially foraging together, given the open-diffusion paradigm and experimental design (in which bees could not freely distribute themselves in space but were forced through the “bottleneck” of the nest entrance tunnel to the foraging arena), this likely reflects temporal factors such differences in when bees began to forage each day, daily changes in foraging activity across bees, and how long each bee takes to return to foraging from the hive.
Analysis of Social Network Structure for the three experimental and three control colonies.
Significant models represent networks where interactions between bees departed from random (i.e., individual bees were more likely to forage with some individuals than others).
[Update] Non-bee insects are important contributors to global crop pollination | bees 2016 – POLLICELEE
Many of the world’s crops are pollinated by insects, and bees are often assumed to be the most important pollinators. To our knowledge, our study is the first quantitative evaluation of the relative contribution of non-bee pollinators to global pollinator-dependent crops. Across 39 studies we show that insects other than bees are efficient pollinators providing 39% of visits to crop flowers. A shift in perspective from a bee-only focus is needed for assessments of crop pollinator biodiversity and the economic value of pollination. These studies should also consider the services provided by other types of insects, such as flies, wasps, beetles, and butterflies—important pollinators that are currently overlooked.
Wild and managed bees are well documented as effective pollinators of global crops of economic importance. However, the contributions by pollinators other than bees have been little explored despite their potential to contribute to crop production and stability in the face of environmental change. Non-bee pollinators include flies, beetles, moths, butterflies, wasps, ants, birds, and bats, among others. Here we focus on non-bee insects and synthesize 39 field studies from five continents that directly measured the crop pollination services provided by non-bees, honey bees, and other bees to compare the relative contributions of these taxa. Non-bees performed 25–50% of the total number of flower visits. Although non-bees were less effective pollinators than bees per flower visit, they made more visits; thus these two factors compensated for each other, resulting in pollination services rendered by non-bees that were similar to those provided by bees. In the subset of studies that measured fruit set, fruit set increased with non-bee insect visits independently of bee visitation rates, indicating that non-bee insects provide a unique benefit that is not provided by bees. We also show that non-bee insects are not as reliant as bees on the presence of remnant natural or seminatural habitat in the surrounding landscape. These results strongly suggest that non-bee insect pollinators play a significant role in global crop production and respond differently than bees to landscape structure, probably making their crop pollination services more robust to changes in land use. Non-bee insects provide a valuable service and provide potential insurance against bee population declines.
Pollinator-dependent crops are increasingly grown to provide food, fiber, and fuel as well as micronutrients essential to human health (1–5). The yield and quality of these crops benefit to varying degrees from flower visitation by animals. The honey bee, Apis mellifera L. (Hymenoptera: Apidae), is the most versatile, ubiquitous, and commonly used managed pollinator (6), but the global reliance on this single pollinator species is a risky strategy, especially given major threats to the health of managed honey bee colonies because of poor nutrition, the ectoparasitic mite Varroa destructor Anderson and Trueman (Mesostigmata: Varroidae), and a number of other pests and diseases (7–10).
However, honey bees are not the only insects that pollinate crops. Apart from a few managed bee taxa, the great majority of other pollinators are free-living or wild, providing an ecosystem service to crops. Wild pollinators other than honey bees recently have been recognized for their role in increasing and stabilizing crop-pollination services (11, 12). Wild bees are known to improve seed set, quality, shelf life, and commercial value of a variety of crops (13–17). Increasingly, studies indicate that insect pollinators other than bees, such as flies, beetles, moths, and butterflies, are equally if not more important for the production of some crops (18–24). Nonetheless, the contribution to crop pollination by non-bee insects has been largely unnoticed, with most global syntheses focusing on bees (25–28) or grouping together all bee and non-bee wild-insect pollinators (11).
Diverse pollinator assemblages have been shown to increase pollination services as a result of complementary resource use arising from variations in morphology and behavior among pollinator taxa (29, 30). For example, pollinator species may visit different parts within a flower or inflorescence or different flowers within a plant (high versus low flowers), improving the quality or quantity of pollination services overall (13, 31–33). Non-bee taxa, in particular, often have broader temporal activity ranges (34–36) and can provide pollination services at different times of the day compared with bees and in weather conditions when bees are unable to forage (37–40). In addition, non-bee taxa may be more efficient in transferring pollen for some crops under certain conditions (18, 19, 38) and/or carry pollen further distances than some bees (41). It has been suggested that this long-distance pollen transfer could have important genetic consequences for wild plants (42, 43). However, there is little information on the overall importance of the diverse group of non-bee wild pollinators (but see refs. 39 and 44) and their importance to global crop production.
Anthropogenic land use change and intensification are considered to be among the main drivers of bee declines (45, 46). One of the mechanisms underlying observed declines is thought to be the loss of habitat that supports host plants (47) and nesting sites (48). However, different pollinator taxa respond differently to disturbances (49, 50). The proximity and area of natural habitat are often associated with higher crop flower visitation and bee diversity (25, 46, 51). Yet, although several studies have investigated the habitat requirements of non-bee taxa (52–55), little is known about how habitat availability affects crop-pollination services from non-bee taxa (but see ref. 44). Thus, differential responses to habitat proximity by bees and non-bees, if such exists, could provide an additional stabilizing effect on crop-pollination services.
In summary, non-bees are often neglected as potential providers of crop ecosystem services by the scientific community and by growers. In the data collection for the present synthesis, for example, 33% of the original 58 pollination studies we obtained did not record or distinguish non-bee pollinators from bee pollinators and thus had to be excluded.
In this study we address the knowledge gap about non-bee crop pollination and ask:
i) How does the crop pollination provided by non-bee insects compare with that provided by honey bees and other bees?
ii) How does the crop pollination provided by non-bees, honey bees, and other bees translate into fruit/seed set?
iii) Do non-bee crop pollinators respond similarly to bees with regard to isolation from natural and semi/natural habitats?
To answer these questions, we compiled a dataset comprising 39 studies of crop pollinators around the world and the pollination services they provide (Table S1).
Pollination Services Provided by Honey Bees, Other Bees, and Non-Bees.
Flower-visitor assemblages were diverse, with representatives from the orders Hymenoptera, Diptera, Lepidoptera, and Coleoptera. Non-bee taxa included flies (Diptera: mainly dominated by Syrphidae, Calliphoridae, Tachinidae, Empididae, and Muscidae), butterflies and moths (Lepidoptera), and various beetle families (Coleoptera) and hymenopterans including ants (Formicidae) and wasps (Fig. S1). Bees observed in the studies included Apidae (e.g., Meliponini, Bombus spp., Xylocopini, and Ceratinini), Halictidae, Colletidae, Megachilidae, and Andrenidae.
The total pollination services provided, which we calculated as the product of visitation frequency and pollen deposition or fruit set per visit (n = 9 studies) (56) did not differ significantly among honey bees, other bees, and non-bees (Fig. 1A). On average, non-bees accounted for 38% [confidence interval (CI): 29–49%], honey bees for 39% (CI: 29–50%), and other bees for 23% (CI: 15–33%) of the visits to crop flowers (n = 37 studies) (Fig. 1B). Visitation rates of other bees and non-bees were very weakly correlated (Pearson’s product–moment correlation: 0.22), and the visitation rates of non-bees and honey bees and of other bees and honey bees were not correlated (0.02 and 0.04, respectively). In contrast, the per-visit pollen deposition or fruit set (n = 11 studies) was significantly lower for non-bees than for either type of bee (Fig. 1C and Fig. S2). Thus, non-bees’ higher visitation frequency and lower per visit effectiveness were compensatory, resulting in levels of pollination-service delivery similar to that provided by bees (Fig. 1A).
The contribution by honey bees, other bees, and non-bees to crop pollination. Data from individual crop studies were standardized by z-scores before analysis. (A) Pollination considered as a function of visits*single-visit effectiveness among guilds for the nine studies with effectiveness and visitation data. Note that per capita effectiveness in each guild is measured only in a subset of dominant species in each study. (B) The contributions of different insect groups to visitation (i.e., percentage of visits). (C) The relative effectiveness of honey bees, other bees, and non-bees as measured by pollen deposition or fruit set per visit, combined across the 11 crop studies for which data were available. Letters depict post hoc test differences (at P < 0.05) among pollinator groups.
Spatial Variation in Pollinator Community Composition.
Observations of insect visitation rates revealed that assemblage composition varied across crop type and region (Fig. 2). Across the 37 crop studies, 31 recorded visits by all three groups of taxa, i.e., honey bees, other bees (all species other than Apis mellifera), and non-bees (Fig. 2). Two custard apple crops in Australia and Brazil (Annona sp.) were visited exclusively by non-bee taxa. Spatial variation in composition of the pollinator community resulted in some crops being visited by a more diverse group of insects than others, even within the same crop type. For example, pollinators of oilseed rape (Brassica napus) were surveyed in Sweden, Germany, the United Kingdom, the Netherlands, Ireland, and Australia, and the contribution to visitation by non-bees differed markedly (5–80%) among these surveys. Even within the three studies in Sweden (oilseed rape A, G, and M), visitation by non-bees ranged from 5–60%, demonstrating that location can have a strong influence, as can crop type, in determining assemblage composition (Fig. 2).
The contribution of different insect groups to flower visitation across the 37 crop studies for which visitation data were available. Crops are ordered, left to right, from mostly bee-dominated to mostly non-bee–dominated.
Higher visitation rates by non-bees and other bees each enhanced crop fruit and seed set more so than similar increases in visitation by honey bees (n = 19 studies) (Fig. 3A). In fact, honey bee visitation was not correlated with fruit set, with the average slope of this relationship centered on zero (β = −0.019, 2.5% CI = −0.164, 97.5% CI = 0.126), whereas non-bees show a positive slope (β = 0.12) minimally overlapping with zero (2.5% CI = −0.016, 97.5% CI = 0.265). The strongest relationship was between other bee visitation and fruit set (β = 0.187, 2.5% CI = 0.044, 97.5% CI = 0.330). Importantly, fruit set increased with non-bee visits independently of bee visitation rates, indicating that non-bee pollinators supplement rather than substitute for bee visitation. Therefore both groups are required for optimal pollination services.
Regression coefficients (i.e., slopes ßi ± 95% CI) representing honey bee, other bees, and non-bee contributions to overall fruit set and distance from natural/seminatural habitat. (A) Overall fruit set measured by seed set across 19 crop studies, estimated from the relationship between visitation and fruit set variation. Visitation by other bees increased fruit set (i.e., the average slope is positive, and CIs for regression coefficients did not include zero). The average regression coefficients across crops for non-bees increased fruit set (i.e., positive mean), but CIs minimally overlapped zero. (B) Distance from natural/seminatural habitat was measured across 23 studies. Visitation by other bees was negatively related to distance from natural/seminatural habitat (i.e., the average slope is negative, CIs for regression coefficients did not include zero). Visitation by honey bees and non-bees was not related to distance from natural/seminatural habitat (i.e., the average slope is negative, but confidence intervals overlapped zero for both taxa). Data from individual crop studies were standardized by z-scores before analysis to permit direct comparison of slopes.
Response to Changes in Land Use.
To test whether non-bees and bees respond differently to isolation from natural or seminatural vegetation, we investigated the relationship between the proximity to these features and the visitation rate of honey bees, other bees, and non-bee taxa across 23 studies. When data across all crop studies are considered, other bee visits declined sharply with increasing isolation from natural/seminatural vegetation (β = −0.263, 2.5% CI = −0.484, 97.5% CI = −0.042) (Fig. 3B). In contrast, non-bee declines are moderate, and the CIs include zero (β = −0.049, 2.5% CI = −0.270, 97.5% CI = 0.182), whereas honey bee visits show no response to proximity to natural/seminatural vegetation (β = 0.070, 2.5% CI = −0.161, 97.5% CI = 0.301).
The clear importance of non-bees as global crop pollinators, as shown in this study, illustrates how important the omission of non-bees from crop pollination studies is to our understanding of crop-pollination services by wild insects. This crop pollination role is in addition to the well-established contributions that non-bees make to the reproduction of wild, native plant species (44, 57). Although on average the amount of pollen deposited per visit to crop flowers is lower for non-bees than for bees, the high visitation frequency of non-bees to crop flowers compensates for the deficit in per-visit effectiveness and results in high pollination services overall (Fig. 1). Thus, our results are consistent with other studies that have found that visitation frequency drives the overall function provided by a species, because the variance across species in their flower visitation is much larger than the variance in per-visit function (28, 58). One outcome is that taxa with less efficient pollen deposition may be the most important pollinators in certain years or seasons when they are at high abundance relative to other taxa (28, 59, 60).
Increased visitation by other bees and by non-bees each enhanced crop fruit and seed set more than increased visitation by honey bees (Fig. 3A). Measuring this downstream outcome variable is important, because pollen deposition does not necessarily lead to fruit set (61) [e.g., if pollinator visits are at saturating levels and result in flower damage or the transfer of poor quality/incompatible pollen (62, 63)]. For example, in our study, honey bees were good at depositing pollen in many crops, but increased honey bee visitation did not increase fruit set, a result that other researchers (11, 64) also have found. In contrast, increased visits from other bees, as from non-bees, were associated with increased fruit set. As argued by Garibaldi et al. (11), these patterns suggest that the effect of other bees and non-bees is additive to the effect of honey bees in the datasets examined.
A final benefit of non-bees documented here is that they respond less negatively than bees to changes in land use (Fig. 3B). Thus, where non-bees and bees pollinate the same crop, the presence of non-bees could help stabilize crop-pollination services against changes in land use through a mechanism known as “response diversity” (49). Hence differences in responses among bee and non-bee taxa potentially could provide pollination “insurance” in the event of bee declines (33). Although other bees responded positively to natural habitat, non-bees and honey bees did not show a clear pattern, perhaps because most other bees are central place foragers, some of which require untilled ground and sparsely vegetated ground for nesting. Other bees also require reliable, long-term pollen and nectar resources, and these habitat features are associated with seminatural or natural vegetation (46). In contrast, many non-bee taxa have diverse nesting habits; e.g., many flies lack central nest locations, and others are dependent on floral resources only during adult life stages (65). For this diverse group of insects the agricultural matrix may be more permeable than it is for many bees (66).
The diversity of life history strategies exhibited by non-bees necessitates an approach to habitat management different from that used for bees to ensure that a wide range of foraging and nesting resources are available. For example, within the hoverfly family (Diptera: Syrphidae) the larvae of some species feed on pollen (67), or aphids (65), or plant matter (68), or dung, among other resources (69), but the adults usually are generalist flower visitors. Furthermore, at least some hoverfly species appear to be less affected by changes in land use than bees, because many hoverfly species are able to use resources from highly modified habitats, including agricultural fields (44, 46, 66). The variability among life histories may explain why some non-bee pollinator populations are known to benefit from the same pollinator-enhancement practices as bees but others do not (54, 70, 71).
There are several reasons why non-bees generally have been overlooked in crop pollination studies until now. The diversity of families and the taxonomy of non-bee taxa are often poorly resolved (72, 73). Some non-bee taxa (such as flies and small wasps) move quickly and are difficult to follow in visual observations (e.g., transects). Further, many researchers have made the erroneous assumption that non-bee taxa are unimportant to pollination, as demonstrated by the 33% of studies reviewed that did not collect data on non-bee taxa as an a priori decision.
With the growing economic importance of crops that require animal-mediated pollination (74), wild insect pollinators are increasingly being recognized for their role in improving and stabilizing crop-pollination services (75). Here, we show that wild pollinators other than bees also make substantial contributions to global crop-pollination services. This study demonstrates the importance of including non-bee pollinators in future crop-pollination surveys, pollination estimates, and pollinator-management practices to ensure that we ascertain the relative contributions from all crop-pollinating taxa, over and above the well-known bee taxa.
Materials and Methods
We analyzed data from 480 fields for 17 crops examined in 39 studies on five continents. Fields ranged from extensive monocultures to small, diversified systems (Tables S1 and S2). All crop studies that were included benefit in some way from insect pollination. The protocols and identity of studies used to investigate the visitation rate, effectiveness, contribution to yield, and response to natural or seminatural vegetation in each study are provided in Tables S1 and S2. Across all the studies, 37 provided data on visitation frequency; 11 studies provided data on pollen transfer or fruit set per-visit effectiveness; 19 studies provided data on seed or fruit set; and 23 provided data on distance to natural/seminatural vegetation. Thirteen of the 39 crop studies have not been included in any previous synthesis on wild pollinator contributions to crop pollination.
Flower Visitation Frequency.
To investigate the frequency with which non-bees visit crop flowers in comparison with bees across our studies, we observed flower visitors within standardized quadrats and transects and measured flower visitation per unit of time for each insect species/group (37 studies). Pollinator observations were carried out during peak flowering. In several studies, visitation was standardized with respect to a unit area or branch (because some crops have hundreds of small flowers per plant, visits per flower could not be accurately assessed). We analyzed visitation by three different groups: honey bees, other bees, and non-bees (i.e., all other insects). In this synthesis across all studies, we considered Apis mellifera as the only species within the honey bee group for consistency across all datasets. Other Apis bees (e.g., Apis cerana indica) were pooled into the other-bee category. We analyzed all feral and managed honey bees as a single group because they cannot be distinguished during field observations. Feral honey bees were uncommon in most studies except for those in South Africa and Argentina. The exact methods and numbers of sampling points surveyed in each study are published elsewhere or are provided in the supporting information (Table S1).
Effectiveness per Flower Visit.
To investigate differences in per-visit effectiveness among bee and non-bee taxa (11 studies) (Table S2), pollen deposition on stigmatic surfaces (76) or fruit set after a single visit was estimated in fine weather conditions from pollination-effectiveness experiments in which virgin inflorescences were bagged with a fine mesh to exclude pollinators. When bagged flowers opened, the bag was removed, and the flowers were observed until an insect visited the flower and contacted the stigma. The stigma then was removed by carefully severing it from the style using finely pointed forceps, and the pollen grains or pollen tubes were counted after one visit by each insect. A variation of this method was used for several crops (i.e., radish, kiwi, avocado, carrot, and watermelon), which involved removing the virgin flower and positioning it to allow visitation by particular taxa (Tables S1 and S2). Single-visit pollen-deposition values generally were available only for the dominant taxa; hence this analysis does not necessarily represent the effectiveness of entire communities.
Calculating Total Pollination per Species.
Total pollination is often considered to be a function of both visitation frequency and per-visit effectiveness (56). We estimated total pollination for the nine studies in which these data were available. We used species-level visitation records and multiplied total visitation of each group (i.e., honey bee, other bees, and non-bees) by the mean per-visit pollen deposition of each group (Fig. 1).
To investigate differences in fruit set or seed set in relation to visitation by bee and non-bee taxa (19 studies) (Table S2), we recorded the proportion of flowers that set fruit or the total number of fruits or seeds as a measure of pollination success.
Isolation from Natural/Seminatural Habitat.
Finally, to investigate the response of bees and non-bees to isolation from natural/seminatural vegetation, we calculated the linear distance (in kilometers) from each field site to the nearest patch of natural or seminatural vegetation (23 studies) (Table S2). For two crops, almond and oilseed rape E, we transformed the percentage of seminatural vegetation within a 1-km area to linear distances following ref. 12.
We initially contacted 58 data holders with the following criteria for inclusion of datasets in the synthesis: field studies must have set out to record all groups of pollinators (i.e., both bee and non-bee groups). Studies were excluded that did not set out to record non-bees (n = 14) or that did not set out to record honey bees (n = 1). If a researcher stated that a systematic survey was performed with the aim of sampling all pollinators (even though an entire group of pollinators was absent), we included that study. Finally, studies that included either bees or non-bees on an ad hoc basis (rather than in a systematic survey) were excluded (n = 4). Although the present study is limited to crop studies in which data were available for non-bee taxa, we do include several crops for which bees are assumed to be the primary visitors, such as almond and watermelon (77, 78). Furthermore, the ratio of bee- to non-bee–visited crops in the FAOSTAT crop database (6) is comparable to the ratio investigated in this synthesis (Table S3). Nonetheless, we acknowledge that the study represents a limited number of crops, and a greater range of datasets is required to obtain a fuller picture of the relative importance of these different groups of pollinators.
Data on visitation rates, pollination effectiveness, fruit or seed set, and isolation from natural/seminatural vegetation were standardized for cross-study analysis with the calculation of z-scores within each study (Datasets S1–S4). Z-scores do not modify the form (e.g., linear or nonlinear) of the relationship between response and predictor variables and allow direct comparison of the values collected in different studies (79).
We analyzed all data using general linear mixed-effects models using R software version 3.0.2, nlme package, lme function, with Gaussian error distribution (80). By including crop study as a random variable, our models estimated different intercepts (αj) for each study (j), accounting for the hierarchical structure of the data, i.e., different fields are nested within each study (79, 81). The overall intercept (μα) reflects a weighted average over crop studies (αj), in which the relative influence of each crop study increases with the precision of its local model fit and its sample size (79, 82).
To answer the first question regarding differences in crop-pollination services provided to crop flowers by non-bee and bee taxa, we ran a different model for each group (honey bees, wild bees, and non-bees) with no predictor. This model enabled calculation of the overall intercept (i.e., mean percent visitation) and CIs for each of the three groups, taking into account the hierarchical structure of the data. Per capita effectiveness values were regressed against pollinator group (categorical: honey bee, other bee, non-bee). Post hoc Tukey tests were used to disentangle the differences in effectiveness among the three groups using the multcomp package (83) with a Hochberg correction for multiple comparisons. To answer the second question, we built three sets of models to examine the relationship between fruit set and the visitation rates of the different insect groups. To determine whether increased visitation rate by each of the three groups was associated with increased fruit set, the first model consisted of fruit set regressed against total visitation of honey bees, other bees, and non-bees, with random intercepts for crop study. The second set of models included both random intercepts and random slopes. A third set of models was run including pairwise interactions among the three groups and only random intercepts. The three models were compared using the Akaike information criterion (AIC) (84). The first model had the greatest support (AIC = 555) followed by both the interaction model (ΔAIC = 5) and the random slopes model (ΔAIC = 4); hence only the random intercept models are presented. Finally, to answer the third question, visitation rate by each group was regressed against isolation from natural habitats in a separate model with random intercepts as described above. We present estimated slopes and CIs for all analyses (Table S4). To meet the assumptions of homoscedasticity, we used a constant variance function when necessary. Variance inflation factors of the predictors were always below 1.5, indicating no multicollinearity (85).
Data collection was funded by a University of New England seed grant (to R.R.). I.B. was supported by European Union Project BeeFun PCIG14-GA-2013-631653; L.A.G. was supported by Universidad Nacional de Río Negro Grant PI 40-B-399 and Consejo Nacional de Investigaciones Científicas y Técnicas Resolución 3260/14, Expediente 3207/14; A.-M.K. and C.B. were supported by the German Science Foundation; D.K., M. Reemer, and J.S. were supported by the Dutch Ministry of Economic Affairs Grants BO-11-011.01-011 and KB-14-003-006; L.G.C. D.K., J.S., R.B., H.S., M.W., M. Rundlöf, and S.G.P. were supported by the European Community’s Seventh Framework Programme FP7/2007–2013 under Grant Agreement 244090, Status and Trends of European Pollinators; H.S. and M.W. were supported by European Community’s Sixth Framework Programme under Grant Agreement GOCE-CT-2003-506675, Assessing Large Scale Risks for Biodiversity with Tested Methods Project; S.A.M.L. was supported the Swedish Farmers’ Foundation for Agricultural Research and the Swedish Board of Agriculture; M.P.D.G. and S.G.P. were supported by a grant from Biotechnology and Biological Sciences Research Council, Defra, the Natural Environment Research Council, the Scottish Government, and the Wellcome Trust under the UK Insect Pollinators Initiative; H.G.S. and R.B. were supported by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning; C.S. was supported by the Swiss National Science Foundation under Grant 3100A0-127632 (FRAGMENT) to F.H. and M.H.E.; J.C.S. and D.A.S. were supported by Irish Environmental Protection Agency Grant EPA 2007-B-CD-1-S1 under the Sectoral Impacts on Biodiversity and Ecosystems Services (SIMBIOSYS) project; B.M.F. and L.G.C. were supported by National Council for Scientific and Technological Development-Brasília Research Grants 05126/2013-0 and 300005/2015-6, respectively; Y.M. and G.P. were supported by The Israel Science Foundation; S.K. was supported by The North-South Centre, Swiss Federal Institute of Technology, Zurich; B.F.V. and J.H. were supported by the Ministry of the Environment and the Brazilian Research Council; and the study on Highland coffee was supported by Grant SEMARNAT-CONACyT 2002-C01-0194 from Mexico’s Environmental Ministry (to C.H.V.). H.T. was supported by the Global Environment Research Fund (E-0801 and S-9) of the Ministry of the Environment, Japan. Funding for kiwi in New Zealand provided by the Thomas J. Watson Foundation (to M.M.M.). B.G.H. and D.E.P. were supported by Ministry for Business Innovation and Employment (C11X1309).
Author contributions: R.R. designed and coordinated the study, collated datasets and interpreted analyses, wrote the first draft of the manuscript, and is the corresponding and senior author; I.B. assisted with the design of the study, conducted and interpreted analyses (with assistance from L.A.G.), discussed, and revised earlier versions of the manuscript; L.A.G., M.P.D.G., B.G.H., R.W., S.A.C., M.M.M., B.G.-H., and C.S.S. contributed data and discussed and revised earlier versions of the project and manuscript; and A.D.A., G.K.S.A., R.B., C. B., L.G.C., N.P.C., M.H.E., B.F., B.M.F., J.G., S.R.G., C.L.G., L.H., F.H., J.H., S.J., F.J., A.-M.K., D.K., S.K., C.Q.L., S.A.M.L., Y.M., V.M.M., W.N., L.N., D.E.P., N.d.O.P., G.P., S.G.P., M. Reemer, M. Rundlöf, J.S., C.S., H.G.S., D.A.S., J.C.S., H.S., H.T., C.H.V., B.F.V., and M.W. collected and formatted field data, and provided several important corrections to subsequent manuscript drafts.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1517092112/-/DCSupplemental.
Freely available online through the PNAS open access option.
Seeds for Bees 2016
Find out more about Project Apis m.’s Seeds for Bees program.
นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูเพิ่มเติม
Pencilmation Compilation #2: Bees, Zombies, Tetris, \u0026 Animals
A compilation of Pencilmation episodes featuring the classic characters of Pencilmation!
Share YOUR fan art in our FB group here!! : http://link.pencilmation.com/FBgroup
NEW PLUSHIES AND MERCH AVAILABLE NOW: http://link.pencilmation.com/plushiesmerchmain
Previous compilation! https://www.youtube.com/watch?v=zQWhr0vtXpA
00:00 BEE MINE (Pencilmation 22)
A love story in which two young doodles are drawn together by unlikely circumstances.
2:09 PLIGHT OF THE LIVING DUDE (Pencilmation 24)
Punning on Night of the Living Dead, an episode about zombies, Tetris, and the end of the world. What more could you want?
4:06 BETTERMORPHOSIS (Pencilmation 20)
Shows the pencil mastering his magician art by metamorphosing animals until he finally finds one funny enough to entertain him…for a while.
6:25 RANCOUR’S AWAY (Pencilmation 21)
Pencilmate travels high and low in this scurvy episode, from the top of a high staircase, to the bottom of the ocean. The pencil brings simple line drawings to life on a page.
8:53 THE WHEEL MCCOY (Pencilmation 23)
Two stick men, cast into bondage by the Pencil, work together in an attempt to break free.
Created by Ross Bollinger
Compilation edited by Francis Florencio
CREDITS FOR BEE MINE :
Animation \u0026 Music by Ross Bollinger
Animation Assistance by Ben Snyder
ADDITIONAL EPISODE CREDITS : https://www.dropbox.com/s/o9toqz4evkt…
ABOUT PENCILMATION :
Pencilmation is a web cartoon created by Ross Bollinger in which pencil drawn stick figures and doodles come to life. It is made with love and a lot of fun by an international team helmed by Ross Bollinger who started the channel alone in his room a long time ago. The Pencilmation channel is for nottooserious grownups alike. Follow the new, wacky, and often times quite silly adventures of Pencilmate, Little Blue Man, Pencilmiss and other doodles every Tuesday and Friday.
Ask questions, share ideas, and draw pictures in the Pencilmation Facebook Group: http://facebook.com/groups/pencilmation
Bees May 2016
Just thought I’d film the bees’ activities for a little bit this morning. . . (And I’ve discovered how edit in slomo footage, so of course, I had to find SOMETHING to film to try it out.) Ha!
Birds n Bees, Clarinet, Keys and Moog: lothenk 2016
Eine musikalische Collage aus Vogelstimmen, Geräuschen, Klarinette und Moog Synthesizer.
Film: \”Die Bestäubung der Taubnessel\”, 16mm, sw, RWU
Ton, Bild und Video: lothenk 2016
Alcohol Mite Wash (Varroa Mite Count)
Learn how to do a alcohol mite wash. Varroa mites kill and infect honey bees with many diseases. It’s important to learn where your mite levels are so you can treat the colony if needed. A colony with high levels of mite will most likely die over winter. In this video I show how to do a alcohol wash. This will tell you how many mites you have per hundred bees. It’s very simple test and the most accurate of all the mite washes done today.
Learn how to treat Varroa mites with Mite Away Quick Strips (MAQS)
Supplies I Used To Mite Wash
Wash Tub: http://amzn.to/2r6LE7C
Rubbing Alcohol: http://amzn.to/2s12u75
Varroa Easy Checker (New Improved Mite Shaker): https://blythewoodbeecompany.com/product/varroaeasycheckeraccuratelycheckthevarroaloadinyourhive/ref/2/
Double Jar Mite Shaker: https://blythewoodbeecompany.com/product/doublejarvarroamitecounter/ref/2/
Mite Away Quick Strips (MAQS): https://blythewoodbeecompany.com/product/miteawayquickstripsvarroamitetreatment/ref/2/
Oxalic Acid Vaporizer:https://blythewoodbeecompany.com/product/oxalicacidvaporizervarroamitetreatment/ref/2/
Commercial Oxalic Vaporizer: https://blythewoodbeecompany.com/product/provap10commercialoxalicacidvaporizer/ref/2/
Beekeeping Equipment I Use:
• Hive Tool: http://amzn.to/2r8MkI0
• Smoker: http://amzn.to/2rl8GX5
• Veil: http://amzn.to/2rSgCQb
• Vented Bee Jacket: http://amzn.to/2rlw8U0
• Queen Excluder: http://amzn.to/2qyWSxl
• 10 Frames Complete Painted Hive Kit: http://amzn.to/2rSguQL
• 10 Deep Frames w/ Cell Rite Foundation: http://amzn.to/2qDRqZa
Beekeeping Books I like
• The Beekeeper Bible: http://amzn.to/2qDPdwB
• Beekeeping For Dummies: http://amzn.to/2qDK8EC
• Queen Rearing Essentials: http://amzn.to/2qDpogc
• Have a question?
• Want a video review on your product?
✉ Email: firstname.lastname@example.org
My Bee Channel https://www.youtube.com/c/JasonChrismanBees
Attention: Just so you know, if you purchase any of the products above, I do get a small kick back from the purchase.
นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูวิธีอื่นๆLeather
ขอบคุณที่รับชมกระทู้ครับ bees 2016