This figure shows the amounts of caffeine and three different types of sugars in nectar from Coffea and Citrus plants. Knowing the amount of these compounds present in these flowering plant species allows the authors to design the subsequent experiments in a way that accurately reflects how honey bees and plants interact in nature.
Nuts and bolts
In both panels, the colored bars represent the mean values measured for each species. The error bars represent the standard error of the mean, which tells you how the actual caffeine or sugar concentrations measured for each sample are distributed around the calculated mean value.
The exact number of measurements taken per species is indicated in the figure legend as “N=”, so Coffea canephora is indicated as having N=34, meaning the authors measured nectar caffeine and sugar concentrations of 34 individual Coffea canephora plants.
The samples collected from Citrus sinensis and Citrus reticulata were pooled, meaning they were mixed together and sampled simultaneously. Though their reasoning is not given in the materials and methods section of the supplemental materials, the authors likely did this because they were unable to collect large enough volumes of nectar from these plants to conduct the assay.
Caffeine and sugar concentrations were measured using a technique called liquid chromatography-mass spectrometry (LC-MS). LC-MS first separates individual chemicals present in a sample (via liquid chromatography) and then identifies what those chemicals are by measuring the masses of the particles in those separated chemicals (via mass spectrometry). For more details on how LC-MS works, see the Glossary section of the SitC annotations.
Panel A shows the amount of caffeine present in a cup of instant coffee, three species of Coffea, and four species of Citrus. Coffea canephora exhibited the highest nectar caffeine concentration at approximately 0.25 mM. Despite the relatively high caffeine concentration of C. canephora, the authors report in the figure legend that the average concentration across all Coffea and all Citrus species was not significantly different.
Panel B shows the amount of sugar present in three species of Coffea and four species of Citrus. The Σ (the uppercase form of the Greek letter “sigma”) on the y-axis title is a mathematic symbol that indicates “sum of.” Thus, “Σ of sucrose, glucose, fructose (M)” means the measurements presented are the combined sum of the nectar molar concentrations of the sugars sucrose, glucose, and fructose. The authors referred to this sum in the text as “total nectar sugar concentration.” The authors present the data for each individual sugar alone in supplemental figure 1B.
Citrus maxima had the highest total nectar sugar concentration. The authors found that the total sugar concentrations in panel B did not correlate with caffeine concentrations presented in panel A. This means that you cannot predict the amount of sugar in a plant’s nectar based on the amount of caffeine present and vice versa.
The authors hypothesized that caffeine present in floral nectar would affect pollinator learning and memory. The figure presents the results of experiments designed to test this hypothesis, in which the authors trained honey bees to associate a specific floral scent with a reward (in this case, sugar) with or without consuming caffeine.
The data show that caffeine has a only mild, but significant, effect on honey bees’ rates of learning a new scent— that is, the honey bees spent about as much time learning the scent with or without caffeine—but it had a profound effect on a honey bee’s ability to actually remember that new scent in the long run.
Nuts and bolts
Panel A represents the rate of learning across subsequent trials as a line graph, with each line representing a different “treatment group.” That is, each line represents bees given sucrose containing different concentrations of caffeine, ranging from 0 M to 109 M caffeine/sucrose solutions.
As with the caffeine concentrations presented in Figure 1, panels B and C show the magnitude of the memory response as bars, with the error bars representing the standard error of the mean. Additionally, significance was indicated in panel B by bright red bars. Dark red/brown bars indicate the results were not significantly different from the sucrose-only control group.
The bees in these experiments were exposed to a specific scent at the same time that they were presented with their reward (sucrose) so that they could learn to associate that scent with reward. Caffeine was added to the sucrose in different concentrations to see how consuming caffeine affected the bees’ ability to associate that scent with reward. After being conditioned, the bees were also presented with an unrelated scent to see whether their memory of reward was associated specifically to the scent they had learned; if the bees responded to the unrelated scent in the same way that they reacted to the scent they had learned, then the response would be nonspecific.
To learn more about how bees are conditioned, see the Glossary section of the SitC annotations.
Rate of learning
Panel A shows that although the effect wasn’t great, adding caffeine to the reward caused the bees to learn to associate the floral scent with reward slightly, but significantly, more quickly than the bees that received no caffeine.
Together, Panels B and C indicate that bees that received caffeine during associative learning were better able to remember scents for longer periods of time.
Panel B shows that compared with sucrose alone, the addition of caffeine concentrations ranging from 10-3 M to 10-7 M improved the bees’ ability to remember the scent 24 hours after training. The two lowest concentrations of caffeine, 10-8 and 10-9 M, did not have a significant effect on long-term memory. Panel C shows that compared with the sucrose-only group (white bars), twice as many bees that had received 10-4 M (0.1 mM) caffeine in their reward (orange bars) were able to remember the learned scent 72 hours after training.
The authors wanted to see what response caffeine induced in Kenyon cells (KCs), the cell type in a bee’s brain that is most similar hippocampal neurons in mammals—the cell type used for associative learning and memory formation.
The results of this experiments revealed that caffeine activates nicotinic acetylcholine receptors in the KCs, making them more likely to fire strongly in response to a sensory stimulus (like, for example, a floral odor). This makes it easier for these cells to help form memories associated with these odors and to learn to associate receiving sugar with that odor.
Nuts and bolts
In order to do see what the bees’ mushroom body neurons were doing, the authors took recordings of KC activity using a technique called “whole-cell patch-clamp electrophysiology.” For a detailed explanation of how this technique works, see the Author’s Experiment section of the SitC annotations.
Panels A and B show the recordings of an individual bee, rather than the whole group. Papers often show results of what they call a “representative sample”—an individual sample that shows the most common result, and so can be said to represent the group as a whole. This is useful for presenting data that are difficult or impossible to consolidate into averages for a whole group; for example, these individual spectra of nerve activity or tissue sections stained to show damage or protein expression patterns.
Panels A and B show one representative sample, but C and D show the mean data across the entire group for three critical time points: when the cells were at rest (before anything was added to them), when caffeine was added, and then when a nicotinic acetylcholine blocker was added. The bars represent standard error of the mean, which tells you how the individual data measured for each sample are distributed around the calculated mean value. An asterisk (*) indicates two measurements are statistically significantly different from each other.
The colored bars in panels E and F show the mean effect of DPCPX, a nicotinic acetylcholine receptor antagonist, on holding current. An asterisk (*) indicates two measurements are significantly different.
Panel G shows a representative trace when DPCPX was added to a KC. The trace on the bottom half of the panel is simply a blown-up view of the first few centimeters of the trace above it—this is the rising phase of the action potential. For a full explanation of action potentials and how neurons fire, see the Glossary section of the SitC annotations.
Panel H shows the mean data of the experiment whose representative trace was presented in panel G. The left y-axis shows the rate of the rising phase and the right y-axis shows the time of decay. The error bars represent standard error of the mean and an asterisk (*) indicates two measurements are significantly different.
Panels A through D show that when added to KCs, caffeine increases nicotinic acetylcholine receptor activation, leading to a depolarized KC membrane potential toward the action potential firing threshold. This means that caffeine can trigger the neurons to start firing, which is required to form memories.
Who's your receptor?
Caffeine is already known to bind to nicotinic acetylcholine receptors and trigger a response, but the authors of this study didn’t want to make any assumptions. To confirm that caffeine was, in fact, using these receptors to elicit the responses seen in panels A through D, they added a compound that prevents caffeine from binding to these receptors a activating them.
Panels E through H show that when this compound, called DPCPX, is added to KCs, caffeine doesn’t induce the effect seen in panels A through D. However, when caffeine is added without DPCPX, that same push of the neurons toward the action potential firing threshold is seen again. These data indicate that caffeine does, in fact, activate nicotinic acetylcholine receptors.
Put another way, if caffeine wasn’t acting through these nicotinic acetylcholine receptors, then why would this effect have disappeared once we blocked these receptors? The most reasonable explanation for the results seen is that caffeine-induced activation of nicotinic acetylcholine receptors is what leads to the depolarization of the KC membrane potential seen in panels A through D.
The authors were interested to see how bees would respond to the nectar levels of caffeine determined in Figure 1. They found that the bees’ bitter taste threshold, and therefore the concentrations of caffeine bees find repellent, are three times higher than those found in the nectar of the flowering plants tested in this paper.
Nuts and bolts
This figure presents the probability that a bee would drink a 0.4-microliter drop of solution on the y-axis and increasing amounts of caffeine on the x-axis. The data points show the average probability for all bees fed the same concentration of sucrose (either 0.3 M, 0.7 M, or 1 M) at that concentration of caffeine. The bars represent the standard error of the mean, which tells you how the individual probabilities measured for each bee are distributed around the calculated mean value.
Bitter taste threshold
This figure shows that bees are more likely to drink a solution if it contains more sucrose. However, once caffeine concentrations reach 1 mM, bees are very unlikely to drink even a high concentration sucrose solution; they are deterred by larger amounts of caffeine present in their reward.
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- Acknowledgments: We thank staff at Centro Agronomico Tropicale de Investigacion et Ensenanza in Costa Rica for access to the Coffea collections and at Technological Education Institute of Crete for access to Citrus orchards; M. Thomson, K. Smith, F. Marion-Poll, and A. Popescu for help with the data collection; M. Thompson for beekeeping; and J. Harvey and C. Connolly for project support. This work was funded in part by the Linnean Society of London and by a UK government Insect Pollinators Initiative grant BB/I000968/1 to G.A.W. and a separate grant to C. Connolly (BB/1000313/1). Methods and additional data are available in the online supplementary materials. All data are archived on the Natural Environment Research Council Environmental Information Data Centre.