Evolutionary Ecology

, Volume 31, Issue 2, pp 143–151 | Cite as

Plant–animal communication: past, present and future

Article

Abstract

Communication between plants and their animal partners underlies some of the planet’s most ecologically and economically important mutualisms. Study of communication in this context offers many opportunities to address fundamental questions about the costs and benefits of signal production, signal honesty, and receiver cognition. In this special issue, contributors highlight several key areas of current research, including how multiple receivers affect floral signaling, and how signaling may be related across different phases of reproduction. Visual signals are a particular emphasis, including how learning can mediate pollinator preferences, and the evolution of conspicuousness. In light of these focal areas, we summarize current trends towards the study of greater complexity both in terms of floral phenotypes and signaling/interaction networks.

Keywords

Communication Floral signals Plant–animal interactions Pollination Herbivory Seed dispersal 

What purpose does the nectar of this or that flower serve? What is the particularly colored spot on it meant for? What connection do all parts of the flower have, and what relationships do they have to the fruit which should originate from the flower? And how does everything which we see and notice in a flower throughout its complete flowering period unite into a single beautiful whole? Sprengel (1793)

In “Discovery of the Secret of Nature in the Structure and Fertilization of Flowers”, Sprengel (1793) amassed evidence to support the idea that plants reward animals with nectar in exchange for the transfer of pollen. Beyond identifying the key elements of pollination mutualisms (and, in passing, those of seed dispersal), this text is also remarkable for its coverage of how floral and fruit signals mediate relationships between plants and their partners. Here, Sprengel famously coined the term nectar guide (Saftmal) to describe floral patterns that direct pollinators towards both rewards and reproductive structures. His careful description of these signals laments, “nobody has recognized what I call nectar covers and nectar guides for what they are, although everyone has seen them.”

To be both seen and recognized remains a theme of research on plant–animal communication more than two centuries later. After all, many questions about how plants “speak” to their mutualistic partners can be addressed in species and locales not much more exotic than those available to an average eighteenth century naturalist. To cite an example from our own work: even the most casual of gardeners would agree that flowers are often both colorful and scented. Yet, researchers have barely begun to understand why plants produce such sensorially complex floral displays (Leonard et al. 2012), much less how pollinators process these multimodal signals (Leonard and Masek 2014). In this case, the critical first step was to identify an interesting question about signal evolution essentially hiding in plain sight.

To achieve a holistic understanding of plant–animal communication, that first step must be followed by a willingness to grapple with the perspectives of both sender and receiver, from proximate and ultimate points of view. Researchers must not only consider the drivers of signal production (e.g. the biochemistry and reproductive biology of the plant) but also the mechanisms and consequences of signal reception (e.g. the sensory and behavioral ecology of the animal). Thus, disciplinary boundaries and a growing emphasis on early-career specialization are challenges to the growth of “Plant–Animal Communication” as a focal area. Beyond the obvious divisions between what students learn in animal communication versus plant ecology courses, parallel trajectories in the literature can yield insights in the absence of discourse. For example, the same plant species could in principle be studied quite separately for its interactions with herbivores versus pollinators versus seed dispersers; the same flower or fruit for its color versus scent; the same receiver for its visual versus olfactory responses to a given plant structure.

For those inclined to bridge these divisions, plant–animal interactions offer an unparalleled testing ground for broader ideas about the evolution of communication. What keeps signals honest? How does conspicuousness evolve or coevolve in relation to sensory systems? What abiotic forces and antagonists shape signal evolution? Is signal form shaped by developmental or phylogenetic constraint? How much does it cost to produce a signal, and what is its fitness benefit? Indeed, as plant behavior goes mainstream (Bradbury and Vehrencamp 2011; Schaefer and Ruxton 2011; Karban 2015), interest in understanding plants’ interactions with animals through the lens of signaling theory will continue to grow. In contrast to many systems of animal communication, here is a case where we can often both easily quantify the signal and connect it indirectly or even directly to fitness. The list of fundamental questions about communication ideally suited to explore in plant–animal systems will likely spiral beyond the few examples noted here.

In this special issue, contributors have taken diverse perspectives on how floral and fruit signals mediate the relationship between plants and their mutualistic partners. Here we highlight four particular themes that emerged:
  1. 1.

    Multiple agents shape floral signaling

     

Plant–animal interactions offer ample opportunities to understand how a complex network of interactants can influence communication in a given context. In the most commonly-studied scenario, herbivores perturb interactions between plants and pollinators via their direct effects on floral displays (e.g. McCall and Irwin 2006) or by inducing defenses that alter the chemistry of floral signals and rewards (e.g. Kessler and Halitschke 2009). In this issue, Hoffmeister and Junker (2016) ask how simulated herbivory affects bees’ responses to not only the flowers of Vicia faba, but also to the visual and olfactory signals of its extrafloral nectaries (EFNs). They outline an indirect pathway by which antagonists might alter interactions with pollinators: increases in EFN activity induced by herbivory can attract the attention of not only natural enemies, but also pollinators. Extrafloral nectar might be a useful resource for the bumblebees in this study, but their visits to these structures do not transfer pollen, and potentially deplete the resources available to antagonists of herbivores.

Shifting from an ecological to an evolutionary perspective, Knauer and Schiestl (2016) continue the theme of asking how multiple receivers shape plant signaling. Namely, floral displays are often attractive not only to pollinators, but also to animals that may act as antagonists. They explore overlap between the visual and olfactory floral signals attractive to pollinators (Bombus terrestris) as well as herbivores that can also act as pollinators (the butterfly Pieris brassicae). When the floral preferences of both interactants align, there is an intriguing potential for such dynamics to drive the evolution of private channels of communication, perhaps one explanation for the observed complexity of floral scent blends.
  1. 2.

    Signals are linked across reproductive phases

     

Beyond the herbivore–plant–pollinator triad described above, connections may also exist between the signals targeting pollinators and seed dispersers. This is not obvious from the literature: flowers and fruits represent two phases of plant reproduction usually studied quite independently of each other. This separation may partly reflect the taxonomic divide between the receivers commonly studied in each context (e.g. insects vs. mammals; hummingbirds vs. other avian species). Nonetheless, both pollination and seed dispersal biologists ask similar questions about signaling, and new findings suggest that, just as these two life history stages are invariably linked, communication in each context may be related.

As a first step towards greater integration, Valenta et al. (2016) review how traits associated with both fruits and flowers mediate interactions with pollinators and seed dispersers. The authors suggest several ways in which findings in each context could be brought together to yield a broader understanding of how communication contributes to plant fitness. For example, a researcher interested in the function of multimodal floral signals might find much to discuss with researchers asking similar questions about fruit traits. Likewise, recent discoveries about mimetic fruits raise new opportunities for researchers broadly interested in deceptive signaling.

Valenta et al.’s review complements a case study by Stournaras and Schaefer (2016) on the connection between flower, fruit, and leaf coloration. Beyond their communicative function, pigments may also buffer plants against abiotic stressors (Strauss and Whittall 2006). In principle, their vegetative function might link reproductive signals in each context. To test these ideas, the authors ask whether the coloration of leaves, flowers, and fruits are coupled in two species of Vaccinium. This work also reminds us that inferring a connection between a particular flower or fruit trait (in this case, conspicuousness) and plant fitness elides over phases where signaling may be even more tightly liked to reproductive success: in principle, fitness components maximized during pollination might or might not translate into successful dispersal.
  1. 3.

    Learning mediates the functional consequences of floral visual signals

     

Despite more than two centuries of research on floral visual displays, many basic aspects of their function and evolutionary history remain obscure. Even familiar nectar guides hold some surprises: although popularly assumed to be beneficial to both plants and pollinators, we have found that their presence may tip the balance of the interaction towards pollen transfer at the expense of nectar collection (Leonard and Papaj 2011). They may even help defend against nectar robbing, by incentivizing “legitimate” flower handling (Leonard et al. 2013). In this issue, de Jager et al. (2016) further unravel the details of how common components of floral patterns (rings and spots) manifest on the continuum of interactions ranging from mutualism to exploitation (Bronstein 1994). In this case, pollinator learning, visual properties of the pattern, and ecological context seem likely to guide these dynamics. This lab-based study is an important first step towards understanding the role of pollinator cognition in mediating visual pattern signaling by real floral phenotypes.

To this end, lab-based studies using greenhouse plant specimens offer a happy medium between the benefits of studying pollinator behavior under controlled conditions versus the difficulties of using real flowers in behavioral work. In their contribution, Russell et al. (2016) use different color morphs of Solanum tridynamum to explore the visual preferences of both naïve and experienced bumblebee foragers (Bombus impatiens). Although color polymorphisms are not uncommon in natural populations of many plant species, the degree to which learning contributes to pollinator-mediated selection in this context is relatively unexplored. Just as in de Jager et al.’s (2016) study of floral patterns, here the authors uncover cognitive complexities that shape bees’ responses to visual floral signals. In this case, learning may be key to understanding the fate of hypochromic morphs: naïve bees show no initial color preferences, but learning biases guide their floral choices over longer timespans. A clever manipulation allowed the authors to consider the colors associated with the anthers versus corolla of each morph separately; creation of such “mosaic” flowers allows researchers to manipulate separate visual components of real flowers in a controlled setting ideal for behavioral experiments.
  1. 4.

    New modeling approaches reveal convergence towards conspicuousness

     

Visual modeling techniques (e.g. Chittka 1992; Vorobyev and Osorio 1998; Endler and Mielke 2005) can open a window into the sensory world of receivers, helping us predict the detectability and discriminability of flower or fruit signals. As animals vary in their visual sensitivity, color space models can also be used to understand how sensory systems might exert similar or different selection pressures on the displays of plant species. These models are also useful for shifting the focus away from our anthropocentric instincts about the function of a visual display. In this special issue, two contributions show how visual modeling can reveal aspects of floral signaling otherwise hidden from view.

Combining computer simulations, behavioral assays, and floral reflectance measurements, Bukovac et al. (2016) tackle a mysterious broad-scale pattern in the visual properties of bee-pollinated flowers. Namely, they find that a spectral “signature” (sharp changes in reflectance at 420–480 nm) rarely found in bee-pollinated flowers is also one which color space models predict should be difficult for bees to detect. Because some bird-pollianted flowers exhibit this visual property, its relative rarity among bee-pollinated plants may indicate pollinator-mediated selection, rather than a production constraint. Despite the utility of visual modeling, the authors also highlight uncertainties associated with key assumptions of these models, including gaps in our understanding of visual processing that might enhance the ability of these tools to explain large-scale patterns in floral signaling.

Finally, a study by Gaskett et al. (2016) reinforces the idea that quantifying the conspicuousness of floral visual traits can shift our understanding of floral evolution. The authors explore signaling strategies in an assemblage of orchid species (representatives of Drakaea and Caladenia), which are pollinated by male thynnine wasps. The authors use a number of analytical techniques to compare the visual properties of floral structures and female wasps, including what may be the first application of color pattern geometry analysis to floral displays (an approach widely used in studies of animal signals: Endler and Mielke 2005; Endler 2012). Although these orchids are assumed to mimic female visual signals, this study returns evidence consistent with an alternative hypothesis: orchids from these two genera appear to have converged on patterns that succeed not via precise mimicry of female wasps, but because they are easy for male wasps to detect against the background. The authors discuss how this pollination strategy might have evolved along a pathway involving elements of both sensory exploitation and sexual deception.

Future directions

As a whole, the contributions to this special issue suggest several lines of research that might yield new insights into communication between plants and animals. We have represented a few key ideas in Fig. 1, illustrated in the context of plant–pollinator communication. As this special issue demonstrates, there is clearly no shortage of important questions involving the simplest dyad (Fig. 1b) wherein a receiver assesses a single type of signal produced by a sender. However, looking ahead, we can envision continuing to expand this scenario along complementary axes of complexity, two of many possible trajectories.
Fig. 1

Areas of current and possible future research on plant–animal communication a Recently research has expanded beyond the classic dyad b to consider plant signals in relation to networks of interacting species (e.g. a Manzanita plant communicates with pollinators, herbivores, and seed dispersers). Understanding increased complexity in both floral rewards (as indicated by line pattern in d) and floral displays (as indicated by line color in e) is also of recent interest. c Combining both themes, future research might address how complex floral phenotypes mediate competition among co-signaling plants via their effects on animal cognition. (Color figure online)

The first axis (x) is that of floral complexity. This effort is already well underway regarding floral advertisements: in the past decade, interest has spiked in understanding how animals use multimodal signals to make decisions, and (relatedly) why plants might produce advertisements that span sensory modalities (Junker and Parachnowitsch 2015); (Fig. 1e). Dovetailing with these developments, we have also recently begun to study how animals forage for multiple reward types offered by their mutualist partners, as well as the reciprocal question of how a plant might benefit from offering a particular combination of resources (Francis et al. 2016); (Fig. 1d).

The second axis (y) represents interaction complexity. The push to expand beyond the classic plant–pollinator dyad is familiar, considering that the direct and indirect effects of plant–herbivore interactions on floral phenotypes have been studied for decades (e.g. Strauss et al. 1996; Johnson et al. 2015), and network approaches are now common in both studies of mutualistic interactions (Bascompte and Jordano 2007) and animal communication (McGregor 2005).

From these foundations, we highlight two areas where an interest in communication might yield unique insights into the ecology and evolution of plant–animal interactions:

Advertisement and decision-making in a community context

Angiosperms are not only partners in a mutualism with pollinators and seed dispersers, but also competitors in a marketplace for the services these animals provide. Fruit and flower traits such as color, luminance, pattern, size and scent can convey accurate information about the quality or quantity of reward available, and generalist pollinators or dispersers use this information to guide resource selection (e.g. Knauer and Schiestl 2015). Across plant species, however, no single trait seems to honestly signal the value of rewards to animals evaluating flowers or fruits. For one plant species, scent may be the best indicator of value; for another, color. To add to this complexity, rewards offered by competing plant species vary not only in quantity and quality but also type (e.g. co-flowering competitors might offer pollinators 2 μl vs. 8 μl of nectar; or alternately, 2 μl of nectar vs. 2 mg of pollen). These cognitive challenges are also relevant to seed dispersal, wherein the most useful signal can vary across plant species: for a non-ecological demonstration, consider the familiar quest to select the ripest avocado (via touch), banana (via vision), and melon (via scent). Consequently, a forager may often encounter relationships between signals and rewards that shift across the different plant species they visit (Fig. 1c). Animals’ interactions with a network of plant signalers thus offer an opportunity to address basic questions about how receivers compare co-signaling species that signal (1) across different sensory modalities and (2) with respect to multiple kinds of rewards.

Research on how pollinators learn and use floral signals might offer a starting point for the question of how animals cope with complex informational landscapes. Bees, for example, can clearly learn associations when signals convey information about different reward types (Muth et al. 2015) and discriminate among floral options using multiple sensory modalities (Leonard and Masek 2014). Understanding the efficacy of decision-making when signals or rewards vary within versus across categories is an obvious next step. Ideally, these scenarios would be inspired by studies establishing the diversity of signal-reward relationships facing foragers within a given plant community. Typically, such surveys focus on a single category (e.g. floral color or nectar volume); it would be more difficult to quantify floral communities along multiple axes (e.g. floral color + scent; nectar + pollen). Nevertheless, information from such field studies should inform the ecological realism of controlled experiments. Likewise, controlled experiments about animal decision-making might also allow us to make predictions regarding the signaling and reward strategies of co-flowering plants. For example, if a focal plant faces a strong visual competitor, would it more effectively boost visitation by investing in floral scent production? Such dynamics could be one driver of signal and reward diversity within a given floral community.

Signaling across interaction contexts

Plant–animal systems may be ideal for understanding how the relative strength of temporally nested interactions impacts signaling, because of the ease with which one can collect longitudinal data during different stages of reproduction (Fig. 1a). As noted by Stournaras and Schaefer (2016), plant signaling traits may be more integrated across life history stages than disciplinary divisions would lead us to believe. Their study shows that abiotic stressors could drive the coloration of both leaves and reproductive structures, with consequences for the degree of visual contrast offered by both flowers and fruits. Besides the physical environment, in principle, biotic interactions might also connect fruit and flower signaling. Most simply, the vegetative coupling Stournaras and Schaefer (2016) describe makes it plausible that foliar herbivory might have related effects on the colors or scents of both fruits and flowers.

Perhaps more intriguingly, integration between flower and fruit signaling should also be impacted by temporal nestedness. Plant reproduction is an inherently hierarchical system where success in earlier contexts (vegetative production and pollination) can have cascading effects on the following context (seed dispersal). For example, the outcome of ineffective floral signaling (pollination failure) may impact both the importance and efficacy of signaling to dispersers as well as the opportunity to do so. Analogous scenarios have been established in other cross-context studies, e.g. those that show that intensity of florivory can alter pollinators’ impact on plant reproduction (Rodríguez-Rodríguez et al. 2015). A plant which has only half its flowers fertilized faces not only a potentially smaller display with which to attract dispersers, but also a scenario in which the efficacy of fruit signals is even more critical, as the dispersal fate of any single seed represents a greater proportion of total reproductive success. Given this, we might expect flexible allocation to fruit signals based upon the success of signaling in the preceding floral stage.

Effects of display size and density offer additional facets for consideration (Howe and Smallwood 1982). For example, when per capita seed dispersal increases with fruit crop (e.g. a plant has 80% of its seeds dispersed when it offers 100 fruits; 90% when it offers 200 fruits) we might expect floral signaling to pollinators to be a relatively stronger source of selection than fruit signaling. Alternatively, if per capita fruit dispersal is independent of crop size (i.e. 80 seeds are dispersed regardless if the crop is 100 or 200 fruits) selection on floral signals may be less important. These ideas could be tested by comparative studies assessing whether investment in floral signal production depends on the relationship between crop size and per capita dispersal.

Hints at a more direct mechanistic coupling between flowers and fruits can be found in agricultural systems, where pollination-associated differences in seed set appear to alter aspects of resulting fruit chemistry (Hogendoorn et al. 2010). Whether the effectiveness of plant–pollinator communication could alter the visual or chemical properties of fruits in a way meaningful to dispersers in natural systems seems to be an open question. However, just as herbivory can alter the chemistry of nectar rewards and floral displays (e.g. Adler et al. 2006), so too might pollinators have functionally-relevant effects on the signals or rewards associated with seed dispersal. If different directional selection regimes characterize signaling during fruit and flowering stages, such biosynthetic linkages could be a source of constraint worth further exploration.

Overall, the two axes of complexity discussed above present a varied landscape where fruit and flower traits may be under selection for their function as signals to mutualists and antagonists, their competitive advantage over simultaneous signalers, and their direct and pleiotropic effects on plants’ interactions with their abiotic environment. An obvious extension of these themes would be to explicitly account for spatial and temporal variation in signaling interactions (c.f. Schaefer and Ruxton 2011). There is obviously plenty of work to be done; given the potential that plant–animal systems offer for addressing these basic questions about the ecology and evolution of communication networks, we are excited about the findings and ideas presented in this special issue, and optimistic about the work they will inspire.

Notes

Acknowledgements

This work was supported by the National Science Foundation (Grant IOS-1257762 to A.S.L.; Graduate Research Fellowship to J.S.F). Thank you to D. Picklum, D.L. Moseley and F. Muth for comments.

References

  1. Adler LS, Wink M, Distl M, Lentz AJ (2006) Leaf herbivory and nutrients increase nectar alkaloids. Ecol Lett 9:960–967. doi:10.1111/j.1461-0248.2006.00944.x CrossRefPubMedGoogle Scholar
  2. Bascompte J, Jordano P (2007) Plant–animal mutualistic networks: the architecture of biodiversity. Annu Rev Ecol Evol Syst 38:567–593. doi:10.1146/annurev.ecolsys.38.091206.09581 CrossRefGoogle Scholar
  3. Bradbury JW, Vehrencamp SL (2011) The broader view: microbes, plants, and humans. In: Bradbury JW, Vehrencamp SL (eds) Principles of animal communication, 2nd edn. Sinauer, Sunderland, pp 651–678Google Scholar
  4. Bronstein JLL (1994) Conditional outcomes in mutualistic interactions. Trends Ecol Evol 9:214–217. doi:10.1016/0169-5347(94)90246-1 CrossRefPubMedGoogle Scholar
  5. Bukovac Z, Dorin A, Finke V et al (2016) Assessing the ecological significance of bee visual detection and colour discrimination on the evolution of flower colours. Evol Ecol. doi:10.1007/s10682-016-9843-6 Google Scholar
  6. Chittka L (1992) The colour hexagon: a chromaticity diagram based on photoreceptor excitations as a generalized representation of colour opponency. J Comp Physiol A 170:533–543. doi:10.1007/BF00199331 Google Scholar
  7. de Jager ML, Willis-Jones E, Critchley S, Glover BJ (2016) The impact of floral spot and ring markings on pollinator foraging dynamics. Evol Ecol. doi:10.1007/s10682-016-9852-5 Google Scholar
  8. Endler JA (2012) A framework for analysing colour pattern geometry: adjacent colours. Biol J Linn Soc 107:233–253. doi:10.1111/j.1095-8312.2012.01937.x CrossRefGoogle Scholar
  9. Endler JA, Mielke PW (2005) Comparing entire colour patterns as birds see them. Biol J Linn Soc 86:405–431. doi:10.1111/j.1095-8312.2005.00540.x CrossRefGoogle Scholar
  10. Francis JS, Muth F, Papaj DR, Leonard AS (2016) Nutritional complexity and the structure of bee foraging bouts. Behav Ecol 27:903–911. doi:10.1093/beheco/arv229 CrossRefGoogle Scholar
  11. Gaskett AC, Endler JA, Phillips RD (2016) Convergent evolution of sexual deception via chromatic and achromatic contrast rather than colour mimicry. Evol Ecol. doi:10.1007/s10682-016-9863-2 Google Scholar
  12. Hoffmeister M, Junker RR (2016) Herbivory-induced changes in the olfactory and visual display of flowers and extrafloral nectaries affect pollinator behavior. Evol Ecol. doi:10.1007/s10682-016-9875-y Google Scholar
  13. Hogendoorn K, Bartholomaeus F, Keller MA (2010) Chemical and sensory comparison of tomatoes pollinated by bees and by a pollination wand. J Econ Entomol 103:1286–1292. doi:10.1603/EC09393 CrossRefPubMedGoogle Scholar
  14. Howe HF, Smallwood J (1982) Ecology of seed dispersal. Annu Rev Ecol Syst 13:201–228CrossRefGoogle Scholar
  15. Johnson MTJ, Campbell SA, Barrett SCH (2015) Evolutionary interactions between plant reproduction and defense against herbivores. Annu Rev Ecol Evol Syst 46:191–213. doi:10.1146/annurev-ecolsys-112414-054215 CrossRefGoogle Scholar
  16. Junker RR, Parachnowitsch AL (2015) Working towards a holistic view on flower traits: how floral scents mediate plant–animal interactions in concert with other floral characters. J Indian Inst Sci 95:43–68. doi:10.1016/S0305-1978(97)00010-0 Google Scholar
  17. Karban R (2015) Plant sensing and communication. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  18. Kessler A, Halitschke R (2009) Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: predictions and case study. Funct Ecol 23:901–912. doi:10.1111/j.1365-2435.2009.01639.x CrossRefGoogle Scholar
  19. Knauer AC, Schiestl FP (2015) Bees use honest floral signals as indicators of reward when visiting flowers. Ecol Lett 18:135–143. doi:10.1111/ele.12386 CrossRefPubMedGoogle Scholar
  20. Knauer AC, Schiestl FP (2016) The effect of pollinators and herbivores on selection for floral signals: a case study in Brassica rapa. Evol Ecol. doi:10.1007/s10682-016-9878-8 Google Scholar
  21. Leonard AS, Masek P (2014) Multisensory integration of colors and scents: insights from bees and flowers. J Comp Physiol A 200:463–474. doi:10.1007/s00359-014-0904-4 CrossRefGoogle Scholar
  22. Leonard AS, Papaj DR (2011) “X” marks the spot: the possible benefits of nectar guides to bees and plants. Funct Ecol 25:1293–1301. doi:10.1111/j.1365-2435.2011.01885.x CrossRefGoogle Scholar
  23. Leonard AS, Dornhaus A, Papaj DR (2012) Why are floral signals complex? An outline of functional hypotheses. In: Patiny S (ed) Evolution of plant–pollinator relationships. Cambridge University Press, Cambridge, pp 261–282Google Scholar
  24. Leonard AS, Brent J, Papaj DR, Dornhaus A (2013) Floral nectar guide patterns discourage nectar robbing by bumble bees. PLoS ONE 8:e55914. doi:10.1371/journal.pone.0055914 CrossRefPubMedPubMedCentralGoogle Scholar
  25. McCall AC, Irwin RE (2006) Florivory: the intersection of pollination and herbivory. Ecol Lett 9:1351–1365. doi:10.1111/j.1461-0248.2006.00975.x CrossRefPubMedGoogle Scholar
  26. McGregor PK (ed) (2005) Animal communication networks. Cambridge University Press, CambridgeGoogle Scholar
  27. Muth F, Papaj DR, Leonard AS (2015) Colour learning when foraging for nectar and pollen: bees learn two colours at once. Biol Lett 11:20150628. doi:10.1098/rsbl.2015.0628 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Rodríguez-Rodríguez MC, Jordano P, Valido A (2015) Hotspots of damage by antagonists shape the spatial structure of plant–pollinator interactions. Ecology 96:2181–2191. doi:10.1890/14-2467.1 CrossRefPubMedGoogle Scholar
  29. Russell AL, Newman CR, Papaj DR (2016) White flowers finish last: foraging bees show learned but not innate biases in a floral color polymorphism. Evol Ecol. doi:10.1007/s10682-016-9848-1 Google Scholar
  30. Schaefer HM, Ruxton GD (2011) Plant–animal communication. Oxford University Press, OxfordCrossRefGoogle Scholar
  31. Sprengel CK (1793) Das Entdeckte Geheimniss der Natur in Bau und in der Befruchtung der Blumen. Viewveg, BerlinCrossRefGoogle Scholar
  32. Stournaras KE, Schaefer HM (2016) Does flower and fruit conspicuousness affect plant fitness? Contrast, color coupling and the interplay of pollination and seed dispersal in two Vaccinium species. Evol Ecol. doi:10.1007/s10682-016-9864-1 Google Scholar
  33. Strauss SY, Whittall JB (2006) Non-pollinator agents of selection on floral traits. In: Harder LD, Barrett CH (eds) Ecology and evolution of flowers. Oxford University Press, Oxford, pp 120–138Google Scholar
  34. Strauss SY, Conner JK, Rush SL (1996) Foliar herbivory affects floral characters and plant attractiveness to pollinators: implications for male and female plant fitness. Am Nat 147:1098–1107CrossRefGoogle Scholar
  35. Valenta K, Nevo O, Martel C, Chapman CA (2016) Plant attractants: integrating insights from pollination and seed dispersal ecology. Evol Ecol. doi:10.1007/s10682-016-9870-3 Google Scholar
  36. Vorobyev M, Osorio D (1998) Receptor noise as a determinant of colour thresholds. Proc R Soc B Biol Sci 265:351–358. doi:10.1098/rspb.1998.0302 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of BiologyUniversity of NevadaRenoUSA

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