Based on this diagram, how do you deduce the keystone species?

Based on this diagram, how do you deduce the keystone species?

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How would you figure out what the keystone species is in the following textbook question? (questions 1 and 2 at the bottom of the image)

Based on my knowledge, a keystone species helps to maintain biodiversity therefore I think it is either the otter or the kelp that is the keystone species. I say this because when either one of them decreases in number, the other species decreases in number as well (less biodiversity).


A keystone species is a plant or animal that plays a unique and crucial role in the way an ecosystem functions. Without keystone species, the ecosystem would be dramatically different or cease to exist altogether.
[… ]
The sea otter is an example of a keystone species in the Pacific Northwest. These mammals feed on sea urchins, controlling their population. If the otters didn't eat the urchins, the urchins would eat up the habitat's kelp. Kelp, or giant seaweed, is a major source of food and shelter for the ecosystem. Some species of crabs, snails, and geese depend on kelp for food. Many types of fish use the huge kelp forests to hide from predators. Without sea otters to control the urchin population, the entire ecosystem would collapse. 1

  1. The organism I would therefore hypothesize to be the keystone species is the sea otter.
  2. The data supports the hypothesis as when there is an decrease in the number of sea otters there is a increase in the biomass of sea urchins, which is accompanied by a decrease in the density of kelp. The lack of otters feeding on sea urchins causes an increase in their abundance, and this leads to an increased consumption of kelp by the sea urchins, which leads to the decrease in the density of kelp. Therefore, the sea otter is the keystone species since a change in their abundance has a significant impact onto the ecosystem and the organisms living in it.

Feedback Inhibition in Metabolic Pathways

Molecules can regulate enzyme function in many ways. The major question remains, however: What are these molecules and where do they come from? Some are cofactors and coenzymes, as you have learned. What other molecules in the cell provide enzymatic regulation such as allosteric modulation, and competitive and non-competitive inhibition? Perhaps the most relevant sources of regulatory molecules, with respect to enzymatic cellular metabolism, are the products of the cellular metabolic reactions themselves. In a most efficient and elegant way, cells have evolved to use the products of their own reactions for feedback inhibition of enzyme activity. Feedback inhibition involves the use of a reaction product to regulate its own further production (Figure 4.11). The cell responds to an abundance of the products by slowing down production during anabolic or catabolic reactions. Such reaction products may inhibit the enzymes that catalyzed their production through the mechanisms described above.

The production of both amino acids and nucleotides is controlled through feedback inhibition. Additionally, ATP is an allosteric regulator of some of the enzymes involved in the catabolic breakdown of sugar, the process that creates ATP. In this way, when ATP is in abundant supply, the cell can prevent the production of ATP. On the other hand, ADP serves as a positive allosteric regulator (an allosteric activator) for some of the same enzymes that are inhibited by ATP. Thus, when relative levels of ADP are high compared to ATP, the cell is triggered to produce more ATP through sugar catabolism.


Landscape Designer

Looking at the well-laid gardens of flowers and fountains seen in royal castles and historic houses of Europe, it is clear that the creators of those gardens knew more than art and design. They were also familiar with the biology of the plants they chose. Landscape design also has strong roots in the United States’ tradition. A prime example of early American classical design is Monticello, Thomas Jefferson’s private estate among his many other interests, Jefferson maintained a passion for botany. Landscape layout can encompass a small private space, like a backyard garden public gathering places, like Central Park in New York City or an entire city plan, like Pierre L’Enfant’s design for Washington, DC.

A landscape designer will plan traditional public spaces—such as botanical gardens, parks, college campuses, gardens, and larger developments—as well as natural areas and private gardens (Figure14.18). The restoration of natural places encroached upon by human intervention, such as wetlands, also requires the expertise of a landscape designer.

With such an array of required skills, a landscape designer’s education includes a solid background in botany, soil science, plant pathology, entomology, and horticulture. Coursework in architecture and design software is also required for the completion of the degree. The successful design of a landscape rests on an extensive knowledge of plant growth requirements, such as light and shade, moisture levels, compatibility of different species, and susceptibility to pathogens and pests. For example, mosses and ferns will thrive in a shaded area where fountains provide moisture cacti, on the other hand, would not fare well in that environment. The future growth of the individual plants must be taken into account to avoid crowding and competition for light and nutrients. The appearance of the space over time is also of concern. Shapes, colors, and biology must be balanced for a well-maintained and sustainable green space. Art, architecture, and biology blend in a beautifully designed and implemented landscape.

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Expanding the focus from pathogens to virus species

Historically, public health and fundamental research have been focused on the detection, containment, treatment and analysis of viruses that are pathogenic to humans following their discovery (a reactive approach). Exploring and defining their biological characteristics in the context of the entire natural diversity as a species has never been a priority. The emergence of SARS-CoV-2 as a human pathogen in December 2019 may thus be perceived as completely independent from the SARS-CoV outbreak in 2002–2003. Although SARS-CoV-2 is indeed not a descendent of SARS-CoV (Fig. 2b), and the introduction of each of these viruses into humans was likely facilitated by independent unknown external factors, the two viruses are genetically so close to each other (Fig. 2c, panel c of the figure in Box 4) that their evolutionary histories and characteristics are mutually informative.

The currently known viruses of the species Severe acute respiratory syndrome-related coronavirus may be as (poorly) representative for this particular species as the few individuals that we selected to represent H. sapiens in Fig. 1. It is thus reasonable to assume that this biased knowledge of the natural diversity of the species Severe acute respiratory syndrome-related coronavirus limits our current understanding of fundamental aspects of the biology of this species and, as a consequence, our abilities to control zoonotic spillovers to humans. Future studies aimed at understanding the ecology of these viruses and advancing the accuracy and resolution of evolutionary analyses 41 would benefit greatly from adjusting our research and sampling strategies. This needs to include an expansion of our current research focus on human pathogens and their adaptation to specific hosts to other viruses in this species. To illustrate the great potential of species-wide studies, it may again be instructive to draw a parallel to H. sapiens, and specifically to the impressive advancements in personalized medicine in recent years. Results of extensive genetic analyses of large numbers of individuals representing diverse populations from all continents have been translated into clinical applications and greatly contribute to optimizing patient-specific diagnostics and therapy. They were instrumental in identifying reliable predictive markers for specific diseases as well as genomic sites that are under selection. It thus seems reasonable to expect that genome-based analyses with a comparable species coverage will be similarly insightful for coronaviruses. Also, additional diagnostic tools that target the entire species should be developed to complement existing tools optimized to detect individual pathogenic variants (a proactive approach). Technical solutions to this problem are already available for example, in the context of multiplex PCR-based assays 42 . The costs for developing and applying (combined or separate) species- and virus-specific diagnostic tests in specific clinical and/or epidemiological settings may help to better appreciate the biological diversity and zoonotic potential of specific virus species and their members. Also, the further reduction of time required to identify the causative agents of novel virus infections will contribute to limiting the enormous social and economic consequences of large outbreaks. To advance such studies, innovative fundraising approaches may be required.

Although this Consensus Statement focuses on a single virus species, the issues raised apply to other species in the family and possibly beyond. A first step towards appreciation of this species and others would be for researchers, journals, databases and other relevant bodies to adopt proper referencing to the full taxonomy of coronaviruses under study, including explicit mentioning of the relevant virus species and the specific virus(es) within the species using the ICTV naming rules explained above. This naming convention is, regretfully, rarely observed in common practice, with mixing of virus and species names being frequently found in the literature (including by the authors of this Consensus Statement on several past occasions). The adoption of accurate virus-naming practices should be facilitated by the major revision of the virus species nomenclature that is currently being discussed by the ICTV and is being planned for implementation in the near future 43 . With this change in place, the CSG is resolved to address the existing significant overlap between virus and species names that complicates the appreciation and use of the species concept in its application to coronaviruses.

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Current state of integrated modelling frameworks

When looking at a range of recent publications, it becomes clear that only a small part of the existent eco-evolutionary theory has been implemented in biodiversity models (Table S1). Nonetheless, despite the above-mentioned limitations, the ecological modelling arena has made tremendous progress in the last few years. Most of the approaches now at least account implicitly for dispersal and abiotic constraints, while a few account for three or more processes simultaneously. However, their interplay is still often modelled less explicitly or simply ignored. Most of the developments so far have dealt with integrating abiotic constraints, dispersal and population demography (e.g. Dullinger et al. 2012 ). Interestingly, as we will demonstrate in the following paragraphs, the approaches with the highest level of integration (i.e. explicit consideration of several interacting processes) are those that have been the most inspired by formalised theory (e.g. metabolic theory, mass-energy theory).

Cheung et al. ( 2012 ), for instance, developed an integrated model based on eco-physiology, dispersal, distribution and population dynamics to predict the climate change impact on more than 600 species of marine fishes due to changes in distribution, abundance and body size. The authors assumed from theory that the maximum body weight of marine fishes and invertebrates was fundamentally limited by the balance between catabolism and anabolism, which both depend on temperature through the Arrhenius equation. Using their integrated model, they show that the averaged maximum body weight of marine assemblages is expected to shrink by 14–24% from 2000 to 2050 under a high-emission scenario, with half of this shrinkage due to physiology and the other half to range shifts. This result predicts a major economic impact, since it may act in synergy with resource over-exploitation and change in primary productivity.

As another example, Kearney et al. ( 2009 ) integrated evolution, dispersal and abiotic constraints and their interplay with biophysical models of energy and mass transfer. The authors suggested that solving the energy balance equation for an ectotherm provides an estimate of the core body temperature under a given set of environmental conditions, further defining physiological function and survival. This approach requires information on essential physiological parameters such as thermal dependence of egg, larval and pupal development for ectotherms, or basal metabolic rate or physiological response curves for endotherms. The approach, which was developed from first principles and experimental data, gave congruent results with a traditional SDM fitted with observed distributional data (Kearney et al. 2010 ). This result could seemingly justify the use of the simpler SDM approach. However, the strong advantage of mechanistic niche modelling, as proposed by Kearney et al. ( 2009 ), is the integration of dispersal and the evolution of some of the modelled traits linked to the distribution (Kearney et al. 2009 ). Using a standard quantitative genetic model, the authors simulated the evolutionary change in egg desiccation resistance and consequently the occurrence and spreading rate of Aedes aegypti in northern Australia (Fig. 2). The model was run with and without climate change. Such an integrated model accounting for range dynamics and evolution is not unique (see Kramer et al. 2008 , 2010 ), but has rarely been applied to biodiversity modelling and is limited to well-studied taxa allowing model parameterisation.

In general, the advantage of integrating several processes simultaneously is not only to provide more informative models of biodiversity but also to raise new ecological questions or hypotheses and to give invaluable insights into the drivers of species distributions. For instance, by integrating abiotic constraints, dispersal and biotic interactions in a single framework, Boulangeat et al. ( 2012a ) managed to quantify the effects of dispersal and plant interactions on the abiotic niche. The unbiased estimation of the niche allowed them to identify potential source-sink areas and the environmental conditions where positive and negative interactions were most important (Fig. 3).

The downside of integrating multiple processes simultaneously is the intricate balance between complexity and tractability (Levins 1966 ). Reducing complexity by identifying unimportant processes and interactions to minimise the number of free parameters will thus remain a key challenge in biodiversity modelling (Box 1). However, the above examples and the approaches listed in Table S1 show that the integration of multiple processes into a modelling framework becomes possible when building the approach on a strong theoretical background. We believe that a theory driven development of simulation tools is necessary for building next-generation biodiversity models. Such an approach should help, among other things, managing complexity and providing more tractable statistical models. In parallel, theoretical simulations also provide intuition of the most important mechanisms by means of sensitivity analyses and provide some mechanistic understanding of parameters and predictions.

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Population genetics in the context of floral volatiles

In specialized pollination systems the distinct ecology and behaviour of a plant’s functional group of pollinators will influence pollination rates, mating system and the extent and distribution of pollen movement. Thus when pollinator specificity is due (at least in part) to floral fragrance ( Waelti et al. 2008 ) and specific pollinators influence these fundamental elements of plant reproductive ecology, floral volatile variation may be expected to influence the genetic composition and evolution of plant populations. For example, changes in floral volatiles that lead to pollinator switching may result in changing patterns of gene flow which in turn may influence the population genetic structure of the species. Presently, these potentially important links between plant volatiles and population genetic structure are poorly understood for any pollination system.

Perhaps the most thorough application of molecular tools in a floral-volatile context has been in the study of sexually-deceptive terrestrial orchid pollination systems in Europe and Australia. Sexual deceit pollination relies on the mimicry of sex-pheromones to attract pollinators ( Schiestl 2005 ) and is characterized by highly specific plant-pollinator relationships which have long been proposed to act as an ethological isolating mechanism between sympatric taxa ( Paulus & Gack 1990 Grant 1994 ). Because this system has benefited from significant collaborative effort between ecologists, chemists and geneticists, a number of examples throughout this review will be drawn from this system. Where possible we also provide examples from other systems, although in many cases complete data are lacking. For example, some cases of well characterized volatile variation have limited supporting pollination ecology and no genetic data. In other cases, genetic patterns and pollination biology are well characterized, but knowledge of volatile composition and variation is lacking. It should be stressed that the bridges between floral scent chemistry, pollination biology and genetics can be built from any direction. Genetics can be revealing when applied to systems for which volatiles and pollinators have been well studied, while establishing population genetic patterns for a poorly studied species may provide evidence of interesting pollination phenomena.

Below, we discuss in turn six basic hypotheses we believe to have utility in the study of floral scent variation within taxa (Table 1). At the most basic level the hypotheses could apply to a study-system of sister taxa for which floral odour plays a key role in the attraction of contrasting functionally specialized pollinator guilds (Fig. 1). Under the scenario in Fig. 1, the interaction between floral volatiles, pollination ecology and population genetics can operate in different ways, and at different levels (species, populations, individuals). Combining knowledge of volatile variation with ecological and genetic knowledge is essential to fully address these hypotheses. Below we consider the six hypotheses in turn.

Hypothesis Volatile knowledge Ecological knowledge Genetic knowledge
1. Pollinator specificity is due to a distinct volatile composition of the floral blend Confirmation of distinct volatile composition activity as attractant Ecological evidence for pollinator specificity associated with specific volatile composition Genetic confirmation of distinct entities (odour types, subspecies, species)
2. Distinct volatile composition reflects species boundaries Distinct volatile composition among species Ecological evidence for reproductive isolation Genetic evidence that volatile composition matches genetically distinct species
3. Hybridization is due to sharing of key volatile components of the floral blend Shared volatile components Morphological and ecological evidence for hybridization Genetic confirmation of hybridization
4. Different pollinators will influence the plant mating system (selfing vs. outcrossing) Confirmation that distinct volatile components attract different pollinators Ecological evidence for behavioural differences among distinct pollinators Genetic evidence for differences in plant mating system
5. Different specific pollinators distinctly influence the extent of pollen flow Confirmation that distinct volatile components attract different pollinators Ecological evidence for different patterns of pollen movement by different pollinators Paternity analysis or other genetic evidence for differences in the extent of pollen flow
6. Different pollinators will influence male contribution to population genetic structure Confirmation that distinct volatile components attract different pollinators Ecological evidence for different mating systems and/or patterns of pollen movement by different pollinators Measures of genetic variation and differentiation within and among populations (ideally for nDNA and cpDNA)

A schematic showing potential links between floral volatiles and population genetics in specialized pollination systems. The flowers represent two hypothetical sister taxa for which floral scent determines pollination by contrasting functionally specialized pollinator groups. Evolutionary lineages are represented by black lines and there is a trend towards finer taxonomic scale at the top of the diagram. Grey arrows depict the direction of gene flow.

Hypothesis 1: volatiles and specific pollination

The first hypothesis: ‘Pollinator specificity is due to the distinct volatile composition of the floral fragrance blend’ is perhaps the most critical and most difficult to satisfactorily test. The aims here are threefold: to determine pollinator specificity, to confirm distinct volatile composition and to obtain evidence for the activity of floral volatile components as pollinator attractants. In most cases to date, even for this seemingly straight-forward hypothesis, there are few studies that have investigated all three lines of evidence.

Despite the modest but expanding bank of information on floral volatile variation within and among species ( Knudsen et al. 2006 ), definitive proof of the links between such variation and associated pollinators remain scarce. Demonstrating the link between specific volatiles and specific pollinators normally requires multiple lines of evidence. Ideally this will include field observations or experiments demonstrating a role for floral odour in pollinator attraction followed by evaluation of physiologically active constituents, characterization and synthesis of those compounds and behavioural testing via bioassay to confirm activity in pollinators ( Schiestl & Marion-Poll 2002b Schiestl & Peakall 2005 Franke et al. 2009 ). Genetic confirmation that pollinator specificity is associated with distinct plant entities can be important, particularly for morphologically similar taxa.

Even before identification of active floral volatile constituents, behavioural experiments should be the first step in examining the importance of scent in pollinator attraction. For example, Okamoto, Kawakita & Kato (2007) confirmed the role of floral scent in the obligate nursery-pollinated Glochidion using choice experiments. When the pollinating Epicephala moths were exposed to air flowing from a pair of bags via a Y-tube they responded only to the air from bags containing flowers of their host Glochidion species. There was no response to flowers of a co-ocurring non-host Glochidion species or to empty control bags. In this way, behavioural bioassays provide elegant and powerful tools for confirming that volatiles play a key role in pollinator attraction.

Once a role for floral odour in pollination attraction has been confirmed, gas chromatography with electroantennographic detection (GC-EAD) offers a powerful tool for identifying the compounds detected by pollinators from the myriad of volatile variation that may be produced by a flower. This method combines gas chromatography (to separate the blend into single constituents) with electroantennographic detection that allows the physiologically active compounds detected by insects to be determined. GC-EAD active compounds are usually subsequently identified by GC-MS and other diagnostic procedures ( Schiestl & Marion-Poll 2002b ). Complementary to the chemical analysis approach of GC-EAD are field or lab-based bioassays to determine the biological activity of putative floral signals. These behavioural experiments typically use synthetic versions of identified floral volatiles in order to show pollinator attraction in vivo. As well as demonstrating the role of fragrance in pollinator attraction, experiments for confirming attractant activity are crucial for distinguishing signals of attraction from the background chemical mosaic which may include chemical signals with other functions, for example those involved in repelling herbivores or plant defensive signalling ( Raguso 2008c ).

The well-studied sexually deceptive orchids of Australia and Europe represent two systems in which GC-EAD, GC-MS and other analytical procedures have identified volatile organic compounds whose activity as attractant in the field has been subsequently confirmed through multiple lines of experimental evidence. The attraction of pollinators to specific odour bouquets in the sexually deceptive orchid genus Ophrys has been explored for several species and more than 50 pollinator-active compounds have been discovered ( Schiestl et al. 1999 Ayasse et al. 2000 Stökl et al. 2005 Ayasse 2006 Paulus 2006 ). Some of these compounds have been identified as commonly occurring molecules including esters, aldehydes, alkanes or alkenes, with specificity determined by ratios of the various compounds that mimic sexual signals of female Andrena bees ( Ayasse et al. 2000 Schiestl & Ayasse 2002a Stökl et al. 2005 ). The important role of specific components for controlling pollinator specificity has been demonstrated by behavioural experiments utilizing synthetic components of the floral bouquet ( Ayasse et al. 2000, 2003 ). In Australian sexually deceptive Chiloglottis orchids pollinated by male thynnine wasps, a previously undiscovered class of natural products (rather than blends of common compounds) provides the chemical basis for pollinator specificity ( Schiestl et al. 2003 Franke et al. 2009 ). The first of these compounds to be described, ‘chiloglottone 1’ (2-ethyl-5-propylcyclohexan-1,3-dione), was confirmed as both the female thynnine wasp sex pheromone and the orchid pollinator attractant ( Schiestl et al. 2003 ). Subsequent study has revealed other chemical variants of this new class of compounds are involved as specific attractants in other orchid pollinator interactions ( Franke et al. 2009 ).

The euglossine bee-pollinated neotropical orchids have probably the longest history of study into odour-mediated pollinator specificity. These orchids lack nectar and attract male euglossine bee pollinators by floral odour. The bees accumulate volatile compounds from the orchids and other floral and non-floral sources to build complex and often species-specific bouquets presumably to attract mates ( Kimsey 1980 Eltz, Roubik & Lunau 2005 Eltz, Ayasse & Lunau 2006 ). In a landmark paper, Dressler (1968) explored hypotheses on the role of odour in pollination, specificity, reproductive isolation and speciation in euglossine bee-pollinated orchids. Surprisingly, despite the early start, much remains to be learnt about this system. A number of euglossine-pollinated orchid species have been the subject of floral volatile analysis and these studies indicate that specific pollinator attraction is probably conferred by a complex cocktail of chemical components ( Hills, Williams & Dodson 1972 Whitten & Williams 1992 Cancino & Damon 2007 ). Behavioural tests with synthetic chemicals have demonstrated the attractiveness of individual components of the floral blend and the influence of volatile mixtures on attractivity ( Williams & Dodson 1972 Ackerman 1983 ). These findings have only recently been augmented by GC-EAD and electroantennography (EAG) which provide evidence that both antennal and central nervous system processes play a role in the specific attraction of bees to odours ( Schiestl & Roubik 2003 Eltz & Lunau 2005 Eltz, Ayasse & Lunau 2006 ). It is apparent we are only beginning to understand the role of floral volatiles in this complex and diverse tropical orchid-pollinator interaction.

The number of plant-pollinator relationships for which floral volatiles have been identified and demonstrated as attractants is small (Table 2) and dominated by deceptive and obligate nursery-pollination systems. Outside sexual-deception, perhaps one of the best examples of an integrated approach to linking volatiles to pollinator is provided by Brodmann et al. (2008) , who showed volatiles emitted by the flowers of the orchid Epipactis helleborine to elicit the attraction of social Vespula wasps in a study that combined tests of natural and synthetic compounds in behavioural assays with GC-EAD confirmation and identification of active floral volatile compounds. Not to be neglected in future studies are those pollination systems outside of these close plant-pollinator relationships where specialization is less extreme. For example, pollination by oligolectic bees (which collect pollen from only one plant species or genus) is common in some parts of the world ( Proctor, Yeo & Lack 1996 Dotterl et al. 2005 ) yet the floral cues involved in these relationships have barely been studied.

System Characterization Experimental confirmation
GC-MS etc GC-EAD Bioassay – floral material Bioassay – synthetics or extracts
Figs – fig-wasp mutualism ( Ware et al. 1993 Grison, Edwards, & Hossaert-Mckey 1999 Grison-Pige, Bessiere & Hossaert-Mckey 2002a Grison-Pige et al. 2002b ) among others ( Gibernau et al. 1998 ) among others ( Grison-Pige, Bessiere & Hossaert-Mckey 2002a )
Sexually deceptive Chiloglottis orchids ( Schiestl et al. 2003 Schiestl & Peakall 2005 Franke et al. 2009 ) ( Schiestl et al. 2003 Schiestl & Peakall 2005 ) ( Bower 1996, 2006 Schiestl et al. 2003 ) ( Schiestl & Peakall 2005 Franke et al. 2009 )
Sexually deceptive Ophrys orchids ( Ayasse et al. 2000, 2003 Stökl et al. 2005 ) ( Schiestl et al. 1999 Ayasse et al. 2000, 2003 Stökl et al. 2005 Gögler et al. 2009 ) ( Schiestl et al. 1999 Ayasse et al. 2000, 2003 ) ( Schiestl et al. 1999 Ayasse et al. 2000, 2003 Gögler et al. 2009 )
Euglossine bees and neotropical orchids ( Hills, Williams & Dodson 1972 Whitten & Williams 1992 Cancino & Damon 2007 ) ( Schiestl & Roubik 2003 Eltz & Lunau 2005 ) ( Williams & Dodson 1972 ) ( Dodson et al. 1969 Williams & Dodson 1972 Ackerman 1983 )
GlochidionEpicephala moth mutualism ( Okamoto, Kawakita & Kato 2007 ) ( Okamoto, Kawakita & Kato 2007 )
Yucca – yucca moth mutualism ( Svensson et al. 2005 )
Dead-horse arum ( Stensmyr et al. 2002 ) ( Stensmyr et al. 2002 ) ( Stensmyr et al. 2002 ) ( Stensmyr et al. 2002 )
Willows and oligolectic bees ( Dotterl et al. 2005 Fussel et al. 2007 ) ( Dotterl et al. 2005 ) ( Dotterl et al. 2005 )
Epipactis helleborine and Vespula social wasps ( Brodmann et al. 2008 ) ( Brodmann et al. 2008 ) ( Brodmann et al. 2008 ) ( Brodmann et al. 2008 )

Hypothesis 2: volatiles and species boundaries

Despite the early discovery that different floral odour composition reflected the delineation of species and their respective pollinators in Catasetum orchids ( Hills, Williams & Dodson 1972 ), there appear to be few studies that evaluate the role of distinct volatile composition in maintaining species boundaries. We therefore draw on our own ongoing studies to provide an example. An interesting feature of the Australian sexually deceptive Chiloglottis orchids is the high frequency of temporally and spatially co-flowering congeneric species ( Bower 1996 Peakall et al. 1997 Mant, Peakall & Weston 2005c Mant et al. 2005a ). One pair of species that can be found co-flowering is C. valida and an undescribed species morphologically similar to C. jeansii (hereafter C. aff jeansii). Chiloglottis valida is known to attract its male thynnine pollinator Neozeloboria monticola with the single volatile compound, chiloglottone 1 (2-ethyl-5-propylcyclohexan-1,3-dione) ( Schiestl & Peakall 2005 ). Similarly, evidence including GC-EAD, GC-MS and bioassays with synthetic compound has revealed that C. aff jeansii attracts its undescribed pollinator (a Neozeloboria species in the impatiens species complex) by a structural isomer of chiloglottone 1, called chiloglottone 3 (2-butyl-5-methylcyclohexan-1,3-dione) ( Franke et al. 2009 R. Peakall unpublished). Field studies have shown no cross-attraction between the two compounds and their respective pollinators.

To test whether chemical composition corresponds with species boundaries we applied GC-MS with selective ion monitoring (SIM, reduces the detection threshold several orders of magnitude and provides the most sensitive measurement of a compound’s presence or absence) of single orchid labella to identify the active compound in flowers from mixed populations of the two taxa. The results of the floral chemistry indicated that our own diagnosis of species based on morphology and conducted in the field was frequently incorrect (R. Peakall unpublished). Subsequently, chloroplast DNA analysis with the taxa defined only by their chemical composition revealed extensive genetic differentiation between these chemically-defined taxa ( Ebert, Hayes & Peakall 2009a ). Thus, this study confirms the hypothesis that distinct floral volatile composition should reflect taxonomic boundaries between morphologically similar species when volatiles function as specific pollinator attractants.

The combination of floral volatile analysis and population genetic analysis can sometimes provide unexpected insights into the nature of species boundaries. Mant, Peakall & Schiestl (2005b) investigated the patterns of odour and genetic variation among several species of sexually deceptive Ophrys. The odour analysis indicated a previously unknown or cryptic taxon that was characterized by distinct odour composition. Remarkably, this entity was not distinct genetically, at least at the level of the nuclear micosatellite loci investigated, nor were its non-active odour compounds distinct from those of related Ophrys species. This discovery in Ophrys may well represent an incipient taxon in the early phases of pollinator mediated speciation with as yet little or no accumulated differentiation evolving at neutral traits not under selection. It now remains to be experimentally confirmed in the field that the odour differences are directly linked to specific pollinators and that hybridization is minimal or absent as a consequence. This example thus represents a case where floral volatile and genetic knowledge is in hand, but now requires further integration with pollination ecology.

Exploring the role of floral scent in taxonomic boundaries (Hypothesis 2) may be approached from different directions. Volatile studies indicating strong odour differences among taxa should seek to integrate ecological and genetic data. Similarly, genetic studies revealing unexpected taxonomic boundaries, particularly among closely related sympatric taxa, should consider whether floral odour variation could be linked to specific pollination and reproductive isolation.

Hypothesis 3: volatiles, hybridization and population genetics

When floral odour is the major determinant of pollinator specificity, changes or variation in floral odour could break down specificity and increase the frequency of interspecific pollen transfer, thereby promoting hybridization and introgression. Alternatively, when hybridization is detected in otherwise highly specific systems, it is of interest to investigate the role floral odour may or may not play in enabling hybridization.

A direct but unexpected link between floral odour and hybridization can be found in Chiloglottis orchids. Extreme pollinator specificity is the norm in these orchids with putative hybrids rarely reported ( Peakall et al. 1997 ). One exception is Chiloglottis X pescottiana which, when described, was hypothesized to be a hybrid between C. trapeziformis and C. valida. Allozyme based genetic analysis subsequently confirmed the hybrid status of this taxon ( Peakall et al. 1997 ). GC-EAD analysis and field bioassays further confirmed that both orchid parents employ the same single volatile compound, chiloglottone, to attract their respective and phylogenetically distinct pollinators ( Schiestl et al. 2003 Schiestl & Peakall 2005 ). By virtue of this shared compound, hybridization between the two taxa (due to pollinator sharing) can occur when their usual geographically and altitudinally separate ranges occasionally overlap ( Peakall et al. 2002 ). This case provides an example of the power of combining floral volatile analysis with ecological and genetic methods to better understand the role of floral volatiles in hybridization.

There are more documented cases of hybridization and introgression in the sexually deceptive Ophrys orchids of Europe compared to Australian sexually deceptive orchids ( Soliva & Widmer 2003 Mant, Peakall & Schiestl 2005b ). This may be due, at least in part, to the differences associated with the chemical basis for pollinator attraction in Ophrys. The volatile blend of Australian sexually deceptive orchids is typically characterized by one, two or three unique active compounds ( Mant et al. 2002 Schiestl et al. 2003 Schiestl & Peakall 2005 Franke et al. 2009 ) while in some of the better-studied Ophrys taxa, specific pollinator attraction is based not on a single chemical odour compound but on emission of distinct blends or ratios of several commonly occurring hydrocarbon compounds ( Schiestl et al. 1999 Stökl et al. 2005 ). Hybridization due to a breakdown of pollinator specificity may occur between some Ophrys more frequently because variation in blends and ratios could result in a floral bouquet more closely resembling that of a sympatric species ( Stökl et al. 2008 ).

An interesting example of marked scent differences among species is found in the genus Silene (Caryophyllaceae). Waelti et al. (2008) investigated floral odour in white and red campions (Silene latifolia and Silene dioica respectively) which are known to be interfertile and to co-occur in parts of their range. GC-MS of floral headspace samples showed distinct odour differences in the relative amounts of biologically active volatile compounds. In a field experiment the biologically active benzenoid phenylacetaldehyde (which dominated the scent of S. dioica and contributed strongly to the odour difference between species) was applied to inflorescences of both species to make floral fragrance more similar. Transfer of fluorescent dye (a pollen analogue) was higher in plots containing scent-manipulated flowers than control plots of unmanipulated inflorescences. Thus, odour differences reduce the potential for gene flow between these species demonstrating the importance of odour for reproductive isolation. This work on volatile variation among species was further supported by an in-depth genetic study of natural hybrid zones among the same two Silene species providing new and detailed insights into the evolutionary role of introgression and hybridization more generally ( Minder, Rothenbuehler & Widmer 2007 Minder & Widmer 2008 ).

The extensive experimental inter-disciplinary work on the Silene system illustrates the interpretive power of integrating floral fragrance analysis, population genetics and pollination ecology. Furthermore, while pollination in this system appears to be functionally specialized, it does not represent a case of extreme specialization like sexual deception or nursery pollination. It is therefore apparent that many other less specialized systems that involve related co-flowering taxa may be candidates for exploring the links between volatiles, hybridisation and population genetics. Such systems may offer the opportunity to investigate whether hybridization is more or less common in species with distinct floral volatile blends and whether attraction due to floral fragrance is reduced or maintained in F1 hybrids between taxa with different floral scents.

Hypothesis 4: volatiles, pollination and plant mating systems

Differences among distinct pollinators in behaviour and abundance can be expected to influence the way pollen is moved within and among individual plants and populations. Therefore, in those systems where floral scent governs pollination specificity, either through innate attraction, floral constancy (see Wright and Schiestl in this feature) or filtering visitor composition, it is likely that floral scent variation will indirectly play a key role in moderating plant mating systems (the degree of selfing vs. outcrossing). To our knowledge there are no studies that have directly linked variation in floral volatile composition to plant mating systems.

A study by Brunet & Sweet (2006) , although not directly linked to plant volatiles, provides a rare example of the application of genetic methods for testing the effects pollinators have on the populations they service. This study investigated the effect of different insect pollinators on outcrossing rates in the Rocky Mountain columbine, Aquilegia coerulea a protandrous, self-compatible herb. Many hours of pollinator observations at eight natural populations over 3 years revealed considerable variation in the relative proportions of different pollinator species among populations. Outcrossing rate estimates, achieved by analysis of seed at five allozyme loci, showed an increase in outcrossing rate with hawkmoth abundance. No effect on mating system was detected for any other pollinator group. This appears to be one of the first studies directly linking different pollinators to outcrossing rates. One explanation for the high outcrossing rates achieved by hawkmoth pollination was that hawkmoths reduced geitnogamous selfing (self-pollination between flowers on the same plant) by preferring to visit female-phase flowers before male-phase flowers. Alternatively, hawkmoths may simply be more effective pollinators. Consequently, A. coerulea populations with low hawkmoth abundance might experience pollen transfer limitation and higher rates of autogamous selfing as reproductive assurance.

In one of the few other examples of studies explicitly investigating the influence of distinct pollinators on plant mating systems, Whelan, Ayre, and Beynon (2009) examined pollination by birds and honey bees in an Australian shrub, Grevillea macleayana. In their experiment they caged some inflorescences to exclude vertebrate pollinators and included in the study one population known to have a high rate of outcrossing. Birds were found to not only deposit more pollen per visit than bees in the high outcrossing population, they also moved longer distances between plants and visited fewer inflorescences on a single plant.

Other relevant insights into how pollinators affect mating system have emerged from research on floral specialization in bees. Pollinator effectiveness, ‘the single-visit contribution by a flower-visitor to the reproductive fitness of a plant’, was compared among specialist bees and generalist pollinators of Knautia arvensis by Larsson (2005) . While specialist bees deposited more pollen per visit (higher pollinator effectiveness), their impact on overall pollination success was moderated by their lower abundance relative to generalist pollinators. Thus, higher reproductive success and outcrossing might result when specialist bees are in high abundance. By contrast, when generalist pollinators are in high abundance pollen limitation through wasted interspecific pollen transfer may occur. Pollinators may therefore have an impact on plant mating systems, and floral scent may indirectly influence plant mating system through its interaction with pollinator fauna.

Floral scent chemistry may influence plant mating systems by influencing pollinator behaviour. The study of Kessler, Gase & Baldwin (2008) on the floral fragrance attractant, benzyl acetone, and the nectar-borne repellent nicotine present in the flowers of self-compatible Nicotiana attenuata offers a novel example. Field experiments with transgenic plants deficient for benzyl acetone synthesis, nicotine synthesis or both demonstrated that outcrossing rates were highest in wild-type plants. This may be due to moderation of the attraction by benzyl acetone by the repellent nicotine that limited the time pollinators spent at any one flower and maximized total number of flower visits.

Even low rates of outcrossing can provide benefits to plants with life-histories such as those with low rates of recruitment ( Raguso 2008a ). As such, a plant’s mating system can be an important factor in the evolution of plant populations. Determining the role pollinators and their behaviour play in moderating plant mating systems and the extent to which this is mediated by floral volatile variation will no doubt provide interesting insights into the evolution of floral scent.

Hypothesis 5: volatiles, pollinators and pollen flow

The spatial patterns of pollen movement determine neighbourhood size and inbreeding rates ( Mitchell et al. 2009 ) and are critical in understanding important evolutionary processes such as population differentiation and speciation. It is well established that pollinator behaviour can control the pattern and extent of pollen dispersal ( Richards 1986 ). For example, nocturnal moth pollinators of Silene alba transport a fluorescent dye pollen analogue further on average than bees ( Young 2002 ). Floral volatiles therefore, through their attraction of different pollinators and influence on pollinator behaviour, could exert an indirect influence on pollen movement within and between plant populations.

In sexually deceptive orchids (where the strong relationship between floral odour and specific pollinator has been repeatedly demonstrated) pollinator behaviour and movements may be controlled by optimal mate seeking strategies potentially leading to quite different patterns of pollen flow compared with other pollination systems ( Peakall & Beattie 1996 ). Two ecological approaches have been taken to investigate pollen flow in sexually deceptive orchids: mark-recapture of pollinators to infer potential pollen movement, and direct measurements of pollen flow by tracking the movements of coloured pollen. In the Australian Caladenia tentaculata, longer distance pollen flow is promoted by the male thynnine pollinator’s avoidance of visits to more than one flower in a patch. Pollen movements approximate a linear rather than a leptokurtic distribution (mean distance – 17 m maximum: 58 m) and mirror movements detected by mark-recapture of the pollinator ( Peakall 1990 ). In Drakaea glyptodon mark-recapture of male thynnine pollinators suggests pollen flow could exceed 130 m ( Peakall 1990 ).While near-neighbour pollination may be avoided in male thynnine pollination systems, pollen flow distances will be bounded by the mate search area. Therefore, the type of pollinator exploited by a sexually deceptive orchid may constrain the maximum pollen flow distance. A mark-recapture study of Colletes cunicularius, a bee pollinator of Ophrys, revealed that individual male bees patrol a specific and restricted portion of the total nesting area in search for mates (mean-recapture distances of 5 m, max 50 m). This behaviour may be expected to limit rather than promote long-distance pollen flow in Ophrys orchids ( Peakall & Schiestl 2004 ). By contrast, while presently unknown, longer distance pollen movements may well occur in those plant species visited by foraging female Colletes bees.

It appears intuitively reasonable that in the classic euglossine trapline pollination ( Janzen 1971 ) and perhaps in some fig-wasp pollination systems ( Nason, Herre & Hamrick 1998 ), long range volatile mediated attraction of pollinators will result in long-range pollinator movement and likely long distance pollen flow. If so, such cases will demonstrate a clear link between volatiles and pollen flow. There is little enough research examining and comparing landscape-level gene flow for different pollinators ( Mitchell et al. 2009 ) let alone drawing the link to floral volatiles. Such research, while technically challenging, is now very achievable and will be best realized by combining volatile knowledge, pollination ecology (e.g. pollinator mark-recapture) and genetics (e.g. paternity analysis).

Hypothesis 6: volatiles and population genetic structure

If different distinct pollinators can be expected to influence both plant mating systems (Hypothesis 4) and the patterns and extent of pollen flow at the population scale (Hypotheses 5), it follows that any differences in plant mating system and pollen flow may in turn influence population genetic structure – the patterns and extent of genetic variation within and among populations. In this way, floral volatiles may have indirect interactions on population genetic structure through their interaction with pollinator fauna.

Hughes et al. (2007) have explored the potential impact of bird vs. fly pollinators on the population genetic structure of two South African species of Streptocarpus. Lower levels of genetic differentiation (based on both nuclear and chloroplast DNA analysis) were detected in the sunbird pollinated S. dunii compared to its long-tongued fly pollinated congener S. primulifolius. This was attributed to the greater vagility and wider distribution of the sunbird that likely facilitates greater population connectivity than that possible by fly pollination. While it was recognized that this conclusion may be confounded by differences in habitat between the two study species, this study highlights a potential impact of pollinator behaviour on population genetic structure. Although no information on floral volatile differences was reported it has been noted by others that ornithophilous flowers often have little odour in comparison to other biotic pollination systems ( Knudsen & Tollsten 1993 Levin, Raguso & Mcdade 2001 Raguso et al. 2003 ). Thus floral volatile differences may indirectly contribute to the population genetic differences between the species.

Population genetic structure in plants is determined by the interaction of multiple factors including mating system, gene flow (both contemporary and historic) by pollen and seed, as well as past population events such as bottlenecks, local extinction and range expansions. A major challenge in linking plant volatile variation to population genetic structure is the need to disentangle these multiple factors. This potential for population genetic structure to be driven by multiple factors can also cloud determination of cause and effect when studying its links to phenotype (e.g. floral scent) and gene flow. The closely related species Clarkia breweri and C. concinna partially overlap in range and conform to the parent-offspring style of rapid speciation due to extreme selection in ecologically marginal populations well characterized in the genus ( Lewis 1962 ). Furthermore, the derivative species, C. breweri, in contrast to its unscented progenitor, C. concinna shows a recent evolution of floral scent production and moth pollination in a largely unscented genus ( Raguso & Pichersky 1995 ). Given that strong selection can dramatically reduce effective population size, with the accompanying founder effects it is conceivable that traits such as floral scent might experience rapid change, elevating rare alleles for floral phenotypes to high frequency by chance within the same genomes as traits under strong selection for fitness (for example drought tolerance).

Given this complexity, careful study design is required in order to be able to definitively identify pollinator-mediated selection as a driver of between-species floral volatile differences. Mant, Peakall & Schiestl (2005b) compared floral odour variation in both putatively selected pollinator-active compounds and non-pollinator-active floral volatile compounds within and among Ophrys species. Population genetic data for neutral markers was also obtained for the same set of samples. In order to enable a meaningful and comparable contrast between odour (both active and non-active components) and genetic data, ( Mant, Peakall & Schiestl 2005b ) adapted the Analysis of Molecular Variance ( amova ) framework for the analysis of odour. Although initially developed for molecular data, this procedure can be applied to the hierarchical analysis of variance for any data set that can be input as a pairwise individual by individual distance matrix. The study found significant floral odour differentiation among allopatric populations within species, among allopatric species and among sympatric species. Active odour compounds were more strongly differentiated among allopatric conspecific populations than non-active compounds. In marked contrast, there was limited population or species level population genetic differentiation. It was concluded that the strong odour differentiation but lack of genetic differentiation among sympatric taxa indicated selection imposed by the distinct odour preferences of different pollinating species. This conclusion was reinforced by the low genetic differentiation observed within species that suggested large effective population sizes and therefore little opportunity for genetic drift to account for the observed patterns ( Mant, Peakall & Schiestl 2005b ). The methods developed and executed in this study may serve as a model for future studies that seek to explore the direct or indirect links between floral odour variation and population genetic structure.


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