Plant-animal pollination interaction networks in Australia
Project Summary
Mutualistic interactions are important in structuring communities but their role may have
been underestimated compared to competition and predation. Recent studies have shown that
pollination is a predominantly generalised interaction, and so interacting species exist within a
network. These networks display properties of asymmetry, nestedness, high connectance and strong
small-world properties. Evidence for this new paradigm has been gathered from a variety of
habitats, with few contributions from Australia and arid systems. Australian arid systems are of
interest due to the suggestion that positive interactions are more likely in harsh environments, and
possess potentially unaltered plant – pollinator networks due to the absence of exotic species. The
aim of this study is to understand the main interaction network of plants and their floral visitors in a
variety of Australian habitats especially arid environments.
Aims and Significance of the Project
Interactions are of direct importance in shaping terrestrial communities and in the
maintenance of biodiversity. Empirical evidence suggests both competition (Fowler 1986; Goldberg
& Novoplansky 1997) and facilitation (Bertness & Callaway 1994; Bruno et al. 2003) are
important; but the balance between these two is unresolved. Mutualistic interactions are
relationships between two species that result in reciprocal benefits (Pellmyr 2002). Such interaction
are particularly important for flowering plant communities (Brooker et al. 2008), as for instance,
flowering plants depend on pollinator mutualists for reproduction. Such mutualisms are ubiquitous
in nature: over 90% of the 240 000 flowering plant species rely on animal pollinators to reproduce
(Buchmann & Nabhan 1996), and the number of pollinator species is predicted to be up to 300 000
(Shepherd et al. 2003). Pollination is directly related to the supply of food for animals as seed, or
directly from consuming nectar and/or pollen (Pellmyr 2002). However, our understanding of the
role of mutualistic interactions play in shaping terrestrial communities is less than that for other
interactions such as competition and predation (Bruno et al. 2003; Bascompte & Jordano 2007).
Thus, such factors are rarely incorporated into models of factors that impact populations and
communities (Stachowicz 2001). The importance of indirect interactions in shaping the diversity
and evolution of plant communities has also been neglected; despite evidence such processes are
important and widespread (Brooker et al. 2008; Sargent & Ackerly 2008).
Pollination ecology is the study of how plants receive and donate pollen, including floral
phenolgy (seasonal timing of biological events), floral adaptation, and animal behaviour (Davila
2006). Pollen transport can be mediated either by abiotic (wind and water) or biotic (animal)
vectors. To attract animal pollinators, plants have evolved a range of visual, olfactory and auditory
cues to signal the presence of a reward, the most common being nectar and pollen (Pellmyr 2002).
Rewards such as nectar have been shown to influence the assemblage of visitors to a plant, and also
to a community (Potts et al. 2004). Floral visitors are not necessarily pollinators; they may be
consuming rewards such as nectar or pollen (Pellmyr 2002; Michener 2007; Zhang et al. 2007),
consuming flowers (florivore) (McCall & Irwin 2006), or preying on other visitors. A visitor
becomes a pollinator when transporting pollen from the anther to the stigma of a flower (Pellmyr
2002). The effectiveness of pollinators can vary depending on quality and quantity of pollen
deposited (Gibson et al. 2006; Lopezaraiza-Mikel et al. 2007).
The study of plant-pollinator interactions has tended to focus on direct pair-wise interactions
(Sargent & Ackerly 2008), where both plant and animal species are specialists: a plant species that
is pollinated by one animal species, and vice versa. This is driven by the view that plant-pollinator
interactions evolve towards specialisation such as those between Ficus and the fig wasp (Michaloud
et al. 1996). However, there is now substantial evidence to suggest that plant-pollinator interactions
are indeed generalised; that is, a given plant species is pollinated by many animal species and an
animal species pollinates many plant species (Waser et al. 1996; Olesen 2000).
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As pollination is a predominantly generalised interaction, indirect and direct interactions are
pervasive in a community of plants and their pollinators. By studying pair-wise interactions
exclusively, the role that direct and indirect interactions play in influencing an organism’s
abundance, phenotypes and genotypes has been largely neglected (Strauss & Irwin 2004). Rather,
we can study the network of plant-pollinator interactions in the manner of conventional food webs
(Memmott 1999). As a result, we can investigate how ecological and evolutionary processes
organise communities of plants and their pollinators which allows us to identify mechanisms behind
the persistence of biodiversity (Bascompte & Jordano 2007). Identifying mechanisms behind
biodiversity persistence is still an area of interest in general ecology (Bruno et al. 2003; Proulx et
al. 2005; Bascompte & Jordano 2007; Brooker et al. 2008).
To date, studies of plant-pollinator networks have revealed patterns of nestedness (generalist
species interacting amongst each other, and specialist species interacting only with generalists)
(Bascompte et al. 2003; Memmott et al. 2004; Bascompte & Jordano 2007), asymmetry of
dependencies (few interactions where a species will depend heavily on another species, and many
interactions where dependencies are weak) (Bascompte et al. 2006), species connectivity
distributions with a power-law regime (few plants with many interactions and many plants with few
interactions) (Jordano et al. 2003; Memmott et al. 2004; Vazquez 2005), and very strong smallworld properties (all species are close to each other and highly clustered) (Olesen et al. 2006a).
These structural attributes have consequences for the resilience of the plant-pollinator communities
(Memmott et al. 2004; Bascompte & Jordano 2007). For instance, connectivity distributions with a
power-law regime give the community heightened resilience to random extinctions (Albert &
Barabasi 2000).
Such characteristics have been found from pollinator networks of predominantly temperate,
tropical and alpine environments !!!!!!!!! Olesen & Jordano (2002) Olesen et al. (2006b). Recently,
a study into the pollinator network in an Australian arid habitat has revealed similar structural
patterns (Popic et al, in prep), despite the environment being of unpredictable ‘boom-bust’ cycles
contrasting to the predictability of other environments. Furthermore, the network was rich in species
and complex in makeup, contradicting the common belief that arid environments are of extremely
low productivity (Knox et al. 2001). As 70% of the Australian continent is classified as arid or
semi-arid, and this level is increasing due to anthropogenic factors, a greater understanding of
communities in such environments can be applied to mitigate the impacts from environmental
problems such as climate change and land degradation.
The few pollinator networks to be sampled in Australia have all featured the introduced
‘super-generalist’ Apis mellifera, except for a single pollinator network from Spinifex-dominated
grassland in the Simpson Desert (Popic et al, in prep). This network was rich in diversity of small
solitary native bees, suggesting such animals are important pollinators in arid Australia. This is in
disagreement with the prevailing paradigm that birds are the major pollinators of arid Australia
(Ford et al. 1979; Keighery 1982; Orians & Milewski 2007). In addition to the role and general
importance of bees being unknown, Australia’s bee fauna is still relatively unknown (Michener
1979, 2007).
Investigations into non-invaded plant-pollinator communities are required in order to
understand impacts when they are altered. Plant phenology is always variable but is expected to
change rapidly with climate change, leading to altered networks (Memmott et al. 2007). The impact
of introduced species, both plants (Lopezaraiza-Mikel et al. 2007; Bartomeus et al. 2008) and
visitors such as A. mellifera and attempts to establish Bombus terrestris (Hingston 2006) in
mainland Australia, can only be fully known with an understanding of plant – pollinator networks
before they are altered.
In this study, the broad aim is to understand interaction networks of plants and their visitors
in different Australian environments, particularly arid environments where they are still largely
unaltered. It will contain the components: plant phenology, plant – visitor networks, pollen
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transport networks, effective pollination by pollen transporters, floral attractants and rewards and
indirect interactions.
Plant phenology (seasonal timing of biological events), along with the diversity and
abundance of potential pollinators dictate what interactions are possible. Investigating flowering
phenology will establish the potential for indirect interactions between plants through their visitors
and between visitors through the plants. For instance, two co-flowering plants can either increase
each other’s reproductive success by facilitating each other’s pollination, decrease each other’s
reproductive output by competing for pollinators or have no effect on each other.
Visitation webs identify the interactions taking place between flowering plants and animal
visitors, and provides a tool to study the way interactions are assembled. Network structure can be
influenced by flowering phenology, floral attractants and rewards and the diversity and abundance
of visitors. A variety of different environments will be sampled to determine difference in structure
between plant – pollinator communities. Commonalities in plant-pollinator networks from different
environments suggest common structural mechanisms shaping plant-pollinator communities. Such
mechanisms may be both evolutionary and ecological ones (ref) and such mechanisms will be
investigated. By sampling a range of environments, the importance of different visitor types in
different environments will also be investigated. Areas of arid Australia are free from exotic insects
such Apis mellifera (Popic et al, in prep) and are rich in native bees which dominate plant-pollinator
networks. Sampling these communities in conjunction with invaded communties, allows us to
investigate the impacts on pollinator networks by exotics such as Apis mellifera.
Determining the type and strength of interactions between species in a plant-pollinator
network is important in order to determine the relative importance of mutualisms. The first step is to
identify pollen transporters. The insect pollen transport web will determine which visitors are likely
pollinators by quantifying which insects move which pollen. Next we can begin detailed
observations and pollinator exclusion experiments (inouye book ref***) to determine which visitors
are importance pollinators. Determining effective pollinators from pollen transporters will also
determine the effectiveness of using pollen transport as an indication of pollination. It will also give
a better indication of the type of interaction between plants and their visitors.
As plant – pollinator networks are highly generalised and small world, constituents within
the networks are interacting indirectly. For instance, plant species could be supporting pollinator
assemblages collectively, attracting a greater diversity and abundance of pollinators that could
increase seed output, and thereby indirectly facilitating each others pollination (ref!!!sargent.
Research Plan, Methods and Technique
Study sites
The arid zone study areas will include the reserves of Cravens Peak (23 S, 138 E) and
Ethabuka (23 S, 138 E) and the station of Carlo (23 S, 138 E), an area in the North Eastern Simpson
Desert, South Western Queensland. The dominant landforms are long parallel sand dunes, 8-10m in
height, divided by interdune valleys (swales). Triodia basedowii (spinifex) dominate swales and
dune sides and common shrubs include Grevillea stenobotrya, Eremophila spp and Acacia spp.
Some swales also have clay soils with the tree Acacia cambagei. Dune crests commonly feature G.
stenobotrya and Eremophila spp. Many ephemeral forbs and herbs are present after rain. Over 150
plant species, seven native small mammal species, 42 reptile species and an unknown number of
invertebrate species exist in the area.
Other arid environments will be chosen according to recent rain events, as rain promotes
flowering in Arid Australia (ref). Other environments may include, and are not limited to heath,
mallee woodlands, open woodlands and sclerophyllus forests.
Plant Phenology
Data on the flowering and seed production for all plant species will be collected across a
spatial and temporal time scale using sampling plots. A schematic portrayal of these data will be
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Spp
generated in MS Excel (Figure 1) to show how long plants flower, what species co-flower and long
they co-flower for.
Time
Figure 1: Plant phenology diagram. Black represents flowering periods, white represents fruiting.
Insect visitation web
Data to construct visitation webs will be collected by catching floral insect visitors
(observations will also be used if the visitor can be identified) using both plots and transects. Ten
minute catching periods in 2x2m plots will be used to sample visitors of common flowering plants,
whereas a 2m wide 100m transect will be used to sample the less common flowering plants. All
visitors will be sampled. Sampling will also occur opportunistically while in the field (these two
catching protocols will be used to generate different visitation webs). Insects will be caught either
using a net, pooter or directly into a vial, and killed using a killing jar containing ethyl acetate.
Insects collected will be differentiated to morphospecies for the purpose of web construction
and sent to experts at the Australian Museum for further resolution.
A visitation web will be constructed in Excel. Power-law distribution will be tested using a
Kolmogorov-Smirnov test in R. To detect structure such as nestedness and compartments,
assemblages will be arranged into binary (presence/absence of interaction) matrices and tested with
Aninhado software (Guimaraes Jr & Guimaraes 2006). Network connectance, average path length
and mean number of interactions across animal and plant species will be calculated in the program
Pajek. These network parameters will be used to compare desert sand dune visitor assemblages to
those from other environments. Networks generated on different trips will also be compared to
determine temporal variation of structure.
Insect Pollen Transport Web
Visitors will be used to construct an insect pollen transport web. Insects will be
systematically dabbed with a 20mm3 section of gelatin-fuchsin to sample and stain pollen. Pollen
storage areas such as pollen baskets will be avoided as these contain pollen unlikely to be available
for pollination (Gibson et al. 2006). The gel will then be placed on a microscope slide, heated to
melting point then covered with a cover slip. Pollen will be identified under a light microscope
using a pollen reference set. Pollinator importance (PI), a measure of the importance of a particular
insect species in pollinating a particular plant species (Schemske & Horvitz 1984), will be
determined for the bees sampled. A modified version from Gibson et al.(2006) will be used:
PI = (relative abundance of pollinator) X (pollen fidelity)
For instance, to calculate the PI of insect α for plant β, the proportion of all insects carrying β pollen
that are of species α, would be multiplied by the mean proportion of individual α pollen loads that
originate from β. A web will be constructed which will take into account the pollinator importance
as in Gibson et al.(2006). Networks will be analysed as described previously.
Determining Pollinators
For certain plant species the relationship between it and its visitors will be determined.
Determining effective pollination includes pollen pickup and transport to a conspecific stigma, and
experiments excluding different visitors so as determine the importance of a given species
(Jennersten 1988; Mayfield et al. 2001; Pellmyr 2002).
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Floral Rewards
Floral attractants and rewards can influence the structure of plant – visitor assemblages
(potts…) !!!!!Floral rewards usually come in two forms: nectar and pollen. Nectar can be quantified
using capillary tubes, and qualified using a refractometer. Pollen will be assessed by amount present
through collection and counting.
Indirect Interactions
Indirect relationships between plants via common pollinators can be investigated by looking
at the reproductive output of plants in different local plant communities, in conjunction with
experimentally setup arrays.
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