Specialization on traits as basis for the niche‐breadth of flower visitors and as structuring mechanism of ecological networks

Abstract

Summary1. Biotic interactions do not occur in isolation but are imbedded in a network of species interactions. Network analysis facilitates the compilation and understanding of the complexity found in natural ecosystems and is a powerful tool to reveal information on the degree of specialization of the interacting partners and their niches. The indices measuring these properties are based on qualitative or quantitative observations of interactions between partners from different trophic levels, which informs about the structure of network patterns, but not about the underlying mechanisms. Functional traits may control the interaction strength between partners and also the (micro-) structure of networks. Here, we ask whether flower visitors specialize on certain plant traits and how this trait specialization contributes to niche partitioning and interaction partner diversity. 2. We introduce two novel statistical approaches suited to evaluate the dimension of the realized niche and to analyse which traits determine niches. As basis for our analysis, we measured 10 quantitative flower traits and evaluated whether 31 arthropod taxa i visited flowers displaying only subsets of the available trait characteristics, indicating a specialization on these traits by narrow trait-widths 〈S i 〉. The product of 10 trait-and species-specific trait-widths 〈S i 〉 was defined as trait-volume V i (expansion of a n-dimensional hypervolume) occupied by each taxon i. These indices are applicable beyond flower-visitor interactions to quantify realized niches based on various biotic and abiotic factors. 3. Each flower visitor species showed some degree of specialization to a unique set of flower traits (realized niche). Overall, our data suggested a hierarchical sequence of flower traits influencing the flower visitors' behaviour and thus network structure: flowering phenology was found to have the strongest effect, followed by flower height, nectar-tube depth and floral reflectance. Less important were pollen-mass/flower, sugar/flower, anther position, phylogeny, display size and abundance. 4. The species-specific specialization on traits suggests that plant communities with more diverse floral niches may sustain a larger number of flower visitors with non-redundant fundamental niches. Our study and statistical approach provide a basis for a better understanding of how plant traits shape interactions between flowers and their visitors and thus network structure.

Experimental manipulation of floral scent bouquets restructures flower–visitor interactions in the field

“…Statistical differences (v 2 tests) and sample size n are presented at the right side of the figure. findings support the notion that floral traits can act in a temporal sequence, with floral scents often preceding or pre-empting other traits during floral approach , suggesting that scents mostly affect initial choices of insects. Foraging choices of animals searching for floral resources are based on floral traits such as scent, colour, morphology and resources that are known to act as floral filters that facilitate or prevent the visitation by animal taxa (Raguso 2008a;Junker et al 2013). In correlative studies, it has been shown that each of these modalities structures part of the observed non-random interaction patterns in natural communities (Hegland & Totland 2005;Stang, Klinkhamer & van der Meijden 2006;L azaro, Hegland & Totland 2008;Campbell et al 2010;Junker, H€ ocherl & Bl€ uthgen 2010), which suggests that individual floral traits do not act in isolation but in concert with other floral features (Junker & Parachnowitsch 2015).…”

“…The demand on rewards by diverse flower visitors may often exceed the supply provided by flowering plants (Roubik 1978;Hanna, Foote & Kremen 2013), forcing the latter to economize their resources by avoiding to waste them on flower visitors that do not effectively contribute to pollen transfer (Irwin, Adler & Alison 2004;Strauss & Whittall 2006;Irwin et al 2010), providing them selectively to mutualists whenever possible. Thus, flower traits that function as attractants to mutualists but simultaneously prevent non-pollinating animals from consumption of floral resources (floral filters) increase the reproductive success of plants (Irwin, Adler & Alison 2004;Kessler, Gase & Baldwin 2008;Junker et al 2013). Accordingly, floral filters have been shown to cause a non-random distribution of flower visitors to the plant species available in a community (Junker, H€ ocherl & Bl€ uthgen 2010;Kaiser-Bunbury et al 2014;Junker et al 2013).…”

“…Thus, flower traits that function as attractants to mutualists but simultaneously prevent non-pollinating animals from consumption of floral resources (floral filters) increase the reproductive success of plants (Irwin, Adler & Alison 2004;Kessler, Gase & Baldwin 2008;Junker et al 2013). Accordingly, floral filters have been shown to cause a non-random distribution of flower visitors to the plant species available in a community (Junker, H€ ocherl & Bl€ uthgen 2010;Kaiser-Bunbury et al 2014;Junker et al 2013). Floral density or abundance, morphological fit and appropriateness of rewards were often discussed to be responsible for the presence or absence of links in plantpollinator networks (Kaiser-Bunbury et al 2014).…”

Flower-visitor and pollen transport networks in a large city: structure and properties

“…(in O and K) had also the longest blooming time and were able to attract pollinators throughout the whole study period, which probably was the reason that for both recorded visitation networks, they were the hubs forming the core of their modules. In the fallow land network studied by Junker et al (2013), spatiotemporal cooccurrence of flowers and animals (phenology) was one of the most important floral traits influencing pollinators' behaviour. In our study site, later in the season, plants of the Fabaceae family were accompanied also by the highly linked biennial generalist umbellifer D. carota.…”

Volatility of network indices due to undersampling of intraspecific variation in plant–insect interactions

“…These properties may be explained by the abundance distributions of plants and animals, phenology, morphological matching, and other factors (Vázquez et al 2009;Junker et al 2013). An on-going methodological debate in community ecology is the impact of sampling effort on the structure and aggregate metrics of Abstract Appropriate sampling effort of interaction networks is necessary to extract robust indices describing the structure of species interactions.…”