DOI: 10.5061/dryad.9cnp5hqjn
title: Pollinator-mediated indirect effects on plant fecundity revealed by network indices

description: Indirect effects arise when one species influences how another species
 interacts with a third. Pollinator-mediated indirect effects are
 widespread in many plant communities and are often not restricted to plant
 species pairs. An analytical framework does not exist yet that allows for
 the evaluation of indirect effects through shared pollinators in a
 community context, as well as their consequences for plant fitness. We
 used network indices describing pollinator sharing to assess the extent to
 which plant species affect and are affected by others in a pollination
 network from a species-rich dune community. For 23 plant species, we
 explore how these indices relate to plant fecundity (seeds/flower) over
 two years. We further linked plant traits and indices to uncover
 functional aspects of pollinator-mediated indirect interactions. Species
 frequently visited by shared pollinators showed higher fecundity and
 exhibited traits that increase pollinator attraction and generalization.
 Conversely, species whose shared pollinators frequently visited other
 plants had lower fecundity and had more specialized traits. Thus,
 pollinator sharing benefited some species while others suffered
 reproductive disadvantages, consistent with competition. The framework
 developed here uses network tools to advance our understanding of how
 pollinator-mediated indirect interactions influence a species’ relative
 reproductive success at the community-level.
description: Fieldwork was carried out in Son Bosc within the protected area
 inside the s’Albufera Natural Park (39º46’28.1” N, 3º07’45.34” E). The
 sampling area consisted of a dune ecosystem at sea level in the northern
 region of Mallorca. We used the same 23 focal species described in Lázaro
 et al. (2020) for which data on plant-pollinator interactions, plant
 fitness and population and flower traits were available. The previous
 study was designed to uncover the link between commonly used network
 metrics and plant reproductive success (Lázaro et al. 2020), while here we
 specifically investigate the role of indirect interactions on plant
 fecundity. Thus, the analyses carried out here are post hoc given the
 availability of adequate data from Lázaro et al. (2020).
 We used observational data on flower visitors to plants recorded
 in 2016 (38 plant and 119 pollinator species) and 2017 (38 plant and 174
 pollinator species), as carried out by Lázaro et al. (2020), during the
 bloom period of the plant species included in this study
 (April-July). Insect censuses on focal plants were conducted once per week
 (15 weeks in 2016, 16 weeks in 2017), during the period of highest
 pollinator activity (10:00 am-17:00 pm), with each census lasting 5 min.
 Surveys were conducted on haphazardly selected individuals of all species
 in bloom each sampling day. Thus, sampling effort was proportional to
 plant species abundance (Lázaro et al. 2020). We considered only visits in
 which the insect touched reproductive organs of the flower. We pooled all
 visits of an insect species to a plant species across all observation
 periods. Then, we built quantitative networks for each year based on two
 visitation variables: visitation frequency (number of visits) and
 visitation rate per flower of each pollinator to each plant
 species. Plant fecundity was estimated in 20 and 30
 individuals per species in 2016 and 2017 (each year using different
 individuals), respectively (Lázaro et al.<i> </i>2020). For
 each species, we obtained: (1) fruit set, i.e. number of
 fruits/infructescences per individual divided by the number of
 flowers/inflorescences, and (2) seed set, estimated as number of seeds per
 fruit (from one fruit/infructescence per individual chosen randomly). We
 used seeds per flower as the final fecundity measure, expressed as fruit
 set (fruits per flower) x seeds per fruit. We used the
 trait data measured for each plant species in the field first reported by
 Lázaro et al. (2020). Briefly, we recorded the following traits: (1)
 Flower abundance (flowers/m<sup>2</sup>); average number of
 open flowers (flower units: flowers or inflorescences depending on the
 species) of each species counted in five transects of 50 x2 m sampled once
 every two weeks each year; (2) Flowering length; number of days the focal
 species was observed in bloom across all temporal surveys each year. (3)
 Flower shape; zygomorphic vs. actinomorphic flower or inflorescences; (4)
 Flower size; average largest diameter (width of the flower/inflorescence
 of each species), measured with digital calliper in 30 randomly chosen
 individuals per species (one fully-open flower unit randomly chosen per
 individual); (5) Corolla tube length (30 individuals per species); (6)
 Nectar volume, measured as the nectar standing crop of 10 randomly chosen
 individuals per species (one flower unit randomly chosen per individual)
 by means of microcapillary tubes; (7) Dependence on pollinators (degree of
 selfing), calculated as 1 – B/C, where B are the seeds per flower produced
 by 10 branches/individuals prevented from insect visitation (i.e. bagged
 before flower anthesis) and C are the seeds per flower of 10 control
 branches/individuals, open to natural pollination. To
 estimate indirect interactions via pollinator sharing, we calculated
 potential indirect effects in the networks (one per year) based on
 visitation frequency and based on visitation rates per flower between all
 plant species pairs using the index proposed by Müller et
 al<i>.</i> (1999), by means of the PAC function in the
 <i>bipartite </i>R-package (Dormann et al<i>.</i>
 2009). We obtained two sets of indirect interaction indices: the first
 based on visitation frequency (number of visits) and the second based on
 visitation rates per flower. For the indices based on visitation
 frequency, we built two plant-plant matrices (one for each year), with
 column values representing how much a plant species influences the other
 species, and rows representing how much the plant species is affected by
 other species. To obtain a single value per species and year, we summed
 all column values per species (separately for each year), representing the
 total effect of the species on all other plant species in the network
 (Acting degree, the sum of the effects of plant <i>j </i>on
 all other plant species in the network). Similarly, we summed all row
 values per species, representing the total effect of all species in the
 network on a particular one (Target degree, the sum of the effects plant
 <i>i </i>received from<i> </i>all other plant
 species in the network). We excluded the diagonal, setting these values to
 zero, as they are estimates of the potential for intraspecific
 competition. Additionally, to obtain indices without the influence of
 abundance, we obtained plant-plant matrices based on pollinator visitation
 rates per flower. We then followed the same procedure to estimate indirect
 effect indices in the pollination networks with visitation rates per
 flower as link weight. In other words, how much a plant species affects
 others by receiving a large fraction of per flower visits of shared
 pollinators (‘Per flower acting degree’) and how a plant species is
 affected by others receiving a large fraction of per flower visits of
 shared pollinators (‘Per flower target degree’).