Material and Methods
Study species
We used a total of eight species (see Table S 1) with different
distribution and IUCN red list status within Switzerland. All species
are co-occurring in the lime-rich crop fields habitat (“Caucalidion”
according to the classification by Delarze, Gonseth, Eggenberg, & Vust,
2015), insect-pollinated and have overlapping flowering times in nature
(Landolt et al., 2010; Lauber, Wagner, & Gygax, 2018). We classified
species as “common” if their IUCN status in Switzerland was “least
concern”, and as rare otherwise. IUCN status in Switzerland correlated
strongly with number of observation within Switzerland pooled between
the years 2000 and 2020 (cor: 0.86, p-value <
2.2*10-16). Breeding system (self-compatible vs
self-incompatible) was extracted from the BioFlor database (Kuehn,
Durka, & Klotz, 2004). To test the relationship between recipient-donor
relatedness and heterospecific pollen interference, we constructed a
phylogenetic tree for our species (see Figure S 1) by pruning a modified
version (Malecore, Dawson, Kempel, Müller, & van Kleunen, 2018) of the
dated DaPhnE supertree of Central European plant species (Durka &
Michalski, 2012) and then calculated the phylogenetic distance using the
cophenetic function of the “ape” package (Paradis, E. Claude &
Strimmer, 2004) in R.
Experimental set-up
We sowed all species into 12 cm x 17 cm trays filled with Seedling
substrate (Klasmann-Deilmann GmbH, 49741 Geeste, Germany) and put them
into the dark coolroom at -4°C for stratification between 5 and 8 weeks.
Once seeds would start to germinate, we moved the trays to a greenhouse
compartment. We transplanted seedlings into 11 cm x 11 cm x 12 cm pots
filled with Selmaterra (fertilized heavy soil with 30% volume peat, see
Table S 2). We randomized pots on tables of a single greenhouse
compartment and watered as well as fertilized regularly. We treated
aphids and fungi whenever necessary.
All species flowered between May 2021 and October 2021. To assure
continuing of flowering, we regularly cut untreated flowers. To assess
the effect size of heterospecific pollen interference, we performed hand
pollinations among all species and measured seed set (yes/no) and seed
number (counts). For the heterospecific pollen treatment, we prepared a
saturated mix of conspecific pollen and heterospecific pollen and
applied it to the stigma of the recipient flower. For each flower
treated with heterospecific pollen mixture, we treated a second flower
on the same plant individual on the same day with conspecific pollen
only as a control, using the same conspecific pollen donor that we used
for the heterospecific pollen mix. The two flowers with heterospecific
and conspecific treatment would constitute a pair with the same “pair
ID” (see Figure 1). Pollen grain number per anther differed greatly
depending on individual and on anther ripeness, thus we standardized
treatment by always applying a pollen amount above saturation level. We
extracted the pollen for the treatments from the anthers by tapping them
on a glass slide and with the help of tweezers, and then mixed it for
the heterospecific treatment using tweezers. We then applied the pollen
mixture (HP) or the conspecific pollen (CP) to the open stigma of the
recipient flower. To avoid selfing, we emasculated recipient flowers by
removing the anthers some days before treatment. For some species
(Bupleurum rotundifolium, Fallopia convolvulus, Myosotis
arvensis ), anther removal would cause too much flower damage due to the
small size, thus anthers were not removed. For these species, selfing
could not be completely excluded.
For each donor-recipient combination, we treated between 2 and 16 flower
pairs (HP and CP; median: 12 flower pairs per donor-recipient
combination). We collected seeds after ripening, and counted them either
by hand or by using an imaging method with imageJ (Abramoff, Magalhaes,
& Ram, 2004) (for Papaver rhoeas , see “Protocol seed counting
in ImageJ” in Supporting Information).
Statistical analysis
Do pollen type and recipient status affect seed set and
seed
number?
To test whether seed set and seed number are affected by pollen type
(conspecific=0, heterospecific=1) and whether the effect size depends on
recipient status (common=0, rare=1), and to account for the high
proportion of zeroes (~24%), we run a hurdle model
using the function glmmTMB of the homonymous package (Brooks et
al., 2017). In a hurdle model, zero counts and non-zero counts are
treated as two separate categories, meaning that a binomial model is
fitted for zeroes vs non-zeroes (the “zero-inflated” model), and a
separate model for the non-zero counts only (“conditional model”). For
the conditional model, we used a truncated negative binomial error
distribution (“truncated nbinom2” in glmmTMB ). We implemented
the same formula for both the zero-inflated and the conditional model,
with pollen type (conspecific vs heterospecific), recipient status (rare
vs common) and their interaction as fixed effects. To account for
non-independence, we included pair ID, treatment date, recipient
species, recipient individual ID and donor individual ID as random
factors.
After running the models, we used the functions emmeans andpairs of the emmeans package (Lenth, 2023) to calculate
the 95% confidence intervals of the estimated marginal mean for each
group (conspecific and heterospecific treatments for common and rare
recipients) and to test for significance of the comparisons of interest
(conspecific vs heterospecific treatment for common recipients,
conspecific vs heterospecific treatment for rare recipients).
Does donor status affect seed set and seed number
?
To explore in more detail the effects of donor and recipient status on
seed set and seed number, we separately analyses a subset including only
the HP treatment. We run a hurdle model using the functionglmmTMB of the homonymous package with recipient status
(common=0, rare=1), a dummy factor indicating whether the heterospecific
pollen donor was of the same or the opposite status (same=0,
opposite=1), as well as their interaction, as fixed effects. We included
the same random factors as in the previous model (pair ID, treatment
date, recipient species, recipient individual ID and donor individual
ID).
After running the models, we used the functions emmeans andpairs of the emmeans package to calculate the 95%
confidence intervals of the estimated marginal mean for each group
(heterospecific treatment for common and rare recipients with common and
rare donors) and to test for significance of the comparisons of interest
(heterospecific treatment: common donors vs rare donors on common
recipients, common donors vs rare donors on rare recipients).
Do recipient and donor self-compatibility affect seed set
and seed
number?
To test whether seed set and seed number are affected by the
self-compatibility of recipient and donor species in interaction with
heterospecific pollen deposition, we repeated the same analyses as
above, replacing recipient and donor status with recipient and donor
self-compatibility.
Does recipient-donor relatedness affects seed
number?
To test whether the phylogenetic distance between recipients and donors
affects seed set and seed number, we calculated for all non-zero counts
the log-response ratio of seed-number with HP treatment on seed number
with CP treatment, within each pair (same pair ID).We then fitted a
Gaussian glmmTMB model including recipient-donor phylogenetic
distance, recipient status (common=0, rare=1), a dummy factor indicating
whether the heterospecific pollen donor was of the same or the opposite
status (same=0, common=1), as well as all their interactions, as fixed
effects. To account for non-independence, we included treatment date,
recipient species, donor species, recipient individual ID and donor
individual ID as random factors.
After running the model, we used the functions emmtrends of theemmeans package to calculate the estimated trends with their 95%
confidence intervals for the relationship between log-response ratio and
recipient-donor relatedness for each group (common recipient with common
donor, common recipient with rare donor, rare recipient with rare donor,
rare recipient with common donor).
For all models, we inspected Pearson residuals for homogeneity of
variance against all grouping variables.