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.