Niche complementarity among pollinators increases community-level plant reproductive success

CENTRE MERSENNE Peer Community Journal is a member of the Centre Mersenne for Open Scientific Publishing http://www.centre-mersenne.org/ Peer Community Journal Section: Ecology RESEARCH ARTICLE Published 2021-11-22 Cite as Ainhoa Magrach, Francisco P. Molina and Ignasi Bartomeus (2021) Niche complementarity among pollinators increases community-level plant reproductive success, Peer Community Journal, 1: e1. Correspondence [email protected] Peer-review Peer reviewed and recommended by PCI Ecology, https://doi.org/10.24072/pci. ecology.100037 This article is licensed under the Creative Commons Attribution 4.0 License. Niche complementarity among pollinators increases community-level plant reproductive success Ainhoa Magrach1,2, Francisco P. Molina3, and Ignasi Bartomeus ,3 Volume 1(2021), article e1 https://doi.org/10.24072/pcjournal.1 Abstract Our understanding of how the structure of species interactions shapes natural communities has increased, particularly regarding plant-pollinator interactions. However, research linking pollinator diversity to reproductive success has focused on pairwise plant-pollinator interactions, largely overlooking community-level dynamics. Here, we present one of the first empirical studies linking pollinator visitation to plant reproduction from a community-wide perspective. We use a well-replicated dataset encompassing 16 plant-pollinator networks and data on reproductive success for 19 plant species from Mediterranean shrub ecosystems. We find that statistical models including simple visitation metrics are sufficient to explain the variability observed. However, a mechanistic understanding of how pollinator diversity affects reproductive success requires additional information on network structure. Specifically, we find positive effects of increasing complementarity in the plant species visited by different pollinators on plant reproductive success. Hence, maintaining communities with a diversity of species but also of functions is paramount to preserving plant diversity. 1Basque Centre for Climate Change-BC3, Edif. Sede 1, 1o, Parque Científico UPV-EHU, Barrio Sarriena s/n, 48940, Leioa, Spain, 2IKERBASQUE, Basque Foundation for Science, María Díaz de Haro 3, 48013, Bilbao, Spain, 3Estación Biológica de Doñana (EBD-CSIC), Avda. Américo Vespucio 26, Isla de la Cartuja, 41092, Sevilla, Spain This document is the Accepted Manuscript version of a Published Work that appeared in final form in: Ainhoa Magrach, Francisco P. Molina, Ignasi Bartomeus. 2019. Niche complementarity among pollinators increases community-level plant reproductive success. PEER COMMUNITY IN ECOLOGY. 35. DOI (10.1101/629931). ©Peer Community Journal This manuscript version is made available under the CC-BY-NC-ND 3.0 license http://creativecommons.org/ licenses/by-nc-nd/3.0/ Introduction Pollinators provide key services to plants by facilitating pollen flow (Garibaldi et al. 2013). Declining trends for some pollinator species in some regions (Potts et al. 2010; Bartomeus et al. 2018) have led researchers to focus on the functional impacts of these changes in pollinator diversity, especially for plant reproductive success (Biesmeijer et al. 2006). Many studies have evaluated reproductive success on individual plant species (Albrecht et al. 2012; Thomson 2018), and used relatively simple visitation metrics (e.g., the number of pollinator species visiting a plant or the number of visits they perform) to explain the differences observed (e.g., Bommarco et al. 2012). Contrastingly, community-level analyses remain scarce (Bennett et al. 2018). Yet plants and pollinators do not interact in isolation but are embedded within larger networks of interactions encompassing other plant and pollinator species (Memmott et al. 2004). We are thus missing an important part of the picture, including direct interactions between the whole ensemble of plants and pollinators, but also indirect ones between species within one guild (e.g., plants) through their shared resources (Pauw 2013; Lázaro et al. 2014; Carvalheiro et al. 2014; Mayfield, Stouffer 2017; Johnson, Bronstein 2019). Understanding how changes in pollinator diversity and community structure affect ecosystem functioning is thus a major challenge that requires attention. The few studies that have analyzed the effects of pollinator diversity on reproductive success at the community level have mainly used experimental setups. As an example, a study that experimentally recreated a plant community with 9 plant species and differing levels of pollinator diversity, found a positive effect of pollinator species diversity on seed set, but also an important effect of niche complementarity between pollinators, a measure of community structure (Fründ et al. 2013). These findings show that not only the diversity of species present, but also the diversity of roles they play and thus the way in which a community is structured are determinant factors of ecosystem functions. Indeed, theoretical research has long suggested that the structure of multitrophic communities has an effect for ecosystem functioning (reviewed in (Thompson et al. 2012)). This line of research, rooted in niche theory and revamped by food-web studies (Macarthur, Levins 1967; May, Arthur 1972; Tilman 1982; Godoy et al. 2018), has greatly advanced theory, but the relationship between structure and function has seldom been tested using empirical data (but see (Poisot et al. 2013; Kaiser-Bunbury et al. 2017; Lázaro et al. 2020)). Specifically, a major knowledge gap resides in understanding which aspects of structure determine which aspects of function (Thompson et al. 2012). This is because although a network perspective has promised to encapsulate complex ecological mechanisms occurring at the community level – such as indirect interactions (Holt 1977; Abrams et al. 1998) or niche overlap (Woodward, Hildrew 2002)- less attention has been given to the ways in which these mechanisms relate to observed ecosystem processes (Blüthgen 2010). We are now at a point where we understand some of the emergent patterns characterizing mutualistic interaction networks at the community level, especially in the case of pollination (Bascompte, Jordano 2007). Amongst them is the prevalence of nested structures, i.e., arrangements where specialist species interact with a subset of the species that generalists interact with (Bascompte et al. 2003). Further, plant-pollinator interaction networks seem to exhibit a relatively high extent of complementary specialization at the community scale, which may be directly related to key ecosystem functions (Blüthgen, Klein 2011). However, the mechanisms by which these attributes affect plant reproduction remain to be understood (Winfree 2013). The time is thus ripe to explore the relationship between community structure and ecosystem functioning empirically, with special emphasis on the underlying ecological mechanisms that drive these relationships. Here, we present an empirical study linking pollinator visitation and plant reproductive success at the community level. We use a well-replicated dataset encompassing plant-pollinator interaction networks collected at 16 sites coupled with data on the reproductive success of 19 plant species recorded in Mediterranean shrub ecosystems. Our study focuses on understanding whether adding information on selected interaction network structure indices to previously used simple visitation metrics (e.g., the number and diversity of pollinator species visiting a plant species) aids in better explaining the differences observed in community-wide reproductive success. In doing so, we conducted our analyses focusing on reproductive success at two different levels: (i) at the species level by considering the association between the position of a focal species within the larger network and its link to individual reproductive success, and 2 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 (ii) at the site level, by evaluating how attributes that describe the whole site might affect average values of reproductive success for all species measured within one particular site. Specifically, our study focuses on how the interplay between the complementarity in plant species visited by different pollinators, and the redundancy in this function relate to reproductive success. Plant reproductive success requires of the delivery of conspecific pollen and thus of a certain degree of niche complementarity (Blüthgen, Klein 2011). Yet, greater values of redundancy in species functions (e.g., that provided by nested structures), are thought to promote species diversity (Bastolla et al. 2009) and stability (Thébault, Fontaine 2010) within plant-pollinator networks. At present, we do not know how either of these network characteristics affects the functions performed by pollinators. Our results suggest that models including information on simple visitation metrics alone are able to explain differences in reproductive success. However, a mechanistic understanding requires additional information on network structure, notably information on the complementarity between the niches occupied by different pollinator species. Specifically, we find a positive effect of increasing niche complementarity between pollinators on plant reproductive success. Material and Method Plant pollinator interactions Our study was conducted in SW Spain within the area of influence of Doñana National Park (Fig. S1). Sites were located within similar elevations (ranging from 50 to 150 m a.s.l.), and similar habitat and soil types, reducing potential confounding factors. Similarity in plant composition between sites was 0.41 (plant mean Sørensen beta-diversity). We surveyed 16 Mediterranean woodland patches with an average distance of 7 km between them (min= 3 km, max= 46.5 km). Each site was surveyed every two weeks for a total of 7 times during the flowering season of 2015 (from February to May) following a 100-m x 2 m transect for 30 mins. Along each transect, we identified all plant species and recorded all the floral visitors that landed on their flowers. Only floral visitors (from now on referred to as pollinators) that could not be identified in the field were captured, stored and identified in the laboratory by FPM and another expert entomologist (see acknowledgements). All surveys were done under similar weather conditions, avoiding windy or rainy days, during mornings and afternoons with the sampling order being established randomly. Within each transect every 10 m we surveyed a 2x2 m quadrant where the number of flowers per species were counted, i.e., 10 quadrats per transect which makes 40m2 of area surveyed overall. Plant reproductive success Within each site, we marked 3-12 individuals (mean ± SD: 6.49 ± 2.37) belonging to 1-6 plant species (mean ± SD:4.06 ± 1.69, Table S2). For each individual, at the end of the season, we recorded fruit set (i.e. the proportion of flowers that set fruit), the average number of seeds per fruit and the average fruit and seed weight per fruit (1-36 fruits subsampled, mean ± SD: 11.17 ± 6.85, Table S3). These last two variables show a strong correlation (Pearson correlation= 0.89), and thus we only present results on fruit weight. Our survey included a total of 19 different totally or partially self-incompatible plant species that depend on pollinators to maximize their reproduction (Table S4) across our 16 sites. All plant species were common and widespread shrubs. Individuals were selected depending on the presence of flowers during the sampling events. We also calculated the average reproductive success at the site level by averaging values of reproductive success obtained for each species. Data analyses To evaluate the sampling completeness, we estimated the asymptotic number of species of plants, pollinators and interactions present (Chao et al. 2009), a non-parametric estimator of species richness for abundance data. This estimator includes non-detected species and allowed us to calculate the proportion detected with our original data. We used Chao 1 asymptotic species richness estimators (Chao et al. 2009) and estimated the richness of pollinators, plants and plant–pollinator links accumulated as sampling effort increased up to 100% sampling coverage using package iNEXT (Hsieh et al. 2016) within the R environment (R Development Core Team 2011). We then extracted the values covered by our sampling. To evaluate differences in network structure between communities, we constructed plant-pollinator interaction networks by pooling the data for the 7 rounds of sampling. We thus obtained one interaction Ainhoa Magrach et al. 3 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 network per site, representing the number of individuals of different pollinator species recorded visiting each different plant species. For each network, we extracted a series of relevant network metrics at the species and site levels. Additionally, we checked for spatial autocorrelation in our data using Mantel correlograms. Autocorrelation values were non-significant for all variables, except for pollinator richness where we have a small but significant effect at small spatial scales (Fig. S2). Hence, we treat each site as independent in our analysis. Species-level network analysis At the species level, we focused on attributes defining the position of a focal plant species within the larger community. As such, we considered two metrics providing complementary non-redundant information: (i) average niche overlap in terms of pollinators between a focal plant species and each of the other plant species in the community, and (ii) the contribution to nestedness of each individual plant species. Niche overlap estimates the potential indirect interactions between plant species through shared resources (in this case pollinators) and the potential for increased heterospecific pollen deposition (ArceoGómez et al. 2019). We calculated it as the average overlap in pollinator species visiting a focal plant and each of the other plants in the community using the Morisita overlap index, a measure of similarity between two sets of data (Zhang 2016). A plant species’ contribution to nestedness is calculated by comparing the nestedness observed in a given community to that generated by randomizing the interactions in which a focal species is involved. Species that show important contributions to overall nestedness will have values >0, while species that do not contribute to overall nestedness wil show values <0 (Saavedra et al. 2011). Site-level network analysis At the site level, we followed the same logic as the one presented at the species level. We also calculated two network metrics providing complementary non-redundant information. In this case, we focused on nestedness, a measure of the redundancy in the plants visited by different pollinators, and pollinator niche complementarity, a measure of the complementarity in plant species visited by different pollinator species. Nestedness is the property by which specialists interact with a subset of the species that generalists interact with (Bascompte et al. 2003). Although there is an ongoing debate in the literature (e.g., (James et al. 2012)), some theoretical studies have found that nested networks are more stable and resilient to perturbations because nestedness promotes a greater diversity by minimizing competition among species in a community (Bastolla et al. 2009). However, many network attributes vary with network size and complexity (Blüthgen et al. 2006). In the case of nestedness, we know it can be affected by network size and connectance (Song et al. 2017). An approach that is often used to correct for this are null models, comparing null-model corrected nestedness values across different networks. However, this approach presents the same issues, as z-scores also change with network size and connectance (Song et al. 2017). We thus used a normalized value of the widely used nestedness metric NODF based on binary matrices (Almeida-Neto, Ulrich 2011), 𝑁𝑂𝐷𝐹𝑐 (Song et al. 2017). This normalized value is calculated as 𝑁𝑂𝐷𝐹𝑐= 𝑁𝑂𝐷𝐹𝑛/(𝐶 ∗ 𝑙𝑜𝑔(𝑆)), where C is connectance and S is network size, calculated as 𝑆 = √(𝑛𝑐𝑜𝑙(𝑤𝑒𝑏) ∗ 𝑛𝑟𝑜𝑤(𝑤𝑒𝑏)). 𝑁𝑂𝐷𝐹𝑛 is calculated as 𝑁𝑂𝐷𝐹/𝑚𝑎𝑥(𝑁𝑂𝐷𝐹), which is independent of network size and thus comparable across different networks (Song et al. 2017). To calculate max(NODF) we used a corrected version of the algorithm (Simmons et al. 2019) whenever possible. Results did not change qualitatively when using the uncorrected version of the algorithm for all sites as both are highly correlated (Spearman correlation = 0.94). To calculate niche complementarity, we used a community-level measure defined as the total branch length of a dendrogram based on qualitative differences in visitor assemblages between plants (Devoto et al. 2012; petchey200?). All network metrics were calculated using package bipartite (Dormann et al. 2009). Statistical analyses To evaluate whether adding information on network structure improves our ability to explain differences in reproductive success - both at the species and the site level - we used generalized linear (GLMs) and generalized linear mixed models (GLMMs) respectively. In both cases we fit three types of 4 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 models: (i) model 0, a null model with no explanatory variables,(ii) model 1, that only included simple visitation metrics and (iii) model 2 that additionally included information on network structure. These models are meant to be additive, so that the network metrics included are intended to complement rather than substitute the simple metrics traditionally used. At the species level, response variables included fruit set analyzed using a binomial distribution and the average number of seeds per fruit, and the average fruit weight fitted using normal distributions. The number of seeds per fruit was centered and scaled (i.e., we subtracted column means and divided by standard deviation) to allow meaningful comparisons across species with contrasting life histories. As explanatory variables, model 1 included the number of pollinator species observed, and the visitation rate received by each plant species. Visitation rate was calculated as the total number of visits received by a plant species divided by the average number of flowers of that species found in the 10 2x2 m quadrats per transect. In turn, model 2 added the two network attributes calculated at the species level: average plant niche overlap and contribution to nestedness. For both models, we included plant species identity nested within site and site as random effects to account for multiple individuals of the same plant species measured at each site. At the site level, response variables were the average reproductive success of all plants surveyed within a site (i.e., average fruit set analyzed using a binomial distribution, average number of seeds per fruit and average fruit weight using a normal distribution). We thus had a single value per site and no random effects are needed. Here, model 1 included total pollinator richness and total pollinator abundance (i.e. number of visits received by all plants within the community) as explanatory variables. Model 2, in turn, added information on network structure by including nestedness and pollinator niche complementarity. Average values of reproductive success at the site level can be driven by a single plant species. Yet, what will determine the persistence of a diverse plant community, is the presence of some sort of “equity” or evenness in reproductive success across the whole community. We therefore calculated the proportion of species with normalized (between 0 and 1) average fruit set values that were above the 50^th percentile as a measure of equity. As any selected threshold is arbitrary, we repeated this using the 25^th and 75^th percentile thresholds (Byrnes et al. 2014). We then used the same framework as that used for species and site-level analyses and fit the same models 0, 1 and 2 using equity in reproductive success as response variable and fitting a binomial distribution. In all cases, we used variance inflation factors to check for collinearity between explanatory variables. Additionally, we ran residual diagnostics to check if model assumptions were met and used the Akaike Information Criterion (AIC) to compare model performance and complexity. Whenever the difference between the AIC of the models was < 2 (𝛥𝐴𝐼𝐶 < 2), we considered all models equally good (Burnham et al. 2010). In the case of mixed models, for comparison, models were fitted by maximum likelihood and then the best model was refitted using restricted maximum likelihood. All predictor variables were standardized prior to analysis. For every model we also calculate the R2 value using the approximation suggested for GLMMs when necessary (Nakagawa et al. 2017). Finally, we tested whether the importance of network structure in explaining differences in equity in reproductive success increases with the number of plant species being considered. We expect that when only one plant species is considered the importance of network structure will be negligible, while we expect it to increase as more plant species are considered (up to a maximum number of 6 species which is the maximum we have measured in our study at a particular site). To test this, we ran a simple simulation in which the number of species considered increased at each step and for each step we re-calculated equity in reproductive success. Instead of drawing plant species randomly for each step, we tested all possible combinations for each plant number level and network, as the number of combinations is small (e.g. for n = 3 plants selected out of 6 there are only 20 possible combinations). Then, we tested if the relationship between equity in reproductive success and niche complementarity (given its importance in determining differences in reproductive success, see Results section) changes as a function of the number of plants considered within our simulated communities. To this end, for each level of species number considered, we randomly selected one of the generated equity values across each of the 16 communities and regressed these 16 values against our network level predictor and extracted the model slope estimates. We repeated this process 1,000 times and averaged all slope estimates. We expect that the more plants considered, the larger the resulting average estimates will Ainhoa Magrach et al. 5 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 be. Note that we only interpret the mean effects, as the variance among different plant number of species considered depends on the initial number of possible combinations. Results Within our sampling we recorded 655 plant-pollinator interactions involving 277 pollinator species and 57 plant species (Table S1). Within the pollinator community the distribution of individuals in different orders was: 92.18% Hymenoptera, 5.69% Diptera, 1.29% Coleoptera and 0.63% Lepidoptera. Our sampling completeness analyses revealed that our survey was able to capture 17-54% of pollinator species (average = 35%), 43-100% of plant species (average = 80%) and 9-32% of plant-pollinator links (average = 20%), in line with that obtained with other studies (e.g., (Chacoff et al. 2011), Fig. S3). Our values of sampling completeness were slightly smaller in the case of pollinators, probably as a consequence of the great diversity found in the Mediterranean region and within our study area in particular, a hotspot of insect diversity (European Commission. Directorate General for the Environment., IUCN (International Union for Conservation of Nature). 2014). Species-level analyses At the species level, in the case of fruit set, our results showed that model 2 had the best fit to our data (lowest AIC value), and fixed effects explained 9% of the variability observed (conditional R^2=17%). We found a positive relationship between fruit set, pollinator species richness, and a network structure metric, the contribution to nestedness of a focal plant within the overall network (Table 1, Fig. 1, Fig. S4). For the average number of seeds per fruit at the species level as well as for fruit weight, our results showed that none of the models fitted were better than the null model explaining differences across plant species. Table 1. Results of GLMM showing the association between simple visitation and network structure metrics and species-level fruit. Bold letters indicate variables with large effects (see Figure S4 for estimate confidence intervals). Fruit set Estimate Std.Error z.value (Intercept) 1.79 0.21 8.38 Pollinator richness 0.51 0.25 2.04 Relative number of visits -0.16 0.25 -0.64 Plant niche overlap 0.20 0.23 0.85 Contribution to nestedness 0.47 0.26 1.81 6 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Figure 1. Partial residual plots showing the effect of A) pollinator species richness and B) the contribution to nestedness of each plant species on fruit set. Dots represent each of the individuals sampled for each species within each site. Site-level analyses At the site level, in the case of fruit set and the number of seeds per fruit, we found that both model 1 and 2 were equally good when penalizing for model complexity (i.e.,𝛥𝐴𝐼𝐶 < 2; (Burnham, Anderson 2004)). This suggests model 2 was a good model despite its added complexity, and actually shows a substantially better predictive ability than model 1 (R^2 = 0.46 for model 2 versus 0.27 for model 1 in the case of fruit set and R^2 = 0.49 for model 2 versus 0.35 for model 1 in the case of the number of seeds per fruit) and therefore we will comment results for this model only. Specifically, we found that both fruit set and the number of seeds per fruit were positively related to niche complementarity between pollinators (Tables 2, Fig. 2, Fig. S5). Additionally, we found a negative association between site-level pollinator richness and average fruit set (Table 2A, Fig. 2, Fig. S5). In the case of fruit weight, we found that both the null model and model 1 were equally good (i.e.,𝛥𝐴𝐼𝐶 < 2; (Burnham, Anderson 2004)). Model 1, i.e., that only including simple visitation metrics, showed an R^2 of 0.23. In this case, we found a positive link with site-level pollinator richness (Table S5A, Figs. S5-S6). This association was maintained even after removing a site that has a particularly large pollinator richness value (Table S5B, Fig. S7, Fig. S5). Ainhoa Magrach et al. 7 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Table 2. Results of GLM showing associations between simple visitation and network structure metrics and A) site-level average fruit set and B) site-level average number of seeds per fruit based on best model selected. Bold letters indicate variables with large effects. A) Fruit set Estimate Std. Error z value (Intercept) 1.20 0.15 7.79 Pollinator richness -0.77 0.26 -2.91 Relative number of visits -0.12 0.19 -0.66 Nestedness 0.02 0.16 0.12 Pollinator niche complementarity 0.40 0.26 1.58 B) Seeds per fruit Estimate Std. Error t value (Intercept) 45.37 8.84 5.13 Pollinator richness 1.56 15.80 0.10 Relative number of visits 4.37 10.78 0.41 Nestedness 3.94 9.80 0.40 Pollinator niche complementarity 26.44 15.49 1.71 Figure 2. Partial residual plots showing the effect of the single predictor which best explains the variability in site-level reproductive success. A) Shows the effect of pollinator richness, and B) of niche complementarity among pollinator species on site-level average fruit set. C) Shows the effect of niche complementarity among pollinator species on the average number of seeds per fruit at the site level. Dots represent average values of fruit set at the level of the community for all plant species considered (N=16 sites). Equity in fruitset When evaluating the relationship between community composition and network structure on equity in reproductive success across the different species within a community, we found that using the 50^th percentile all models were equally good (i.e.,𝛥𝐴𝐼𝐶 < 2; (Burnham, Anderson 2004)), but none of the 8 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 variables considered showed any strong associations (Table S6). In the case of the other two thresholds considered (25^th and 75^th percentiles) model 0, the null model, was the best model. Within our simulation evaluating the relationship between niche complementarity and equity in reproductive success at increasing number of plant species considered, we found that the link to complementarity became more important as more species were considered (Fig. 3). This importance seemed to reach a plateau at 6 species. However, this should be further evaluated, as this was the maximum number of species simultaneously observed in a community for our study, which precludes us from simulating further numbers of species. Figure 3. Results of simulation evaluating the importance of niche complementarity in determining differences in equity in reproductive across communities harboring from one to six species. Points represent average values across 1,000 simulated combinations. Discussion The existence of relationships between interaction network structure and ecosystem function have been long hypothesized, yet, the specific mechanisms underlying this relationship remain elusive (Thompson et al. 2012). Our results suggest that different aspects of network structure affect different dimensions of ecosystem functioning. Specifically, we find that the contribution to nestedness of a plant species within a community has a positive association with its fruit set. From a plant’s perspective, this indicates that being connected to other plant species via shared pollinators has a positive outcome (e.g. by ensuring a stable pollinator supply through time) rather than a negative one (e.g. via heterospecific pollen transport). At the site level, we find that greater values of niche complementarity between pollinators result in larger average values of reproductive success. Most of our analyses reveal that model 1 and 2 were equally good, which suggests that the added complexity of measuring the full network of interactions may not pay off for rapid assessments. Hence, simple visitation metrics, such as pollinator richness, might be enough to describe general patterns (Garibaldi et al. 2013; Garibaldi et al. 2014). Yet, adding network level information may inform us of the potential ecological mechanisms underlying the processes driving these observed patterns. Further, although we sampled each site seven times in a randomized order in an attempt to better represent interactions through time, our surveys were able to capture 20% of interactions given the great diversity of our study system. This could be explaining part of the low effect sizes we find at the species level, where a stronger contribution of pollinator visits is expected given their obligate dependence. In addition, plant reproductive success is affected by other variables which we do not attempt to measure in this study and that could explain a large portion of the variability observed. Consistent with previous experimental (Fontaine et al. 2005; Fründ et al. 2013), theoretical (Pauw 2013), and empirical studies (Poisot et al. 2013; Valdovinos et al. 2016), we find that niche complementarity Ainhoa Magrach et al. 9 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Figure S6. Partial residual plots showing the effect of pollinator richness on site-level average fruit weight. Dots represent values for each site (N=16 sites). Figure S7. Partial residual plots showing the effect of pollinator richness on site-level average fruit weight. Here, a site with a particularly large pollinator richness value is removed to test whether it might be driving the significant relationship. Dots represent values for each site (N=15 sites). 16 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Table S1A. List of all plant species present at each of the sites and included in network analyses. Site Plant species Aznalcazar Asphodelus fistulosus Aznalcazar Cistus crispus Aznalcazar Cistus ladanifer Aznalcazar Cistus monspeliensis Aznalcazar Cistus salvifolius Aznalcazar Echium plantagineum Aznalcazar Lavandula pedunculata Aznalcazar Lavandula stoechas Aznalcazar Lavatera cretica Aznalcazar Rosmarinus officinalis Aznalcazar Teucrium fruticans Bonares Andryala integrifolia Bonares Cistus crispus Bonares Cistus ladanifer Bonares Cistus salvifolius Bonares Halimium commutatum Bonares Lavandula pedunculata Bonares Lavandula stoechas Bonares Thapsia villosa Bonares Thymus mastichina ConventodelaLuz Cistus crispus ConventodelaLuz Cistus ladanifer ConventodelaLuz Cistus salvifolius ConventodelaLuz Halimium halimifolium ConventodelaLuz Lavandula stoechas ConventodelaLuz Retama sp. ConventodelaLuz Rosmarinus officinalis ConventodelaLuz Spartium junceum ConventodelaLuz Teucrium fruticans CotitodeSantaTeresa Astragalus lusitanicus CotitodeSantaTeresa Cistus crispus CotitodeSantaTeresa Cistus salvifolius CotitodeSantaTeresa Lavandula pedunculata CotitodeSantaTeresa Lavandula stoechas CotitodeSantaTeresa Rosmarinus officinalis CotitodeSantaTeresa Thapsia villosa Elpinar Cistus albidus Elpinar Cistus salvifolius Elpinar Convolvulus arvensis Elpinar Halimium commutatum Elpinar Lavandula stoechas Elpinar Rosmarinus officinalis Elpozo Cistus ladanifer Elpozo Cistus salvifolius Elpozo Erica scoparia Elpozo Erica umbellata Elpozo Rosmarinus officinalis Esparragal Armeria velutina Esparragal Chamaemelum fuscatum Esparragal Cistus libanotis Esparragal Cistus salvifolius Ainhoa Magrach et al. 17 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Plant species Esparragal Halimium commutatum Esparragal Lavandula pedunculata Esparragal Lavandula stoechas Esparragal Scabiosa atropurpurea LaCunya Andryala integrifolia LaCunya Cerinthe gymnandra LaCunya Cistus salvifolius LaCunya Echium plantagineum LaCunya Erica ciliaris LaCunya Halimium commutatum LaCunya Lavandula pedunculata LaCunya Leontodon longirostris LaCunya Rosmarinus officinalis LaCunya Tuberaria guttata LaCunya Ulex australis LaRocina Anchusa azurea LaRocina Andryala integrifolia LaRocina Cistus salvifolius LaRocina Diplotaxis virgata LaRocina Halimium commutatum LaRocina Halimium halimifolium LaRocina Lavandula pedunculata LaRocina Lavandula stoechas LaRocina Linaria viscosa LaRocina Rosmarinus officinalis LaRocina Spartium junceum Lasmulas Cistus crispus Lasmulas Cistus ladanifer Lasmulas Cistus monspeliensis Lasmulas Cistus salvifolius Lasmulas Echium plantagineum Lasmulas Lavandula stoechas Lasmulas Ranunculus sp. Lasmulas Rosmarinus officinalis Lasmulas Thapsia villosa Niebla Andryala integrifolia Niebla Arctotheca calendula Niebla Asphodelus fistulosus Niebla Astragalus lusitanicus Niebla Calendula arvensis Niebla Carduus sp. Niebla Cistus crispus Niebla Cistus ladanifer Niebla Cistus monspeliensis Niebla Convolvulus arvensis Niebla Lavandula pedunculata Niebla Lavandula stoechas Niebla Leontodon sp. Niebla Linaria viscosa Niebla Linum bienne Niebla Lupinus angustifolius Niebla Phlomis purpurea Niebla Taraxacum vulgare 18 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Plant species Niebla Thapsia villosa PinaresdeHinojos Andryala integrifolia PinaresdeHinojos Cistus crispus PinaresdeHinojos Cistus libanotis PinaresdeHinojos Cistus salvifolius PinaresdeHinojos Diplotaxis virgata PinaresdeHinojos Rosmarinus officinalis PinaresdeHinojos Spartium junceum PinaresdeHinojos Ulex australis Pinodelcuervo Asphodelus fistulosus Pinodelcuervo Chamaemelum fuscatum Pinodelcuervo Cistus crispus Pinodelcuervo Cistus ladanifer Pinodelcuervo Cistus salvifolius Pinodelcuervo Halimium commutatum Pinodelcuervo Lavandula pedunculata Pinodelcuervo Lavandula stoechas Pinodelcuervo Ranunculus sp. Pinodelcuervo Rosmarinus officinalis Pinodelcuervo Thapsia villosa Pinodelcuervo Ulex australis Urbanizaciones Calendula arvensis Urbanizaciones Cistus crispus Urbanizaciones Cistus salvifolius Urbanizaciones Halimium commutatum Urbanizaciones Lavandula pedunculata Urbanizaciones Lavandula stoechas Urbanizaciones Rosmarinus officinalis Urbanizaciones Tuberaria guttata Urbanizaciones Ulex australis Villamanriqueeste Cistus crispus Villamanriqueeste Cistus ladanifer Villamanriqueeste Cistus salvifolius Villamanriqueeste Genista hirsuta Villamanriqueeste Rosmarinus officinalis Villamanriqueeste Spartium junceum Villamanriquesur Andryala integrifolia Villamanriquesur Armeria velutina Villamanriquesur Cistus crispus Villamanriquesur Cistus salvifolius Villamanriquesur Convolvulus arvensis Villamanriquesur Genista hirsuta Villamanriquesur Halimium halimifolium Villamanriquesur Lavandula stoechas Villamanriquesur Rosmarinus officinalis Table S1B. List of all pollinator species present at each of the sites and included in network analyses. Site Pollinator species Aznalcazar Andrena flavipes Aznalcazar Andrena nigroaenaea Ainhoa Magrach et al. 19 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species Aznalcazar Andrena nitidiuscula Aznalcazar Andrena sp. Aznalcazar Andrena tenuistriata Aznalcazar Anthophora dispar Aznalcazar Anthophora sp. Aznalcazar Apis mellifera Aznalcazar Bombus terrestris Aznalcazar Calliphora sp. Aznalcazar Cerceris sabulosa Aznalcazar Dasypoda argentata Aznalcazar Dasypoda cingulata Aznalcazar Dasypoda crassicornis Aznalcazar Empis morpho1 Aznalcazar Eristalis arbustorum Aznalcazar Eucera alternans Aznalcazar Eucera codinai Aznalcazar Eucera collaris Aznalcazar Eucera elongatula Aznalcazar Eucera hispaliensis Aznalcazar Eucera sp. Aznalcazar Flavipanurgus venustus Aznalcazar Heliotaurus ruficollis Aznalcazar Hoplitis adunca Aznalcazar Lasioglossum morpho1 Aznalcazar Macroglossum stellatarum Aznalcazar Merodon sp. Aznalcazar Osmia leaiana Aznalcazar Panurgus calcaratus Aznalcazar Pseudoanthidium lituratum Aznalcazar Rhyncomyia cuprea Aznalcazar Syrphidae sp. Aznalcazar Tabanus morpho1 Aznalcazar Tabanus morpho2 Aznalcazar Volucella elegans Aznalcazar Xylocopa cantabrita Bonares Ammophila heydeni Bonares Ancistrocerus biphaleratus Bonares Andrena hispania Bonares Andrena nigroaenaea Bonares Andrena ovatula Bonares Andrena rhyssonota Bonares Andrena vulpecula Bonares Anthaxia morpho1 Bonares Anthidium septemspinosum Bonares Apis mellifera Bonares Bombus terrestris Bonares Bombylius sp. Bonares Ceratina cucurbitina Bonares Colletes acutus Bonares Colletes ligatus Bonares Dasypoda hirtipes Bonares Dasypogon morpho1 Bonares Empis morpho1 20 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species Bonares Empis sp. Bonares Eristalis sp. Bonares Eucera collaris Bonares Eucera elongatula Bonares Eucera sp. Bonares Graphosoma lineatum Bonares Halictus scabiosae Bonares Hoplitis papaveris Bonares Lasioglossum sp. Bonares Megachile sp. Bonares Musca sp. Bonares Platynochaetus setosus Bonares Trypoxylon morpho1 ConventodelaLuz Ammophila heydeni ConventodelaLuz Anthophora retusa ConventodelaLuz Apis mellifera ConventodelaLuz Bombus terrestris ConventodelaLuz Chasmatopterus villosulus ConventodelaLuz Eucera alternans ConventodelaLuz Exosoma lusitanicum ConventodelaLuz Ichneumonidae morpho1 ConventodelaLuz Oxythyrea funesta ConventodelaLuz Platynochaetus setosus ConventodelaLuz Syrphidae sp. ConventodelaLuz Tropinota squalida ConventodelaLuz Vespula germanica ConventodelaLuz Xylocopa cantabrita CotitodeSantaTeresa Andrena rhyssonota CotitodeSantaTeresa Anthophora aestivalis CotitodeSantaTeresa Anthophora hispanica CotitodeSantaTeresa Apis mellifera CotitodeSantaTeresa Bombus terrestris CotitodeSantaTeresa Dasypoda cingulata CotitodeSantaTeresa Dasypoda crassicornis CotitodeSantaTeresa Eucera chrysopyga CotitodeSantaTeresa Eucera codinai CotitodeSantaTeresa Eucera sp. CotitodeSantaTeresa Heriades crenulatus CotitodeSantaTeresa Lasioglossum albocinctum CotitodeSantaTeresa Lasioglossum malachurum CotitodeSantaTeresa Lasioglossum sp. CotitodeSantaTeresa Lestica clypeata CotitodeSantaTeresa Macroglossum stellatarum CotitodeSantaTeresa Merodon sp. CotitodeSantaTeresa Musca morpho1 CotitodeSantaTeresa Nemotelus morpho1 CotitodeSantaTeresa Nomada agrestis CotitodeSantaTeresa Nomada sp. CotitodeSantaTeresa Platynochaetus setosus CotitodeSantaTeresa Trypoxylon morpho1 CotitodeSantaTeresa Xylocopa cantabrita Elpinar Andrena ferrugineicrus Elpinar Andrena nigroaenaea Ainhoa Magrach et al. 21 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species Elpinar Apis mellifera Elpinar Bombus terrestris Elpinar Ceratina cucurbitina Elpinar Empis tessellata Elpinar Eucera alternans Elpinar Eucera sp. Elpinar Lestica clypeata Elpinar Platynochaetus setosus Elpinar Psilothrix viridicoerulea Elpinar Xylocopa cantabrita Elpozo Andrena hispania Elpozo Andrena sp. Elpozo Apis mellifera Elpozo Bombus terrestris Elpozo Bombylius morpho1 Elpozo Bombylius sp. Elpozo Bombylius torquatus Elpozo Colletes nigricans Elpozo Dasypoda crassicornis Elpozo Heliotaurus ruficollis Elpozo Lasioglossum bimaculatus Elpozo Lasioglossum imminutus Elpozo Merodon sp. Elpozo Musca sp. Elpozo Panurgus sp. Elpozo Psilothrix viridicoerulea Elpozo Xylocopa violacea Esparragal Andrena sp. Esparragal Anthophora atroalba Esparragal Apidae sp. Esparragal Apis mellifera Esparragal Cerceris morpho1 Esparragal Chasmatopterus illigeri Esparragal Dasypoda sp. Esparragal Episyrphus balteatus Esparragal Eucera collaris Esparragal Halictus tridivisus Esparragal Lasioglossum bimaculatus Esparragal Lasioglossum leucozonium Esparragal Lasioglossum malachurum Esparragal Lasioglossum morpho1 Esparragal Osmia fulviventris Esparragal Pieris rapae Esparragal Tenthredo sp. Esparragal Usia morpho1 LaCunya Andrena rhyssonota LaCunya Anthophora dispar LaCunya Anthophora retusa LaCunya Apis mellifera LaCunya Bombus terrestris LaCunya Ceratina cucurbitina LaCunya Dasypoda cingulata LaCunya Empis morpho1 22 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species LaCunya Heliotaurus ruficollis LaCunya Lasioglossum albocinctum LaCunya Lasioglossum imminutus LaCunya Lasioglossum malachurum LaCunya Lasioglossum sp. LaCunya Lasioglossum tridivisus LaCunya Lomatia morpho1 LaCunya Panurgus banksianus LaCunya Pieris brassicae LaCunya Pseudoanthidium melanorum LaRocina Andrena sp. LaRocina Anthophora bimaculata LaRocina Anthophora retusa LaRocina Apis mellifera LaRocina Arachnospila morpho1 LaRocina Bombus terrestris LaRocina Ceratina sp. LaRocina Colletes acutus LaRocina Colletes sp. LaRocina Dasypoda cingulata LaRocina Dasypoda crassicornis LaRocina Dasypoda sp. LaRocina Dischistus morpho1 LaRocina Dischistus senex LaRocina Episyrphus balteatus LaRocina Eristalis tenax LaRocina Helophilus trivittatus LaRocina Heriades crenulatus LaRocina Heriades truncorum LaRocina Hoplitis tridentata LaRocina Lasioglossum imminutus LaRocina Lasioglossum malachurum LaRocina Lasioglossum sp. LaRocina Malachius morpho1 LaRocina Merodon sp. LaRocina Nomada agrestis LaRocina Nomada fucata LaRocina Nomada melathoracica LaRocina Osmia caerulescens LaRocina Panurgus banksianus LaRocina Panurgus sp. LaRocina Rhyncomyia cuprea LaRocina Sphecodes sp. LaRocina Syrphidae sp. LaRocina Xylocopa cantabrita Lasmulas Andrena flavipes Lasmulas Andrena nigroaenaea Lasmulas Andrena rhyssonota Lasmulas Anthophora dispar Lasmulas Anthophora hispanica Lasmulas Apis mellifera Lasmulas Bombylius sp. Lasmulas Bombylius torquatus Ainhoa Magrach et al. 23 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species Lasmulas Conopidae sp. Lasmulas Dasypoda albimana Lasmulas Empis sp. Lasmulas Empis testacea Lasmulas Eristalis similis Lasmulas Eucera chrysopyga Lasmulas Eucera sp. Lasmulas Flavipanurgus venustus Lasmulas Heliotaurus ruficollis Lasmulas Lasioglossum imminutus Lasmulas Lasioglossum malachurum Lasmulas Mycterus curculioides Lasmulas Panurgus calcaratus Lasmulas Panurgus dargius Lasmulas Xylocopa cantabrita Niebla Andrena flavipes Niebla Andrena labialis Niebla Andrena ovatula Niebla Andrena rhyssonota Niebla Andrena tenuistriata Niebla Anthidium septemspinosum Niebla Anthophora dispar Niebla Anthophora hispanica Niebla Anthophora sp. Niebla Apis mellifera Niebla Bombus terrestris Niebla Bombylius fimbriatus Niebla Bombylius sp. Niebla Ceratina callosa Niebla Colletes sp. Niebla Episyrphus balteatus Niebla Eucera collaris Niebla Eucera notata Niebla Exosoma lusitanicum Niebla Halictus scabiosae Niebla Heliotaurus ruficollis Niebla Heriades crenulatus Niebla Lasioglossum malachurum Niebla Lasioglossum sp. Niebla Macrophya montana Niebla Merodon sp. Niebla Osmia bicornis Niebla Osmia submicans Niebla Panurgus banksianus Niebla Panurgus dargius Niebla Platynochaetus setosus Niebla Potosia cuprea Niebla Rhodanthidium sticticum Niebla Sphaerophoria scripta Niebla Systropha planidens Niebla Usia morpho1 Niebla Usia morpho2 Niebla Usia sp. 24 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Site Pollinator species Niebla Vespula germanica Niebla Xylocopa violacea PinaresdeHinojos Andrena hispania PinaresdeHinojos Apis mellifera PinaresdeHinojos Bombus terrestris PinaresdeHinojos Chrysura refulgens PinaresdeHinojos Colletes acutus PinaresdeHinojos Colletes nigricans PinaresdeHinojos Colletes sp. PinaresdeHinojos Dasypoda crassicornis PinaresdeHinojos Lasioglossum bimaculatus PinaresdeHinojos Lasioglossum malachurum PinaresdeHinojos Lasioglossum sp. PinaresdeHinojos Nomada melathoracica PinaresdeHinojos Panurgus dargius PinaresdeHinojos Psilothrix viridicoerulea PinaresdeHinojos Tenthredo corynetes PinaresdeHinojos Xylocopa cantabrita Pinodelcuervo Ancistrocerus gazella Pinodelcuervo Ancistrocerus reconditus Pinodelcuervo Andrena hispania Pinodelcuervo Andrena sp. Pinodelcuervo Apis mellifera Pinodelcuervo Bombylella atra Pinodelcuervo Bombylius sp. Pinodelcuervo Ceratina cucurbitina Pinodelcuervo Cerceris morpho1 Pinodelcuervo Dasypoda cingulata Pinodelcuervo Flavipanurgus venustus Pinodelcuervo Lasioglossum sexnotatum Pinodelcuervo Lomatia morpho1 Pinodelcuervo Megascolia maculata Pinodelcuervo Merodon sp. Pinodelcuervo Musca sp. Pinodelcuervo Nomada melathoracica Pinodelcuervo Nomada merceti Pinodelcuervo Nomada sp. Pinodelcuervo Panurgus cephalotes Pinodelcuervo Pelecocera tricincta Pinodelcuervo Systoechus morpho1 Pinodelcuervo Usia sp. Pinodelcuervo Xylocopa cantabrita Urbanizaciones Andrena sp. Urbanizaciones Andrena vulpecula Urbanizaciones Apis mellifera Urbanizaciones Bombus terrestris Urbanizaciones Bombylidae morpho1 Urbanizaciones Bombylius sp. Urbanizaciones Ceratina sp. Urbanizaciones Colletes nigricans Urbanizaciones Dasypoda cingulata Urbanizaciones Dasypoda sp. Urbanizaciones Dischistus senex Ainhoa Magrach et al. 25 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Table S4. List of plant species surveyed and their mating system. Plant_family Plant_genus Plant_species reproductive_system Cistaceae Cistus monspeliensis self-incompatible Cistaceae Cistus crispus self-incompatible Cistaceae Cistus ladanifer self-incompatible Cistaceae Cistus salviifolius self-incompatible Cistaceae Cistus albidus self-incompatible Cistaceae Cistus libanotis self-incompatible Cistaceae Halimium commutatum self-incompatible Cistaceae Halimium halimifolium self-incompatible Lamiaceae Lavandula pedunculata partially self-compatible Lamiaceae Lavandula stoechas partially self-compatible Lamiaceae Teucrium fruticans partially self-compatible Lamiaceae Rosmarinus officinalis partially self-compatible Lamiaceae Phlomis purpurea self-incompatible Xanthorrhoeaceae Asphodelus fistulosus partially self-compatible Fabaceae Ulex australis self-incompatible Fabaceae Spartium junceum self-incompatible Fabaceae Astragalus lusitanicus partially self-compatible Fabaceae Retama sphaerocarpa partially self-compatible Boraginaceae Anchusa azurea self-incompatible 32 Ainhoa Magrach et al. Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Table S5. Results of GLM showing effect of simple visitation metrics on A) site-level average fruit weight based on best model selected and B) the same analysis removing one site that has a particularly large pollinator richness value to test whether this point might be driving the relationship. A) Estimate Std. Error t value (Intercept) 0.08 0.01 8.56 Pollinator richness 0.02 0.01 2.11 Relative number of visits 0.01 0.01 0.78 B) Estimate Std. Error t value (Intercept) 0.08 0.01 8.04 Pollinator richness 0.02 0.01 1.97 Relative number of visits 0.01 0.01 0.76 Ainhoa Magrach et al. 33 Peer Community Journal, Vol. 1 (2021), article e1 https://doi.org/10.24072/pcjournal.1 Table S6. Results of GLM showing effect of simple visitation metrics on equity in reproductive success across plant species within a site based on best model selected (0.50 threshold). Estimate Std. 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