Next Article in Journal
Insight into the Taxonomic Resolution of Apiospora: Introducing Novel Species and Records from Bamboo in China and Thailand
Previous Article in Journal
Introduced, Mixed, and Peripheral: Conservation of Mitochondrial-DNA Lineages in the Wild Boar (Sus scrofa L.) Population in the Urals
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact of the Honeybee Apis mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range

1
Red de Ecoetología, Instituto de Ecología AC, Xalapa 91070, Veracruz, Mexico
2
Instituto de Biología, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México 04510, Mexico
3
Departamento de Genética, Ecologia e Evolução, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Minas Gerais, Brazil
4
Biodiversity Monitoring and Assessment, Royal Belgian Institute of Natural Sciences, 1000 Brussels, Belgium
5
Evolutionary Biology and Ecology, Université Libre de Bruxelles, 1050 Brussels, Belgium
6
Red de Biología Evolutiva, Instituto de Ecología AC, Xalapa 91070, Veracruz, Mexico
*
Authors to whom correspondence should be addressed.
Diversity 2022, 14(11), 917; https://doi.org/10.3390/d14110917
Submission received: 24 August 2022 / Revised: 11 October 2022 / Accepted: 21 October 2022 / Published: 27 October 2022
(This article belongs to the Section Biodiversity Conservation)

Abstract

:
Studies at local spatial scales have shown that the generalist honeybee Apis mellifera L. can strongly affect the structural organization and properties of pollination networks. However, there is still little knowledge on how the connectivity of the honeybee within networks (i.e., interactive role) could affect pollination networks at a global scale. To fill this gap, we evaluated how the interactive role of A. mellifera could affect niche overlap, specialization, and robustness of pollination networks. We used 109 weighted pollination networks spread across about 94 degrees of latitude and 227 degrees of longitude. We developed a new standardized framework based on species removal to assess the impact of the honeybee on plant-pollinator networks. We found that when the honeybee was removed from the networks, plant species had less niche overlap (i.e., shared fewer interactions via their pollinators) and the networks became more specialized and more robust to species extinctions. Our findings indicate that A. mellifera’s effects on pollination networks vary geographically and could influence several ecological and evolutionary factors acting at local scales, including pollination services. We hope this contribution will stimulate new macroecological studies involving abundant and generalist species and their functional roles within ecological communities.

1. Introduction

A central goal of ecology is understanding the mechanisms and drivers that determine the ecological dominance of species (i.e., high ability of one species to outcompete another due to its large size, population, or productivity) [1,2]. The honeybee, Apis mellifera Linnaeus 1758 (Hymenoptera: Apidae), a eusocial insect species, is a prime example of ecological dominance in plant and floral visitor interactions. Apis mellifera currently has a nearly cosmopolitan distribution, thriving in regions far beyond its native distribution (i.e., Africa, Europe, and parts of Asia) and occupies a broader range of climatic conditions than those encountered in its native range [3,4,5]. The ecological success of A. mellifera is linked to the species’ capacity to adapt to new climatic environments, and because of its flexible foraging behavior, A. mellifera is capable of foraging for resources from a wide diversity of flowering plants [6,7]. Moreover, this species can digest a large range of nutrients and dilute toxins and unpalatable compounds due to social behaviors and stores [8,9,10]. Another aspect of the wide geographic distribution of A. mellifera is the diversity of its subspecies/breeds, species’ domestication, and introduction by humans [11,12]. In fact, honeybee keeping has steadily grown in recent decades worldwide, which is linked to the fact that A. mellifera is an effective pollinator of many crops and honey production is a profitable activity [13]. In this context, understanding how A. mellifera interacts with plants and influences other pollinators is an excellent study system to assess the effects of abundant species on biodiversity patterns and ecosystem functioning.
Studies have shown that generalist species such as A. mellifera have a major impact on pollinators in their native and introduced distributional ranges [14,15]. Overall, A. mellifera tends to be a locally abundant species and often dominates food and nesting resources [16,17]. Indeed, A. mellifera can aggressively and indirectly displace other species of pollinators from floral hosts and, consequently, lead to a reduction in foraging efficiency, fitness, and pollination services of those other species [18,19,20]. As a result of such exploitative and interference competition with other species, the presence of A. mellifera influences how pollinator communities are structured (e.g., abundance, richness, and interaction patterns) and patterns of pathogen transmission [14,21,22]. However, our theoretical and empirical understanding of how the presence of the honeybee influences such patterns, namely the interaction structure among plants and pollinators, remains to be thoroughly elucidated.
The conceptualization of ecological networks of interactions between species allows us to understand how interactive communities are structured and the roles of different species in structuring such communities [23]. In ecological interaction networks, species are represented by nodes and their interactions by links describing the ecological relationships between species. The number of studies using interaction network concepts is increasing and providing new insights into ecosystem functioning, stability, and coevolutionary processes [24,25,26,27]. Most of these studies addressed network structural properties covering the general organization of species interactions with biological significance: niche overlap (i.e., the similarity of the compositions of species interactions among species within a trophic level), specialization (i.e., the dependency between species based on the range of resources available), and network robustness (i.e., the ability of a network to avoid coextinctions after species loss) [28,29]. These network descriptors are biologically meaningful because together they describe the degree of niche partitioning and interaction similarity among species and evaluate potential coextinction cascades. Moreover, they are commonly used to describe the organization of bipartite networks, allowing direct comparisons with previous studies on species interaction networks. By using these network descriptors, we can evaluate how the presence or absence of a given species can change network organization. In this sense, empirical studies have shown that some species have high network importance and great influence on other species (i.e., a highly interactive role) [30]. These findings indicate that not all species contribute similarly to the ecological and coevolutionary dynamics of these interaction networks [31,32], and therefore some species are expected to be more influential than others in the organization of species interactions.
Studies have shown that the honeybee may strongly affects the structure and organization of plant–pollinator interaction networks at sites where A. mellifera is considered exotic [7,33,34,35,36]. In general, the presence of A. mellifera increases connectance and nestedness and decreases modularity and interaction links in these pollination networks. Such effects are associated with the foraging behavior of the honeybee which interacts with multiple types of flowering resources, leading to biotic homogenization of interactions in networks with negative impacts on pollination services [7,33,34,35,36]. However, these studies have been mainly focused on local spatial scales and consider small regions (e.g., Brazilian biomes, Canary Islands, and the Italian Alps). Therefore, our knowledge and accumulated information on the effect of A. mellifera on pollinator networks does not represent the global extent of its current distribution. Consequently, the impacts of the interactive role of honeybees on pollination network organization at the global scale, including both its native and introduced range, remain to be determined.
In this study, we evaluated how the interactive role of A. mellifera impacts the niche overlap, specialization, and robustness of pollination networks. For this, we developed a standardized framework based on in-silico species removal from networks to assess the potential impact of A. mellifera in over 109 pollination networks across the globe. In general, we postulated that the impact of A. mellifera on the organization of pollination networks should be positively correlated with its interactive role within such networks. This is because Cruz et al. [37] recently showed that A. mellifera has a highly interactive role in global pollination networks. However, this role varies geographically and is shaped by environmental (e.g., temperature and precipitation) and anthropogenic factors (e.g., disturbance). Therefore, despite some studies showing that A. mellifera establishes a large number of interactions within pollination networks [7,33,34,35,36], it is expected that in those sites where A. mellifera is more connected to other species by multiple direct and indirect pathways (i.e., higher interactive role), its removal from these sites should have a greater simulated effect on the organization of these networks. Here, we calculated the potential impact of A. mellifera on the values of network metrics by removing this species from the networks and compared them to random species removal simulations (i.e., standardized effect). It is very important to note that removal simulation studies may not represent the real experimental conditions in the field but help us produce theoretical outcomes that improve our ability to predict potential changes under controlled conditions. Overall, we advanced the knowledge of macroecological patterns of plant-pollinator interaction networks, demonstrating how the interactive role of A. mellifera across its geographic range directly impacts the organization of plant-pollination networks. We hope this contribution will stimulate new macroecological studies involving abundant and generalist species and their functional roles within ecological communities.

2. Materials and Methods

2.1. Interaction Network Dataset

We compiled species interaction networks available in the literature from 1976 to 2021 that contained information on floral visitor species (including A. mellifera) foraging on plant species (Figure 1 and Supplementary Materials Table S1) [38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63]. Such information was obtained by using the scientific web search engines Google Scholar, Web of Science, and Scopus using the keywords “pollination networks”, “plant–floral visitor networks”, “floral–visitor networks”, and “flower-visitor networks”. Moreover, we also compiled interaction networks available in the online repositories Web of Life (http://www.web-of-life.es) and Dryad (https://datadryad.org/stash) (accessed on 24 January 2022). Note that we only used weighted networks as metric estimates are less susceptible to sample effort biases than binary networks [64]. Plant-hummingbird networks, plant-bee networks, plant-bat networks, and other taxonomically restricted networks were excluded. Detailed information on the exclusion and inclusion criteria of the interaction networks used in this study is shown in Figure S1. Of the 109 networks compiled, only three (2.75%) were obtained from thesis. All other networks were taken from papers and online datasets.

2.2. The Interactive Role of Apis mellifera

To measure the interactive role of A. mellifera within each pollination network, we considered four species-level descriptors: species degree, betweenness centrality, species strength, and Katz centrality using the bipartite package [65] implemented in R [66]. We chose these descriptors because they provide complementary biological information on species centrality within interaction networks. Therefore, estimates of the interactive roles of species in interaction networks are expected to be more robust when based on complementary information than on a single measure. Species degree (k) is the number of plant species with which A. mellifera interacts [67]. Betweenness centrality describes the shortest paths connecting pairs of floral visitor species using A. mellifera as a potential bridge [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. Species strength quantifies the cumulative importance of A. mellifera using the sum of dependencies across all the flowering plants visited by this bee species [69,70]. Katz centrality calculates the number of immediate neighbors and the direct and indirect paths of A. mellifera to other species in the network, either plants or floral visitors [71]. All equations and data distributions of the four species-level descriptors are shown in File S1 and Figure S2, respectively.
Because centrality measures are highly correlated (Figure S3) we used a principal component analysis (PCA) to reduce the four-dimensional space to a single generalized centrality index represented by the scores on the first principal component as often used in specialized literature [30,72,73]. This generalized index summarized the information on the interactive role of A. mellifera. The first principal component (PC1) accounted for 68.2% of the variability, indicating complementarity among the four metrics of centrality despite being correlated (Figure S1). Then, we rescaled this PC1 axis by adding up the absolute value of the minimum PC1 score plus one ten-thousandth of a unit. Therefore, all PC1 scores were greater than zero, but the order and distance among them remained the same. Because the four-centrality metrics correlated positively with PC1, high PC1 scores indicated that A. mellifera has a highly interactive role and is connected to other species by multiple direct and indirect pathways.

2.3. Estimating Specialization, Niche Overlap, and Robustness

Each pollination network was built by an interaction matrix A with r floral visitors and c plant species. The matrix body, Ar,c contained the number of times the plant species c interacted with the floral visitor species r. We calculated the plant’s niche overlap, specialization, and robustness for each pollination network using the bipartite package [65] in R [40].
We calculated the plant’s niche overlap using Horn’s index (NO), which provides information on the similarity of plant species’ interactions [74]. This index ranges from 0 to 1, where lower values indicate no common use of partners and higher values indicate high similarity in plant interactions. We focused only on the plant’s niche overlap to assess how A. mellifera could influence the similarity of floral visitor composition shared between plants. Network specialization was estimated using the complementary specialization (H2) index. This index quantifies the extent to which interaction frequencies deviate from an expected random probability distribution of interactions. This probability is based on random encounters between trophic levels and is weighted by the total frequency of each species using Shannon entropy [75]. The values of complementary specialization H2 range from 0 (complete generalization) to 1 (complete specialization) [75].
We calculated the robustness of pollination networks to floral visitor extinction based on simulating cumulative removals of species from the network using their number of links (i.e., species degree) as an extinction criterion [76]. We used a systematic removal from the most to the least connected species as expected in a catastrophic scenario, where the most connected species have a higher extinction risk. Specifically, we removed one floral visitor species and those plant species connected to it, indicating secondary losses. Afterward, we removed all remaining species based on their degrees until all floral visitor species were removed. Then, we calculated the area under the coextinction curve to estimate the network robustness (R) [76]. Here, R = 1 corresponds to a very slight decrease in the extinction curve until the point at which almost all species were removed (i.e., a more robust network), and R = 0 corresponds to a swift reduction in the extinction curve and, therefore, represents a less robust network.

2.4. Weighted Impact of Apis mellifera on Pollination Networks

Some studies have calculated the impact of A. mellifera on network metrics by removing this bee species from the network [33,77]; that is, all the honeybee interactions and those plant species exclusively connected to this species. Then, they estimated the percentage change in the values of each metric with and without A. mellifera. However, this is not an adequate measure to assess the impact of A. mellifera on network organization mainly because when removing any species and their interactions from a network, the metric values will change. In other words, with this approach, it is difficult to accurately estimate whether the impact is only due to removing A. mellifera itself or any other species that go with it (i.e., changes in network size). To control this possible bias, we calculated the weighted impact of A. mellifera on the values of network metrics (i.e., specialization, niche overlap, and robustness).
For each of the networks (A[r,c], r = floral visitors and c = plant species), we extinguished one floral visitor node ( r i ) at a time. Removing a specific floral visitor implies removing all of its interactions and plant nodes that exclusively interacted with the removed floral visitor node. Thus, for each network we have as many estimates of the metric values as there are floral visitors in the network. Each network had a specific metric estimate, M A m , representing the extinction of A. mellifera, while all other M i estimates represent the extinction of other floral visitors. For each network, we averaged the metric estimates when floral visitors other than A. mellifera were removed ( M ¯ ). Then, the weighted impact of the removal of A. mellifera from the networks was calculated as ( M A m M ¯ )/ ( M A m + M A m ) . A schematic representation of the calculation is presented in Figure S4. Positive values of the weighted impact indicate higher metric values when A. mellifera is excluded. In contrast, negative values of the weighted impact indicate that the average response when floral visitors other than the honeybee were removed was higher.

3. Data analysis

To test whether the standardized impact of A. mellifera on specialization, niche overlap, and robustness could be affected by the interactive role of A. mellifera in the pollination networks, we used generalized additive mixed models (GAMM) with gaussian error distributions and identity link functions. In these models, we used the positive scores of PC1, describing the interactive role of A. mellifera as the smoothed explicative variable and network metrics and the weight impacts of A. mellifera as response variables. GAM models are sensitive to the number of splines (k), and even a relatively low number of splines (4+) can lead to overfitted models with response oscillations with no biological meaning. Therefore, we fixed the number of splines to three (k = 3) with the low rank isotropic smoother (bs = tp), which is sufficient to predict nonlinear responses including those with marked asymptotic behavior [78]. Because some networks were sampled within the same site, we used the network’s original location as a random factor to control for this hierarchical nature of our study and to incorporate dependency among measurements within sites. All models were built in R [66] using the gamm function in the mgcv package [79].
To verify that GAMM models represented a good fit to the data, we visually explored the residual behavior in two ways. First, we plot the theoretical quantiles of a normal distribution against the residuals’ quantiles. If normally distributed, the plot will depict a straight-linear pattern in the middle sector with slight to marked departures at the tails, a common pattern even when data are truly randomly extracted from a theoretical normal distribution. Also, we plotted the predicted values against the residuals to assess the variability of the error (departure from the theoretical expectation, zero) all through the model predictions. Here, we can conclude that the assumption of homoscedasticity is met when there is an equally variable normal-random departure from zero all through the predicted values.

4. Results

Across all 109 networks, A. mellifera interacted with 380 different plant species. Within individual networks, A. mellifera’s degree ranged from 1–38 plant species with an average degree of 6.23 ± 5.72 (mean ± standard deviation) plant species at each site.
When evaluating the weighted impact of A. mellifera on pollination networks, we observed a negative impact of the interactive role of A. mellifera (PC1 scores) on the values of the plant’s niche overlap (NO), indicating that pollination networks without A. mellifera had less niche overlap (GAMM: F = 49.06; p < 0.001; R2(adj) = 0.441). Our findings showed that pollination networks without A. mellifera had less niche overlap and decreased sharply as the interactive role of A. mellifera increased in the networks (Figure 2A). For specialization, we found a positive impact of the interactive role of A. mellifera (PC1 scores) on the values of network specialization (H2), indicating that pollination networks without A. mellifera were more specialized (GAMM: F = 31.68; p < 0.001; R2(adj) = 0.341) (Figure 2B). Finally, we found a positive impact of the interactive role of A. mellifera (PC1 scores) on the values of network robustness (R), indicating that pollination networks without A. mellifera were more robust to species extinction (GAMM: F = 12.52; p < 0.001; R2(adj) = 0.156) (Figure 2C). The three GAMM models were a good fit to the data. Residuals followed the pattern expected from a theoretical normal distribution, and there was no marked bias in models’ predictions (homoscedasticity). Support plots are shown in Figure S5.

5. Discussion

Using an extensive and geographically broadly distributed dataset, we showed that the impact of A. mellifera on the niche overlap, specialization, and robustness of pollination networks is correlated with the interactive role A. mellifera in these networks. These findings show that A. mellifera’s effects on pollination networks were not homogeneous across space, suggesting that the impact of A. mellifera on pollination networks could influence both ecological and evolutionary factors acting at local scales. Our results also indicate that the impact of A. mellifera on the organization of pollination networks differs between sites (i.e., some sites tend to be more vulnerable to A. mellifera than others).
Previous efforts e.g., [33] compared network estimates with and without A. mellifera overseen that fact that any in-silico extinction will affect the metric values. Here, we used a new framework in which the effect of in-silico extinction of A. mellifera was weighted against the average effect of the extinction of the other floral visitors. It is important to highlight that our framework could be applied to any study involving in-silico extinction of species within interaction networks Here, we showed that when the honeybee is removed from the networks, plant species tend to have less niche overlap (i.e., share fewer interactions via their pollinators) and the networks tend to become more specialized. Specifically, the more A. mellifera is directly and indirectly connected with other species within the networks (i.e., higher centrality), the more it could affect plant niche overlap and the specialization of the interactions between all species, meaning that A. mellifera tends to increase the homogenization of interactions within pollination networks. In fact, a study performed by Giannini et al. [7] showed that the number of interactions of A. mellifera within pollination networks is directly related to a higher niche overlap. Our findings also agree with a recent study by Corcos et al. [35], which showed that an increase in the abundance of A. mellifera leads to greater specialization of other pollinators on plants, that possibly change their floral hosts possibly to avoid competition with A. mellifera. (i.e., pollinator niche rewiring [54,80]). Moreover, A. mellifera would lead to greater exploitative or interference competition per resource, not only between pollinators and their preferred floral hosts, but also between plants by their effective pollinators. This is because A. mellifera could monopolize the resource and move pollen loads among different species (which does not favor effective pollination), impairing the reproductive success of plant species that are highly visited by honeybees [34], disrupt the seasonal dynamics [81], and alter plant genetic structure [82]. Furthermore, the homogenization of interactions by generalist species could disrupt native coevolutionary systems [33,83], mainly because these generalist species promote trait-matching and convergence through direct and indirect interactions [32]. Therefore, this threat is of particular concern in regions where the honeybee has been introduced [33,84] and where it plays a higher interactive role in plant-floral visitor networks [37].
We also found that when A. mellifera is removed from pollination networks, these networks tend to become more robust to species extinction. These results partially contrast with those of Santos et al. [33] where the authors showed that although A. mellifera did not affect network robustness, this bee species changes the structure of six native pollination networks in the Brazilian Caatinga (i.e., decreased nestedness and increased modularity caused by the removal of this bee species). Also, in partial contrast to our results, Corcos et al. [35] showed that network robustness increased with increasing honeybee abundance in the Italian Alps. However, note that like niche overlap and specialization, we show that the magnitude of the impact of A. mellifera on network robustness to species extinction is proportional to its interactive role within pollination networks and not just to its presence and abundance as studied by Santos et al. [33] and Corcos et al. [35]. It is imperative to highlight that all these studies have been focused on small spatial scales. Therefore, our study is the first assessment of the interactive role of A. mellifera, covering much of the global extent of the species’ current distribution. Overall, by using a simulation approach, we found that a higher interactive role of A. mellifera could affect not only how plants and pollinators interact within pollination networks, but also the fragility of those interactions to possible species extinction events.
We suggest that the significant impact of A. mellifera on the niche overlap, specialization, and robustness of pollination networks shown in this study occurs precisely in disturbed sites and where it is exotic, which could strongly impact the stability of these pollination systems. This is because as we recently showed in Cruz et al. [37], A. mellifera has a higher interactive role at sites where it is exotic compared to sites where the species is native. Moreover, some studies have shown that A. mellifera tends to be more abundant in disturbed habitats [37,85] and is likely to favor invasion by exotic plant species [86]. Therefore, efforts related to the conservation and restoration of the ecosystems need to reestablish community function and mitigate the homogenization of interactions generated by A. mellifera [57,87] since this species could have a greater interacting role in more disturbed environments.
Mainly due to the domestication, management, and commercialization of A. mellifera as a pollinator and honey producer in different regions of the planet, the number of honeybee workers and the foraging of this species on plants has increased in recent decades (i.e., greater interactive role in networks’ pollination) around the world. Recent studies have shown that resource overlap between wild pollinators and A. mellifera increase with increasing trait similarities [88]. Further, the short-term deployment of honeybee colonies could negatively affect the foraging of other bee species within pollination networks (mainly bumble bees and carpenter bees) [36]. In this sense, Valido et al. [34] also assessed the effects of pre- and post-establishment stages of hives of A. mellifera. These authors showed that this honeybee disrupts the hierarchical structural organization of pollination networks (i.e., loss of interactions by generalist species and impairs pollination services by wild pollinators) and reduces plant reproductive success. This increase in the interactive role of A. mellifera could have a strong effect on the organization and structure of biotic interactions between pollinators and plants at the local level, especially on those pollinators that share floral hosts with A. mellifera. On the other hand, it is important to mention that this species can be an effective pollinator of wild plants and crops, especially at those sites where native bees have already been extirpated by other factors (e.g., habitat loss and fragmentation) [89].
We emphasize that all patterns reported here are based on in-silico extinctions, a first step that need to be supplemented by field experimentation. Future studies should assess what determines a greater or lesser interactive role and abundance of the honeybees within local pollination networks since this species disrupts and influences the organization of pollination networks [7,33,34,35,36]. Also, there is a need of field experimental research to address changes in abundances of floral visitors, temporal variations, frequency of flowers visitation, rewiring and reweighting of interactions in interactive networks when A. mellifera is locally excluded. Because A. mellifera is managed worldwide to provide pollination services in may crops, further research must consider not only the impacts of A. mellifera on interactive networks of wild floral resources but there is an urgent need to explicitly incorporate cultivated floral resources.
In conclusion, we showed that the higher the interactive role of the honeybee A. mellifera within pollination networks, the higher its impact on plants’ niche overlap, specialization, and robustness in such networks. Specifically, in sites where the honeybee was a higher generalist floral visitor, the niche overlap of the plants increased while the specialization and robustness of the interaction networks decreased.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d14110917/s1, Table S1: List of plant-pollinator networks compiled in this study. Figure S1: Flow diagram showing how the interaction networks search was done and what were the exclusion and inclusion criteria of this study. Figure S2: Density plots showing the distribution in the values of species degree, betweenness centrality, species strength, and Katz centrality. Figure S3: Correlation coefficients between each species-level descriptor used to measure the interactive role of Apis mellifera. Figure S4: Schematic diagram of the calculation of the weighted impact of Apis mellifera on network metrics. Figure S5: Behavior of residuals of fitted GAMM models. File S1: Detailed information on the indices used to measure species centrality.

Author Contributions

W.D. and R.G. conceived the research questions and designed the study. C.P.C. and P.L. collected the data. W.D. and R.G. analyzed the data and led the writing of the manuscript. All authors (W.D., C.P.C., P.L., B.R., I.A.H.-D., F.S.N., M.L., F.V. and R.G.) wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT, Mexico) in 2022 under Grant No. FOP16-2021-01-319227 awarded to Wesley Dáttilo.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary Materials.

Acknowledgments

Carlos Pinilla Cruz thanks the Consejo Nacional de Ciencia y Tecnología (CONACyT, Mexico) for the scholarship that they provided (CVU: 1086750).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. DeMaynadier, P.; Hunter, M.L. Keystone support. BioScience 1994, 44, 2. [Google Scholar] [CrossRef]
  2. Simberloff, D. Flagships, umbrellas, and keystones: Is single species management passé in the landscape era? Biol. Conserv. 1998, 83, 247–257. [Google Scholar] [CrossRef]
  3. Switanek, M.; Crailsheim, K.; Truhetz, H.; Brodschneider, R. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 2017, 579, 1581–1587. [Google Scholar] [CrossRef] [PubMed]
  4. Leclercq, G.; Gengler, N.; Francis, F. How humans reshaped diversity in honeybees (Apis mellifera L.): A review. Entomol. Faun. 2018, 2, id71. [Google Scholar]
  5. Medina, A.M.; Almeida-Neto, M. Grinnelian and Eltonian niche conservatism of the European honeybee (Apis mellifera) in its exotic distribution. Sociobiology 2020, 67, 239–246. [Google Scholar] [CrossRef]
  6. Ruttner, F. Biogeography and Taxonomy of Honeybees; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
  7. Giannini, T.C.; Garibaldi, L.A.; Acosta, A.L.; Silva, J.S.; Maia, K.P.; Saraiva, A.M.; Guimarães, P.R., Jr.; Kleinert, A.M. Native and non-native supergeneralist bee species have different effects on plant-bee networks. PLoS ONE 2015, 10, e0137198. [Google Scholar] [CrossRef]
  8. Stevenson, P.C. For antagonists and mutualists: The paradox of insect toxic secondary metabolites in nectar and pollen. Phytochem. Rev. 2020, 19, 603–614. [Google Scholar] [CrossRef] [Green Version]
  9. Wright, G.A.; Nicolson, S.W.; Shafir, S. Nutritional physiology and ecology of honey bees. Annu. Rev. Entomol. 2018, 63, 327–344. [Google Scholar] [CrossRef]
  10. Nicholls, E.; Hempel de Ibarra, N. Assessment of pollen rewards by foraging bees. Func. Ecol. 2017, 31, 76–87. [Google Scholar] [CrossRef] [Green Version]
  11. Dogantzis, K.A.; Zayed, A. Recent advances in population and quantitative genomics of honey bees. Curr. Opin. Insect. Sci. 2019, 31, 93–98. [Google Scholar] [CrossRef]
  12. Ilyasov, R.A.; Lee, M.L.; Takahashi, J.I.; Kwon, H.W.; Nikolenko, A.G. A revision of subspecies structure of western honey bee Apis mellifera. Saudi J. Biol. Sci. 2020, 27, 3615–3621. [Google Scholar] [CrossRef]
  13. Aizen, M.A.; Harder, L.D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 2009, 19, 915–918. [Google Scholar] [CrossRef] [Green Version]
  14. Mallinger, R.E.; Gaines-Day, H.R.; Gratton, C. Do managed bees have negative effects on wild bees?: A systematic review of the literature. PLoS ONE 2017, 12, e0189268. [Google Scholar] [CrossRef] [Green Version]
  15. Hung, K.L.J.; Kingston, J.M.; Albrecht, M.; Holway, D.A.; Kohn, J.R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Biol. Sci. 2018, 285, 20172140. [Google Scholar] [CrossRef] [Green Version]
  16. Winfree, R.; Aguilar, R.; Vázquez, D.P.; LeBuhn, G.; Aizen, M.A. A meta-analysis of bees' responses to anthropogenic disturbance. Ecology 2009, 8, 2068–2076. [Google Scholar] [CrossRef]
  17. Goulson, D.; Nicholls, E.; Botías, C.; Rotheray, E.L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 2015, 347, 1255957. [Google Scholar] [CrossRef]
  18. Rogers, S.; Cajamarca, P.; Tarpy, D.; Burrack, H. Honey bees and bumble bees respond differently to inter- and intra-specific encounters. Apidologie 2013, 44, 621–629. [Google Scholar] [CrossRef] [Green Version]
  19. Thomson, D.M. Local bumble bee decline linked to recovery of honeybees, drought effects on floral resources. Ecol. Lett. 2016, 19, 1247–1255. [Google Scholar] [CrossRef]
  20. Wojcik, V.A.; Morandin, L.A.; Davies Adams, L.; Rourke, K.E. Floral resource competition between honey bees and wild bees: Is there clear evidence and can we guide management and conservation? Environ. Entom. 2018, 47, 822–833. [Google Scholar] [CrossRef]
  21. Lindström, S.A.M.; Herbertsson, L.; Rundlöf, M.; Bommarco, R.; Smith, H.G. Experimental evidence that honeybees depress wild insect densities in a flowering crop. Proc. R. Soc. B Biol. Sci 2016, 283, 20161641. [Google Scholar] [CrossRef] [Green Version]
  22. Torné-Noguera, A.; Rodrigo, A.; Osorio, S.; Bosch, J. Collateral effects of beekeeping: Impacts on pollen-nectar resources and wild bee communities. Basic Appl. Ecol. 2016, 17, 199–209. [Google Scholar] [CrossRef]
  23. Luna, P.; Dáttilo, W. Disentangling plant-animal interactions into complex networks: A multi-view approach and perspectives. In Plant-Animal Interactions: Sources of Biodiversity; Del-Claro, K., Torezan-Silingardi, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2021; pp. 261–281. [Google Scholar]
  24. Harvey, E.; Gounand, I.; Ward, C.L.; Altermatt, F. Bridging ecology and conservation: From ecological networks to ecosystem function. J. Appl. Ecol. 2017, 54, 371–379. [Google Scholar] [CrossRef] [Green Version]
  25. Tylianakis, J.M.; Morris, R.J. Ecological networks across environmental gradients. Annu. Rev. Ecol. Evol. Syst. 2017, 48, 25–48. [Google Scholar] [CrossRef]
  26. Guimaraes, P.R., Jr. The structure of ecological networks across levels of organization. Ann. Rev. Ecol. Evol. Syst. 2020, 51, 433–460. [Google Scholar] [CrossRef]
  27. Burin, G.; Guimaraes, P.R., Jr.; Quental, T.B. Macroevolutionary stability predicts interaction patterns of species in seed dispersal networks. Science 2021, 372, 733–737. [Google Scholar] [CrossRef]
  28. Antoniazzi, R., Jr.; Dáttilo, W.; Rico-Gray, V. A useful guide of main indices and software used for ecological networks studies. In Ecological Networks in the Tropics: An Integrative Overview of Species Interactions from Some of the Most Species-Rich Habitats on Earth; Dáttilo, W., Rico-Gray, V., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; pp. 185–196. [Google Scholar]
  29. Delmas, E.; Besson, M.; Brice, M.H.; Burkle, L.A.; Dalla Riva, G.V.; Fortin, M.J.; Gravel, D.; Guimarães, P.R., Jr.; Hembry, D.H.; Newman, E.A.; et al. Analysing ecological networks of species interactions. Biol. Rev. 2019, 94, 16–36. [Google Scholar] [CrossRef] [Green Version]
  30. Sazima, C.; Guimarães, P.R., Jr.; Dos Reis, S.F.; Sazima, I. What makes a species central in a cleaning mutualism network? Oikos 2010, 119, 1319–1325. [Google Scholar] [CrossRef]
  31. Guimarães, P.R., Jr.; Jordano, P.; Thompson, J.N. Evolution and coevolution in mutualistic networks. Ecol. Lett. 2011, 14, 877–885. [Google Scholar] [CrossRef]
  32. Dáttilo, W.; Lara-Rodríguez, N.; Jordano, P.; Guimarães, P.R., Jr.; Thompson, J.N.; Marquis, R.J.; Medeiros, L.P.; Ortiz-Pulido, R.; Marcos-García, M.A.; Rico-Gray, V. Unravelling Darwin’s entangled bank: Architecture and robustness of mutualistic networks with multiple interaction types. Proc. R. Soc. B Biol. Sci. 2016, 283, 20161564. [Google Scholar] [CrossRef] [Green Version]
  33. Santos, G.M.M.; Aguiar, C.M.; Genini, J.; Martins, C.F.; Zanella, F.C.; Mello, M.A. Invasive Africanized honeybees change the structure of native pollination networks in Brazil. Biol. Invasions 2012, 14, 2369–2378. [Google Scholar] [CrossRef]
  34. Valido, A.; Rodríguez-Rodríguez, M.C.; Jordano, P. Honeybees disrupt the structure and functionality of plant-pollinator networks. Sci. Rep. 2019, 9, 4711. [Google Scholar] [CrossRef]
  35. Corcos, D.; Cappellari, A.; Mei, M.; Paniccia, D.; Cerretti, P.; Marini, L. Contrasting effects of exotic plant invasions and managed honeybees on plant-flower visitor interactions. Divers. Distrib. 2020, 26, 1397–1408. [Google Scholar] [CrossRef]
  36. Weaver, J.R.; Ascher, J.S.; Mallinger, R.E. Effects of short‚ Äêterm managed honey bee deployment in a native ecosystem on wild bee foraging and plant-pollinator networks. Insect Conserv. Divers. 2022, in press. [Google Scholar] [CrossRef]
  37. Cruz, C.P.; Luna, P.; Guevara, R.; Hinojosa-Díaz, I.A.; Villalobos, F.; Dáttilo, W. Climate and human influence shape the interactive role of the honeybee in pollination networks beyond its native distributional range. Basic Appl. Ecol. 2022, 63, 186–195. [Google Scholar] [CrossRef]
  38. Lima, G.; Leite, A.; Souza, C.; Castro, C.; de Santana Bezerra, E. A multilayer network in an herbaceous tropical community reveals multiple roles of floral visitors. Oikos 2020, 129, 1141–1151. [Google Scholar] [CrossRef]
  39. Oleques, S.; Vizentin-Bugoni, J.; Overbeck, G. Influence of grazing intensity on patterns and structuring processes in plant-pollinator networks in a subtropical grassland. Arthropod-Plant Interac. 2019, 13, 757–770. [Google Scholar] [CrossRef]
  40. Beal-Neves, M.; Vogel Ely, C.; Westerhofer-Esteves, M.; Blochtein, B.; Lahm, R.A.; Quadros, E.L.; Abreu-Ferreira, P.M. The influence of urbanization and fire disturbance on plant-floral visitor mutualistic networks. Diversity 2020, 12, 141. [Google Scholar] [CrossRef] [Green Version]
  41. Lara-Romero, C.; Seguí, J.; Pérez-Delgado, A.; Nogales, M.; Traveset, A. Beta diversity and specialization in plant–pollinator networks along an elevational gradient. J. Biogeogr. 2019, 46, 1598–1610. [Google Scholar] [CrossRef]
  42. Dicks, L.V.; Corbet, S.A.; Pywell, R.F. Compartmentalization in plant–insect flower visitor webs. J. Anim. Ecol. 2002, 71, 32–43. [Google Scholar] [CrossRef]
  43. Ollerton, J.; Johnson, S.D.; Cranmer, L.; Kellie, S. The pollination ecology of an assemblage of grassland asclepiads in South Africa. Ann. Bot. 2003, 92, 807–834. [Google Scholar] [CrossRef] [Green Version]
  44. Memmott, J. The structure of a plant-pollinator food web. Ecol. Lett. 1999, 2, 276–280. [Google Scholar] [CrossRef] [PubMed]
  45. Inouye, D.W.; Pyke, G.H. Pollination biology in the Snowy Mountains of Australia: Comparisons with montane Colorado, USA. Aust. J. Ecol. 1988, 13, 191–210. [Google Scholar] [CrossRef]
  46. Motten, A.F. Pollination Ecology of the Spring Wildflower Community in the Deciduous Forests of Piedmont North Carolina. Ph.D. Thesis, Duke University, Duhram, NC, USA, 1982. [Google Scholar]
  47. Motten, A.F. Pollination ecology of the spring wildflower community of a temperate deciduous forest. Ecol. Monog. 1986, 56, 21–42. [Google Scholar] [CrossRef]
  48. Small, E. Insect pollinators of the Mer Bleue peat bog of Ottawa. Can. Field-Nat. 1976, 90, 22–28. [Google Scholar]
  49. Ingversen, T.T. Plant–pollinator interactions on Jamaica and Dominica: The centrality, asymmetry and modularity of networks. Master’s Thesis, University of Aarhus, Aarhus, Denmark, 2006. [Google Scholar]
  50. Kakutani, T.; Inoue, T.; Kato, M.; Ichihashi, H. Insect-flower relationship in the campus of Kyoto University, Kyoto: An overview of the flowering phenology and the seasonal pattern of insect visits. Contrib. Biol. Lab. Kyoto Univ. 1990, 27, 465–521. [Google Scholar]
  51. Kato, M.; Miura, R. Flowering phenology and anthophilous insect community at a threatened natural lowland marsh at Nakaikemi in Tsuruga, Japan. Contrib. Biol. Lab. Kyoto Univ. 1996, 29, 1–48. [Google Scholar]
  52. Bartomeus, I.; Vilà, M.; Santamaria, L. Contrasting effects of invasive plants in plant-pollinator networks. Oecologia 2008, 155, 761–770. [Google Scholar] [CrossRef]
  53. Bezerra, E.L.S.; Machado, I.C.S.; Mello, M.A.R. Pollination networks of oil-flowers: A tiny world within the smallest of all worlds. J. Anim. Ecol. 2009, 78, 1096–1101. [Google Scholar] [CrossRef]
  54. Kaiser-Bunbury, C.N.; Muff, S.; Memmott, J.; Müller, C.B.; Caflisch, A. The robustness of pollination networks to the loss of species and interactions: A quantitative approach incorporating pollinator behaviour. Ecol. Lett. 2010, 13, 442–452. [Google Scholar] [CrossRef] [Green Version]
  55. Kaiser-Bunbury, C.N.; Vázquez, D.P.; Stang, M.; Ghazoul, J. Determinants of the microstructure of plant-pollinator networks. Ecology 2014, 95, 3314–3324. [Google Scholar] [CrossRef]
  56. Trøjelsgaard, K.; Jordano, P.; Carstensen, D.W.; Olesen, J.M. Geographical variation in mutualistic networks: Similarity, turnover and partner fidelity. Proc. R. Soc. B. 2015, 282, 20142925. [Google Scholar] [CrossRef] [Green Version]
  57. Benadi, G.; Hovestadt, T.; Poethke, H.J.; Blüthgen, N. Specialization and phenological synchrony of plant–pollinator interactions along an altitudinal gradient. J. Anim. Ecol. 2014, 83, 639–650. [Google Scholar] [CrossRef]
  58. Hagen, M.; Kraemer, M. Agricultural surroundings support flower–visitor networks in an Afrotropical rain forest. Biol. Conserv. 2010, 143, 1654–1663. [Google Scholar] [CrossRef]
  59. Carstensen, D.W.; Trøjelsgaard, K.; Ollerton, J.; Morellato, L.P.C. Local and regional specialization in plant–pollinator networks. Oikos 2018, 127, 531–537. [Google Scholar] [CrossRef]
  60. Souza, C.S.; Maruyama, P.K.; Aoki, C.; Sigrist, M.R.; Raizer, J.; Gross, C.L.; de Araujo, A.C. Temporal variation in plant–pollinator networks from seasonal tropical environments: Higher specialization when resources are scarce. J. Ecol. 2018, 106, 2409–2420. [Google Scholar] [CrossRef]
  61. Montero-Castaño, A.; Vilà, M. Influence of the honeybee and trait similarity on the effect of a non-native plant on pollination and network rewiring. Func. Ecol. 2017, 31, 142–152. [Google Scholar] [CrossRef] [Green Version]
  62. Zhao, Y.H.; Memmott, J.; Vaughan, I.P.; Li, H.D.; Ren, Z.X.; Lázaro, A.; Zhou, W.; Xu, X.; Wang, W.-J.; Liang, H.; et al. The impact of a native dominant plant, Euphorbia jolkin ii, on plant–flower visitor networks and pollen deposition on stigmas of co-flowering species in subalpine meadows of Shangri-La, SW China. J. Ecol. 2021, 109, 2107–2120. [Google Scholar] [CrossRef]
  63. Motivans-Švara, E.; Ştefan, V.; Sossai, E.; Feldmann, R.; Aguilon, D.J.; Bontsutsnaja, A.; E-Vojtkó, A.; Kilian, I.C.; Lang, P.; Mõtlep, M.; et al. Effects of different types of low-intensity management on plant-pollinator interactions in Estonian grasslands. Ecol. Evol. 2021, 11, 16909–16926. [Google Scholar] [CrossRef]
  64. Miranda, P.N.; da Silva Ribeiro, J.E.L.; Luna, P.; Brasil, I.; Delabie, J.H.C.; Dáttilo, W. The dilemma of binary or weighted data in interaction networks. Ecol. Complex. 2019, 38, 1–10. [Google Scholar] [CrossRef]
  65. Dormann, C.F.; Fründ, J.; Blüthgen, N.; Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open J. Ecol. 2009, 2, 7–24. [Google Scholar] [CrossRef]
  66. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: http://www.R-project.org/ (accessed on 15 February 2022).
  67. Borrett, S.R. Throughflow centrality is a global indicator of the functional importance of species in ecosystems. Ecol. Indic. 2013, 32, 182–196. [Google Scholar] [CrossRef] [Green Version]
  68. Brandes, U. On variants of shortest-path betweenness centrality and their generic computation. Social Net. 2008, 30, 136–145. [Google Scholar] [CrossRef] [Green Version]
  69. Bascompte, J.; Jordano, P. Plant-animal mutualistic networks: The architecture of biodiversity. Annu. Rev. Ecol. Evol. 2007, 38, 567–593. [Google Scholar] [CrossRef] [Green Version]
  70. Bascompte, J.; Jordano, P.; Olesen, J.M. Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science 2006, 312, 431–433. [Google Scholar] [CrossRef] [PubMed]
  71. Katz, L. A new status index derived from sociometric analysis. Psychometrika 1953, 18, 39–43. [Google Scholar] [CrossRef]
  72. Medeiros, L.P.; Garcia, G.; Thompson, J.N.; Guimarães, P.R., Jr. The geographic mosaic of coevolution in mutualistic networks. Proc. Natl. Acad. Sci. USA 2018, 115, 12017–12022. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Maia, K.P.; Rasmussen, C.; Olesen, J.M.; Guimarães, P.R., Jr. Does the sociality of pollinators shape the organisation of pollination networks? Oikos 2019, 128, 741–752. [Google Scholar] [CrossRef] [Green Version]
  74. Horn, H.S. Measurement of “overlap” in comparative ecological studies. Am. Nat. 1966, 100, 419–424. [Google Scholar] [CrossRef]
  75. Blüthgen, N.; Menzel, F.; Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 2006, 6, 9. [Google Scholar] [CrossRef] [Green Version]
  76. Burgos, E.; Ceva, H.; Perazzo, R.P.; Devoto, M.; Medan, D.; Zimmermann, M.; Delbue, A.M. Why nestedness in mutualistic networks? J. Theor Bol. 2007, 249, 307–313. [Google Scholar] [CrossRef] [Green Version]
  77. Silva, J.R.C.; da Silva Mouga, D.M.D.; Dec, E. The bee community (Hymenoptera, Apidae) of Ilha Grande, Babitonga bay, Santa Catarina State, Brazil: Structure, insularity and interaction network. Sociobiology 2022, 69, e7360. [Google Scholar] [CrossRef]
  78. Hastie, T.J.; Tibshirani, R.J. Generalized Additive Models; Chapman and Hall: London, UK, 1990. [Google Scholar]
  79. Wood, S.N. Generalized Additive Models: An Introduction with R, 2nd ed.; Chapman and Hall/CRC: London, UK, 2017. [Google Scholar]
  80. Batra, S.W. Solitary bees. Sci. Am. 1984, 250, 120–127. [Google Scholar] [CrossRef]
  81. Arroyo-Correa, B.; Burkle, L.A.; Emer, C. Alien plants and flower visitors disrupt the seasonal dynamics of mutualistic networks. J. Ecol. 2020, 108, 1475–1486. [Google Scholar] [CrossRef]
  82. Traveset, A.; Richardson, D.M. Biological invasions as disruptors of plant reproductive mutualisms. Trends Ecol. Evol. 2006, 21, 208–216. [Google Scholar] [CrossRef]
  83. Aizen, M.A.; Morales, C.L.; Morales, J.M. Invasive mutualists erode native pollination webs. PLoS Biol. 2008, 6, e31. [Google Scholar] [CrossRef]
  84. Prendergast, K.S.; Ollerton, J. Impacts of the introduced European honeybee on Australian bee-flower network properties in urban bushland remnants and residential gardens. Austral Ecol. 2022, 47, 35–53. [Google Scholar] [CrossRef]
  85. Carman, K.; Jenkins, D.G. Comparing diversity to flower-bee interaction networks reveals unsuccessful foraging of native bees in disturbed habitats. Biol. Conserv. 2016, 202, 110–118. [Google Scholar] [CrossRef]
  86. Richardson, D.M.; Allsopp, N.; D'Antonio, C.M.; Milton, S.J.; Rejmánek, M. Plant invasions-the role of mutualisms. Biol. Rev. 2000, 75, 65–93. [Google Scholar] [CrossRef]
  87. Valiente-Banuet, A.; Aizen, M.A.; Alcántara, J.M.; Arroyo, J.; Cocucci, A.; Galetti, M.; García, M.B.; García, D.; Gómez, J.M.; Jordano, P.; et al. Beyond species loss: The extinction of ecological interactions in a changing world. Funct. Ecol. 2015, 29, 299–307. [Google Scholar] [CrossRef] [Green Version]
  88. Cappellari, A.; Bonaldi, G.; Mei, M.; Paniccia, D.; Cerretti, P.; Lorenzi, M. Functional traits of plants and pollinators explain resource overlap between honeybees and wild pollinators. Oecologia 2022, 198, 1019–1029. [Google Scholar] [CrossRef]
  89. Potts, S.G.; Biesmeijer, J.C.; Kremen, C.; Neumann, P.; Schweiger, O.; Kunin, W.E. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 2010, 25, 345–353. [Google Scholar] [CrossRef] [PubMed]
Figure 1. World map showing the spatial distribution of the 109 weighted pollination networks compiled in this study. Although some networks were sampled within the same or nearby sites, they are at 68 different geographic coordinates.
Figure 1. World map showing the spatial distribution of the 109 weighted pollination networks compiled in this study. Although some networks were sampled within the same or nearby sites, they are at 68 different geographic coordinates.
Diversity 14 00917 g001
Figure 2. Standardized impacts of the interactive role (PC1) of the honeybee Apis mellifera after its exclusion from the pollination networks by using random species removal simulations on (A) plants’ niche overlap (GAMM: F = 49.06; p < 0.001; R2(adj) = 0.441), (B) specialization (GAMM: F = 31.68; p < 0.001; R2(adj) = 0.341), and (C) robustness to species extinction (GAMM: F = 12.52; p < 0.001; R2(adj) = 0.156). Negative impacts indicate that pollination networks without A. mellifera had lower values of the metrics, while positive impacts indicate that pollination networks without A. mellifera had higher values of the metrics (see text for details). The dotted lines represent 95% confidence limits.
Figure 2. Standardized impacts of the interactive role (PC1) of the honeybee Apis mellifera after its exclusion from the pollination networks by using random species removal simulations on (A) plants’ niche overlap (GAMM: F = 49.06; p < 0.001; R2(adj) = 0.441), (B) specialization (GAMM: F = 31.68; p < 0.001; R2(adj) = 0.341), and (C) robustness to species extinction (GAMM: F = 12.52; p < 0.001; R2(adj) = 0.156). Negative impacts indicate that pollination networks without A. mellifera had lower values of the metrics, while positive impacts indicate that pollination networks without A. mellifera had higher values of the metrics (see text for details). The dotted lines represent 95% confidence limits.
Diversity 14 00917 g002
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Dáttilo, W.; Cruz, C.P.; Luna, P.; Ratoni, B.; Hinojosa-Díaz, I.A.; Neves, F.S.; Leponce, M.; Villalobos, F.; Guevara, R. The Impact of the Honeybee Apis mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range. Diversity 2022, 14, 917. https://doi.org/10.3390/d14110917

AMA Style

Dáttilo W, Cruz CP, Luna P, Ratoni B, Hinojosa-Díaz IA, Neves FS, Leponce M, Villalobos F, Guevara R. The Impact of the Honeybee Apis mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range. Diversity. 2022; 14(11):917. https://doi.org/10.3390/d14110917

Chicago/Turabian Style

Dáttilo, Wesley, Carlos Pinilla Cruz, Pedro Luna, Brenda Ratoni, Ismael A. Hinojosa-Díaz, Frederico S. Neves, Maurice Leponce, Fabricio Villalobos, and Roger Guevara. 2022. "The Impact of the Honeybee Apis mellifera on the Organization of Pollination Networks Is Positively Related with Its Interactive Role throughout Its Geographic Range" Diversity 14, no. 11: 917. https://doi.org/10.3390/d14110917

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop