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      Bee genetics and conservation

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          DNA Barcoding: Promise and Pitfalls

          In this issue of PLoS Biology, Hebert et al. (2004) have set out to test the resolution and performance of “DNA barcoding,” using a single mtDNA gene, cytochrome c oxidase I (COI), for a sample of North American birds. Before turning to details of this study, it is useful as context to consider the following questions: What is DNA barcoding, and what does it promise? What is new about it? Why is it controversial? What are the potential pitfalls? Put simply, the intent of DNA barcoding is to use large-scale screening of one or a few reference genes in order to (i) assign unknown individuals to species, and (ii) enhance discovery of new species (Hebert et al. 2003; Stoeckle 2003). Proponents envisage development of a comprehensive database of sequences, preferably associated with voucher specimens representing described species, against which sequences from sampled individuals can be compared. Given the long history of use of molecular markers (e.g., allozymes, rDNA, and mtDNA) for these purposes (Avise 2004), there is nothing fundamentally new in the DNA barcoding concept, except increased scale and proposed standardization. The former is inevitable. Standardization, i.e., the selection of one or more reference genes, is of proven value in the microbial community and in stimulating large-scale phylogenetic analyses, but whether “one gene fits all” is open to debate. Why, then, all the fuss? Initial reactions to the DNA barcoding concept have ranged from unbridled enthusiasm, especially from ecologists (Janzen 2004), to outright condemnation, largely from taxonomists (e.g., see the February 2003 issue of Trends in Ecology and Evolution). The former view reflects a real need to connect different life history stages and to increase the precision and efficiency of field studies involving diverse and difficult-to-identify taxa. The criticisms are mainly in response to the view that single-gene sequences should be the primary identifier for species (“DNA taxonomy”; Tautz et al. 2002; see also Blaxter 2004). At least for the macrobiota, the DNA barcoding community has moved away from this to emphasize the importance of embedding any large-scale sequence database within the existing framework and practice of systematics, including the importance of voucher specimens and of integrating molecular with morphological characters. Another point of contention—that DNA barcodes have limited phylogenetic resolution—arises from confusion about the scope of inference. At best, single-gene assays can hope to identify an individual to species or reveal inconsistencies between molecular variation and current perceptions of species boundaries. DNA barcoding should not be confused with efforts to resolve the “tree of life.” It should connect with and benefit from such projects, but resolving phylogeny at scales from species to major eukaryotic clades requires a very different strategy for selecting genes. Indeed, the very characteristic that makes the COI gene a candidate for high-throughput DNA barcoding—highly constrained amino acid sequence and thus broad applicability of primers (Hebert et al. 2003)—also limits its information content at deeper phylogenetic levels (e.g., Russo et al. 1996; Zardoya and Meyer 1996; Naylor and Brown 1997). Finally, while superficially appealing, the very term DNA barcoding is unfortunate, as it implies that each species has a fixed and invariant characteristic—like a barcode on a supermarket product. As evolutionary biologists, we should question this analogy. In evaluating the promise and pitfalls of DNA barcoding, we need to separate the two areas of application: molecular diagnostics of individuals relative to described taxa, and DNA-led discovery of new species. Both are inherently phylogenetic and rely on a solid taxonomic foundation, including adequate sampling of variation within species and inclusion of all previously described extant species within a given genus. Accurate diagnosis depends on low intraspecific variation compared with that between species, such that a short DNA sequence will allow precise allocation of an individual to a described taxon. The extensive literature on mtDNA phylogeography (Avise 2000) indicates that this condition often holds, although there are exceptions. Furthermore, within many species there is sufficient structure that it will be possible to allocate an individual to a particular geographic population. Such identifications should be accompanied by a statement of confidence—e.g., node support in a phylogenetic analysis and caveats in relation to the breath of sampling in the reference database (e.g., whale forensics; Palumbi and Cipriano 1998). DNA-led species discovery is more contentious, but again is not new. In animals, inclusion of mtDNA evidence in biogeographic and systematic analyses often reveals unexpected diversity or discordance with morphology, which then prompts re-evaluation of morphological and ecological characteristics and, if warranted, taxonomic revision. But, despite recent proposals (Wiens and Penkrot 2002; Hebert et al. 2004), it does not follow that mtDNA divergence should be a primary criterion for recognizing species boundaries (see also Sites and Marshall 2003). Potential limitations of using mtDNA to infer species boundaries include retention of ancestral polymorphism, male-biased gene flow, selection on any mtDNA nucleotide (as the whole genome is one linkage group), introgression following hybridization, and paralogy resulting from transfer of mtDNA gene copies to the nucleus. These are acknowledged by Hebert et al. (2004) and well documented in the literature (Bensasson et al. 2001; Ballard and Whitlock 2004), including that on birds (Degnan 1993; Quinn and White 1987; Lovette and Bermingham 2001; Weckstein et al. 2001). More specifically, using some level of mtDNA divergence as a yardstick for species boundaries ignores the low precision with which coalescence of mtDNA predicts phylogenetic divergence at nuclear genes (Hudson and Turelli 2003). An additional problem with focusing on mtDNA (or any other molecular) divergence as a primary criterion for recognizing species is that it will lead us to overlook new or rapidly diverged species, such as might arise through divergent selection or polyploidy, and thus to conclude that speciation requires long-term isolation. For example, a recent mtDNA analysis of North American birds (Johnson and Cicero 2004) showed that numerous avian species have low divergences and that speciation can occur relatively rapidly under certain circumstances. We contend, therefore, that whereas divergent or discordant mtDNA sequences might stimulate taxonomic reassessment based on nuclear genes as well as morphology, ecology, or behavior, mtDNA divergence is neither necessary nor sufficient as a criterion for delineating species. This view accords with existing practice: taxonomic splits in North American birds typically are based on multiple lines of biological evidence, e.g., morphological and vocal differences as well as genetic data (American Ornithologists' Union 1998). We turn now to the core of Hebert et al.'s paper—COI sequencing of a substantial sample of North American birds (260 of 667 species) and its validity as a test of the barcoding concept. Their aim is to test “the correspondence between species boundaries signaled by COI barcodes and those established by prior taxonomic research.” North American birds are an interesting choice because their species-level taxonomy is relatively well resolved and there has been extensive previous analysis of levels of mtDNA sequence divergence within and among described species (Klicka and Zink 1997; Avise and Walker 1998; Johnson and Cicero 2004). Herbert et al. (2004) found differences in COI sequences “between closely related species” that were 19–24 times greater in magnitude than the differences within species (7.05%–7.93% versus 0.27%–0.43%, respectively). From these data, they conclude that most North American bird species can be discriminated via molecular diagnosis of individuals and propose a “standard sequence threshold” of ten times the mean intraspecific variation (yielding a 2.7% threshold in birds) to flag genetically divergent taxa as “provisional species.” Thus, their analysis seeks to address both potential applications of DNA barcoding. Although Herbert et al. sampled a large number of species, a true test of the precision of mtDNA barcodes to assign individuals to species would include comparisons with sister species—the most closely related extant relatives. This would require that all members of a genus be examined, rather than a random sample of imprecisely defined close relatives, and that taxa be included from more than one geographic region. Johnson and Cicero (2004) showed the importance of comparing sister species when examining genetic divergence values in North American birds, with results that contrast strongly with those of Hebert et al. as well as previous studies (e.g., Klicka and Zink 1997). For 39 pairs of avian sister species, mtDNA sequence divergences ranged from 0.0% to 8.2%, with an average of 1.9% (cf. 7% to 8% among closely related species in Hebert et al.). Of these, 29 pairs (74%) are at or below the 2.7% threshold proposed by Herbert et al. and thus would not be recognized as species despite biological differences. Moreover, although only a few of these 39 pairs (see Table 1 in Johnson and Cicero [2004]) had sufficient sampling to assess intraspecific variation in mtDNA sequences, these typically showed paraphyly in mtDNA haplotypes. Therefore, there are still too few cases with adequate sampling of intraspecific diversity for sister species pairs to know how common paraphyly is, although a recent meta-analysis found that 17% of bird species deviated from mtDNA monophyly (Funk and Omland 2003). Collectively, these observations cast doubt on the precision of DNA barcoding for allocating individuals to previously described avian species. Empidonax flycatchers, which are renowned for their morphological similarity and could thereby benefit from DNA-based identification tools, provide an example of the importance of a more detailed analysis. A complete molecular phylogeny for this group (Johnson and Cicero 2002) yielded distances between four pairs of sister species that ranged from 0.7% (E. difficilis versus E. occidentalis) to 4.6% (E. traillii versus E. alnorum); notably, the genetic distance between mainland and island populations of E. difficilis (E. d. difficilis and E. d. insulicola, 0.9%) was greater than that between sister species (Johnson and Cicero 2002). Herbert et al.'s analysis included only two species of Empidonax (E. traillii and E. virescens), which are not sisters but members of divergent clades. Because E. virescens is genetically distant from all other species of Empidonax (10.3% to 12.5% uncorrected distance; Johnson and Cicero 2002), its comparison with E. trailli therefore inflates estimates of interspecific distances within the genus. Another key point of Hebert et al.'s analysis was to estimate levels of intraspecific diversity. For 130 species of the 260 examined, more than two individuals were sequenced (n = 2 to 12 individuals per species, mean = 2.4), and pooled pairwise genetic distances were found to be uncorrelated with geographic distances, leading Hebert et al. to conclude that “high levels of intraspecific divergence in COI in North American birds appear uncommon.” However, this makes the assumption that there is a common underlying pattern of phylogeographic structure, which is unlikely for North American birds (Zink 1996, Zink et al. 2001). If there is significant variation, assessment of intraspecific diversity can be based on a small sample of individuals only if individuals are sampled across existing population subdivisions for which geography and phenotypic variation are reasonable initial surrogates. The analyses presented by Hebert et al. will certainly stimulate further debate (a reply by Hebert et al. to the present letter is posted at http://www.barcodinglife.com), but, for the reasons outlined here, they are not yet a definitive test of the utility of DNA barcoding for either diagnosis of individuals or discovery of species. We also question whether the results for North American birds can be extrapolated to the tropics, where DNA barcoding could have maximum value. In general, among-population sequence divergence increases with decreasing latitude, even excluding previously glaciated regions (Martin and MacKay 2004), and studies of intraspecific genetic diversity in Neotropical birds have revealed a higher level of phylogeographic subdivision compared to temperate species (Remsen 1997, Lovette and Bermingham 2001). Thus, the general utility of mtDNA barcoding across different biogeographic regions—and between resident versus migratory taxa—requires further scrutiny. There is little doubt that large-scale and standardized sequencing, when integrated with existing taxonomic practice, can contribute significantly to the challenges of identifying individuals and increasing the rate of discovering biological diversity. But to determine when and where this approach is applicable, we now need to discover the boundary conditions. The real challenge lies with tropical taxa and those with limited dispersal and thus substantial phylogeographic structure. Such analyses need to be taxonomically broad and need to extend beyond the focal geographic region to ensure that potential sister taxa are evaluated and can be discriminated. There is also the need to examine groups with frequent (possibly cryptic) hybridization, recent radiations, and high rates of gene transfer from mtDNA to the nucleus. Only then will the skeptics be satisfied.
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            Functional Diversity of Plant–Pollinator Interaction Webs Enhances the Persistence of Plant Communities

            Introduction Understanding the consequences of biodiversity loss for ecosystem functioning and services is currently a major aim of ecology [1,2]. Animal-mediated pollination is one of the essential ecosystem services provided to humankind [3,4]. The negative impact of pollinator decline on the reproductive success of flowering plants has been documented at the species level [5–7], but little information is available at the community level [8]. Increasing the scale of study to the community level is essential to account for potential competitive or facilitative effects among species that belong to the plant–pollinator network. Such effects, which are often linked to diversity [9,10], are known to have large influences on ecological processes such as community productivity and stability [11,12]. Experimental evidence for diversity effects on the functioning of terrestrial ecosystems is mainly available for plants. As primary producers, plants play a central role in the flow of energy within ecosystems [13,14]. Animal-pollinated angiosperms represent up to 70% of plant species in numerous communities and ecosystems [15]. Mutualistic interactions between animals and plants form several intricate interaction webs [16]. Recent analysis of plant–pollinator and plant–frugivore interaction webs demonstrates that these contain a continuum from fully specialist to fully generalist species [17,18]. However, these networks are structured in a nested way [19,20], with specialists mainly interacting with generalists. Such a pattern might have important consequences for ecosystem functioning, because it might confer resilience to perturbations such as the extinction of species [21] if, for example, generalist pollinators buffer the loss of specialist pollinators [18,22–24]. Furthermore, this hypothesis does not take into account the dynamical properties of these networks. In a plant–pollinator community, variations in species diversity at different trophic levels may lead to an adaptation of interaction strengths [25], which may in turn affect the total effectiveness of pollination. We conclude that more information is urgently needed concerning the impacts of biodiversity loss on multispecies and multitrophic interactions. To experimentally test the effect of functional diversity on the functioning and persistence of plant–pollinator communities, we defined functional groups of plants and pollinators based on morphological traits. For plants, two functional groups with three species each were defined according to accessibility of floral rewards (pollen and nectar; see Figure 1). The first group (group 1) included Matricaria officinalis, Erodium cicutarium, and Raphanus raphanistrum, which have easily accessible floral rewards and will be called “open flowers.” The second group (group 2), called “tubular flowers,” included Mimulus guttatus, Medicago sativa, and Lotus corniculatus, all of which present floral rewards hidden at the bottom of a tubular corolla. For pollinators, two functional groups were defined according to mouthparts length (Figure 1). The first group (group A) included three species of syrphid flies (Diptera) with short mouthparts: Saephoria sp., Episyrphus balteatus, and Eristalis tenax. The second group (group B) included three species of bumble bees with longer mouthparts: Bombus terrestris, B, pascuorum, and B, lapidarius. Note that in this case a functional trait (long mouthparts) and a phylogenetic group are confounded. Preliminary observations showed that these six insect species contribute up to 70% of all pollinating visits to flowers in our study area in France. Constructing a plant–pollinator network with these four functional groups leads to a nested structure with specialists interacting with generalists (Figure 1, third column). In principle, syrphid flies cannot efficiently pollinate tubular flowers because their mouthparts are too short. At the beginning of spring 2003, we set up 36 4-m2 caged experimental plant communities. There were three plant treatments following a “substitutive” design [26]. Two of them contained one of the two plant functional groups alone (group 1 or 2), whereas the third contained both plant functional groups in combination (group 3). We applied three different pollination treatments to each plant treatment, by introducing each pollinator functional group alone (group A or B), or both groups together (group C). This full factorial design led to nine experimental treatments, which were replicated four times each, making a total of 36 experimental units. The pollination treatments were applied in two consecutive years (June–July 2003 and 2004). We controlled for the total number of pollinator visits received by each plot during the two pollination seasons (1,000 visits in 2003 and 1,200 visits in 2004) to allow an unbiased comparison of pollination efficiency among the various experimental treatments. In August and September 2003, we counted the number of fruits on each plant in every plot. We also counted the number of seeds per fruit on five collected fruits per plant. Lastly, in April 2004 and 2005, we measured both the number of plant species present at the seedling stage (recruitment richness) and the total number of seedlings (recruitment density) to determine the effects of the experimental treatments on the natural recruitment of the next plant generation. Results Effects on Plant Reproductive Success The reproductive success of the two plant functional groups after the first season is analysed in Table 1. There was a significant effect of pollination treatment on the number of fruits per plant (Table 1, left; standardized means ± standard error [SE]: syrphid −0.278 ± 0.061, bumble bee 0.221 ± 0.065, and both 0.063 ± 0.068). Orthogonal contrasts on pollination treatment indicate that the identity of the pollinator guild (syrphid [A] versus bumble bee [B]) had a significant effect. There was a higher fruit production in bumble bee–pollinated communities than in those pollinated by syrphids. Moreover, the breakdown of the interaction of pollination and plant treatments into the orthogonal contrasts A1 versus B1 and A2 versus B2 indicates that the two plant functional groups responded differently to the identity of the pollinator functional group. Tubular 3 flowers (group 2) produced significantly fewer fruits in the syrphid treatment, whereas open flowers (group 1) produced the same amount of fruits whatever the identity of the pollinator functional group (Figure 2A). This supports our hypothesis that bumble bees were able to pollinate both plant functional groups whereas syrphids could only efficiently pollinate open flowers. Although the functional diversity of plant or pollinator treatment alone had no significant effect, fruit production tended to increase with both plant and pollinator functional diversity (contrast [A1 + A2 + B1 + B2] versus C3; Figure 2B). With respect to seed set per fruit, the interaction between plant and pollination treatment was marginally significant (Table 1, right). As with fruit production, the contrasts A1 versus B1 and A2 versus B2 indicate that the two plant functional groups responded differently to pollinator functional group identity. The pattern, however, was different: Open flowers produced significantly fewer seeds per fruit in the bumble bee treatment than in the syrphid treatment (Figure 2C). This means that bumblebees were less-efficient pollinators than syrphids for open flowers. This could be due to the higher rate of geitonogamous visits (i.e., consecutive visits to different flowers of the same plant, resulting in self-fertilization) by bumblebees. Indeed, preliminary observations using a similar experimental design showed that bumble bees perform a higher percentage of geitonogamous visits than do syrphids (I. Dajoz, unpublished data). Finally, the mean number of seeds per fruit in the plant communities tended to increase with functional diversity of pollination treatments (contrast [A + B] versus C; Figure 2D). Effects on Natural Recruitment We analysed the long-term effects of our pollination treatments on the natural recruitment of our experimental plant treatments after the first and second pollination seasons. The results are presented in Table 2. There was a significant effect of year on recruitment richness with a higher richness after the second pollination season (mean ± SE: 1.916 ± 0.075 in 2004, and 2.291 ± 0.0856 in 2005). Among the possible causes was a severe drought in 2003 [27], which likely affected both plant and insect populations. Such a drought did not occur in 2004. This difference in climate between years may account for a large part of the year effect. Recruitment richness was significantly different among plant treatments, with fewer species recruiting in tubular communities (Figure 3). This is very likely due to two perennial species (whereas all species are annuals in the other group) which may have different reproductive traits and create differences in competitive intensity among the plant treatments. There was a significant effect of pollination treatment, with a higher recruitment richness when both groups of pollinators were present (means ± SE: syrphid 1.854 ± 0.973, bumble bee 2.052 ± 0.826, and both 2.406 ± 1.062). However, as suggested by the significant interaction between plant and pollination treatments, the pattern was more complex (Figure 3A). In fact, pollination treatments had no effect on recruitment richness in open-flower plant treatment (Figure 3A, left). In the tubular-flower plant treatment, recruitment in the syrphid fly treatment tended to be lower than in the other pollination treatments (Figure 3A, centre). But the positive effect of pollinator functional diversity was obvious in the plant treatment that contained both plant functional groups (Figure 3A, right). In the mixed plant treatment, recruitment richness under the most functionally diverse pollination treatment was substantially above that in the two other treatments. Considering recruitment density, there was also a significant effect of year, with a higher density after the second pollination season (mean ± SE: 26.784 ± 2.324 in 2004 and 31.319 ± 1.937 in 2005), and a significant effect of plant treatment, with fewer individuals recruiting in tubular communities (Figure 3B, centre). These year and plant-treatment effects can be explained in the same way as for recruitment richness (see above). There was also a significant effect of pollination treatment, with a lower recruitment density when plant communities were pollinated by syrphid flies alone (means ± SE: syrphids: 24.104 ± 20.464, bumble bees: 34.364 ± 32.781, and both 28.688 ± 21.459). This is congruent with our results on the number of fruits produced per plant (see Table 1, contrast A versus B). As for recruitment richness, there was a significant interaction between plant and pollination treatments (Figure 3B). In the open-flower plant treatment, recruitment density was not significantly different among pollination treatments (Figure 3B, left). But in the tubular-flower plant treatment, recruitment density was significantly higher in the bumble bee treatment than in the other pollination treatments (Figure 3B, centre). Finally, in the mixed plant treatment, the same pattern as for recruitment richness was observed: There was a higher density in the mixed pollination treatment than in single-guild pollination treatments (Figure 3B, right). Note that these results on natural recruitment are not an artefact caused by sampling small quadrats in heterogeneous experimental plots since the same patterns were observed when data from all quadrats in a plot were pooled. Pollination Visitation Web in the Mixed Plant Treatment To explain the strong effect of pollinator functional diversity on the persistence of mixed plant communities, we carried out a log-linear analysis on the visitation rate of each insect species in a given pollination treatment, for the six plant species of the mixed plant treatment. Data from the year 2003 are illustrated in Figure 4, and the results of the analysis on both years are presented in Table 3. In the second year, there was a significant effect of plant functional group identity: Tubular flowers received a higher number of visits than did open flowers (mean visitation frequency ± SE: for open flowers 0.236 ± 0.097 and for tubular flowers 0.763 ± 0.097). This is very likely due to the two well-established perennial species, which produced a more attractive floral display during the second year of the experiment. For the two years of the experiment, there was a significant interaction between plant functional group and pollinator functional group. This indicates that the two pollinator functional groups were specialised on different plant functional groups (mean visitation frequency ± SE on open flowers and tubular flowers, respectively: in 2003, for bumble bees 0.128 ± 0.058 and 0.433 ± 0.075; for syrphids 0.327 ± 0.043 and 0.113 ± 0.052; in 2004, for bumble bees 0.01 ± 0.005 and 0.58 ± 0.075; for syrphids 0.23 ± 0.055 and 0.18 ± 0.087). Syrphids mainly visited open flowers whereas bumble bees preferentially visited tubular flowers (Figure 4). Even though bumble bees can pollinate open flowers quite efficiently when this is the only plant functional group present (as shown by the reproductive success, recruitment diversity, and recruitment density of the open-flower plant treatment in the bumble bee treatment, Figures 2 and 3), they focus on the tubular-flower group in the mixed plant treatment. In the mixed pollination treatment, the match between plant and pollinator functional groups leads to a more homogenous distribution of pollinator visits among plant groups than in the other pollination treatments. Ultimately, this significantly increases the reproductive success of plants, most likely through the homogenisation of pollinator visits and the minimization of inefficient pollinator visits. Discussion Previous studies on the diversity of plant–pollinator interaction webs were either descriptive [16], carried out on a single plant species [6,7,28–30], or based on simulation [21] and theoretical approaches [22,31]. To our knowledge, this is the first experimental evidence that the persistence of a plant community can be affected by a loss of diversity of its pollinating fauna. Of course, our experimental communities differed from natural ones in several respects. Among other things, the interaction networks we studied were much simpler than those occurring in nature; in particular, they contained fewer species in each trophic level. But such simplifications from natural situations are often necessary to carry out controlled experiments. In plant communities that contained only open flowers, plants produced fewer seeds per fruit in the bumblebee treatment than in the syrphid treatment (Figure 2C), but this was compensated by a sufficiently high fruit production, leading to a richness and density of natural recruitment that was similar to the other pollination treatments (Figure 3A and 3B left). Thus, in these communities, all pollination treatments were equally effective in the long term. In plant communities that contained only tubular flowers, syrphids were inefficient pollinators; fruit production was very low (Figure 2A) and insufficient to allow a good natural recruitment. Bumble bees were the most effective pollinators (Figure 3A and 3C, centre). Note that in the bumble bee treatment, the very high value of average recruitment density was due to three measurements in two replicates, in which only M. guttatus seedlings were recorded at a very high density (more than 150 seedlings per quadrat). To test the effect of these outliers, we removed them and repeated our analysis. The same significant effects were observed, except for the effect of pollination treatment, which became marginally significant (p = 0.0645). The new mean number of seedlings per quadrat for this experimental treatment was 32.17 ± 4.55 (SE), which is still slightly above the value for the pollination treatment with both pollinator groups. For plant communities that contained only tubular flowers, recruitment richness in the two pollination treatments that contained bumblebees was similar. These results are in agreement with our theoretical pollination network presented in Figure 1. In our experimental system, syrphids can be considered as specialist pollinators since they efficiently pollinate only open flowers. Bumble bees were potentially generalists as they induced an important fruit production of the two plant types and a good recruitment in the open- and tubular-flower plant treatments. Our results on the reproductive success and recruitment of single-guild plant treatments indicate that there are strong functional group identity effects since our plant functional groups responded differently to our pollinator functional groups. However, the functional diversity of both the plant and pollination treatments was also important. Plant reproductive success tended to increase with pollinator functional diversity when the number of seeds per fruit was considered, and with both plant and pollinator functional diversity when the number of fruits per plant was considered (Figures 2B and 2D). Although recruitment in single-guild plant treatments was mainly affected by the identity of functional groups, the effect of functional diversity was dramatic in the mixed plant treatment. Natural recruitment of plant communities visited by mixed pollinator guilds was largely above that in other pollination treatments. Pollination by syrphids alone allowed the reproduction of open flowers but not tubular flowers, as expected from the specialisation of syrphids. More surprisingly, however, bumble bees failed to be efficient generalist pollinators. Most of their visits occurred on tubular flowers (Figure 4), resulting in a relatively poor recruitment of open flowers. The only pollination treatment that achieved a high recruitment of both open and tubular flowers when they were mixed, was the one containing the two insect functional groups (Figure 3, right). When syrphids and bumble bees simultaneously pollinated mixed plant communities, they each focused on their target plant functional group, leading to more efficient visits and a better distribution of visits among plant functional groups (Figure 4). Ultimately, it was the pollination treatment with both pollinator functional groups that produced the highest richness and density of natural recruitment. Consequently, since most natural plant communities contain both open and tubular flowers, pollinator functional diversity should strongly enhance the persistence of these communities. Although our experimental system differed from natural communities, and information about the reciprocal effects of the functional diversity of plant communities on the diversity of pollinator communities would be useful, our study indicates that the functional diversity of plant–pollinator interaction webs may be critical for the persistence and functioning of ecosystems and should be carefully monitored and protected. The loss of pollinator functional diversity is likely to trigger plant population decline or extinctions [4], which in turn are likely to affect the structure and composition of natural plant communities and the productivity of many agroecosytems that rely on insect pollination [8]. Ultimately, higher trophic levels may be affected since the diversity and biomass of consumers depend on primary production. Our results strongly suggest that the functional diversity of complex interaction webs plays a crucial role in the sustainability of ecosystems. Materials and Methods Experimental plant communities At the beginning of spring 2003, plant communities were set up in a meadow that remained almost undisturbed for 10 years at the Station Biologique de Foljuif, France, 80 km southwest of Paris. Prior to the establishment of the communities, soil was sterilized by injecting 120 °C steam (30 min) to destroy the seed bank and soil pathogens. In each of the 36 4-m2 plots, a total of 30 adult plants were planted on a grid, spaced 25 cm from each other, to minimize competition and homogenise spatial distribution. Thus, plant density was the same in all experimental plots. We selected a moderate density to maintain within- and among-species competition to a low level, and to allow enough space for future recruitment in the plots. Each of these plant communities was enclosed in a 2-m–high nylon mesh cage in order to eliminate natural pollinator visitation. Pollination rounds During the flowering seasons (June–July 2003 and 2004), pollinators were captured around the study area and introduced into the cages. The relative abundance of pollinator species in the various pollination treatments reflects their natural abundances. From preliminary observations, we had noticed that, in order to have no more than three insects active at the same moment in a 4-m2 plot, it was necessary to put about eight syrphid flies, or six bumble bees, or a mixture of six syrphids and four bumble bees in each pollination cage. Each pollination round in a given plot included 200 visits in the year 2003 and 300 in the year 2004. In total, each plot received either four (in 2004) or five (in 2003) pollination rounds, leading to a total of 1,000 visits per plot in 2003 and 1,200 in 2004. Pollination activity Bumble bees needed approximately 30 min after introduction in the cages to calm down and start to pollinate. In the pollination treatment with both pollinator guilds, we then introduced syrphids, which started to pollinate immediately. Mean visitation time was not significantly different between insects in the cages and in nature. This was true both for bumble bees (mean visitation time in cages: 3.25 ± 0.92 s, mean visitation time in nature: 2.91 ± 1.33 s, t = 1.51, df = 96, p = 0.133) and for syrphids (mean visitation time in cages: 40.21 ± 8.89 s, mean visitation time in nature: 35.38 ± 14.75 s, t = 0.77, df = 12, p = 0.45). Measurement of reproductive success One month after the first pollination treatments, we counted the total number of fruits on each plant, except for M. guttatus and M. officinalis in which fruits cannot be counted without collecting them. We randomly took five fruits per plant of each species to estimate the number of seeds per fruit. Measurement of recruitment richness and density Recruitment richness and density were estimated during the second (April 2004) and third (April 2005) year of the experiment by counting the number of seedlings of each species in four 1,600-cm2 quadrats in each plot. Statistical analysis Statistical analyses were performed using SAS 8.2 software. For the analysis of plant reproductive success, we log-transformed the data to ensure normality. We standardized the data by species using the formula: x − μ/σ (where μ = the mean and σ = the standard deviation of number of fruits or number of seeds per fruit for a given plant species) in order to make the data comparable among the various species and functional groups. We used a mixed analysis of variance (ANOVA) model (SAS proc mixed), in which the fixed effects were plant treatment, pollination treatment, and their interaction term. To investigate the effects of the various plant and pollination treatments, we subdivided a priori each main effect into two components using orthogonal contrasts. The first contrast tested the effect of the identity of the plant or pollinator functional group, i.e. one group versus the other. The second tested the effect of the functional diversity of the plant or pollination treatment, i.e. single-guild versus mixed-guild plant or pollination treatments. Similarly, we subdivided the interaction into three orthogonal contrasts testing the effects of pollinator functional group identity on each plant guild, and the effect of the functional diversity of both plant and pollination treatments. See Table 1 for the construction of the contrasts. For the analysis of plant recruitment, we used a repeated measure ANOVA model (SAS proc mixed). The fixed effects were pollination treatment, plant treatment, year, and all the interaction terms. The repeated effect was year, and the subject effect was replicate. For recruitment density, data were log transformed. For each year of the experiment, the visitation rate of pollinators on each plant species in the communities with both plant functional groups was analysed using a mixed log-linear model (glimix macro, SAS). We subdivided the pollination treatment into two effects: pollinator functional diversity (one or two pollinator functional groups) and identity of the pollinator functional group (bumble bees or syrphids). The model included pollinator species nested within identity of pollinator functional groups, plant species nested within identity of plant functional group, identity of pollinator functional groups, identity of plant functional groups, pollinator functional diversity, and all interaction terms. The replicate was a random effect.
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              How are deleterious mutations purged? Drift versus nonrandom mating.

              Accumulation of deleterious mutations has important consequences for the evolution of mating systems and the persistence of small populations. It is well established that consanguineous mating can purge a part of the mutation load and that lethal mutations can also be purged in small populations. However, the efficiency of purging in natural populations, due to either consanguineous mating or to reduced population size, has been questioned. Consequences of consanguineous mating systems and small population size are often equated under "inbreeding" because both increase homozygosity, and selection is though to be more efficient against homozygous deleterious alleles. I show that two processes of purging that I call "purging by drift" and "purging by nonrandom mating" have to be distinguished. Conditions under which the two ways of purging are effective are derived. Nonrandom mating can purge deleterious mutations regardless of their dominance level, whereas only highly recessive mutations can be purged by drift. Both types of purging are limited by population size, and sharp thresholds separate domains where purging is either effective or not. The limitations derived here on the efficiency of purging are compatible with some experimental studies. Implications of these results for conservation and evolution of mating systems are discussed.
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                Author and article information

                Journal
                Apidologie
                Apidologie
                EDP Sciences
                0044-8435
                1297-9678
                May 2009
                July 7 2009
                : 40
                : 3
                : 237-262
                Article
                10.1051/apido/2009026
                dc6b6a6b-2c2f-425d-91ec-1b3b6bfbe94b
                © 2009
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